Back to the Control Room: Managing Artistic Work

Control rooms have long been a key domain of investigation in HCI and CSCW as sites for understanding distributed work and fragmented settings, as well as the role and design of digital technologies in that work. Although research has tended to focus mainly on ‘command and control’ configurations, such as rail transport, ambulance dispatch, air traffic and CCTV rooms, centres of coordination shaped by artistic and performative concerns have much to contribute. Our study examines how a professional team of artists and volunteers stage manage and direct the performance of a mixed reality game from a central control room, with remote runners performing live video streaming from the streets nearby to online players. We focus on the work undertaken by team members to bring this about, exploring three key elements that enable it. First, we detail how team members oriented to the work as an artistic performance produced for an audience, how they produced compelling, varied content for online players, and how the quality of the work was ongoingly assessed. Second, we unpack the organisational hierarchy in the control room’s division of labour, and how this was designed to manage the challenges of restricted informational visibility there. Third, we explore the interactional accomplishment of the performance by looking at the role of radio announcements from the event’s director to orchestrate how the performance developed over time. Announcements were used to resolve trouble and provide instructions for avoiding future performative problems; but more centrally, to give artistic direction to runners in order to shape the performance itself. To close we discuss how this study of a performance impacts CSCW’s understandings of control room work, how the problem of ‘diffuse’ tasks like artistic work is co-ordinated, and how orientations towards quality as an artistic concern is manifest in / as control room practices. We also reflect on hierarchical and horizontal control room arrangements, and the role of video as both collaborative resource and product.

Graphical abstract

Evaluating Virtual Hand Illusion through Realistic Appearance and Tactile Feedback

We conducted a virtual reality study to explore virtual hand illusion through three levels of appearance (Appearance dimension: realistic vs. pixelated vs. toon hand appearances) and two levels of tactile feedback (Tactile dimension: no tactile vs. tactile feedback). We instructed our participants to complete a virtual assembly task in this study. Immediately afterward, we asked them to provide self-reported ratings on a survey that captured presence and five embodiment dimensions (hand ownership, touch sensation, agency and motor control, external appearance, and response to external stimuli). The results of our study indicate that (1) tactile feedback generated a stronger sense of presence, touch sensation, and response to external stimuli; (2) the pixelated hand appearance provided the least hand ownership and external appearance; and (3) in the presence of the pixelated hand, prior virtual reality experience of participants impacted their agency and motor control and their response to external stimuli ratings. This paper discusses our findings and provides design considerations for virtual reality applications with respect to the realistic appearance of virtual hands and tactile feedback.

A Systematic Review on Visualizations for Self-Generated Health Data for Daily Activities

Due to the development of sensing technology people can easily track their health in various ways, and the interest in personal healthcare data is increasing. Individuals are interested in controlling their wellness, which requires self-awareness and an understanding of various health conditions. Self-generated health data are easily accessed through mobile devices, and data visualization is commonly used in applications. A systematic literature review was conducted to better understand the role of visualizations and learn how to develop effective ones. Thirteen papers were analyzed for types of data, characteristics of visualizations, and effectiveness for healthcare management. The papers were selected because they represented research on personal health data and visualization in a non-clinical setting, and included health data tracked in everyday life. This paper suggests six levels for categorizing the efficacy of visualizations that take into account cognitive and physical changes in users. Recommendations for future work on conducting evaluations are also identified. This work provides a foundation for personal healthcare data as more applications are developed for mobile and wearable devices.

A Framework for Service-Oriented Architecture (SOA)-Based IoT Application Development

In the last decades, the increasing complexity of industrial information technology has led to the emergence of new trends in manufacturing. Factories are using multiple Internet of Things (IoT) platforms to harvest sensor information to improve production. Such a transformation contributes to efficiency growth and reduced production costs. To deal with the heterogeneity of the services within an IoT system, Service-Oriented Architecture (SOA) is referred to in the literature as being advantageous for the design and development of software to support IoT-based production processes. The aim of SOA-based design is to provide the leverage to use and reuse loosely coupled IoT services at the middleware layer to minimise system integration problems. We propose a system architecture that follows the SOA architectural pattern and enables developers and business process designers to dynamically add, query or use instances of existing modular software in the IoT context. Furthermore, an analysis of utilization of modular software that presents some challenges and limitations of this approach is also in the scope of this work.

Protecting Private Information for Two Classes of Aggregated Database Queries

An important direction of informatics is devoted to the protection of privacy of confidential information while providing answers to aggregated queries that can be used for analysis of data. Protecting privacy is especially important when aggregated queries are used to combine personal information stored in several databases that belong to different owners or come from different sources. Malicious attackers may be able to infer confidential information even from aggregated numerical values returned as answers to queries over large collections of data. Formal proofs of security guarantees are important, because they can be used for implementing practical systems protecting privacy and providing answers to aggregated queries. The investigation of formal conditions which guarantee protection of private information against inference attacks originates from a fundamental result obtained by Chin and Ozsoyoglu in 1982 for linear queries. The present paper solves similar problems for two new classes of aggregated nonlinear queries. We obtain complete descriptions of conditions, which guarantee the protection of privacy of confidential information against certain possible inference attacks, if a collection of queries of this type are answered. Rigorous formal security proofs are given which guarantee that the conditions obtained ensure the preservation of privacy of confidential data. In addition, we give necessary and sufficient conditions for the protection of confidential information from special inference attacks aimed at achieving a group compromise.

Culture knowledge graph construction techniques

This article describes the development process of the Thai cultural knowledge graph, which facilitates a more precise and rapid comprehension of the culture and customs of Thailand. The construction process is as follows: First, data collection technologies and techniques were used to obtain text data from the Wikipedia encyclopedia about cultural traditions in Thailand. Second, entity recognition and relationship extraction were performed on the structured text set. A natural language processing (NLP) technique was used to characterize and extract better textual resources from Wikipedia to support a deeper understanding of user-generated content by using automatic tools. Regarding entity recognition, a BiLSTM model was used to extract relationships between entities. After the entities and their relationships were obtained, triple data were generated from the semistructured data in the existing knowledge base. Then, a knowledge graph was created, knowledge bases were stored in the Neo4j Desktop, and the quality and performance of the created knowledge graph were assessed. According to the experimental findings, the precision value is 84.73%, the recall value is 82.26%, and the F1-score value is 83.47%; therefore, BiLSTM-CNN-CRF can successfully extract entities from the structured text.

Impact of Cognitive Biases on Progressive Visualization

Progressive visualization is fast becoming a technique in the visualization community to help users interact with large amounts of data. With progressive visualization, users can examine intermediate results of complex or long running computations, without waiting for the computation to complete. While this has shown to be beneficial to users, recent research has identified potential risks. For example, users may misjudge the uncertainty in the intermediate results and draw incorrect conclusions or see patterns that are not present in the final results. In this article, we conduct a comprehensive set of studies to quantify the advantages and limitations of progressive visualization. Based on a recent report by Micallef et al., we examine four types of cognitive biases that can occur with progressive visualization: uncertainty bias, illusion bias, control bias, and anchoring bias. The results of the studies suggest a cautious but promising use of progressive visualization - while there can be significant savings in task completion time, accuracy can be negatively affected in certain conditions. These findings confirm earlier reports of the benefits and drawbacks of progressive visualization and that continued research into mitigating the effects of cognitive biases is necessary.

The role of deep learning in urban water management: A critical review

Deep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world.

Sign Language Recognition Method Based on Palm Definition Model and Multiple Classification

Technologies for pattern recognition are used in various fields. One of the most relevant and important directions is the use of pattern recognition technology, such as gesture recognition, in socially significant tasks, to develop automatic sign language interpretation systems in real time. More than 5% of the world's population-about 430 million people, including 34 million children-are deaf-mute and not always able to use the services of a living sign language interpreter. Almost 80% of people with a disabling hearing loss live in low- and middle-income countries. The development of low-cost systems of automatic sign language interpretation, without the use of expensive sensors and unique cameras, would improve the lives of people with disabilities, contributing to their unhindered integration into society. To this end, in order to find an optimal solution to the problem, this article analyzes suitable methods of gesture recognition in the context of their use in automatic gesture recognition systems, to further determine the most optimal methods. From the analysis, an algorithm based on the palm definition model and linear models for recognizing the shapes of numbers and letters of the Kazakh sign language are proposed. The advantage of the proposed algorithm is that it fully recognizes 41 letters of the 42 in the Kazakh sign alphabet. Until this time, only Russian letters in the Kazakh alphabet have been recognized. In addition, a unified function has been integrated into our system to configure the frame depth map mode, which has improved recognition performance and can be used to create a multimodal database of video data of gesture words for the gesture recognition system.

Semantic-Based Dynamic Service Adaptation in Context-Aware Mobile Cloud Learning

Self-adaptable system concerns on service adaptation whenever errors persist within the system. Changes in contextual information such as networks or sensors will affect the system’s effectiveness because the service adaptation process is not comprehensively handled in those contexts. Besides, the correctness to get the most equivalence services to be substituted is limitedly being addressed from previous works. A dynamic service adaptation framework is introduced to monitor and run a reasoning control to solve these issues. Hence, this paper presents a case study to proof the dynamic service adaptation framework that leverages on semantic-based approach in a context-aware environment. The evaluation of the case study resulted in a significant difference for the effectiveness at a 95% confidence level, which can be interpreted to confirm that the framework is promising to be used in operating dynamic adaptation process in a pervasive environment.

The role of trust and privacy concerns in using social media for e-retail services: The moderating role of COVID-19

The COVID-19 pandemic has disrupted the customers habits of purchasing as well as shopping behaviours. This study seeks to develop an integrated model of the critical role of trust and privacy concerns in influencing consumers purchase behaviour through social media. It also explored the moderating role of COVID-19 on these relationships. Quantitative data were collected using survey strategy through questionnaires to address different levels of the study. Our proposed model was tested with 1,200 consumers, 600 prior to COVID-19 and 600 during COVID-19. Partial Least Squares Structural Equation Modelling was conducted to assess the hypotheses. The findings revealed that purchase intention depends on trust and privacy concerns. Information quality, security concerns, ease of use, privacy/security assurance seal, and disposition to third party certification are the main drivers of trust and privacy concerns. Furthermore, our proposed model during COVID-19 period has higher explanator power (R2 = 0.741) than before COVID-19 period (R2 = 0.603 and consumers buying behaviour has been increased during COVID-19. The results offer important implications for retailers and are likely to stimulate further research in the area of purchase behaviour through social media.

Social Media Use in Hearing Loss, Tinnitus, and Vestibular Disorders: A Systematic Review

Background:

People are increasingly using social media outlets for gathering health-related information. There has also been considerable interest from researchers and clinicians in understanding how social media is used by the general public, patients, and health professionals to gather health-related information. Interest in the use of social media for audiovestibular disorders has also received attention, although published evidence synthesis of this use is lacking. The objective of this review article was to synthesize existing research studies related to social media use concerning hearing loss, tinnitus, and vestibular disorders.

Method:

Comprehensive searches were performed in multiple databases between October and November 2020 and again in June 2021 and March 2022, with additional reports identified from article citations and unpublished literature. This review article was presented using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Results:

A total of 1,512 articles were identified. Of these, 16 publications met the inclusion criteria. Overall, social media offered people the platform to learn about hearing loss, tinnitus, and vestibular disorders via advice and support seeking, personal experience sharing, general information sharing, and relationship building. Research studies were more common on information and user activities seen on Facebook Pages, Twitter, and YouTube videos. Misinformation was identified across all social media platforms for each of these conditions.

Conclusions:

Online discussions about audiovestibular disorders are evident, although inconsistencies in study procedures make it difficult to compare these discussion groups. Misinformation is a concern needing to be addressed during clinical consultations as well as via other public health means. Uniform guidelines are needed for research regarding the use of social media so that outcomes are comparable. Moreover, clinical studies examining how exposure to and engagement with social media information may impact outcomes (e.g., help seeking, rehabilitation uptake, rehabilitation use, and satisfaction) require exploration.

Supplemental Material:

https://doi.org/10.23641/asha.20667672

Human Activity Recognition: Review, Taxonomy and Open Challenges

Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains, and vision and sensor-based data enable cutting-edge technologies to detect, recognize, and monitor human activities. Several reviews and surveys on HAR have already been published, but due to the constantly growing literature, the status of HAR literature needed to be updated. Hence, this review aims to provide insights on the current state of the literature on HAR published since 2018. The ninety-five articles reviewed in this study are classified to highlight application areas, data sources, techniques, and open research challenges in HAR. The majority of existing research appears to have concentrated on daily living activities, followed by user activities based on individual and group-based activities. However, there is little literature on detecting real-time activities such as suspicious activity, surveillance, and healthcare. A major portion of existing studies has used Closed-Circuit Television (CCTV) videos and Mobile Sensors data. Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Support Vector Machine (SVM) are the most prominent techniques in the literature reviewed that are being utilized for the task of HAR. Lastly, the limitations and open challenges that needed to be addressed are discussed.

A survey of uncover misleading and cyberbullying on social media for public health

Misleading health information is a critical phenomenon in our modern life due to advance in technology. In fact, social media facilitated the dissemination of information, and as a result, misinformation spread rapidly, cheaply, and successfully. Fake health information can have a significant effect on human behavior and attitudes. This survey presents the current works developed for misleading information detection (MLID) in health fields based on machine learning and deep learning techniques and introduces a detailed discussion of the main phases of the generic adopted approach for MLID. In addition, we highlight the benchmarking datasets and the most used metrics to evaluate the performance of MLID algorithms are discussed and finally, a deep investigation of the limitations and drawbacks of the current progressing technologies in various research directions is provided to help the researchers to use the most proper methods in this emerging task of MLID.

Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry

The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review's main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel's feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.

A new feature for handwritten signature image description based on local binary patterns

Objectives. The problem of describing the invariant features of a digital image of handwritten signature that describes the distribution of its local features is considered. The formation of fundamentally new approach to the calculation of such features is described.Methods. Digital image processing methods are used. First an image is converted into a binary representation, then its morphological and median filtering is performed. Then using the method of principal components, the image is rotated to give the signature a horizontal orientation. A rectangle describing the signature is cut out, then it is scaled to the template of a certain size. In the article the template of 300×150 pixels was used. Then the border of the signature is formed. Local binary patterns are calculated from its binary contour, i.e. each pixel is assigned a number from 0 to 255, which describes the location of the edge pixels in 3×3 neighborhood of each pixel. A histogram of calculated patterns for 256 intervals is formed. The first and last intervals are discarded because they correspond to all black and white pixels in the neighborhood and are not informative. The remaining 254 numbers of the array form new local features of the signature.Results. The studies were performed on the bases of digitized signatures TUIT and CEDAR containing true and fake signatures of 80 persons. The accuracy of correct verification of signatures on these bases was about 78 % and 70 %.Conclusion. The possibility of using the proposed possibilities for solving the problems of verifying the authenticity of handwritten signatures has been experimentally confirmed.

Cluster system load balancing model with consideration of hardware characteristics of server hardware

Objectives. To upgrade and complement the existing load balancing model in multi-server systems, taking into account the hardware characteristics of the server equipment, as well as the most loaded components of the server equipment in the video conferencing service cluster in educational processes and distance education.Methods. The existing mathematical model of load balancing as a mass exchange system is considered, when significant changes are introduced: penalties for equipment downtime and penalties for waiting in a queue will depend on the load on the server hardware components in the cluster architecture of video conferencing service.Results. Formulas are given for calculating the total performance of a cluster of n servers with the maximum and minimum load of server hardware components in a videoconferencing system cluster.Conclusion. A modeling complex has been developed to test the mathematical model on a system of up to n < 10 servers in a cluster of a videoconferencing system. Based on the results of calculations of the modeling complex, it was concluded that it is necessary to upgrate the existing algorithm for balancing the load on the selected BigBlueButton video conferencing service.

The predictors of mobile government services adoption through social media: A case of Chinese citizens

This research studied the factors accounting for Chinese citizens’ behavioral adoption of mobile government services via social media platforms. Social media innovations have empowered governments to better interact and stay in touch with citizens, and thus understanding citizens’ adoption of government services via social media will enable policymakers to leverage social media to better meet the service requirements of citizens. Drawing upon the Chinese mobile-government context, this research framework was made on the Technology Acceptance Model (TAM) while the analysis of data was completed with Smart PLS by the use of the SEM procedure. The analysis has surprisingly discovered that perceived usefulness (PU) does not predict the adoption of mobile government services through social media. However, perceived information quality was significant in determining both the PU and adoption behavior. It was also shown that factors such as perceived security, perceived mobility, trendiness, and interactivity were all significant determinants of both the perceived usefulness and adoption intention respectively. The research and managerial consequences of the study outcomes on m-government development and diffusion are thoroughly considered.

The development and validation of a dashboard prototype for real-time suicide mortality data

Introduction/Aim

Data visualisation is key to informing data-driven decision-making, yet this is an underexplored area of suicide surveillance. By way of enhancing a real-time suicide surveillance system model, an interactive dashboard prototype has been developed to facilitate emerging cluster detection, risk profiling and trend observation, as well as to establish a formal data sharing connection with key stakeholders via an intuitive interface.

Materials and Methods

Individual-level demographic and circumstantial data on cases of confirmed suicide and open verdicts meeting the criteria for suicide in County Cork 2008–2017 were analysed to validate the model. The retrospective and prospective space-time scan statistics based on a discrete Poisson model were employed via the R software environment using the “rsatscan” and “shiny” packages to conduct the space-time cluster analysis and deliver the mapping and graphic components encompassing the dashboard interface.

Results

Using the best-fit parameters, the retrospective scan statistic returned several emerging non-significant clusters detected during the 10-year period, while the prospective approach demonstrated the predictive ability of the model. The outputs of the investigations are visually displayed using a geographical map of the identified clusters and a timeline of cluster occurrence.

Discussion

The challenges of designing and implementing visualizations for suspected suicide data are presented through a discussion of the development of the dashboard prototype and the potential it holds for supporting real-time decision-making.

Conclusions

The results demonstrate that integration of a cluster detection approach involving geo-visualisation techniques, space-time scan statistics and predictive modelling would facilitate prospective early detection of emerging clusters, at-risk populations, and locations of concern. The prototype demonstrates real-world applicability as a proactive monitoring tool for timely action in suicide prevention by facilitating informed planning and preparedness to respond to emerging suicide clusters and other concerning trends.

Unsupervised Domain Adaptation for Vertebrae Detection and Identification in 3D CT Volumes Using a Domain Sanity Loss

A variety of medical computer vision applications analyze 2D slices of computed tomography (CT) scans, whereas axial slices from the body trunk region are usually identified based on their relative position to the spine. A limitation of such systems is that either the correct slices must be extracted manually or labels of the vertebrae are required for each CT scan to develop an automated extraction system. In this paper, we propose an unsupervised domain adaptation (UDA) approach for vertebrae detection and identification based on a novel Domain Sanity Loss (DSL) function. With UDA the model’s knowledge learned on a publicly available (source) data set can be transferred to the target domain without using target labels, where the target domain is defined by the specific setup (CT modality, study protocols, applied pre- and processing) at the point of use (e.g., a specific clinic with its specific CT study protocols). With our approach, a model is trained on the source and target data set in parallel. The model optimizes a supervised loss for labeled samples from the source domain and the DSL loss function based on domain-specific “sanity checks” for samples from the unlabeled target domain. Without using labels from the target domain, we are able to identify vertebra centroids with an accuracy of 72.8%. By adding only ten target labels during training the accuracy increases to 89.2%, which is on par with the current state-of-the-art for full supervised learning, while using about 20 times less labels. Thus, our model can be used to extract 2D slices from 3D CT scans on arbitrary data sets fully automatically without requiring an extensive labeling effort, contributing to the clinical adoption of medical imaging by hospitals.

Evaluating and Revising the Digital Citizenship Scale

Measuring citizen activities in online environments is an important enterprise in fields as diverse as political science, informatics, and education. Over the past decade, a variety of scholars have proposed survey instruments for measuring digital citizenship. This study investigates the psychometric properties of one such measure, the Digital Citizenship Scale (DCS). While previous investigations of the DCS drew participants exclusively from single educational environments (college students, teachers), this study is the first with a survey population (n = 1820) that includes both students and the general public from multiple countries. Four research questions were addressed, two of which were focused on the validity of the DCS for this wider population. Our results suggest refining the 26-item five-factor DCS tool into an abbreviated 19-item four-factor instrument. The other two research questions investigated how gender, generation, and nationality affect DCS scores and the relationship between the different DCS factors. While gender was found to have a minimal effect on scores, nationality and age did have a medium effect on the online political activism factor. Technical skills by themselves appear to play little role in predicting online political engagement; the largest predictor of online political engagement was critical perspective and a willingness to use the Internet in active ways beyond simply consuming content.

Do Digital Finance and the Technology Acceptance Model Strengthen Financial Inclusion and SME Performance?

Digital inclusive finance, as a vital engine for the country’s high-quality growth, provides new impetus and prospects for encouraging economic development during the looming economic downturn. SMEs play a significant role in economic growth and development, particularly in developing countries. However, value promoting financial inclusion for SMEs through digitalization is still understudied. The objectives aimed at by this investigation were: to study the impact of financial inclusion on SME performances, to observe the influence of digital financing on financial inclusion and SME performance association as a mediator and to examine how the Technology Acceptance Model (TAM) supports financial inclusion and SME performance. A well-structured questionnaire using a quantitative research approach was utilized to gather data from 366 owner-managers among Sri Lankan SMEs. The study’s findings are presented: financial inclusion, digital financing and TAM play influential roles in SME performance. More precisely, digital financing and TAM mediate positively the relationship between financial inclusion and performance in SMEs. The findings of this research endeavor to shed light on developing and popularizing digital financing by providing services which are cheap, secure and low risk from a supply-side perspective, as well as adopting and adjusting digital financing by enhancing financial literacy, which would be necessary from the demand-side perspective.

Prevention of Cyber Security with the Internet of Things Using Particle Swarm Optimization

High security for physical items such as intelligent machinery and residential appliances is provided via the Internet of Things (IoT). The physical objects are given a distinct online address known as the Internet Protocol to communicate with the network's external foreign entities through the Internet (IP). IoT devices are in danger of security issues due to the surge in hacker attacks during Internet data exchange. If such strong attacks are to create a reliable security system, attack detection is essential. Attacks and abnormalities such as user-to-root (U2R), denial-of-service, and data-type probing could have an impact on an IoT system. This article examines various performance-based AI models to predict attacks and problems with IoT devices with accuracy. Particle Swarm Optimization (PSO), genetic algorithms, and ant colony optimization were used to demonstrate the effectiveness of the suggested technique concerning four different parameters. The results of the proposed method employing PSO outperformed those of the existing systems by roughly 73 percent.

An Improved Trilateration Positioning Algorithm with Anchor Node Combination and K-Means Clustering

As a classic positioning algorithm with a simple principle and low computational complexity, the trilateration positioning algorithm utilizes the coordinates of three anchor nodes to determine the position of an unknown node, which is widely applied in various positioning scenes. However, due to the environmental noise, environmental interference, the distance estimation error, the uncertainty of anchor nodes' coordinates, and other negative factors, the positioning error increases significantly. For this problem, we propose a new trilateration algorithm based on the combination and K-Means clustering to effectively remove the positioning results with significant errors in this paper, which makes full use of the position and distance information of the anchor nodes in the area. In this method, after analyzing the factors affecting the optimization of the trilateration and selecting optimal parameters, we carry out experiments to verify the effectiveness and feasibility of the proposed algorithm. We also compare the positioning accuracy and positioning efficiency of the proposed algorithm with those of other algorithms in different environments. According to the comparison of the least-squares method, the maximum likelihood method, the classical trilateration and the proposed trilateration, the results of the experiments show that the proposed trilateration algorithm performs well in the positioning accuracy and efficiency in both light-of-sight (LOS) and non-light-of-sight (NLOS) environments. Then, we test our approach in three realistic environments, i.e., indoor, outdoor and hall. The experimental results show that when there are few available anchor nodes, the proposed localization method reduces the mean distance error compared with the classical trilateration, the least-squares method, and the maximum likelihood.

Advantages of virtual reality for the participative design of work processes: An integrative perspective

BACKGROUND: The participative design of work processes is hampered by as-yet unresolved challenges. A root cause is seen in high information-pass-on-barriers. Virtual Reality (VR) may have a significant potential to overcome these challenges. Yet, there is no systematic understanding of which advantages provided by VR can support the participative design of work processes. OBJECTIVE: The present study aims to assess the potential of VR to support the participative design of work processes by conducting an integrative literature review identifying the advantages of VR in general work contexts and mapping them to known challenges in participative design of work processes. METHODS: The integrative literature review was conducted based on 268 sources of which 52 were considered for an in-depth analysis of the advantages offered by VR. RESULTS: The resulting conceptual framework consisted of 13 characteristic-related advantages (e.g., immersion, interactivity, flexibility) and 10 effect-related advantages (e.g., attractivity, involvement, cost efficiency) which readily address known challenges in the participative design of work processes. CONCLUSION: Mapping the advantages of VR to the challenges in participative design of work processes revealed a substantial potential of VR to overcome high information-pass-on-barriers. As such, employing VR in work process design initiatives represents a fruitful avenue for the promotion of prevention and employee health.

Sustainable Development Goal for Quality Education (SDG 4): A study on SDG 4 to extract the pattern of association among the indicators of SDG 4 employing a genetic algorithm

Sustainable Development Goals (SDG) are at the forefront of government initiatives across the world. The SDGs are primarily concerned with promoting sustainable growth via ensuring wellbeing, economic growth, environmental legislation, and academic advancement. One of the most prominent goals of the SDG is to provide learners with high-quality education (SDG 4). This paper aims to look at the perspectives of the Sustainable Development Goals improvised to provide quality education. We also analyze the existing state of multiple initiatives implemented by the Indian government in the pathway to achieving objectives of quality education (SDG 4). Additionally, a case study is considered for understanding the association among the observed indicators of SDG4. For this purpose, exploratory data analysis, and numerical association rule mining in combination with QuantMiner genetic algorithm approaches have been applied. The outcomes reveal the presence of a significant degree of association among these parameters pointing out the fact that understanding the impact of one (or more) indicator on other related indicators is critical for achieving SDG 4 goals (or factors). These findings will assist governing bodies in taking preventive measures while modifying existing policies and ensuring the effective enactment of SDG 4 goals, which also will subsequently aid in the resolution of issues related to other SDGs.

Development of Ontology for Knowledge of Traditions Common Culture of Countries in the Greater Mekong Subregion

The development of ontology is one important research area in the digital humanities. This study aims at creating a semantic search system for traditions common culture in the Greater Mekong Subregion (GMS) to solve problems in semantic gaps. This paper presents the second phase of the main research. It will present how to develop ontologies for the traditions and common culture in the GMS to gain a perspicuous understanding of the traditions and common culture of those countries in the region. A theoretical concept of seven steps for ontology development was applied by using an ontology editor called Hozo Ontology Editor. The main ontology found in this study included 15 main classes: common culture, history, belief, purpose, location, ritual, activity, literature, values, place, time, principle, person, equipment, and ethnic group. Traditions common culture is a subclass of common culture classes that were found to be related to all classes in ontology. This ontology will be useful for developing a semantic search system of the traditions common culture of the GMS in the next steps of the main study.

Machine fault detection methods based on machine learning algorithms: A review

Preventive identification of mechanical parts failures has always played a crucial role in machine maintenance. Over time, as the processing cycles are repeated, the machinery in the production system is subject to wear with a consequent loss of technical efficiency compared to optimal conditions. These conditions can, in some cases, lead to the breakage of the elements with consequent stoppage of the production process pending the replacement of the element. This situation entails a large loss of turnover on the part of the company. For this reason, it is crucial to be able to predict failures in advance to try to replace the element before its wear can cause a reduction in machine performance. Several systems have recently been developed for the preventive faults detection that use a combination of low-cost sensors and algorithms based on machine learning. In this work the different methodologies for the identification of the most common mechanical failures are examined and the most widely applied algorithms based on machine learning are analyzed: Support Vector Machine (SVM) solutions, Artificial Neural Network (ANN) algorithms, Convolutional Neural Network (CNN) model, Recurrent Neural Network (RNN) applications, and Deep Generative Systems. These topics have been described in detail and the works most appreciated by the scientific community have been reviewed to highlight the strengths in identifying faults and to outline the directions for future challenges.

Automatic Assessment of Abdominal Exercises for the Treatment of Diastasis Recti Abdominis Using Electromyography and Machine Learning

Diastasis Recti Abdominis (DRA) is a medical condition in which the two sides of the rectus abdominis muscle are separated by at least 2.7 cm. This happens when the collagen sheath that exists between the rectus muscles stretches beyond a certain limit. The recti muscles generally separate and move apart in pregnant women due to the development of fetus in the womb. In some cases, this intramuscular gap will not be closed on its own, leading to DRA. The primary treatment procedures of DRA involve different therapeutic exercises to reduce the inter-recti distance. However, it is tedious for the physiotherapists to constantly monitor the patients and ensure that the exercises are being done correctly. The objective of this research is to analyze the correctness of such performed exercises using electromyogram (EMG) signals and machine learning. To the best of our knowledge, this is the first work reporting the objective evaluation of rehabilitation exercises for DRA. Experimental studies indicate that the surface EMG signals were effective in classifying the correctly and incorrectly performed movements. An extensive analysis was carried out with different machine learning models for classification. It was inferred that the RUSBoosted Ensembled classifier was effective in differentiating these movements with an accuracy of 92.3%.

Visualization and Semantic Labeling of Mood States Based on Time-Series Features of Eye Gaze and Facial Expressions by Unsupervised Learning

This study is intended to develop a stress measurement and visualization system for stress management in terms of simplicity and reliability. We present a classification and visualization method of mood states based on unsupervised machine learning (ML) algorithms. Our proposed method attempts to examine the relation between mood states and extracted categories in human communication from facial expressions, gaze distribution area and density, and rapid eye movements, defined as saccades. Using a psychological check sheet and a communication video with an interlocutor, an original benchmark dataset was obtained from 20 subjects (10 male, 10 female) in their 20s for four or eight weeks at weekly intervals. We used a Profile of Mood States Second edition (POMS2) psychological check sheet to extract total mood disturbance (TMD) and friendliness (F). These two indicators were classified into five categories using self-organizing maps (SOM) and U-Matrix. The relation between gaze and facial expressions was analyzed from the extracted five categories. Data from subjects in the positive categories were found to have a positive correlation with the concentrated distributions of gaze and saccades. Regarding facial expressions, the subjects showed a constant expression time of intentional smiles. By contrast, subjects in negative categories experienced a time difference in intentional smiles. Moreover, three comparative experiment results demonstrated that the feature addition of gaze and facial expressions to TMD and F clarified category boundaries obtained from U-Matrix. We verify that the use of SOM and its two variants is the best combination for the visualization of mood states.

Prevalence of symptoms, comorbidities, fibrin amyloid microclots and platelet pathology in individuals with Long COVID/Post-Acute Sequelae of COVID-19 (PASC)

BACKGROUND

Fibrin(ogen) amyloid microclots and platelet hyperactivation previously reported as a novel finding in South African patients with the coronavirus 2019 disease (COVID-19) and Long COVID/Post-Acute Sequelae of COVID-19 (PASC), might form a suitable set of foci for the clinical treatment of the symptoms of Long COVID/PASC. A Long COVID/PASC Registry was subsequently established as an online platform where patients can report Long COVID/PASC symptoms and previous comorbidities.

METHODS

In this study, we report on the comorbidities and persistent symptoms, using data obtained from 845 South African Long COVID/PASC patients. By using a previously published scoring system for fibrin amyloid microclots and platelet pathology, we also analysed blood samples from 80 patients, and report the presence of significant fibrin amyloid microclots and platelet pathology in all cases.

RESULTS

Hypertension, high cholesterol levels (dyslipidaemia), cardiovascular disease and type 2 diabetes mellitus (T2DM) were found to be the most important comorbidities. The gender balance (70% female) and the most commonly reported Long COVID/PASC symptoms (fatigue, brain fog, loss of concentration and forgetfulness, shortness of breath, as well as joint and muscle pains) were comparable to those reported elsewhere. These findings confirmed that our sample was not atypical. Microclot and platelet pathologies were associated with Long COVID/PASC symptoms that persisted after the recovery from acute COVID-19.

CONCLUSIONS

Fibrin amyloid microclots that block capillaries and inhibit the transport of O₂ to tissues, accompanied by platelet hyperactivation, provide a ready explanation for the symptoms of Long COVID/PASC. Removal and reversal of these underlying endotheliopathies provide an important treatment option that urgently warrants controlled clinical studies to determine efficacy in patients with a diversity of comorbidities impacting on SARS-CoV-2 infection and COVID-19 severity. We suggest that our platelet and clotting grading system provides a simple and cost-effective diagnostic method for early detection of Long COVID/PASC as a major determinant of effective treatment, including those focusing on reducing clot burden and platelet hyperactivation.

Towards Generalizing the Information Theory for Neural Communication

Neuroscience extensively uses the information theory to describe neural communication, among others, to calculate the amount of information transferred in neural communication and to attempt the cracking of its coding. There are fierce debates on how information is represented in the brain and during transmission inside the brain. The neural information theory attempts to use the assumptions of electronic communication; despite the experimental evidence that the neural spikes carry information on non-discrete states, they have shallow communication speed, and the spikes' timing precision matters. Furthermore, in biology, the communication channel is active, which enforces an additional power bandwidth limitation to the neural information transfer. The paper revises the notions needed to describe information transfer in technical and biological communication systems. It argues that biology uses Shannon's idea outside of its range of validity and introduces an adequate interpretation of information. In addition, the presented time-aware approach to the information theory reveals pieces of evidence for the role of processes (as opposed to states) in neural operations. The generalized information theory describes both kinds of communication, and the classic theory is the particular case of the generalized theory.

A New 360° Framework to Predict Customer Lifetime Value for Multi-Category E-Commerce Companies Using a Multi-Output Deep Neural Network and Explainable Artificial Intelligence

Online purchasing has developed rapidly in recent years due to its efficiency, convenience, low cost, and product variety. This has increased the number of online multi-category e-commerce retailers that sell a variety of product categories. Due to the growth in the number of players, each company needs to optimize its own business strategy in order to compete. Customer lifetime value (CLV) is a common metric that multi-category e-commerce retailers usually consider for competition because it helps determine the most valuable customers for the retailers. However, in this paper, we introduce two additional novel factors in addition to CLV to determine which customers will bring in the highest revenue in the future: distinct product category (DPC) and trend in amount spent (TAS). Then, we propose a new framework. We utilized, for the first time in the relevant literature, a multi-output deep neural network (DNN) model to test our proposed framework while forecasting CLV, DPC, and TAS together. To make this outcome applicable in real life, we constructed customer clusters that allow the management of multi-category e-commerce companies to segment end-users based on the three variables. We compared the proposed framework (constructed with multiple outputs: CLV, DPC, and TAS) against a baseline single-output model to determine the combined effect of the multi-output model. In addition, we also compared the proposed model with multi-output Decision Tree (DT) and multi-output Random Forest (RF) algorithms on the same dataset. The results indicate that the multi-output DNN model outperforms the single-output DNN model, multi-output DT, and multi-output RF across all assessment measures, proving that the multi-output DNN model is more suitable for multi-category e-commerce retailers’ usage. Furthermore, Shapley values derived through the explainable artificial intelligence method are used to interpret the decisions of the DNN. This practice demonstrates which inputs contribute more to the outcomes (a significant novelty in interpreting the DNN model for the CLV).

A blockchain-based infection tracing and notification system by non-fungible tokens

SARS-CoV2 pandemic is heavily affecting our lives. Many actions have been undertaken to slow down its expansion and, among the others, contact tracing applications are the less invasive to monitor the spread of the virus. The idea behind contact tracing is to track contacts between people by the exchange of identifiers, not linked to individuals, exploiting the use of Bluetooth Low Energy (BLE) technology to estimate the duration and proximity of contacts. The data collected in this way is used for the sole purpose of notifying a potential contact with an infected person without revealing their identity and location. This paper presents a contact tracing protocol based on blockchain technology that exploits smart contracts for reporting contacts at risk of contagion. The novelty of the proposed solution is the use of Non Fungible Tokens (NFT) to guarantee user privacy through a decentralized approach, equipped with a reliable non-proprietary notification mechanism that allows public access to anonymous infections data.

Wearable Sensor-Based Human Activity Recognition with Hybrid Deep Learning Model

It is undeniable that mobile devices have become an inseparable part of human’s daily routines due to the persistent growth of high-quality sensor devices, powerful computational resources and massive storage capacity nowadays. Similarly, the fast development of Internet of Things technology has motivated people into the research and wide applications of sensors, such as the human activity recognition system. This results in substantial existing works that have utilized wearable sensors to identify human activities with a variety of techniques. In this paper, a hybrid deep learning model that amalgamates a one-dimensional Convolutional Neural Network with a bidirectional long short-term memory (1D-CNN-BiLSTM) model is proposed for wearable sensor-based human activity recognition. The one-dimensional Convolutional Neural Network transforms the prominent information in the sensor time series data into high level representative features. Thereafter, the bidirectional long short-term memory encodes the long-range dependencies in the features by gating mechanisms. The performance evaluation reveals that the proposed 1D-CNN-BiLSTM outshines the existing methods with a recognition rate of 95.48% on the UCI-HAR dataset, 94.17% on the Motion Sense dataset and 100% on the Single Accelerometer dataset.

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning

Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO₂, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO₂SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).

Carbonic: A Framework for Creating and Visualizing Complex Compound Graphs

Advances in data generation and acquisition have resulted in a volume of available data of such magnitude that our ability to interpret and extract valuable knowledge from them has been surpassed. Our capacity to analyze data is hampered not only by their amount or their dimensionality, but also by their relationships and by the complexity of the systems they model. Compound graphs allow us to represent the existing relationships between nodes that are themselves hierarchically structured, so they are a natural substrate to support multiscale analysis of complex graphs. This paper presents Carbonic, a framework for interactive multiscale visual exploration and editing of compound graphs that incorporates several strategies for complexity management. It combines the representation of graphs at multiple levels of abstraction, with techniques for reducing the number of visible elements and for reducing visual cluttering. This results in a tool that allows both the exploration of existing graphs and the visual creation of compound graphs following a top-down approach that allows simultaneously observing the entities and their relationships at different scales. The results show the applicability of the developed framework to two use cases, demonstrating the usefulness of Carbonic for moving from information to knowledge.

Knowing the intention behind limb movements of a partner increases embodiment towards the limb of joint avatar

We explored a concept called "virtual co-embodiment", which enables users to share their virtual avatars with others. Co-embodiment of avatars and robots can be applied for collaboratively performing complicated tasks, skill training, rehabilitation, and aiding disabled users. We conducted an experiment where two users could co-embody one "joint avatar" in first person view and control different arms to collaboratively perform three types of reaching tasks. We measured their senses of agency and ownership towards the two arms of the avatar and changes in skin conductance levels in response to visual stimuli threatening the two virtual arms. We found that sense of agency, ownership, and skin conductance were significantly higher towards the virtual arm with control compared to the arm controlled by the partner. Furthermore, the senses of agency and ownership towards the arm controlled by the partner were significantly higher when the participant dyads shared a common intention or when they were allowed to see their partner's target, compared to when the partner's target was invisible. These results show that while embodiment towards partner-controlled limbs is lower compared to limbs with control, visual information necessary for predicting the partner's intentions can significantly enhance embodiment towards partner-controlled limbs during virtual co-embodiment.

A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net

In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface.

Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview

Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

Data Management and Processing in Seismology: An Application of Big Data Analysis for the Doublet Earthquake of 2021, 03 March, Elassona, Central Greece

On 3 March 2021 (10:16, UTC), a strong earthquake, Mw 6.3, occurred in Elassona, Central Greece. The epicenter was reported 10 km west of Tyrnavos. Another major earthquake followed this event on the same day at Mw 5.8 (3 March 2021, 11:45, UTC). The next day, 4 March 2021 (18:38, UTC), there was a second event with a similar magnitude as the first, Mw 6.2. Both events were 8.5 km apart. The following analysis shows that the previous events and the most significant aftershocks were superficial. However, historical and modern seismicity has been sparse in this area. Spatially, the region represents a transitional zone between different tectonic domains; the right-lateral slip along the western end of the North Anatolian Fault Zone (NAFZ) in the north Aegean Sea plate-boundary structure ends, and crustal extension prevails in mainland Greece. These earthquakes were followed by rich seismic activity recorded by peripheral seismographs and accelerometers. The installation of a dense, portable network from the Aristotle University of Thessaloniki team also helped this effort, installed three days after the seismic excitation, as seismological stations did not azimuthally enclose the area. In the present work, a detailed analysis was performed using seismological data. A seismological catalogue of 3.787 events was used, which was processed with modern methods to calculate 34 focal mechanisms (Mw > 4.0) and to recalculate the parameters of the largest earthquakes that occurred in the first two days.

Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review

Despite the increasing utilization of lean practices and digital technologies (DTs) related to Industry 4.0, the impact of such dual interventions on healthcare services remains unclear. This study aims to assess the effects of those interventions and provide a comprehensive understanding of their dynamics in healthcare settings. The methodology comprised a systematic review following the PRISMA guidelines, searching for lean interventions supported by DTs. Previous studies reporting outcomes related to patient health, patient flow, quality of care, and efficiency were included. Results show that most of the improvement interventions relied on lean methodology followed by lean combined with Six Sigma. The main supporting technologies were simulation and automation, while emergency departments and laboratories were the main settings. Most interventions focus on patient flow outcomes, reporting positive effects on outcomes related to access to service and utilization of services, including reductions in turnaround time, length of stay, waiting time, and turnover time. Notably, we found scarce outcomes regarding patient health, staff wellbeing, resource use, and savings. This paper, the first to investigate the dual intervention of DTs with lean or lean–Six Sigma in healthcare, summarizes the technical and organizational challenges associated with similar interventions, encourages further research, and promotes practical applications.

Multi-Perspective Representation to Part-Based Graph for Group Activity Recognition

Group activity recognition that infers the activity of a group of people is a challenging task and has received a great deal of interest in recent years. Different from individual action recognition, group activity recognition needs to model not only the visual cues of individuals but also the relationships between them. The existing approaches inferred relations based on the holistic features of the individual. However, parts of the human body, such as the head, hands, legs, and their relationships, are the critical cues in most group activities. In this paper, we establish the part-based graphs from different viewpoints. The intra-actor part graph is designed to model the spatial relations of different parts for an individual, and the inter-actor part graph is proposed to explore part-level relations among actors, in which visual relation and location relation are both considered. Furthermore, a two-branch framework is utilized to capture the static spatial and dynamic temporal representations simultaneously. On the Volleyball Dataset, our approach obtains a classification accuracy of 94.8%, achieving very competitive performance in comparison with the state of the art. As for the Collective Activity Dataset, our approach improves the accuracy by 0.3% compared with the state-of-the-art results.

Robotic Complex for Harvesting Apple Crops

The article deals with the concept of building an automated system for the harvesting of apple crops. This system is a robotic complex mounted on a tractor cart, including an industrial robot and a packaging system with a container for fruit collection. The robot is equipped with a vacuum gripper and a vision system. A generator for power supply, a vacuum pump for the gripper and an equipment control system are also installed on the cart. The developed automated system will have a high degree of reliability that meets the requirements of operation in the field.