ALBERT

Description: As difficult vision-based tasks like object detection and monocular depth estimation are making their way in real-time applications and as more light weighted solutions for autonomous vehicles navigation systems are emerging, obstacle detection and collision prediction are two very challenging tasks for small embedded devices like drones. We propose a novel light weighted and time-efficient vision-based solution to predict Time-to-Collision from a monocular video camera embedded in a smartglasses device as a module of a navigation system for visually impaired pedestrians. It consists of two modules: a static data extractor made of a convolutional neural network to predict the obstacle position and distance and a dynamic data extractor that stacks the obstacle data from multiple frames and predicts the Time-to-Collision with a simple fully connected neural network. This paper focuses on the Time-to-Collision network’s ability to adapt to new sceneries with different types of obstacles with supervised learning.

Description: Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.

Description: Schemes generating cryptographic keys from arrays of pre-formed Resistive Random Access (ReRAM) cells, called memristors, can also be used for the design of fast true random number generators (TRNG’s) of exceptional quality, while consuming low levels of electric power. Natural randomness is formed in the large stochastic cell-to-cell variations in resistance values at low injected currents in the pre-formed range. The proposed TRNG scheme can be designed with three interconnected blocks: (i) a pseudo-random number generator that acts as an extended output function to generate a stream of addresses pointing randomly at the array of ReRAM cells; (ii) a method to read the resistance values of these cells with a low injected current, and to convert the values into a stream of random bits; and, if needed, (iii) a method to further enhance the randomness of this stream such as mathematical, Boolean, and cryptographic algorithms. The natural stochastic properties of the ReRAM cells in the pre-forming range, at low currents, have been analyzed and demonstrated by measuring a statistically significant number of cells. Various implementations of the TRNGs with ReRAM arrays are presented in this paper.

Description: Many low-cost platforms that support floating-point arithmetic, such as microcontrollers and field-programmable gate arrays, do not include fast hardware or software methods for calculating the square root and/or reciprocal square root. Typically, such functions are implemented using direct lookup tables or polynomial approximations, with a subsequent application of the Newton–Raphson method. Other, more complex solutions include high-radix digit-recurrence and bipartite or multipartite table-based methods. In contrast, this article proposes a simple modification of the fast inverse square root method that has high accuracy and relatively low latency. Algorithms are given in C/C++ for single- and double-precision numbers in the IEEE 754 format for both square root and reciprocal square root functions. These are based on the switching of magic constants in the initial approximation, depending on the input interval of the normalized floating-point numbers, in order to minimize the maximum relative error on each subinterval after the first iteration—giving 13 correct bits of the result. Our experimental results show that the proposed algorithms provide a fairly good trade-off between accuracy and latency after two iterations for numbers of type float, and after three iterations for numbers of type double when using fused multiply–add instructions—giving almost complete accuracy.

Description: The Disjoint Connecting Paths problem and its capacitated generalization, called Unsplittable Flow problem, play an important role in practical applications such as communication network design and routing. These tasks are NP-hard in general, but various polynomial-time approximations are known. Nevertheless, the approximations tend to be either too loose (allowing large deviation from the optimum), or too complicated, often rendering them impractical in large, complex networks. Therefore, our goal is to present a solution that provides a relatively simple, efficient algorithm for the unsplittable flow problem in large directed graphs, where the task is NP-hard, and is known to remain NP-hard even to approximate up to a large factor. The efficiency of our algorithm is achieved by sacrificing a small part of the solution space. This also represents a novel paradigm for approximation. Rather than giving up the search for an exact solution, we restrict the solution space to a subset that is the most important for applications, and excludes only a small part that is marginal in some well-defined sense. Specifically, the sacrificed part only contains scenarios where some edges are very close to saturation. Since nearly saturated links are undesirable in practical applications, therefore, excluding near saturation is quite reasonable from the practical point of view. We refer the solutions that contain no nearly saturated edges as safe solutions, and call the approach safe approximation. We prove that this safe approximation can be carried out efficiently. That is, once we restrict ourselves to safe solutions, we can find the exact optimum by a randomized polynomial time algorithm.

Description: With the progressive deterioration of cyber threats, collecting cyber threat intelligence (CTI) from open-source threat intelligence publishing platforms (OSTIPs) can help information security personnel grasp public opinions with specific pertinence, handle emergency events, and even confront the advanced persistent threats. However, due to the explosive growth of information shared on multi-type OSTIPs, manually collecting the CTI has had low efficiency. Articles published on the OSTIPs are unstructured, leading to an imperative challenge to automatically gather CTI records only through natural language processing (NLP) methods. To remedy these limitations, this paper proposes an automatic approach to generate the CTI records based on multi-type OSTIPs (GCO), combing the NLP method, machine learning method, and cybersecurity threat intelligence knowledge. The experiment results demonstrate that the proposed GCO outperformed some state-of-the-art approaches on article classification and cybersecurity intelligence details (CSIs) extraction, with accuracy, precision, and recall all over 93%; finally, the generated records in the Neo4j-based CTI database can help reveal malicious threat groups.

Description: Since the inception of the Internet of Things (IoT), we have adopted centralized architecture for decades. With the vastly growing number of IoT devices and gateways, this architecture struggles to cope with the high demands of state-of-the-art IoT services, which require scalable and responsive infrastructure. In response, decentralization becomes a considerable interest among IoT adopters. Following a similar trajectory, this paper introduces an IoT architecture re-work that enables three spheres of IoT workflows (i.e., computing, storage, and networking) to be run in a distributed manner. In particular, we employ the blockchain and smart contract to provide a secure computing platform. The distributed storage network maintains the saving of IoT raw data and application data. The software-defined networking (SDN) controllers and SDN switches exist in the architecture to provide connectivity across multiple IoT domains. We envision all of those services in the form of separate yet integrated peer-to-peer (P2P) overlay networks, which IoT actors such as IoT domain owners, IoT users, Internet Service Provider (ISP), and government can cultivate. We also present several IoT workflow examples showing how IoT developers can adapt to this new proposed architecture. Based on the presented workflows, the IoT computing can be performed in a trusted and privacy-preserving manner, the IoT storage can be made robust and verifiable, and finally, we can react to the network events automatically and quickly. Our discussions in this paper can be beneficial for many people ranging from academia, industries, and investors that are interested in the future of IoT in general.

Description: The accurate of i identificationntrinsically disordered proteins or protein regions is of great importance, as they are involved in critical biological process and related to various human diseases. In this paper, we develop a deep neural network that is based on the well-known VGG16. Our deep neural network is then trained through using 1450 proteins from the dataset DIS1616 and the trained neural network is tested on the remaining 166 proteins. Our trained neural network is also tested on the blind test set R80 and MXD494 to further demonstrate the performance of our model. The MCC value of our trained deep neural network is 0.5132 on the test set DIS166, 0.5270 on the blind test set R80 and 0.4577 on the blind test set MXD494. All of these MCC values of our trained deep neural network exceed the corresponding values of existing prediction methods.

Description: In this study, we address the problem originated from the fact that “The Corona 19 Epidemiological Research Support System,” developed by the Korea Centers for Disease Control and Prevention, is limited to analyzing the Global Positioning System (GPS) information of the confirmed COVID-19 cases alone. Consequently, we study a method that the authority predicts the transmission route of COVID-19 between visitors in the community from a spatiotemporal perspective. This method models a contact network around the first confirmed case, allowing the health authorities to conduct tests on visitors after an outbreak of COVID-19 in the community. After securing the GPS data of community visitors, it traces back to the past from the time the first confirmed case occurred and creates contact clusters at each time step. This is different from other researches that focus on identifying the movement paths of confirmed patients by forward tracing. The proposed method creates the contact network by assigning weights to each contact cluster based on the degree of proximity between contacts. Identifying the source of infection in the contact network can make us predict the transmission route between the first confirmed case and the source of infection and classify the contacts on the transmission route. In this experiment, we used 64,073 simulated data for 100 people and extracted the transmission route and a top 10 list for centrality analysis. The contacts on the route path can be quickly designated as a priority for COVID-19 testing. In addition, it is possible for the authority to extract the subjects with high influence from the centrality theory and use them for additional COVID-19 epidemic investigation that requires urgency. This model is expected to be used in the epidemic investigation requiring the quick selection of close contacts.

Description: Beach erosion is a natural phenomenon that is not compensated by depositing fresh material on the shoreline while transporting sand away from the shoreline. There are three phenomena that have a serious influence on the coastal structure, such as increases in flooding, accretion, and water levels. In addition, the prediction of coastal evolution is used to investigate the topography of the beach. In this research, we present a one-dimensional mathematical model of shoreline evolution, and the parameters that influence this model are described on a monthly basis over a period of one year. Consideration is given to the wave crest impact model for evaluating the impact of the wave crest at that stage. It focuses on the evolution of the shoreline in environments where groins are installed on both sides. The initial and boundary condition setting techniques are proposed by the groins and their environmental parameters. The non-uniform influence of the crest of the breaking wave is so often considered. We then used the traditional forward time centered space technique and the Saulyev finite difference technique to estimate the monthly evolution of the shoreline for each year.

Description: Image denoising is a challenging research problem that aims to recover noise-free images from those that are contaminated with noise. In this paper, we focus on the denoising of images that are contaminated with additive white Gaussian noise. For this purpose, we propose an ensemble learning model that uses the output of three image denoising models, namely ADNet, IRCNN, and DnCNN, in the ratio of 2:3:6, respectively. The first model (ADNet) consists of Convolutional Neural Networks with attention along with median filter layers after every convolutional layer and a dilation rate of 8. In the case of the second model, it is a feed forward denoising CNN or DnCNN with median filter layers after half of the convolutional layers. For the third model, which is Deep CNN Denoiser Prior or IRCNN, the model contains dilated convolutional layers and median filter layers up to the dilated convolutional layers with a dilation rate of 6. By quantitative analysis, we note that our model performs significantly well when tested on the BSD500 and Set12 datasets.

Description: The optimization of bus scheduling is a key method to improve bus service. So, the purpose of this paper is to address the regional public transportation dispatching problem, while taking into account the association between the departure time of buses and the waiting time of passengers. A bi-objective optimization model for regional public transportation scheduling is established to minimize the total waiting cost of passengers and to maximize the comprehensive service rate of buses. Moreover, a NSGA-II algorithm with adaptive adjusted model for crossover and mutation probability is designed to obtain the Pareto solution set of this problem, and the entropy weight-TOPSIS method is utilized to make a decision. Then the algorithms are compared with examples, and the results show that the model is feasible, and the proposed algorithms are achievable in solving the regional public transportation scheduling problem.

Description: A detailed knowledge of the influence of a particle’s shape on its settling behavior is useful for the prediction and design of separation processes. Models in the available literature usually fit a given function to experimental data. In this work, a constructive and data-driven approach is presented to obtain new drag correlations. To date, the only considered shape parameters are derivatives of the axis lengths and the sphericity. This does not cover all relevant effects, since the process of settling for arbitrarily shaped particles is highly complex. This work extends the list of considered parameters by, e.g., convexity and roundness and evaluates the relevance of each. The aim is to find models describing the drag coefficient and settling velocity, based on this extended set of shape parameters. The data for the investigations are obtained by surface resolved simulations of superellipsoids, applying the homogenized lattice Boltzmann method. To closely study the influence of shape, the particles considered are equal in volume, and therefore cover a range of Reynolds numbers, limited to [9.64, 22.86]. Logistic and polynomial regressions are performed and the quality of the models is investigated with further statistical methods. In addition to the usually studied relation between drag coefficient and Reynolds number, the dependency of the terminal settling velocity on the shape parameters is also investigated. The found models are, with an adjusted coefficient of determination of 0.96 and 0.86, in good agreement with the data, yielding a mean deviation below 5.5% on the training and test dataset.

Description: This research explores the factors that influence students’ continuous usage intention regarding online learning platforms from the perspectives of social capital, perceived usefulness, and perceived ease of use. The questionnaire survey method was used in the research to analyze the relationship between the research variables and verify the hypothesis based on data from 248 collected valid questionnaire responses. The following results were obtained: (1) “Social interaction ties” positively affect students’ continuous usage intention. (2) “Shared language” negatively affects students’ continuous usage intention. (3) “Shared vision” positively affects students’ continuous usage intention. (4) “Perceived usefulness” positively affects students’ continuous usage intention. (5) “Perceived ease of use” positively affects students’ continuous usage intention. According to the results, students believe in useful teaching that promotes knowledge and skills. The ease of use of learning tools is key to whether they can learn successfully. Paying attention to the interaction and communication between students, so that students have a shared goal and participate in teamwork, is something that teachers must pay attention to in the course of operation. The professional vocabulary of the teaching content and the way of announcing information should avoid using difficult terminology, which is also a point to which teachers need to pay attention.

Description: Cicerone and Di Stefano defined and studied the class of k-distance-hereditary graphs, i.e., graphs where the distance in each connected induced subgraph is at most k times the distance in the whole graph. The defined graphs represent a generalization of the well known distance-hereditary graphs, which actually correspond to 1-distance-hereditary graphs. In this paper we make a step forward in the study of these new graphs by providing characterizations for the class of all the k-distance-hereditary graphs such that k

Description: This study aims to investigate the role of several aspects that may influence human–robot interaction in assistive scenarios. Among all, we focused on semi-permanent qualities (i.e., personality and cognitive state) and temporal traits (i.e., emotion and engagement) of the user profile. To this end, we organized an experimental session with 11 elderly users who performed a cognitive assessment with the non-humanoid ASTRO robot. ASTRO robot administered the Mini Mental State Examination test in Wizard of Oz setup. Temporal and long-term qualities of each user profile were assessed by self-report questionnaires and by behavioral features extrapolated by the recorded videos. Results highlighted that the quality of the interaction did not depend on the cognitive state of the participants. On the contrary, the cognitive assessment with the robot significantly reduced the anxiety of the users, by enhancing the trust in the robotic entity. It suggests that the personality and the affect traits of the interacting user have a fundamental influence on the quality of the interaction, also in the socially assistive context.

Description: Multi-facility location problem is a type of task often solved (not only) in logistics. It consists in finding the optimal location of the required number of centers for a given number of points. One of the possible solutions is to use the principle of the genetic algorithm. The Solver add-in, which uses the evolutionary method, is available in the Excel office software. It was used to solve the benchmark in 4 levels of difficulty (from 5 centers for 25 points to 20 centers for 100 points), and one task from practice. The obtained results were compared with the results obtained by the metaheuristic simulated annealing method. It was found that the results obtained by the evolutionary method are sufficiently accurate. Their accuracy depends on the complexity of the task and the performance of the HW used. The advantage of the proposed solution is easy availability and minimal requirements for user knowledge.

Description: As a crucial task in surveillance and security, person re-identification (re-ID) aims to identify the targeted pedestrians across multiple images captured by non-overlapping cameras. However, existing person re-ID solutions have two main challenges: the lack of pedestrian identification labels in the captured images, and domain shift issue between different domains. A generative adversarial networks (GAN)-based self-training framework with progressive augmentation (SPA) is proposed to obtain the robust features of the unlabeled data from the target domain, according to the preknowledge of the labeled data from the source domain. Specifically, the proposed framework consists of two stages: the style transfer stage (STrans), and self-training stage (STrain). First, the targeted data is complemented by a camera style transfer algorithm in the STrans stage, in which CycleGAN and Siamese Network are integrated to preserve the unsupervised self-similarity (the similarity of the same image between before and after transformation) and domain dissimilarity (the dissimilarity between a transferred source image and the targeted image). Second, clustering and classification are alternately applied to enhance the model performance progressively in the STrain stage, in which both global and local features of the target-domain images are obtained. Compared with the state-of-the-art methods, the proposed method achieves the competitive accuracy on two existing datasets.

Description: Structural and metabolic imaging are fundamental for diagnosis, treatment and follow-up in oncology. Beyond the well-established diagnostic imaging applications, ultrasounds are currently emerging in the clinical practice as a noninvasive technology for therapy. Indeed, the sound waves can be used to increase the temperature inside the target solid tumors, leading to apoptosis or necrosis of neoplastic tissues. The Magnetic resonance-guided focused ultrasound surgery (MRgFUS) technology represents a valid application of this ultrasound property, mainly used in oncology and neurology. In this paper; patient safety during MRgFUS treatments was investigated by a series of experiments in a tissue-mimicking phantom and performing ex vivo skin samples, to promptly identify unwanted temperature rises. The acquired MR images, used to evaluate the temperature in the treated areas, were analyzed to compare classical proton resonance frequency (PRF) shift techniques and referenceless thermometry methods to accurately assess the temperature variations. We exploited radial basis function (RBF) neural networks for referenceless thermometry and compared the results against interferometric optical fiber measurements. The experimental measurements were obtained using a set of interferometric optical fibers aimed at quantifying temperature variations directly in the sonication areas. The temperature increases during the treatment were not accurately detected by MRI-based referenceless thermometry methods, and more sensitive measurement systems, such as optical fibers, would be required. In-depth studies about these aspects are needed to monitor temperature and improve safety during MRgFUS treatments.

Description: Visible Light Communication (VLC) has been emerging as a promising technology to address the increasingly high data-rate and time-critical demands that the Internet of Things (IoT) and 5G paradigms impose on the underlying Wireless Sensor Actuator Networking (WSAN) technologies. In this line, the IEEE 802.15.7 standard proposes several physical layers and Medium Access Control (MAC) sub-layer mechanisms that support a variety of VLC applications. Particularly, at the MAC sub-layer, it can support contention-free communications using Guaranteed Timeslots (GTS), introducing support for time-critical applications. However, to effectively guarantee accurate usage of such functionalities, it is vital to derive the worst-case bounds of the network. In this paper, we use network calculus to carry out the worst-case bounds analysis for GTS utilization of IEEE 802.15.7 and complement our model with an in-depth performance analysis. We also propose the inclusion of an additional mechanism to improve the overall scalability and effective bandwidth utilization of the network.

Description: Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.

Description: Societies are entering the age of technological disruption, which also impacts governance institutions such as parliamentary organizations. Thus, parliaments need to adjust swiftly by incorporating innovative methods into their organizational culture and novel technologies into their working procedures. Inter-Parliamentary Union World e-Parliament Reports capture digital transformation trends towards open data production, standardized and knowledge-driven business processes, and the implementation of inclusive and participatory schemes. Nevertheless, there is still a limited consensus on how these trends will materialize into specific tools, products, and services, with added value for parliamentary and societal stakeholders. This article outlines the rapid evolution of the digital parliament from the user perspective. In doing so, it describes a transformational framework based on the evaluation of empirical data by an expert survey of parliamentarians and parliamentary administrators. Basic sets of tools and technologies that are perceived as vital for future parliamentary use by intra-parliamentary stakeholders, such as systems and processes for information and knowledge sharing, are analyzed. Moreover, boundary conditions for development and implementation of parliamentary technologies are set and highlighted. Concluding recommendations regarding the expected investments, interdisciplinary research, and cross-sector collaboration within the defined framework are presented.

Description: This article presents the smoothed shock filter, which iteratively produces local segmentations in image’s inflection zones with smoothed morphological operators (dilations, erosions). Hence, it enhances contours by creating smoothed ruptures, while preserving homogeneous regions. After describing the algorithm, we show that it is a robust approach for denoising, compared to related works. Then, we expose how we exploited this filter as a pre-processing step in different image analysis tasks (medical image segmentation, fMRI, and texture classification). By means of its ability to enhance important patterns in images, the smoothed shock filter has a real positive impact upon such applications, for which we would like to explore it more in the future.

Description: With Electric Vehicles (EV) emerging as the dominant form of green transport in the UK, it is critical that we better understand existing infrastructures in place to support the uptake of these vehicles. In this multi-disciplinary paper, we demonstrate a novel end-to-end workflow using deep learning to perform automated surveys of urban areas to identify residential properties suitable for EV charging. A unique dataset comprised of open source Google Street View images was used to train and compare three deep neural networks and represents the first attempt to classify residential driveways from streetscape imagery. We demonstrate the full system workflow on two urban areas and achieve accuracies of 87.2% and 89.3% respectively. This proof of concept demonstrates a promising new application of deep learning in the field of remote sensing, geospatial analysis, and urban planning, as well as a major step towards fully autonomous artificially intelligent surveying techniques of the built environment.

Description: We study the problem of quickly computing point-to-point shortest paths in massive road networks with traffic predictions. Incorporating traffic predictions into routing allows, for example, to avoid commuter traffic congestions. Existing techniques follow a two-phase approach: In a preprocessing step, an index is built. The index depends on the road network and the traffic patterns but not on the path start and end. The latter are the input of the query phase, in which shortest paths are computed. All existing techniques have large index size, slow query running times or may compute suboptimal paths. In this work, we introduce CATCHUp (Customizable Approximated Time-dependent Contraction Hierarchies through Unpacking), the first algorithm that simultaneously achieves all three objectives. The core idea of CATCHUp is to store paths instead of travel times at shortcuts. Shortcut travel times are derived lazily from the stored paths. We perform an experimental study on a set of real world instances and compare our approach with state-of-the-art techniques. Our approach achieves the fastest preprocessing, competitive query running times and up to 38 times smaller indexes than competing approaches.

Description: Graph variational auto-encoder (GVAE) is a model that combines neural networks and Bayes methods, capable of deeper exploring the influential latent features of graph reconstruction. However, several pieces of research based on GVAE employ a plain prior distribution for latent variables, for instance, standard normal distribution (N(0,1)). Although this kind of simple distribution has the advantage of convenient calculation, it will also make latent variables contain relatively little helpful information. The lack of adequate expression of nodes will inevitably affect the process of generating graphs, which will eventually lead to the discovery of only external relations and the neglect of some complex internal correlations. In this paper, we present a novel prior distribution for GVAE, called Dirichlet process (DP) construction for Student’s t (St) distribution. The DP allows the latent variables to adapt their complexity during learning and then cooperates with heavy-tailed St distribution to approach sufficient node representation. Experimental results show that this method can achieve a relatively better performance against the baselines.

Description: At present, our online activity is almost constant, either producing information or consuming it, both for the social and academic fields. The spaces in which people move and travel every day, innocently divided between the face-to-face and the virtual, affect the way we communicate and perceive ourselves. In this document, a characterization of the academic digital identity of Chilean university students is proposed and an invitation to teachers to redefine learning spaces is made, allowing integrating all those technological tools that the student actually uses. This study was developed within the logic of pragmatism based on mixed methodology, non-experimental design, and a descriptive–quantitative cross-sectional approach. A non-probabilistic sample was made up of 509 students, who participated voluntarily with an online questionnaire. The Stata Version-14 program was used, applying the Mann–Whitney–Wilcoxon and Kruskal–Wallis U tests. To develop characterizations, a conglomerate analysis was performed with a hierarchical dissociative method. In general, Chilean university students are highly truthful on the Internet without making significant differences between face-to-face and digital interactions, with low awareness of their ID, being easily recognizable on the Web. Regarding their educational process, they manage it with analogical/face-to-face mixing formal and informal technological tools to optimize their learning process. These students manifest a hybrid academic digital identity, without gender difference in the deployment of their PLEs, but maintaining stereotypical gender behaviors in the construction of their digital identity on the Web, which shows a human-technological development similar to that of young Asians and Europeans.

Description: Intra-city delivery has developed rapidly along with the expansion of the logistics industry. Timely delivery is one of the main requirements of consumers and has become a major challenge to delivery service providers. To compensate for the adverse effects of delivery delays, platforms have launched delay compensation services for consumers who order. This study quantitatively evaluated consumer perception of the delay compensation service in intra-city deliveries using a choice experiment. We explored how different attributes of the delay compensation service plan affect consumer preference and their willingness to pay for the services. These service attributes are “delay probability display”, “compensation amount”, “compensation method”, “penalty method for riders”, and “one-time order price”. Using a multinomial logit model to analyze the questionnaire results, the respondents showed a positive preference for on-time delivery probability display, progressive compensation amount, and cash compensation. The results also show that the respondents opposed the penalty scheme where the riders would bear the compensation costs. Positive preference attributes are conducive to enhancing consumers’ willingness to order and pay for the program. Based on our findings and research conclusions, we proposed several recommendations to improve the delay compensation service program.

Description: Until recently, traditional machine learning techniques (TMLTs) such as multilayer perceptrons (MLPs) and support vector machines (SVMs) have been used successfully for churn prediction, but with significant efforts expended on the configuration of the training parameters. The selection of the right training parameters for supervised learning is almost always experimentally determined in an ad hoc manner. Deep neural networks (DNNs) have shown significant predictive strength over TMLTs when used for churn predictions. However, the more complex architecture of DNNs and their capacity to process huge amounts of non-linear input data demand more time and effort to configure the training hyperparameters for DNNs during churn modeling. This makes the process more challenging for inexperienced machine learning practitioners and researchers. So far, limited research has been done to establish the effects of different hyperparameters on the performance of DNNs during churn prediction. There is a lack of empirically derived heuristic knowledge to guide the selection of hyperparameters when DNNs are used for churn modeling. This paper presents an experimental analysis of the effects of different hyperparameters when DNNs are used for churn prediction in the banking sector. The results from three experiments revealed that the deep neural network (DNN) model performed better than the MLP when a rectifier function was used for activation in the hidden layers and a sigmoid function was used in the output layer. The performance of the DNN was better when the batch size was smaller than the size of the test set data, while the RemsProp training algorithm had better accuracy when compared with the stochastic gradient descent (SGD), Adam, AdaGrad, Adadelta, and AdaMax algorithms. The study provides heuristic knowledge that could guide researchers and practitioners in machine learning-based churn prediction from the tabular data for customer relationship management in the banking sector when DNNs are used.

Description: The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High-Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also uses the advances of image classification techniques. Our best-performing interlock-to-stable classifier reaches an Area under the ROC Curve value of 0.71±0.01 compared to 0.65±0.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5±0.2 s per interlock.

Description: Metric Multidimensional Scaling is commonly used to solve multi-sensor location problems in 2D or 3D spaces. In this paper, we show that such technique provides poor results in the case of indoor location problems based on 802.11 Fine Timing Measurements, because the number of anchors is small and the ranging error asymmetrically distributed. We then propose a two-step iterative approach based on geometric resolution of angle inaccuracies. The first step reduces the effect of poor ranging exchanges. The second step reconstructs the anchor positions, starting from the distances of highest likely-accuracy. We show that this geometric approach provides better location accuracy results than other Euclidean Distance Metric techniques based on Least Square Error logic. We also show that the proposed technique, with the input of one or more known location, can allow a set of fixed sensors to auto-determine their position on a floor plan.

Description: The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness of the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial training approach called GANSan for learning a sanitizer whose objective is to prevent the possibility of any discrimination (i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our method GANSan is partially inspired by the powerful framework of generative adversarial networks (in particular Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible, thus preserving the interpretability of the sanitized data. Consequently, once the sanitizer is trained, it can be applied to new data locally by an individual on their profile before releasing it. Finally, experiments on real datasets demonstrate the effectiveness of the approach as well as the achievable trade-off between fairness and utility.

Description: The combination of mobile edge computing (MEC) and wireless power transfer (WPT) is recognized as a promising technology to solve the problem of limited battery capacities and insufficient computation capabilities of mobile devices. This technology can transfer energy to users by radio frequency (RF) in wireless powered mobile edge computing. The user converts the harvested energy, stores it in the battery, and utilizes the harvested energy to execute corresponding local computing and offloading tasks. This paper adopts the Frequency Division Multiple Access (FDMA) technique to achieve task offloading from multiple mobile devices to the MEC server simultaneously. Our objective is to study multiuser dynamic joint optimization of computation and wireless resource allocation under multiple time blocks to solve the problem of maximizing residual energy. To this end, we formalize it as a nonconvex problem that jointly optimizes the number of offloaded bits, energy harvesting time, and transmission bandwidth. We adopt convex optimization technology, combine with Karush–Kuhn–Tucker (KKT) conditions, and finally transform the problem into a univariate constrained convex optimization problem. Furthermore, to solve the problem, we propose a combined method of Bisection method and sequential unconstrained minimization based on Reformulation-Linearization Technique (RLT). Numerical results demonstrate that the performance of our joint optimization method outperforms other benchmark schemes for the residual energy maximization problem. Besides, the algorithm can maximize the residual energy, reduce the computation complexity, and improve computation efficiency.

Description: Opinion mining techniques, investigating if text is expressing a positive or negative opinion, continuously gain in popularity, attracting the attention of many scientists from different disciplines. Specific use cases, however, where the expressed opinion is indisputably positive or negative, render such solutions obsolete and emphasize the need for a more in-depth analysis of the available text. Emotion analysis is a solution to this problem, but the multi-dimensional elements of the expressed emotions in text along with the complexity of the features that allow their identification pose a significant challenge. Machine learning solutions fail to achieve a high accuracy, mainly due to the limited availability of annotated training datasets, and the bias introduced to the annotations by the personal interpretations of emotions from individuals. A hybrid rule-based algorithm that allows the acquisition of a dataset that is annotated with regard to the Plutchik’s eight basic emotions is proposed in this paper. Emoji, keywords and semantic relationships are used in order to identify in an objective and unbiased way the emotion expressed in a short phrase or text. The acquired datasets are used to train machine learning classification models. The accuracy of the models and the parameters that affect it are presented in length through an experimental analysis. The most accurate model is selected and offered through an API to tackle the emotion detection in social media posts.

Description: This paper has introduced an algorithm for the identification of islanding events in the remotely located distribution grid with renewable energy (RE) sources using the voltage signals. Voltage signal is processed using Stockwell transform (ST) to compute the median-based islanding recognition factor (MIRF). The rate of change in the root mean square (RMS) voltage is computed by differentiating the RMS voltage with respect to time to compute the voltage rate of change in islanding recognition factor (VRCIRF). The proposed voltage-based islanding recognition factor (IRFV) is computed by multiplying the MIRF and VRCIRF element to element. The islanding event is discriminated from the faulty and operational events using the simple decision rules using the peak magnitude of IRFV by comparing peak magnitude of IRFV with pre-set threshold values. The proposed islanding detection method (IDM) effectively identified the islanding events in the presence of solar energy, wind energy and simultaneous presence of both wind and solar energy at a fast rate in a time period of less than 0.05 cycles compared to the voltage change rate (ROCOV) and frequency change rate (ROCOF) IDM that detects the islanding event in a time period of 0.25 to 0.5 cycles. This IDM provides a minimum non-detection zone (NDZ). This IDM efficiently discriminated the islanding events from the faulty and switching events. The proposed study is performed on an IEEE-13 bus test system interfaced with renewable energy (RE) generators in a MATLAB/Simulink environment. The performance of the proposed IDM is better compared to methods based on the use of ROCOV, ROCOF and discrete wavelet transform (DWT).

Description: Wind turbine gearboxes are known to be among the weakest components in the system and the possibility to study and understand the behavior of geared transmissions when subject to several types of faults might be useful to plan maintenance and eventually reduce the costs by preventing further damage. The aim of this work is to develop a high-fidelity numerical model of a single-stage planetary gearbox selected as representative and to evaluate its behavior in the presence of surface fatigue and tooth-root bending damage, i.e., pits and cracks. The planetary gearbox is almost entirely modelled, including shafts, gears as well as bearings with all the rolling elements. Stresses and strains in the most critical areas are analyzed to better evaluate if the presence of such damage can be somehow detected using strain gauges and where to place them to maximize the sensitivity of the measures to the damage. Several simulations with different levels, types and positions of the damage were performed to better understand the mutual relations between the damaged and the stress state. The ability to introduce the effect of the damage in the model of a gearbox represents the first indispensable step of a Structural Health Monitoring (SHM) strategy. The numerical activity was performed taking advantage of an innovative hybrid numerical–analytical approach that ensures a significant reduction of the computational effort. The developed model shows good sensitivity to the presence, type and position of the defects. For the studied configuration, the numerical results show clearly show a relation between the averaged rim stress and the presence of root cracks. Moreover, the presence of surface defects seems to produce local stress peaks (when the defects pass through the contact) in the instantaneous rim stress.

Description: The paper sheds a light on the interconnections between touristic sector development and regional development in Kazakhstan. The paper covers analysis of the current competitiveness of the touristic destinations in Kazakhstan. Based on qualitative and quantitative research, the study shows that there is a huge need for a transformation in marketing communications tools in order to increase the competitiveness and image of Kazakhstani tourism. The study provides potential scenarios and solutions to increase touristic attractiveness, which would lead to enticing more investors and increase tourism capacity and potential. Also, the paper provides insights in ecotourism and the regional economy by outlining the older and newer managerial and governmental approaches in supporting the entire tourism sector in Kazakhstan.

Description: In this paper, we present a new parametric family of three-step iterative for solving nonlinear equations. First, we design a fourth-order triparametric family that, by holding only one of its parameters, we get to accelerate its convergence and finally obtain a sixth-order uniparametric family. With this last family, we study its convergence, its complex dynamics (stability), and its numerical behavior. The parameter spaces and dynamical planes are presented showing the complexity of the family. From the parameter spaces, we have been able to determine different members of the family that have bad convergence properties, as attracting periodic orbits and attracting strange fixed points appear in their dynamical planes. Moreover, this same study has allowed us to detect family members with especially stable behavior and suitable for solving practical problems. Several numerical tests are performed to illustrate the efficiency and stability of the presented family.

Description: Color coding is an algorithmic technique used in parameterized complexity theory to detect “small” structures inside graphs. The idea is to derandomize algorithms that first randomly color a graph and then search for an easily-detectable, small color pattern. We transfer color coding to the world of descriptive complexity theory by characterizing—purely in terms of the syntactic structure of describing formulas—when the powerful second-order quantifiers representing a random coloring can be replaced by equivalent, simple first-order formulas. Building on this result, we identify syntactic properties of first-order quantifiers that can be eliminated from formulas describing parameterized problems. The result applies to many packing and embedding problems, but also to the long path problem. Together with a new result on the parameterized complexity of formula families involving only a fixed number of variables, we get that many problems lie in FPT just because of the way they are commonly described using logical formulas.

Description: The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint. We focus on two applications of CT, namely, low-dose CT and sparse-angle CT. This enables us to fairly compare different methods using standardized settings. As a general result, we observe that the deep learning-based methods are able to improve the reconstruction quality metrics in both CT applications while the top performing methods show only minor differences in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). We further discuss a number of other important criteria that should be taken into account when selecting a method, such as the availability of training data, the knowledge of the physical measurement model and the reconstruction speed.

Description: Device-to-Device (D2D) communications, which enable direct communication between nearby user devices over the licensed spectrum, have been considered a key technique to improve spectral efficiency and system throughput in cellular networks (CNs). However, the limited spectrum resources cannot be sufficient to support more cellular users (CUs) and D2D users to meet the growth of the traffic data in future wireless networks. Therefore, Long-Term Evolution-Unlicensed (LTE-U) and D2D-Unlicensed (D2D-U) technologies have been proposed to further enhance system capacity by extending the CUs and D2D users on the unlicensed spectrum for communications. In this paper, we consider an LTE network where the CUs and D2D users are allowed to share the unlicensed spectrum with Wi-Fi users. To maximize the sum rate of all users while guaranteeing each user’s quality of service (QoS), we jointly consider user access and resource allocation. To tackle the formulated problem, we propose a matching-iteration-based joint user access and resource allocation algorithm. Simulation results show that the proposed algorithm can significantly improve system throughput compared to the other benchmark algorithms.

Description: This paper explores how the concepts of information and technics have been leveraged differently by a variety of philosophical and epistemological frameworks over time. Using the Foucauldian methodology of genealogical historiography, it analyzes how the use of these concepts have impacted the way we understand the world and what we can know about that world. As these concepts are so ingrained in contemporary technologies of the information age, understanding how these concepts have changed over time can help make clearer how they continue to impact our processes of subjectivation. Analysis reveals that the predominant understanding of information and technics today is based on a cybernetic approach that conceptualizes information as a resource. However, this analysis also reveals that Michel Foucault’s conceptualization of technics resonates with that of the Sophists, offering an opportunity to rethink contemporary conceptualizations of information and technics in a way that connects to posthuman philosophic systems that afford new approaches to communication and media studies.

Description: Digital images represent the primary tool for diagnostics and documentation of the state of preservation of artifacts. Today the interpretive filters that allow one to characterize information and communicate it are extremely subjective. Our research goal is to study a quantitative analysis methodology to facilitate and semi-automate the recognition and polygonization of areas corresponding to the characteristics searched. To this end, several algorithms have been tested that allow for separating the characteristics and creating binary masks to be statistically analyzed and polygonized. Since our methodology aims to offer a conservator-restorer model to obtain useful graphic documentation in a short time that is usable for design and statistical purposes, this process has been implemented in a single Geographic Information Systems (GIS) application.

Description: Using the single premise entailment (SPE) model to accomplish the multi-premise entailment (MPE) task can alleviate the problem that the neural network cannot be effectively trained due to the lack of labeled multi-premise training data. Moreover, the abundant judgment methods for the relationship between sentence pairs can also be applied in this task. However, the single-premise pre-trained model does not have a structure for processing multi-premise relationships, and this structure is a crucial technique for solving MPE problems. This paper proposes adding a multi-premise relationship processing module based on not changing the structure of the pre-trained model to compensate for this deficiency. Moreover, we proposed a three-step training method combining this module, which ensures that the module focuses on dealing with the multi-premise relationship during matching, thus applying the single-premise model to multi-premise tasks. Besides, this paper also proposes a specific structure of the relationship processing module, i.e., we call it the attention-backtracking mechanism. Experiments show that this structure can fully consider the context of multi-premise, and the structure combined with the three-step training can achieve better accuracy on the MPE test set than other transfer methods.

Description: Methods for No-Reference Video Quality Assessment (NR-VQA) of consumer-produced video content are largely investigated due to the spread of databases containing videos affected by natural distortions. In this work, we design an effective and efficient method for NR-VQA. The proposed method exploits a novel sampling module capable of selecting a predetermined number of frames from the whole video sequence on which to base the quality assessment. It encodes both the quality attributes and semantic content of video frames using two lightweight Convolutional Neural Networks (CNNs). Then, it estimates the quality score of the entire video using a Support Vector Regressor (SVR). We compare the proposed method against several relevant state-of-the-art methods using four benchmark databases containing user generated videos (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC). The results show that the proposed method at a substantially lower computational cost predicts subjective video quality in line with the state of the art methods on individual databases and generalizes better than existing methods in cross-database setup.

Description: Compared to single source systems, stereo X-ray CT systems allow acquiring projection data within a reduced amount of time, for an extended field-of-view, or for dual X-ray energies. To exploit the benefit of a dual X-ray system, its acquisition geometry needs to be calibrated. Unfortunately, in modular stereo X-ray CT setups, geometry misalignment occurs each time the setup is changed, which calls for an efficient calibration procedure. Although many studies have been dealing with geometry calibration of an X-ray CT system, little research targets the calibration of a dual cone-beam X-ray CT system. In this work, we present a phantom-based calibration procedure to accurately estimate the geometry of a stereo cone-beam X-ray CT system. With simulated as well as real experiments, it is shown that the calibration procedure can be used to accurately estimate the geometry of a modular stereo X-ray CT system thereby reducing the misalignment artifacts in the reconstruction volumes.

Description: Automated driving technologies are rapidly being developed. However, until vehicles are fully automated, the control of the dynamic driving task will be shifted between the driver and automated driving system. This paper aims to explore how transitions from automated driving to manual driving affect user experience and how that experience correlates to take-over performance. In the study 20 participants experienced using an automated driving system during rush-hour traffic in the San Francisco Bay Area, CA, USA. The automated driving system was available in congested traffic situations and when active, the participants could engage in non-driving related activities. The participants were interviewed afterwards regarding their experience of the transitions. The findings show that most of the participants experienced the transition from automated driving to manual driving as negative. Their user experience seems to be shaped by several reasons that differ in temporality and are derived from different phases during the transition process. The results regarding correlation between participants’ experience and take-over performance are inconclusive, but some trends were identified. The study highlights the need for new design solutions that do not only improve drivers’ take-over performance, but also enhance user experience during take-over requests from automated to manual driving.

Description: Buruli ulcer caused by Mycobacterium ulcerans (M. ulcerans) is identified by a pain-free cyst or edema which develops into a massive skin ulcer if left untreated. There are reports of chemoresistance, toxicity, noncompliance, and poor efficacy of current therapeutic options. Previously, we used cheminformatics approaches to identify potential antimycobacterial compounds targeting major receptors in M. ulcerans. In this paper, we sought to identify potential bioactive compounds by targeting Cystathionine gamma-synthase (CGS) MetB, a key receptor involved in methionine synthesis. Inhibition of methionine synthesis restricts the growth of M. ulcerans. Two potent inhibitors Juglone (IC50 0.7 +/− 0.7 µmol/L) and 9-hydroxy-alpha-lapachone (IC50 0.9 +/− 0.1 µmol/L) were used to generate 3D chemical feature pharmacophore model via LigandScout with a score of 0.9719. The validated model was screened against a pre-filtered library of 2530 African natural products. Compounds with fit scores above 66.40 were docked against the structure of CGS to generate hits. Three compounds, namely Gentisic 5-O glucoside (an isolate of African tree Alchornea cordifolia), Isoscutellarein (an isolate of Theobroma plant) and ZINC05854400, were identified as potential bioactive molecules with high binding affinities of −7.1, −8.4 and −8.4 kcal/mol against CGS, respectively. Novel structural insight into the binding mechanisms was elucidated using LigPlot+ and molecular dynamics simulations. All three molecules were predicted to possess antibacterial, anti-ulcerative, and dermatological properties. These compounds have the propensity to disrupt the methionine synthesis mechanisms with the potential of stagnating the growth of M. ulcerans. As a result of reasonably good pharmacological profiling, the three drug-like compounds are potential novel scaffolds that can be optimized into antimycobacterial molecules.

Description: Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

Description: The authors wish to make the following corrections to their paper [...]

Description: Federated learning is a novel distributed learning framework, which enables thousands of participants to collaboratively construct a deep learning model. In order to protect confidentiality of the training data, the shared information between server and participants are only limited to model parameters. However, this setting is vulnerable to model poisoning attack, since the participants have permission to modify the model parameters. In this paper, we perform systematic investigation for such threats in federated learning and propose a novel optimization-based model poisoning attack. Different from existing methods, we primarily focus on the effectiveness, persistence and stealth of attacks. Numerical experiments demonstrate that the proposed method can not only achieve high attack success rate, but it is also stealthy enough to bypass two existing defense methods.

Description: Person re-identification (ReID) plays a significant role in video surveillance analysis. In the real world, due to illumination, occlusion, and deformation, pedestrian features extraction is the key to person ReID. Considering the shortcomings of existing methods in pedestrian features extraction, a method based on attention mechanism and context information fusion is proposed. A lightweight attention module is introduced into ResNet50 backbone network equipped with a small number of network parameters, which enhance the significant characteristics of person and suppress irrelevant information. Aiming at the problem of person context information loss due to the over depth of the network, a context information fusion module is designed to sample the shallow feature map of pedestrians and cascade with the high-level feature map. In order to improve the robustness, the model is trained by combining the loss of margin sample mining with the loss function of cross entropy. Experiments are carried out on datasets Market1501 and DukeMTMC-reID, our method achieves rank-1 accuracy of 95.9% on the Market1501 dataset, and 90.1% on the DukeMTMC-reID dataset, outperforming the current mainstream method in case of only using global feature.

Description: In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

Description: Recurrent Neural Networks (RNNs) are known for their ability to learn relationships within temporal sequences. Gated Recurrent Unit (GRU) networks have found use in challenging time-dependent applications such as Natural Language Processing (NLP), financial analysis and sensor fusion due to their capability to cope with the vanishing gradient problem. GRUs are also known to be more computationally efficient than their variant, the Long Short-Term Memory neural network (LSTM), due to their less complex structure and as such, are more suitable for applications requiring more efficient management of computational resources. Many of such applications require a stronger mapping of their features to further enhance the prediction accuracy. A novel Quaternion Gated Recurrent Unit (QGRU) is proposed in this paper, which leverages the internal and external dependencies within the quaternion algebra to map correlations within and across multidimensional features. The QGRU can be used to efficiently capture the inter- and intra-dependencies within multidimensional features unlike the GRU, which only captures the dependencies within the sequence. Furthermore, the performance of the proposed method is evaluated on a sensor fusion problem involving navigation in Global Navigation Satellite System (GNSS) deprived environments as well as a human activity recognition problem. The results obtained show that the QGRU produces competitive results with almost 3.7 times fewer parameters compared to the GRU. The QGRU code is available at.

Description: In this article a sincere effort has been made to address the origin of the incommensurability/irrationality of numbers. It is folklore that the starting point was several unsuccessful geometric attempts to compute the exact values of 2 and π. Ancient records substantiate that more than 5000 years back Vedic Ascetics were successful in approximating these numbers in terms of rational numbers and used these approximations for ritual sacrifices, they also indicated clearly that these numbers are incommensurable. Since then research continues for the known as well as unknown/expected irrational numbers, and their computation to trillions of decimal places. For the advancement of this broad mathematical field we shall chronologically show that each continent of the world has contributed. We genuinely hope students and teachers of mathematics will also be benefited with this article.

Description: Precisely assessing the severity of persons with COVID-19 at an early stage is an effective way to increase the survival rate of patients. Based on the initial screening, to identify and triage the people at highest risk of complications that can result in mortality risk in patients is a challenging problem, especially in developing nations around the world. This problem is further aggravated due to the shortage of specialists. Using machine learning (ML) techniques to predict the severity of persons with COVID-19 in the initial screening process can be an effective method which would enable patients to be sorted and treated and accordingly receive appropriate clinical management with optimum use of medical facilities. In this study, we applied and evaluated the effectiveness of three types of Artificial Neural Network (ANN), Support Vector Machine and Random forest regression using a variety of learning methods, for early prediction of severity using patient history and laboratory findings. The performance of different machine learning techniques to predict severity with clinical features shows that it can be successfully applied to precisely and quickly assess the severity of the patient and the risk of death by using patient history and laboratory findings that can be an effective method for patients to be triaged and treated accordingly.

Description: Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

Description: Porcine reproductive and respiratory syndrome virus (PRRSV) causes reproductive failure in sows and respiratory disease in piglets and growing pigs. The disease rapidly spreads in swine populations, making it a serious problem causing great financial losses to the swine industry. However, past mathematical models used to describe the spread of the disease have not yielded sufficient understanding of its spatial transmission. This work has been designed to investigate a mathematical model for the spread of PRRSV considering both time and spatial dimensions as well as the observed decline in infectiousness as time progresses. Moreover, our model incorporates into the dynamics the assumption that some members of the infected population may recover from the disease and become immune. Analytical solutions are derived by using the modified extended hyperbolic tangent method with the introduction of traveling wave coordinate. We also carry out a stability and phase analysis in order to obtain a clearer understanding of how PRRSV spreads spatially through time.

Description: In order to model information dissemination in social networks, a special methodology of sampling statistical data formation has been implemented. The probability distribution laws of various characteristics of personal and group accounts in four social networks are investigated. Stochastic aspects of interrelations between these indicators were analyzed. The classification of groups of social network users is proposed, and their characteristic features and main empirical regularities of mutual transitions are marked. Regression models of forecasting changes in the number of users of the selected groups have been obtained.

Description: The prognosis of diffuse large B-cell lymphoma (DLBCL) is heterogeneous. Therefore, we aimed to highlight predictive biomarkers. First, artificial intelligence was applied into a discovery series of gene expression of 414 patients (GSE10846). A dimension reduction algorithm aimed to correlate with the overall survival and other clinicopathological variables; and included a combination of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) artificial neural networks, gene-set enrichment analysis (GSEA), Cox regression and other machine learning and predictive analytics modeling [C5.0 algorithm, logistic regression, Bayesian Network, discriminant analysis, random trees, tree-AS, Chi-squared Automatic Interaction Detection CHAID tree, Quest, classification and regression (C&R) tree and neural net)]. From an initial 54,613 gene-probes, a set of 488 genes and a final set of 16 genes were defined. Secondly, two identified markers of the immune checkpoint, PD-L1 (CD274) and IKAROS (IKZF4), were validated in an independent series from Tokai University, and the immunohistochemical expression was quantified, using a machine-learning-based Weka segmentation. High PD-L1 associated with poor overall and progression-free survival, non-GCB phenotype, Epstein–Barr virus infection (EBER+), high RGS1 expression and several clinicopathological variables, such as high IPI and absence of clinical response. Conversely, high expression of IKAROS was associated with a good overall and progression-free survival, GCB phenotype and a positive clinical response to treatment. Finally, the set of 16 genes (PAF1, USP28, SORT1, MAP7D3, FITM2, CENPO, PRCC, ALDH6A1, CSNK2A1, TOR1AIP1, NUP98, UBE2H, UBXN7, SLC44A2, NR2C2AP and LETM1), in combination with PD-L1, IKAROS, BCL2, MYC, CD163 and TNFAIP8, predicted the survival outcome of DLBCL with an overall accuracy of 82.1%. In conclusion, building predictive models of DLBCL is a feasible analytical strategy.

Description: In a community with an aging population, helping each other is a must society function. Lacking mutual trust makes the need for a fair and transparent service exchange platform on top of the public service administration’s list. We present an efficient blockchain-based TimeBank realization with a newly proposed dynamic service matching algorithm (DSMA) in this work. The Hyperledger Fabric (or Fabric in short), one of the well-known Consortium Blockchains, is chosen as our system realization platform. It provides the identity certification mechanism and has an extendable network structure. The performance of a DSMA is measured by the waiting time for a service to get a match, called the service-matching waiting time (SMWT). In our DSMA, the decision as to whether a service is to get a match or wait for a later chance depends dynamically on the total number of contemporarily available services (i.e., the thickness of the service market). To better the proposed TimeBank system’s service quality, a Dynamic Tuning Strategy (DTS) is designed to thicken the market size. Experimental results show that a thicker market makes on-chain nodes have more links, and in turn, they find a match easier (i.e., consume a shorter SMWT).

Description: This paper aims at showing a state of the art about digital citizenship from the methodological point of view when it comes to measuring this construct. The review of the scientific literature offers at least ten definitions and nine different scales of measurement. The comparative and diachronic analysis of the content of the definitions shows us two conceptions of digital citizenship, some more focused on digital competences and others on critical and activist aspects. This paper replicates and compares three scales of measurement of digital citizenship selected for their relevance and administered in a sample of 366 university students, to analyze their psychometric properties and the existing coincidences and divergences between the three. The most outstanding conclusion is that not all of them seem to measure the same construct, due to its diversity of dimensions. An online activism dimension needs to be incorporated if digital citizenship is to be measured. There is an urgent need to agree internationally on a definition of digital citizenship with its corresponding dimensions to elaborate a reliable and valid measuring instrument.

Description: In today’s Industrial Internet of Things (IIoT) environment, where different systems interact with the physical world, the state proposed by the Industry 4.0 standards can lead to escalating vulnerabilities, especially when these systems receive data streams from multiple intermediaries, requiring multilevel security approaches, in addition to link encryption. At the same time taking into account the heterogeneity of the systems included in the IIoT ecosystem and the non-institutionalized interoperability in terms of hardware and software, serious issues arise as to how to secure these systems. In this framework, given that the protection of industrial equipment is a requirement inextricably linked to technological developments and the use of the IoT, it is important to identify the major vulnerabilities and the associated risks and threats and to suggest the most appropriate countermeasures. In this context, this study provides a description of the attacks against IIoT systems, as well as a thorough analysis of the solutions for these attacks, as they have been proposed in the most recent literature.

Description: This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. First, it presents a Big-Data driven methodology that studies the multimodal components of user interactions and analyzes the data from Bluetooth Low Energy (BLE) beacons and BLE scanners to detect a user’s indoor location in a specific ‘activity-based zone’ during Activities of Daily Living. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data from diverse behavioral patterns to detect the ‘zone-based’ indoor location of a user in any Internet of Things (IoT)-based environment. These two approaches achieved performance accuracies of 81.36% and 81.13%, respectively, when tested on a dataset. Third, it presents a methodology to detect the spatial coordinates of a user’s indoor position that outperforms all similar works in this field, as per the associated root mean squared error—one of the performance evaluation metrics in ISO/IEC18305:2016—an international standard for testing Localization and Tracking Systems. Finally, it presents a comprehensive comparative study that includes Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, to address the challenge of identifying the optimal machine learning approach for Indoor Localization.

Description: Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.

Description: The Border Gateway Protocol (BGP) is the standard inter-domain route protocol on the Internet. Autonomous System (AS) traffic is forwarded by the BGP neighbors. In the route selection, if there are malicious or inactive neighbors, it will affect the network’s performance or even cause the network to crash. Therefore, choosing trusted and safe neighbors is an essential part of BGP security research. In response to such a problem, in this paper we propose a BGP Neighbor Trust Establishment Mechanism based on the Bargaining Game (BNTE-BG). By combining service quality attributes such as bandwidth, packet loss rate, jitter, delay, and price with bargaining game theory, it allows the AS to select trusted neighbors which satisfy the Quality of Service independently. When the trusted neighbors are forwarding data, we draw on the gray correlation algorithm to calculate neighbors’ behavioral trust and detect malicious or inactive BGP neighbors.

Description: Big data have become a global strategic issue, as increasingly large amounts of unstructured data challenge the IT infrastructure of global organizations and threaten their capacity for strategic forecasting. As experienced in former massive information issues, big data technologies, such as Hadoop, should efficiently tackle the incoming large amounts of data and provide organizations with relevant processed information that was formerly neither visible nor manageable. After having briefly recalled the strategic advantages of big data solutions in the introductory remarks, in the first part of this paper, we focus on the advantages of big data solutions in the currently difficult time of the COVID-19 pandemic. We characterize it as an endemic heterogeneous data context; we then outline the advantages of technologies such as Hadoop and its IT suitability in this context. In the second part, we identify two specific advantages of Hadoop solutions, globality combined with flexibility, and we notice that they are at work with a “Hadoop Fusion Approach” that we describe as an optimal response to the context. In the third part, we justify selected qualifications of globality and flexibility by the fact that Hadoop solutions enable comparable returns in opposite contexts of models of partial submodels and of models of final exact systems. In part four, we remark that in both these opposite contexts, Hadoop’s solutions allow a large range of needs to be fulfilled, which fits with requirements previously identified as the current heterogeneous data structure of COVID-19 information. In the final part, we propose a framework of strategic data processing conditions. To the best of our knowledge, they appear to be the most suitable to overcome COVID-19 massive information challenges.

Description: A choice to use a seat belt is largely dependent on the psychology of the vehicles’ occupants, and thus those decisions are expected to be characterized by preference heterogeneity. Despite the importance of seat belt use on the safety of the roadways, the majority of existing studies ignored the heterogeneity in the data and used a very standard statistical or descriptive method to identify the factors of using a seatbelt. Application of the right statistical method is of crucial importance to unlock the underlying factors of the choice being made by vehicles’ occupants. Thus, this study was conducted to identify the contributory factors to the front-seat passengers’ choice of seat belt usage, while accounting for the choice preference heterogeneity. The latent class model has been offered to replace the mixed logit model by replacing a continuous distribution with a discrete one. However, one of the shortcomings of the latent class model is that the homogeneity is assumed across a same class. A further extension is to relax the assumption of homogeneity by allowing some parameters to vary across the same group. The model could still be extended to overlay some attributes by considering attributes non-attendance (ANA), and aggregation of common-metric attributes (ACMA). Thus, this study was conducted to make a comparison across goodness of fit of the discussed models. Beside a comparison based on goodness of fit, the share of individuals in each class was used to see how it changes based on various model specifications. In summary, the results indicated that adding another layer to account for the heterogeneity within the same class of the latent class (LC) model, and accounting for ANA and ACMA would improve the model fit. It has been discussed in the content of the manuscript that accounting for ANA, ACMA and an extra layer of heterogeneity does not just improve the model goodness of fit, but largely impacts the share of class allocation of the models.

Description: The provision of wellness in workplaces gained interest in recent decades. A factor that contributes significantly to workers’ health is their diet, especially when provided by canteen services. The assessment of such a service involves questions as food cost, its sustainability, quality, nutritional facts and variety, as well as employees’ health and disease prevention, productivity increase, economic convenience vs. eating satisfaction when using canteen services. Even if food habits have already been studied using traditional statistical approaches, here we adopt an approach based on Network Science that allows us to deeply study, for instance, the interconnections among people, company and meals and that can be easily used for further analysis. In particular, this work concerns a multi-company dataset of workers and dishes they chose at a canteen worksite. We study eating habits and health consequences, also considering the presence of different companies and the corresponding contact network among workers. The macro-nutrient content and caloric values assessment is carried out both for dishes and for employees, in order to establish when food is balanced and healthy. Moreover, network analysis lets us discover hidden correlations among people and the environment, as communities that cannot be usually inferred with traditional or methods since they are not known a priori. Finally, we represent the dataset as a tripartite network to investigate relationships between companies, people, and dishes. In particular, the so-called network projections can be extracted, each one being a network among specific kind of nodes; further community analysis tools will provide hidden information about people and their food habits. In summary, the contribution of the paper is twofold: it provides a study of a real dataset spanning over several years that gives a new interesting point of view on food habits and healthcare, and it also proposes a new approach based on Network Science. Results prove that this kind of analysis can provide significant information that complements other traditional methodologies.

Description: With the development of mobile technology, the usage of media data has increased dramatically. Therefore, data reduction represents a research field to maintain valuable information. In this paper, a new scheme called Multi Chimera Transform (MCT) based on data reduction with high information preservation, which aims to improve the reconstructed data by producing three parameters from each 16×16 block of data, is proposed. MCT is a 2D transform that depends on constructing a codebook of 256 picked blocks from some selected images which have a low similarity. The proposed transformation was applied on solid and soft biometric modalities of AR database, giving high information preservation with small resulted file size. The proposed method produced outstanding performance compared with KLT and WT in terms of SSIM and PSNR. The highest SSIM was 0.87 for the proposed scheme MCT of the full image of AR database, while the existed method KLT and WT had 0.81 and 0.68, respectively. In addition, the highest PSNR was 27.23 dB for the proposed scheme on warp facial image of AR database, while the existed methods KLT and WT had 24.70 dB and 21.79 dB, respectively.

Description: This paper presents the structure of an encyclopedia-based framework (EbF) in which to develop computer vision systems that incorporate the principles of agile development with focussed knowledge-enhancing information. The novelty of the EbF is that it specifies both the use of drop-in modules, to enable the speedy implementation and modification of systems by the operator, and it incorporates knowledge of the input image-capture devices and presentation preferences. This means that the system includes automated parameter selection and operator advice and guidance. Central to this knowledge-enhanced framework is an encyclopedia that is used to store all information pertaining to the current system operation and can be used by all of the imaging modules and computational runtime components. This ensures that they can adapt to changes within the system or its environment. We demonstrate the implementation of this system over three use cases in computer vision for unmanned aerial vehicles (UAV) showing how it is easy to control and set up by novice operators utilising simple computational wrapper scripts.

Description: A typhoon is an extreme weather event with strong destructive force, which can bring huge losses of life and economic damage to people. Thus, it is meaningful to reduce the prediction errors of typhoon intensity forecasting. Artificial and deep neural networks have recently become widely used for typhoon forecasting in order to ensure typhoon intensity forecasting is accurate and timely. Typhoon intensity forecasting models based on long short-term memory (LSTM) are proposed herein, which forecast typhoon intensity as a time series problem based on historical typhoon data. First, the typhoon intensity forecasting models are trained and tested with processed typhoon data from 2000 to 2014 to find the optimal prediction factors. Then, the models are validated using the optimal prediction factors compared to a feed-forward neural network (FNN). As per the results of the model applied for typhoons Chan-hom and Soudelor in 2015, the model based on LSTM using the optimal prediction factors shows the best performance and lowest prediction errors. Thus, the model based on LSTM is practical and meaningful for predicting typhoon intensity within 120 h.

Description: Network caching is a technique used to speed-up user access to frequently requested contents in complex data networks. This paper presents a two-layer overlay network caching system for content distribution. It is used to define some caching scenarios with increasing complexity, which refers to real situations, including mobile 5G connectivity. For each scenario our aim is to maximize the hit ratio, which leads to the formulation of NP-complete optimization problems. The heuristic solutions proposed are based on the theory of the maximization of monotone submodular functions under matroid constraints. After the determination of the approximation ratio of the greedy heuristic algorithms proposed, a numerical performance analysis is shown. This analysis includes a comparison with the Least-Frequently Used (LFU) eviction strategy adapted to the analyzed systems. Results show very good performance, under the hypotheses of either known or unknown popularity of contents.

Description: Binary MQ arithmetic coding is widely used as a basic entropy coder in multimedia coding system. MQ coder esteems high in compression efficiency to be used in JBIG2 and JPEG2000. The importance of arithmetic coding is increasing after it is adopted as a unique entropy coder in HEVC standard. In the binary MQ coder, arithmetic approximation without multiplication is used in the process of recursive subdivision of range interval. Because of the MPS/LPS exchange activity that happens in the MQ coder, the output byte tends to increase. This paper proposes an enhanced binary MQ arithmetic coder to make use of look-up table (LUT) for (A × Qe) using quantization skill to improve the coding efficiency. Multi-level quantization using 2-level, 4-level and 8-level look-up tables is proposed in this paper. Experimental results applying to binary documents show about 3% improvement for basic context-free binary arithmetic coding. In the case of JBIG2 bi-level image compression standard, compression efficiency improved about 0.9%. In addition, in the case of lossless JPEG2000 compression, compressed byte decreases 1.5% using 8-level LUT. For the lossy JPEG2000 coding, this figure is a little lower, about 0.3% improvement of PSNR at the same rate.

Description: Structural health monitoring (SHM) is a promising technique for in-service inspection of technical structures in a broad field of applications in order to reduce maintenance efforts as well as the overall structural weight. SHM is basically an inverse problem deriving physical properties such as damages or material inhomogeneity (target features) from sensor data. Often models defining the relationship between predictable features and sensors are required but not available. The main objective of this work is the investigation of model-free distributed machine learning (DML) for damage diagnostics under resource and failure constraints by using multi-instance ensemble and model fusion strategies and featuring improved scaling and stability compared with centralised single-instance approaches. The diagnostic system delivers two features: A binary damage classification (damaged or non-damaged) and an estimation of the spatial damage position in case of a damaged structure. The proposed damage diagnostics architecture should be able to be used in low-resource sensor networks with soft real-time capabilities. Two different machine learning methodologies and architectures are evaluated and compared posing low- and high-resolution sensor processing for low- and high-resolution damage diagnostics, i.e., a dedicated supervised trained low-resource and an unsupervised trained high-resource deep learning approach, respectively. In both architectures state-based recurrent artificial neural networks are used that process spatially and time-resolved sensor data from experimental ultrasonic guided wave measurements of a hybrid material (carbon fibre laminate) plate with pseudo defects. Finally, both architectures can be fused to a hybrid architecture with improved damage detection accuracy and reliability. An extensive evaluation of the damage prediction by both systems shows high reliability and accuracy of damage detection and localisation, even by the distributed multi-instance architecture with a resolution in the order of the sensor distance.

Description: In recent years, the Markov Logic Network (MLN) has emerged as a powerful tool for knowledge-based inference due to its ability to combine first-order logic inference and probabilistic reasoning. Unfortunately, current MLN solutions cannot efficiently support knowledge inference involving arithmetic expressions, which is required to model the interaction between logic relations and numerical values in many real applications. In this paper, we propose a probabilistic inference framework, called the Numerical Markov Logic Network (NMLN), to enable efficient inference of hybrid knowledge involving both logic and arithmetic expressions. We first introduce the hybrid knowledge rules, then define an inference model, and finally, present a technique based on convex optimization for efficient inference. Built on decomposable exp-loss function, the proposed inference model can process hybrid knowledge rules more effectively and efficiently than the existing MLN approaches. Finally, we empirically evaluate the performance of the proposed approach on real data. Our experiments show that compared to the state-of-the-art MLN solution, it can achieve better prediction accuracy while significantly reducing inference time.

Description: Focusing on virtual reality (VR) and film cutting, this study compared and evaluated the effect of visual mode (2D, VR) and cutting rate (fast, medium, slow) on a load, to make an attempt for VR research to enter the cognitive field. This study uses a 2 × 3 experimental research design. Forty participants were divided into one of two groups randomly and watched films with three cutting rates. The subjective and objective data were collected during the experiment. The objective results confirm that VR films bring more powerful alpha, beta, theta wave activities, and bring a greater load. The subjective results confirm that the fast cutting rate brings a greater load. These results provide a theoretical support for further exploring the evaluation methods and standards of VR films and improving the viewing experience in the future.

Description: The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.

Description: This paper studies a novel intelligent motion control algorithm for Autonomous Underwater Vehicles (AUV) and develops a virtual reality system for a new interactive experimental platform. The paper designs a robust neuro-fuzzy controller to tackle system uncertainties and external disturbances. Fuzzy control can solve the uncertainty problem of control systems. The neural network model self-tunes the controller parameters to improve the anti-interference ability. The designed control algorithm is verified using a MATLAB implementation and a virtual reality system. The virtual reality system developed in this paper can be used to debug the control algorithm, simulate the marine environment, and establish an ocean current interference model. The paper uses the MATLAB engine to realize the data communication between the MATLAB and the AUV virtual reality system. This allows the output order of the controller in MATLAB to drive the AUV in a virtual simulation system to simulate the 3D space motion.

Description: Information on the management of local administrations and the actions of the political leaders who govern them is essential for citizens to exercise their political rights. It is therefore necessary for these administrations to provide quality information that the media can use as sources for their news stories. At the same time, these media outlets have to compare and report while taking into account the plurality of their audiences. However, in local settings, collusion exists between political power and media owners that restricts the plurality of news, favoring the dominant political interests and hiding the demands, interests and protagonism of other social actors. We study this problem in the Caribbean Region of Colombia. We analyze the information that the town halls of the main cities in the region provide to the media and how the largest print newspapers and main regional television news broadcasters report on local politics. We compare these news stories to establish whether there is a plurality of news reports. In addition, we analyze the key elements of the news items disseminated by private media outlets to establish whether they report a limited vision of reality: the topics covered, the protagonists referred to in headlines and news stories, and the sources against which the news and images are compared. The results reveal shortcomings that result in similar information between public information and private media content, thus limiting the plurality of news reports and the social protagonism of other social agents. Ultimately, this hinders quality journalism that satisfies the interests of citizens.

Description: This article describes the current landscape in the fields of social media and socio-technical systems. In particular, it analyzes the different ways in which social media are adopted in organizations, workplaces, educational and smart environments. One interesting aspect of this integration, is the use of social media for members’ participation and access to the processes and services of their organization. Those services cover many different types of daily routines and life activities, such as health, education, transports. In this survey, we compare and classify current research works according to multiple features, including: the use of Social Network Analysis and Social Capital models, users’ motivations for participation and organizational costs, adoption of the social media platform from below. Our results show that many of these current systems are developed without taking into proper consideration the social structures and processes, with some notable and positive exceptions.

Description: To reconstruct point geometry from multiple images, computation of the fundamental matrix is always necessary. With a new optimization criterion, i.e., the re-projective 3D metric geometric distance rather than projective space under RANSAC (Random Sample And Consensus) framework, our method can reveal the quality of the fundamental matrix visually through 3D reconstruction. The geometric distance is the projection error of 3D points to the corresponding image pixel coordinates in metric space. The reasonable visual figures of the reconstructed scenes are shown but only some numerical result were compared, as is standard practice. This criterion can lead to a better 3D reconstruction result especially in 3D metric space. Our experiments validate our new error criterion and the quality of fundamental matrix under the new criterion.

Description: In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive the UAV swarm from an initial geographical region to another geographical region where the swarm must form a three-dimensional shape (e.g., surface of a sphere). As most decision-theoretic formulations suffer from the curse of dimensionality, we adapt an existing fast approximate dynamic programming method called nominal belief-state optimization (NBO) to approximately solve the formation control problem. We perform numerical studies in MATLAB to validate the performance of the above control algorithms.

Description: Playability is a key concept in game studies defining the overall quality of video games. Although its definition and frameworks are widely studied, methods to analyze and evaluate the playability of video games are still limited. Using heuristics for playability evaluation has long been the mainstream with its usefulness in detecting playability issues during game development well acknowledged. However, such a method falls short in evaluating the overall playability of video games as published software products and understanding the genuine needs of players. Thus, this paper proposes an approach to analyze the playability of video games by mining a large number of players’ opinions from their reviews. Guided by the game-as-system definition of playability, the approach is a data mining pipeline where sentiment analysis, binary classification, multi-label text classification, and topic modeling are sequentially performed. We also conducted a case study on a particular video game product with its 99,993 player reviews on the Steam platform. The results show that such a review-data-driven method can effectively evaluate the perceived quality of video games and enumerate their merits and defects in terms of playability.

Description: The purpose of this study was to investigate security evaluation practices among small and medium enterprises (SMEs) in small South African towns when adopting cloud business intelligence (Cloud BI). The study employed a quantitative design in which 57 SMEs from the Limpopo Province were surveyed using an online questionnaire. The study found that: (1) the level of cybersecurity threats awareness among decision-makers was high; (2) decision-makers preferred simple checklists and guidelines over conventional security policies, standards, and frameworks; and (3) decision-makers considered financial risks, data and application security, and cloud service provider reliability as the main aspects to consider when evaluating Cloud BI applications. The study conceptualised a five-component security framework for evaluating Cloud BI applications, integrating key aspects of conventional security frameworks and methodologies. The framework was validated for relevance by IT specialists and acceptance by SME owners. The Spearman correlational test for relevance and acceptance of the proposed framework was found to be highly significant at p 〈 0.05. The study concluded that SMEs require user-friendly frameworks for evaluating Cloud BI applications. The major contribution of this study is the security evaluation framework conceptualised from the best practices of existing security standards and frameworks for use by decision-makers from small towns in Limpopo. The study recommends that future research consider end-user needs when customising or proposing new solutions for SMEs in small towns.

Description: Topic Detection and Tracking (TDT) on Twitter emulates human identifying developments in events from a stream of tweets, but while event participants are important for humans to understand what happens during events, machines have no knowledge of them. Our evaluation on football matches and basketball games shows that identifying event participants from tweets is a difficult problem exacerbated by Twitter’s noise and bias. As a result, traditional Named Entity Recognition (NER) approaches struggle to identify participants from the pre-event Twitter stream. To overcome these challenges, we describe Automatic Participant Detection (APD) to detect an event’s participants before the event starts and improve the machine understanding of events. We propose a six-step framework to identify participants and present our implementation, which combines information from Twitter’s pre-event stream and Wikipedia. In spite of the difficulties associated with Twitter and NER in the challenging context of events, our approach manages to restrict noise and consistently detects the majority of the participants. By empowering machines with some of the knowledge that humans have about events, APD lays the foundation not just for improved TDT systems, but also for a future where machines can model and mine events for themselves.

Description: The pre-training fine-tuning mode has been shown to be effective for low resource neural machine translation. In this mode, pre-training models trained on monolingual data are used to initiate translation models to transfer knowledge from monolingual data into translation models. In recent years, pre-training models usually take sentences with randomly masked words as input, and are trained by predicting these masked words based on unmasked words. In this paper, we propose a new pre-training method that still predicts masked words, but randomly replaces some of the unmasked words in the input with their translation words in another language. The translation words are from bilingual data, so that the data for pre-training contains both monolingual data and bilingual data. We conduct experiments on Uyghur-Chinese corpus to evaluate our method. The experimental results show that our method can make the pre-training model have a better generalization ability and help the translation model to achieve better performance. Through a word translation task, we also demonstrate that our method enables the embedding of the translation model to acquire more alignment knowledge.

Description: The purpose of this paper is to examine whether salience of neighbor comparison information attracts more attention from residents and consequently leads to significant energy conservation. An eye-tracking experiment on 54 residents in a local apartment complex in Korea found that the average time of attention to the neighbor comparison information increased to 277 ms when the size of the information was four times larger and the information was located to the far left. However, the interviews with the subjects suggest that salience of the information is seemingly unrelated to energy conservation, because most of them did not agree with the social consensus that individuals need to refrain from consuming energy when they know that they have consumed more than the neighbor’s average. Utility data on 502 households in the apartments revealed that, of the households notified that they consumed more than their neighbors, only less than 50% reduced their energy consumption, which supports the interview results. Therefore, it was concluded that neighbor comparison information did not lead to significant energy conservation effects in the community, although salience of the information contributed to attracting more attention to the information. Unavailable household data remained as limitation to clarify the effect by households.

Description: The continuous increase in network traffic has sharply increased the demand for high-performance packet processing systems. For a high-performance packet processing system based on multi-core processors, the packet scheduling algorithm is critical because of the significant role it plays in load distribution, which is related to system throughput, attracting intensive research attention. However, it is not an easy task since the canonical flow-level packet scheduling algorithm is vulnerable to traffic locality, while the packet-level packet scheduling algorithm fails to maintain cache affinity. In this paper, we propose an adaptive throughput-first packet scheduling algorithm for DPDK-based packet processing systems. Combined with the feature of DPDK burst-oriented packet receiving and transmitting, we propose using Subflow as the scheduling unit and the adjustment unit making the proposed algorithm not only maintain the advantages of flow-level packet scheduling algorithms when the adjustment does not happen but also avoid packet loss as much as possible when the target core may be overloaded Experimental results show that the proposed method outperforms Round-Robin, HRW (High Random Weight), and CRC32 on system throughput and packet loss rate.

Description: Question-answering systems based on knowledge graphs are extremely challenging tasks in the field of natural language processing. Most of the existing Chinese Knowledge Base Question Answering(KBQA) can only return the knowledge stored in the knowledge base by extractive methods. Nevertheless, this processing does not conform to the reading habits and cannot solve the Out-of-vocabulary(OOV) problem. In this paper, a new generative question answering method based on knowledge graph is proposed, including three parts of knowledge vocabulary construction, data pre-processing, and answer generation. In the word list construction, BiLSTM-CRF is used to identify the entity in the source text, finding the triples contained in the entity, counting the word frequency, and constructing it. In the part of data pre-processing, a pre-trained language model BERT combining word frequency semantic features is adopted to obtain word vectors. In the answer generation part, one combination of a vocabulary constructed by the knowledge graph and a pointer generator network(PGN) is proposed to point to the corresponding entity for generating answer. The experimental results show that the proposed method can achieve superior performance on WebQA datasets than other methods.

Description: For a given positive integer k, the k-circle formation problem asks a set of autonomous, asynchronous robots to form disjoint circles having k robots each at distinct locations, centered at a set of fixed points in the Euclidean plane. The robots are identical, anonymous, oblivious, and they operate in Look–Compute–Move cycles. This paper studies the k-circle formation problem and its relationship with the k-epf problem, a generalized version of the embedded pattern formation problem, which asks exactly k robots to reach and remain at each fixed point. First, the k-circle formation problem is studied in a setting where the robots have an agreement on the common direction and orientation of one of the axes. We have characterized all the configurations and the values of k, for which the k-circle formation problem is deterministically unsolvable in this setting. For the remaining configurations and the values of k, a deterministic distributed algorithm has been proposed, in order to solve the problem. It has been proved that for the initial configurations with distinct robot positions, if the k-circle formation problem is deterministically solvable then the k-epf problem is also deterministically solvable. It has been shown that by modifying the proposed algorithm, the k-epf problem can be solved deterministically.

Description: Assessing the business performance is an important aspect of almost all economic decisions at the microeconomic and macroeconomic level, in the short and long term. Information about the partners’ relationship to the business, their interest in the evaluation of investments can be explained by various indicators. It is relevant to understand the dependencies of the business performance and the amount of equity, while negative equity can be considered as critical information of existence. The purpose of quantitative research is to identify the relationship between reported negative equity and the business performance in Slovakia on an exhaustive sample of financial data of businesses with negative equity in the period 2014–2018. The business performance with negative equity is assessed through the Altman Z-score and the IN05 index, by classifying businesses into bankruptcy, prosperity and gray zones. Pearson’s correlation analysis between negative equity and Altman Z-score performance confirms the strong direct relationship between negative equity and the bankruptcy zone, the weaker indirect relationship between negative equity and the gray zone, and almost no dependence of negative equity and prosperity zone. In the case of the IN05 index, a low correlation was found between negative equity and all three zones. Although businesses with negative equity are in a bankruptcy zone, they do not have to close automatically, but they have to improve resource management, in particular to increase equity, for example by making a profit and good financial management.

Description: Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data. However, most existing methods based on variational (graph) autoencoder assume that the prior of latent variables obeys the standard normal distribution which encourages all nodes to gather around 0. That leads to the inability to fully utilize the latent space. Therefore, it becomes a challenge on how to choose a suitable prior without incorporating additional expert knowledge. Given this, we propose a novel noninformative prior-based interpretable variational graph autoencoder (NPIVGAE). Specifically, we exploit the noninformative prior as the prior distribution of latent variables. This prior enables the posterior distribution parameters to be almost learned from the sample data. Furthermore, we regard each dimension of a latent variable as the probability that the node belongs to each block, thereby improving the interpretability of the model. The correlation within and between blocks is described by a block–block correlation matrix. We compare our model with state-of-the-art methods on three real datasets, verifying its effectiveness and superiority.

Description: Quality control of heat sealed bottles is very important to minimize waste and in some cases protect people’s health. The present paper describes a case study where an automated non invasive and non destructive quality control system was designed to assess the quality of the seals of bottles containing pesticide. In this case study, the integrity of the seals is evaluated using an artificial neural network based on images of the seals processed with computer vision techniques. Because the seals are not directly visible from the bottle exterior, the images are infrared pictures obtained using a thermal camera. The method is non invasive, automated, and can be applied to common conveyor belts currently used in industrial plants. The results show that the inspection process is effective in identifying defective seals with a precision of 98.6% and a recall of 100% and because it is automated it can be scaled up to large bottle processing plants.

Description: The purpose of this review is to describe the landscape of scientific literature enriched by an author’s keyword analysis to develop and test blockchain’s capabilities for enhancing supply chain resilience in times of increased risk and uncertainty. This review adopts a dynamic quantitative bibliometric method called systematic literature network analysis (SLNA) to extract and analyze the papers. The procedure consists of two methods: a systematic literature review (SLR) and bibliometric network analysis (BNA). This paper provides an important contribution to the literature in applying blockchain as a key component of cyber supply chain risk management (CSRM), manage and predict disruption risks that lead to resilience and robustness of the supply chain. This systematic review also sheds light on different research areas such as the potential of blockchain for privacy and security challenges, security of smart contracts, monitoring counterfeiting, and traceability database systems to ensure food safety and security.

Description: The sharing mode of the logistics industry can effectively solve the new problems arising from the rapid development of the express industry. However, only when the interests are reasonably distributed can the sharing mode be implemented for a long time. This paper discusses the connotation of unified warehouse and distribution, designs the operation mode of a unified warehouse and distribution, and solves the profit distribution problem of a unified warehouse and distribution alliance based on the improved Shapley value method. Firstly, the traditional Shapley value method is improved by using a comprehensive correction factor, including the proportions of investment, risk, and innovative research contributions. Secondly, each factor’s weight is determined by the analytic hierarchy process (AHP), and the profits are distributed according to the contribution of each express enterprise to the alliance. Finally, an example is given to verify the validity of the modified algorithm. It proves that the modified Shapley value method can effectively solve the problem of profit distribution.

Description: This paper proposes a new machine vision method to test the quality of a semi-transparent automotive illuminant component. Difference images of Frangi filtered surface images are used to enhance defect-like image structures. In order to distinguish allowed structures from defective structures, morphological features are extracted and used for a nearest-neighbor-based anomaly score. In this way, it could be demonstrated that a segmentation of occurring defects is possible on transparent illuminant parts. The method turned out to be fast and accurate and is therefore also suited for in-production testing.

Description: Cluster analysis is widely applied in the neuropsychological field for exploring patterns in cognitive profiles, but traditional hierarchical and non-hierarchical approaches could be often poorly effective or even inapplicable on certain type of data. Moreover, these traditional approaches need the initial specification of the number of clusters, based on a priori knowledge not always owned. For this reason, we proposed a novel method for cognitive clustering through the affinity propagation (AP) algorithm. In particular, we applied the AP clustering on the regression residuals of the Mini Mental State Examination scores—a commonly used screening tool for cognitive impairment—of a cohort of 49 Parkinson’s disease, 48 Progressive Supranuclear Palsy and 44 healthy control participants. We found four clusters, where two clusters (68 and 30 participants) showed almost intact cognitive performance, one cluster had a moderate cognitive impairment (34 participants), and the last cluster had a more extensive cognitive deficit (8 participants). The findings showed, for the first time, an intra- and inter-diagnostic heterogeneity in the cognitive profile of Parkinsonisms patients. Our novel method of unsupervised learning could represent a reliable tool for supporting the neuropsychologists in understanding the natural structure of the cognitive performance in the neurodegenerative diseases.

Description: Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids “reinventing the wheel,” but also presents a lightweight solution to otherwise computationally heavy problems.