Reviewerly/exHarmony huggingface dataset:
Pelatihan Alat Peraga Penggaris Geser Untuk Meningkatkan Pemahaman Berhitung Siswa SD

Authors:
raw_author_name: Rospala Hanisah Yukti Sari

Abstract: Teaching aids are media that can help stimulate students' skills, thoughts, attention and desires so that they can stimulate student learning activities. On the other hand, using teaching aids can create space for students to be able to participate in every learning process. In fact, at SDN 2 Kereng Bangkirai class III and SDIT Hasanka class II, learning activities have not used teaching aids. The props that are made and used are counting boards made from cardboard. This teaching aid is used in the learning process for class III students at SDN 2 Kereng Bangkirai and class II at SDIT Hasanka. The aim of learning using these teaching aids is to facilitate students in the learning process and the hope is that by using these props, learning activities can be more active and enjoyable in learning activities, and will not make students bored in learning so that their material skills will be better.
A special target detection called You Only Looks Once

Authors:
raw_author_name: Liyun Li
raw_affiliation_string: School of Engineering Science

Abstract: This paper proposes a unique target detection called You Only Look Once (YOLO for short) and a recognition method. Unlike the previous idea of many classifiers with object detection functions, the object detection box is set as a spatially separated bounding box and the regression problem of related class probabilities is realized. The neural network model can directly scan the entire image during testing, predicting bounding boxes and class probabilities from the complete picture. At the same time, because the whole detection channel relies on a single neural network, it is more straightforward and concise when upgrading and updating. The unified architecture used in this paper is fast, with smaller versions of the YOLO model processing a staggering 155 frames per second. At the same time, it has also achieved excellent results in mAP. Compared with other detection systems, YOLOv3 has optimized many past problems, including positioning errors. At the same time, the probability of predicting false detections in the absence of false detections is small. Finally, like the YOLO base model, YOLOv3 may produce significant errors when processing abstract works of art and images with a large number of small objects. However, its actual results are still better than detection methods such as Region -Convolutional Neural Networks (R-CNN).
The application of artificial intelligence in the game field

Authors:
raw_author_name: Xiangyu Zhou
raw_affiliation_string: Southwest University

Abstract: With the rapid development of artificial intelligence technology, its application in the field of games is increasingly widespread. At present, the application of artificial intelligence in games has become a research hotspot. This article mainly explores the application of AI in various types of games such as first person shooter (FPS), Role playing Games (RPGs), Strategy, and analyzes their advantages and disadvantages based on the category of the game. Specifically, this article explores the application of AI in FPS games, focusing on how AI controls the behavior and strategies of enemies to provide players with challenging and realistic experiences. In the context of RPGs, the paper analyzes AI's impact on intelligent decision-making and interaction with non-playable characters (NPCs). Intelligent NPCs create a more authentic and immersive interaction between players and game characters. In the case of strategy games, AI enhances the strategic and challenging aspects, offering a more layered and dynamic gaming experience, and pushing players' decision-making abilities and strategic thinking. Additionally, the article discusses the limitations and challenges of AI in games. This paper summarizes and prospects the application of AI in the field of games.
Randomized Pacman maze generation algorithm

Authors:
raw_author_name: Xinyu Dou
raw_affiliation_string: Pennsylvania State University

Abstract: Pacman, a cultural icon since its debut in 1980, is celebrated for its captivating static maze design. This research explores innovative algorithms, including an improved version of Sidewinder, in the context of dynamic maze generation for Pacman. Traditional algorithms, constituting the initial focus, serve as counterexamples to highlight their unsuitability in creating Pacman mazes due to their generic design unrelated to the game's unique gameplay. By showcasing these limitations, the study emphasizes the necessity for tailored solutions that align with Pacman's dynamic environment. Subsequently, attention shifts to an innovative algorithm exhibiting promise for Pacman maze generation. An in-depth analysis, using the same rigorous criteria applied to traditional algorithms provides essential insights into its potential for enhancing the Pacman gaming experience.
A comparison of recent progress in breast cancer diagnosis models using machine learning algorithms

Authors:
raw_author_name: Zhiyu Zhang
raw_affiliation_string: University of Electronic Science and Technology of China

Abstract: Breast cancer is the leading cause of mortality among women suffering from cancer, so the accurate diagnosis is important. This review aims to provide a thorough examination of advancements and trends in breast cancer diagnosis by analysing recognized papers published between 2020 and 2023.The paper firstly gives a brief overview of breast cancer, machine learning algorithms, followed by an introduction of basic process for ML in breast cancer diagnosis. After that, by focusing on two emerging trends, hybridization and newly invented modalities, the review introduces existing achievements in the field. Subsequently, it highlights nine notable or novel designs in breast cancer diagnosis, while presenting their comparative properties in a tabular format. Hopefully, this review can equip researchers with valuable insights for future studies and references, helping them gain a better understanding of the field and facilitating further improvements in breast cancer detection and classification.
Electronic Health Record Interoperability System in Peru Using Blockchain

Authors:
raw_author_name: David Mauricio
raw_author_name: Pedro Castañeda
raw_author_name: José Luis Castillo-Sequera

Abstract: In Peru, there is currently no integrated electronic health record (EHR) system that can be automatically shared between healthcare facilities. This leads to increased service costs due to duplicated examinations and records, as well as additional time required to manage patients’ clinical information. One alternative for ensuring the secure interoperability of EHRs while preserving data privacy is the use of blockchain technology. However, existing works consider a pre-established format for exchanging EHRs, which is not applicable when systems have different formats, as is the case in Peru. This work proposes an architecture and a web application for exchanging EHRs in heterogeneous systems. The proposed system includes the homologation of an EHR with rapid interoperability resources for medical attention using FHIR HL7, and vice versa, to achieve interoperability. Additionally, it utilizes blockchain technology to ensure data security and privacy. The web application was tested using a case simulation to demonstrate EHR interoperability between clinics in a clear, secure, and efficient manner. In addition, a survey was conducted with 30 patients regarding adoption, and another survey was conducted with 10 doctors from a public hospital in Peru regarding usability. The results demonstrate a very high level of adoption and usability for them all. Unlike other studies, the proposal does not necessitate alterations to existing EHR systems for interoperability. In other words, the proposal presents a feasible and cost-effective alternative to addressing the EHR interoperability issue in clinics and hospitals in Peru.
Application of Deep Learning Models for Bone-Conducted Speech Signals Extracted in the Form of Bone Conduction Headphones

Authors:
raw_author_name: Shikang Sun
raw_author_name: Byeong-Hee Kim

Abstract: In this study, we used deep learning to align bone-conducted speech signals with air-conducted speech signals, aiming to replace traditional air conduction microphones in voice-based services capturing surrounding sounds. We fabricated headphones, placing bone conduction microphones on the rami (the branches of a bone in the jaw area), in line with traditional bone conduction headphone configurations. Using LSTM, CNN, and CRNN models, we created databases that aligned bone-conducted speech signals with their air-conducted counterparts and tested them with bone-conducted speech signals captured via our custom-made headphones. The CNN model demonstrated superior performance in accurately distinguishing three English words (“apple,” “hello,” and “pass”), including their voiceless pronunciations. In conclusion, our study shows that deep learning models can effectively use bone-conducted speech signals extracted from the rami for automatic speech recognition (ASR), paving the way for future ASR technology that precisely recognizes only the speaker’s voice.
Attribute-specific Cyberbullying Detection Using Artificial Intelligence

Authors:
raw_author_name: Chidubem Ejiofor
raw_affiliation_string: Department of Computer Science, Western Illinois University, Macomb, Illinois, 61455, USA

Abstract: Cyberbullying, a pervasive issue in the digital age, poses threats to individuals’ well-being across various attributes such as religion, age, ethnicity, and gender. This research employs artificial intelligence to detect cyberbullying instances in Twitter data, utilizing both traditional and deep learning models. The study repurposes the Sentiment140 dataset, originally intended for sentiment analysis, for the nuanced task of cyberbullying detection. Ethical considerations guide the dataset transformation process, ensuring responsible AI development. The Naive Bayes algorithm demonstrates commendable precision, recall, and accuracy, showcasing its efficacy. The Bi-LSTM model, leveraging deep learning capabilities, exhibits nuanced cyberbullying detection. The study also underscores limitations, emphasizing the need for refined models and diverse datasets.
ANALYSIS OF THE NEEDS OF MANDARIN TEACHING USERS ON LIVE STREAMING PLATFORMS

Authors:
raw_author_name: Hanafi Mohd Yasin
raw_affiliation_string: Faculty of Education, Universiti Kebangsaan Malaysia

Abstract: The rapid development of live streaming platforms has attracted an increasing number of knowledge sharers and learners. These platforms have expanded from entertainment and live commerce to knowledge sharing and monetization. Mandarin Chinese teaching has gained popularity due to its vast user base and relatively low teaching threshold. However, after a period of practice, some drawbacks have been observed, impacting teaching effectiveness and students' learning outcomes. Through questionnaires, specific student needs will be identified, and collaborative efforts among the platform, regulatory authorities, and content providers will be undertaken to improve and enhance Mandarin Chinese teaching on live-streaming platforms. This paper employs quantitative analysis to investigate the distribution of Mandarin learners on live streaming platforms, factors influencing their learning, and their satisfaction levels with the learning process. The aim is to provide valuable insights for future Mandarin teaching on live-streaming platforms and other educational domains. The rapid development of live streaming platforms has attracted increasing knowledge sharers and learners. These platforms have expanded from entertainment and live commerce to knowledge sharing and monetisation. Mandarin Chinese teaching has gained popularity due to its vast user base and relatively low teaching threshold. However, after a period of practice, some drawbacks have been observed, impacting teaching effectiveness and students' learning outcomes. Through questionnaires, specific student needs will be identified, and collaborative efforts among the platform, regulatory authorities, and content providers will be undertaken to improve and enhance Mandarin Chinese teaching on live-streaming platforms. This paper employs quantitative analysis to investigate the distribution of Mandarin learners on live streaming platforms, factors influencing their learning, and their satisfaction levels with the learning process. The aim is to provide valuable insights for future Mandarin teaching on live-streaming platforms and other educational domains.
Exploring the Levels of Undergraduate University Students' Study Skills

Authors:
raw_author_name: Mushtaq Ahmad Malik
raw_affiliation_string: University of Sargodha, Sargodha, Pakistan

Abstract: The ability to study well is crucial to students' academic performance. The aims of this research were to investigate the study skills levels of undergraduate students, to determine the correlation between academic performance and the study skills of undergraduate students, and to identify any noteworthy variations in study skills based on gender and location. Multi-stage stratified random sampling technique was used to select representative sample of 2207 undergraduate students from Punjab, Pakistan's general public universities. With the author's consent, a modified version of the study skills evaluation questionnaire was adapted, and the academic performance scale was created, verified, and subjected to a reliability coefficient of .094 and 0.85. Four research hypotheses were developed and put to the test using the t-test, Pearson r, mean, and standard deviation. The results indicated a substantial difference in terms of gender and location, as well as a favorable link between study skills and academic performance. Moreover, the majority of undergraduate students employed study skills at a moderate level; therefore, it is recommended to design and incorporate study skills in educational courses for students in academic curricula.
Reconstructing near-water-wall temperature in coal-fired boilers using improved transfer learning and hidden layer configuration optimization

Authors:
raw_author_name: Wenyuan Xue
raw_affiliation_string: School of Automation Engineering, Northeast Electric Power University, Jilin, PR China
raw_author_name: Zhi Wang
raw_affiliation_string: School of Automation Engineering, Northeast Electric Power University, Jilin, PR China
raw_author_name: Shengxian Cao
raw_affiliation_string: School of Automation Engineering, Northeast Electric Power University, Jilin, PR China
raw_author_name: Mengxuan Sui
raw_affiliation_string: School of Automation Engineering, Northeast Electric Power University, Jilin, PR China
raw_author_name: Yuan Yang
raw_affiliation_string: School of Automation Engineering, Northeast Electric Power University, Jilin, PR China
raw_author_name: Jiyuan Li
raw_affiliation_string: School of Automation Engineering, Northeast Electric Power University, Jilin, PR China

Abstract: The temperature field is a critical factor for ensuring the safe combustion and energy conservation in boilers. However, an effective method for reconstructing the temperature field near the water wall is still under exploration. In this paper, a method for online reconstruction of the temperature field distribution near the water wall in a 330 MW tangentially fired coal boiler is proposed, which progresses from a well-established model for the entire furnace. A two-branch fusion network for transfer learning (TBFN-TL) method is proposed, incorporating additional key parameters, heat flux, during the transfer process to enhance the effectiveness. A Bayesian hierarchical neural architecture search (BHNAS) method is proposed to optimize the configuration of the hidden layers in building neural networks. Compared with computational fluid dynamics (CFD) results, the mean absolute percentage error (MAPE) of the reconstruction results for the entire furnace model, traditional transfer learning methods, and the proposed TBFN-TL are 11.57%, 5.738%, and 2.052%, respectively, demonstrating a significant enhancement. The proposed BHNAS method extends the search optimization space, obtaining more excellent configurations for the hidden layer nodes. The proposed methods have significant implications for temperature field reconstruction, the field of transfer learning, and the optimization of hidden layer configurations.
Optimizing Disaster Response: OptiDrone- Advanced Integration of Dijkstra's Algorithm and Cutting-Edge Software Solutions for UAV Path Planning and Obstacle Avoidance Efficiency

Authors:
raw_author_name: JEZREEL E. RODRIGUEZ

Abstract: The objective of the research is development of a specific concept called "OptiDrone," which is based on the integration of Dijkstra's Algorithm and the latest development of aerial vehicles' path planning and obstacle avoidance efficacy. The study involves a combination of theoretical and algorithmic refinement as well simulation-based experiments to test the effectiveness of OptiDrone in dynamic disaster scenarios. OptiDrone's combination of powerful route planning techniques along with advanced software programs is set to achieve a substantially higher response rate and operational resilience in the face of difficulties. The results add much value to the body of knowledge about UAV-assisted disaster management systems, indicating possible gains in efficiency and timeliness while addressing crisis situations. Different disciplines of disaster management, robotics, and software engineering can benefit from this research, which is a significant milestone towards the use of more efficient and innovative unmanned aerial systems in disaster response.
FOSTERING LITERACY SKILLS IN KINDERGARTEN THROUGH PROJECT-BASED POP-UP BOOK CRAFTING

Authors:
raw_author_name: Herman Herman
raw_affiliation_string: Pendidikan Guru Pendidikan Anak Usia Dini, Universitas Negeri Makassar
raw_author_name: Parwoto Parwoto
raw_affiliation_string: Pendidikan Guru Pendidikan Anak Usia Dini, Universitas Negeri Makassar

Abstract: This study is an experimental research aimed at determining the influence of project-based pop-up book creafting on literacy skills. The population in this study consists of all kindergarten children in the city of Makassar. The research sample was selected using a probability sampling technique, and the selected kindergartens were Teratai UNM Kindergarten with 15 children and Ceria Kindergarten with 15 children. The study analyzes findings using descriptive analysis techniques and Independent-sample T-test. The results showed that the average post-test score of students who participated in project learning in the creation of pop-up books increased to 83.52, while students who participated in conventional learning reached 66.48. The hypothesis test results showed that the t value was 5.486 with a significance of 0.000, confirming a highly significant difference in literacy skills between the groups that followed project-based learning and conventional learning. Therefore, based on the research findings, improving literacy skills in children requires attention to the learning model used, ensuring that the provided learning activities are suitable and capable of stimulating child development.
A Building Model for Enhancing Information Systems Security

Authors:
raw_author_name: Abdualmajed A.G. Al-Khulaidi
raw_author_name: Saleh Noman Al-Assali

Abstract: Today, electronic systems and information have become no less important than food and water for human life. No one can deny this fact. Electronic information systems contribute to the development of life in many areas. But some electronic information systems should not be available for anyone to use, such as reading, operating, modifying or destroying without permission. This type of electronic information systems must be protected in some way based on the related situation. . Password-based authentication is the first defense for most information systems. Password security enhances the security of the entire information system. Therefore, password protection models become very important in such cases to help users improve authentication security. Researchers published various methods based on salting, hashing, symmetric encryption, and key splitting methods. The paper conducted by Saleh Al-Asali describes a method for generating a suitable secret key that is easy to memorize and remember, as he succeeded in generating a password that is strong and easy to remember. This paper improves the previous method by adding some improvements mentioned in the section described in the suggested method.
Needs Analysis for Education for Students with Health Impairments

Authors:
raw_author_name: Jaewoo Do
raw_author_name: Minjeong Kim

Abstract: As the number of students with health impairments continues to increase and the need for customized educational support increases, interest in education for students with health impairments is increasing. This study explored deeply and comprehensively the development tasks in education for students with health impairments to solve current issues and response to educational changes. In specific, the needs of all subjects(public officials, teachers, teachers of consigned educational institutions, students, and parents) related to education for students with health impairments were investigated through focus group interviews. As a result of the study, a total of 10 needs related to the curriculum and contents, educational methods and evaluations, student and teacher support, and the new educational policies were identified, and we found that each need was closely related. Each need derived from this research explains the development tasks for the education of students with health impairments and the necessity of establishing a role for each subjects.
Analysis of early childhood teachers’ climate change teaching capabilities using IPA technique

Authors:
raw_author_name: Min Jeong Kim
raw_author_name: Hyeryeon Ryu

Abstract: This study examined the importance and implementation of climate change teaching competencies for early childhood teachers. An IPA analysis was conducted to examine the priorities of educational content to strengthen early childhood teachers' climate change teaching capabilities. For this study, questionnaires were distributed and collected online to early childhood teachers working in kindergartens in Chungcheongbuk-do and Gyeonggi-do. The final research subjects sampled were 51 people. As a result of this study, looking at the difference between the current level and importance of early childhood teachers' climate change response practice competency and ecological citizenship competency, there was a significant difference. The importance of overall climate change response capabilities and ecological citizenship capabilities was higher than the current level. And looking at the results of the IPA analysis of early childhood teachers' climate change response practice capabilities, the strengthening and maintenance of teaching capabilities was found to be the will to implement climate change and climate change sensitivity. The capacity that requires intensive management was found to be the ability to reflect on climate change. According to the IPA analysis results among early childhood teachers' teaching competencies on ecological citizenship, the competency that required intensive management was participation, and the competency that required gradual improvement was justice. Through the results of this study, climate change teaching capabilities necessary for early childhood teachers were derived and the direction of teacher education for this was discussed.