Reviewerly/exHarmony huggingface dataset:
Action Segmentation Through Self-Supervised Video Features and Positional-Encoded Embeddings

Authors:
raw_author_name: Guilherme de A. P. Marques
raw_affiliation_string: Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil
raw_author_name: José Matheus C. Boaro
raw_affiliation_string: Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil
raw_author_name: Antonio José G. Busson
raw_affiliation_string: BTG Pactual, São Paulo, Brazil
raw_author_name: Alan L. V. Guedes
raw_affiliation_string: Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil
raw_author_name: Julio Cesar Duarte
raw_affiliation_string: Military Institute of Engineering (IME), Rio de Janeiro, Brazil
raw_author_name: Sérgio Colcher
raw_affiliation_string: Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil

Abstract: Action segmentation consists of temporally segmenting a video and labeling each segmented interval with a specific action label. In this work, we propose a novel action segmentation method that requires no initial video analysis and no annotated data. Our proposal involves extracting features from videos using several pre-trained deep-learning models, including spatiotemporal and self-supervised methods. Data is then transformed using a positional encoder, and finally, a clustering algorithm is applied, where each produced cluster presumably corresponds to a different single and distinguishable action. For self-supervised features, we explored DINO, and for spatiotemporal features, we investigated I3D and SlowFast methods. Moreover, two different clustering algorithms (FINCH and KMeans) were investigated, and we also explored how varying the length of video snippets that generate the feature vectors affected the quality of the segmentation. Experiments show that our method produces competitive results on the Breakfast and INRIA Instructional Videos dataset benchmarks. Our best result was produced using a composition of self-supervised features generated by DINO, FINCH clustering, and positional encoding.
Design and analysis of adaptive control systems: Adaptive control algorithms using machine learning

Authors:
raw_author_name: Zikai Zhou
raw_affiliation_string: The University of Manchester

Abstract: A growing field of research is the use of adaptive control algorithms with machine learning techniques like Q-learning and SARSA. With prospective applications in robotics, healthcare, and other fields, this interdisciplinary method strives to combine the stability and robustness of conventional control systems with the self-learning skills of reinforcement learning. Making sure that systems are stable and that learning occurs effectively is the main research problem. This paper demonstrates the stability and convergence of existing adaptive control algorithms when integrating with machine learning. There are mainly four primary methods of control algorithms: reinforcement learning, neutral network, support vector machine and deep learning. Reinforcement learning is the main focus of this paper. Data efficiency, robustness, and generalization are the main problems with reinforcement learning. Q-learning and SARSA (State, Action, Reward, State, Action) are two algorithms for reinforcement learning. The research will be done by analyzing these two algorithms based on existing material and the actual application of these two algorithms. SARSA is believed to be more safe as its on-policy methodology, and Q-learning is believed to be more proactive as its off-policy methodology.
Development effects of industrialization, labor force participation and electrification on productive capacity in Africa: A panel data investigation

Authors:
raw_author_name: Abdulrahman Ibrahim
raw_affiliation_string: Department of Economics, University of Nigeria, Nsukka, Nigeria

Abstract: In our study spanning 2010–2022 across 50 African economies, we investigated the effects of industrialization, labor force participation, and electrification on Africa’s productive capacity. We also examined how electrification moderates the effect of industrialization and labor force participation on productive capacity. We adopted the pooled OLS, the fixed effect estimator, and the dynamic system GMM estimation technique for our analysis. Our findings indicate industrialization, labor force participation, and electrification as potent channels for enhancing Africa's productive capacity. In tandem with Africa’s reality, we find that electrification isn’t moderating the positive impact of industrialization and labor force participation on productivity. Our findings also revealed human capital development, government health expenditure, and institutional quality as pivotal drivers of productive capacity in Africa, while factors like global economic policy uncertainty and natural resource endowment exhibit predominantly negative roles. These results underscore the need for African policymakers and leaders to foster an enabling environment that facilitates industrialization, increased labor force participation, and improved electricity access to promote economic performance and productivity.
Effects of Students’ Autonomy Support on their Self-Regulated Learning Strategies: Three Field Experiments in Secondary Education

Authors:
raw_author_name: Wilfried Admiraal
raw_affiliation_string: Oslo Metropolitan Universty
raw_author_name: Ditte Lockhorst
raw_affiliation_string: Oberon Research and Consultancy
raw_author_name: Lysanne Post
raw_affiliation_string: Leiden University
raw_author_name: Liesbeth Kester
raw_affiliation_string: Utrecht University

Abstract: Providing students with autonomy over their learning process can support the development of their self-regulation skills. This study aimed to examine the effects of autonomy-support interventions on students’ self-regulated learning strategies. The participants were 432 students from three secondary schools in the Netherlands. In each school, a one-year field experiment was performed, in which teachers and students shared control over learning processes. At the start and end of the school year, students from both the experimental and control conditions completed a questionnaire on perceived autonomy support and self-regulation strategies used. Univariate analyses of covariance were performed to examine the effects of the interventions on perceived autonomy support and the use of self-regulated learning strategies. The intervention with the highest amount of student control showed positive effects on self-regulated learning strategies ‘task orientation’, ‘planning’, and ‘process evaluation’, although it did show a small decrease in perceived autonomy support. The other two interventions showed an increase in perceived autonomy support and a positive trend in ‘monitoring’. The field experiments suggest that different ways of supporting student autonomy can yield different effects on students’ self-regulated learning strategies. The tentative conclusion is that a focus on student control is the most effective.
Peningkatan Kemampuan Menulis Karangan Narasi Dengan Mengunakan Media Gambar Seri

Authors:
raw_author_name: Resnita Dewi
raw_affiliation_string: Universitas Kristen Indonesia Toraja

Abstract: The purpose of this research is to determine the use of series image media in improving students' skills and motivation in writing narrative essays and to find out the factors that influence students in writing narrative essays using series image media for class IV SDN 010 Pakasassan Mamasa. The research carried out was classroom action research. Type of quantitative descriptive research. Data collection procedures were carried out using interviews, journals and tests. Data analysis techniques use qualitative and quantitative data analysis techniques. The aim of data analysis techniques is to find out in detail how to obtain data and the development of research results. The results of the research found in this study were the students' completeness in learning Indonesian after applying the series of picture media in cycle I. It was found that 6 students or 24% had successfully completed the ability to write narrative essays. Meanwhile, in cycle II it was found that the highest student score was 95 with a score range of 25 and an average score of 86.01 from a total of 25 students. It can be concluded that the use of series image media has a positive influence on learning to write narrative essays.
Numerical solution of the Schrödinger equation using Neural Networks in Python

Authors:
raw_author_name: D. Vlachos
raw_affiliation_string: Department of Economics, University of Peloponnese, Tripoli, Greece

Abstract: Abstract The motion of quantum mechanical systems in physical sciences is described by partial differential equations, usually of second order with respect to spatial coordinates. The required solutions of the time-dependent type of equations are, in general, functions of the temporal variable t and the state vectors determining the positions of the system’s particles at the time t. Only a small number, however of those differential equations can however be solved analytically, while the majority of them must be solved numerically by applying specific very advanced integration techniques. Among these equations, the fundamental Schrödinger equation offers great insight towards numerical solving. In this work we present an effective method that solves numerically the time-independent Schrodinger equation on the basis of neural networks techniques. Analytical and numerical results for the radial part of this equation with the corresponding energies are then compared so as to estimate the performance of our method.
Few-shot Learning for Heterogeneous Information Networks

Authors:
raw_author_name: Yang Fang
raw_affiliation_string: National University of Defense Technology, Changsha, China
raw_author_name: Xiang Zhao
raw_affiliation_string: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China
raw_author_name: Weidong Xiao
raw_affiliation_string: Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, China
raw_author_name: Maarten de Rijke
raw_affiliation_string: University of Amsterdam, Amsterdam, The Netherlands

Abstract: Heterogeneous information networks (HINs) are a key resource in many domain-specific retrieval and recommendation scenarios, and in conversational environments. Current approaches to mining graph data often rely on abundant supervised information. However, supervised signals for graph learning tend to be scarce for a new task and only a handful of labeled nodes may be available. Meta-learning mechanisms are able to harness prior knowledge that can be adapted to new tasks. In this paper, we design a meta-learning framework, called META-HIN , for few-shot learning problems on HINs. To the best of our knowledge, we are among the first to design a unified framework to realize the few-shot learning of HINs and facilitate different downstream tasks across different domains of graphs. Unlike most previous models, which focus on a single task on a single graph, META-HIN is able to deal with different tasks (node classification, link prediction, and anomaly detection are used as examples) across multiple graphs. Subgraphs are sampled to build the support and query set. Before being processed by the meta-learning module, subgraphs are modeled via a structure module to capture structural features. Then, a heterogeneous GNN module is used as the base model to express the features of subgraphs. We also design a GAN-based contrastive learning module that is able to exploit unsupervised information of the subgraphs. In our experiments, we fuse several datasets from multiple domains to verify META-HIN ’s broad applicability in a multiple-graph scenario. META-HIN consistently and significantly outperforms state-of-the-art alternatives on every task and across all datasets that we consider.
The use of blockchain in the insurance industry: A bibliometric analysis

Authors:
raw_author_name: Shorouq Fathi Eletter
raw_affiliation_string: Associate Professor, Business College, Department of Business Administration, Al Ain University, UAE

Abstract: Blockchain technology has surfaced as a formidable catalyst, capable of reshaping the operational dynamics of conventional businesses. The integration of blockchain technology within the insurance industry has prompted a need for comprehensive understanding and assessment. This study aims to elucidate the relevance of blockchain in the insurance sector, identify key trends, influential research, and the overarching goals motivating the incorporation of blockchain in insurance practices. The study employed a systematic review to collect 125 research articles published between 2017 and 2023 focused on blockchain in insurance from the Web of Science database. This study used Biblioshiny. The analysis encompasses a wide range of parameters, including publication trends, influential authors, thematic clusters, and the geographic distribution of research. IEEE Access emerges as a prominent platform, hosting 11 articles dedicated to this topic. The results of keyword co-occurrence highlighted two keywords: blockchain and insurance. The results of the country collaboration map highlighted particular shortcomings, such as the concentration of research in countries like China, the USA, India, KSA, Malaysia, and Australia. A lack of research in developing and underdeveloped countries and insufficient knowledge sharing among researchers highlights a research gap. In conclusion, the analysis offers valuable insights into the evolving landscape of blockchain within the insurance industry.
Professional Training of a Future Teacher for the Organization of Pupils' Innovative Activity in the Educational Process

Authors:
raw_author_name: Zhanna Petrochko
raw_affiliation_string: Borys Grinchenko Kyiv Metropolitan University, Bulvarno-Kudriavska, 18/2,04053, Kyiv, Ukraine
raw_author_name: Chen Liang
raw_affiliation_string: Dragomanov Ukrainian State University, Pyrohova str., 9, Kyiv, Ukraine
raw_author_name: Viktoria Ostapchuk
raw_affiliation_string: Dragomanov Ukrainian State University, Pyrohova str., 9, Kyiv, Ukraine

Abstract: The study concept is based on the awareness of the process of training of a future physics teacher for the organization of pupils' innovative activity as an important component of modern professional education, which makes it possible to form a teacher as a competitive specialist capable of creative pedagogical work. The main study method was statistical methods: the methods of testing statistical hypotheses using Pearson, Wilcoxon-Mann-Whitney and binomial tests, the method of empirical data analysis using convolution matrices, the methods of visualizing experimental results. Four universities of Ukraine were the base for the formative pedagogical experiment: Zaporizhzhya National University, Berdyansk State Pedagogical University, Pavlo Tychyna Uman Pedagogical University, and Kherson State University. The measurement and comparison of the levels of readiness of the members of the control and experimental groups for the organization of pupils' innovative activity was carried out for separate components of readiness (motivational-value, cognitive, activity-creative ones) and as a whole.
Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization

Authors:
raw_author_name: Yang Luo
raw_affiliation_string: Computer Science, China CITIC Bank Software Development Center, Beijing, China
raw_author_name: Guokun Xu
raw_affiliation_string: Computer Science, Beijing Foreign Studies University, Beijing, China
raw_author_name: Ying Xie
raw_affiliation_string: Computer Science, San Francisco Bay University, Fremont, USA
raw_author_name: Yibo Yin
raw_affiliation_string: Computer Science, Contemporary Amperex Technology USA Inc, Auburn Hills, USA

Abstract: E-commerce chatbots play a crucial role in customer service but often struggle with understanding complex queries. This study introduces a breakthrough approach leveraging the Falcon-7B model, a state-of-the-art Large Language Model (LLM) with 7 billion parameters. Trained on a vast dataset of 1,500 billion tokens from RefinedWeb and curated corpora, the Falcon-7B model excels in natural language understanding and generation. Notably, its 16-bit full quantization transformer ensures efficient computation without compromising scalability or performance. By harnessing cutting-edge machine learning techniques, our method aims to redefine e-commerce chatbot systems, providing businesses with a robust solution for delivering personalized customer experiences.
WEB-BASED INFORMATION SYSTEM PREDICTION OF VEHICLE THEFT VULNERABILITY IN JAYAPURA USING REGRESSION ANALYSIS

Authors:
raw_author_name: Fegie Yoanti Wattimena
raw_affiliation_string: Universitas Ottow Geissler Papua, Jayapura
raw_author_name: Halomoan Edy Manurung
raw_affiliation_string: Universitas Ottow Geissler Papua, Jayapura

Abstract: Vehicle theft in Jayapura Regency is quite high and there is no application to assist the police in making estimates or predictions of the number of theft cases that will occur in the next year. In 2022, cases of theft in Jayapura district will start to increase. to make these predictions the authors designed and built a system that can predict the number of these cases in building this application the authors use the Regression Analysis method this process can help the police predict the number of cases in the coming year. The development method used is SDLC, linear regression analysis and using the PHP programming language, the database uses MYSQL, Sublime Text. This research was conducted because there was no system that could assist the staff of the Resort Police (Polres) of Jayapura Regency. From this research, a system for predicting the level of vulnerability to motorized vehicle theft has been successfully built at the Jayapura District Police with data processed for attendance data using face region, reporting data using barcodes, queue data using counters and digital archive data helping the police store important documents.
TermIt: Managing normative thesauri

Authors:
raw_author_name: Petr Křemen
raw_affiliation_string: Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
raw_author_name: Michal Med
raw_affiliation_string: Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
raw_author_name: Miroslav Blaško
raw_affiliation_string: Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
raw_author_name: Lama Saeeda
raw_affiliation_string: Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
raw_author_name: Martin Ledvinka
raw_affiliation_string: Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic

Abstract: Thesauri are popular, as they represent a manageable compromise – they are well-understood by domain experts, yet formal enough to boost use cases like semantic search. Still, as the thesauri size and complexity grow in a domain, proper tracking of the concept references to their definitions in normative documents, interlinking concepts defined in different documents, and keeping all the concepts semantically consistent and ready for subsequent conceptual modeling, is difficult and requires adequate tool support. We present TermIt, a web-based thesauri manager aimed at supporting the creation of thesauri based on decrees, directives, standards, and other normative documents. In addition to common editing capabilities, TermIt offers term extraction from documents, including a web document annotation browser plug-in, tracking term definitions in documents, term quality and ontological correctness checking, community discussions over term meanings, and seamless interlinking of concepts across different thesauri. We also show that TermIt features better fit the E-government scenarios in the Czech Republic than other tools. Additionally, we present the feasibility of TermIt for these scenarios by preliminary user experience evaluation.
Study on the Impact of the Quality of Teaching on the Attractiveness of Vocational Colleges

Authors:
raw_author_name: Ali Khatibi
raw_author_name: Jacquline Tham
raw_author_name: S. M. Ferdous Azam

Abstract: This study investigates the correlation between the quality of teaching and the attractiveness of vocational colleges. The research aims to discern how the effectiveness of teaching methods, faculty expertise, and educational resources influence students' perceptions and choices regarding vocational education. Through a comprehensive analysis of various factors, including teaching styles, curriculum design, and extracurricular support, the study seeks to identify key elements that contribute to the overall appeal of vocational colleges. The findings are expected to provide valuable insights for educators, policymakers, and stakeholders in enhancing the quality of teaching in vocational institutions, ultimately fostering greater interest and enrollment in these educational settings.
Development of the sociocyberphysical systems` multi-contour security methodology

Authors:
raw_author_name: Stanislav Milevskyi
raw_affiliation_string: National Technical University "Kharkiv Polytechnic Institute", Ukraine
raw_author_name: Olha Korol
raw_affiliation_string: National Technical University "Kharkiv Polytechnic Institute", Ukraine
raw_author_name: Galyna Mykytyn
raw_affiliation_string: Lviv Polytechnic National University, Ukraine
raw_author_name: Andrii Vlasov
raw_affiliation_string: Kharkiv National University of Radio Electronics, Ukraine

Abstract: The constant increase in the number of threats to the security of critical infrastructure objects, which include socio-cyberphysical systems, leads to a decrease in the quality of security services and the level of security of infrastructure elements. The object of research is the process of building a complex system of protection in socio-cyberphysical systems. The imperfection of the mechanisms for ensuring the security of critical infrastructure objects, which include socio-cyberphysical systems, the technological complexity of identifying new security threats necessitates an urgent need for a radical revision of the current approaches to its provision. So, it becomes clear that the development of a new approach to ensuring the security of information resources in socio-cyberphysical systems is needed. The article proposes a new approach to the methodological foundations of building multi-contour information protection systems with internal and external circuits on each of the platforms of socio-cyberphysical systems. This approach is based on a universal classifier of threats, which takes into account not the technical aspect of threats, but also their integration with social engineering methods, their synergy of hybridity. The sociopolitical influence on the realization of threats is taken into account, and practical mechanisms for providing basic security services based on post-quantum algorithms are also proposed. To provide basic security services in the proposed multi-contour protection system, it is proposed to use post-quantum algorithms – McEliece crypto-code constructions, which provide Perr=10-9–10-12, safe time Tsec=1025–1035 group operations. Within the framework of the proposed approach, the problem of increasing the level of information security has been formalized and further ways of solving it have been determined