Reviewerly/exHarmony huggingface dataset:
Effects of Students’ Autonomy Support on their Self-Regulated Learning Strategies: Three Field Experiments in Secondary Education

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

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.
Heavy metal concentrations and water quality assessment of different types of drinking water wells in the Erdenet Cu–Mo mining area

Abstract The Erdenet mine (Erdenet, Mongolia) is a copper–molybdenum open pit mine with a huge tailing pond and is located next to a residential area. As the sources of drinking water in this area rely on groundwater and can be categorized into public or private wells, we aimed to assess the groundwater quality of the different types of drinking water wells. To accomplish our goal, we analyzed 18 trace metals (As, Ba, Bi, Cd, Co, Cr, Cs, Cu, Ga, Mo, Ni, Pb, Se, Sr, Th, U, V, Zn) and 8 major metals (Al, Ca, Fe, K, Mg, Mn, Na, Si) samples using ICP-MS and ICP-OES, and the heavy metal pollution index, heavy metal evaluation index, and degree of contamination were calculated. The most concerning point is that the Mo concentration of waste water in the Erdenet mine tailing pond and at a natural spring located 300 m from the tailing pond was 1100 μg/L, which greatly exceeds the WHO permissible concentration for Mo of 70 μg/L. Furthermore, high Ca and Mg concentrations at all private wells indicated that consumers of these wells are more vulnerable to any type of water pollution, as these wells are not built with any disinfection or treatment system. A modified categorization of the water quality indices showed that the public and private wells have low–medium contamination and that the tailing pond seepage water, along with its nearest spring, have a high degree of contamination. Because many private wells are located along the tailing pond and used directly without any treatment, we suggest a continuous evaluation and monitoring of the groundwater quality in this area.
Numerical solution of the Schrödinger equation using Neural Networks in Python

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

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

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

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

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.
Teachers’ technological (pedagogical) knowledge–predictors for students’ ICT literacy?

With the integration of information and communication technologies (ICT) into curricula, teachers are responsible for promoting ICT literacy among secondary school students, which requires in-depth technological knowledge (TK) and technological-pedagogical knowledge (TPK) on the part of teachers. This study uses a multilevel analysis to examine how teachers’ professional knowledge at TK and TPK influences secondary school students’ ICT literacy. Using data from n = 1,566 students from a larger sample taught in N = 134 classes by N = 220 teachers in N = 39 schools, our results show contrary to our hypotheses, no significant relationship between teachers’ professional knowledge regarding TK and TPK and students’ cross-curricula ICT literacy. Furthermore, we did not find any significant relationship in our model between students’ study-related ICT use and ICT literacy. By analyzing the relationship between teachers’ TK and TPK and students’ ICT literacy, our study provides new insights into the relationship between teachers’ cross-curricular knowledge and students’ cross-curricular achievements. It discusses further possible explanations and directions for future research.
OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images

Object detection is a fundamental task of remote-sensing image processing. Most existing object detection detectors handle regression and classification tasks through learning from a fixed set of learnable anchors or queries. To simplify object candidates, we propose a denoising diffusion process for remote-sensing image object detection, which directly detects objects from a set of random boxes. During the training phase, the horizontal detection boxes are transformed into oriented detection boxes firstly. Then, the model learns to reverse this transformation process by diffusing from the ground truth-oriented box to a random distribution. During the inference phase, the model incrementally refines a set of randomly generated boxes to produce the final output result. Remarkable results have been achieved using our proposed method. For instance, on commonly used object detection datasets such as DOTA, our approach achieves a mean average precision (mAP) of 76.59%. Similarly, on the HRSC2016 dataset, our method achieves a 72.4% mAP.
WEB-BASED INFORMATION SYSTEM PREDICTION OF VEHICLE THEFT VULNERABILITY IN JAYAPURA USING REGRESSION ANALYSIS

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

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

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

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
Penerapan Metode Yolov5 dan Teknologi Text-To-Speech dalam Aplikasi Pengenalan Abjad dan Objek Sekitar untuk Anak Usia Dini

Technological developments have provided significant benefits for various levels of society, including young children. There are several internal and external factors that can hinder children from remembering the alphabet and objects around them. To overcome these challenges, an innovative and interesting approach is needed to increase children's interest and involvement in learning to recognize alphabets and objects. Research has been carried out to develop an interactive learning media aimed at early childhood using an artificial intelligence approach. The You Only Look Once (YOLO) algorithm will be used to detect letters and surrounding objects in real-time, while Text-to-Speech (TTS) technology is used to convert text into sound. The dataset consists of 2511 images with 36 classes, including alphabets, fruits, animals, and stationery. In this research, the YOLOv5s method obtained a significant level of accuracy, reaching 85% so it could work well in detecting objects.&#x0D;
NotFYCEX: A Simulation Based Price Prediction and Notification System Using Continuous Machine Learning Method

This research study presents NotFyCEX - a price notification systems model based on Internet of Things (IoT) and Blockchain technology for reporting important trends in a Cryptocurrency Exchange (CRYPTEX) market. The system is simulated in near real time using a co-simulator approach including web-based PHP server-side application for emulating price fluctuations, a dynamic neural network model application integrated within the MATLAB/SIMULINK software environment and ArduinoUNO hardware for continual physical signalling/alerting. Dynamic simulation results showed exact matches between synthesized price state representations and the predicted pattern states. Thus, we consider NotFyCEX a novel effort that can effectively serve as a notification and price prediction system to the end-user or cryptocurrency trader in a given CRYPTEX market.
CNN Anchor Anderson Cooper To Participate In ‘Fireside Chat’ at Opening Session

Back to table of contents Previous article Next article APA & MeetingsFull AccessCNN Anchor Anderson Cooper To Participate In ‘Fireside Chat’ at Opening SessionMark MoranMark MoranSearch for more papers by this authorPublished Online:22 Jan 2024https://doi.org/10.1176/appi.pn.2024.02.2.18AbstractSince the start of his career in 1992, Cooper has worked in nearly 80 countries and has covered numerous major news events around the world, often reporting from the scene. Cooper has also played a pivotal role in CNN’s political and election coverage.Anderson Cooper, anchor of CNN’s “Anderson Cooper 360°,” will be the featured guest at a “fireside chat” during the Opening Session at APA’s 2024 Annual Meeting in New York City.“Anderson Cooper 360°” is a global newscast that goes beyond the headlines with in-depth reporting and investigations. The show airs weeknights at 8 p.m. ET on CNN and is simulcast to an international audience on CNN International. Since April 2023, Cooper has also anchored “The Whole Story,” airing Sundays on CNN, which showcases character-driven stories, special interviews, profiles, and investigative deep dives from CNN anchors and correspondents.Since the start of his career in 1992, Cooper has worked in nearly 80 countries and has covered nearly all major news events around the world, often reporting from the scene. Cooper has also played a pivotal role in CNN’s political and election coverage. He has anchored from political conventions and moderated several presidential primary debates and town halls. In 2016, Cooper was selected by the Committee on Presidential Debates to co-moderate one of the three debates between Donald Trump and Hillary Clinton.In addition to his shows on CNN, Cooper is also a regular correspondent for CBS’s “60 Minutes.” He has won a number of major journalism awards. He helped lead CNN’s Peabody Award–winning coverage of Hurricane Katrina and duPont Award-winning coverage of the 2004 tsunami in the Indian Ocean. Additionally, he has been awarded 20 Emmy Awards, including two for his coverage of the earthquake in Haiti and an Edward R. Murrow Award.Cooper’s four books—Astor: The Rise and Fall of an American Fortune; Vanderbilt: The Rise and Fall of an America Dynasty; The Rainbow Comes and Goes: A Mother and Son on Life, Love, and Loss; and Dispatches From the Edge: A Memoir of Wars, Disaster, and Survival—all topped The New York Times bestseller list.Before joining CNN, Cooper was an ABC News correspondent and anchor of the network’s reality program “The Mole.” Cooper anchored ABC’s overnight newscast “World News Now” and was a correspondent for “World News Tonight” as well as “20/20.”Cooper graduated from Yale University in 1989 with a bachelor’s degree in political science. He also studied Vietnamese at the University of Hanoi. Cooper is based in New York City. ■The Opening Session will be held Sunday, May 5, from 5:30 p.m. to 7 p.m. at the Javits Center. ISSUES New Archived