ALBERT

Description: Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

Description: In this work, we propose a mechanism for knowledge transfer between Convolutional Neural Networks via the geometric regularization of local features produced by the activations of convolutional layers. We formulate appropriate loss functions, driving a “student” model to adapt such that its local features exhibit similar geometrical characteristics to those of an “instructor” model, at corresponding layers. The investigated functions, inspired by manifold-to-manifold distance measures, are designed to compare the neighboring information inside the feature space of the involved activations without any restrictions in the features’ dimensionality, thus enabling knowledge transfer between different architectures. Experimental evidence demonstrates that the proposed technique is effective in different settings, including knowledge-transfer to smaller models, transfer between different deep architectures and harnessing knowledge from external data, producing models with increased accuracy compared to a typical training. Furthermore, results indicate that the presented method can work synergistically with methods such as knowledge distillation, further increasing the accuracy of the trained models. Finally, experiments on training with limited data show that a combined regularization scheme can achieve the same generalization as a non-regularized training with 50% of the data in the CIFAR-10 classification task.

Description: Both the statistical machine translation (SMT) model and neural machine translation (NMT) model are the representative models in Uyghur–Chinese machine translation tasks with their own merits. Thus, it will be a promising direction to combine the advantages of them to further improve the translation performance. In this paper, we present a hybrid framework of developing a system combination for a Uyghur–Chinese machine translation task that works in three layers to achieve better translation results. In the first layer, we construct various machine translation systems including SMT and NMT. In the second layer, the outputs of multiple systems are combined to leverage the advantage of SMT and NMT models by using a multi-source-based system combination approach and the voting-based system combination approaches. Moreover, instead of selecting an individual system’s combined outputs as the final results, we transmit the outputs of the first layer and the second layer into the final layer to make a better prediction. Experiment results on the Uyghur–Chinese translation task show that the proposed framework can significantly outperform the baseline systems in terms of both the accuracy and fluency, which achieves a better performance by 1.75 BLEU points compared with the best individual system and by 0.66 BLEU points compared with the conventional system combination methods, respectively.

Description: With the flourishing development of the hotel industry, the study of customer satisfaction based on online reviews and data has become a new model. In this paper, customer reviews and ratings on Ctrip.com are used, and TF-IDF and K-means algorithms are used to extract and cluster the keywords of reviews texts. Finally, 10 first-level influencing factors of hotel customer satisfaction are determined: epidemic prevention, consumption emotion, convenience, environment, facilities, catering, target group, perceived value, price, and service. Based on backpropagation neural network and weight matrix operation, an influencing factor analysis model of hotel customer satisfaction is constructed to explore the role of these factors. The results show that consumption emotion, perceived value, epidemic prevention, target group, and convenience would significantly affect customer satisfaction, among which epidemic prevention becomes a new factor affecting customer satisfaction. Environment, facilities, catering, and service have relatively little effect on customer satisfaction, while price has the least effect. This study provides a path and method for online reviews of hotel management to improve customer satisfaction and provides a theoretical basis for the study of online reviews of hotels.

Description: The e-commerce industry in Indonesia is growing in line with the increasing number of internet users in Indonesia. Unfortunately, many internet users in Indonesia are still unsure about shopping online because of the lack of buyer trust with sellers and service providers. This study aims to identify the factors that influence online shop consumers to conduct transactions online. This research used a questionnaire survey distributed to customers who had ever used an online shop application. The sample used in this research was 468 respondents. The data collected was then analyzed using Partial Least Square. The results of this research indicated that trust, perceived value, and buying interest positively influence consumers’ decisions to purchase using an online shop application.

Description: Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) in an efficient and scalable manner. The key to success of these models is their ability to learn low-rank vector representations for knowledge graph entities and relations. Despite the rapid development of KGE models, state-of-the-art approaches have mostly focused on new ways to represent embeddings interaction functions (i.e., scoring functions). In this paper, we argue that the choice of other training components such as the loss function, hyperparameters and negative sampling strategies can also have substantial impact on the model efficiency. This area has been rather neglected by previous works so far and our contribution is towards closing this gap by a thorough analysis of possible choices of training loss functions, hyperparameters and negative sampling techniques. We finally investigate the effects of specific choices on the scalability and accuracy of knowledge graph embedding models.

Description: The article deals with the topic of attention paid to online privacy policy statements by university students. Privacy policy statements were originally intended to mitigate the users’ privacy con-cerns and support trust, but users disregard them. The article uses the theory of planned behav-iour combined with privacy calculus to find and verify determinants of reading privacy policy statements. We used the survey method and evaluated the results with partial least square struc-tural equation modelling. We concluded that the attitude towards reading privacy policy state-ments is influenced by privacy risks and privacy benefits. The intention to read privacy policy statements is influenced by social norms, understanding the privacy policy and mainly by the willingness to spend time and effort reading the statements. The effect of attitude was also signif-icant, but its size was smaller. Finally, wider conclusions are drawn, as the confusion around pri-vacy policy statements is a symptom of a wider social change in the information society.

Description: In the light of the increasing importance of the societal impact of research, this article attempts to address the question as to how social sciences and humanities (SSH) research outputs from 2019 are represented in Slovak research portfolios in comparison with those of the EU-28 and the world. The data used for the analysis originate from the R&D SK CRIS and bibliographic Central Register of Publication Activities (CREPČ) national databases, and WoS Core Collection/InCites. The research data were appropriate for the analysis at the time they were structured, on the national level; of high quality and consistency; and covering as many components as possible and in mutual relations. The data resources should enable the research outputs to be assigned to research categories. The analysis prompts the conclusion that social sciences and humanities research outputs in Slovakia in 2019 are appropriately represented and in general show an increasing trend. This can be documented by the proportion represented by the SSH research projects and other entities involved in the overall Slovak research outputs, and even the higher ratio of SSH research publications in comparison with the EU-28 and the world. Recommendations of a technical character include research data management, data quality, and the integration of individual systems and available analytical tools.

Description: Entity Resolution (ER) is the problem of identifying co-referent entity pairs across datasets, including knowledge graphs (KGs). ER is an important prerequisite in many applied KG search and analytics pipelines, with a typical workflow comprising two steps. In the first ’blocking’ step, entities are mapped to blocks. Blocking is necessary for preempting comparing all possible pairs of entities, as (in the second ‘similarity’ step) only entities within blocks are paired and compared, allowing for significant computational savings with a minimal loss of performance. Unfortunately, learning a blocking scheme in an unsupervised fashion is a non-trivial problem, and it has not been properly explored for heterogeneous, semi-structured datasets, such as are prevalent in industrial and Web applications. This article presents an unsupervised algorithmic pipeline for learning Disjunctive Normal Form (DNF) blocking schemes on KGs, as well as structurally heterogeneous tables that may not share a common schema. We evaluate the approach on six real-world dataset pairs, and show that it is competitive with supervised and semi-supervised baselines.

Description: Exploratory data analysis (EDA) is an iterative process where data scientists interact with data to extract information about their quality and shape as well as derive knowledge and new insights into the related domain of the dataset. However, data scientists are rarely experienced domain experts who have tangible knowledge about a domain. Integrating domain knowledge into the analytic process is a complex challenge that usually requires constant communication between data scientists and domain experts. For this reason, it is desirable to reuse the domain insights from exploratory analyses in similar use cases. With this objective in mind, we present a conceptual system design on how to extract domain expertise while performing EDA and utilize it to guide other data scientists in similar use cases. Our system design introduces two concepts, interaction storage and analysis context storage, to record user interaction and interesting data points during an exploratory analysis. For new use cases, it identifies historical interactions from similar use cases and facilitates the recorded data to construct candidate interaction sequences and predict their potential insight—i.e., the insight generated from performing the sequence. Based on these predictions, the system recommends the sequences with the highest predicted insight to data scientist. We implement a prototype to test the general feasibility of our system design and enable further research in this area. Within the prototype, we present an exemplary use case that demonstrates the usefulness of recommended interactions. Finally, we give a critical reflection of our first prototype and discuss research opportunities resulting from our system design.

Description: The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric errors of radiotherapy from relevant literature using machine learning and natural language processing (NLP) approaches. A framework is developed in this study that initially builds a training corpus by extracting sentences containing different types of geometric errors of radiotherapy from relevant publications. The publications are retrieved from PubMed following a given set of rules defined by a domain expert. Subsequently, the method develops a training corpus by extracting relevant sentences using a sentence similarity measure. A support vector machine (SVM) classifier is then trained on this training corpus to extract the sentences from new publications which contain relevant geometric errors. To demonstrate the proposed approach, we have used 60 publications containing geometric errors in radiotherapy to automatically extract the sentences stating the mean and standard deviation of different types of errors between planned and executed radiotherapy. The experimental results show that the recall and precision of the proposed framework are, respectively, 97% and 72%. The results clearly show that the framework is able to extract almost all sentences containing required data of geometric errors.

Description: Malware creators generate new malicious software samples by making minor changes in previously generated code, in order to reuse malicious code, as well as to go unnoticed from signature-based antivirus software. As a result, various families of variations of the same initial code exist today. Visualization of compiled executables for malware analysis has been proposed several years ago. Visualization can greatly assist malware classification and requires neither disassembly nor code execution. Moreover, new variations of known malware families are instantly detected, in contrast to traditional signature-based antivirus software. This paper addresses the problem of identifying variations of existing malware visualized as images. A new malware detection system based on a two-level Artificial Neural Network (ANN) is proposed. The classification is based on file and image features. The proposed system is tested on the ‘Malimg’ dataset consisting of the visual representation of well-known malware families. From this set some important image features are extracted. Based on these features, the ANN is trained. Then, this ANN is used to detect and classify other samples of the dataset. Malware families creating a confusion are classified by a second level of ANNs. The proposed two-level ANN method excels in simplicity, accuracy, and speed; it is easy to implement and fast to run, thus it can be applied to antivirus software, smart firewalls, web applications, etc.

Description: The preliminary credibility assessment screening system (PCASS) is a US-based program, which is currently being implemented by intelligence units of the North Atlantic Treaty Organization (NATO) to make the initial screening of individuals suspected of infiltrating the Afghan National Defense and Security Forces (ANDSF). Sensors have been instrumental in the PCASS, leading to organizational change. The aim of this research is to describe how the ANDSF adapted to the implementation of PCASS, as well as implemented changes since the beginning of the program. To do so, we have conducted a qualitative, exploratory, and descriptive case study that allows one to understand, through the use of a series of data collection sources, a real-life phenomenon of which little is known. The results suggest that the sensors used in PCASS empower security forces with reliable technologies to identify and neutralize internal threats. It then becomes evident that the technological leadership that PCASS provides allows the developing of a relatively stable and consistent organizational change, fulfilling the objectives of the NATO and the ANDSF.

Description: The notion of comfort with respect to rides, such as roller coasters, is typically addressed from the perspective of a physical ride, where the convenience of transportation is redefined to minimize risk and maximize thrill. As a popular form of entertainment, roller coasters sit at the nexus of rides and games, providing a suitable environment to measure both mental and physical experiences of rider comfort. In this paper, the way risk and comfort affect such experiences is investigated, and the connection between play comfort and ride comfort is explored. A roller coaster ride simulation is adopted as the target environment for this research, which combines the feeling of being thrill and comfort simultaneously. At the same time, this paper also expands research on roller coaster rides while bridging the rides and games via the analogy of the law of physics, a concept currently known as motion in mind. This study’s contribution involves a roller coaster ride model, which provides an extended understanding of the relationship between physical performance and the mental experience relative to the concept of motion in mind while establishing critical criteria for a comfortable experience of both the ride and play.

Description: The emergence of the fourth industrial revolution (Industry 4.0, hereinafter I 4.0) has led to an entirely fresh approach to production, helping to enhance the key industrial processes and therefore increase the growth of labor productivity and competitiveness. Simultaneously, I 4.0 compels changes in the organization of work and influences the lives of employees. The paper intends to construct a model for predicting the allocation of human resources in the sectors of the national economy of the Czech Republic in connection with I 4.0. The model used in this research visualizes the shift of labor in the economic sectors of the Czech Republic from the year 2013 to the following years in the near future. The main contribution of this article is to show the growth of employment in the high-tech services sector, which will have an ascending trend.

Description: Automatic Weather Stations (AWS) are extensively used for gathering meteorological and climatic data. The World Meteorological Organization (WMO) provides publications with guidelines for the implementation, installation, and usages of these stations. Nowadays, in the new era of the Internet of Things, there is an ever-increasing necessity for the implementation of automatic observing systems that will provide scientists with the real-time data needed to design and apply proper environmental policy. In this paper, an extended review is performed regarding the technologies currently used for the implementation of Automatic Weather Stations. Furthermore, we also present the usage of new emerging technologies such as the Internet of Things, Edge Computing, Deep Learning, LPWAN, etc. in the implementation of future AWS-based observation systems. Finally, we present a case study and results from a testbed AWS (project AgroComp) developed by our research team. The results include test measurements from low-cost sensors installed on the unit and predictions provided by Deep Learning algorithms running locally.

Description: By only storing a unique copy of duplicate data possessed by different data owners, deduplication can significantly reduce storage cost, and hence is used broadly in public clouds. When combining with confidentiality, deduplication will become problematic as encryption performed by different data owners may differentiate identical data which may then become not deduplicable. The Message-Locked Encryption (MLE) is thus utilized to derive the same encryption key for the identical data, by which the encrypted data are still deduplicable after being encrypted by different data owners. As keys may be leaked over time, re-encrypting outsourced data is of paramount importance to ensure continuous confidentiality, which, however, has not been well addressed in the literature. In this paper, we design SEDER, a SEcure client-side Deduplication system enabling Efficient Re-encryption for cloud storage by (1) leveraging all-or-nothing transform (AONT), (2) designing a new delegated re-encryption (DRE), and (3) proposing a new proof of ownership scheme for encrypted cloud data (PoWC). Security analysis and experimental evaluation validate security and efficiency of SEDER, respectively.

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: 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: 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: 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: 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: 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: 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 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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.

Description: Previous studies have pointed out that it is hard to achieve the level of herd immunity for the population and then effectively stop disease propagation from the perspective of public health, if individuals just make vaccination decisions based on individualism. Individuals in reality often exist in the form of groups and cooperate in or among communities. Meanwhile, society studies have suggested that we cannot ignore the existence and influence of collectivism for studying individuals’ decision-making. Regarding this, we formulate two vaccination strategies: individualistic strategy and collectivist strategy. The former helps individuals taking vaccination action after evaluating their perceived risk and cost of themselves, while the latter focuses on evaluating their contribution to their communities. More significantly, we propose a reinforcement learning mechanism based on policy gradient. Each individual can adaptively pick one of these two strategies after weighing their probabilities with a two-layer neural network whose parameters are dynamically updated with his/her more and more vaccination experience. Experimental results on scale-free networks verify that the reinforcement learning mechanism can effectively improve the vaccine coverage level of communities. Moreover, communities can always get higher total payoffs with fewer costs paid, comparing that of pure individualistic strategy. Such performance mostly stems from individuals’ adaptively picking collectivist strategy. Our study suggests that public health authorities should encourage individuals to make vaccination decisions from the perspective of their local mixed groups. Especially, it is more worthy of noting that individuals with low degrees are more significant as their vaccination behaviors can more sharply improve vaccination coverage of their groups and greatly reduce epidemic size.

Description: While users benefit greatly from the latest communication technology, with popular platforms such as social networking services including Facebook or search engines such as Google, scientists warn of the effects of a filter bubble at this time. A solution to escape from filtered information is urgently needed. We implement an approach based on the mechanism of a metasearch engine to present less-filtered information to users. We develop a practical application named MosaicSearch to select search results from diversified categories of sources collected from multiple search engines. To determine the power of MosaicSearch, we conduct an evaluation to assess retrieval quality. According to the results, MosaicSearch is more intelligent compared to other general-purpose search engines: it generates a smaller number of links while providing users with almost the same amount of objective information. Our approach contributes to transparent information retrieval. This application helps users play a main role in choosing the information they consume.

Description: Certified public accounts’ (CPAs) audit opinions of going concern are the important basis for evaluating whether enterprises can achieve normal operations and sustainable development. This study aims to construct going concern prediction models to help CPAs and auditors to make more effective/correct judgments on going concern opinion decisions by deep learning algorithms, and using the following methods: deep neural networks (DNN), recurrent neural network (RNN), and classification and regression tree (CART). The samples of this study are companies listed on the Taiwan Stock Exchange and the Taipei Exchange, a total of 352 companies, including 88 companies with going concern doubt and 264 normal companies (with no going concern doubt). The data from 2002 to 2019 are taken from the Taiwan Economic Journal (TEJ) Database. According to the empirical results, with the important variables selected by CART and modeling by RNN, the CART-RNN model has the highest going concern prediction accuracy (the accuracy of the test dataset is 95.28%, and the average accuracy is 93.92%).

Description: Responding to the recent COVID-19 outbreak, several organizations and private citizens considered the opportunity to design and publish online explanatory data visualization tools for the communication of disease data supported by a spatial dimension. They responded to the need of receiving instant information arising from the broad research community, the public health authorities, and the general public. In addition, the growing maturity of information and mapping technologies, as well as of social networks, has greatly supported the diffusion of web-based dashboards and infographics, blending geographical, graphical, and statistical representation approaches. We propose a broad conceptualization of Web visualization tools for geo-spatial information, exceptionally employed to communicate the current pandemic; to this end, we study a significant number of publicly available platforms that track, visualize, and communicate indicators related to COVID-19. Our methodology is based on (i) a preliminary systematization of actors, data types, providers, and visualization tools, and on (ii) the creation of a rich collection of relevant sites clustered according to significant parameters. Ultimately, the contribution of this work includes a critical analysis of collected evidence and an extensive modeling effort of Geo-Online Exploratory Data Visualization (Geo-OEDV) tools, synthesized in terms of an Entity-Relationship schema. The COVID-19 pandemic outbreak has offered a significant case to study how and how much modern public communication needs spatially related data and effective implementation of tools whose inspection can impact decision-making at different levels. Our resulting model will allow several stakeholders (general users, policy-makers, and researchers/analysts) to gain awareness on the assets of structured online communication and resource owners to direct future development of these important tools.

Description: Digital advertising has been frequently used for the promotion of e-commerce among individuals. However, little is known about the function of cultural factors that can outline the effectiveness of digital advertising practices to alter attitude and consumer behavior toward clothing brands. This research examines how norm-congruent attitudes toward digital advertising (hereafter ADA) may operate as a process variable that mediates the relationship between perception about digital advertising (hereafter PDA) and online purchase intention of fashion brands (hereafter OPI). We propose a gender egalitarianism (hereafter GE)-moderated mediation model whereby ADA mediates the relationships between PDA and OPI in two culturally diverse nations: Malaysia and Pakistan. The model was tested by using 2 (GE appeal: present vs. absent) × 2 (nation: Pakistan vs. Malaysia) × 2 (no exposure to ads/exposure to ads) experimental design with data obtained from a sample of 260. Findings show that there is a significant difference in the relationship between PDA and OPI that is mediated by the attitude in both nations. However, the mediation implication of the attitude is significantly dependent on the interaction of the GE. In this way, the study provides some practical recommendations for the marketers by highlighting the salient advertising features that may be more useful in both nations.

Description: Sensors continue to pervade our surroundings in undiminished ways [...]

Description: This article discusses the impact of a lockdown caused by the novel coronavirus disease 2019 on the educational process at a selected faculty of a public university in the Czech Republic focused on economic education. The aim was to capture relevant aspects in the context of impacts on the management of the educational process in the organization. The unique situation brought the possibility of analyzing the flexibility of the organization, its ability to adapt. A questionnaire survey was conducted among academics. We found out how they coped with this situation, their technical equipment, support from the faculty, and whether they encountered any problems. The goal of the article was not to bring an exact evaluation of selected questions, but to show the state of the actual situation, to point out possible problems of users, and to link these things with the approach to the management of the organization. Based on the analysis, we bring suggestions and recommendations for improving the process of transition to online learning as well as distance education management and recommendation to support teaching, regardless of the teacher’s workplace. The basic areas and activities that need to be managed were also identified.

Description: Long Range (LoRa) communication is widely adapted in long-range Internet of Things (IoT) applications. LoRa is one of the powerful technologies of Low Power Wide Area Networking (LPWAN) standards designed for IoT applications. Enormous IoT applications lead to massive traffic results, which affect the entire network’s operation by decreasing the quality of service (QoS) and minimizing the throughput and capacity of the LoRa network. To this end, this paper proposes a novel cluster throughput model of the throughput distribution function in a cluster to estimate the expected value of the throughput capacity. This paper develops two main clustering algorithms using dense LoRa-based IoT networks that allow clustering of end devices according to the criterion of maximum served traffic. The algorithms are built based on two-common methods, K-means and FOREL. In contrast to existing methods, the developed method provides the maximum value of served traffic in a cluster. Results reveal that our proposed cluster throughput model obtained a higher average throughput value by using a normal distribution than a uniform distribution.

Description: In this study, we proposed a method to improve the safety level of control software (CSW) by managing the CSW’s design information and safety analysis results, and combining failure mode and effects analysis (FMEA) and fault tree analysis (FTA). Here, the CSW is developed using structured analysis and design methodology. In the upper stage of the CSW’s development process, as the input of the preliminary design information (data flow diagrams (DFDs) and control flow diagrams (CFDs)), the causes of undesirable events of the CSW are clarified by FMEA, and the countermeasures are reflected in the preliminary design information. In the lower stage of the CSW’s development process, as the inputs of the detailed design information (DFDs and CFDs in the lower level) and programs, the causes of the specific undesirable event are clarified by FTA, and the countermeasures are reflected in the detailed design specifications and programs. The processes are repeated until the impact of undesirable events become the acceptable safety level. By applying the proposed method to the CSW installed into a communication control equipment on the space system, we clarified several undesirable events and adopted adequate countermeasures. Consequently, a safer CSW is developed by applying the proposed method.

Description: This framework for human behavior monitoring aims to take a holistic approach to study, track, monitor, and analyze human behavior during activities of daily living (ADLs). The framework consists of two novel functionalities. First, it can perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs to identify a list of distinct behavioral patterns associated with different complex activities. Second, it consists of an intelligent decision-making algorithm that can analyze these behavioral patterns and their relationships with the dynamic contextual and spatial features of the environment to detect any anomalies in user behavior that could constitute an emergency. These functionalities of this interdisciplinary framework were developed by integrating the latest advancements and technologies in human–computer interaction, machine learning, Internet of Things, pattern recognition, and ubiquitous computing. The framework was evaluated on a dataset of ADLs, and the performance accuracies of these two functionalities were found to be 76.71% and 83.87%, respectively. The presented and discussed results uphold the relevance and immense potential of this framework to contribute towards improving the quality of life and assisted living of the aging population in the future of Internet of Things (IoT)-based ubiquitous living environments, e.g., smart homes.

Description: Computer-aided methods, based on the entropic linear program framework, have been shown to be effective in assisting the study of information theoretic fundamental limits of information systems. One key element that significantly impacts their computation efficiency and applicability is the reduction of variables, based on problem-specific symmetry and dependence relations. In this work, we propose using the disjoint-set data structure to algorithmically identify the reduction mapping, instead of relying on exhaustive enumeration in the equivalence classification. Based on this reduced linear program, we consider four techniques to investigate the fundamental limits of information systems: (1) computing an outer bound for a given linear combination of information measures and providing the values of information measures at the optimal solution; (2) efficiently computing a polytope tradeoff outer bound between two information quantities; (3) producing a proof (as a weighted sum of known information inequalities) for a computed outer bound; and (4) providing the range for information quantities between which the optimal value does not change, i.e., sensitivity analysis. A toolbox, with an efficient JSON format input frontend, and either Gurobi or Cplex as the linear program solving engine, is implemented and open-sourced.

Description: In the distance estimation scheme using Frequency-Modulated-Continuous-Wave (FMCW) radar, the frequency difference, which was caused by the time delay of the received signal reflected from the target, is calculated to estimate the distance information of the target. In this paper, we propose a distance estimation scheme exploiting the deep learning technology of artificial neural network to improve the accuracy of distance estimation over the conventional Fast Fourier Transform (FFT) Max value index-based distance estimation scheme. The performance of the proposed scheme is compared with that of the conventional scheme through the experiments evaluating the accuracy of distance estimation. The average estimated distance error of the proposed scheme was 0.069 m, while that of the conventional scheme was 1.9 m.

Description: Due to the limited bandwidth of Low-Power Wide-Area Networks (LPWAN), the application layer is currently often tied straight above the link layer, limiting the evolution of sensor networks distributed over a large area. Consequently, the highly efficient Static Context Header Compression (SCHC) standard was introduced, where devices can compress the IPv6 and upper layer protocols down to a single byte. This approach, however, assumes that every compression context is distributed before deployment, again limiting the evolution of such networks. Therefore, this paper presents two context registration mechanisms leveraging on the SCHC adaptation layer. This is done by analyzing current registration solutions in order to find limitations and optimizations with regard to very constrained networks. Both solutions and the current State-of-The-Art (SoTA) are evaluated in a Lightweight Machine to Machine (LwM2M) environment. In such situation, both developed solutions decrease the energy consumption already after 25 transmissions, compared with the current SoTA. Furthermore, simulations show that Long Range (LoRa) devices still have a 80% chance to successfully complete the registration flow in a network with a 50% Packet Error Ratio. Briefly, the work presented in this paper delivers bootstrapping tools to constrained, SCHC-enabled networks while still being able to reduce energy consumption.

Description: The application of cosegmentation in remote sensing image change detection can effectively overcome the salt and pepper phenomenon and generate multitemporal changing objects with consistent boundaries. Cosegmentation considers the image information, such as spectrum and texture, and mines the spatial neighborhood information between pixels. However, each pixel in the minimum cut/maximum flow algorithm for cosegmentation change detection is regarded as a node in the network flow diagram. This condition leads to a direct correlation between computation times and the number of nodes and edges in the diagram. It requires a large amount of computation and consumes excessive time for change detection of large areas. A superpixel segmentation method is combined into cosegmentation to solve this shortcoming. Simple linear iterative clustering is adopted to group pixels by using the similarity of features among pixels. Two-phase superpixels are overlaid to form the multitemporal consistent superpixel segmentation. Each superpixel block is regarded as a node for cosegmentation change detection, so as to reduce the number of nodes in the network flow diagram constructed by minimum cut/maximum flow. In this study, the Chinese GF-1 and Landsat satellite images are taken as examples, the overall accuracy of the change detection results is above 0.80, and the calculation time is only one-fifth of the original.

Description: In this theoretical paper, we explore Big Data ethics in the broader context of general data ethics, stakeholder groups, demand for governance and regulation, social norms, and human values. We follow and expand on the digital divide, governance, and regulatory theories, and we apply them to many levels and contexts, such as state and society, organization, enterprise governance of IT (EGIT), and data projects, among others. We introduce the new role and responsibility of data experts as an important stakeholder group in the balance of power of Big Data ethics because they simultaneously hold a position in groups of data-rich organizations and data-poor users. We argue that the balancing role of data experts consists of motivation and competence, a sense of responsibility for data ethics, and the possibility and means to influence Big Data issues. Finally, we conclude our research by model mapping the role of data experts in Big Data ethics and proposing them as a balancing power.

Description: Serious Games (SG) provide a comfortable learning environment and are productive for various disciplines ranging from Science, Technology, Engineering, and Mathematics (STEM) to computer programming. The Object Oriented (OO) paradigm includes objects related to real life, and is considered a natural domain that can be worked with. Nonetheless, mapping those real-life objects with basic Object-Oriented Programming (OOP) concepts becomes a challenge for students to understand. Therefore, this study is concerned with designing and developing an SG prototype to overcome students’ difficulties and misconceptions in learning OOP and achieving positive learning outcomes. An experimental evaluation was carried out to show the difference between the experimental group students’ performance, who interact with the developed game, and students of the control group, who learn via the traditional instructional method. The experimental evaluations’ main finding is that the experimental group’s performance is better than the control group. The experimental group’s Normalized Learning Gain (NLG) is significantly higher than the control group (p〈 0.005, paired t-test). The evaluation study results show that the developed prototype’s perceived motivation on the Instructional Materials Motivation Survey (IMMS) 5-point Likert scale resulted in the highest mean score for attention (3.87) followed by relevance (3.66) subcategories. The results of this study show that the developed SG prototype is an effective tool in education, which improves learning outcomes and it has the potential to motivate students to learn OOP.

Description: The field of natural language processing (NLP) has witnessed a boom in language representation models with the introduction of pretrained language models that are trained on massive textual data then used to fine-tune downstream NLP tasks. In this paper, we aim to study the evolution of language representation models by analyzing their effect on an under-researched NLP task: emotion analysis; for a low-resource language: Arabic. Most of the studies in the field of affect analysis focused on sentiment analysis, i.e., classifying text into valence (positive, negative, neutral) while few studies go further to analyze the finer grained emotional states (happiness, sadness, anger, etc.). Emotion analysis is a text classification problem that is tackled using machine learning techniques. Different language representation models have been used as features for these machine learning models to learn from. In this paper, we perform an empirical study on the evolution of language models, from the traditional term frequency–inverse document frequency (TF–IDF) to the more sophisticated word embedding word2vec, and finally the recent state-of-the-art pretrained language model, bidirectional encoder representations from transformers (BERT). We observe and analyze how the performance increases as we change the language model. We also investigate different BERT models for Arabic. We find that the best performance is achieved with the ArabicBERT large model, which is a BERT model trained on a large dataset of Arabic text. The increase in F1-score was significant +7–21%.

Description: Nowadays, industries are implementing heterogeneous systems from different domains, backgrounds, and operating systems. Manufacturing systems are becoming more and more complex, which forces engineers to manage the complexity in several aspects. Technical complexities bring interoperability, risk management, and hazards issues that must be taken into consideration, from the business model design to the technical implementation. To solve the complexities and the incompatibilities between heterogeneous components, several distributed and cosimulation standards and tools can be used for data exchange and interconnection. High-level architecture (HLA) and functional mockup interface (FMI) are the main international standards used for distributed and cosimulation. HLA is mainly used in academic and defense domains while FMI is mostly used in industry. In this article, we propose an HLA/FMI implementation with a connection to an external business process-modeling tool called Papyrus. Papyrus is configured as a master federate that orchestrates the subsimulations based on the above standards. The developed framework is integrated with external heterogeneous components through an FMI interface. This framework is developed with the aim of bringing interoperability to a system used in a power generation company.

Description: The concept of information is foundational to many disciplines yet also problematic and contested. This article contributes to the understanding of information through discussion of the findings of the interdisciplinary Difference That Makes a Difference (DTMD) project. DTMD used international conferences and workshops to bring together individuals from a wide range of disciplines to share how their field understands information, to engage in interdisciplinary conversations, and to contribute to edited publications. A simple answer to the question ‘what is information?’ is not forthcoming, but, it is argued, should no more be expected than would be an answer to ‘what is matter?’. Nevertheless, through exploration of the areas of consensus that emerged from the bottom-up process of interdisciplinary dialogue, this paper offers ten assertions about the nature of information narratives for further debate. The assertions range from ‘information requires a body’, through ‘information always has meaning’ and ‘information cannot be stored or communicated’ to ‘information is always shaped by power, authority and hierarchy’. This article finishes by illustrating and testing the assertions against an information case study of a team of medical experts disseminating information to the general public about the COVID-19 virus.

Description: The purpose of this study is to conduct topic modeling and sentiment analysis on the posts of Skytrax (airlinequality.com), where there are many interests and participation of the people who have used or are willing to use it for airlines. The purpose of people gathering at Skytrax is to make better choices using the actual experiences of other customers who have experienced airlines. Online reviews written by customers with experience using airlines in Asia were collected. The data collected were online reviews from 27 airlines, with more than 14,000 reviews. Topic modeling and sentiment analysis were used with the collected data to figure out what kinds of important words are in the online reviews. As a result of the topic modeling, ‘seat’, ‘service’, and ‘meal’ were significant issues in the flight through frequency analysis. Additionally, the result revealed that delay was the main issue, which can affect customer dissatisfaction while ‘staff service’ can make customers satisfied through sentiment analysis as the result shows the ‘staff service’ with meal and food in the topic modeling.

Description: Introduction. Are viewers of video-on-demand (VoD) services more intrinsically (i.e., preferentially self-determined) or extrinsically (i.e., externally determined) motivated when selecting movies and series? For extrinsic motivation, we distinguish between algorithmically generated suggestions from the services and personal recommendations from other users. Methods. We empirically investigated the information behavior on video streaming services of users from German-speaking countries with the help of an online survey (N = 1258). Results. Active VoD users watch videos online mainly on a daily basis. They are externally determined in the selection of their videos both by algorithmically generated recommendations from the systems and―to a higher extent―from personal suggestions from acquaintances, friends, and relatives. However, there is a clear indication that intrinsic motivation plays a major role in the selection of videos. Discussion. Users of VoD services move in a cycle between machine-generated recommendations, suggestions, and exchange of opinions from and with other people, and self-determined information behavior.

Description: Academic text recommendation, as a kind of text recommendation, has a wide range of application prospects. Predicting texts of interest to scholars in different fields based on anonymous sessions is a challenging problem. However, the existing session-based method only considers the sequential information, and pays more attention to capture the session purpose. The relationship between adjacent items in the session is not noticed. Specifically in the field of session-based text recommendation, the most important semantic relationship of text is not fully utilized. Based on the graph neural network and attention mechanism, this paper proposes a session-based text recommendation model (TXT-SR) incorporating the semantic relations, which is applied to the academic field. TXT-SR makes full use of the tightness of semantic connections between adjacent texts. We have conducted experiments on two real-life academic datasets from CiteULike. Experimental results show that TXT-SR has better effectiveness than existing session-based recommendation methods.

Description: With the propagation of cyberbullying in social networks as a trending subject, cyberbullying detection has become a social problem that researchers are concerned about. Developing intelligent models and systems helps detect cyberbullying automatically. This work focuses on text-based cyberbullying detection because it is the commonly used information carrier in social networks and is the widely used feature in this regard studies. Motivated by the documented success of neural networks, we propose a complete model combining the bidirectional gated recurrent unit (Bi-GRU) and the self-attention mechanism. In detail, we introduce the design of a GRU cell and Bi-GRU’s advantage for learning the underlying relationships between words from both directions. Besides, we present the design of the self-attention mechanism and the benefit of this joining for achieving a greater performance of cyberbullying classification tasks. The proposed model could address the limitation of the vanishing and exploding gradient problems. We avoid using oversampling or downsampling on experimental data which could result in the overestimation of evaluation. We conduct a comparative assessment on two commonly used datasets, and the results show that our proposed method outperformed baselines in all evaluation metrics.

Description: (1) Background: The aim of this study is to describe manager–employee and employee–employee relations during the COVID-19 pandemic and their impact on measures of the likely use of elements of remote teaching by university employees in the future. (2) Methods: The study used a descriptive-correlation research design with a survey as the primary instrument for data gathering. A total of 732 personnel took part in the survey, selected by a convenience sampling technique. The researchers used an adapted and modified instrument to gather data. The instrument underwent a reliability test. This study used structural equation modeling to confirm hypotheses. (3) Results: It was shown that manager–employee relations at Polish universities during the COVID-19 pandemic were of low quality. However, employee–employee relations were of above-average quality, and have a significant positive impact on intentions to use elements of remote working in the future. (4) Conclusions: Based on the results of the study, some general recommendations are presented for change management and relationship-building.

Description: The authors would like to add the following reference to the “Reference” section of their paper [...]

Description: Ensuring the security of IoT devices and chips at runtime has become an urgent task as they have been widely used in human life. Embedded memories are vital components of SoC (System on Chip) in these devices. If they are attacked or incur faults at runtime, it will bring huge losses. In this paper, we propose a run-time detection architecture for memory security (RDAMS) to detect memory threats (fault and Hardware Trojans attack). The architecture consists of a Security Detection Core (SDC) that controls and enforces the detection procedure as a “security brain”, and a memory wrapper (MEM_wrapper) which interacts with memory to assist the detection. We also design a low latency response mechanism to solve the SoC performance degradation caused by run-time detection. A block-based multi-granularity detection approach is proposed to render the design flexible and reduce the cost in implementation using the FPGA’s dynamic partial reconfigurable (DPR) technology, which enables online detection mode reconfiguration according to the requirements. Experimental results show that RDAMS can correctly detect and identify 10 modeled memory faults and two types of Hardware Trojans (HTs) attacks without leading a great performance degradation to the system.

Description: Fault Trees are well-known models for the reliability analysis of systems, used to compute several kinds of qualitative and quantitative measures, such as minimal cut-sets, system failure probability, sensitivity (importance) indices, etc [...]

Description: Motion sickness (MS) is a syndrome associated with symptoms like nausea, dizziness, and other forms of physical discomfort. Automated vehicles (AVs) are potent at inducing MS because users are not adapted to this novel form of transportation, are provided with less information about the own vehicle’s trajectory, and are likely to engage in non-driving related tasks. Because individuals with an especially high MS susceptibility could be limited in their use of AVs, the demand for MS mitigation strategies is high. Passenger anticipation has been shown to have a modulating effect on symptoms, thus mitigating MS. To find an effective mitigation strategy, the prototype of a human–machine interface (HMI) that presents anticipatory ambient light cues for the AV’s next turn to the passenger was evaluated. In a realistic driving study with participants (N = 16) in an AV on a test track, an MS mitigation effect was evaluated based on the MS increase during the trial. An MS mitigation effect was found within a highly susceptible subsample through the presentation of anticipatory ambient light cues. The HMI prototype was proven to be effective regarding highly susceptible users. Future iterations could alleviate MS in field settings and improve the acceptance of AVs.

Description: Sampling is an important step in the machine learning process because it prioritizes samples that help the model best summarize the important concepts required for the task at hand. The process of determining the best sampling method has been rarely studied in the context of graph neural networks. In this paper, we evaluate multiple sampling methods (i.e., ascending and descending) that sample based off different definitions of centrality (i.e., Voterank, Pagerank, degree) to observe its relation with network topology. We find that no sampling method is superior across all network topologies. Additionally, we find situations where ascending sampling provides better classification scores, showing the strength of weak ties. Two strategies are then created to predict the best sampling method, one that observes the homogeneous connectivity of the nodes, and one that observes the network topology. In both methods, we are able to evaluate the best sampling direction consistently.

Description: Fighting crime in cyberspace requires law enforcement authorities to immerse in a digital ocean of vast amount of information and also to acquire and objectify the evidence of criminal activity. Handling digital evidence is a complex and multifaceted process as they can provide critical evidentiary information in an unquestionable and irrefutable way. When digital evidence resides in a cloud storage environment the criminal investigation is faced with unprecedented contemporary legal challenges. In this paper, the authors identify three main legal challenges that arise from the current cloud-based technological landscape, i.e., territoriality (the loss of location), possession (the cloud content ownership) and confiscation procedure (user authentication/data preservation issues). On the onset of the identified challenges, the existing American, European and International legal frameworks are thoroughly evaluated. Finally, the authors discuss and endorse the Power of Disposal, a newly formed legal notion and a multidisciplinary solution with a global effect as a result of collaboration between technical, organizational and legal perspectives as an effective first step to mitigate the identified legal challenges.

Description: Collective learning has been advocated to be at the source for innovation, particularly as serendipity seems historically to have been the driving force not only behind innovation, but also behind scientific discovery and artistic creation. Informal learning is well known to represent the most significant learning effects in humans, far better than its complement: formal learning with predefined objectives. We have designed an approach—ViewpointS—based on a digital medium—the ViewpointS Web Application—that enables and enhances the processes for sharing knowledge within a group and is equipped with metrics aimed at assessing collective and informal learning. In this article, we introduce by giving a brief state of the art about collective and informal learning, then outline our approach and medium, and finally, present and exploit a real-life experiment aimed at evaluating the ViewpointS approach and metrics.

Description: In order to solve the oversupply and repositioning problems of bike-sharing, this paper proposes an optimization model to obtain a reasonable supply volume scheme for bike-sharing and infrastructure configuration planning. The optimization model is constrained by the demand for bike-sharing, urban traffic carrying capacity (road network and parking facilities carrying capacities), and the flow conservation of shared bikes in each traffic analysis zone. The model was formulated through mixed-integer programming with the aim of minimizing the total costs for users and bike-sharing enterprises (including the travel cost of users, production and maintenance costs of shared bikes, and repositioning costs). CPLEX was used to obtain the optimal solution for the model. Then, the optimization model was applied to 183 traffic analysis zones in Nanjing, China. The results showed that not only were user demands met, but the load ratios of the road network and parking facilities with respect to bike-sharing in each traffic zone were all decreased to lower than 1.0 after the optimization, which established the rationality and effectiveness of the optimization results.

Description: This paper addresses the use of network coding algorithms combined with adequate retransmission techniques to improve the communication reliability of Wireless Sensor Networks (WSN). Basically, we assess the recently proposed Optimized Relay Selection Technique (ORST) operating together with four different retransmission techniques, three of them applying network coding algorithms. The target of this assessment is to analyze the impact upon the communication reliability from each of the proposed retransmission techniques for WSN applications. In addition, this paper presents an extensive state-of-the-art study in what concerns the use of network coding techniques in the WSN context. The initial assumption of this research work was that the ORST operating together network coding would improve the communication reliability of WNS. However, the simulation assessment highlighted that, when using the ORST technique, retransmission without network coding is the better solution.

Description: Year after year, mobile malware attacks grow in both sophistication and diffusion. As the open source Android platform continues to dominate the market, malware writers consider it as their preferred target. Almost strictly, state-of-the-art mobile malware detection solutions in the literature capitalize on machine learning to detect pieces of malware. Nevertheless, our findings clearly indicate that the majority of existing works utilize different metrics and models and employ diverse datasets and classification features stemming from disparate analysis techniques, i.e., static, dynamic, or hybrid. This complicates the cross-comparison of the various proposed detection schemes and may also raise doubts about the derived results. To address this problem, spanning a period of the last seven years, this work attempts to schematize the so far ML-powered malware detection approaches and techniques by organizing them under four axes, namely, the age of the selected dataset, the analysis type used, the employed ML techniques, and the chosen performance metrics. Moreover, based on these axes, we introduce a converging scheme which can guide future Android malware detection techniques and provide a solid baseline to machine learning practices in this field.

Description: In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency representations by the software accompanying the device. A simple average is used to aggregate multiple spectral components into EEG bands, such as α, β, and γ bands. The chosen classifiers are SVM (support vector machine) and multi-layer feedforward network trained individually for each subject. Experimental results show that features, with α+β+γ bands and bandwidth 4 Hz, the average accuracy over all subjects in both runs can reach more than 80% and some subjects up to 90+% with the SVM classifier. The results suggest that a brain machine interface could be implemented based on the mental states of the user even with the use of a cheap EEG device.

Description: Most aircraft in the world are tracked by various surveillance radar systems. Currently there is no legal requirement for light aircraft to be fitted with a transponder; however, this does not mean light aircraft should not be tracked. By adding a cheap, live tracking solution for light aircraft, the safety of low-flying aircraft pilots can be greatly increased. The radio operators who coordinate the aircraft can have an improved understanding of the air traffic and in the event of an emergency, the position of the aircraft can be relayed to emergency services. This paper proposes an approach to use a smartphone as an aircraft transponder to improve the radar tracking capabilities of low-flying aircraft. This study presents a practical and effective approach as well as a prototype implementation. The study includes the development of the three main components: (1) A mobile application that transforms a smartphone into an aircraft transponder; exploiting the GPS functionalities, (2) a desktop application that visualizes the aircraft data in real time on a map, and (3) a backend that bridges the mobile and the desktop application. To evaluate the study, flight tests were performed in a real aircraft over the Isle of Wight in the UK.

Description: Cross-domain authenticated asymmetric group key agreement allows group members in different domains to establish a secure group communication channel and the senders can be anyone. However, the existing schemes do not meet the requirement of batch verification in the group key negotiation phase, which makes the schemes have low efficiency. To address this problem, an identity-based cross-domain authenticated asymmetric group key agreement is proposed that supports batch verification. The performance analysis shows that this protocol is highly efficient. Finally, the proposed protocol is proved to be secure under the k-Bilinear Diffie–Hellman Exponent assumption.

Description: Information technology (IT) service management is considered a collection of frameworks that support organizations managing services. The implementation of these kinds of frameworks is constantly increasing in the IT service provider domain. The main objective is to define and manage IT services through its life cycle. However, from observing the literature, scarcely any research exists describing the main concepts of ITSM. Many organizations still struggle in several contexts in this domain, mainly during implementation. This research aims to develop a reference study detailing the main concepts related with ITSM. Thus, a systematic literature review is performed. In total, 47 articles were selected from top journals and conferences. The benefits, challenges, opportunities, and practices for ITSM implementation were extracted, critically analysed, and then discussed.

Description: The paper analyzes the behavior and habits of expectant and new mothers on a specialized pregnancy/parenthood-oriented social network, especially whether and how the pregnancy, and later the age of infants, impact the online activity of mothers. The authors compared almost 5000 parents divided into 23 “term groups”—long-term discussion platforms of parents with the same due month. The age of the child (due date) was taken as the basis for the activity analysis—determining the phases in which the users were more or less active online. Results are shown as charts supported by verification of the following statistical hypotheses: (a) users in later-term groups are less active than those in earlier ones; (b) users’ activity peaks around their due dates; (c) users are still very active six months after the due date; (d) activity shortly rises again around the child’s first birthday. We concluded that expectant mothers were most active two months before their due dates and around their due dates. After that, the observed activity decreased, with a slight increase around the child’s first birthday. Our findings can be useful for sociological and psychological studies, as well as for marketing purposes.

Description: This paper discusses the creation of an agent-based simulation model for interactive robotic faces, built based on data from physical human–robot interaction experiments, to explore hypotheses around how we might create emergent robotic personality traits, rather than pre-scripted ones based on programmatic rules. If an agent/robot can visually attend and behaviorally respond to social cues in its environment, and that environment varies, then idiosyncratic behavior that forms the basis of what we call a “personality” should theoretically be emergent. Here, we evaluate the stability of behavioral learning convergence in such social environments to test this idea. We conduct over 2000 separate simulations of an agent-based model in scaled-down, abstracted forms of the environment, each one representing an “experiment”, to see how different parameters interact to affect this process. Our findings suggest that there may be systematic dynamics in the learning patterns of an agent/robot in social environments, as well as significant interaction effects between the environmental setup and agent perceptual model. Furthermore, learning from deltas (Markovian approach) was more effective than only considering the current state space. We discuss the implications for HRI research, the design of interactive robotic faces, and the development of more robust theoretical frameworks of social interaction.

Description: With the progressive development of a wide range of applications, interconnect things and internet of things (IoT) became an imperative required trend by industries and academicians. IoT became a base infrastructure for remote access or control depending on internet protocol (IP) networks, especially after the COVID-19 pandemic. The huge application domain’s infrastructure, which depends on IoT, requires a trusted connection to guarantee security and privacy while transferring data. This paper proposes a hybrid identity authentication pipeline that integrates three schemes, namely, an elliptic curve cryptography (ECC) scheme is integrated with the Ong, Schnorr, and Shamir (OSS) signature scheme and chaotic maps. The latter satisfies both security and guarantee criteria. The novelty of the proposal is in using chaotic mapping and a cyclic group to deduce a substitution box (S-Box) and a reversible matrix as a portion of the OSS signature equation. The ECC-based security part is an efficient public key cryptography mechanism with less computational cost, which makes it the most convenient to be used in IoT devices for authentication and privacy. The strength of the proposed scheme relies on combining the discrete logarithm problem (DLP) and integer factorization problem (IFP). The proposed approach was simulated using Lab-View and compared with other state-of-the art schemes. Extensive simulation results and analysis of the security and time rendering results confirmed its durability against different types of attacks, such as linear and differential attacks.

Description: Information retrieval (IR) is about making systems for finding documents or information. Knowledge organization (KO) is the field concerned with indexing, classification, and representing documents for IR, browsing, and related processes, whether performed by humans or computers. The field of IR is today dominated by search engines like Google. An important difference between KO and IR as research fields is that KO attempts to reflect knowledge as depicted by contemporary scholarship, in contrast to IR, which is based on, for example, “match” techniques, popularity measures or personalization principles. The classification of documents in KO mostly aims at reflecting the classification of knowledge in the sciences. Books about birds, for example, mostly reflect (or aim at reflecting) how birds are classified in ornithology. KO therefore requires access to the adequate subject knowledge; however, this is often characterized by disagreements. At the deepest layer, such disagreements are based on philosophical issues best characterized as “paradigms”. No IR technology and no system of knowledge organization can ever be neutral in relation to paradigmatic conflicts, and therefore such philosophical problems represent the basis for the study of IR and KO.

Description: Face recognition algorithms based on deep learning methods have become increasingly popular. Most of these are based on highly precise but complex convolutional neural networks (CNNs), which require significant computing resources and storage, and are difficult to deploy on mobile devices or embedded terminals. In this paper, we propose several methods to improve the algorithms for face recognition based on a lightweight CNN, which is further optimized in terms of the network architecture and training pattern on the basis of MobileFaceNet. Regarding the network architecture, we introduce the Squeeze-and-Excitation (SE) block and propose three improved structures via a channel attention mechanism—the depthwise SE module, the depthwise separable SE module, and the linear SE module—which are able to learn the correlation of information between channels and assign them different weights. In addition, a novel training method for the face recognition task combined with an additive angular margin loss function is proposed that performs the compression and knowledge transfer of the deep network for face recognition. Finally, we obtained high-precision and lightweight face recognition models with fewer parameters and calculations that are more suitable for applications. Through extensive experiments and analysis, we demonstrate the effectiveness of the proposed methods.

Description: In order to guarantee the privacy of users’ data, the Brazilian government created the Brazilian General Data Protection Law (LGPD). This article made a diagnostic of Brazilian organizations in relation to their suitability for LGPD, based on the perception of Information Technology (IT) practitioners who work in these organizations. We used a survey with 41 questions to diagnose different Brazilian organizations, both public and private. The diagnostic questionnaire was answered by 105 IT practitioners. The results show that 27% of organizations process personal data of public access based on good faith and LGPD principles. In addition, our findings also revealed that 16.3% of organizations have not established a procedure or methodology to verify that the LGPD principles are being respected during the development of services that will handle personal data from the product or service design phase to its execution and 20% of the organizations did not establish a communication process to the personal data holders, regarding the possible data breaches. The result of the diagnostic allows organizations and data users to have an overview of how the treatment of personal data of their customers is being treated and which points of attention are in relation to the principles of LGPD.

Description: This paper presents a novel bio-inspired predictive model of visual navigation inspired by mammalian navigation. This model takes inspiration from specific types of neurons observed in the brain, namely place cells, grid cells and head direction cells. In the proposed model, place cells are structures that store and connect local representations of the explored environment, grid and head direction cells make predictions based on these representations to define the position of the agent in a place cell’s reference frame. This specific use of navigation cells has three advantages: First, the environment representations are stored by place cells and require only a few spatialized descriptors or elements, making this model suitable for the integration of large-scale environments (indoor and outdoor). Second, the grid cell modules act as an efficient visual and absolute odometry system. Finally, the model provides sequential spatial tracking that can integrate and track an agent in redundant environments or environments with very few or no distinctive cues, while being very robust to environmental changes. This paper focuses on the architecture formalization and the main elements and properties of this model. The model has been successfully validated on basic functions: mapping, guidance, homing, and finding shortcuts. The precision of the estimated position of the agent and the robustness to environmental changes during navigation were shown to be satisfactory. The proposed predictive model is intended to be used on autonomous platforms, but also to assist visually impaired people in their mobility.

Description: Imitation of human behaviors is one of the effective ways to develop artificial intelligence. Human dancers, standing in front of a mirror, always achieve autonomous aesthetics evaluation on their own dance motions, which are observed from the mirror. Meanwhile, in the visual aesthetics cognition of human brains, space and shape are two important visual elements perceived from motions. Inspired by the above facts, this paper proposes a novel mechanism of automatic aesthetics evaluation of robotic dance motions based on multiple visual feature integration. In the mechanism, a video of robotic dance motion is firstly converted into several kinds of motion history images, and then a spatial feature (ripple space coding) and shape features (Zernike moment and curvature-based Fourier descriptors) are extracted from the optimized motion history images. Based on feature integration, a homogeneous ensemble classifier, which uses three different random forests, is deployed to build a machine aesthetics model, aiming to make the machine possess human aesthetic ability. The feasibility of the proposed mechanism has been verified by simulation experiments, and the experimental results show that our ensemble classifier can achieve a high correct ratio of aesthetics evaluation of 75%. The performance of our mechanism is superior to those of the existing approaches.

Description: This paper presents a study about augmented-reality-based chemistry learning in a university lecture. Organic chemistry is often perceived as particularly difficult by students because spatial information must be processed in order to understand subject specific concepts and key ideas. To understand typical chemistry-related representations in books or literature, sophisticated mental rotation- and other spatial abilities are needed. Providing an augmented reality (AR) based learning support in the learning setting together with text and pictures is consistent with the idea of multiple external representations and the cognitive theory of multimedia learning. Using multiple external representations has proven to be beneficial for learning success, because different types of representations are processed separately in working memory. Nevertheless, the integration of a new learning medium involves the risk to hinder learning, in case of being not suitable for the learning topic or learning purpose. Therefore, this study investigates how the AR-use affects students’ cognitive load during learning in three different topics of organic chemistry. For this purpose also the usability of AR learning support is considered and the possible reduction of the influence of the mental rotation on learning success will be investigated.

Description: Research trends in gamification have shown a significant diversity in various areas of e-health, particularly in addressing the issues of rehabilitation and physical activity. Rehabilitation requires better engaging tools that help to increase the patient’s motivation and engagement in particular forms of rehabilitation training. Adopting gamification in rehabilitation offers different treatment and care environments when implementing rehabilitation training. As gamification is increasingly being explored in rehabilitation, one might not realize that using various techniques in gamified applications yields a different effect on gameplay. To date, varied gamification techniques have been utilized to provide useful experiences from the perspective of health applications. However, a limited number of surveys have investigated the gamification of rehabilitation and the use of suitable game techniques for rehabilitation in the literature. The objective of this paper is to examine and analyze the existing gamification techniques for rehabilitation applications. A classification of rehabilitation gamification is developed based on the rehabilitation gamifying requirements and the gamification characteristics that are commonly applied in rehabilitation applications. This classification is the main contribution of this paper. It provides insight for researchers and practitioners into suitable techniques to design and apply gamification with increased motivation and sustainable engagement for rehabilitation treatment and care. In addition, different game elements, selection blocks, and gamification techniques are identified for application in rehabilitation. In conclusion, several challenges and research opportunities are discussed to improve gamification deployment in rehabilitation in the future.

Description: Sensing and mapping its surroundings is an essential requirement for a mobile robot. Geometric maps endow robots with the capacity of basic tasks, e.g., navigation. To co-exist with human beings in indoor scenes, the need to attach semantic information to a geometric map, which is called a semantic map, has been realized in the last two decades. A semantic map can help robots to behave in human rules, plan and perform advanced tasks, and communicate with humans on the conceptual level. This survey reviews methods about semantic mapping in indoor scenes. To begin with, we answered the question, what is a semantic map for mobile robots, by its definitions. After that, we reviewed works about each of the three modules of semantic mapping, i.e., spatial mapping, acquisition of semantic information, and map representation, respectively. Finally, though great progress has been made, there is a long way to implement semantic maps in advanced tasks for robots, thus challenges and potential future directions are discussed before a conclusion at last.

Description: In recent years, the growing number of devices connected to the internet has increased significantly. These devices can interact with the external environment and with human beings through a wide range of sensors that, perceiving reality through the digitization of some parameters of interest, can provide an enormous amount of data. All this data is then shared on the network with other devices and with different applications and infrastructures. This dynamic and ever-changing world underlies the Internet of Things (IoT) paradigm. To date, countless applications based on IoT have been developed; think of Smart Cities, smart roads, and smart industries. This article analyzes the current architectures, technologies, protocols, and applications that characterize the paradigm.

Description: The reputation of companies is one of their key success factors. It is therefore necessary to value this intangible asset. In order to detect possible threats quickly, continuous monitoring of corporate reputation plays an important role in this valuation process. Family businesses are an ideal object for reputation management research, as through their brands they integrate tradition and addressability at the same time. The main aim of the paper is to discuss the issue of innovative approaches to the online reputation management. We performed an in-depth analysis of online reputation through an Advanced sentiment analysis on the significant sample of ten largest family-owned businesses in the world. Taking into account all relevant determinants of reputation such as Google as well as major social networks, namely Facebook, Twitter, YouTube, and LinkedIn. As there is a noticeable difference between the marketing communication of the parent company and the marketing communication of the brand owned by the company, the findings of the analyses will provide a better insight into the issue of sustainable brand development. By identify good practices, as well as highlighting weaknesses, our research has the ambition to contribute to the shift of knowledge in the field of reputation management.

Description: Up until quite recently, our contemporary society has faced various challenges and issues related to accelerated urbanization and industrialization, consumers, and organizations’ rather limited range of possibilities to completely satisfy needs and wants regarding environmental pollution, the capacity of our planet to regenerate its used goods annually and ensuring that the living conditions of future generations are considered alongside those of the current generations [...]

Description: With the increasing number of cybercrimes, the digital forensics team has no choice but to implement more robust and resilient evidence-handling mechanisms. The capturing of digital evidence, which is a tangible and probative piece of information that can be presented in court and used in trial, is very challenging due to its volatility and improper handling procedures. When computer systems get compromised, digital forensics comes into play to analyze, discover, extract, and preserve all relevant evidence. Therefore, it is imperative to maintain efficient evidence management to guarantee the credibility and admissibility of digital evidence in a court of law. A critical component of this process is to utilize an adequate chain of custody (CoC) approach to preserve the evidence in its original state from compromise and/or contamination. In this paper, a practical and secure CustodyBlock (CB) model using private blockchain protocol and smart contracts to support the control, transfer, analysis, and preservation monitoring is proposed. The smart contracts in CB are utilized to enhance the model automation process for better and more secure evidence preservation and handling. A further research direction in terms of implementing blockchain-based evidence management ecosystems, and the implications on other different areas, are discussed.

Description: Coronavirus-19 (COVID-19) started from Wuhan, China, in late December 2019. It swept most of the world’s countries with confirmed cases and deaths. The World Health Organization (WHO) declared the virus a pandemic on 11 March 2020 due to its widespread transmission. A public health crisis was declared in specific regions and nation-wide by governments all around the world. Citizens have gone through a wide range of emotions, such as fear of shortage of food, anger at the performance of governments and health authorities in facing the virus, sadness over the deaths of friends or relatives, etc. We present a monitoring system of citizens’ concerns using emotion detection in Twitter data. We also track public emotions and link these emotions with COVID-19 symptoms. We aim to show the effect of emotion monitoring on improving people’s daily health behavior and reduce the spread of negative emotions that affect the mental health of citizens. We collected and annotated 5.5 million tweets in the period from January to August 2020. A hybrid approach combined rule-based and neural network techniques to annotate the collected tweets. The rule-based technique was used to classify 300,000 tweets relying on Arabic emotion and COVID-19 symptom lexicons while the neural network was used to expand the sample tweets that were annotated using the rule-based technique. We used long short-term memory (LSTM) deep learning to classify all of the tweets into six emotion classes and two types (symptom and non-symptom tweets). The monitoring system shows that most of the tweets were posted in March 2020. The anger and fear emotions have the highest number of tweets and user interactions after the joy emotion. The results of user interaction monitoring show that people use likes and replies to interact with non-symptom tweets while they use re-tweets to propagate tweets that mention any of COVID-19 symptoms. Our study should help governments and decision-makers to dispel people’s fears and discover new symptoms associated with the symptoms that were declared by the WHO. It can also help in the understanding of people’s mental and emotional issues to address them before the impact of disease anxiety becomes harmful in itself.

Description: Designing and delivering outcome-based courses that emphasize learner-centric educational discourse and active learning is challenging, especially in online learning environments. Ensuring quality in the design and delivery of such courses in the virtual space requires a well-defined framework with key constituents that interact based on ordered sequences of events. Despite the pressing need for a quality assurance system for today’s virtual, real-time courses, such a system has not been systematically designed. A coherent quality assurance system requires a clear framework that defines the interacting constituents. This work proposes a conceptual and generic “Quality Assurance” (QA) framework, based on experiences primarily in Science, Technology, Engineering, and Mathematics (STEM) fields, for the effective design and delivery of outcome-based virtual, real-time courses that incorporate active learning practices. This Quality Assurance framework may be adjusted to serve as a blueprint that, once adjusted by institutions to accommodate their missions, guides institutions in developing or amending their policies and procedures for the design and delivery of virtual, real-time courses; in addition, such a framework is important for institutions to develop Quality Assurance systems that integrate mechanisms for continuous improvement. The proposed quality assurance framework includes three constituents: a “Teaching and Learning Support” (TLS) that trains educators on pedagogical approaches and the capabilities of the institution’s Learning Management System (LMS); an “Information and Communication Technology Support” (ICTS) that assists educators with the technologies and tools available in the learning management system; and a “Course Management System” (CMS) that encapsulates course design, delivery, and assessment; this study focuses primarily on this “Course Management System” constituent.

Description: Linear complexity is an important criterion to characterize the unpredictability of pseudo-random sequences, and large linear complexity corresponds to high cryptographic strength. Pseudo-random Sequences with a large linear complexity property are of importance in many domains. In this paper, based on the theory of inverse Gray mapping, two classes of new generalized cyclotomic quaternary sequences with period pq are constructed, where pq is a product of two large distinct primes. In addition, we give the linear complexity over the residue class ring Z4 via the Hamming weights of their Fourier spectral sequence. The results show that these two kinds of sequences have large linear complexity.

Description: Smart cities leverage large amounts of data acquired in the urban environment in the context of decision support tools. These tools enable monitoring the environment to improve the quality of services offered to citizens. The increasing diffusion of personal Internet of things devices capable of sensing the physical environment allows for low-cost solutions to acquire a large amount of information within the urban environment. On the one hand, the use of mobile and intermittent sensors implies new scenarios of large-scale data analysis; on the other hand, it involves different challenges such as intermittent sensors and integrity of acquired data. To this effect, edge computing emerges as a methodology to distribute computation among different IoT devices to analyze data locally. We present here a new methodology for imputing environmental information during the acquisition step, due to missing or otherwise out of order sensors, by distributing the computation among a variety of fixed and mobile devices. Numerous experiments have been carried out on real data to confirm the validity of the proposed method.

Description: Currently, analyzing the microscopic image of cotton fiber cross-section is the most accurate and effective way to measure its grade of maturity and then evaluate the quality of cotton samples. However, existing methods cannot extract the edge of the cross-section intact, which will affect the measurement accuracy of maturity grade. In this paper, a new edge detection algorithm that is based on the RCF convolutional neural network (CNN) is proposed. For the microscopic image dataset of the cotton fiber cross-section constructed in this paper, the original RCF was firstly used to extract the edge of the cotton fiber cross-section in the image. After analyzing the output images of RCF in each convolution stage, the following two conclusions are drawn: (1) the shallow layers contain a lot of important edge information of the cotton fiber cross-section; (2) because the size of the cotton fiber cross-section in the image is relatively small and the receptive field of the convolutional layer gradually increases with the deepening of the number of layers, the edge information detected by the deeper layers becomes increasingly coarse. In view of the above two points, the following improvements are proposed in this paper: (1) modify the network supervision model and loss calculation structure; (2) the dilated convolution in the deeper layers is removed; therefore, the receptive field in the deeper layers is reduced to adapt to the detection of small objects. The experimental results show that the proposed method can effectively improve the accuracy of edge extraction of cotton fiber cross-section.

Description: To tackle the issues of semantic collision and inconsistencies between ontologies and the original data model while learning ontology from relational database (RDB), a semi-automatic semantic consistency checking method based on graph intermediate representation and model checking is presented. Initially, the W-Graph, as an intermediate model between databases and ontologies, was utilized to formalize the semantic correspondences between databases and ontologies, which were then transformed into the Kripke structure and eventually encoded with the SMV program. Meanwhile, description logics (DLs) were employed to formalize the semantic specifications of the learned ontologies, since the OWL DL showed good semantic compatibility and the DLs presented an excellent expressivity. Thereafter, the specifications were converted into a computer tree logic (CTL) formula to improve machine readability. Furthermore, the task of checking semantic consistency could be converted into a global model checking problem that could be solved automatically by the symbolic model checker. Moreover, an example is given to demonstrate the specific process of formalizing and checking the semantic consistency between learned ontologies and RDB, and a verification experiment was conducted to verify the feasibility of the presented method. The results showed that the presented method could correctly check and identify the different kinds of inconsistencies between learned ontologies and its original data model.