Combined Use of Virtual Reality and a Chatbot Reduces Emotional Stress More Than Using Them Separately

In recent years, various organizations, such as companies and governments, have been required to take measures for the mental health of their employees, and the importance of self-care for mental health by employees themselves has been increasing, as well as being supported by administrators, such as doctors and workplace managers. As a means of self-care of mental health that can be implemented by busy professionals during their workdays and daily lives, the Digital-SAT method has been developed to implement the stress-care process of the SAT method, a psychological counseling technique for resolving psychological stress problems, in a self-guided manner using digital media. To realize the Digital-SAT method, two issues need to be addressed: first, to obtain the same emotional stress reduction effect as the SAT method and, second, to ensure the continuous implementation of the Digital-SAT method. Previous studies have shown that applications (apps) using virtual reality are effective in solving the former issue, and an app using a chatbot can be effective in solving the latter. In this research, an intervention study was conducted to verify the effectiveness of combined use of the two apps to encourage continuous use, resulting in increased emotional stress reduction, with the aim of making it feasible in actual work environments. An intervention of four weeks of app use was conducted with 70 nurses working in two hospitals where measures for mental health due to emotional labour and overwork were required. The emotional stress reduction effects of the intervention were evaluated using psychological scales and blood pressure levels, and it was confirmed that combined use of apps was more effective than using them separately to practice the Digital-SAT method in an actual work environment.

An Integration of Health Monitoring System in Public Transport Using the Semantic Web of Things

The integration of the Internet of Things (IoT) technology and artificial intelligence has become essential in many aspects of daily life since the expansion of the communications and information field. Healthcare is one area that urgently needs to benefit from these technologies to keep up with the dramatic evolution of communications for contemporary human life. IoT, through wearable devices, provides real-time data related to the measurement of a person’s vital signs of health. However, for this data to become more relevant and valuable, it needs to be linked to other domains. Public transport is a domain related to the daily activity of people who take advantage of the IoT to provide exemplary transport services whose quality of service can greatly affect people’s health. The integration of these two domains offers many benefits, especially when providing services adapted to passengers’ health status, making them safer and healthier. This paper proposes an approach based on an IoT architecture using Semantic Web technologies; it aims to integrate health monitoring in public transport, provide passengers with quality transport services, and ensure continuous health monitoring. The use of Semantic Web technologies overcomes the lack of interoperability due to the heterogeneity of data collected by different devices and generated by two different domains. An experimental study was conducted, and the proposed approach’s results were compared with those obtained by the evaluation of a physician. The results show that the approach is effective and should allow passengers to benefit from appropriate transport services that better match their health status.

Designing, Realizing, Running, and Evaluating Virtual Museum – a Survey on Innovative Concepts and Technologies

This paper presents a survey of innovative concepts and technologies involved in virtual museums (ViM) that shows their advantages and disadvantages in comparison with physical museums. We describe important lessons learned during the creation of three major virtual museums between 2010 and 2020 with partners at universities from Armenia, Germany, and Chile. Based on their categories and features, we distinguish between content-, communication- and collaboration-centric museums with a special focus on learning and co-curation. We give an overview of a generative approach to ViMs using the ViMCOX metadata format, the curator software suite ViMEDEAS, and a comprehensive validation and verification management. Theoretical considerations include exhibition design and new room concepts, positioning objects in their context, artwork authenticity, digital instances and rights management, distributed items, private museum and universal access, immersion, and tour and interaction design for people of all ages. As a result, this survey identifies different approaches and advocates for stakeholders’ collaboration throughout the life cycle in determining the ViM's direction and evolution, its concepts, collection type, and the technologies used with their requirements and evaluation methods. The paper ends with a brief perspective on the use of artificial intelligence in ViMs.

Big Data between Quality and Security: Dynamic Access Control for Collaborative Platforms

Big Data often refers to a set of technologies dedicated to deal with large volumes of data. Data Quality and Data Security are two essential aspects for any Big Data project. While Data Quality Management Systems are about putting in place a set of processes to assess and improve certain characteristics of data such as Accuracy, Consistency, Completeness, Timeliness, etc., Security Systems are designed to protect the Confidentiality, Integrity and Availability of data. In a Big Data environment, data quality processes can be blocked by data security mechanisms. Indeed, data is often collected from external sources that could impose their own security policies. In many research works, it has been recognized that merging and integrating access control policies are real challenges for Big Data projects. To address this issue, we suggest in this paper a framework to secure data collection in collaborative platforms. Our framework extends and combines two existing frameworks namely: PolyOrBAC and SLA- Framework. PolyOrBAC is a framework intended for the protection of collaborative environments. SLA-Framework, for its part, is an implementation of the WS-Agreement Specification, the standard for managing bilaterally negotiable SLAs (Service Level Agreements) in distributed systems; its integration into PolyOrBAC will automate the implementation and application of security rules. The resulting framework will then be incorporated into a data quality assessment system to create a secure and dynamic collaborative activity in the Big Data context.

J.UCS special issue on Challenges for Smart Environments – Human-Centered Computing, Data Science, and Ambient Intelligence. Smart Human-Centered Computing (volume 2)

Based on a successful funded collaboration between the American University of Armenia, the University of Duisburg-Essen and the University of Chile, in previous years a network was built, and in September 2020 a group of researchers gathered (although virtually) for the 2nd CODASSCA workshop on “Collaborative Technologies and Data Science in Smart City Applications”. This event has attracted 25 paper submissions which deal with the problems and challenges mentioned above. The studies are in specialized areas and disclose novel solutions and approaches based on existing theories suitably applied. The authors of the best papers published in the conference proceedings on Collaborative Technologies and Data Science in Artificial Intelligence Applications by Logos edition Berlin were invited to submit significantly extended and improved versions of their contributions to be considered for a journal special issue of J.UCS. There was also a J.UCS open call so that any author could submit papers on the highlighted subject. For this volume, we selected those devoted mainly to human-computer interaction problematics, which were rigorously reviewed in three rounds and 6 papers nominated to be published.

Uncertainty Handling in Genetic Risk Assessment and Counseling

In this paper, we consider genetic risk assessment and genetic counseling for breast cancer from the point of view of reliable uncertainty handling. In medical practice, there exist fairly accurate numerical tools predicting breast cancer (or gene mutation) probability based on such factors as the family history of a patient. However, they are too complex to be applied in normal doctors’ offices, so that several simplified, questionnaire-type support tools appeared. This process is highly affected by uncertainty. At the same time, reliability of test interpretations and counseling conclusions is especially important since they have direct influence on humans and their decisions. We show how expert opinions on mutation probabilities can be combined using the Dempster-Shafer theory. Based on multi-criteria binary decision trees and interval analysis, we combine the referral screening tool designed to determine patients at risk of breast cancer (and recommend genetic counseling or testing for them) with three further risk assessment tools available for this purpose. A patient’s confidence in the outcome of a genetic counseling session can be heightened by the proposed method since it combines different sources to provide score ranges leading to more information. Finally, based on this approach, a decision tree for assigning a risk category is proposed which enhances the existing methodology. The great impact of epistemic uncertainty is reflected through large overlapping intervals for the risk classes.

Deep Semi-Supervised Image Classification Algorithms: a Survey

Semi-supervised learning is a branch of machine learning focused on improving the performance of models when the labeled data is scarce, but there is access to large number of unlabeled examples. Over the past five years there has been a remarkable progress in designing algorithms which are able to get reasonable image classification accuracy having access to the labels for only 0.1% of the samples. In this survey, we describe most of the recently proposed deep semi-supervised learning algorithms for image classification and identify the main trends of research in the field. Next, we compare several components of the algorithms, discuss the challenges of reproducing the results in this area, and highlight recently proposed applications of the methods originally developed for semi-supervised learning.

On Recurrent Neural Network Based Theorem Prover For First Order Minimal Logic

There are three main problems for theorem proving with a standard cut-free system for the first order minimal logic. The first problem is the possibility of looping. Secondly, it might generate proofs which are permutations of each other. Finally, during the proof some choice should be made to decide which rules to apply and where to use them. New systems with history mechanisms were introduced for solving the looping problems of automated theorem provers in the first order minimal logic. In order to solve the rule selection problem, recurrent neural networks are deployed and they are used to determine which formula from the context should be used on further steps. As a result, it yields to the reduction of time during theorem proving.

Incremental autoencoders for text streams clustering in social networks

Clustering data streams in order to detect trending topic on social networks is a chal- lenging task that interests the researchers in the big data field. In fact, analyzing such data needs several requirements to be addressed due to their large amount and evolving nature. For this purpose, we propose, in this paper, a new evolving clustering method which can take into account the incremental nature of the data and meet with its principal requirements. Our method explores a deep learning technique to learn incrementally from unlabelled examples generated at high speed which need to be clustered instantly. To evaluate the performance of our method, we have conducted several experiments using the Sanders, HCR and Terr-Attacks datasets.

Leveraging multifaceted proximity measures among developers in predicting future collaborations to improve the social capital of software projects

Social capital is an asset earned by people through their social connections. One of the motivations among developers to contribute to open source development and maintenance tasks is to earn social capital. Recent studies suggest that the social capital of the project has an impact on the sustained participation of the developers in open source software (OSS). One way to improve the social capital of the project is to help the developers in connecting with their peers. However, to the best of our knowledge, there is no prior research which attempts to predict future collaborations among developers and establish the significance of these collaborations on improving the social capital at the project level. To address this research gap, in this paper, we model the past collaborations among developers on version control system (VCS) and issue tracking system (ITS) as homogeneous and heterogeneous developer social network (DSN). Along with the novel path count based features, defined on proposed heterogeneous DSN, multifaceted proximity features are used to generate a feature set for machine learning classifiers. Our experiments performed on 5 popular open source projects (Spark, Kafka, Flink, WildFly, Hibernate) indicate that the proposed approach can predict the future collaborations among developers on both the platforms i.e. VCS as well as ITS with a significant accuracy (AUROC up to 0.85 and 0.9 for VCS and ITS respectively). A generic metric- recall of gain in social capital is proposed to investigate the efficacy of these predicted collaborations in improving the social capital of the project. We also concretised this metric on various measures of social capital and found that collaborations predicted by our approach have significant potential to improve the social capital at project level (e.g. Recall of gain in cohesion index up to 0.98 and Recall of gain in average godfather index up to 0.99 for VCS). We also showed that structure of collaboration network has an impact on the accuracy and usefulness of predicted collaborations. Since the past research suggests that many newcomers abandon the open source project due to social barriers which they face after joining the project, our research outcomes can be used to build the recommendation systems which might help to retain such developers by improving their social ties based on similar skills/interests.