ALBERT

Description: Continuous electronic fetal monitoring and the access to databases of fetal heart rate (FHR) data have sparked the application of machine learning classifiers to identify fetal pathologies. However, most fetal heart rate data are acquired using Doppler ultrasound (DUS). DUS signals use autocorrelation (AC) to estimate the average heartbeat period within a window. In consequence, DUS FHR signals loses high frequency information to an extent that depends on the length of the AC window. We examined the effect of this on the estimation bias and discriminability of frequency domain features: low frequency power (LF: 0.03–0.15 Hz), movement frequency power (MF: 0.15–0.5 Hz), high frequency power (HF: 0.5–1 Hz), the LF/(MF + HF) ratio, and the nonlinear approximate entropy (ApEn) as a function of AC window length and signal to noise ratio. We found that the average discriminability loss across all evaluated AC window lengths and SNRs was 10.99% for LF 14.23% for MF, 13.33% for the HF, 10.39% for the LF/(MF + HF) ratio, and 24.17% for ApEn. This indicates that the frequency domain features are more robust to the AC method and additive noise than the ApEn. This is likely because additive noise increases the irregularity of the signals, which results in an overestimation of ApEn. In conclusion, our study found that the LF features are the most robust to the effects of the AC method and noise. Future studies should investigate the effect of other variables such as signal drop, gestational age, and the length of the analysis window on the estimation of fHRV features and their discriminability.

Description: A key challenge for the secondary prevention of Alzheimer’s dementia is the need to identify individuals early on in the disease process through sensitive cognitive tests and biomarkers. The European Prevention of Alzheimer’s Dementia (EPAD) consortium recruited participants into a longitudinal cohort study with the aim of building a readiness cohort for a proof-of-concept clinical trial and also to generate a rich longitudinal data-set for disease modelling. Data have been collected on a wide range of measurements including cognitive outcomes, neuroimaging, cerebrospinal fluid biomarkers, genetics and other clinical and environmental risk factors, and are available for 1,828 eligible participants at baseline, 1,567 at 6 months, 1,188 at one-year follow-up, 383 at 2 years, and 89 participants at three-year follow-up visit. We novelly apply state-of-the-art longitudinal modelling and risk stratification approaches to these data in order to characterise disease progression and biological heterogeneity within the cohort. Specifically, we use longitudinal class-specific mixed effects models to characterise the different clinical disease trajectories and a semi-supervised Bayesian clustering approach to explore whether participants can be stratified into homogeneous subgroups that have different patterns of cognitive functioning evolution, while also having subgroup-specific profiles in terms of baseline biomarkers and longitudinal rate of change in biomarkers.

Description: Tether is a stablecoin, namely a cryptocurrency associated with an underlying security. Tether provides one of the most relevant ways to buy bitcoins and has been the centre of many controversies. In fact, it has been hypothesized that new tethers are issued without the underlying reserves, and that new massive Tether emissions are the basis of strong speculative movements on the Bitcoin, with consequent bubble effects. In the course of this article, we conduct a Social Network Analysis focused on the Tether transaction graph to identify the main actors that play a leading role on the network and characterize the transaction flow between them. From our analysis, we conclude that 1) the Tether transaction network does not enjoy the Smalltalk property, with the robustness and reliability it carries with it; 2) cryptopcurrency exchanges are the nodes with the greatest centrality; 3) even Assortativity is not found, as the subjects who move Tether on a large scale do not give continuity to their presence and operations, therefore do not get a chance to consolidate stable links between them; and 4) among the exchanges, Bitfinex, which has co-ownership and co-administration relationships with the Tether issuer, can be mostly associated with the Rich-gets-Richer property.

Description: Earthworm-like robots have received great attention due to their prominent locomotion abilities in various environments. In this research, by exploiting the extraordinary three-dimensional (3D) deformability of the Yoshimura-origami structure, the state of the art of earthworm-like robots is significantly advanced by enhancing the locomotion capability from 2D to 3D. Specifically, by introducing into the virtual creases, kinematics of the non-rigid-foldable Yoshimura-ori structure is systematically analyzed. In addition to exhibiting large axial deformation, the Yoshimura-ori structure could also bend toward different directions, which, therefore, significantly expands the reachable workspace and makes it possible for the robot to perform turning and rising motions. Based on prototypes made of PETE film, mechanical properties of the Yoshimura-ori structure are also evaluated experimentally, which provides useful guidelines for robot design. With the Yoshimura-ori structure as the skeleton of the robot, a hybrid actuation mechanism consisting of SMA springs, pneumatic balloons, and electromagnets is then proposed and embedded into the robot: the SMA springs are used to bend the origami segments for turning and rising motion, the pneumatic balloons are employed for extending and contracting the origami segments, and the electromagnets serve as anchoring devices. Learning from the earthworm’s locomotion mechanism--retrograde peristalsis wave, locomotion gaits are designed for controlling the robot. Experimental tests indicate that the robot could achieve effective rectilinear, turning, and rising locomotion, thus demonstrating the unique 3D locomotion capability.

Description: AntAlate is a software framework for Unmanned Aerial Vehicle (UAV) autonomy, designed to streamline and facilitate the work of application developers, particularly in deployment of Multi-Agent Robotic Systems (MARS). We created AntAlate in order to bring our research in the field of multi-agent systems from theoretical results to both advanced simulations and to real-life demonstrations. Creating a framework capable of catering to MARS applications requires support for distributed, decentralized, control using local sensing, performed autonomously by groups of identical anonymous agents. Though mainly interested in the emergent behavior of the system as a whole, we focused on the single agent and created a framework suitable for a system of systems approach, while minimizing the hardware requirements of the single agent. Global observers or even a centralized control can be added on top of AntAlate, but the framework does not require a global actor to finalize an application. The same applies to a human in the loop, and fully autonomous UAV applications can be written in as straightforward a way as can semi-autonomous applications. In this paper we describe the AntAlate framework and demonstrate its utility and versatility.

Description: Background: There is growing interest in the connection between the gut microbiome and human health and disease. Conventional approaches to analyse microbiome data typically entail dimensionality reduction and assume linearity of the observed relationships, however, the microbiome is a highly complex ecosystem marked by non-linear relationships. In this study, we use topological data analysis (TDA) to explore differences and similarities between the gut microbiome across several countries.Methods: We used curated adult microbiome data at the genus level from the GMrepo database. The dataset contains OTU and demographical data of over 4,400 samples from 19 studies, spanning 12 countries. We analysed the data with tmap, an integrative framework for TDA specifically designed for stratification and enrichment analysis of population-based gut microbiome datasets.Results: We find associations between specific microbial genera and groups of countries. Specifically, both the USA and UK were significantly co-enriched with the proinflammatory genera Lachnoclostridium and Ruminiclostridium, while France and New Zealand were co-enriched with other, butyrate-producing, taxa of the order Clostridiales.Conclusion: The TDA approach demonstrates the overlap and distinctions of microbiome composition between and within countries. This yields unique insights into complex associations in the dataset, a finding not possible with conventional approaches. It highlights the potential utility of TDA as a complementary tool in microbiome research, particularly for large population-scale datasets, and suggests further analysis on the effects of diet and other regionally varying factors.

Description: This paper discusses results from two successive rounds of virtual mines rescue training. The first round was conducted in a surround projection environment (360-VR), and the second round was conducted in desktop virtual reality (Desktop-VR). In the 360-VR condition, trainees participated as groups, making collective decisions. In the Desktop-VR condition, trainees could control their avatars individually. Overall, 372 participants took part in this study, including 284 mines rescuers who took part in 360-VR, and 243 in Desktop-VR. (155 rescuers experienced both.) Each rescuer who trained in 360-VR completed a battery of pre- and post-training questionnaires. Those who attended the Desktop-VR session only completed the post-training questionnaire. We performed principal components analysis on the questionnaire data, followed by a multiple regression analysis, the results of which suggest that the chief factor contributing to positive learning outcome was Learning Context, which extracted information about the quality of the learning content, the trainers, and their feedback. Subjective feedback from the Desktop-VR participants indicated that they preferred Desktop-VR to 360-VR for this training activity, which highlights the importance of choosing an appropriate platform for training applications, and links back to the importance of Learning Context. Overall, we conclude the following: 1) it is possible to train effectively using a variety of technologies but technology that is well-suited to the training task is more useful than technology that is “more advanced,” and 2) factors that have always been important in training, such as the quality of human trainers, remain critical for virtual reality training.

Description: The human ability of keeping balance during various locomotion tasks is attributed to our capability of withstanding complex interactions with the environment and coordinating whole-body movements. Despite this, several stability analysis methods are limited by the use of overly simplified biped and foot structures and corresponding contact models. As a result, existing stability criteria tend to be overly restrictive and do not represent the full balance capabilities of complex biped systems. The proposed methodology allows for the characterization of the balance capabilities of general biped models (ranging from reduced-order to whole-body) with segmented feet. Limits of dynamic balance are evaluated by the Boundary of Balance (BoB) and the associated novel balance indicators, both formulated in the Center of Mass (COM) state space. Intermittent heel, flat, and toe contacts are enabled by a contact model that maps discrete contact modes into corresponding center of pressure constraints. For demonstration purposes, the BoB and balance indicators are evaluated for a whole-body biped model with segmented feet representative of the human-like standing posture in the sagittal plane. The BoB is numerically constructed as the set of maximum allowable COM perturbations that the biped can sustain along a prescribed direction. For each point of the BoB, a constrained trajectory optimization algorithm generates the biped’s whole-body trajectory as it recovers from extreme COM velocity perturbations in the anterior–posterior direction. Balance capabilities for the cases of flat and segmented feet are compared, demonstrating the functional role the foot model plays in the limits of postural balance. The state-space evaluation of the BoB and balance indicators allows for a direct comparison between the proposed balance benchmark and existing stability criteria based on reduced-order models [e.g., Linear Inverted Pendulum (LIP)] and their associated stability metrics [e.g., Margin of Stability (MOS)]. The proposed characterization of balance capabilities provides an important benchmarking framework for the stability of general biped/foot systems.

Description: Background: The introduction of new visual technologies increases the risk of visually induced motion sickness (VIMS). The aim was to evaluate the 6-item Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ; also known as the VIMSSQ-short) and other predictors for individual susceptibility to VIMS.Methods: Healthy participants (10M + 20F), mean age 22.9 (SD 5.0) years, viewed a 360° panoramic city scene projected in the visual equivalent to the situation of rotating about an axis tilted from the vertical. The scene rotated at 0.2 Hz (72° s−1), with a ‘wobble’ produced by superimposed 18° tilt on the rotational axis, with a field of view of 83.5°. Exposure was 10 min or until moderate nausea was reported. Simulator Sickness Questionnaire (SSQ) was the index of VIMS. Predictors/correlates were VIMSSQ, Motion Sickness Susceptibility Questionnaire (MSSQ), migraine (scale), syncope, Social & Work Impact of Dizziness (SWID), sleep quality/disturbance, personality (“Big Five” TIPI), a prior multisensory Stepping-Vection test, and vection during exposure.Results: The VIMSSQ had good scale reliability (Cronbach’s alpha = 0.84) and correlated significantly with the SSQ (r = 0.58). Higher MSSQ, migraine, syncope, and SWID also correlated significantly with SSQ. Other variables had no significant relationships with SSQ. Regression models showed that the VIMSSQ predicted 34% of the individual variation of VIMS, increasing to 56% as MSSQ, migraine, syncope, and SWID were incorporated as additional predictors.Conclusion: The VIMSSQ is a useful adjunct to the MSSQ in predicting VIMS. Other predictors included migraine, syncope, and SWID. No significant relationship was observed between vection and VIMS.

Description: Increased participation in activities has been associated with improved positive mental health outcomes. However, there is much debate regarding the net effects of video games on individuals. Typified as a socially isolating activity, many games inherently contain socialization within the environment with game-generated characters or other players. Coinciding with the time of the initial pandemic/quarantine period was the release of a popular socializing and life simulation game, Animal Crossing: New Horizons. We investigated whether participation in this game was related to emotional outcomes associated with pandemics (e.g., loneliness and anxiety). The relationship between deleterious mental health and social gaming, amid a time of enforced reduction in socializing, would allow us to isolate the impact of the introduction of a social video game on improving the quality of life for players of this game. Participants (n = 1053) were asked about their time spent playing video games via an online survey, their socialization in game play, loneliness, and anxiety. We predicted that participants with higher levels of social interaction within the game would report less loneliness and anxiety. Utilizing multiple linear regression analyses, the research found that increased gaming and related activities were predictive of higher anxiety and somewhat related to increased loneliness. However, increased visits to another island were associated with lower levels of loneliness. As such, players may be utilizing gaming as a coping mechanism for anxiety. This research may inform generalized research regarding the influence that social games may have on feelings of loneliness and anxiety.

Description: BackgroundBlockchain is a new methodology involving a data structure with list of records, called blocks, which are linked using cryptography. The aim of the review is to overview the existing publication, projects, and platforms on the use of blockchain in Medicine and Neurology.MethodsWe searched the bibliographic database of MEDLINE and BASE. We also accessed ICObench, Coinmarketcap, and Mobihealthnews databases to explore upcoming, ongoing, and ended projects.ResultsIn medicine, there are many projects related to health care, disease prevention, and promotion of healthy life style. In neurology, only one project looks promising: Neuro, an ongoing scientific-technical project uniting scientists, engineers, and programmers for development of new architectures and algorithms of neural networks. Bibliographic searches found 117 publications on Medline and 203 publications on BASE referring to the use of blockchain technology in medicine. Most of them are presented as reviews (narrative, systematic, or minireview), opinions and hypotheses, commentaries, or perspectives. As for Neurology, only one publication refers to the use of blockchain, specifically to Parkinson’s disease.DiscussionAmong the problems related to medicine, there is the lack of information on the patient’s clinical history that could allow accurate diagnosis and treatment. The possibility of having a register based on blockchain technology could help doctors in many ways, including patient management, choosing and monitoring treatments, and standardization of clinical trials.ConclusionThe use of the blockchain technology in medicine has been repetitively proposed to solve different problems. In this article, we highlight the possible benefits of this technology, with attention to Neurology. Blockchain use can lead to quantifiable benefits in the treatment of neurodegenerative diseases, especially in clinical trials that can fail because of an incorrect patient recruitment.

Description: This paper introduces the development of an anthropomorphic soft robotic hand integrated with multiple flexible force sensors in the fingers. By leveraging on the integrated force sensing mechanism, grip state estimation networks have been developed. The robotic hand was tasked to hold the given object on the table for 1.5 s and lift it up within 1 s. The object manipulation experiment of grasping and lifting the given objects were conducted with various pneumatic pressure (50, 80, and 120 kPa). Learning networks were developed to estimate occurrence of object instability and slippage due to acceleration of the robot or insufficient grasp strength. Hence the grip state estimation network can potentially feedback object stability status to the pneumatic control system. This would allow the pneumatic system to use suitable pneumatic pressure to efficiently handle different objects, i.e., lower pneumatic pressure (50 kPa) for lightweight objects which do not require high grasping strength. The learning process of the soft hand is made challenging by curating a diverse selection of daily objects, some of which displays dynamic change in shape upon grasping. To address the cost of collecting extensive training datasets, we adopted one-shot learning (OSL) technique with a long short-term memory (LSTM) recurrent neural network. OSL aims to allow the networks to learn based on limited training data. It also promotes the scalability of the network to accommodate more grasping objects in the future. Three types of LSTM-based networks have been developed and their performance has been evaluated in this study. Among the three LSTM networks, triplet network achieved overall stability estimation accuracy at 89.96%, followed by LSTM network with 88.00% and Siamese LSTM network with 85.16%.

Description: Techniques from artificial intelligence have been widely applied in optical communication and networks, evolving from early machine learning (ML) to the recent deep learning (DL). This paper focuses on state-of-the-art DL algorithms and aims to highlight the contributions of DL to optical communications. Considering the characteristics of different DL algorithms and data types, we review multiple DL-enabled solutions to optical communication. First, a convolutional neural network (CNN) is used for image recognition and a recurrent neural network (RNN) is applied for sequential data analysis. A variety of functions can be achieved by the corresponding DL algorithms through processing the different image data and sequential data collected from optical communication. A data-driven channel modeling method is also proposed to replace the conventional block-based modeling method and improve the end-to-end learning performance. Additionally, a generative adversarial network (GAN) is introduced for data augmentation to expand the training dataset from rare experimental data. Finally, deep reinforcement learning (DRL) is applied to perform self-configuration and adaptive allocation for optical networks.

Description: Two of the major revolutions of this century are the Artificial Intelligence and Robotics. These technologies are penetrating through all disciplines and faculties at a very rapid pace. The application of these technologies in medicine, specifically in the context of Covid 19 is paramount. This article briefly reviews the commonly applied protocols in the Health Care System and provides a perspective in improving the efficiency and effectiveness of the current system. This article is not meant to provide a literature review of the current technology but rather provides a personal perspective of the author regarding what could happen in the ideal situation.

Description: The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and to facilitate their acceptance in public environments, they are often equipped with social abilities for navigation and interaction. Socially compliant robot navigation is increasingly being learned from human observations or demonstrations. We argue that these techniques that typically aim to mimic human behavior do not guarantee fair behavior. As a consequence, social navigation models can replicate, promote, and amplify societal unfairness, such as discrimination and segregation. In this work, we investigate a framework for diminishing bias in social robot navigation models so that robots are equipped with the capability to plan as well as adapt their paths based on both physical and social demands. Our proposed framework consists of two components: learning which incorporates social context into the learning process to account for safety and comfort, and relearning to detect and correct potentially harmful outcomes before the onset. We provide both technological and societal analysis using three diverse case studies in different social scenarios of interaction. Moreover, we present ethical implications of deploying robots in social environments and propose potential solutions. Through this study, we highlight the importance and advocate for fairness in human-robot interactions in order to promote more equitable social relationships, roles, and dynamics and consequently positively influence our society.

Description: The notion that blockchains offer decentralized, “trustless” guarantees of security through technology is a fundamental misconception held by many advocates. This misconception hampers participants from understanding the security differences between public and private blockchains and adopting blockchain technology in suitable contexts. This paper introduces the notion of “people security” to argue that blockchains hold inherent limitations in offering accurate security guarantees to people as participants in blockchain-based infrastructure, due to the differing nature of the threats to participants reliant on blockchain as secure digital infrastructure, as well as the technical limitations between different types of blockchain architecture. This paper applies a sociotechnical security framework to assess the social, software, and infrastructural layers of blockchain applications to reconceptualize “blockchain security” as “people security.” A sociotechnical security analysis of existing macrosocial level blockchain systems surfaces discrepancies between the social, technical, and infrastructural layers of a blockchain network, the technical and governance decisions that characterize the network, and the expectations of, and threats to, participants using the network. The results identify a number of security and trust assumptions against various blockchain architectures, participants, and applications. Findings indicate that private blockchains have serious limitations for securing the interests of users in macrosocial contexts, due to their centralized nature. In contrast, public blockchains reveal trust and security shortcomings at the micro and meso-organizational levels, yet there is a lack of suitable desktop case studies by which to analyze sociotechnical security at the macrosocial level. These assumptions need to be further investigated and addressed in order for blockchain security to more accurately provide “people security”.

Description: In recent years, communication robots aiming to offer mental support to the elderly have attracted increasing attention. Dialogue systems consisting of two robots could provide the elderly with opportunities to hold longer conversations in care homes. In this study, we conducted an experiment to compare two types of scenario-based dialogue systems with different types of bodies—physical and virtual robots—to investigate the effects of embodying such dialogue systems. Forty elderly people aged from 65 to 84 interacted with either an embodied desktop-sized humanoid robot or computer graphic agent displayed on a monitor. The elderly participants were divided into groups depending on the success of the interactions. The results revealed that (i) in the group where the robots responded more successfully with the expected conversation flow, the elderly are more engaged in the conversation with the physical robots than the virtual robots, and (ii) the elderly in the group in which robots responded successfully are more engaged in the conversation with the physical robots than those in the group in which the robots responded with ambiguous responses owing to unexpected utterances from the elderly. These results suggest that having a physical body is advantageous in promoting high engagement, and the potential advantage appears depending on whether the system can handle the conversation flow. These findings provide new insight into the development of dialogue systems assisting elderly in maintaining a better mental health.

Description: Alzheimer’s dementia (AD) is a type of neurodegenerative disease that is associated with a decline in memory. However, speech and language impairments are also common in Alzheimer’s dementia patients. This work is an extension of our previous work, where we had used spontaneous speech for Alzheimer’s dementia recognition employing log-Mel spectrogram and Mel-frequency cepstral coefficients (MFCC) as inputs to deep neural networks (DNN). In this work, we explore the transcriptions of spontaneous speech for dementia recognition and compare the results with several baseline results. We explore two models for dementia recognition: 1) fastText and 2) convolutional neural network (CNN) with a single convolutional layer, to capture the n-gram-based linguistic information from the input sentence. The fastText model uses a bag of bigrams and trigrams along with the input text to capture the local word orderings. In the CNN-based model, we try to capture different n-grams (we use n = 2, 3, 4, 5) present in the text by adapting the kernel sizes to n. In both fastText and CNN architectures, the word embeddings are initialized using pretrained GloVe vectors. We use bagging of 21 models in each of these architectures to arrive at the final model using which the performance on the test data is assessed. The best accuracies achieved with CNN and fastText models on the text data are 79.16 and 83.33%, respectively. The best root mean square errors (RMSE) on the prediction of mini-mental state examination (MMSE) score are 4.38 and 4.28 for CNN and fastText, respectively. The results suggest that the n-gram-based features are worth pursuing, for the task of AD detection. fastText models have competitive results when compared to several baseline methods. Also, fastText models are shallow in nature and have the advantage of being faster in training and evaluation, by several orders of magnitude, compared to deep models.

Description: In this research, the evolution of blockchain applied to supply chains has been mapped from the inception of the technology until June 2020, utilizing primarily public data sources. We have analyzed 271 blockchain projects on parameters such as their inception dates, types of blockchain, status, sectors applied to and type of organization that founded the project. We confirm generally understood trends in the blockchain market with new projects following the industry’s general hype and funding levels. We observe most activity in the Agriculture/Grocery sector and the Freight/Logistics sector. We see the shift of market interest from private companies (startups) to public companies and consortia and the change in blockchain adoption from Ethereum to Hyperledger. Finally, we observe more market-ready solutions and fewer inactive projects for Hyperledger-based projects than Ethereum-based projects.

Description: The field of musical robotics presents an interesting case study of the intersection between creativity and robotics. While the potential for machines to express creativity represents an important issue in the field of robotics and AI, this subject is especially relevant in the case of machines that replicate human activities that are traditionally associated with creativity, such as music making. There are several different approaches that fall under the broad category of musical robotics, and creativity is expressed differently based on the design and goals of each approach. By exploring elements of anthropomorphic form, capacity for sonic nuance, control, and musical output, this article evaluates the locus of creativity in six of the most prominent approaches to musical robots, including: 1) nonspecialized anthropomorphic robots that can play musical instruments, 2) specialized anthropomorphic robots that model the physical actions of human musicians, 3) semi-anthropomorphic robotic musicians, 4) non-anthropomorphic robotic instruments, 5) cooperative musical robots, and 6) individual actuators used for their own sound production capabilities.

Description: This paper presents a ranking method of operating sequences based on the actual condition of complex systems. This objective is achieved using the health checkup concept and the multiattribute utility theory. Our contribution is the proposal of sequences ranking process using data and experts’ judgments. The ranking results in a decision-making element; it allows experts to have an objective and concise overall ranking to be used for decision making. A case study is presented based on an experimental platform; it allows us to compare two aggregation operators: the weighted mean and the Choquet integral.

Description: Since its introduction in 1994, Milgram and Kishino's reality-virtuality (RV) continuum has been used to frame virtual and augmented reality research and development. While originally, the RV continuum and the three dimensions of the supporting taxonomy (extent of world knowledge, reproduction fidelity, and extent of presence metaphor) were intended to characterize the capabilities of visual display technology, researchers have embraced the RV continuum while largely ignoring the taxonomy. Considering the leaps in technology made over the last 25 years, revisiting the RV continuum and taxonomy is timely. In reexamining Milgram and Kishino's ideas, we realized, first, that the RV continuum is actually discontinuous; perfect virtual reality cannot be reached. Secondly, mixed reality is broader than previously believed, and, in fact, encompasses conventional virtual reality experiences. Finally, our revised taxonomy adds coherence, accounting for the role of users, which is critical to assessing modern mixed reality experiences. The 3D space created by our taxonomy incorporates familiar constructs such as presence and immersion, and also proposes new constructs that may be important as mixed reality technology matures.

Description: Background: Pulmonary rehabilitation (PR) has been proven effective but is not well accessed due to transport, time, cost, and physical limitations of patients. We have developed a mobile phone-based PR program (mPR) that could be offered as an alternative for those unable to attend in-person. This was developed following formative research with patients, their families and clinicians. mPR has a core text message program plus an app that includes an action plan, exercise videos, lung visualization, symptom score questionnaire and 1-min sit-to-stand test.Aims: To determine the feasibility of delivering pulmonary rehabilitation by mobile phone.Methods: A 9-week non-randomized (1-arm) pilot study was conducted. Participants were 26 adults with chronic obstructive pulmonary disease plus four family members, who were offered participation at first assessment or during group PR sessions. Outcomes included satisfaction, engagement with the program, and perceived impacts.Results: Eight people (31%) opted for text messages only, and 18 (69%) chose text messages plus the app. Three people stopped the program early, 20 said they would recommend it to others, 19 said it helped them to feel more supported, 17 said it helped them to change their behavior.Conclusion: It is feasible to deliver PR support via mobile phone, including exercise prescription and support. Our mPR program was appreciated by a small number of people with chronic respiratory disorders and family members. Suggestions for improvements are being used to inform the further development of the program, which will then be tested for effectiveness. Registered with the Australia New Zealand Clinical Trials Registry ACTRN12619000884101 (www.anzctr.org.au).

Description: Entering the 5G/6G era, the core concept of human-centric communications has intensified the research effort into analytical frameworks for integrating technological and non-technological domains. Among non-technological domains, human behavioral, psychological, and socio-economic contexts are widely considered as indispensable elements for characterizing user experience (UE). In this study, we introduce the prospect theory as a promising methodology for modeling UE and perceptual measurements for human-centric communications. As the founding pillar of behavioral economics, the prospect theory proposes the non-linear quantity and probability perception of human psychology, which extends to five fundamental behavioral attributes that have profound implications for diverse disciplines. An example of applying the novel theoretic framework is also provided to illustrate how the prospect theory can be utilized to incorporate human factors and analyze human-centric communications. By expatiating on the prospect theoretic framework, we aim to provide a guideline for developing human-centric communications and articulate a novel interdisciplinary research area for further investigation.

Description: Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new generation of long-range autonomous underwater vehicles (LRAUVs), such as the Slocum glider and Tethys-class AUV, has emerged with high endurance, long-range, and energy-aware capabilities. These new vehicles provide an effective solution to study different oceanic phenomena across multiple spatial and temporal scales. For these vehicles, the ocean environment has forces and moments from changing water currents which are generally on the order of magnitude of the operational vehicle velocity. Therefore, it is not practical to generate a simple trajectory from an initial location to a goal location in an uncertain ocean, as the vehicle can deviate significantly from the prescribed trajectory due to disturbances resulted from water currents. Since state estimation remains challenging in underwater conditions, feedback planning must incorporate state uncertainty that can be framed into a stochastic energy-aware path planning problem. This article presents an energy-aware feedback planning method for an LRAUV utilizing its kinematic model in an underwater environment under motion and sensor uncertainties. Our method uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goal-constrained belief space to make the feedback plan synthesis computationally tractable. Energy-aware feedback plans for different water current layers are synthesized through sampling and ocean dynamics. The synthesized feedback plans provide strategies for the vehicle that drive it from an environment’s initial location toward the goal location. We validate our method through extensive simulations involving the Tethys vehicle’s kinematic model and incorporating actual ocean model prediction data.

Description: Wrist disability caused by a series of diseases or injuries hinders the patient’s capability to perform activities of daily living (ADL). Rehabilitation devices for the wrist motor function have gained popularity among clinics and researchers due to the convenience of self-rehabilitation. The inherent compliance of soft robots enabled safe human-robot interaction and light-weight characteristics, providing new possibilities to develop wearable devices. Compared with the conventional apparatus, soft robotic wearable rehabilitation devices showed advantages in flexibility, cost, and comfort. In this work, a compact and low-profile soft robotic wrist brace was proposed by directly integrating eight soft origami-patterned actuators on the commercially available wrist brace. The linear motion of the actuators was defined by their origami pattern. The extensions of the actuators were constrained by the brace fabrics, deriving the motions of the wrist joint, i.e., extension/flexion, ulnar/radial deviation. The soft actuators were made of ethylene-vinyl acetate by blow molding, achieving mass-production capability, low cost, and high repeatability. The design and fabrication of the soft robotic wrist brace are presented in this work. The experiments on the range of motion, output force, wearing position adaptivity, and performance under disturbance have been carried out with results analyzed. The modular soft actuator approach of design and fabrication of the soft robotic wrist brace has a wide application potential in wearable devices.

Description: The recent outbreak of the novel coronavirus (COVID-19) has infected millions of citizens worldwide and claimed many lives. This paper examines the impact of COVID-19 on Chinese e-commerce by analyzing behavioral changes observed on a large online shopping platform. We first conduct a time series analysis to identify product categories that faced the most extensive disruptions. The time-lagged analysis shows that behavioral patterns of shopping actions are highly responsive to the epidemic's development. Based on these findings, we present a consumer demand prediction method by encompassing the epidemic statistics and behavioral features of COVID-19-related products. Experimental results demonstrate that our predictions outperform existing baselines and further extend to long-term and province-level forecasts. Finally, we discuss how our market analysis and prediction can help better prepare for future pandemics by gaining extra time to launch preventive measures.

Description: COVID-19, the illness caused by the SARS-CoV-2 virus, is now a worldwide pandemic with mortality in hundreds of thousands as infections continue to increase. Containing the spread of this viral infection and decreasing the mortality rate is a major challenge. Identifying appropriate antigenic epitopes from the viral proteins is a very important task for vaccine production and the development of diagnostic kits and antibody therapy. A novel antigenic epitope would be specific to the SARS-CoV-2 virus and can distinguish infections caused by common cold viruses. In this study two approaches are employed to identify both continuous and conformational B-cell antigenic epitopes. To achieve this goal, we modeled a complete structure of the receptor binding domain (RBD) of the spike protein using recently deposited coordinates (6vxx, 6vsb, and 6w41) in the protein data bank. In addition, we also modeled the RBD-ACE2 receptor complex for SARS-CoV-2 using the SARS-CoV RBD-ACE2 complex (3D0J) as a reference model. Finally, structure based predicted antigenic epitopes were compared to the ACE2 binding region of RBD of SARS-CoV-2. The identified conformational epitopes show overlaps with the ACE2-receptor binding region of the RBD of SARS-CoV-2. Strategies defined in the current study identified novel antigenic epitope that is specific to the SARS-CoV-2 virus. Integrating such approach in the diagnosis can distinguish infections caused by common cold viruses from SARS-CoV-2 virus.

Description: The solutions to many computer vision problems, including that of 6D object pose estimation, are dominated nowadays by the explosion of the learning-based paradigm. In this paper, we investigate 6D object pose estimation in a practical, real-word setting in which a mobile device (smartphone/tablet) needs to be localized in front of a museum exhibit, in support of an augmented-reality application scenario. In view of the constraints and the priorities set by this particular setting, we consider an appropriately tailored classical as well as a learning-based method. Moreover, we develop a hybrid method that consists of both classical and learning based components. All three methods are evaluated quantitatively on a standard, benchmark dataset, but also on a new dataset that is specific to the museum guidance scenario of interest.

Description: AI is one of the most debated subjects of today and there seems little common understanding concerning the differences and similarities of human intelligence and artificial intelligence. Discussions on many relevant topics, such as trustworthiness, explainability, and ethics are characterized by implicit anthropocentric and anthropomorphistic conceptions and, for instance, the pursuit of human-like intelligence as the golden standard for Artificial Intelligence. In order to provide more agreement and to substantiate possible future research objectives, this paper presents three notions on the similarities and differences between human- and artificial intelligence: 1) the fundamental constraints of human (and artificial) intelligence, 2) human intelligence as one of many possible forms of general intelligence, and 3) the high potential impact of multiple (integrated) forms of narrow-hybrid AI applications. For the time being, AI systems will have fundamentally different cognitive qualities and abilities than biological systems. For this reason, a most prominent issue is how we can use (and “collaborate” with) these systems as effectively as possible? For what tasks and under what conditions, decisions are safe to leave to AI and when is human judgment required? How can we capitalize on the specific strengths of human- and artificial intelligence? How to deploy AI systems effectively to complement and compensate for the inherent constraints of human cognition (and vice versa)? Should we pursue the development of AI “partners” with human (-level) intelligence or should we focus more at supplementing human limitations? In order to answer these questions, humans working with AI systems in the workplace or in policy making have to develop an adequate mental model of the underlying ‘psychological’ mechanisms of AI. So, in order to obtain well-functioning human-AI systems, Intelligence Awareness in humans should be addressed more vigorously. For this purpose a first framework for educational content is proposed.

Description: The effectiveness of cyber security measures are often questioned in the wake of hard hitting security events. Despite much work being done in the field of cyber security, most of the focus seems to be concentrated on system usage. In this paper, we survey advancements made in the development and design of the human centric cyber security domain. We explore the increasing complexity of cyber security with a wider perspective, defining user, usage and usability (3U’s) as three essential components for cyber security consideration, and classify developmental efforts through existing research works based on the human centric security design, implementation and deployment of these components. Particularly, the focus is on studies that specifically illustrate the shift in paradigm from functional and usage centred cyber security, to user centred cyber security by considering the human aspects of users. The aim of this survey is to provide both users and system designers with insights into the workings and applications of human centric cyber security.

Description: Soft robots are ideal for underwater manipulation in sampling and other servicing applications. Their unique features of compliance, adaptability, and being naturally waterproof enable robotic designs to be compact and lightweight, while achieving uncompromized dexterity and flexibility. However, the inherent flexibility and high nonlinearity of soft materials also results in combined complex motions, which creates both soft actuator and sensor challenges for force output, modeling, and sensory feedback, especially under highly dynamic underwater environments. To tackle these limitations, a novel Soft Origami Optical-Sensing Actuator (SOSA) with actuation and sensing integration is proposed in this paper. Inspired by origami art, the proposed sensorized actuator enables a large force output, contraction/elongation/passive bending actuation by fluid, and hybrid motion sensing with optical waveguides. The SOSA design brings two major novelties over current designs. First, it involves a new actuation-sensing mode which enables a superior large payload output and a robust and accurate sensing performance by introducing the origami design, significantly facilitating the integration of sensing and actuating technology for wider applications. Secondly, it simplifies the fabrication process for harsh environment application by investigating the boundary features between optical waveguides and ambient water, meaning the external cladding layer of traditional sensors is unnecessary. With these merits, the proposed actuator could be applied to harsh environments for complex interaction/operation tasks. To showcase the performance of the proposed SOSA actuator, a hybrid underwater 3-DOFs manipulator has been developed. The entire workflow on concept design, fabrication, modeling, experimental validation, and application are presented in detail as reference for wider effective robot-environment applications.

Description: Tendon actuation is one of the most prominent actuation principles for continuum robots. To date, a wide variety of modelling approaches has been derived to describe the deformations of tendon-driven continuum robots. Motivated by the need for a comprehensive overview of existing methodologies, this work summarizes and outlines state-of-the-art modelling approaches. In particular, the most relevant models are classified based on backbone representations and kinematic as well as static assumptions. Numerical case studies are conducted to compare the performance of representative modelling approaches from the current state-of-the-art, considering varying robot parameters and scenarios. The approaches show different performances in terms of accuracy and computation time. Guidelines for the selection of the most suitable approach for given designs of tendon-driven continuum robots and applications are deduced from these results.

Description: Contracts regulate most of our professional and personal life: they enable modern society to operate. The term “Smart Contract,” coined in 1994 by Nick Szabo, means different things to different people. This editorial perspective explores the meanings of the term “smart contract” and the challenges about the legality of “smart contracts.” We are familiar with contracts written in natural language, yet our relationships with smart contracts is yet to be defined. The advent of blockchain technology seems to have accelerated the development and the opportunities for the adoption of smart contracts. The purpose of this editorial is to create an interdisciplinary section where computer scientists and members of the legal profession participate in a constructive debate around smart contracts to positively influence future development.

Description: We introduce a soft robot actuator composed of a pre-stressed elastomer film embedded with shape memory alloy (SMA) and a liquid metal (LM) curvature sensor. SMA-based actuators are commonly used as electrically-powered limbs to enable walking, crawling, and swimming of soft robots. However, they are susceptible to overheating and long-term degradation if they are electrically stimulated before they have time to mechanically recover from their previous activation cycle. Here, we address this by embedding the soft actuator with a capacitive LM sensor capable of measuring bending curvature. The soft sensor is thin and elastic and can track curvature changes without significantly altering the natural mechanical properties of the soft actuator. We show that the sensor can be incorporated into a closed-loop “bang-bang” controller to ensure that the actuator fully relaxes to its natural curvature before the next activation cycle. In this way, the activation frequency of the actuator can be dynamically adapted for continuous, cyclic actuation. Moreover, in the special case of slower, low power actuation, we can use the embedded curvature sensor as feedback for achieving partial actuation and limiting the amount of curvature change.

Description: Over the past two decades, scholars developed various unmanned sailboat platforms, but most of them have specialized designs and controllers. Whereas these robotic sailboats have good performance with open-source designs, it is actually hard for interested researchers or fans to follow and make their own sailboats with these open-source designs. Thus, in this paper, a generic and flexible unmanned sailboat platform with easy access to the hardware and software architectures is designed and tested. The commonly used 1-m class RC racing sailboat was employed to install Pixhawk V2.4.8, Arduino Mega 2,560, GPS module M8N, custom-designed wind direction sensor, and wireless 433 Mhz telegram. The widely used open-source hardware modules were selected to keep reliable and low-cost hardware setup to emphasize the generality and feasibility of the unmanned sailboat platform. In software architecture, the Pixhawk V2.4.8 provided reliable states’ feedback. The Arduino Mega 2,560 received estimated states from Pixhawk V2.4.8 and the wind vane sensor, and then controlled servo actuators of rudder and sail using simplified algorithms. Due to the complexity of introducing robot operating system and its packages, we designed a generic but real-time software architecture just using Arduino Mega 2,560. A suitable line-of-sight guidance strategy and PID-based controllers were used to let the autonomous sailboat sail at user-defined waypoints. Field tests validated the sailing performance in facing WRSC challenges. Results of fleet race, station keeping, and area scanning proved that our design and algorithms could control the 1-m class RC sailboat with acceptable accuracy. The proposed design and algorithms contributed to developing educational, low-cost, micro class autonomous sailboats with accessible, generic, and flexible hardware and software. Besides, our sailboat platform also facilitates readers to develop similar sailboats with more focus on their missions.

Description: Model-Based Reinforcement Learning (MBRL) algorithms have been shown to have an advantage on data-efficiency, but often overshadowed by state-of-the-art model-free methods in performance, especially when facing high-dimensional and complex problems. In this work, a novel MBRL method is proposed, called Risk-Aware Model-Based Control (RAMCO). It combines uncertainty-aware deep dynamics models and the risk assessment technique Conditional Value at Risk (CVaR). This mechanism is appropriate for real-world application since it takes epistemic risk into consideration. In addition, we use a model-free solver to produce warm-up training data, and this setting improves the performance in low-dimensional environments and covers the shortage of MBRL’s nature in the high-dimensional scenarios. In comparison with other state-of-the-art reinforcement learning algorithms, we show that it produces superior results on a walking robot model. We also evaluate the method with an Eidos environment, which is a novel experimental method with multi-dimensional randomly initialized deep neural networks to measure the performance of any reinforcement learning algorithm, and the advantages of RAMCO are highlighted.

Description: Living beings modulate the impedance of their joints to interact proficiently, robustly, and safely with the environment. These observations inspired the design of soft articulated robots with the development of Variable Impedance and Variable Stiffness Actuators. However, designing them remains a challenging task due to their mechanical complexity, encumbrance, and weight, but also due to the different specifications that the wide range of applications requires. For instance, as prostheses or parts of humanoid systems, there is currently a need for multi-degree-of-freedom joints that have abilities similar to those of human articulations. Toward this goal, we propose a new compact and configurable design for a two-degree-of-freedom variable stiffness joint that can match the passive behavior of a human wrist and ankle. Using only three motors, this joint can control its equilibrium orientation around two perpendicular axes and its overall stiffness as a one-dimensional parameter, like the co-contraction of human muscles. The kinematic architecture builds upon a state-of-the-art rigid parallel mechanism with the addition of nonlinear elastic elements to allow the control of the stiffness. The mechanical parameters of the proposed system can be optimized to match desired passive compliant behaviors and to fit various applications (e.g., prosthetic wrists or ankles, artificial wrists, etc.). After describing the joint structure, we detail the kinetostatic analysis to derive the compliant behavior as a function of the design parameters and to prove the variable stiffness ability of the system. Besides, we provide sets of design parameters to match the passive compliance of either a human wrist or ankle. Moreover, to show the versatility of the proposed joint architecture and as guidelines for the future designer, we describe the influence of the main design parameters on the system stiffness characteristic and show the potential of the design for more complex applications.

Description: This research summarizes the implementation of blockchain technology in the food and agriculture industry in Canada. Our research indicates that blockchain solutions are an existing and proven set of technologies. We also describe how blockchain based supply chain traceability information has many more benefits than its current use for food safety and product recalls. We recommend that costs for development of blockchain based solutions should also be distributed across stakeholders, and apportioned by the relevant industry associations. Our research indicates that adoption of blockchain technology in agriculture will achieve critical mass earlier when the industry applies a consortium approach, in a regulatory environment that is supported by government. This report also makes recommendations relevant to the integration of blockchain for end consumers of food.

Description: In the past few decades, there has been a sharp rise of research irreproducibility and retraction, to a point that now is deemed as a crisis. Addressing this crisis, we present a peer-to-peer (P2P) publication model that utilizes blockchain and smart contract technologies. Focusing primarily on researchers and reviewers, the conceptual P2P publication model addresses the sociocultural and incentivization aspects of the irreproducibility crisis. In the P2P publication model, instead of a complete publication, a preapproved experimental design will be published on an incremental basis (unit-by-unit) and authorship will be shared with reviewers. The concept of the P2P publication model was inspired by the transformational journey the music publishing industry has undertaken as it traverses through vinyl age (complete albums) to the Spotify age (single-by-single), where there is a growing inclination among artists toward building an incremental album, taking account of feedback from fans and utilizing automated revenue collection and sharing systems. The ability to publish incrementally through the P2P publication model will relieve researchers from the burden of publishing complete and “good results” while simultaneously incentivizing reviewers to undertake rigorous review work to gain authorship credit in the research. The proposed P2P publication model aims to transform the century-old publication model and incentivization structure in alignment with open access publication ethos of the 21st century.

Description: Food products are usually difficult to handle for robots because of their large variations in shape, size, softness, and surface conditions. It is ideal to use one robotic gripper to handle as many food products as possible. In this study, a scooping-binding robotic gripper is proposed to achieve this goal. The gripper was constructed using a pneumatic parallel actuator and two identical scooping-binding mechanisms. The mechanism consists of a thin scooping plate and multiple rubber strings for binding. When grasping an object, the mechanisms actively makes contact with the environment for scooping, and the object weight is mainly supported by the scooping plate. The binding strings are responsible for stabilizing the grasping by wrapping around the object. Therefore, the gripper can perform high-speed pick-and-place operations. Contact analysis was conducted using a simple beam model and a finite element model that were experimentally validated. Tension property of the binding string was characterized and an analytical model was established to predict binding force based on object geometry and binding displacement. Finally, handling tests on 20 food items, including products with thin profiles and slippery surfaces, were performed. The scooping-binding gripper succeeded in handling all items with a takt time of approximately 4 s. The gripper showed potential for actual applications in the food industry.

Description: This paper introduces and validates a real-time dynamic predictive model based on a neural network approach for soft continuum manipulators. The presented model provides a real-time prediction framework using neural-network-based strategies and continuum mechanics principles. A time-space integration scheme is employed to discretize the continuous dynamics and decouple the dynamic equations for translation and rotation for each node of a soft continuum manipulator. Then the resulting architecture is used to develop distributed prediction algorithms using recurrent neural networks. The proposed RNN-based parallel predictive scheme does not rely on computationally intensive algorithms; therefore, it is useful in real-time applications. Furthermore, simulations are shown to illustrate the approach performance on soft continuum elastica, and the approach is also validated through an experiment on a magnetically-actuated soft continuum manipulator. The results demonstrate that the presented model can outperform classical modeling approaches such as the Cosserat rod model while also shows possibilities for being used in practice.

Description: Measures of perceived affordances—judgments of action capabilities—are an objective way to assess whether users perceive mediated environments similarly to the real world. Previous studies suggest that judgments of stepping over a virtual gap using augmented reality (AR) are underestimated relative to judgments of real-world gaps, which are generally overestimated. Across three experiments, we investigated whether two factors associated with AR devices contributed to the observed underestimation: weight and field of view (FOV). In the first experiment, observers judged whether they could step over virtual gaps while wearing the HoloLens (virtual gaps) or not (real-world gaps). The second experiment tested whether weight contributes to underestimation of perceived affordances by having participants wear the HoloLens during judgments of both virtual and real gaps. We replicated the effect of underestimation of step capabilities in AR as compared to the real world in both Experiments 1 and 2. The third experiment tested whether FOV influenced judgments by simulating a narrow (similar to the HoloLens) FOV in virtual reality (VR). Judgments made with a reduced FOV were compared to judgments made with the wider FOV of the HTC Vive Pro. The results showed relative underestimation of judgments of stepping over gaps in narrow vs. wide FOV VR. Taken together, the results suggest that there is little influence of weight of the HoloLens on perceived affordances for stepping, but that the reduced FOV of the HoloLens may contribute to the underestimation of stepping affordances observed in AR.

Description: We present a primer on multisensory experiences, the different components of this concept, as well as a reflection of its implications for individuals and society. We define multisensory experiences, illustrate how to understand them, elaborate on the role of technology in such experiences, and present the three laws of multisensory experiences, which can guide discussion on their implications. Further, we introduce the case of multisensory experiences in the context of eating and human-food interaction to illustrate how its components operationalize. We expect that this article provides a first point of contact for those interested in multisensory experiences, as well as multisensory experiences in the context of human-food interaction.

Description: A robot swarm is a decentralized system characterized by locality of sensing and communication, self-organization, and redundancy. These characteristics allow robot swarms to achieve scalability, flexibility and fault tolerance, properties that are especially valuable in the context of simultaneous localization and mapping (SLAM), specifically in unknown environments that evolve over time. So far, research in SLAM has mainly focused on single- and centralized multi-robot systems—i.e., non-swarm systems. While these systems can produce accurate maps, they are typically not scalable, cannot easily adapt to unexpected changes in the environment, and are prone to failure in hostile environments. Swarm SLAM is a promising approach to SLAM as it could leverage the decentralized nature of a robot swarm and achieve scalable, flexible and fault-tolerant exploration and mapping. However, at the moment of writing, swarm SLAM is a rather novel idea and the field lacks definitions, frameworks, and results. In this work, we present the concept of swarm SLAM and its constraints, both from a technical and an economical point of view. In particular, we highlight the main challenges of swarm SLAM for gathering, sharing, and retrieving information. We also discuss the strengths and weaknesses of this approach against traditional multi-robot SLAM. We believe that swarm SLAM will be particularly useful to produce abstract maps such as topological or simple semantic maps and to operate under time or cost constraints.

Description: With the significant growth of internet usage, people increasingly share their personal information online. As a result, an enormous amount of personal information and financial transactions become vulnerable to cybercriminals. Phishing is an example of a highly effective form of cybercrime that enables criminals to deceive users and steal important data. Since the first reported phishing attack in 1990, it has been evolved into a more sophisticated attack vector. At present, phishing is considered one of the most frequent examples of fraud activity on the Internet. Phishing attacks can lead to severe losses for their victims including sensitive information, identity theft, companies, and government secrets. This article aims to evaluate these attacks by identifying the current state of phishing and reviewing existing phishing techniques. Studies have classified phishing attacks according to fundamental phishing mechanisms and countermeasures discarding the importance of the end-to-end lifecycle of phishing. This article proposes a new detailed anatomy of phishing which involves attack phases, attacker’s types, vulnerabilities, threats, targets, attack mediums, and attacking techniques. Moreover, the proposed anatomy will help readers understand the process lifecycle of a phishing attack which in turn will increase the awareness of these phishing attacks and the techniques being used; also, it helps in developing a holistic anti-phishing system. Furthermore, some precautionary countermeasures are investigated, and new strategies are suggested.

Description: The sustained increase in new cases of COVID-19 across the world and potential for subsequent outbreaks call for new tools to assist health professionals with early diagnosis and patient monitoring. Growing evidence around the world is showing that lung ultrasound examination can detect manifestations of COVID-19 infection. Ultrasound imaging has several characteristics that make it ideally suited for routine use: small hand-held systems can be contained inside a protective sheath, making it easier to disinfect than X-ray or computed tomography equipment; lung ultrasound allows triage of patients in long term care homes, tents or other areas outside of the hospital where other imaging modalities are not available; and it can determine lung involvement during the early phases of the disease and monitor affected patients at bedside on a daily basis. However, some challenges still remain with routine use of lung ultrasound. Namely, current examination practices and image interpretation are quite challenging, especially for unspecialized personnel. This paper reviews how lung ultrasound (LUS) imaging can be used for COVID-19 diagnosis and explores different image processing methods that have the potential to detect manifestations of COVID-19 in LUS images. Then, the paper reviews how general lung ultrasound examinations are performed before addressing how COVID-19 manifests itself in the images. This will provide the basis to study contemporary methods for both segmentation and classification of lung ultrasound images. The paper concludes with a discussion regarding practical considerations of lung ultrasound image processing use and draws parallels between different methods to allow researchers to decide which particular method may be best considering their needs. With the deficit of trained sonographers who are working to diagnose the thousands of people afflicted by COVID-19, a partially or totally automated lung ultrasound detection and diagnosis tool would be a major asset to fight the pandemic at the front lines.

Description: Dynamic systems theory transformed our understanding of motor control by recognizing the continual interaction between the organism and the environment. Movement could no longer be visualized simply as a response to a pattern of stimuli or as a demonstration of prior intent; movement is context dependent and is continuously reshaped by the ongoing dynamics of the world around us. Virtual reality is one methodological variable that allows us to control and manipulate that environmental context. A large body of literature exists to support the impact of visual flow, visual conditions, and visual perception on the planning and execution of movement. In rehabilitative practice, however, this technology has been employed mostly as a tool for motivation and enjoyment of physical exercise. The opportunity to modulate motor behavior through the parameters of the virtual world is often ignored in practice. In this article we present the results of experiments from our laboratories and from others demonstrating that presenting particular characteristics of the virtual world through different sensory modalities will modify balance and locomotor behavior. We will discuss how movement in the virtual world opens a window into the motor planning processes and informs us about the relative weighting of visual and somatosensory signals. Finally, we discuss how these findings should influence future treatment design.

Description: As social media influence become increasingly popular, understanding why some posts are highly followed than others, especially from the perspective of those leading the discussion allows us to gain insight on how followership is being influenced. A qualitative study of eight participants leading active discussions on Quora was conducted using semi-structured in-depth interviews, followed by thematic analysis. The open coding method was used to iteratively code related answers to develop themes. Results suggest that copyright tactics, controversial answers and sharing new information are some of the mechanisms for influencing followership. These mechanisms are built overtime through conscious strong engagement and by writing a consistently well-thought-out answer. The motivation for leading and writing answers on Quora were more intrinsic than extrinsic, and most participants believed influencing followership should not be a concern if one has the right message.

Description: The cricket is one of the model animals used to investigate the neuronal mechanisms underlying adaptive locomotion. An intact cricket walks mostly with a tripod gait, similar to other insects. The motor control center of the leg movements is located in the thoracic ganglia. In this study, we investigated the walking gait patterns of the crickets whose ventral nerve cords were surgically cut to gain an understanding of how the descending signals from the head ganglia and ascending signals from the abdominal nervous system into the thoracic ganglia mediate the initiation and coordination of the walking gait pattern. Crickets whose paired connectives between the brain and subesophageal ganglion (SEG) (circumesophageal connectives) were cut exhibited a tripod gait pattern. However, when one side of the circumesophageal connectives was cut, the crickets continued to turn in the opposite direction to the connective cut. Crickets whose paired connectives between the SEG and prothoracic ganglion were cut did not walk, whereas the crickets exhibited an ordinal tripod gait pattern when one side of the connectives was intact. Crickets whose paired connectives between the metathoracic ganglion and abdominal ganglia were cut initiated walking, although the gait was not a coordinated tripod pattern, whereas the crickets exhibited a tripod gait when one side of the connectives was intact. These results suggest that the brain plays an inhibitory role in initiating leg movements and that both the descending signals from the head ganglia and the ascending signals from the abdominal nervous system are important in initiating and coordinating insect walking gait patterns.

Description: Next generation wireless networks are expected to provide much higher data throughput and reliable connections for a far larger number of wireless service subscribers and machine-type nodes, which results in increasingly stringent requirements of spectral efficiency (SE) and energy efficiency (EE). Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) stands out as a promising solution to satisfy the SE requirement with a reasonable increase in system complexity. However, the EE of OFDM-IM is still required to be enhanced. Moreover, diversity gain is difficult to harvest from the frequency domain without affecting the SE for OFDM-IM systems, which hinders further reliability enhancement. In this regard, relay-assisted OFDM-IM, as a promising joint paradigm to achieve both high SE and EE, was proposed and has been studied since last year. The objectives of this study are to summarize the recent achievements of this joint paradigm, articulate its pros and cons, and reveal the corresponding challenges and future work. More importantly, we provide a full picture and insights into the implementation of this new paradigm in next generation networks.

Description: Upcoming cryptocurrency approaches claim to enable feeless micropayments. Assuming that this promise will be kept, this is likely to give rise to new business models that entail transactions worth fractions of a cent. In this mini review, we describe two exemplary application cases and outline the associated research activities for each. The first case considers subscription models in consumer markets for which feeless micropayments might be used to expand and, thus, enrich the range of plans by offering shorter subscription times. The second case is concerned with prosumers who might be willing to sell self-generated data (e.g., data generated through their smart meters).

Description: The outbreak of COVID-19, caused by the SARS-CoV-2 coronavirus, has been declared a pandemic by the World Health Organization (WHO) in March, 2020 and rapidly spread to over 210 countries and territories around the world. By December 24, there are over 77M cumulative confirmed cases with more than 1.72M deaths worldwide. To mathematically describe the dynamic of the COVID-19 pandemic, we propose a time-dependent SEIR model considering the incubation period. Furthermore, we take immunity, reinfection, and vaccination into account and propose the SEVIS model. Unlike the classic SIR based models with constant parameters, our dynamic models not only predicts the number of cases, but also monitors the trajectories of changing parameters, such as transmission rate, recovery rate, and the basic reproduction number. Tracking these parameters, we observe the significant decrease in the transmission rate in the U.S. after the authority announced a series of orders aiming to prevent the spread of the virus, such as closing non-essential businesses and lockdown restrictions. Months later, as restrictions being gradually lifted, we notice a new surge of infection emerges as the transmission rates show increasing trends in some states. Using our epidemiology models, people can track, timely monitor, and predict the COVID-19 pandemic with precision. To illustrate and validate our model, we use the national level data (the U.S.) and the state level data (New York and North Dakota), and the resulting relative prediction errors for the infected group and recovered group are mostly lower than 0.5%. We also simulate the long-term development of the pandemic based on our proposed models to explore when the crisis will end under certain conditions.

Description: Recommender systems aim to provide item recommendations for users and are usually faced with data sparsity problems (e.g., cold start) in real-world scenarios. Recently pre-trained models have shown their effectiveness in knowledge transfer between domains and tasks, which can potentially alleviate the data sparsity problem in recommender systems. In this survey, we first provide a review of recommender systems with pre-training. In addition, we show the benefits of pre-training to recommender systems through experiments. Finally, we discuss several promising directions for future research of recommender systems with pre-training. The source code of our experiments will be available to facilitate future research.

Description: We apply various seq2seq models on programming language correction tasks on Juliet Test Suite for C/C++ and Java of Software Assurance Reference Datasets and achieve 75% (for C/C++) and 56% (for Java) repair rates on these tasks. We introduce pyramid encoder in these seq2seq models, which significantly increases the computational efficiency and memory efficiency, while achieving similar repair rate to their nonpyramid counterparts. We successfully carry out error type classification task on ITC benchmark examples (with only 685 code instances) using transfer learning with models pretrained on Juliet Test Suite, pointing out a novel way of processing small programming language datasets.

Description: Context-dependency effects on memory exist, whereby people’s context influences their ability to accurately recall items from memory. This effect was not previously studied when considering virtual reality as an environmental context. We show that adverse effects on recall of memorized items exist when changing between virtual and real environments. The effect was not present when memorizing and recall were both done in VR; it appears to be caused by the change of environmental context. This previously unknown effect may impact how we use VR for memorization tasks, particularly when accurate recall of memorized information in a real environment is important. In a memory-recall experiment (n = 51) participants that underwent a context change involving VR after memorizing performed significantly worse on 24-h later item recall than those who did not change context (17% lower accuracy, p 〈 0.001). In particular memorizing in VR as opposed to a real environment lowers accuracy of recall in a real environment (24% lower, p = 0.001).

Description: Representations of gender in new technologies like the Siri, Pepper, and Sophia robotic assistants, as well as the commodification of features associated with gender on platforms like Instagram, inspire questions about how and whether robotic tools can have gender and what it means to people if they do. One possible response to this is through artistic creation of dance performance. This paper reports on one such project where, along the route to this inquiry, creation of machine augmentation – of both the performer and audience member – was necessary to communicate the artistic ideas grappled with therein. Thus, this article describes the presentation of Babyface, a machine-augmented, participatory contemporary dance performance. This work is a reaction to feminized tropes in popular media and modern technology, and establishes a parallel between the ways that women and machines are talked about, treated, and – in the case of machines – designed to look and behave. This paper extends prior reports on the creation of this piece and its accompanying devices to describe extensions with audience member participation, and reflect on the responses of these audience members. These fabricated elements alongside the actions of the performer and a soundscape that quotes statements made by real “female” robots create an otherwordly, sad cyborg character that causes viewers to question their assumptions about and pressures on the feminine ideal.

Description: In hydrogeology, inverse techniques have become indispensable to characterize subsurface parameters and their uncertainty. When modeling heterogeneous, geologically realistic discrete model spaces, such as categorical fields, Monte Carlo methods are needed to properly sample the solution space. Inversion algorithms use a forward operator, such as a numerical groundwater solver. The forward operator often represents the bottleneck for the high computational cost of the Monte Carlo sampling schemes. Even if efficient sampling methods (for example Posterior Population Expansion, PoPEx) have been developed, they need significant computing resources. It is therefore desirable to speed up such methods. As only a few models generated by the sampler have a significant likelihood, we propose to predict the significance of generated models by means of machine learning. Only models labeled as significant are passed to the forward solver, otherwise, they are rejected. This work compares the performance of AdaBoost, Random Forest, and convolutional neural network as classifiers integrated with the PoPEx framework. During initial iterations of the algorithm, the forward solver is always executed and subsurface models along with the likelihoods are stored. Then, the machine learning schemes are trained on the available data. We demonstrate the technique using a simulation of a tracer test in a fluvial aquifer. The geology is modeled by the multiple-point statistical approach, the field contains four geological facies, with associated permeability, porosity, and specific storage values. MODFLOW is used for groundwater flow and transport simulation. The solution of the inverse problem is used to estimate the 10 days protection zone around the pumping well. The estimated speed-ups with Random Forest and AdaBoost were higher than with the convolutional neural network. To validate the approach, computing times of inversion without and with machine learning schemes were computed and the error against the reference solution was calculated. For the same mean error, accelerated PoPEx achieved a speed-up rate of up to 2 with respect to the standard PoPEx.

Description: COVID-19 has severely impacted mental health in vulnerable demographics, in particular older adults, who face unprecedented isolation. Consequences, while globally severe, are acutely pronounced in low- and middle-income countries (LMICs) confronting pronounced gaps in resources and clinician accessibility. Social robots are well-recognized for their potential to support mental health, yet user compliance (i.e., trust) demands seamless affective human-robot interactions; natural ‘human-like’ conversations are required in simple, inexpensive, deployable platforms. We present the design, development, and pilot testing of a multimodal robotic framework fusing verbal (contextual speech) and nonverbal (facial expressions) social cues, aimed to improve engagement in human-robot interaction and ultimately facilitate mental health telemedicine during and beyond the COVID-19 pandemic. We report the design optimization of a hybrid face robot, which combines digital facial expressions based on mathematical affect space mapping with static 3D facial features. We further introduce a contextual virtual assistant with integrated cloud-based AI coupled to the robot’s facial representation of emotions, such that the robot adapts its emotional response to users’ speech in real-time. Experiments with healthy participants demonstrate emotion recognition exceeding 90% for happy, tired, sad, angry, surprised and stern/disgusted robotic emotions. When separated, stern and disgusted are occasionally transposed (70%+ accuracy overall) but are easily distinguishable from other emotions. A qualitative user experience analysis indicates overall enthusiastic and engaging reception to human-robot multimodal interaction with the new framework. The robot has been modified to enable clinical telemedicine for cognitive engagement with older adults and people with dementia (PwD) in LMICs. The mechanically simple and low-cost social robot has been deployed in pilot tests to support older individuals and PwD at the Schizophrenia Research Foundation (SCARF) in Chennai, India. A procedure for deployment addressing challenges in cultural acceptance, end-user acclimatization and resource allocation is further introduced. Results indicate strong promise to stimulate human-robot psychosocial interaction through the hybrid-face robotic system. Future work is targeting deployment for telemedicine to mitigate the mental health impact of COVID-19 on older adults and PwD in both LMICs and higher income regions.

Description: The effective disinfection of hospitals is paramount in lowering the COVID-19 transmission risk to both patients and medical personnel. Autonomous mobile robots can perform the surface disinfection task in a timely and cost-effective manner, while preventing the direct contact of disinfecting agents with humans. This paper proposes an end-to-end coverage path planning technique that generates a continuous and uninterrupted collision-free path for a mobile robot to cover an area of interest. The aim of this work is to decrease the disinfection task completion time and cost by finding an optimal coverage path using a new graph-based representation of the environment. The results are compared with other existing state-of-the-art coverage path planning approaches. It is shown that the proposed approach generates a path with shorter total travelled distance (fewer number of overlaps) and smaller number of turns.

Description: This paper presents the design, fabrication, and operation of a soft robotic compression device that is remotely powered by laser illumination. We combined the rapid and wireless response of hybrid nanomaterials with state-of-the-art microengineering techniques to develop machinery that can apply physiologically relevant mechanical loading. The passive hydrogel structures that constitute the compliant skeleton of the machines were fabricated using single-step in situ polymerization process and directly incorporated around the actuators without further assembly steps. Experimentally validated computational models guided the design of the compression mechanism. We incorporated a cantilever beam to the prototype for life-time monitoring of mechanical properties of cell clusters on optical microscopes. The mechanical and biochemical compatibility of the chosen materials with living cells together with the on-site manufacturing process enable seamless interfacing of soft robotic devices with biological specimen.

Description: One of the main limiting factors in deployment of marine robots is the issue of energy sustainability. This is particularly challenging for traditional propeller-driven autonomous underwater vehicles which operate using energy intensive thrusters. One emerging technology to enable persistent performance is the use of autonomous recharging and retasking through underwater docking stations. This paper presents an integrated navigational algorithm to facilitate reliable underwater docking of autonomous underwater vehicles. Specifically, the algorithm dynamically re-plans Dubins paths to create an efficient trajectory from the current vehicle position through approach into terminal homing. The path is followed using integral line of sight control until handoff to the terminal homing method. A light tracking algorithm drives the vehicle from the handoff location into the dock. In experimental testing using an Oceanserver Iver3 and Bluefin SandShark, the approach phase reached the target handoff within 2 m in 48 of 48 tests. The terminal homing phase was capable of handling up to 5 m offsets with approximately 70% accuracy (12 of 17 tests). In the event of failed docking, a Dubins path is generated to efficiently drive the vehicle to re-attempt docking. The vehicle should be able to successfully dock in the majority of foreseeable scenarios when re-attempts are considered. This method, when combined with recent work on docking station design, intelligent cooperative path planning, underwater communication, and underwater power transfer, will enable true persistent undersea operation in the extremely dynamic ocean environment.

Description: Interest in the use of virtual reality technologies for individuals with autism spectrum disorders has been increasing for over two decades. Recently, research interest has been growing in the area of head mounted display-based virtual reality technologies, thanks to increased availability and affordability. Affordances and theorized benefits of headset-based virtual reality for individuals with autism spectrum disorders are quite promising. However, very little attention has been given in the literature to implementation safety and ethics. This is a particular concern in light of documented adverse effects associated with headset-based virtual reality. To approach this gap, this article details how the authors approached the issue of minimizing adverse effects with related and overlapping methods, but from two separate, independent research sites—one in the United States and one in the United Kingdom. A structured within- and across-case analysis of the two independent studies was conducted to identify central implementation processes and procedures. Analysis resulted in development of a model for minimizing potential adverse effects of headset-based virtual reality for this population. We assert that our model could provide clarity in terms of design and implementation of headset-based virtual reality for individuals with autism spectrum disorders, guide implementations of future researchers and practitioners, and contribute to minimizing and controlling for potential adverse effects.

Description: During an ultrasound (US) scan, the sonographer is in close contact with the patient, which puts them at risk of COVID-19 transmission. In this paper, we propose a robot-assisted system that automatically scans tissue, increasing sonographer/patient distance and decreasing contact duration between them. This method is developed as a quick response to the COVID-19 pandemic. It considers the preferences of the sonographers in terms of how US scanning is done and can be trained quickly for different applications. Our proposed system automatically scans the tissue using a dexterous robot arm that holds US probe. The system assesses the quality of the acquired US images in real-time. This US image feedback will be used to automatically adjust the US probe contact force based on the quality of the image frame. The quality assessment algorithm is based on three US image features: correlation, compression and noise characteristics. These US image features are input to the SVM classifier, and the robot arm will adjust the US scanning force based on the SVM output. The proposed system enables the sonographer to maintain a distance from the patient because the sonographer does not have to be holding the probe and pressing against the patient's body for any prolonged time. The SVM was trained using bovine and porcine biological tissue, the system was then tested experimentally on plastisol phantom tissue. The result of the experiments shows us that our proposed quality assessment algorithm successfully maintains US image quality and is fast enough for use in a robotic control loop.

Description: Strategies to mitigate the spread of COVID-19, namely quarantine and social distancing protocols, have exposed a troubling paradox: mandated isolation meant to preserve well-being has inadvertently contributed to its decline. Prolonged isolation has been associated with widespread loneliness and diminished mental health, with effects compounded by limited face-to-face access to clinical and social support systems. While remote communication technologies (e.g., video chat) can connect individuals with healthcare providers and social networks, remote technologies might have limited effectiveness in clinical and social contexts. In this review, we articulate the promise of Virtual Reality as a conduit to clinical resources and social connection. Furthermore, we outline various social and economic factors limiting the virtual reality industry’s ability to maximize its potential to address mental health issues brought upon by the pandemic. These barriers are delineated across five dimensions: sociocultural, content, affordability, supply chain, and equitable design. After examining potential short- and long-term solutions to these hurdles, we outline potential avenues for applied and theoretical research seeking to validate these solutions. Through this evaluation we seek to (a) emphasize virtual reality’s capacity to improve mental health by connecting communities to clinical and social support systems, (b) identify socioeconomic barriers preventing users from accessing these systems through virtual reality, and (c) discuss solutions that ensure these systems can be equitably accessed via changes to existing and future virtual reality infrastructures.

Description: The aim of this paper is to further the understanding of embodiment by 1) analytically determining the components defining embodiment, 2) increasing comparability and standardization of the measurement of embodiment across experiments by providing a universal embodiment questionnaire that is validated and reliable, and 3) motivating researchers to use a standardized questionnaire. In this paper we validate numerically and refine our previously proposed Embodiment Questionnaire. We collected data from nine experiments, with over 400 questionnaires, that used all or part of the original embodiment 25-item questionnaire. Analysis was performed to eliminate non-universal questions, redundant questions, and questions that were not strongly correlated with other questions. We further numerically categorized and weighted sub-scales and determined that embodiment is comprised of interrelated categories of Appearance, Response, Ownership, and Multi-Sensory. The final questionnaire consists of 16 questions and four interrelated sub-scales with high reliability within each sub-scale, Chronbach’s α ranged from 0.72 to 0.82. Results of the original and refined questionnaire are compared over all nine experiments and in detail for three of the experiments. The updated questionnaire produced a wider range of embodiment scores compared to the original questionnaire, was able to detect the presence of a self-avatar, and was able to discern that participants over 30 years of age have significantly lower embodiment scores compared to participants under 30 years of age. Removed questions and further research of interest to the community are discussed.

Description: Web accessibility monitoring systems support users in checking entire websites for accessibility issues. Although these tools can only check the compliance with some of the many success criteria of the Web Content Accessibility Guidelines, they can assist quality assurance personnel, web administrators and web authors to discover hotspots of barriers and overlooked accessibility issues in a continuous manner. These tools should be effective in identifying accessibility issues. Furthermore, they should motivate users, as this promotes employee productivity and increases interest in accessibility in general. In a comparative study, we applied four commercial monitoring systems on two of the Stuttgart Media University’s websites. The tools are: 1) The Accessibility module of Siteimprove from Siteimprove, 2) Pope Tech from Pope Tech, 3) WorldSpace Comply (now called axe Monitor) from Deque, and 4) ARC Monitoring from The Paciello Group. The criteria catalogue consists of functional criteria that we gleaned from literature and user experience criteria based on the User Experience Questionnaire. Based on a focus group consisting of experts of Stuttgart Media University, we derived individual weights for the criteria. The functional evaluation criteria are: Coverage of the website and the guidelines, completeness, correctness, support in locating errors, support for manual checks, degree of implementing gamification patterns, support for various input and report formats, and methodological support for the Website Accessibility Conformance Evaluation Methodology 1.0 and for the German procurement law for public authorities Barrierefreie Informationstechnik-Verordnung 2.0. For determination of the user experience criteria, we conducted exploratory think-aloud user tests (n = 15) using a coaching approach. Every participant tested all tools for 15 min (within-subject design). The participants completed post-test questionnaires, including the User Experience Questionnaire. According to our results, Siteimprove turned out to be the best tool for our purposes.

Description: Alzheimer’s dementia (AD) is a chronic neurodegenerative illness that manifests in a gradual decline of cognitive function. Early identification of AD is essential for managing the ensuing cognitive deficits, which may lead to a better prognostic outcome. Speech data can serve as a window into cognitive functioning and can be used to screen for early signs of AD. This paper describes methods for learning models using speech samples from the DementiaBank database, for identifying which subjects have Alzheimer’s dementia. We consider two machine learning tasks: 1) binary classification to distinguish patients from healthy controls, and 2) regression to estimate each subject’s Mini-Mental State Examination (MMSE) score. To develop models that can use acoustic and/or language features, we explore a variety of dimension reduction techniques, training algorithms, and fusion strategies. Our best performing classification model, using language features with dimension reduction and regularized logistic regression, achieves an accuracy of 85.4% on a held-out test set. On the regression task, a linear regression model trained on a reduced set of language features achieves a root mean square error (RMSE) of 5.62 on the test set. These results demonstrate the promise of using machine learning for detecting cognitive decline from speech in AD patients.

Description: This paper reports on end users' perspectives on the use of a blockchain solution for private and secure individual “omics” health data management and sharing. This solution is one output of a multidisciplinary project investigating the social, data, and technical issues surrounding application of blockchain technology in the context of personalized healthcare research. The project studies potential ethical, legal, social, and cognitive constraints of self-sovereign healthcare data management and sharing, and whether such constraints can be addressed through careful design of a blockchain solution.

Description: Advanced imaging and DNA sequencing technologies now enable the diverse biology community to routinely generate and analyze terabytes of high resolution biological data. The community is rapidly heading toward the petascale in single investigator laboratory settings. As evidence, the single NCBI SRA central DNA sequence repository contains over 45 petabytes of biological data. Given the geometric growth of this and other genomics repositories, an exabyte of mineable biological data is imminent. The challenges of effectively utilizing these datasets are enormous as they are not only large in the size but also stored in geographically distributed repositories in various repositories such as National Center for Biotechnology Information (NCBI), DNA Data Bank of Japan (DDBJ), European Bioinformatics Institute (EBI), and NASA’s GeneLab. In this work, we first systematically point out the data-management challenges of the genomics community. We then introduce Named Data Networking (NDN), a novel but well-researched Internet architecture, is capable of solving these challenges at the network layer. NDN performs all operations such as forwarding requests to data sources, content discovery, access, and retrieval using content names (that are similar to traditional filenames or filepaths) and eliminates the need for a location layer (the IP address) for data management. Utilizing NDN for genomics workflows simplifies data discovery, speeds up data retrieval using in-network caching of popular datasets, and allows the community to create infrastructure that supports operations such as creating federation of content repositories, retrieval from multiple sources, remote data subsetting, and others. Named based operations also streamlines deployment and integration of workflows with various cloud platforms. Our contributions in this work are as follows 1) we enumerate the cyberinfrastructure challenges of the genomics community that NDN can alleviate, and 2) we describe our efforts in applying NDN for a contemporary genomics workflow (GEMmaker) and quantify the improvements. The preliminary evaluation shows a sixfold speed up in data insertion into the workflow. 3) As a pilot, we have used an NDN naming scheme (agreed upon by the community and discussed in Section 4) to publish data from broadly used data repositories including the NCBI SRA. We have loaded the NDN testbed with these pre-processed genomes that can be accessed over NDN and used by anyone interested in those datasets. Finally, we discuss our continued effort in integrating NDN with cloud computing platforms, such as the Pacific Research Platform (PRP). The reader should note that the goal of this paper is to introduce NDN to the genomics community and discuss NDN’s properties that can benefit the genomics community. We do not present an extensive performance evaluation of NDN—we are working on extending and evaluating our pilot deployment and will present systematic results in a future work.

Description: Verbal communication is an expanding field in robotics showing a significant increase in both the industrial and research field. The application of verbal communication in robotics aims to reach a natural human-like interaction with robots. In this study, we investigated how salient terms related to verbal communication in robotics have evolved over the years, what are the topics that recur in the related literature, and what are their trends. The study is based on a computational linguistic analysis conducted on a database of 7,435 scientific publications over the last 2 decades. This comprehensive dataset was extracted from the Scopus database using specific key-words. Our results show how relevant terms of verbal communication evolved, which are the main coherent topics and how they have changed over the years. We highlighted positive and negative trends for the most coherent topics and the distribution over the years for the most significant ones. In particular, verbal communication resulted in being highly relevant for social robotics. Potentially, achieving natural verbal communication with a robot can have a great impact on the scientific, societal, and economic role of robotics in the future.

Description: The Office of the National Coordinator for Health Information Technology estimates that 96% of all U.S. hospitals use a basic electronic health record, but only 62% are able to exchange health information with outside providers. Barriers to information exchange across EHR systems challenge data aggregation and analysis that hospitals need to evaluate healthcare quality and safety. A growing number of hospital systems are partnering with third-party companies to provide these services. In exchange, companies reserve the rights to sell the aggregated data and analyses produced therefrom, often without the knowledge of patients from whom the data were sourced. Such partnerships fall in a regulatory grey area and raise new ethical questions about whether health, consumer, or health and consumer privacy protections apply. The current opinion probes this question in the context of consumer privacy reform in California. It analyzes protections for health information recently expanded under the California Consumer Privacy Act (“CA Privacy Act”) in 2020 and compares them to protections outlined in the Health Information Portability and Accountability Act (“Federal Privacy Rule”). Four perspectives are considered in this ethical analysis: 1) standards of data deidentification; 2) rights of patients and consumers in relation to their health information; 3) entities covered by the CA Privacy Act; 4) scope and complementarity of federal and state regulations. The opinion concludes that the CCPA is limited in its application when health information is processed by a third-party data aggregation company that is contractually designated as a business associate; when health information is deidentified; and when hospital data are sourced from publicly owned and operated hospitals. Lastly, the opinion offers practical recommendations for facilitating parity between state and federal health data privacy laws and for how a more equitable distribution of informational risks and benefits from the sale of aggregated hospital data could be fostered and presents ways both for-profit and nonprofit hospitals can sustain patient trust when negotiating partnerships with third-party data aggregation companies.

Description: Birth registration is a critical element of newborn care. Increasing the coverage of birth registration is an essential part of the strategy to improve newborn survival globally, and is central to achieving greater health, social, and economic equity as defined under the United Nations Sustainable Development Goals. Parts of Eastern and Southern Africa have some of the lowest birth registration rates in the world. Mobile technologies have been used successfully with mothers and health workers in Africa to increase coverage of essential newborn care, including birth registration. However, mounting concerns about data ownership and data protection in the digital age are driving the search for scalable, user-centered, privacy protecting identity solutions. There is increasing interest in understanding if a self-sovereign identity (SSI) approach can help lower the barriers to birth registration by empowering families with a smartphone based process while providing high levels of data privacy and security in populations where birth registration rates are low. The process of birth registration and the barriers experienced by stakeholders are highly contextual. There is currently a gap in the literature with regard to modeling birth registration using SSI technology. This paper describes the development of a smartphone-based prototype system that allows interaction between families and health workers to carry out the initial steps of birth registration and linkage of mothers-baby pairs in an urban Kenyan setting using verifiable credentials, decentralized identifiers, and the emerging standards for their implementation in identity systems. The goal of the project was to develop a high fidelity prototype that could be used to obtain end-user feedback related to the feasibility and acceptability of an SSI approach in a particular Kenyan healthcare context. This paper will focus on how this technology was adapted for the specific context and implications for future research.

Description: Refurbishment and remanufacturing are the industrial processes whereby used products or parts that constitute the product are restored. Remanufacturing is the process of restoring the functionality of the product or a part of it to “as-new” quality, whereas refurbishment is the process of restoring the product itself or part of it to “like-new” quality, without being as thorough as remanufacturing. Within this context, the EU-funded project RECLAIM presents a new idea on refurbishment and remanufacturing based on big data analytics, machine learning, predictive analytics, and optimization models using deep learning techniques and digital twin models with the aim of enabling the stakeholders to make informed decisions about whether to remanufacture, upgrade, or repair heavy machinery that is toward its end-of-life. The RECLAIM project additionally provides novel strategies and technologies that enable the reuse of industrial equipment in old, renewed, and new factories, with the goal of saving valuable resources by recycling equipment and using them in a different application, instead of discarding them after use. For instance, RECLAIM provides a simulation engine using digital twin in order to predict maintenance needs and potential faults of large industrial equipment. This simulation engine keeps the virtual twins available to store all available information during the lifetime of a machine, such as maintenance operations, and this information can be used to perform an economic estimation of the machine's refurbishment costs. The RECLAIM project envisages developing new technologies and strategies aligned with the circular economy and in support of a new model for the management of large industrial equipment that approaches the end of its design life. This model aims to reduce substantially the opportunity cost of retaining strategies (both moneywise and resourcewise) by allowing relatively old equipment that faces the prospect of decommissioning to reclaim its functionalities and role in the overall production system.

Description: In Ethereum blockchain, smart contracts are immutable, public, and distributed. However, they are subject to many vulnerabilities stemming from coding errors made by developers. Seven cybersecurity incidents occurred in Ethereum smart contracts between 2016 and 2018, which led to financial losses estimated to be over US$ 289 million. Reentrancy vulnerability was the cause of two of these incidents, and the impacts went far beyond financial loss. Several reentrancy countermeasures are available, which are based on predefined patterns that are used to prevent vulnerability exploitation before the deployment of a smart contract; however, several limitations have been identified in these countermeasures. Motivated by all these issues, the objective of this article is to help developers improve the cybersecurity of smart contracts by proposing a solution that calculates the difference between the contract balance and the total balance of all participants in a smart contract before and after any operation in a transaction that changes its state. Proof-of-concept implementations show that this solution can provide a detection and prevention mechanism against reentrancy attacks during the execution of any smart contract.

Description: Background: Sawing of bone is an essential part of an autopsy procedure. An oscillating saw always generates noise, fine infectious dust particles, and the possibility of traumatic injuries, all of which can induce occupational hazard risks to autopsy workers, especially during the COVID-19 pandemic.Objectives: The first goal of this study was to explore the production of noise and bone dust emission, comparing an oscillating saw and a robotic autopsy saw during an autopsy. The second goal was to evaluate the performance of a new robotic autopsy method, used during skull opening. The third goal was to encourage mortuary workers to use robotic technology during the autopsy procedure to protect us away from occupational injuries as well as airborne infections.Materials and Methods: The experiments involved a comparison of noise levels and aerosol production during skull cutting between the oscillating saw and the robotic autopsy saw.Results: The results confirmed that noise production from the robotic autopsy saw was lower than the oscillating saw. However, the bone dust levels, produced by the robotic autopsy saw, were greater than the oscillating saw, but were not greater than the dust concentrations which were present before opening the skull.Conclusions: The use of a new robotic system might be an alternative choice for protecting against occupational damage among the healthcare workers. Further research might attempt to consider other healthcare problems which occur in the autopsy workplace and apply the robotic-assisted technology in autopsy surgery.

Description: Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel. Motivated by advances in whole-brain imaging, we examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. As one of the only species for which neuron-level dynamics can be recorded, C. elegans serves as the ideal organism for designing and testing models bridging recent advances in deep learning and established concepts in neuroscience. We show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, we compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen organisms, implying a potential path to generalizable machine learning in neuroscience.

Description: The development of soft hands is an important progress to empower robotic grasping with passive compliance while greatly decreasing the complexity of control. Despite the advances during the past decades, it is still not clear how to design optimal hands or fingers given the task requirements. In this paper, we propose a framework to learn the optimal design parameter for a fin-ray finger in order to achieve stable grasping. First, the pseudo-kinematics of the soft finger is learned in simulation. Second, the task constraints are encoded as a combination of desired grasping force and the empirical grasping quality function in terms of winding number. Finally, the effectiveness of the proposed approach is validated with experiments in simulation and using real-world examples as well.

Description: Anxiety and stress are prominent issues for the adolescent population. Physical activity is known to reduce symptoms of anxiety and stress; however, many adolescents lack the time or motivation to exercise regularly, particularly during stressful exam weeks. Virtual Reality (VR) has the potential to make exercise more enjoyable and more engaging than exercise alone. We aimed to investigate the immediate effect of a 10-min dodgeball exercise session, with and without a VR headset, on self-reported stress, anxiety and cognitive performance in adolescents during times known to induce stress in high school, such as exam weeks. Participants were randomly assigned to a VR group (n = 16) where participants were immersed in a virtual dodgeball environment (exergame), or a dodgeball group (n = 14) which played a simple game of one-on-one dodgeball. Executive function was measured using the Trail Making Test (TMT) Parts A and B. Anxiety was self-reported on the Pediatric Anxiety Short Form 8a (PASF). Stress was self-reported on the Psychological Stress Experiences-Short Form 8a (PSES). Both groups significantly improved their TMT A and B performance and reduced stress and anxiety scores with effect size ranging from 0.59 to 1.2 (main effect of time p 〈 0.001 for all outcomes). There were no significant differences between groups and no time by group interaction for any outcome. A short bout of exercise, with or without VR, during stressful high school exam weeks was shown to be effective for immediate reduction of stress and anxiety and enhancement of cognitive function in a small sample of high school students. High schools looking to apply interventions to help their students manage anxiety and stress should consider encouraging them to take a “time-out” to exercise and play. The cost-effectiveness of exergames inside the school settings and implications for academic success should be investigated in future research.

Description: Concerns for spectrum congestion have spurred extensive research efforts on efficient spectrum management. Therefore, devising schemes for spectrum sharing between radar and wireless communication systems has become an important area of research. Joint communications-radar (JCR) systems are among the several approaches proposed to achieve this objective. In JCR systems, additional components and processes are added to an existing standardized communication platform to enable radar functions. Moreover, the communication waveform is used as an integrated JCR waveform, i.e., the same signal is used to communicate information to a receiver and to perform radar detection and estimation operations for a nearby target. The most common application of JCR systems is found in vehicle-to-vehicle (V2V) communication scenarios. In this article, an overview of the spectrum sharing methods is presented, with a focus on JCR systems in automotive and other applications. We first review the recent works on IEEE 802.11p- and IEEE 802.11ad-based radars. A basic description of the modeling of a JCR system and channels is presented, followed by discussions on the main components and processes employed in various JCR systems. We are mainly interested in how radar detection and estimation functions are performed in conjunction with the communication receiver functions with minimal alterations to the existing system. At the end of the paper, some performance trade-offs between the communication and radar sub-systems are also discussed.

Description: To ensure the continuity of healthcare and to counter the spread of the COVID-19 pandemic, doctors and nursing staff at hospitals must face an insidious, invisible danger that is stretching the healthcare system far past its capacity. Excessive workload, inadequate protection from contamination, the need to manage patients experiencing extreme suffering and being kept apart from their families put medical personnel at high risk to experience stress and anxiety. Numerous scientific studies have shown that, among various therapeutic programs, virtual reality represents a highly specialized and effective tool for the prevention and treatment of stress and anxiety. However, the solutions developed using this technology for the management of stress and anxiety induced by the COVID-19 pandemic are still very limited, and none of these have been developed specifically for use with healthcare professionals. Therefore, this paper will detail the design and evaluation protocol of MIND-VR, a virtual reality-based psychoeducational experience on stress and anxiety developed following a user-centered design approach. The virtual experience will be tested on a sample of Italian hospital healthcare personnel involved in the COVID-19 pandemic emergency. MIND-VR is available free of charge, both in Italian and English, on the project website (https://mind-vr.com/).

Description: Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the “music mainstream” strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervized learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user’s country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-à-vis state-of-the-art algorithms that do not exploit this type of context information.

Description: Purpose: To assess image quality and uncertainty in organ-at-risk segmentation on cone beam computed tomography (CBCT) enhanced by deep-learning convolutional neural network (DCNN) for head and neck cancer.Methods: An in-house DCNN was trained using forty post-operative head and neck cancer patients with their planning CT and first-fraction CBCT images. Additional fifteen patients with repeat simulation CT (rCT) and CBCT scan taken on the same day (oCBCT) were used for validation and clinical utility assessment. Enhanced CBCT (eCBCT) images were generated from the oCBCT using the in-house DCNN. Quantitative imaging quality improvement was evaluated using HU accuracy, signal-to-noise-ratio (SNR), and structural similarity index measure (SSIM). Organs-at-risk (OARs) were delineated on o/eCBCT and compared with manual structures on the same day rCT. Contour accuracy was assessed using dice similarity coefficient (DSC), Hausdorff distance (HD), and center of mass (COM) displacement. Qualitative assessment of users’ confidence in manual segmenting OARs was performed on both eCBCT and oCBCT by visual scoring.Results: eCBCT organs-at-risk had significant improvement on mean pixel values, SNR (p 〈 0.05), and SSIM (p 〈 0.05) compared to oCBCT images. Mean DSC of eCBCT-to-rCT (0.83 ± 0.06) was higher than oCBCT-to-rCT (0.70 ± 0.13). Improvement was observed for mean HD of eCBCT-to-rCT (0.42 ± 0.13 cm) vs. oCBCT-to-rCT (0.72 ± 0.25 cm). Mean COM was less for eCBCT-to-rCT (0.28 ± 0.19 cm) comparing to oCBCT-to-rCT (0.44 ± 0.22 cm). Visual scores showed OAR segmentation was more accessible on eCBCT than oCBCT images.Conclusion: DCNN improved fast-scan low-dose CBCT in terms of the HU accuracy, image contrast, and OAR delineation accuracy, presenting potential of eCBCT for adaptive radiotherapy.

Description: The visual design of antagonists—typically thought of as “bad guys”—is crucial for game design. Antagonists are key to providing the backdrop to a game's setting and motivating a player's actions. The visual representation of antagonists is important because it affects player expectations about the character's personality and potential actions. Particularly important is how players perceive an antagonist's morality. For example, an antagonist appearing disloyal might foreshadow betrayal; a character who looks cruel suggests that tough fights are ahead; or, a player might be surprised when a friendly looking character attacks them. Today, the art of designing character morality is informed by archetypal elements, existing characters, and the artist's own background. However, little work has provided insight into how an antagonist's appearance can lead players to make moral judgments. Using Mechanical Turk, we collected participant ratings on a stimulus image set of 105 antagonists from popular video games. The results of our work provide insights into how the visual attributes of antagonists can influence judgments of character morality. Our findings provide a valuable new lens for understanding and deepening an important aspect of game design. Our results can be used to help ensure that a particular character design has the best chance to be universally seen as “evil,” or to help create more complex and conflicted emotional experiences through carefully designed characters that do not appear to be bad. Our research extends current research practices that seek to build an understanding of game design and provides exciting new directions for exploring how design and aesthetic practices can be better studied and supported.

Description: We present UnrealHaptics, a plugin-architecture that enables advanced virtual reality (VR) interactions, such as haptics or grasping in modern game engines. The core is a combination of a state-of-the-art collision detection library with support for very fast and stable force and torque computations and a general device plugin for communication with different input/output hardware devices, such as haptic devices or Cybergloves. Our modular and lightweight architecture makes it easy for other researchers to adapt our plugins to their requirements. We prove the versatility of our plugin architecture by providing two use cases implemented in the Unreal Engine 4 (UE4). In the first use case, we have tested our plugin with a haptic device in different test scenes. For the second use case, we show a virtual hand grasping an object with precise collision detection and handling multiple contacts. We have evaluated the performance in our use cases. The results show that our plugin easily meets the requirements of stable force rendering at 1 kHz for haptic rendering even in highly non-convex scenes, and it can handle the complex contact scenarios of virtual grasping.

Description: Large amounts of labeled data are a prerequisite to training accurate and reliable machine learning models. However, in the medical domain in particular, this is also a stumbling block as accurately labeled data are hard to obtain. DementiaBank, a publicly available corpus of spontaneous speech samples from a picture description task widely used to study Alzheimer's disease (AD) patients' language characteristics and for training classification models to distinguish patients with AD from healthy controls, is relatively small—a limitation that is further exacerbated when restricting to the balanced subset used in the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) challenge. We build on previous work showing that the performance of traditional machine learning models on DementiaBank can be improved by the addition of normative data from other sources, evaluating the utility of such extrinsic data to further improve the performance of state-of-the-art deep learning based methods on the ADReSS challenge dementia detection task. To this end, we developed a new corpus of professionally transcribed recordings from the Wisconsin Longitudinal Study (WLS), resulting in 1366 additional Cookie Theft Task transcripts, increasing the available training data by an order of magnitude. Using these data in conjunction with DementiaBank is challenging because the WLS metadata corresponding to these transcripts do not contain dementia diagnoses. However, cognitive status of WLS participants can be inferred from results of several cognitive tests including semantic verbal fluency available in WLS data. In this work, we evaluate the utility of using the WLS ‘controls’ (participants without indications of abnormal cognitive status), and these data in conjunction with inferred ‘cases’ (participants with such indications) for training deep learning models to discriminate between language produced by patients with dementia and healthy controls. We find that incorporating WLS data during training a BERT model on ADReSS data improves its performance on the ADReSS dementia detection task, supporting the hypothesis that incorporating WLS data adds value in this context. We also demonstrate that weighted cost functions and additional prediction targets may be effective ways to address issues arising from class imbalance and confounding effects due to data provenance.

Description: Medical training simulators have the potential to provide remote and automated assessment of skill vital for medical training. Consequently, there is a need to develop “smart” training devices with robust metrics that can quantify clinical skills for effective training and self-assessment. Recently, metrics that quantify motion smoothness such as log dimensionless jerk (LDLJ) and spectral arc length (SPARC) are increasingly being applied in medical simulators. However, two key questions remain about the efficacy of such metrics: how do these metrics relate to clinical skill, and how to best compute these metrics from sensor data and relate them with similar metrics? This study addresses these questions in the context of hemodialysis cannulation by enrolling 52 clinicians who performed cannulation in a simulated arteriovenous (AV) fistula. For clinical skill, results demonstrate that the objective outcome metric flash ratio (FR), developed to measure the quality of task completion, outperformed traditional skill indicator metrics (years of experience and global rating sheet scores). For computing motion smoothness metrics for skill assessment, we observed that the lowest amount of smoothing could result in unreliable metrics. Furthermore, the relative efficacy of motion smoothness metrics when compared with other process metrics in correlating with skill was similar for FR, the most accurate measure of skill. These results provide guidance for the computation and use of motion-based metrics for clinical skill assessment, including utilizing objective outcome metrics as ideal measures for quantifying skill.

Description: The massive deployment of advanced wireless networks is essential to support broadband connectivity, low latency communication, and Internet of Things applications. Nevertheless, in the time of coronavirus disease (COVID-19) there is a massive amount of misinformation and uncertainty about the impact of fifth-generation cellular network (5G) networks on human health. In this paper, we investigate the main categories of misinformation regarding 5G, i.e., fake theories, the misconception of 5G features, and open questions that require further research. Then, we propose two novel approaches for the design of electromagnetic field (EMF)-aware cellular networks that can reduce human exposure to radio frequency radiation.

Description: Over the decades, fashions in Computational Linguistics have changed again and again, with major shifts in motivations, methods and applications. When digital computers first appeared, linguistic analysis adopted the new methods of information theory, which accorded well with the ideas that dominated psychology and philosophy. Then came formal language theory and the idea of AI as applied logic, in sync with the development of cognitive science. That was followed by a revival of 1950s-style empiricism—AI as applied statistics—which in turn was followed by the age of deep nets. There are signs that the climate is changing again, and we offer some thoughts about paths forward, especially for younger researchers who will soon be the leaders.

Description: Chronic conditions represent a significant twenty first century challenge. Education and self-management training are the mainstay of clinical intervention for such conditions since care is dependent on health literacy and self-management. This intervention not only imparts the necessary understanding and skills for self-management, but also helps people to overcome personal barriers to positive behavioral change, such as low self-efficacy. Moreover, education maximizes dignity, by enabling shared decision-making. A plethora of research supports the role of education and self-management training in the management of chronic conditions, whilst at the same time highlighting that not all approaches lead to meaningful behavioral change. Immersive virtual reality (VR) offers a unique set of features and tools for delivering these interventions. For example, the immersive nature focuses attention and promotes engagement; the ability to simulate authentic and interactive real-world scenarios can be used to promote the benefits of active learning; and the ability to facilitate embodiment of avatars with distinct appearance and capability can be used to bias new perceptions and behaviors in-line with the avatar's characteristics. Moreover, the ability to use VR independent of a clinician renders a potential solution to instances where significant barriers to healthcare access exist. This short perspective paper will discuss how VR may be used to host education and self-management interventions in the domain of chronic condition management. Further, it will outline considerations for developers and conclude with a call for the co-creation of new VR-based education and self-management interventions.

Description: Introduction: Patients boarding in the Emergency Department can contribute to overcrowding, leading to longer waiting times and patients leaving without being seen or completing their treatment. The early identification of potential admissions could act as an additional decision support tool to alert clinicians that a patient needs to be reviewed for admission and would also be of benefit to bed managers in advance bed planning for the patient. We aim to create a low-dimensional model predicting admissions early from the paediatric Emergency Department.Methods and Analysis: The methodology Cross Industry Standard Process for Data Mining (CRISP-DM) will be followed. The dataset will comprise of 2 years of data, ~76,000 records. Potential predictors were identified from previous research, comprising of demographics, registration details, triage assessment, hospital usage and past medical history. Fifteen models will be developed comprised of 3 machine learning algorithms (Logistic regression, naïve Bayes and gradient boosting machine) and 5 sampling methods, 4 of which are aimed at addressing class imbalance (undersampling, oversampling, and synthetic oversampling techniques). The variables of importance will then be identified from the optimal model (selected based on the highest Area under the curve) and used to develop an additional low-dimensional model for deployment.Discussion: A low-dimensional model comprised of routinely collected data, captured up to post triage assessment would benefit many hospitals without data rich platforms for the development of models with a high number of predictors. Novel to the planned study is the use of data from the Republic of Ireland and the application of sampling techniques aimed at improving model performance impacted by an imbalance between admissions and discharges in the outcome variable.

Description: In comparison to field crops such as cereals, cotton, hay and grain, specialty crops often require more resources, are usually more sensitive to sudden changes in growth conditions and are known to produce higher value products. Providing quality and quantity assessment of specialty crops during harvesting is crucial for securing higher returns and improving management practices. Technical advancements in computer and machine vision have improved the detection, quality assessment and yield estimation processes for various fruit crops, but similar methods capable of exporting a detailed yield map for vegetable crops have yet to be fully developed. A machine vision-based yield monitor was designed to perform size categorization and continuous counting of shallots in-situ during the harvesting process. Coupled with a software developed in Python, the system is composed of a video logger and a global navigation satellite system. Computer vision analysis is performed within the tractor while an RGB camera collects real-time video data of the crops under natural sunlight conditions. Vegetables are first segmented using Watershed segmentation, detected on the conveyor, and then classified by size. The system detected shallots in a subsample of the dataset with a precision of 76%. The software was also evaluated on its ability to classify the shallots into three size categories. The best performance was achieved in the large class (73%), followed by the small class (59%) and medium class (44%). Based on these results, the occasional occlusion of vegetables and inconsistent lighting conditions were the main factors that hindered performance. Although further enhancements are envisioned for the prototype system, its modular and novel design permits the mapping of a selection of other horticultural crops. Moreover, it has the potential to benefit many producers of small vegetable crops by providing them with useful harvest information in real-time.

Description: Introduction: Prognostic scores are important tools in oncology to facilitate clinical decision-making based on patient characteristics. To date, classic survival analysis using Cox proportional hazards regression has been employed in the development of these prognostic scores. With the advance of analytical models, this study aimed to determine if more complex machine-learning algorithms could outperform classical survival analysis methods.Methods: In this benchmarking study, two datasets were used to develop and compare different prognostic models for overall survival in pan-cancer populations: a nationwide EHR-derived de-identified database for training and in-sample testing and the OAK (phase III clinical trial) dataset for out-of-sample testing. A real-world database comprised 136K first-line treated cancer patients across multiple cancer types and was split into a 90% training and 10% testing dataset, respectively. The OAK dataset comprised 1,187 patients diagnosed with non-small cell lung cancer. To assess the effect of the covariate number on prognostic performance, we formed three feature sets with 27, 44 and 88 covariates. In terms of methods, we benchmarked ROPRO, a prognostic score based on the Cox model, against eight complex machine-learning models: regularized Cox, Random Survival Forests (RSF), Gradient Boosting (GB), DeepSurv (DS), Autoencoder (AE) and Super Learner (SL). The C-index was used as the performance metric to compare different models.Results: For in-sample testing on the real-world database the resulting C-index [95% CI] values for RSF 0.720 [0.716, 0.725], GB 0.722 [0.718, 0.727], DS 0.721 [0.717, 0.726] and lastly, SL 0.723 [0.718, 0.728] showed significantly better performance as compared to ROPRO 0.701 [0.696, 0.706]. Similar results were derived across all feature sets. However, for the out-of-sample validation on OAK, the stronger performance of the more complex models was not apparent anymore. Consistently, the increase in the number of prognostic covariates did not lead to an increase in model performance.Discussion: The stronger performance of the more complex models did not generalize when applied to an out-of-sample dataset. We hypothesize that future research may benefit by adding multimodal data to exploit advantages of more complex models.

Description: Cranes are widely used in the field of construction, logistics, and the manufacturing industry. Cranes that use wire ropes as the main lifting mechanism are deeply troubled by the swaying of heavy objects, which seriously restricts the working efficiency of the crane and even cause accidents. Compared with the single-pendulum crane, the double-pendulum effect crane model has stronger nonlinearity, and its controller design is challenging. In this paper, cranes with a double-pendulum effect are considered, and their nonlinear dynamical models are established. Then, a controller based on the radial basis function (RBF) neural network compensation adaptive method is designed, and a stability analysis is also presented. Finally, the hardware-in-the-loop experimental results show that the neural network compensation control can effectively improve the control performance of the controller in practice.