Reviewerly/exHarmony huggingface dataset:
Specifics of information and communication developments in education: Analysis of value changes before and after 2020 (A critical review)

Introduction. The study examines the ambiguous attitude of the scholarly community to the changes occurring in the education system, in general, and to the use of digital technologies within education, in particular. The aim of this study is to reveal the specifics of the educational changes on the basis of a critical review of research investigations, analysis of value factors determining the problems of integration of digital information and communication technologies in education before and after the COVID-19 pandemic. Materials and Methods. The research methodology includes the analysis and generalization of international and Russian refereed research literature investigating the problems of information developments of society, virtualization of cognitive processes and the development of education. Results. The research findings indicate the specificity of highly critical attitudes of the scholarly community to the use of digital technologies in education and to the current outcomes of the use of the virtual environment in the educational process. The authors emphasize that, in contrast to the situation of the previous stage, there is a particularly pronounced mediated nature of communication in the virtual space, which, as a consequence, determines the possibility of losing a range of informative elements in the process of communicative exchange. In this regard, the focus of the study is on the issues of formative assessment and feedback in the process of virtual communication in education. The authors clarify and define modern educational changes. They argue that virtualization in modern educational practice is the most important factor influencing, first of all, value orientations of the individual. The content of value orientations is changing as well. Values are being re-estimated; attention is focused on the values of safety and health. In this concern, the main attention is shifted to the following issues: the influence of digital learning on the functional body responses, the safety of educational interaction, individuals’ adaptation to altered conditions, and formation of adaptive developmental mechanisms. Conclusions. In conclusion, the authors summarize the main characteristic features and value factors which determine the problems of integration of digital information and communication technologies in education.
Techno-Pedagogical Skills for 21st Century Digital Classrooms: An Extensive Literature Review

Techno-pedagogical ability is a way and reasonable value education available to everyone. Indeed, although in the past it has been found that there are many benefits to using innovation in the classroom environment, there are still obstacles or experiences related to the use of innovation. The literature review was utilized to gather and choose the papers for this research. This article investigates the techno-pedagogical skills requirements of the science and art teachers of the Sukkur State Government College. In this paper, literature is collected from different real sites, such as Google, Google Scholar, Science Direct (Elsevier), Sage, Springer, Emerald, Taylor &amp; Francis, and Eric databases. Ten themes emerged from the literature to analyze the techno-pedagogical skills of science and art educators and provide suggestions and solutions for improving educational institutions. Quantitative, qualitative, and mixed research papers are all included in this study. This research also identified the knowledge gap and methodological and implication gap in this research article as per the need for 21st Century Digital Classrooms. The results show that there is no significant difference in techno-pedagogical skills between science teachers and art teachers who use technology in teaching. This is because of the epidemic. Talking about the current scourge, COVID-19 has transformed traditional courses into digitization through ICT integration. Information and Communication Technology (ICT) is seen as a tool that can help schools adapt to the transition from industry to information. It is also considered as a tool to provide, support, and strengthen educational reforms in accordance with the educational needs of the information society.
Surrounding Objects Detection and Tracking for Autonomous Driving Using LiDAR and Radar Fusion

Abstract Radar and LiDAR are two environmental sensors commonly used in autonomous vehicles, Lidars are accurate in determining objects’ positions but significantly less accurate as Radars on measuring their velocities. However, Radars relative to Lidars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution. In order to compensate for the low detection accuracy, incomplete target attributes and poor environmental adaptability of single sensors such as Radar and LiDAR, in this paper, an effective method for high-precision detection and tracking of surrounding targets of autonomous vehicles. By employing the Unscented Kalman Filter, Radar and LiDAR information is effectively fused to achieve high-precision detection of the position and speed information of targets around the autonomous vehicle. Finally, the real vehicle test under various driving environment scenarios is carried out. The experimental results show that the proposed sensor fusion method can effectively detect and track the vehicle peripheral targets with high accuracy. Compared with a single sensor, it has obvious advantages and can improve the intelligence level of autonomous cars.
The Machine-to-Everything (M2X) Economy: Business Enactments, Collaborations, and e-Governance

Nowadays, business enactments almost exclusively focus on human-to-human business transactions. However, the ubiquitousness of smart devices enables business enactments among autonomously acting machines, thereby providing the foundation for the machine-driven Machine-to-Everything (M2X) Economy. Human-to-human business is governed by enforceable contracts either in the form of oral, or written agreements. Still, a machine-driven ecosystem requires a digital equivalent that is accessible to all stakeholders. Additionally, an electronic contract platform enables fact-tracking, non-repudiation, auditability and tamper-resistant storage of information in a distributed multi-stakeholder setting. A suitable approach for M2X enactments are electronic smart contracts that allow to govern business transactions using a computerized transaction protocol such as a blockchain. In this position paper, we argue in favor of an open, decentralized and distributed smart contract-based M2X Economy that supports the corresponding multi-stakeholder ecosystem and facilitates M2X value exchange, collaborations, and business enactments. Finally, it allows for a distributed e-governance model that fosters open platforms and interoperability. Thus, serving as a foundation for the ubiquitous M2X Economy and its ecosystem.
Device Identity-Based User Authentication on Electronic Payment System for Secure E-Wallet Apps

E-wallets are a modern electronic payment system technology that easily recognize consumer interest, making our transactions very convenient and efficient. E-wallets are intended to substitute the existing physical wallet, which may tell others something about us as a person. That is why using a physical wallet is a unique, personal experience that cannot be duplicated. A solution would be to replace the physical wallet with an e-wallet on an existing mobile device. The personal nature of the e-wallet is that it should be installed on a unique device. One of the fundamental protections against any illegal access to e-wallet application is through authentication. In particular, the fundamental authentication category used in an existing e-wallet is based on knowledge (i.e., what you know), ownership (i.e., what you have), and biometric (i.e., what you are) authentication, which are sometimes prone to security threats such as account takeover, sim swapping, app cloning, or know your customer verification attacks. The design of an e-wallet authentication on mobile device solution must take into consideration the intensity of the security. To address this problem, this study proposes a design of e-wallet apps with an extension security element that focuses on the device identity in the existing user authentication mechanism. This study covers four fundamental categories of authentication: password, one time password, fingerprints, and international mobile equipment identifier. Using IMEI limits an e-wallet to be in one specific device in one time; this brings it into line with the nature of a physical wallet. In addition, it will be ready to handle the mentioned threats above, which will ultimately result in the far more reliable to use of e-wallet apps. The proposed authentication design has two phases, a registration phase and an authentication phase. The proposed method has been developed and implemented based on an Android Studio Firebase real-time database management and PayPal. In addition, the complete design has been evaluated using functional requirement testing to see how closely it meets functionality requirements. The results obtained from functional testing show that the functionalities of the proposed method meet the requirements, and one cannot use a same account on two devices; hence, it is secure from attacks. The result also shows that the proposed method has no errors. Moreover, it has been shown that our proposed method has better security parameters in terms of the existing method.
Ethical, Legal and Social Issues of Digital Phenotyping as a Future Solution for Present-Day Challenges: A Scoping Review

Abstract Digital phenotyping represents an avenue of consideration in patients' self-management. This scoping review aims to explore the trends in the body of literature on ethical, legal, and social challenges relevant to the implementation of digital phenotyping technologies in healthcare. The study followed the PRISMA-ScR methodology (Tricco et al. in Ann Int Med 169(7):467–473, 2018. https://doi.org/10.7326/M18-0850 ). The review systematically identified relevant literature, characterised the discussed technology, explored its impacts and the proposed solutions to identified challenges. Overall, the literature, perhaps unsurprisingly, concentrates on technical rather than ethical, legal, and social perspectives, which limits understanding of the more complex cultural and social factors in which digital phenotyping technologies are embedded. ELS issues mostly concern privacy, security, consent, lack of regulation, and issues of adoptability, and seldom expand to more complex ethical issues. Trust was chosen as an umbrella theme of a continuum of major ELS and technical issues. Sustained critical analysis of digital phenotyping showed to be sparse and geographically exclusive. There is a continuum and overlap between ELS issues, suggesting the need for a holistic, interdisciplinary approach to each of the challenges posed by the various technologies of digital phenotyping.
MEASURING VALUE AT RISK USING GARCH MODEL - EVIDENCE FROM THE CRYPTOCURRENCY MARKET

There is a growing interest in the activities of the crypto market by various stakeholders. These stakeholders generally include investors, entrepreneurs, governments, fund managers, climate activists, institutional managers, employees with surplus funds, and crypto miners. This study aims to investigate the accuracy of the GARCH models for measuring and estimating Value-at-risk (VaR) using the Cryptocurrency index for future investment and managerial decision making. Because of this, the present study uses the top 30 Cryptocurrencies index in terms of Market capitalization excluding stable coins to determine the best GARCH models. Many entrepreneurs, institutional managers, fund managers, and other stakeholders have recently included cryptocurrency in their investment portfolio because of the increase in transactions and high returns growth in the global financial market with its associated high returns and volatility. Information communication technology has paved the way for such activities in the global markets. The daily data frequency was applied because of the availability of the data. The empirical analysis has been carried out for the period from January 2017 to December 2020 for a total of 1461observation. The returns volatility is estimated using SGARCH and EGARCH models. The findings evidenced that, using both normal distribution and Student t distribution, EGARCH provides a better measure and estimate than SGARCH concerning high persistence and volatility. Against this background, the present study also examined Backtesting to estimate Value at Risk. Interestingly, the findings of the available study would provide industry players, practitioners, entrepreneurs, and investors the maximum edge on how to use or measure such variables against others to make investment decisions. Also, the findings would subsequently contribute more insight into academia on the study area.
Low-rank supervised and semi-supervised multi-metric learning for classification

Multi-metric learning is an important technique for improving classification performance since learning a single metric is usually insufficient for complex data. Most of the existing multi-metric learning approaches have high computational complexity. In this work, two multi-metric learning frameworks proposed to perform supervised and semi-supervised classifications respectively. Based on the frameworks, we first design a low-rank multi-metric learning model (LSMML) for supervised classification, in which multiple local class metrics as well as one global metric are jointly trained. A joint regularization scheme, composed of LogDet divergence and low-rank term, is also well-designed to incorporate prior knowledge for improving generalization. By learning appropriate metrics, LSMML not only captures the local nonlinear discriminate information of each class to reduce the probability of misclassification but also enhances the stability, alleviates the computational burden, and avoids the risk of overfitting. Then, we extend LSMML to a semi-supervised learning scenario and propose a low-rank semi-supervised multi-metric learning approach (LSeMML) to process data with scarce labels. Alternating iterative algorithms are designed to optimize both LSMML and LSeMML. At each iteration, we only require to perform geodesic convex optimizations, with closed-form solutions and low computational cost. In supervised and semi-supervised settings respectively, numerical simulations are carried out on different databases, which shows that the proposed LSMML and LSeMML have a simple form, fast training speed, and good classification performance.
Emotional Intelligence and Academic Self-Efficacy in Relation to the Psychological Well-Being of University Students During COVID-19 in Venezuela

Due to the COVID-19 pandemic, educational centers and universities in Venezuela have closed their physical plants and are migrating to emergency remote education to continue with academic programs. This empirical study aimed to analyze the predictive capacity of academic self-efficacy and emotional intelligence skills on each of the dimensions of psychological well-being. We employed a cross-sectional predictive design. The sample comprised 277 university students, of which 252 were female (91.00%). Their ages ranged from 18 to 45 years, with a mean of 20.35 ( SD = 2.29). Non-probabilistic chance sampling was used. For data collection, we used an anonymous online form, contacted students by mail, and invited them to participate in the study. Questionnaires were available between 217 and 227 days of decreed quarantine in Venezuela. The results indicated average levels of academic self-efficacy (Me = 4; IQR = 2), emotional intelligence: clarity (Me = 27; IQR = 10), attention (Me = 25; IQR = 10) y repair (Me = 25; IQR = 12), and psychological well-being (Me = 35; IQR = 5). We found differences according to sex and age, specifically in emotional regulation ( z = 3.73, p &amp;lt; 0.001, d = 0.438) and in bonds of psychological well-being ( z = 2.51, p = 0.012, d = 0.276) favoring men (Me = 33, IQR = 9; Me = 8, IQR = 1), respectively. Regarding age, statistically significant differences were found in the group of students older than 21 years with higher perception of psychological well-being ( z = 3.69, p &amp;lt; 0.001, d = 0.43) and in each of its dimensions. Emotional intelligence and academic self-efficacy were found to be significant predictors of psychological well-being and its dimensions, specifically on control ( R 2 -Cox = 0.25, R 2 -Nagelkerke = 0.34, 69.90% of total correct classification), links ( R 2 -Cox = 0.09, R 2 -Nagelkerke = 0.12, 65.07% of total correct classification), projects ( R 2 -Cox = 0.32, R 2 -Nagelkerke = 0.46, 78.40% of total correct classification), acceptance ( R 2 -Cox = 0.17, R 2 -Nagelkerke = 0.23, 68.28% of total correct classification), and total well-being ( R 2 -Cox = 0.52, R 2 -Nagelkerke = 0.71, 87.16% of total correct classification). It was concluded that emotional intelligence and academic self-efficacy are protective psychological resources of psychological well-being that should be promoted at university to mitigate the negative effects of the pandemic on the mental health of young people.
Ecological Validity through Gamification: an Experiment with a Mixed Reality Escape Room

Increasing the ecological validity of subjective laboratory experiments is still an open problem for the Quality of Experience research community. To contribute to this target, we have designed a subjective experiment based on a five-minute mixed reality escape room game, and we have conducted the experiment in an uncontrolled environment outside the lab. Our experiment addresses the main dimensions of ecological validity (stimulus, response, and setting) while still getting precision level similar to a lab trial, at the cost of reducing the number of responses to measure and their potential dynamic range.
Context-Aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

With the wide application of electronic health records (EHR) in healthcare facilities, health event prediction with deep learning has gained more and more attention. A common feature of EHR data used for deep-learning-based predictions is historical diagnoses. Existing work mainly regards a diagnosis as an independent disease and does not consider clinical relations among diseases in a visit. Many machine learning approaches assume disease representations are static in different visits of a patient. However, in real practice, multiple diseases that are frequently diagnosed at the same time reflect hidden patterns that are conducive to prognosis. Moreover, the development of a disease is not static since some diseases can emerge or disappear and show various symptoms in different visits of a patient. To effectively utilize this combinational disease information and explore the dynamics of diseases, we propose a novel context-aware learning framework using transition functions on dynamic disease graphs. Specifically, we construct a global disease co-occurrence graph with multiple node properties for disease combinations. We design dynamic subgraphs for each patient's visit to leverage global and local contexts. We further define three diagnosis roles in each visit based on the variation of node properties to model disease transition processes. Experimental results on two real-world EHR datasets show that the proposed model outperforms state of the art in predicting health events.
Machine Translation: Friend or Foe in the Language Classroom?

Machine translation (MT) provides a seemingly accelerated alternative way to communicate in the target language (L2).A convenient service to the public, MT renders a potential disservice to language learners.In this pedagogically focused article, we show concrete and detailed examples of how language instructors can turn MT and other electronic tools such as translation memories, grammar-and spellcheckers, or mapping tools into virtual assistants to empower students to use them responsibly.Two classroom interventions, one at a large public research university on the West coast and the second one at a medium-sized public university in the Midwest, aimed to develop students' awareness of the language learning process, while introducing them to various online tools that can help them communicate better in L2 without blindly using MT.The interventions were designed for intermediate level students.The first group of students were part of an advanced composition course who were shown limitations of MT and alternative editorial tools in L2, while the second group was part of an introductory literature course in which students were introduced to reasoning maps, such as mind, concept, and argument maps, to assist them with L2 communication.The main takeaways from these interventions were the need to readjust the students' attitudes as much as the instructors' mindsets if we want to make MT an ally.Shifting focus from accuracy to comprehensibility changes the stakes in L2 communication as the production of meaning becomes an exercise in student agency and leads to the satisfaction of being able to communicate spontaneously in the target language.
A Novel Video Steganographic Scheme Incorporating the Consistency Degree of Motion Vectors

In this letter, a novel steganographic scheme in motion vector domain (MV) for H.264 video is presented, which can significantly improve the security performance against the newly emerged powerful multi-domain feature set MVC (motion vector consistency). By taking into account both the consistency degree of motion vectors for sub-blocks within a macroblock (MB) or sub-macroblock (sub-MB), and the MV statistics, the corresponding distortion function called dMVC is proposed. The proposed dMVC is also shown to be capable of integrating with existing methods to resist the joint steganalytic attacks of both MVC feature and local optimality features, e.g., NPELO, in the framework of minimal distortion embedding. Compared with other state-of-the-art MV-based steganographic schemes, experimental results on YUV sequences at various embedding rates and QPs show that the proposed method gains significant performance improvement while maintaining good coding efficiency.
Mobile sensor for clouds shadow detection and direct normal irradiance estimation

This paper presents a mobile sensor system to detect and estimate low direct normal irradiance (DNI) areas caused by clouds shadows. This work proposes using a team of unmanned aerial vehicles (UAVs) to localise and characterise the shadow of mobile clouds. This information can be used by the plant control system to minimise its effects over a solar plant. Simulations to test and discuss the algorithm are presented. The work presented here obtains a similar degree of precision as far as the estimation of the shape of the cloud shadow is concerned but with a much faster computational time than other algorithms described in literature.