Reviewerly/exHarmony huggingface dataset:
Assessing the Usage of Library Electronic Resources by Postgraduate Students at the University of Ghana Balme Library Research Common

This study aims to examine the extent of use of library electronic resources by postgraduate students at the University of Ghana Balme Library Research Commons to determine their level of awareness, the purpose of use, the training received, and the challenges of using the electronic resources.The target population was the postgraduate students who were using the Research Commons of the Balme Library during the study.A mixed-method was adopted for the study, questionnaires and interviews were the main instruments used for data collection.The findings of the study revealed that the respondents mostly use electronic resources, especially the institutional repository (UGspace) as their first point of search for online journals.There was a high level of awareness of the electronic resources through library training programs and fresher's orientations.The electronic resources were purposely used by the postgraduate students for their research, dissertation, assignment, and literature that support their studies.The major source of electronic resources training was the library.Some challenges such as many irrelevant search results, inability to identify the best resources, and too much time to identify resources were identified.It is recommended that information literacy skills training needs to be intensified to improve the usage of electronic resources.
Management of educational programs of a regional university: evaluation of efficiency and specifics of development

The article presents a model of an educational program from the point of view of its consumers and organizers; the contingent of undergraduate students studying on a budgetary and contract basis in state and non-state organizations of Russia was analyzed; methods for evaluating the effectiveness of educational programs of regional universities are presented.
IMPACTOS DA PANDEMIA DE COVID-19 NA SAÚDE MENTAL DOS IDOSOS

OBJECTIVE: Covid-19 caused by SARS-CoV-2 began in Wuhan, China and in a short time spread across the world, becoming a pandemic. With this problematization, several governments adopted policies such as social isolation in an effort to reduce infection rates, and limiting the spread of the disease, especially in populations at greater risk, such as older adults. This study aimed to evaluate the impact of the pandemic on the mental health of older adults. METHODS: A quantitative, cross-sectional, analytical observational study was carried out, with a population of older adults (over 65 years old), from the city of São Paulo - SP, Brazil; through an online questionnaire (Google Forms®) guided by the Patient Health Questionnaire-9 (PHQ- 9), Geriatric Depression Scale (GDS), Geriatric Anxiety Inventory (GAI), and the Beck Anxiety Scale (BAI). In the statistical analysis, the Mann Whitney test was used, considering p&lt;0.05. RESULTS: A total of 208 older people answered the questionnaire, 33% men and 67% women, of whom 30% lived alone. There was a statistically significant association between sex and feeling alone (p= 0.015) and in considering that professional help, even if virtual, would minimize the effects of social isolation (p=0.030), with evidence of this relationship among women. CONCLUSION: It is concluded that the older population deserves attention with regard to mental health care during the pandemic, due to the consequences of social isolation. It is inferred that older adults have a critical sense of their own mental health, signaling the need for social and clinical assistance.
SPIKE: secure and private investigation of the kidney exchange problem

The kidney exchange problem (KEP) addresses the matching of patients in need for a replacement organ with compatible living donors. Ideally many medical institutions should participate in a matching program to increase the chance for successful matches. However, to fulfill legal requirements current systems use complicated policy-based data protection mechanisms that effectively exclude smaller medical facilities to participate. Employing secure multi-party computation (MPC) techniques provides a technical way to satisfy data protection requirements for highly sensitive personal health information while simultaneously reducing the regulatory burdens.We have designed, implemented, and benchmarked SPIKE, a secure MPC-based privacy-preserving KEP protocol which computes a locally optimal solution by finding matching donor-recipient pairs in a graph structure. SPIKE matches 40 pairs in cycles of length 2 in less than 4 min and outperforms the previous state-of-the-art protocol by a factor of [Formula: see text] in runtime while providing medically more robust solutions.We show how to solve the KEP in a robust and privacy-preserving manner achieving significantly more practical performance than the current state-of-the-art (Breuer et al., WPES'20 and CODASPY'22). The usage of MPC techniques fulfills many data protection requirements on a technical level, allowing smaller health care providers to directly participate in a kidney exchange with reduced legal processes. As sensitive data are not leaving the institutions' network boundaries, the patient data underlie a higher level of protection than in the currently employed (centralized) systems. Furthermore, due to reduced legal barriers, the proposed decentralized system might be simpler to implement in a transnational, intereuropean setting with mixed (national) data protecion laws.
Weak PassPoint Passwords Detected by the Perimeter of Delaunay Triangles

PassPoint is a graphical authentication technique that is based on the selection of five points in an image. A detected vulnerability lies in the possible existence of a pattern in the points that make up the password. The objective of this work is to detect nonrandom graphical passwords in the PassPoint scenario. A spatial randomness test based on the average of Delaunay triangles’ perimeter is proposed, given the ineffectiveness of the classic tests in this scenario, which only consists of five points. A state-of-the-art of various applications of Voronoi polygons and Delaunay triangulations are presented to detect clustered and regular patterns. The distributions of the averages of the triangles’ perimeters in the PassPoint scenario for various sizes of images are disclosed, which were unknown. The test’s decision criterion was constructed from one of the best distributions to which the data were adjusted. Type I and type II errors were estimated, and it was concluded that the proposed test could detect clustered and regular graphical passwords in PassPoint, therefore being more effective in detecting clustering than regularity.
Determination of support vector regression parameters using African buffalo optimization algorithm

&lt;span lang="EN-US"&gt;The use of support vector regression (SVR) for regression tasks has been on increase over the past few years. Unfortunately, the practical application of SVR for regression task is limited due to its dependence on proper setting of its hyper-parameters and associated kernel parameter. Therefore, it become imperative to device a reliable and fast mechanism of determining the value of these parameters that could guarantee lowest generalization error. This paper presents SVR parameter optimization approaches using African buffalo optimisation (ABO) algorithm, i.e. SVR-ABO. The SVR parameters are optimized by using African buffalo optimisation algorithm. Results obtained from several experiments performed has shown that the proposed ABO algorithm has the capability of determining SVR hyper-parameters which most of time has to be done through estimation.&lt;/span&gt;
Intelligent Reading of English Text Based on the Generative Model Constraint Label Fusion

The intelligent reading of English text is affected by complex environmental factors, which will result in low reading accuracy and poor reader experience. Based on the artificial intelligence model, this study constructs the artificial intelligence English text reading model by using the generative model constraint label, which helps to improve the intelligence of the English text reading effect. This study also designs a multigraph label fusion algorithm based on generative model constraints. By making full use of the prior knowledge of multiple graphs, the result of fusion graph segmentation is achieved. Moreover, this study also uses the combination of two algorithms, namely, the combination of GMM and MRF, to express the spatial correlation of local statistical features and image pixels in a comprehensive and all-round way. Another model design also includes a series of joint distributions of the learning data for the construction of the image energy function, and the conditional probability distribution is used as the model for prediction. At the end of the study, another variable control experiment is carried out to analyze the performance of the model and the accuracy of the model in English text recognition and classification is studied and counted. The research results show that the intelligent reading model constructed based on this study can meet the needs of the actual situation.
Towards a new method of estimating the student attention based on the eye gaze

This paper presents a new system for automating the monitoring and estimation of student attention during the course session. The followed approach is based on the analysis of the student's gaze to predict his state of attention. A simple hardware device consisting of a camera and a pc was used in this study. Existing machine learning algorithms were used for the student gaze estimation. The principles of homography were used to ensure the transformation from an image coordinates system to a real-world coordinates system. 5 students took part in this experiment and whose gaze was detected and analyzed during 10 minutes of the class session in order to analyze their states of attention and inattention.
Virtual tools in distance education: university satisfaction regarding its application as part of teaching strategies

When virtual education was implemented in Peru, the limitations of teachers in technological management were evident. For this reason, the research seeks to analyze the perception of university satisfaction regarding the use of virtual teacher tools as part of teaching strategies, in order to improve virtual teaching-learning, achieving student motivation and facilitating this meaningful learning through the use of virtual tools. The method used according to the investigative approach is qualitative, according to its scope it is descriptive and correlational. During the development of the research, it was identified that the satisfaction regarding the use of virtual tools by the teacher is focused on the critical, constructive and positive attitude towards virtual tools and in the acquittal of students' questions regarding the use of virtual tools. On the other hand, the indicator that is related to low student satisfaction focuses on the low diversity of methodological strategies used for the development of virtual learning sessions. Likewise, the Chi-square test shows the significant relationship between the perception of the teacher's competences regarding the use of virtual tools and the perception of the quality of the teaching offered to students during distance education.
Communication, culture, competency, and stakeholder that contribute to requirement elicitation effectiveness

&lt;span lang="EN-US"&gt;In the context of software development, requirement engineering is one of the crucial phases that leads to software project success or failure. According to several disruptive changes in the software engineering landscape as well as the world’s challenge of virus pandemic, the provision of practical and innovative software applications is required. Therefore, issues resolution in requirement elicitation is potentially one of the key success factors resulting in enhanced quality of system requirement. The authors have striven to create new ways of requirement elicitation according to factor effects of communication, culture, competency, and stakeholder, by incorporating tools, processes, methods, and techniques to solve the problems comprehensively, and then proposed an adaptive and applicable conceptual framework. To illustrate these effects, the authors performed a literature review from the past 8 years, and then data analysis from interviews of 27 practitioners, observations and focus groups of software development in real-life projects.&lt;/span&gt;
Forecasting movie rating using k-nearest neighbor based collaborative filtering

&lt;p&gt;&lt;span lang="EN-US"&gt;Expressing reviews in the form of sentiments or ratings for item used or movie seen is the part of human habit. These reviews are easily available on different social websites. Based on interest pattern of a user, it is important to recommend him the items. Recommendation system is playing a vital role in everyone’s life as demand of recommendation for user’s interest increasing day by day. Movie recommendation system based on available ratings for a movie has become interesting part for new users. Till today, a lot many recommendation systems are designed using several machine learning algorithms. Still, sparsity problems, cold start problem, scalability, grey sheep problem are the hurdles for the recommendation systems that must be resolved using hybrid algorithms. We proposed in this paper, a movie rating system using a k-nearest neighbor (KNN-based) collaborative filtering (CF) approach. We compared user’s ratings for different movies to get top K users. Then we have used this top K set to find missing ratings by user for a movie using CF. Our proposed system when evaluated for various criteria shows promising results for movie recommendations compared with existing systems.&lt;/span&gt;&lt;/p&gt;
Selective local binary pattern with convolutional neural network for facial expression recognition

&lt;span lang="EN-US"&gt;Variation in images in terms of head pose and illumination is a challenge in facial expression recognition. This research presents a hybrid approach that combines the conventional and deep learning, to improve facial expression recognition performance and aims to solve the challenge. We propose a selective local binary pattern (SLBP) method to obtain a more stable image representation fed to the learning process in convolutional neural network (CNN). In the preprocessing stage, we use adaptive gamma transformation to reduce illumination variability. The proposed SLBP selects the discriminant features in facial images with head pose variation using the median-based standard deviation of local binary pattern images. We experimented on the Karolinska directed emotional faces (KDEF) dataset containing thousands of images with variations in head pose and illumination and Japanese female facial expression (JAFFE) dataset containing seven facial expressions of Japanese females’ frontal faces. The experiments show that the proposed method is superior compared to the other related approaches with an accuracy of 92.21% on KDEF dataset and 94.28% on JAFFE dataset.&lt;/span&gt;
Behavicker: Eavesdropping Computer-Usage Activities through Acoustic Side Channel

Computers are widely used for business and entertainment purposes throughout our modern lives. Computer kits provide a variety of services such as text processing, programming, shopping, and gaming. Computers have greatly enhanced the quality of our lives; however, we discover an often-overlooked fact that engaging in computer-related activities may be eavesdropped upon by an attacker by sniffing the emitted acoustic signals from keyboard and mouse. The activity of eavesdropping via acoustic side channel has lower requirements in terms of hardware instrumentation and is easier to implement in real-world applications than other side channel attacks that have been presented in previous work. In this paper, we design and implement a system, namely, Behavicker, to validate the feasibility of this kind of attack. Unlike conventional activity recognition, Behavicker infers high-level computer-usage activities with a semantics-preserving multiscale learning scheme, based on the recognition of basic keyboard and mouse events including left click, right click, middle click, scrolling up, and scrolling down. Real-world experiments show that Behavicker can recognize six interaction events with an accuracy of 88.3% and infer computer-usage activities with an accuracy of 82.7% in an indoor environment.
The Role of the Regional Economic Communities and their Strategies to Promote and Uphold Credible and Legitimate Electoral Processes in Africa

Electoral malpractices in Africa are multifaceted, intertwined, and complex. Most African countries have held regular elections, albeit often flawed and contested. Elections have been contested because of structural problems within States and elections have become highly competitive. The rise of authoritarian democracies where most elections serve as a means of survival for African leaders determined to remain in power has resulted in disputed elections. The shrinking political spaces within States has led to a slide towards authoritarian democracy. The capture and neutralisation of democracy-enhancing institutions has caused vote-rigging, voter intimidation, and manipulation of vote results to mention a few. The phenomena of third termism and increasing prospects of life presidencies have been common causes of disputed elections. The use of constitutional and soft coups to retain power has often resulted in contested elections. These and many other structural issues have affected the credibility and legitimacy of elections in Africa mostly. The Regional Economic Communities (RECs) have assumed a role to address these challenges. They have adopted legal instruments to promote democratic elections. They are involved in observing elections (pre- and post-electoral audits), mediation, political negotiations, and dialogues. In some cases, they have imposed sanctions on States violating constitutional order to protect democracy within States. This article discusses how the RECs have dealt with election disputes in the past decades, and their strategies to promote and uphold credible and legitimate electoral processes in their regions. This article concludes by highlighting the challenges of electoral democracy within the RECs, and what they could do better to promote credible and legitimate elections in their regions.
Improving Students’ Motivation and Reading Ability in Learning Narrative Text using Problem-Based Learning

This study is conducted because the author finds out that most of his students have a low reading ability and motivation in learning Narrative text. This study aims to improve students’ motivation and reading ability in learning Narrative text by applying the Problem-based Learning model in the 9C grade student s of SMP N 1 Watukumpul. Data collection techniques used were observation, and testing. Data analysis techniques used comparative descriptive techniques and critical analysis. The conclusions of this study were Problem-based Learning can improve students’ learning motivation and their reading ability in learning narrative text. The average score of motivation obtained in the first cycle of action amounted to 59,25 and 76,125 in the second cycle. While the average of student reading ability in learning narrative text in pre-action amounted to 58, in the first cycle amounted to 68 and 79 in the second cycle. From the average score of students’ motivation and students’ reading ability, it can be concluded that Problem-based Learning can improve students’ motivation and reading ability in learning Narrative text.
BOUNDS FOR THE MINIMUM VERTEX-CONNECTIVITY ENERGY OF A GRAPH.

In this paper, we put forward the new variant of graph energy namely, the minimum vertex connectivity energy of a graph.Further, we have obtained some bounds for this newly introduced parameter.
DAMPAK PERILAKU KECANDUAN GAME ONLINE PLAYER UNKNOWN’S BATTLE GROUND (PUBG) TERHADAP PRESTASI BELAJAR KOGNITIF PESERTA DIDIK PADA MATERI LAJU REAKSI KELAS XI IPA DI SMA YAPIS MANOKWARI

Abstract Activity of playing is the Player Unknown's Battle Ground (PUBG) online game is done excessively with outrealizing it canimpact students' low learning achievement, especially chemistry subjects, which are considere ddifficult and tedious. The purpose of this study was to determine the effect of addiction behavior playing online games PUBG (Player Unknown's Battle Ground) on cognitive chemistry learning achievement of students at Yapis Manokwari Senior High School, to determine the relationship between addiction behavior playing PUBG online games (Player Unknown's Battle Ground) and cognitive learning achievement. Chemistry of students at Yapis Manokwari High School and the impact of addictive behavior on playing the Player Unknown's Battle Ground (PUBG)online game on students' chemistry learning achievement at Yapis Manokwari High School. This research uses quantitative methods with a descriptive approach. This study indicates that there is an influence of addictive behavior to play The Player Unknown's Battle Ground (PUBG) online game on the cognitive chemistry learning achievement of students at Yapis Manokwari High School. The effect was 43.8%, while the relationship between addiction behavior to play online games PUBG (Player Unknown's Battle Ground) and chemistry learning achievement was strongly correlated or correlated with 0.67.Keywords: Quantitative methods, Learning achievement, Game’s, Addiction
THE CAPABILITY OF SENTINEL 1 (SAR) FOR FLOOD MAPPING: A CASE STUDY IN SERANG WATERSHED, KULONPROGO REGENCY

Floods are the most common natural disaster in Indonesia, with high intensity than any other natural disaster. A flood is a condition where an area is inundated due to an overflow of water that exceeds the water disposal capacity in a room, resulting in physical, social, and economic losses. The Serang watershed (DAS) belongs in the Priority I (critical) watershed condition, so it is necessary to determine flood-prone areas in future management. Sentinel 1 Remote Sensing Image (SAR) can record at any time, day or night, and in all weather conditions, making it suitable for flood analysis. This study aimed to determine the ability of Sentinel 1 Image (SAR) in determining the inundation flood area in Serang watershed, Kulonprogo Regency, Indonesia. The method used was the Otsu algorithm with threshold determination by measuring and evaluating the variance between classes of a threshold at a certain level calculated from the normalized histogram of the image. Floods in the Serang watershed, Kulonprogo Regency, mainly occur in agricultural areas. The ability of Sentinel images to obtain land surface data in all conditions can be used for flood analysis where passive sensor images cannot record it. In addition, the withdrawal of inundation areas on Sentinel 1 imagery using the Otsu algorithm can determine a threshold to separate inundated and unflooded areas with a confusion matrix of 77%.Keywords: Flood, Sentinel 1 (SAR), Otsu Algorithm