Reviewerly/exHarmony huggingface dataset:
The role of academic electronic books in undergraduate students' achievement in higher education

The study objective was to seek the role of utilizing academic electronic books on Ajman University undergraduate students' achievement and faculty members viewpoints about their use. The study participants were 91 students, split into two groups the first group was empirical (46) and the other group was control (45) plus 220 members of the faculty. A performance test and a questionnaire were designed and implemented as tools of study. The results detected significant differences among both the empirical groups and the control groups, for the benefit of the empirical group; and faculty members exhibited highly favorable perspectives on the use of academic electronic books at their university. Faculty members' perceptions varied according to gender, college, and experience teaching, but the academic rank showed no influence.
Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining

Ambient Assisted Living (AAL) is becoming crucial to help governments face the consequences of the emerging ageing population. It aims to motivate independent living of older adults at their place of residence by monitoring their activities in an unobtrusive way. However, challenges are still faced to develop a practical AAL system. One of those challenges is detecting failures in non-intrusive sensors in the presence of the non-deterministic human behaviour. This paper proposes sensor failure detection and isolation system in the AAL environments equipped with event-driven, ambient binary sensors. Association Rule mining is used to extract fault-free correlations between sensors during the nominal behaviour of the resident. Pruning is then applied to obtain a non-redundant set of rules that captures the strongest correlations between sensors. The pruned rules are then monitored in real-time to update the health status of each sensor according to the satisfaction and/or unsatisfaction of rules. A sensor is flagged as faulty when its health status falls below a certain threshold. The results show that detection and isolation of sensors using the proposed method could be achieved using unlabelled datasets and without prior knowledge of the sensors’ topology.
Using Virtual Reality to Promote Physical Activity

The purpose of this research is to develop an immersive virtual fitness center as an intervention to promote physical activity and examine its impacts on users' exercise plans and exercise behaviors.The virtual reality (VR)-based intervention includes three main features: enabling the users to control their avatar, personalizing the avatar to look the same as the user, and visualizing the positive consequences of exercising.We conducted an experiment to randomly assign participants to two treatment groups: experiencing either the self or another person losing weight because of exercising in the virtual environment.The findings demonstrated that the self-avatar group exercised more in the voluntary section than the other-avatar group.However, participants in the self-avatar condition perceived a lower level of sense of presence compared to participants in the other-avatar condition.Compared to people in the control condition who watched and followed the exercise from a workout video, those who exercised in the virtual fitness center, regardless of whether the avatar was based on the self's image or another person's image, planned to spend more time on exercising in the following week.Theoretical and practical implications for using VR technology to promote health-related behavioral change, and why personalization decreases perceived sense of presence in the virtual environment are discussed.
Challenges and Opportunities to Overcome The Impact Of COVID-19 Pandemic on Power Engineering Education

The overall objective of this paper is to propose a viable approach to quality Education in Power Engineering during the COVID-19 period. To this end, we base the study on our previous steps in implementing Augmented Reality technology for training future engineers in the field of energy. To cope with the present challenges brought by the COVID-19 pandemic, we analyzed the necessary paradigm shift to maintain the benefits of using Augmented Reality in teaching.
Intelligent Blended Agents: Reality–Virtuality Interaction with Artificially Intelligent Embodied Virtual Humans

Intelligent virtual agents (VAs) already support us in a variety of everyday tasks such as setting up appointments, monitoring our fitness, and organizing messages. Adding a humanoid body representation to these mostly voice-based VAs has enormous potential to enrich the human–agent communication process but, at the same time, raises expectations regarding the agent’s social, spatial, and intelligent behavior. Embodied VAs may be perceived as less human-like if they, for example, do not return eye contact, or do not show a plausible collision behavior with the physical surroundings. In this article, we introduce a new model that extends human-to-human interaction to interaction with intelligent agents and covers different multi-modal and multi-sensory channels that are required to create believable embodied VAs. Theoretical considerations of the different aspects of human–agent interaction are complemented by implementation guidelines to support the practical development of such agents. In this context, we particularly emphasize one aspect that is distinctive of embodied agents, i.e., interaction with the physical world. Since previous studies indicated negative effects of implausible physical behavior of VAs, we were interested in the initial responses of users when interacting with a VA with virtual–physical capabilities for the first time. We conducted a pilot study to collect subjective feedback regarding two forms of virtual–physical interactions. Both were designed and implemented in preparation of the user study, and represent two different approaches to virtual–physical manipulations: (i) displacement of a robotic object, and (ii) writing on a physical sheet of paper with thermochromic ink. The qualitative results of the study indicate positive effects of agents with virtual–physical capabilities in terms of their perceived realism as well as evoked emotional responses of the users. We conclude with an outlook on possible future developments of different aspects of human–agent interaction in general and the physical simulation in particular.
Prediction of Cranial Radiotherapy Treatment in Pediatric Acute Lymphoblastic Leukemia Patients Using Machine Learning: A Case Study at MAHAK Hospital

Background: Acute Lymphoblastic Leukemia (ALL) is the most common blood disease in children and is responsible for the most deaths amongst children. Due to major improvements in the treatment protocols in the 50-years period, the survivability of this disease has witnessed dramatic rise until this date which is about 90 percent. There are many investigations tending to indicate the efficiency of cranial radiotherapy found out that without that, outcome of the patients did not change and even it improved at some cases. Methods: the main aim of this study is predicting cranial radiotherapy treatment in pediatric acute lymphoblastic leukemia patients using machine learning. Scope of this paper is intertwined with predicting the necessity of one of the treatment modalities that has been used for many years for this group of patients named Cranial Radiotherapy (CRT). For this purpose, a case study is considered at Mahak charity hospital. In this paper, our focus is on ALL patients aged 0 to 17 treated at Mahak hospital, one of the best centers for treatment of childhood malignancies in Iran. Dataset analyzed in this study is gathered by the research team from patient’s paper-based files. Our dataset consists of 241 observations on patients with 31 attributes after the data cleaning process. Our designed machine learning model for predicting cranial radiotherapy treatment in pediatric acute lymphoblastic leukemia patients is a stacked ensemble classifier of independently strong models with a meta-learner to tune the weights and parameters of the base classifiers. Results: The stacked ensemble classifier show highly reasonable performance with AUC of 87.52%. Moreover, the attributes are ranked based on their predictive power and the most important variable for CRT necessity prediction is the disease relapse. Conclusion: In order to conclude, derived from previous studies regarding CRT it is not only cost-effective but also more healthy to eradicate the use of CRT for the treatment of childhood ALL. Furthermore, it is valuable to increase the clinical databases by creating more synthetic health databases not only for research purposes but also for physicians to keep track of their patient’s status.
SANS: Self-Sovereign Authentication for Network Slices

5G communications proposed significant improvements over 4G in terms of efficiency and security. Among these novelties, the 5G network slicing seems to have a prominent role: deploy multiple virtual network slices, each providing a different service with different needs and features. Like this, a Slice Operator (SO) ruling a specific slice may want to offer a service for users meeting some requirements. It is of paramount importance to provide a robust authentication protocol, able to ensure that users meet the requirements, providing at the same time a privacy-by-design architecture. This makes even more sense having a growing density of Internet of Things (IoT) devices exchanging private information over the network. In this paper, we improve the 5G network slicing authentication using a Self-Sovereign Identity (SSI) scheme: granting users full control over their data. We introduce an approach to allow a user to prove his right to access a specific service without leaking any information about him. Such an approach is SANS, a protocol that provides nonlinkable protection for any issued information, preventing an SO or an eavesdropper from tracking users’ activity and relating it to their real identities. Furthermore, our protocol is scalable and can be taken as a framework for improving related technologies in similar scenarios, like authentication in the 5G Radio Access Network (RAN) or other wireless networks and services. Such features can be achieved using cryptographic primitives called Zero-Knowledge Proofs (ZKPs). Upon implementing our solution using a state-of-the-art ZKP library and performing several experiments, we provide benchmarks demonstrating that our approach is affordable in speed and memory consumption.
Annotated corpus creation for sentiment analysis in code-mixed Hindi-English (Hinglish) social network data

Background: Evaluating the sentiments of tweets, blogs, comments and posts have become a crucial part of many applications.Sentiment analysis of social network data is very helpful for decision-making in application areas like movie reviews, product feedback and impact of the speech of a politician etc.The users often comment in their native languages or in slang languages or more often they use abbreviations and do not even stick to grammatical rules of the language.The bilingual and multilingual community often mixes two or more than two languages in their comments.Unavailability of annotated code-mixed data for native language adds to the difficulty in performing sentiment analysis.Objectives The motive of this article is to present the process of creating annotated corpus for code mixed social media text in Hindi, English and Hinglish which is collected from twitter.Ambiguous meaning words and inconsistent spellings of both the languages have also been included in the study to provide wide spread canvas.Method: This study will provide significant elements that should be considered while developing the annotated corpus of Hindi, Hinglish & English dataset.The annotation is calculated on the basis of polarity of words in three categories as positive, negative and neutral.There are words which have mixed feeling i.e. these words have positive as well as negative sentiments.To consider these words, inner agreement among the polarities has been considered.The words used for sarcasm or slangs have also been taken into account.The study has included ambiguous meaning and inconsistent spelling words of both languages as well.Findings: The proposed work provides a standard annotated corpus for code switched social media text in Hindi-English (Hinglish).The process of developing the corpus and calculating the polarity has been shown.It is found that if one considers the code-mixed text, the accuracy can be enhanced.Application: The proposed corpus can be utilized in the area of market analysis, customer behavior, polling analysis, brand monitoring, etc.The corpus serves as dataset which can further be extended according to the problem definition.
Web-Based Privacy-Preserving Multicenter Medical Data Analysis Tools Via Threshold Homomorphic Encryption: Design and Development Study

Background Data sharing in multicenter medical research can improve the generalizability of research, accelerate progress, enhance collaborations among institutions, and lead to new discoveries from data pooled from multiple sources. Despite these benefits, many medical institutions are unwilling to share their data, as sharing may cause sensitive information to be leaked to researchers, other institutions, and unauthorized users. Great progress has been made in the development of secure machine learning frameworks based on homomorphic encryption in recent years; however, nearly all such frameworks use a single secret key and lack a description of how to securely evaluate the trained model, which makes them impractical for multicenter medical applications. Objective The aim of this study is to provide a privacy-preserving machine learning protocol for multiple data providers and researchers (eg, logistic regression). This protocol allows researchers to train models and then evaluate them on medical data from multiple sources while providing privacy protection for both the sensitive data and the learned model. Methods We adapted a novel threshold homomorphic encryption scheme to guarantee privacy requirements. We devised new relinearization key generation techniques for greater scalability and multiplicative depth and new model training strategies for simultaneously training multiple models through x-fold cross-validation. Results Using a client-server architecture, we evaluated the performance of our protocol. The experimental results demonstrated that, with 10-fold cross-validation, our privacy-preserving logistic regression model training and evaluation over 10 attributes in a data set of 49,152 samples took approximately 7 minutes and 20 minutes, respectively. Conclusions We present the first privacy-preserving multiparty logistic regression model training and evaluation protocol based on threshold homomorphic encryption. Our protocol is practical for real-world use and may promote multicenter medical research to some extent.
Motivating employees to acquire digital skills

Motivating the working population to master digital skills is an important condition for the digital transformation of the Russian economy and companies. The article examines the relationship between the general level of motivation for learning digital skills and a number of factors, assesses the average level of motivation for four groups of skills, and compares the level of motivation between groups of workers and their motives. Empirical data were obtained by conducting an online survey of the working population in March 2020, the target sample was 116 respondents. It was revealed that the general level of motivation of workers to learn decreases with the increasing complexity of digital skills. The hypotheses about the relationship between the level of motivation and the perceived difficulty of using information and communication technologies and previous learning experience were confirmed. Differences between the groups of workers in the level of motivation and the degree of mastering specialized digital skills, related to the nature of the work were revealed. A higher interest in mastering this group of skills, as well as the degree of mastering them, was demonstrated by the group of managers. The strongest motive for acquiring digital skills is the need to use them at work.
Perceptions of Foreign Language Teachers for M-Learning

Educational technology is a set of systems consisting of tech savvy equipment, processes and methods for the effective and positive implementation of educational theories. Nowadays, when the internet is becoming widespread, and mobile device technologies such as smartphones and tablets have been developing rapidly, it is possible to access educational content without being connected to a place. Today, almost everyone has smartphones. Many social networks, sites, applications are available. Many studies have shown that technology is effective in the field of education. For teachers with foreign language and their perceptions of learning through the help of technology are important. In this study, it is important to determine teachers' perception levels of mobile applications that contribute to foreign language learning. The "mobile learning perception scale" developed to access the research data. Items that are mentioned in the scale are basically positive question, "absolutely disagree", "disagree", "undecided", "agree", "strongly agree". After analysing the reliability of the survey, the overall result was found as Cronbach ? = 0,963. A total of 180 teachers attended from North Cyprus and Russian universities. According to the result of the research, the teachers' perception of mobile learning is high. But this rate is proportional to age. There is a difference in the findings regarding the use of mobile technology when looking at the professional seniority and age ranges. This is due to the fact that recently, technology-supported trainings have been increased and the addition of "material design courses" and "technology-supported trainings" at universities play an important role in increasing perceptions. The use of mobile technology can be increased by providing in-service trainings to teachers with high professional experience and ages.
A Multiscale Topographical Analysis Based on Morphological Information: The HEVC Multiscale Decomposition

In this paper, we evaluate the effect of scale analysis as well as the filtering process on the performances of an original compressed-domain classifier in the field of material surface topographies classification. Each surface profile is multiscale analyzed by using a Gaussian Filter analyzing method to be decomposed into three multiscale filtered image types: Low-pass (LP), Band-pass (BP), and High-pass (HP) filtered versions, respectively. The complete set of filtered image data constitutes the collected database. First, the images are lossless compressed using the state-of-the art High-efficiency video coding (HEVC) video coding standard. Then, the Intra-Prediction Modes Histogram (IPHM) feature descriptor is computed directly in the compressed domain from each HEVC compressed image. Finally, we apply the IPHM feature descriptors as an input of a Support Vector Machine (SVM) classifier. SVM is introduced here to strengthen the performances of the proposed classification system thanks to the powerful properties of machine learning tools. We evaluate the proposed solution we called “HEVC Multiscale Decomposition” (HEVC-MD) on a huge database of nearly 42,000 multiscale topographic images. A simple preliminary version of the algorithm reaches an accuracy of 52%. We increase this accuracy to 70% by using the multiscale analysis of the high-frequency range HP filtered image data sets. Finally, we verify that considering only the highest-scale analysis of low-frequency range LP was more appropriate for classifying our six surface topographies with an accuracy of up to 81%. To compare these new topographical descriptors to those conventionally used, SVM is applied on a set of 34 roughness parameters defined on the International Standard GPS ISO 25178 (Geometrical Product Specification), and one obtains accuracies of 38%, 52%, 65%, and 57% respectively for Sa, multiscale Sa, 34 roughness parameters, and multiscale ones. Compared to conventional roughness descriptors, the HEVC-MD descriptors increase surfaces discrimination from 65% to 81%.
MAGNETO

Universal Serial Bus (USB) Flash Drives are nowadays one of the most convenient and diffused means to transfer files, especially when no Internet connection is available. However, USB flash drives are also one of the most common attack vectors used to gain unauthorized access to host devices. For instance, it is possible to replace a USB drive so that when the USB key is connected, it would install passwords stealing tools, root-kit software, and other disrupting malware. In such a way, an attacker can steal sensitive information via the USB-connected devices, as well as inject any kind of malicious software into the host. To thwart the above-cited raising threats, we propose MAGNETO, an efficient, non-interactive, and privacy-preserving framework to verify the authenticity of a USB flash drive, rooted in the analysis of its unintentional magnetic emissions. We show that the magnetic emissions radiated during boot operations on a specific host are unique for each device, and sufficient to uniquely fingerprint both the brand and the model of the USB flash drive, or the specific USB device, depending on the used equipment. Our investigation on 59 different USB flash drives---belonging to 17 brands, including the top brands purchased on Amazon in mid-2019---, reveals a minimum classification accuracy of 98.2% in the identification of both brand and model, accompanied by a negligible time and computational overhead. MAGNETO can also identify the specific USB Flash drive, with a minimum classification accuracy of 91.2%. Overall, MAGNETO proves that unintentional magnetic emissions can be considered as a viable and reliable means to fingerprint read-only USB flash drives. Finally, future research directions in this domain are also discussed.
Accelerator-Aware Fast Spatial Feature Network for Real-Time Semantic Segmentation

Semantic segmentation is performed to understand an image at the pixel level; it is widely used in the field of autonomous driving. In recent years, deep neural networks achieve good accuracy performance; however, there exist few models that have a good trade-off between high accuracy and low inference time. In this paper, we propose a fast spatial feature network (FSFNet), an optimized lightweight semantic segmentation model using an accelerator, offering high performance as well as faster inference speed than current methods. FSFNet employs the FSF and MRA modules. The FSF module has three different types of subset modules to extract spatial features efficiently. They are designed in consideration of the size of the spatial domain. The multi-resolution aggregation module combines features that are extracted at different resolutions to reconstruct the segmentation image accurately. Our approach is able to run at over 203 FPS at full resolution (1024×2048) in a single NVIDIA 1080Ti GPU, and obtains a result of 69.13% mIoU on the Cityscapes test dataset. Compared with existing models in real-time semantic segmentation, our proposed model retains remarkable accuracy while having high FPS that is over 30% faster than the state-of-the-art model. The experimental results proved that our model is an ideal approach for the Cityscapes dataset. The code is publicly available at: https://github.com/computervision8/FSFNet.