Reviewerly/exHarmony huggingface dataset:
Efficient high-resolution human pose estimation with integrated attention mechanism

Authors:
raw_author_name: Fuchen Liu
raw_affiliation_string: Wenzhou University (China)

Abstract: Deep learning has brought about a revolutionary transformation in the field of human pose estimation, offering significant advantages. Traditional approaches often seek performance improvement by expanding and deepening networks, resulting in increased parameters and complexity. In response to this challenge, we introduce DNNet, a novel framework rooted in HRNet. Its unique basic blocks incorporate feature mapping, channel weighting, and the fusion of different receptive fields. In comparison to HRNet, DNNet stands out with fewer parameters, lower computational complexity, and more resilient lightweight features. By leveraging features at various resolutions, DNNet's performance is further enhanced. Extensive validation on both the COCO dataset and a dataset focused on dangerous driving behavior reveals that DNNet's accuracy is comparable to that of HRNet. Notably, DNNet achieves a remarkable 69% reduction in parameters and a 59% decrease in computational complexity under similar accuracy conditions. In practical real-world applications, when applied to a dataset addressing dangerous behavior, DNNet outperforms both ShuffleNet and MobileNet in accuracy, highlighting its adaptability and efficacy in diverse scenarios. DNNet's unique features position it as a promising solution in the field of human pose estimation, providing a balanced trade-off between accuracy, efficiency, practical applicability, and the organic fusion of different receptive fields.
Research on the Integrated Solution of Physical Education Based on Smart Campus

Authors:
raw_author_name: Jiangtao Li
raw_affiliation_string: Education, Sanya University, Hainan, China

Abstract: In order to improve the physical education experience for students, this study investigates the idea of an integrated solution based on a smart campus for physical education. Within the physical education curriculum, the study focuses on the integration of smart fitness monitoring and wearables, interactive fitness equipment and gamification, mobile applications and customized workout plans, smart facilities, indoor navigation, and data-driven curriculum enhancements. The data gathered from a study involving two groups of students, an experimental group with access to smart campus solutions and a control group without such access, was analyzed using a well-designed framework. The data study looks at how the smart campus solutions affect the students' academic performance, fitness levels, motivation, and adherence to exercise regimens. The analyzed findings point to a number of advantages of using smart campus technologies into physical education. Compared to the control group, students in the experimental group showed higher levels of academic performance, increased motivation, improved exercise adherence, and fitness. Students who had access to smart campus solutions reported much better experiences with physical education overall. The results imply that smart campus technologies have the potential to produce a physical education learning environment that is more interesting, focused on the needs of the students, and effective. Smart campus solutions help optimize curriculum design, boost motivation, and enhance academic performance by utilizing data-driven insights and personalized learning experiences.
The Impact of a Mobile App on Student-Alumni Communication and Engagement: A Case Study

Authors:
raw_author_name: Jyotsna Anthal
raw_author_name: ShanuKumar Singh

Abstract: Abstract: The purpose of this study was to examine the effects of a mobile application that connects college students and alumni on their communication, networking, and career development. The app was developed by a team of students as a novel idea to foster strong alumni relations and provide mentorship opportunities for current students. The study used a mixed-methods approach, combining quantitative data from surveys and qualitative data from interviews. The surveys were conducted before and after the app was launched, and the interviews were conducted after the app was used for three months. The results showed that the app was beneficial to both students and alumni, as it increased their frequency and quality of communication, enhanced their sense of belonging and community, and improved their skills and experiences. The paper discusses the implications, limitations, and recommendations of the study for future research and practice.
Trusted Data Acquisition in Microelectronics Manufacturing as a Basis for ML-Optimized Processing

Authors:
raw_author_name: M. Heimann
raw_affiliation_string: Siemens AG
raw_author_name: M. Thies
raw_affiliation_string: University of Bielefeld
raw_author_name: B. Schroeder
raw_affiliation_string: Technical University Berlin
raw_author_name: M. Schneider-Ramelow
raw_affiliation_string: Technical University Berlin

Abstract: In the digitization of manufacturing processes, one of the major goals is the connection of production facilities and the use of data to digitize business processes. In order to optimize manufacturing processes and maximize the quality of the resulting product, further process information and data directly from the work piece and from the manufacturing environment are required to achieve a holistic system view in addition to the selected data that the manufacturing systems already provide. Within the SiEvEI 4.0 project, a research consortium from industry and academia works on process digitization for a manufacturing scenario where high value electronic goods are built in a distributed manufacturing environment. The key research topics addressed are the implementation of a Chain of Trust [CoT] for trustworthy distributed manufacturing and the application of artificial intelligence/machine learning to analyze and eventually optimize manufacturing processes. The basic concepts of this approach have been presented at IMAPS 2021. As an update, this paper reports on the actual experimental evaluation of these concepts in two different assembly lines, including data acquisition, data handling and AI processing with the goal to optimize processing targeting higher production yield and product quality. Specific for this work is the focus on high mix/low volume SMD assembly using fully automated equipment and Solder Ball Application. As a result, the paper presents the experimental validation of the manufacturing process digitization and the use this digital description of a process combination to make a distributed manufacturing flow safe and increase product/process quality.
Autonomous Driving System Driven by Artificial Intelligence Perception Fusion

Authors:
raw_author_name: Yong Wang
raw_author_name: Shuqian Du
raw_author_name: Qi Xin
raw_author_name: Yuhang He
raw_author_name: Wenpin Qian

Abstract: Perception, as the information input module of the automatic driving system, determines the lower limit of the entire automatic driving system. Both autonomous driving perception and robot perception are constantly approaching the real physical world through digital methods, and this real physical world is only limited to the scope of human perception, such as lane lines, traffic lights, driving obstacles, and so on. The main premise of this process is that humans already know the categories or properties of the physical world, and only allow machines and systems to replicate human responses. Whether it is a pure visual route or a multi-source fusion route, the essence is the difference between the perceptual system schemes, one focusing on the vertical and the other on the horizontal. The pure vision solution represented by Tesla or the multi-source sensor fusion file represented by Waymo. In fact, the perception module of the automatic driving system usually has multiple sensors to achieve information redundancy and information complementarity through multiple dimensions, but there is the possibility of information conflict between different sensors. This paper aims at the advantages of perception-driven artificial intelligence to achieve breakthroughs in autonomous driving innovation, and analyzes how perception fusion drive is applied to the practical application of autonomous driving, so as to analyze the future development prospects of artificial intelligence.
Anomaly Detection in Heterogeneous Data Using Graph Convolutional Networks

Authors:
raw_author_name: Shubham Kumar -
raw_author_name: Shivam -
raw_author_name: Aarti -

Abstract: Closed-circuit television (CCTV) surveillance systems are ubiquitous tools for ensuring public and private safety. However, manual monitoring of surveillance footage is arduous and time-consuming, often resulting in delayed identification and response to potential threats. In this paper, we present a novel real-time threat detection system tailored for CCTV surveillance, employing cutting-edge deep learning methodologies, particularly graph convolutional networks (GCNs), to detect and classify high-movement levels within video frames. By treating videos as segments and delineating between anomalous (potentially threatening) and normal (safe) segments, our system facilitates continuous real-time monitoring of surveillance footage, enabling the timely identification of potential threats such as abuse, burglaries, explosions, shootings, fighting, shoplifting, road accidents, arson, robbery, stealing, assault, and vandalism. To assess the efficacy of our system, we conducted extensive experiments on a large-scale dataset comprising CCTV footage, yielding promising results. Additionally, we introduce a novel, comprehensive dataset comprising 128 hours of real-world surveillance videos, encompassing various anomalies and normal activities. This dataset serves dual purposes: general anomaly detection, and the recognition of specific anomalous activities. Our experimental findings demonstrate the superior performance of our method in anomaly detection compared to state-of-the-art approaches. Furthermore, we provide an analysis of several recent deep learning baselines for anomalous activity recognition, revealing the challenging nature of our dataset and the potential it holds for future research endeavors. Through the integration of advanced deep learning techniques and comprehensive dataset creation, our proposed system stands to significantly enhance the efficiency and effectiveness of CCTV surveillance, facilitating faster response times and bolstering security for individuals in diverse environments.
Development and Validation of Secondary School Educational Fairness Diagnostic Tool

Authors:
raw_author_name: Suk-Yeol Lee
raw_author_name: Jae Bok Seo
raw_author_name: Sung soo Jung

Abstract: The purpose of this study was to explore the concept and attributes of educational fairness in secondary schools and to develop and validate an educational fairness diagnostic tool. This study conducted a Delphi survey to explore the concept, factors, diagnostic areas, and items of educational fairness in secondary schools and a survey of 159 preliminary and 747 main surveys of middle and high school students to develop and validate the diagnostic tool by conducting factor analysis, reliability, and correlation analysis. In this study, the definition of educational fairness in secondary schools was defined as "the degree to which secondary students perceive that they are receiving appropriate education according to their interests, abilities, circumstances, and backgrounds in terms of opportunities, processes, and outcomes of school education.” In addition, the components of educational fairness were set as distributive fairness, procedural fairness, and interactional fairness, and through the validation process, 30 items were derived, including 10 items on distributive fairness, 10 items on procedural fairness, and 10 items on interactional fairness. The diagnostic tool developed in this study can be used to identify differences between individual variables related to educational fairness perceived by secondary students and to reveal causal relationships between variables that affect educational fairness.
Identification of Network Disruptions Using the Fuzzy K-Nearest Neighbor Algorithm in Case Based Reasoning at STMIK Pranata Indonesia

Authors:
raw_author_name: Asep Sumantri
raw_affiliation_string: Universitas Budi Luhur Jakarta
raw_author_name: Denni Kurniawan
raw_affiliation_string: Universitas Budi Luhur Jakarta

Abstract: Case-Based Reasoning (CBR) is a computer reasoning system that uses old knowledge to solve new problems. CBR provides solutions to new cases by looking at old cases that are closest to new cases. This would be very beneficial as it would eliminate the need to extract models as required by rule-based systems. The main problem in this research is the frequent disruption/difficulty for students and staff to connect to the network at the Indonesian institutional system. The method used is fuzzy K-Nearest Neighbor (FK-NN) is a variant of the K-Nearest Neighbor (KNN) method with fuzzy techniques. Using case based reasoning to make it easier for the system to reason, which prioritizes the use of information and previous knowledge to represent experience as a source of learning basis for reasoning. This system can help the IT team at STMIK Pranata Indonesia to more quickly handle types of network disturbances. Testing the system using a user acceptance test produces acceptable results and is used properly according to user needs. The scoring method uses a Likert scale with acceptance results of 94% with the criteria of strongly agree. The highest accuracy was obtained from test results with K = 5 with a value of 94.33%
Navigating the Transformative Impact of Artificial Intelligence on English Language Teaching: Exploring Challenges and Opportunities

Authors:
raw_author_name: Wahyuddin Rauf
raw_author_name: Abd. Rajab

Abstract: This study aims to examine the impact of artificial intelligence (AI) on English language teaching in higher education. The scope of the study was limited to the opportunities and challenges of English language teaching. This study was conducted using a mixed method of conducting surveys and in-depth interviews. A questionnaire link using Spreadsheet’s Google Form was sent to the English lecturer through the WhatsApp group and personal contact No. The study sample consisted of 16 English lecturers. Analyzing the questionnaire and interview transcript indicates that the emergence of artificial intelligence (AI) technology has created opportunities and challenges for English language teaching and learning. AI has changed the landscape of English language teaching in higher education. English lecturers utilized different types of AI for various purposes, for example, asking and solving questions and checking for grammatical errors, checking plagiarism, paraphrasing, and reviewing literature. The study also revealed that AI has a variety of advantages for language teaching and learning, including the detection of plagiarism and grammatical errors. In addition, AI has created opportunities and challenges for the future of English language teaching. AI required digital literacy to utilize. The English teaching profession might be taken over by AI in the future, so English lecturers must continuously improve their digital literacy.
An Analysis of The English Summative Test: EFL Teacher-Made Test

Authors:
raw_author_name: Yessicka Noviasmy
raw_author_name: None Risma

Abstract: This study aims to determine the quality of the multiple-choice summative test items for senior high school EFL teachers in Parepare. The analysis consisted of validity, reliability, level of difficulty, and distractors. The type of research used in this research was quantitative descriptive. The subjects in this study were 33 students. The data in this study was collected using documentation techniques. Data analysis in the form of item analysis was carried out using the Biserial point correlation. The results of the study showed that the valid category was 80% and invalid are 20%. Included in the unreliable test with KR-20 &lt; 0.70 or 0,6. The difficult category was 1 item, the moderate category was 8 items, and easy category was 1 item. Distractors function very good was 70%, good category was 20%, and the quite good category was 10%. It can be resumed that the final multiple-choice test made by EFL teacher has very good quality of item validity, unreliable test, good quality level of difficulty, and very good quality distractor. Based on the result analysis of test items, it can be concluded that several good test items can still be used for the future summative test, and several test items should be revised.
Developing Vocabulary Handbook for the Eighth Grade Students of Junior High School

Authors:
raw_author_name: Salsabila Anggraeni
raw_author_name: Andi Tenrisanna Syam
raw_author_name: Magfirah Thayyib

Abstract: This study aimed to develop a vocabulary handbook for eighth-grade students at SMP Negeri 8 Palopo. The researcher used Research and Development by adapting the ADDIE model—the participants of this study were eighth-grade students of SMP Negeri 8 Palopo and three expert validators. Data were collected through observation, interviews, students' questionnaires, and expert validation. The vocabulary handbook was developed by referring to the needs analysis and adapting a task-based language approach. The appropriate vocabulary handbook as a research product consists of: 1. three units of vocabulary handbook material, namely: What are you doing? My uncle is a zookeeper, and Bigger is sometimes better. 2. Variety of activities adapted from task-based 3. Attractive layout and media 4. Other skills like listening, reading, and writing are completed with exciting grammar and vocabulary. The suitability of the student vocabulary guidebook is evidenced by the results of student and teacher perceptions that were categorized into "Very Good" classification, which indicated that the vocabulary handbook is ready to use with little revisions.
The power of quantum algorithms

Authors:
raw_author_name: Hamish Johnston

Abstract: Building quantum computers isn’t just about developing clever new hardware. Toby Cubitt, who co-founded the quantum-tech firm Phasecraft, tells Hamish Johnston why algorithms are vital too.
Quantum processor integrates 48 logical qubits

Authors:
raw_author_name: Sam Jarman

Abstract: A quantum processor with 48 logical qubits that can execute algorithms while correcting errors in real time has been unveiled in the US.
Global Trends on Electronic Learning and Entrepreneurship: Bibliometric Analysis (2001 – 2022)

Authors:
raw_author_name: Dewi Kusuma Wardani
raw_author_name: Tri Murwaningsih

Abstract: The use of electronic learning in entrepreneurship education will increase entrepreneurial intentions and behaviors in university students. The purpose of this study is to comprehensively analyze electronic learning in entrepreneurship globally. The research method used is VOSviewer-assisted bibliometric analysis. The review showed that there were 398 academic documents published in the Scopus database between 2001 and 2022. The results showed that the most publications occurred in 2021, and keyword identification formed 7 clusters that grouped research themes in recent years. Based on the studies conducted, this study presents a roadmap for prospective researchers as well as critical themes that are very likely for future research success.
Analysis of news article on the Cross-Disciplinary College Applications in response to National Curriculum Reform and College Admissions Reform

Authors:
raw_author_name: Hyun Sook Yi

Abstract: The purpose of this study is to analyze the impact of national curriculum reform and college admissions reform on the evolving nature of cross-disciplinary college applications and to identify the key issues associated with cross-disciplinary college applications through an analysis of news articles published by mainstream mass media outlets. For this purpose, a total of 2,371 news articles related to cross-disciplinary college applications published by mainstream mass media outlets from January 1, 1997, to Novermber 30, 2023, were collected using web scraping method, and keyword frequency analysis and Structural Topic Modeling (STM) were conducted. The main results of the study are as follows. First, over the course of 26 years, 7 topics related to cross-disciplinary college applications reported in news articles were extracted, including ‘Plans for the promotion of public education and reduction of private education expenses’, ‘Policies for supporting students with specialized talents’, ‘Early admissions procedures and support strategies’, ‘Trends of natural science students enrolling in liberal arts majors and the introduction of integrated CSAT’, ‘Avoidance of science and engineering majors and support policies’, ‘Application strategies based on admission types’, and ‘Trends of liberal arts students enrolling in natural science majors and advantages and disadvantages based on admission types and subjects’. Second, the appearance rate of topics and the major issues varied across different phases of the integrated education system. Lastly, based on the results of this study, implications regarding the issues and directions for improvement of support were derived.
Problematics of Students' Discipline Value in Academic Activities and Non Academic

Authors:
raw_author_name: Yan Dirk Wabiser
raw_affiliation_string: Pancasila and Citizenship Education Study Program, Cenderawasih University

Abstract: Discipline is a state that is formed and trained through the values of obedience, adherence, loyalty, serenity, order, and order. This research aims to know the concepts of discipline and how to shape the values of student discipline in academic and non-academic activities on FKIP IPS Management at one of the universities in Papua. This research involved 36 students. The method used in this research is qualitative method with purposive sampling techniques. Research results show that the concepts of discipline in academic and non-academic activities are values and mental attitudes. The results of this research clarify and produce forms of disciplinary awareness of students of IPS FKIP in one of the universities in Papua, among others: a) time commitment; b) appreciation of time; c) feelings of shame, guilt and fear; d) agreement as a rule. Furthermore, the student discipline concept of IPS is part of the discipline of democracy and self-imposed discipline that arises from within because of the impulse of feelings, wills and own minds to obey or do by themselves with the emphasis of educational approach. Rules are guidelines and directions, accomplishments are rewards. If the student's attitude shows obedience and adherence, then it is awarded as an achievement.Discipline is a state that is formed and trained through the values of obedience, adherence, loyalty, serenity, order, and order. This research aims to know the concepts of discipline and how to shape the values of student discipline in academic and non-academic activities on FKIP IPS Management at one of the universities in Papua. This research involved 36 students. The method used in this research is qualitative method with purposive sampling techniques. Research results show that the concepts of discipline in academic and non-academic activities are values and mental attitudes. The results of this research clarify and produce forms of disciplinary awareness of students of IPS FKIP in one of the universities in Papua, among others: a) time commitment; b) appreciation of time; c) feelings of shame, guilt and fear; d) agreement as a rule. Furthermore, the student discipline concept of IPS is part of the discipline of democracy and self-imposed discipline that arises from within because of the impulse of feelings, wills and own minds to obey or do by themselves with the emphasis of educational approach. Rules are guidelines and directions, accomplishments are rewards. If the student's attitude shows obedience and adherence, then it is awarded as an achievement.