Reviewerly/exHarmony huggingface dataset:
Penerapan PI Hole DNS Server Sebagai ADS-Blocker Dan Sistem Filtering Website Pada Jaringan Hotspot

Abstract This research aims to build a Hotspot network infrastructure that is free from advertisements to maximize bandwidth usage, implement a virus prevention system by filtering websites, and reduce router performance in hotspot networks. This research uses the Network Development Life Cycle (NDLC) method. By using the research method, it is hoped that the author can define the process cycle of designing or developing a computer network system, especially in the process of implementing the Pi Hole DNS server as an ads-blocker and website filtering system on a hotspot network. The results of this study indicate that the application of a pi hole DNS server on a hotspot network can block advertisements on sites visited via a web browser on a hotspot network. In addition, the pi hole DNS server can also perform filtering on advertising provider sites and the pi hole DNS server does not reduce the quality of service on the hotspot network.&#x0D; Keywords: NDLC, pi hole, android, network, hotspot.
Small Target Detection for Unmanned Aerial Vehicle Images Based on YOLOv5l

Object detection on the unmanned aerial vehicle(UAV)image is a popular and challengeable research orientation. UAV images consist of a large number of targets, complex backgrounds, and affluent small targets. So as to accurately detect small targets on UAV images, we put forward a small target detection model with a type of attention mechanism based on YOLOv5. Firstly, the ECA attention mechanism module[1] is added after the structure block of CSPdarknet[2]in the backbone of YOLOv5 to focus on the region of interest. Then we improve the structure of YOLOV5 by adding an extra prediction head which is useful to detect different-scale objects. We also substitute the sampling method for transpose convolution[3] to keep more features. To reduce the parameters and expedite the training speed, we use GhostConv[5]to reduce parameters and train time. A mass of experiments on the dataset VisDrone21 [6] shows that the improved YOLOv5l has good performance on UAV images. As a benchmark model, YOLOv5l is more suitable for UAV small target detection due to its network structure. Under the YOLOv5l benchmark, the mAP50 of our model increased by 10.3% and the mAP50:95 increased by 7.4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> . The mAP50 of the model trained at the input resolution of 1536*1536 can reach 62.8%.
Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review

This review article presents recent advancements in deep learning methodologies and applications for autonomous navigation. It analyzes state-of-the-art deep learning frameworks used in tasks like signal processing, attitude estimation, obstacle detection, scene perception, and path planning. The implementation and testing methodologies of these approaches are critically evaluated, highlighting their strengths, limitations, and areas for further development. The review emphasizes the interdisciplinary nature of autonomous navigation and addresses challenges posed by dynamic and complex environments, uncertainty, and obstacles. With a particular focus on mobile robots, self-driving cars, unmanned aerial vehicles, and space vehicles to underscore the importance of navigation in these domains. By synthesizing findings from multiple studies, the review aims to be a valuable resource for researchers and practitioners, contributing to the advancement of novel approaches. Key aspects covered include the classification of deep learning applications, recent advancements in methods, general applications in the field, innovations, challenges, and limitations associated with learning-based navigation systems. This review also explores current research trends and future directions in the field. This extensive overview, initiated in 2020, provides a valuable resource for researchers of all levels, from seasoned experts to newcomers. Its main purpose is to streamline the process of identifying, evaluating, and interpreting relevant research, ultimately contributing to the progress and development of autonomous navigation technologies.
Edge-Supervised Linear Object Skeletonization for High-Speed Camera

This paper presents a high-speed skeletonization algorithm for detecting the skeletons of linear objects from their binary images. The primary objective of our research is to achieve rapid extraction of the skeletons from binary images while maintaining accuracy for high-speed cameras. The proposed algorithm uses edge supervision and a branch detector to efficiently search inside the object, avoiding unnecessary computation on irrelevant pixels outside the object. Additionally, our algorithm addresses the challenge of self-intersections in linear objects with a branch detection module, which detects existing intersections and initializes new searches on emerging branches when necessary. Experiments on various binary images, such as numbers, ropes, and iron wires, demonstrated the reliability, accuracy, and efficiency of our approach. We compared the performance of our method with existing skeletonization techniques, showing its superiority in terms of speed, especially for larger image sizes.
Three-Dimensional Tracking of a Target under Angle-Frequency Measurements with Multiple Frequency Lines

This article considers tracking a constant-velocity underwater target, which emits sound with distinct frequency lines. By analyzing the target's azimuth, elevation and multiple frequency lines, the ownship can estimate the target's position and (constant) velocity. In our paper, this tracking problem is called the 3D Angle-Frequency Target Motion Analysis (AFTMA) problem. We consider the case where some frequency lines disappear and appear occasionally. Instead of tracking every frequency line, this paper proposes to estimate the average emitting frequency by setting the average frequency as the state vector in the filter. As the frequency measurements are averaged, the measurement noise decreases. In the case where we use the average frequency line as our filter state, both the computational load and the root mean square error (RMSE) decrease, compared to the case where we track every frequency line one by one. As far as we know, our manuscript is unique in addressing 3D AFTMA problems, such that an ownship can track an underwater target while measuring the target's sound with multiple frequency lines. The performance of the proposed 3D AFTMA filter is demonstrated utilizing MATLAB simulations.
PERAN ARTIFICIAL INTELLIGENCE CHATGPT DALAM PERENCANAAN PEMBELAJARAN DI ERA REVOLUSI INDUSTRI 4.0

This study aims to provide an alternative solution to the problems that teachers often experience in preparing lesson plans and assessment instruments. Problems that often arise are learning plans that are not contextualized, only using teaching plans that already exist on the internet, lack of time in preparing assessment instrument. Through a quantitative approach with library research method, the researcher collected data about ChatGPT then analyzed with two prompts. The results showed that ChatGPT can generate teaching plans and assessment instruments in less than 10 minutes. The first prompt only took about 5 minutes, while the second prompt took less than 2 minutes. This shows that the use of ChatGPT is very effective in teaching planning. Penelitian ini bertujuan untuk memberikan salah satu alternatif solusi terhadap masalah yang sering dihadapi guru dalam menyusun perencanaan pembelajaran baik berupa RPP maupun instrumen penilaian. Masalah yang sering muncul adalah perencanaan pembelajaran yang tidak kontekstual, hanya menggunakan RPP yang sudah ada di internet, kurangnya waktu dalam mempersiapkan perangkat pembelajaran. Hal ini dapat diselesaikan dengan menggunakan Artificial Intelligence (AI) ChatGPT. Melalui pendekatan kuantitatif dengan metode penelitian pustaka, peneliti mengumpulkan data tentang ChatGPT kemudian menganalisis dengan dua prompt. Hasil penelitian menunjukkan bahwa ChatGPT dapat menghasilkan RPP dan instrumen penilaian kurang dari 10 menit. Prompt pertama hanya membutuhkan waktu sekitar 5 menit, sedangkan prompt kedua kurang dari 2 menit. Hal ini menunjukkan bahwa penggunaan ChatGPT sangat efektif dalam perencanaan pembelajaran.
Temporal Relation Extraction with Contrastive Prototypical Sampling

Temporal relation extraction aims to infer the temporal order of either two events in the document. Because of the nature of events in real life, there exist severe imbalanced temporal relation classes in the temporal relation extraction task. Even though various methods have been proposed to improve the overall performance, these methods' accuracy on small temporal classes is limited. In this work, we present a contrastive prototypical learning framework to address this problem, which explicitly models the spatial similarity between instances in the embedding space so that instances from small classes can be distinguished from the large classes. To make it compatible with current temporal relation extraction settings, we propose a novel sampling memory queue-based method so that the framework can be applied to a limited batch size scenario. We further design a context encoding layer that incorporates both contextualized information and linguistic features such as tense information and dependency. Our extensive experiments on TimeBank-Dense, TDDiscourse, and MATRES datasets demonstrate that our model can significantly improve the performance of small relation classes and therefore increase the overall learning ability.
Effect of Quantum Learning Model on Mathematics Learning Outcomes of Elementary School Students

The background of this research is the low learning outcomes on the content of learning mathematics in fourth grade elementary school students. The purpose of this study was to determine the differences in learning outcomes of the quantum learning model and the conventional learning model. The research is a quasi-experimental design nonequivalent control group design. The population of this research is the fourth grade students of SD Negeri 2 Mowila and SD Negeri 5 Mowila as many as 83 students. The sample of this research is the fourth grade students of SD Negeri 2 Mowila (57 students) as the experimental group and the fourth grade students of SD Negeri 5 Mowila (26 students) as the control group. The research findings indicate that thelearning model quantum has an effect on students' mathematics learning outcomes. This is indicated by the results of the one sample t-test with sig. (2-tailed) of 0.00 ( p 0.05) . The effect of thelearning model quantum on mathematics learning outcomes with independent sample t-test test with value sig. (2-tailed) of 0.008 ˂ 0.05.
Ore mineralogy and geochemistry of the productive intrusive bodies in Guydash Fe-Skarn deposit, East Azarbaijan Province

Ore mineralogy and geochemistry of the productive intrusive bodies in Guydash Fe-Skarn deposit, East Azarbaijan Province
Advanced techniques in the analysis of traffic accidents

Modern vehicles in road traffic are equipped with mutually integrated electronic devices used for managing systems. A chip in the airbag module identifies collisions and, at the same time, memorizes key parameters prepared for the analysis of traffic accidents. After the transfer of data, it is possible to make an undoubted temporal-spatial analysis and give answers to all those questions which the previous practice had no answers for and instead relied on the principle of in dubio pro reo. The paper describes the current cognitive methods, presents modern digital forensics devices, its history, organization of digital forensics systems, highlights the formal and factual dilemmas of this novelty, describes the technique and method of data access as well as different perspectives. A list of questions that have not been answered by previous practice is provided, as well as several examples from practice, demonstrating the advantages that digital forensics achieves in relation to the traditional way of working. Applying the results of digital forensics will undoubtedly have a substantial effect on eradicating false accusations, erroneous verdicts, and unnecessary lawsuits. It will completely change the work of traffic-technical experts by eradicating all known weaknesses, reduce the number of lawsuits, and largely shorten the existing ones, which will affect the economy of proceedings in this area.
Developing a Culture of Continuous Learning for Sustainable Job Performance: TEVT Sector as Learning Organization, Pakistan.

This study explores the role of Technical Support (TS) and Leadership Support (LS) in enhancing Employee Performance (EP) through the development of a Culture of Continuous Learning (CCL) in the Technical Education and Vocational Training (TEVT) sector of southern Punjab. Data from 291 TEVT trainers were analyzed using Smart PLS4. The results show that TS and LS have a positive impact on CCL, leading to improved EP. Employee Engagement (EE) moderates these effects, revealing barriers such as overwhelmed employees, limited relevance of learning programs, and a perceived lack of support and recognition. To overcome these challenges, organizations can provide support structures like mentoring and coaching. These findings emphasize the importance of TS, LS, and CCL in enhancing EP in the TEVT sector and suggest further research to explore additional factors for improved employee performance in resource-constrained environments.
Innovation in micro and small businesses: how inbound open innovation and dynamic capabilities work together to explain innovation performance

Purpose: This study examined how inbound open innovation and dynamic capabilities (sensing, seizing, and transforming capacity) work together to explain innovation performance. Methodology: The study used a case research method that involved interviewing ten purposively selected managers of micro and small furniture industries in Arusha, Dar es Salaam and Mbeya cities of Tanzania. Subsequently, the study used illustrative and content analysis methods to compare and align predetermined theoretical relationships on how dynamic capabilities and inbound open innovation explain innovation performance to empirical results.Originality: The use of illustrative analysis to compare extant theoretical relationships between dynamic capabilities, inbound open innovation, and innovation performance to case studies’ processes in this study is novel to the open innovation literature. Also, the resultant conceptual framework and relationships linking inbound open innovation to innovation performance through systematic processes of dynamic capabilities are novel.Main results: This study showed that inbound open innovation explains innovation performance through systematic complementary processes of dynamic capabilities. Moreover, the study showed that if inbound open innovation generates complex external ideas, micro and small firms adopt coupled open innovation as a predecessor of dynamic capabilities to unlock complexity.Theoretical contributions: This study has integrated the theories of open innovation and dynamic capabilities by developing a conceptual framework and relationships that show how inbound open innovation explains innovation performance through dynamic capabilities.Practical contributions: This study has revealed alternative combinations of dynamic capabilities that business managers need to benefit innovation performance from inbound open innovation.
Special Issue “Recent Trends in Natural Language Processing and Its Applications”

The recent advancements in Artificial Intelligence have paved the way for remarkable achievements in tasks that have traditionally posed challenges even for humans [...]
Pre-Monitoring Biogeochemical Research of the landscapes in the Area of Ada Tepe Gold Mining Site (Eastern Rhodopes)

The article depicts the biogeochemical properties of the landscapes in the area of the Ada Tepe gold mine (Krumovitsa River Catchment, Eastern Rhodopes) before its launching as an active ore-extraction site. The research is conducted by examination of heavy metals content in particular samples of representative native plant species in the local landscapes. The conducted research surveyed the content of 7 chemical elements (heavy metals) in the tissues of native plants from the landscapes in the Eastern Rhodopes. These microelements are generally prioritized in terms of environmental protection. The interpretation of the obtain results was made by applying the Coefficient of Biological Absorption (Ax). It is as a result of the proportions between the chemical content in the plant tissue (lx) and the chemical content in the local soil/bedrock (nx). By default, this coefficient represents the absorption (and accumulation) intensity of chemical elements in the plants.
Realization of a source-device-independent quantum random number generator secured by nonlocal dispersion cancellation

Quantum random number generators (QRNGs) can provide genuine randomness by exploiting the intrinsic probabilistic nature of quantum mechanics, which play important roles in many applications. However, the true randomness acquisition could be subjected to attacks from untrusted devices involved or their deviations from the theoretical modeling in real-life implementation. We propose and experimentally demonstrate a source-device-independent QRNG, which enables one to access true random bits with an untrusted source device. The random bits are generated by measuring the arrival time of either photon of the time–energy entangled photon pairs produced from spontaneous parametric downconversion, where the entanglement is testified through the observation of nonlocal dispersion cancellation. In experiment, we extract a generation rate of 4 Mbps by a modified entropic uncertainty relation, which can be improved to gigabits per second by using advanced single-photon detectors. Our approach provides a promising candidate for QRNGs with no characterization or error-prone source devices in practice.
Teachers' Teaching Style as Perceived by Students and its Influence on Students' Level of Self-Regulation and Motivation in Learning Psychology

The emergence of COVID-19 resulted from educational institutions to adapt flexible mode of learning. In this type of modality, teacher’s teaching styles is one of the crucial factors that may influence the student’s motivation in learning. This quantitative descriptive-correlation study investigated both the students’ perception on what type of teaching style their teachers have and students’ level of motivation in learning Psychology subjects. Data was collected from 349 students taking BS Psychology from Dalubhasaan ng Lungsod ng San Pablo, a locally funded college in the City of San Pablo, Philippines. Results shows that as perceived by the students, 86% of their teachers used learner-centered approach while the remaining 14% used teacher-centered approach. Participation in the learning process is the most common approach used by teachers for specific teaching styles, followed by personalizing experience, relating to experience, and climate building, in that order. Students' motivation to learn psychology was found to be high to very high, as was their self-regulation level. Learner-centered, participation in the learning process, personalizing experience, relating to experience, and climate building was found to have low positive correlation to intrinsic motivation and task value but have negligible correlation to extrinsic motivation and self-regulation.&#x0D;
The Seduction of Meme Music

Meme Music went viral during the COVID-19 pandemic. Distributed via apps like TikTok, it might transform not only the way music is made, but also how it is consumed. An article about the cultural implications of an emerging digital format – and why we all might be Memecubators.
The role of author identities in peer review

There is widespread debate on whether to anonymize author identities in peer review. The key argument for anonymization is to mitigate bias, whereas arguments against anonymization posit various uses of author identities in the review process. The Innovations in Theoretical Computer Science (ITCS) 2023 conference adopted a middle ground by initially anonymizing the author identities from reviewers, revealing them after the reviewer had submitted their initial reviews, and allowing the reviewer to change their review subsequently. We present an analysis of the reviews pertaining to the identification and use of author identities. Our key findings are: (I) A majority of reviewers self-report not knowing and being unable to guess the authors' identities for the papers they were reviewing. (II) After the initial submission of reviews, 7.1% of reviews changed their overall merit score and 3.8% changed their self-reported reviewer expertise. (III) There is a very weak and statistically insignificant correlation of the rank of authors' affiliations with the change in overall merit; there is a weak but statistically significant correlation with respect to change in reviewer expertise. We also conducted an anonymous survey to obtain opinions from reviewers and authors. The main findings from the 200 survey responses are: (i) A vast majority of participants favor anonymizing author identities in some form. (ii) The "middle-ground" initiative of ITCS 2023 was appreciated. (iii) Detecting conflicts of interest is a challenge that needs to be addressed if author identities are anonymized. Overall, these findings support anonymization of author identities in some form (e.g., as was done in ITCS 2023), as long as there is a robust and efficient way to check conflicts of interest.
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed.