Reviewerly/exHarmony huggingface dataset:
Online Communitarian Appeals Increase Opposition to Violent Extremism

Governments and NGOs have implemented hundreds of countering violent extremism (CVE) programs designed to combat radicalization and reduce support for violent extremist organizations (VEOs). However, the effectiveness of these programs is rarely evaluated rigorously, providing little guidance to scholars or practitioners. We therefore pair a large-scale Facebook experiment (39.8 million impressions) with an online survey experiment (N=7,761 respondents) to compare the effectiveness of three brief videos designed with local partners to reduce support for violent extremism and VEOs in Bangladesh while minimizing risk to participants. We find that a message of communal harmony from religious authorities received greater Facebook engagement and more frequent supportive emojis than messages stating that all religions oppose terrorism or warning about the costs of terrorism. We test the robustness of these messages by randomly exposing respondents to brief footage of an anti-government protest organized by a local VEO. Communal harmony was the only message that managed to reduce net opposition to extremism after this counter-programming was introduced. An exploratory analysis further reveals that the messages do not affect intentions to attend VEO events or share VEO messages online. Instead, they increase intentions to oppose VEOs by sharing messages or attending rallies by mainstream or anti-extremist groups. These results highlight the value of communitarian appeals by religious authorities as well as the need for more experimental, multi-platform evaluations of CVE messaging that separately measure VEO support and opposition.
ASM Sci. J., Vol. 1, No. 1, 2007

The ASM Science Journal (ASM Sc. J.) is intended to be one of the primary vehicles of the Academy of Sciences Malaysiato stimulate and nurture excellence in science and technology for the development of the nation and for the benefit ofmankind. Its purpose is also to widely disseminate significant research developments and deliberate on R&amp;D strategiesand policies. It will further provide a major opportunity for showcasing the effectiveness of scientific and technical effortsand increasing the efficiency of management of research and development which is linked with the ability to communicateinformation about current research efforts and the results of past work. This journal will pay attention to the importanceof scientific and technical endeavour and contribute to the better understanding of problems of science and technologythrough its regular publication and wide circulation, thus promoting cooperation and exchange of views among the sciencecommunity worldwide. All these objectives are fully in line with the aims and objectives of the Academy.In this maiden issue, there are contributions from many fields covering both fundamental and applied research: medicine,engineering, chemistry, electronics, mechanics and physics. Our future plan is to gear one issue a year towards a particulartheme. The papers in that issue will address current and impending issues, and the demands and needs of the scientificcommunity and society pertaining to the selected theme.It is hoped that this varied, vital and vigorous exchange of scientific information that the Journal provides will assist inbetter understanding the various forces and factors that affect the progress of humanity and safeguard its survival. TheAcademy of Sciences Malaysia, therefore, in earnestly discharging its responsibility, proudly associates itself with thisJournal in striving to promote an international exchange of scientific information.
Current Areas of Activity of the Adviser to the Director for Education and Interaction with Childrenʼs Public Associations

Modern challenges to the implementation of education in a general education organization have led to the emergence of a new position – adviser to the director for education and interaction with childrenʼs public associations. Relevant is the scientific understanding and justification of the activities of the education adviser to identify key competencies, functions and areas of his work, in order to subsequently ensure high-quality training of these specialists. The purpose of the article is to characterize the features of the current areas of work of the adviser to the director for education and interaction with childrenʼs public associations. At the moment, in the field of activity of an adviser in education, such issues are being investigated as: socio-pedagogical technologies of interaction between an adviser and childrenʼs public associations, a competence model of an adviserʼs activity, a methodology for researching the activity of advisers, an adviserʼs work with the parent community, the strategic status of an adviser, a study of the professionalism of advisers, environmental direction in the adviserʼs work, etc. In our opinion, the priority directions of the adviserʼs work are: interaction with childrenʼs and youth public associations, interaction with parents of students, involvement of children in socially significant activities. The article also reveals the main types of pedagogical activity carried out by the adviser: prognostic, design, organizational, communicative, coordinating and reflective.
Functional Literacy as a Pedagogical Problem

The article discusses the content and correlation of the concepts of “literacyˮ, “functional literacyˮ. The issues of the evolution of the concept of “functional literacyˮ in pedagogical theory and practice at various stages of social development, the expansion of the concept of “literacyˮ to the concept of “functional literacyˮ in connection with the growing demands of society to the educational process, the heterogeneity of the perception of the concept of “functional literacyˮ by various researchers are touched upon. Attention is focused on the role of the school in the formation of a functionally literate personality. The author comes to the reasonable conclusion that in modern pedagogy functional literacy is considered as the basis for the manifestation of adaptive abilities to the conditions of society by a person and the expansion of the range of demand in various fields of activity. The purpose of the article is to consider the problem of functional literacy in the context of pedagogical sciences.
Immersive Theatricalization: Art-Pedagogical Technology in Professional Education

The article examines new opportunities and risks in modern professional education associated with digitalization, as well as the variability of the introduction of immersive theatricalization as part of art-pedagogical activity and its potential from the point of view of preserving the humanistic orientation of education. Immersiveness is presented in the article as an independent technology that includes elements of theatricalization, play and reflection, and provides interactivity in the learning process. The advantages of using immersive theatricalization in professional education are considered: activity in an educational and professional environment as close as possible to the real activity of a future specialist, increased motivation and development of professional and personal capabilities, in particular pedagogical artistry, communicative qualities and organizational abilities. Promising directions for the introduction of immersive theatricalization are identified: special training of teachers, correlations objective competence and subjective assessment of students’ achievements, development of psychophysiological, emotional and cognitive capabilities of future specialists.
A Multi-Objective Optimization Problem on Evacuating 2 Robots from the Disk in the Face-to-Face Model; Trade-Offs between Worst-Case and Average-Case Analysis

&lt;p&gt;The problem of evacuating two robots from the disk in the face-to-face model was first introduced by Czyzowicz et al. [DISC’2014], and has been extensively studied (along with many variations) ever since with respect to worst-case analysis. We initiate the study of the same problem with respect to average-case analysis, which is also equivalent to designing randomized algorithms for the problem. In particular, we introduce constrained optimization problem &lt;sub&gt;2&lt;/sub&gt;Evac&lt;sub&gt;𝐹2𝐹&lt;/sub&gt;, in which one is trying to minimize the average-case cost of the evacuation algorithm given that the worst-case cost does not exceed &lt;em&gt;w&lt;/em&gt;. The problem is of special interest with respect to practical applications, since a common objective in search-and-rescue operations is to minimize the average completion time, given that a certain worst-case threshold is not exceeded, e.g., for safety or limited energy reasons. Our main contribution is the design and analysis of families of new evacuation parameterized algorithms which can solve &lt;sub&gt;2&lt;/sub&gt;Evac&lt;sub&gt;𝐹2𝐹&lt;/sub&gt;, for every &lt;em&gt;w&lt;/em&gt; for which the problem is feasible. Notably, the worst-case analysis of the problem, since its introduction, has been relying on technical numerical, computer-assisted calculations, following tedious robot trajectory analysis. Part of our contribution is a novel systematic procedure, which given &lt;em&gt;any evacuation algorithm&lt;/em&gt;, can derive its worst- and average-case performance in a clean and unified way. &lt;/p&gt;
Sentiment Analysis on Cryptocurrency Tweets Using Machine Learning

Cryptocurrency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Many investors believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of sentiment analysis and emotion for cryptocurrencies. For the study, we targeted (BTC) Bitcoin and collected related data. The data collection, cleaning were essential components of the study. Analysis of the sentiments about cryptocurrency is highly desirable to provide a holistic view of peoples' perceptions. In this regard, this study performs both sentiment analysis and emotion detection using the tweets related to the Bitcoin which are widely used for predicting the market prices of cryptocurrency. For increasing the efficacy of the analysis, a deep learning ensemble model LSTM-GRU is proposed that combines two recurrent neural networks applications. Comparatively, a larger number of people feel happy with the use of cryptocurrency, followed by fear and surprise emotions. The model achieves the highest performance for sentiment analysis with a 0.91 accuracy score and the highest emotion 0.83. Similarly, LSTM-GRU outperforms all other models in terms of correct and wrong predictions for both sentiment analysis 0.99 and emotion detection 0.98. Key Words: bitcoin, sentiment analysis, machine learning, cryptocurrencies, tweets
EVALUATION OF THE HELIOSAT-4 AND MCCLEAR MODELS FOR SOLAR GLOBALIRRADIATION ESTIMATE AT TWO SITES IN ARGENTINA

Satellite-derived solar radiation information is necessary for assessing the financial risk of solar energy projects at arbitrary locations. These estimates must be evaluated against high-quality ground measurements to ensure the reliability of the information. In this work, 15-minute Global Horizontal Irradiance (GHI) data from the Heliosat-4 model for all-sky conditions and the McClear model for clear-sky conditions were evaluated against ground-based measurements at two Saver-Net stations (http: //www.savernetsatreps.org): Pilar (Córdoba) and Villa Martelli (Buenos Aires). Clear sky detection of the measurements was done using the modified clarity index and analyzing the variability of consecutive measurements. A bias close to -5.5 % was found for the Heliosat-4 model at both sites and 1.15 % and -0.73 % for the MCClear model in Pilar and Villa Martelli, respectively. Furthermore, the relative root mean square error (rRMSD) was2 % and 20 % for the McClear and Heliosat-4 models, respectively at both sites.
Gamification and ubiquitous learning in higher education: applying learning styles

Education has gained increasing importance in the current context. Both teachers and students face difficulties due to the lack of knowledge in the use of technological tools and good practices of this type of learning. With the aim of improving academic performance and motivation in the learning process, an investigation was carried out to evaluate the effectiveness of gamification and ubiquitous learning in higher education, taking into account the learning styles of students. The methodology used was Design-Based Research, using a quantitative approach and a convenience sample made up of several faculties of the Autonomous University of Queretaro, Mexico. Likert scale surveys were conducted before and after implementing gamification and ubiquitous learning practices. Subsequently, an analysis was performed using the T test for paired samples. The results showed a significant difference before and after the implementation of these practices, which supports the idea that students improved their academic performance and motivation in the teachinglearning process. The research concluded that the effectiveness of gamification and ubiquitous learning in higher education was due to the fact that a predominant learning style was considered in each group, thereby enabling the formulation of strategies to enhance students’ academic performance and motivation in the learning process.
DYNAMIC EVALUATION AND TEST OF ORALS: DIGITAL EFFICACY ON UNIVERSITY UNDERGRADUATES’ PERFORMANCE IN SOUTHWEST NIGERIA

This paper presents a study exploring the efficacy of customised software, dynamic evaluation and Test of Orals (TEO) on the performance of university undergraduates in Southwest Nigeria. A multistage sampling procedure was used in selecting 200 students from two public universities in Southwest Nigeria. A quasi-experimental design and descriptive survey-type research design were used to investigate the impact of customised software ( Kollit ) on 200 students randomly selected for the study. Two hypotheses were raised. The instrument for data collection was the Students' Oral English Proficiency Test (SOEPT). The Cronbach Alfa method was used to establish the reliability coefficient for SOEPT, which was found to be 0.68. A One-Way Analysis of Variance was utilised to test the hypotheses at a 0.05 significance level. The study results indicated a significant increase in academic performance on the part of the students after implementing the customised software. The findings suggested that the customised software could improve the performance and evaluation of students in the Test of Orals. The study concludes by discussing the potential limitations of the study while offering recommendations for future research
Ledger of Trust: Investigating Blockchain’s Impact on Credit Business Efficiency and Security

&lt;p&gt;The integration of blockchain technology in financial sectors has emerged as a topic of burgeoning interest, with credit business being a pivotal area of exploration. This study aims to investigate the impact of blockchain technology on the operational efficiency and security of credit business processes. Employing a mixed-methods research design, data was collected from 15 credit institutions that have adopted blockchain technology, alongside a control group of 15 credit institutions operating on traditional digital platforms. Key performance indicators (KPIs) such as transaction speed, error rate, fraud incidence, and operational costs were analyzed. The findings reveal a significant enhancement in operational efficiency and security in credit institutions leveraging blockchain technology. Blockchain adoption was associated with a 70 % reduction in fraudulent activities, a 50 % increase in transaction speed, and a 40 % reduction in operational costs. Furthermore, the decentralized nature of blockchain significantly enhanced transparency and trust among stakeholders, fostering a more robust and resilient credit business ecosystem. The study underscores the transformative potential of blockchain technology in redefining the operational paradigms of credit businesses, thereby contributing to the broader discourse on blockchain’s applicability in financial sectors. Future research is recommended to explore the long-term sustainability and regulatory implications of blockchain integration in credit business operations. &lt;/p&gt;
Real-Time and Approximate Iterative Optical Flow Implementation on Low-Power Embedded CPUs

Optical flow estimation is used in many embedded computer vision applications, and it is known to be computationally intensive. In the literature, many methods exist to estimate optical flow. Thus, the challenge is to find a method that matches the applicative constraints. In an embedded system, a trade-off between power consumption and execution time has to be made to meet both energy and framerate constraints. This work proposes methods to implement an approximate Horn & Schunck optical flow estimation that meets embedded CPUs constraints. This is achieved thanks to architectural optimizations, software optimizations and algorithm tuning. For instance, on the NVIDIA Jetson Nano, and for HD video sequences, the achieved frame latency is 12 ms for 5 Watts. To the best of our knowledge, this is the fastest optical flow implementation on embedded CPUs.
RDF named graphs

With the Resource Description Framework (RDF) you can represent Linked Data as subject-predicate-object triples. But what if you have to represent additional context of those triples? In this Blog post I will briefly introduce the advanced data management feature “named graphs” with two examples: different data source named graphs in the BELTRANS project and different story-contexts for Digital Humanities research.
Mise En Place Efficiency in the Kitchen of 18th Restaurant, the Trans Luxury Hotel

This study aims to examine the efficiency of mise en place in the kitchen of the 18th restaurant, located in The Trans Luxury Hotel. Mise en place is a systematic and organized preparation practice carried out before cooking, involving the collection, preparation, and arrangement of all ingredients, equipment, and tools required during the cooking process. Efficiency in mise en place is crucial in achieving optimal performance in a restaurant kitchen, including time savings, increased productivity, and reduced errors.The research methodology utilized direct observation and interviews with kitchen staff at the 18th Restaurant. Data collection encompassed preparation time, efficiency of arrangement, equipment usage, and adherence to established mise en place procedures. Data analysis involved comparing actual preparation time with predetermined target times. The findings indicate that the implementation of mise en place in the kitchen of the 18th Restaurant still has some inefficiencies. Several factors influencing mise en place efficiency include a lack of coordination among employees, unavailability of required ingredients or equipment, and a lack of understanding of established procedures. Recommendations to enhance mise en place efficiency in the kitchen of the 18th Restaurant include improving training and understanding among employees regarding mise en place procedures, ensuring the availability of adequate equipment, and enhancing coordination among the kitchen team. Additionally, the implementation of supervision and control systems is necessary to ensure compliance with established mise en place procedures. This study provides a better understanding of the importance of mise en place efficiency in a restaurant kitchen and offers practical recommendations to enhance mise en place performance in the 18th Restaurant of The Trans Luxury Hotel. The findings can serve as a guide for restaurant management in optimizing preparation and organization in the kitchen, thereby improving operational efficiency and the quality of service provided to hotel guests.
Analysis of psychological characteristics of tourists

The article analyzes the psychological characteristics of a person in the organization of leisure and recreation, the main features of tourism as an experience of moving in space from a place of permanent residence, the behavior of tourists through the choice of a place of travel. Independence of tourists of different categories. the positions are compared depending on the features of the choice of mountain, beach and historical and cultural centers. In the system of reasons and psychological functions of the choice of certain types of tourism, an individual psychological analysis of various groups of tourists (mountain tourists, historical and cultural tourists, beach tourists) is carried out, the internal psychological goals of tour operators and the positions of external objects of tourist activity, justified in geographical space relative to the time of rest, are considered. The purpose of the article is to identify the psychological characteristics of people using various types of tourism, and to analyze the functions of leisure when evaluating tourist destinations.
Survey on Imbalanced Dataset Classification—Machine Learning

Algorithms of Machine Learning (ML) are most widely used to process imbalanced datasets with accurate patterns. Classification of unbalanced data is a significant research area in ML to overcome various issues. This can be very useful in real-time applications such as disease prediction, sentiment analysis, and pattern recognition. It is very difficult for traditional algorithms to process huge unbalanced data and this may lead to mismatched results. Nowadays many ensemble algorithms are developed to process unbalanced data by solving several issues. In this chapter, ML algorithms earnestly discuss implementation aspects, and experimental outcomes for each learning, offering a further understanding of their potencies as well as weaknesses. Numerous conventional techniques for class imbalance, such as data sampling and cost-sensitive learning, are relevant in machine learning. The survey deduces an insightful analysis that highpoints innumerable breaks in machine learning based on class-imbalanced data for future research.
Performance Analysis of American Sign Language Using Wavelet Transform and CNN

Sign languages play an important role to bridge the communication gap with hearing-impaired people. A lot of research is carried out to provide efficient, portable, and economical, tools, techniques, and products to make communication smooth, fast, and correct for both static and dynamic sign language recognition. For vision-based sign language recognition, various machine learning algorithms are used. Convolutional Neural Network (CNN) is one such method popularly used. However, the accuracy and speed of recognition are still a matter of concern, especially in dynamic sign language recognition. This paper presents the analysis of the performance of two methods Wavelet Transform and CNN for static American Sign Language (ASL) recognition in terms of (i) the average time of recognition, i.e., processing time for a single sign image, and (ii) accuracy, i.e., recognition rate. This work is carried out on signer-dependent static ASL images. It is observed that using CNN, the accuracy of recognition is 97.30%, and for Wavelet Transform it is 96.20%, indicating a very small difference. However, the average time required for recognition or the processing time for a single image using CNN is 41.2 s of CPU time, and using Wavelet Transform, it is 13.5 s of CPU time, which is almost 67% less than that required using CNN. Thus, compared to CNN, Wavelet Transform can be preferred in applications where timing is a major constraint.