Reviewerly/exHarmony huggingface dataset:
Empowering photovoltaic power generation with edge computing: A recognition and location approach for hot spot

Abstract The presented paper proposes a photovoltaic module hot spot detection algorithm based on YOLOv8‐BCB. The algorithm addresses issues such as component efficiency degradation and poor contact in long‐term operation of PV systems, with a focus on the hot spot effect’. To achieve rapid detection and localization of small targets in complex scenes, the algorithm incorporates the Weighted Bi‐directional Feature Pyramid Network (BiFPN) into YOLOv8. Additionally, a lightweight upsampling operator called Content‐Aware ReAssembly of FEatures (CARAFE) is used to reduce the detection load on the unmanned aerial vehicle (UAV) detection system. The BiFormer attention mechanism is also integrated into the C2f module to better capture dependencies between sequences and reduce background interference. To highlight the superiority of the authors’ algorithm compared to other high‐quality algorithms, the authors conducted comparative experiments on the same dataset. The YOLOv8‐BCB algorithm surpasses the SSD, faster‐cnn, and RetinaNet algorithms in both accuracy and detection speed. It achieves a precision of 97.1% and a recall rate of 94.9%, with an FPS of 110. The result is a target detection algorithm that is both fast and accurate.
СТАН ПІДГОТОВКИ ФАХІВЦІВ МИСТЕЦЬКОГО СПРЯМУВАННЯ В УНІВЕРСИТЕТАХ ЄВРОПЕЙСЬКОГО СОЮЗУ ТА США ДО ЗАСТОСУВАННЯ МУЗИЧНОЇ ТЕРАПІЇ У ПРОФЕСІЙНІЙ ДІЯЛЬНОСТІ

The goal of modern university education in Europe and the USA is training, upbringing, harmonious development and socialization of a highly moral, spiritually developed personality, which takes place with the use of health-preserving technologies. This goal acquires great importance precisely in the conditions of the educational space, since it covers a significant part of the population, which is characterized by differences in cultural level, material status, and age. The number of students with special needs is increasing in university education. The use of music therapy in the modern university space contributes to solving the identified problems, for which it is necessary to implement: highly qualified training of personnel in music therapy and educational projects and programs in universities in Europe and the USA. During the 20th and 21st centuries in Europe and the USA, music therapy began to be intensively introduced into university education to activate educational and intellectual activity, model a health-saving educational environment, meet the special needs of student youth in inclusive education, and create a STEAM learning environment.To date, Ukrainian scientists have systematized only certain aspects of scientific knowledge about music therapy, which is introduced into the content of modern university education in Europe and the USA. In this way, the leading international experience on the training of specialists in the artistic field to apply music therapy in professional activities has not been summarized, the system of measures to attract students to these classes has not been substantiated, and data on the specifics of the content of classes with music therapy tools have not been systematized. The consequence of using music therapy in the university environment should be the expression of feelings and positive changes in the behavior of subjects of education during communication with others and reflection. Music therapy solves communication issues, improves motivation, attention, behavior, improves memory and the ability to learn, affects the emotional sphere of the individual.
Mineralogy and geochemistry of tellurium in the some mining sites of Transbaikalia

The first results of the study of the occurrence and mineral forms of tellurium in hypogenic and hypergenic conditions on the example of some deposits in Transbaikalia are presented in the article. Their paragenesis and the variations of tellurium content in ores and the hypergenesis zone are considered. Experiments on water leaching of tellurium, bismuth, lead, uranium and silver were carried out. The physicochemical model of mineral associations with the participation of these elements in conditions of the hypergenesis zone was calculated.
Penerapan Metode Skoring Untuk Penilain Hasil Belajar Siswa Di SDN Pondok Kelor II

Scoring and scoring systems are fundamental methods in the field of education, especially in Indonesia, because the results of scoring and scoring systems can measure students' abilities, learning attitudes, and learning outcomes within a certain period of time for basic considerations and to determine children's performance. . This article explains in detail about scoring and assessment systems in education.
Study on the Physical Training of Men's Basketball Team of Naval Aeronautical University preparing for the "Strong Army Cup" Basketball Tournament of Military Academies

The Naval Aeronautical University basketball team represented the Navy to participate in the 2023 military academies "Strong Army Cup" basketball tournament, and won the runner-up, creating the best result of the Navy. This paper studies the special physical training and competition performance of basketball training for three months, analyzes the winning factors and shortcomings, and puts forward suggestions for improvement.
Development of a Web-Based Assignment Information System by Applying Leaderboard and Quest Gamification Elements at SMK Negeri 2 Surakarta

&lt;p&gt;This study aims to find out how to build a web-based assignment information system by applying Leaderboard and Quest gamification elements at SMK Negeri 2 Surakarta TKJ Expertise Program and determine the eligibility level developed according to ISO 25010 Standards. Information system development using the Laravel web application framework. Development is carried out using the agile development software development model consisting of 5 stages, namely planning to produce functional and non-functional requirements. The research applies the Object Oriented Analysis and Design (OOAD) method in the analysis and design process. Analysis produces use case diagrams, Design produces activity diagrams and class diagrams. Implementation produces program code from development results. The system produces products uploaded to the server, which can later be accessed online and in real time. This information system has three levels of users, namely admin, teachers and students. The suitability test results using the ISO 25010 Standard on the three sub-characteristics of functional suitability, namely functional completeness, have an average value of 1 (Good). Functional correctness has an average value of 1 (Good). Functional appropriateness has an average value of 1 (Good). Characteristics of performance efficiency obtained an average result of GTMatrix Grade 85% (grade B). The average largest contentful paint is 1.423s (OK, but considering improvement). Average total blocking time 97.82ms (Good - nothing to do here). Average cumulative layout shift 0.023 (Longer than recommended). Portability characteristics have excellent results in that the system can run on desktop or mobile devices. Usability characteristics of 85% (Very Decent). The results of the respondent gamification test average score of 4.44 indicate that respondents are categorized as strongly agreeing with the existence of a web-based assignment information system by applying leaderboard and quest gamification elements. From the results of these tests, it can be concluded that the web-based assignment information system, using Leaderboard and Quest gamification elements at SMK Negeri 2 Surakarta TKJ Expertise Program, meets ISO 25010 quality standards. &lt;/p&gt;
ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation

We propose a novel Attentional Scale Sequence Fusion based You Only Look Once (YOLO) framework (ASF-YOLO) which combines spatial and scale features for accurate and fast cell instance segmentation. Built on the YOLO segmentation framework, we employ the Scale Sequence Feature Fusion (SSFF) module to enhance the multi-scale information extraction capability of the network, and the Triple Feature Encoder (TPE) module to fuse feature maps of different scales to increase detailed information. We further introduce a Channel and Position Attention Mechanism (CPAM) to integrate both the SSFF and TPE modules, which focus on informative channels and spatial position-related small objects for improved detection and segmentation performance. Experimental validations on two cell datasets show remarkable segmentation accuracy and speed of the proposed ASF-YOLO model. It achieves a box mAP of 0.91, mask mAP of 0.887, and an inference speed of 47.3 FPS on the 2018 Data Science Bowl dataset, outperforming the state-of-the-art methods. The source code is available at https://github.com/mkang315/ASF-YOLO.
SemiSAM: Exploring SAM for Enhancing Semi-Supervised Medical Image Segmentation with Extremely Limited Annotations

Semi-supervised learning has attracted much attention due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods, which is especially important for medical image segmentation which typically requires intensive pixel/voxel-wise labeling by domain experts. Although semi-supervised methods can improve the performance by utilizing unlabeled data, there are still gaps between fully supervised methods under extremely limited annotation scenarios. In this paper, we propose a simple yet efficient strategy to explore the usage of the Segment Anything Model (SAM) for enhancing semi-supervised medical image segmentation. Concretely, the segmentation model trained with domain knowledge provides information for localization and generating input prompts to the SAM. Then the generated pseudo-labels of SAM are utilized as additional supervision to assist in the learning procedure of the semi-supervised framework. Experimental results demonstrate that SAM's assistance significantly enhances the performance of existing semi-supervised frameworks, especially when only one or a few labeled images are available.
A note on restriction rules

We consider a certain class of infinitary rules of inference, called here restriction rules, using of which allows us to deduce complete theories of given models. The first instance of such rules was the $\omega$-rule introduced by Hilbert, and generalizations of the $\omega$-rule were first considered by Henkin. Later on Barwise showed that within countable languages, for any countable model $\mathfrak M$, first-order logic expanded by the corresponding $\mathfrak M$-rule deduces from the diagram of $\mathfrak M$ all formulas that are true in all models that include $\mathfrak M$, in particular, it deduces the (relativized) complete theory of $\mathfrak M$. We show that, if the aim is only deducing the complete theory of a given model, these countability assumptions can be omitted. Moreover, similar facts hold for infinitary and higher-order logics, even if these logics are highly incomplete. Finally, we show that Barwise's theorem in its stronger form, for vocabularies of arbitrary cardinality, holds for infinitary logics $\mathscr L_{\kappa,\lambda}$ whenever $\kappa$ is supercompact. The note also contains brief historical remarks.
OCWYOLO: A Road Depression Detection Method

In terms of road depression detection, there are four existing methods: (1) based on two-dimensional image processing methods, (2) three-dimensional point cloud modeling and segmentation based methods, (3) based on machine/deep learning methods, and (2) (3) hybrid methods. This article proposes a road depression detection algorithm based on Yolov7, which utilizes the Yolov7 network model to make different improvements: to make the localization more accurate, we use WIoU, which reduces the total loss and improves the AP to a certain extent. Due to the presence of some small objects in the dataset, small objective detection has always been a focus of object detection, so ODConv, which can effectively detect small objects, has been introduced. In the past, adding attention mechanisms directly added an attention component, which would make the model more complex. Therefore, we directly applied attention mechanisms to existing components without increasing the complexity of the model and achieving the goal of improving accuracy. In addition, we conducted a large number of experiments to verify the superiority of our model. We not only compare it on our road depression dataset but also conducted comparative experiments with the recent state-of-the-art models in the Yolo field on the COCO dataset to verify its universality. Compared to Yolov7, our model has improved by 14.9% on AP, while also comparing with the newly proposed Yolov8 model, we have improved by 2.0% on AP.
High-speed overtaking trajectory planning based on multi-objective optimization

Due In order to ensure the safety of high-speed overtaking planning and improve the trajectory comfort, a multi-objective trajectory planning algorithm for high-speed overtaking was proposed. The algorithm solves the boundary conditions of high-speed overtaking on the basis of feasible convex space, and then analyzes the feasibility of overtaking behavior through the safety overtaking strategy. Then, according to the established overtaking trajectory planning objective function and comfort optimization function, the trajectory ride comfort and other objectives are optimized, and the algorithm plans the trajectory in real time until the overtaking behavior is completed. To verify the effectiveness of the proposed algorithm, Prescan and Matlab /Simulink were used to build a co-simulation platform, and two common overtaking scenarios were verified and analyzed. The simulation results show that the proposed method can effectively improve the comfort and safety of the planned overtaking trajectory compared with the single objective optimization.
Advancements in Synthetic Data Extraction for Industrial Injection Molding

Machine learning has significant potential for optimizing various industrial processes. However, data acquisition remains a major challenge as it is both time-consuming and costly. Synthetic data offers a promising solution to augment insufficient data sets and improve the robustness of machine learning models. In this paper, we investigate the feasibility of incorporating synthetic data into the training process of the injection molding process using an existing Long Short-Term Memory architecture. Our approach is to generate synthetic data by simulating production cycles and incorporating them into the training data set. Through iterative experimentation with different proportions of synthetic data, we attempt to find an optimal balance that maximizes the benefits of synthetic data while preserving the authenticity and relevance of real data. Our results suggest that the inclusion of synthetic data improves the model’s ability to handle different scenarios, with potential practical industrial applications to reduce manual labor, machine use, and material waste. This approach provides a valuable alternative for situations where extensive data collection and maintenance has been impractical or costly and thus could contribute to more efficient manufacturing processes in the future.
Natural Language-Driven Dialogue Systems for Support in Physical Medicine and Rehabilitation

This paper presents a natural language-driven dialogue system designed to support healthcare professionals and students in the field of physical medicine and rehabilitation. The system seamlessly integrates concepts from intelligent information systems, data mining, ontologies, and human-computer interaction, employing at its core a rule-based dialogue mechanism. The system harnesses the power of ontology-based graph knowledge, underscoring its domain-specific efficacy. This article delves into the automated knowledge base formation, utilising Python scripts to translate EBSCO’s dataset of articles on physical medicine and rehabilitation into an OWL ontology. This methodology ensures adaptability to the ever-evolving landscape of medical insights. The system’s approach to natural language processing encompasses text preprocessing, semantic category discernment, andSPARQL query creation, providing 26 predefined categories. As an innovation in performance optimisation, the system integrates a strategy to cache precomputed responses using a PostgreSQL database, which aids in resource conservation and reduction in query execution latency. The system’s user engagement avenues are further detailed, showcasing a Telegram bot and an API, enhancing accessibility and user experience. In essence, this article illuminates an advanced, efficient dialogue system for physical medicine and rehabilitation, synthesising multiple computational paradigms, and standing as a beacon for healthcare practitioners and students alike.
Teachers’ Perception, Plan, and Implementation of Cambridge Curriculum in Teaching Writing at Cambridge Bilingual High School Yogyakarta

Cambridge English Language Assessment is one of the curriculum pathways used in Senior High School. This curriculum has required subjects, including English as a Second Language/ESL. At the end of the course, Cambridge curriculum has a final test named Cambridge testing/ IGCSE test. Based on previous studies, it found that writing is the students’ lowest point. In addition, preliminary research at CBHS Yogyakarta also found that writing skills, especially hortatory exposition text, is the most challenging material faced by 11th-grade students. Therefore, the role of the teachers in the teaching-learning process, especially in writing, is essential. This thesis aimed to evaluate how English teachers perceive, plan, and implement the Cambridge curriculum in teaching writing. The objectives of the research are (1) to explain the teachers’ perception of the Cambridge curriculum in teaching writing in CBHS Yogyakarta (2) to explain the teachers’ plan of the Cambridge curriculum in teaching writing in CBHS Yogyakarta (3) to explain the teachers’ implementation of the Cambridge curriculum in teaching writing in CBHS Yogyakarta (4) to show the relationship between the teachers’ perception, the teachers’ plan, and the teachers’ implementation of Cambridge curriculum in teaching the students writing in CBHS Yogyakarta. It was collected by qualitative research. The sample involved four English teachers of CBHS Yogyakarta. Data collection included interview guidelines, questionnaires, document analysis, and observation. The technique of analyzing the data will use the steps of analyzing data proposed by Creswell (2014). The results showed the four teachers of CBHS Yogyakarta had positive perception in teaching writing. They have adequate knowledge and experience dealing with Cambridge curriculum implementation. The teachers’ implementation aligned with their perception and plan. However, although their lesson plan did not include all aspects based on the local curriculum qualification, it was completed enough to implement in the classroom. Keywords: Teachers’ perception, teachers’ plan, teachers’ implementation, Cambridge curriculum, hortatory exposition texts.
دور القيادة الريادية في مواجهة الازمات التسويقية دراسة استطلاعية من وجهة نظر الإداريين في عدد من شركات الاتصالات في محافظة دهوك

This research aims to shed light on the importance of entrepreneurial leadership in facing marketing crises, through an applied study based on the opinions and viewpoints of administrators in a number of telecommunications companies in Duhok Governorate. Marketing crises are an important challenge facing the commercial sector at the present time, and they require effective leadership strategies to overcome them and succeed in facing them. Data was collected by distributing a questionnaire to a number of administrators in telecommunications companies, whereby statistical analysis techniques were used to analyze the data and extract the desired results and main ideas related to entrepreneurial leadership and its role in facing marketing crises. The results of the study showed the importance of entrepreneurial leadership in facing marketing crises. The administrators have adopted innovative leadership styles and driven the application of innovation and change to adapt to the changing challenges in the telecom market. This was achieved by promoting innovation in marketing operations, and encouraging employees to develop new ideas and innovative marketing strategies. The importance of building trust and promoting positive relations between the leadership team and employees was also identified, as this led to raising team morale and increasing interaction and cooperation in facing marketing crises. It has also been proven that entrepreneurial leaders enhance employee capabilities and encourage continuous learning, which contributes to strengthening the organization's ability to face marketing crises and achieve sustainable success. Based on these results, the researchers suggest that entrepreneurial leadership should have a central role in developing strategies for facing marketing crises. Leaders must pay great attention to developing and enhancing entrepreneurial leadership skills and fostering a culture of innovation and excellence in the organization.
Reference based AI Decision Support for Cybersecurity

In the cyber environment, massive amounts of data are generated daily. Artificial Intelligence (AI) technologies can effectively manage this vast data to support efficient operations in the cyber environment. Thanks to active research, AI has advanced significantly in this regard. However, as AI achieves higher performance, it becomes increasingly complex, which results in the low interpretability of AI outputs. This black-box nature of AI technology makes AI chal-lenging to apply in fields like cybersecurity, where the risk of false positives is significant. To address this issue, researchers have been working on eXplainable Artificial Intelligence (XAI) technology, with the intention to enhance the utility of AI by providing interpretations of AI predictions. Most previous research has focused on understanding how models function in terms of feature importance to interpret AI results. However, this approach fails to provide clear interpretations in fields where interpretability is crucial, such as security. Therefore, this paper proposes a framework that offers interpretations of AI results, even in unsupervised environ-ments that are suitable for security scenarios. Additionally, we have improved the logic of cal-culation Reference and have enhanced the function and performance compared with previous research. We provide additional information that supports interpretation, such as P-Values and References, to offer more effective decision support to security analysts and to ultimately reduce false alarms and enhance model performance. Overall, we aim to improve the model’s perfor-mance by providing clear interpretations that are suitable for security tasks, thereby contrib-uting to more effective decision-making by security analysts.