Reviewerly/exHarmony huggingface dataset:
Collective Decision of One-vs-Rest Networks for Open-Set Recognition

Unknown examples that are unseen during training often appear in real-world pattern recognition tasks, and an intelligent self-learning system should be able to distinguish between known examples and unknown examples. Accordingly, open-set recognition (OSR), which addresses the problem of classifying knowns and identifying unknowns, has recently been highlighted. However, conventional deep neural networks (DNNs) using a softmax layer are vulnerable to overgeneralization, producing high confidence scores for unknowns. In this article, we propose a simple OSR method that is based on the intuition that the OSR performance can be maximized by setting strict and sophisticated decision boundaries that reject unknowns while maintaining satisfactory classification performance for knowns. For this purpose, a novel network structure, in which multiple one-vs-rest networks (OVRNs) follow a convolutional neural network (CNN) feature extractor, is proposed. Here, an OVRN is a simple feedforward neural network that is designed to assign confidence scores that are lower than those in the softmax layer to unknown samples so that unknown samples can be more effectively separated from known classes. Furthermore, the collective decision score is modeled by combining the multiple decisions reached by the OVRNs to alleviate overgeneralization. Extensive experiments were conducted on various datasets, and the experimental results show that the proposed method performs significantly better than the state-of-the-art methods by effectively reducing overgeneralization. The code is available at https://github.com/JaeyeonJang/Openset-collective-decision.
ViTPose++: Vision Transformer for Generic Body Pose Estimation

In this paper, we show the surprisingly good properties of plain vision transformers for body pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model dubbed ViTPose. ViTPose employs the plain and non-hierarchical vision transformer as an encoder to encode features and a lightweight decoder to decode body keypoints in either a top-down or a bottom-up manner. It can be scaled to 1B parameters by taking the advantage of the scalable model capacity and high parallelism, setting a new Pareto front for throughput and performance. Besides, ViTPose is very flexible regarding the attention type, input resolution, and pre-training and fine-tuning strategy. Based on the flexibility, a novel ViTPose++ model is proposed to deal with heterogeneous body keypoint categories via knowledge factorization, i.e., adopting task-agnostic and task-specific feed-forward networks in the transformer. We also demonstrate that the knowledge of large ViTPose models can be easily transferred to small ones via a simple knowledge token. Our largest single model ViTPose-G sets a new record on the MS COCO test set without model ensemble. Furthermore, our ViTPose++ model achieves state-of-the-art performance simultaneously on a series of body pose estimation tasks, including MS COCO, AI Challenger, OCHuman, MPII for human keypoint detection, COCO-Wholebody for whole-body keypoint detection, as well as AP-10K and APT-36K for animal keypoint detection, without sacrificing inference speed.
Estimation of monthly global solar radiation over twelve major cities of Libya

• 7 empirical models are compared for estimation of solar radiation over Libya. • The models are established using measured data of global solar radiation. • Statistical indicators are calculated to evaluate the performance of the models. • The best model performance for each site is determined. • The results show that there is no unique model valid for all considered sites. This study aims to estimate monthly averaged daily horizontal global solar radiation. Measured climatological data collected at twelve major cities located across Libya's map were used to establish 7 different empirical models. The empirical coefficients of the models were calculated using the least square method. The accuracy of the models was evaluated using different statistical criteria such as Taylor diagram, mean absolute percentage error, MAPE, and root mean square error, RMSE. The results indicated that the sunshine duration-based models are more accurate than air temperature-based models, and the best performance was obtained by the quadratic regression model for all twelve Libyan cities. Moreover, this regression model can be used for the prediction of monthly mean horizontal global solar radiation at a specific site across Libya's regions with minimum error. Furthermore, the results of the global solar irradiance produced by this method can be used for designing solar systems applications.
PEMANFAATAN INTERNET OF THINGS PADA SMARTHOME DENGAN MODEL SIMULASI PROTOTYPE

Internet of Things has been a recent focus in technology discussion. This tecnology enables every devices we own to connect to the internet, allowing remote control throught smartphones or even voice commands. The development of iot extends beyond large industries like power grids and factories, reaching into everyday household electronics. For instance, light can now be connected to the internet. The final project involves designing and creating an internet controlled relay system with comamands sent throught a telegram bot. This project aims to implement iot tecnology in a simple and affordable manner. The control system utilizes the nodeMCU microcontroller with a two -channel relay output to remote turn light on and off via telegram.
The Implementation of Bottom-Up in Reading Comprehension for Vocational School Students

This research aims to investigate the improvement reading comprehension of SMK PGRI 2 Mojokerto by using Bottom-Up approach in reading comprehension of recount text. The research was conducted in two Cycles of Classroom Action Research (CAR) where each Cycle was conducted in 4 (four) phases, they were: planning, action, observation, reflection. Each Cycle was applied in (four) meetings. The subjects of the research were 35 students of tenth grade (X-C Mechatronics). In collecting data, the researcher applied three instruments: observation, questionnaire and reading test. There were 2 (two) observations in field note which conducted by the researcher: the first observation which was done before implementing Bottom-Up to find the problem during reading activity in English lesson. Meanwhile, the second observation was to find the improvement in having Bottom-Up that conducted by English teacher during the approach applied. The questionnaire was delivered to students in the fourth meeting of the second Cycle to find out the responses of the students in learning reading by using Bottom-Up. The data was analyzed through descriptive and statistical method. In this research, the researcher acted as a teacher and the English teacher as an observer. The research findings showed that the students reading comprehension improved from Cycle I to Cycle II. Furthermore, the data from the observation and questionnaire indicated that the majority of the students gave positive response toward the use of Bottom-Up approach in teaching reading. It emphasized that the approach could improve students’ ability in reading, effective, interesting, and grab the students’ enthusiasm in reading activity.
Detecting Shadows in Computer Vision: A MATLAB-Based Approach

The abstract outlines a novel MATLAB-based technique for enhancing shadow detection accuracy in computer vision applications. This MATLAB-based method uses thresholding and color analysis to improve the accuracy of shadow identification. Luminance (L*) is distinguished from chromaticity (a* and b*) in digital photographs by converting them into the Lab color space. This makes it easier to identify possible shadowed areas. The luminance channel's thresholding makes it possible to distinguish between regions that are well-lit and those that are shadowed. The computation of chromaticity distance also helps in detecting modest color changes that are suggestive of shadows. The culmination of the suggested technique is the creation of a binary shadow mask, which isolates the image's shaded regions. The research addresses limitations of existing approaches by maintaining robustness in diverse lighting conditions and complex scenarios, thereby enhancing image comprehension and the precision of shadow detection. The proposed technique holds promise for improving object recognition and scene analysis in computer vision applications, offering a robust and reliable solution for identifying and isolating shadows in digital images.
CRIISL: Convert Rasterize Image Into SVG Layers

Raster images in multiple layers or vector format can be challenging for digital artists. However, converting them into Scalable Vector Graphics (SVG) format, such as Adobe Illustrator, Inkscape, or CorelDRAW, offers advantages. SVG files allow for easy resizing without loss of quality, making them ideal for creating logos, icons, and other graphics that need to be scaled to different sizes. They are lightweight and can be easily edited using code, providing more control over design elements. This versatile and flexible tool streamlines the design process and provides greater flexibility for artists in their creative projects. SVG files are smaller in size compared to raster images, making them easier to work with and share. Developers can also use code-based approaches to further manipulate and customize SVG images as needed.
Review on green resources and AI for biogenic solar power

The need for clean and renewable energy has grown dramatically during the past few years. As potential candidates for producing green energy in this region, photovoltaic and bio-solar energy technologies have arisen. This review presents a novel approach for designing and developing photovoltaics and bio-solar cells using eco-friendly materials and artificial intelligence (AI) techniques. An intriguing architecture is outlined for a bio-solar cell that fuses photovoltaic electronics with photosynthetic organisms. A recyclable thin-film solar cell serves as the basis of our photovoltaic system. To further maximize the effectiveness of the device, we use AI algorithms. According to statistical calculations, the proposed bio-solar cell can produce a sizable amount of electricity while being ecologically sound. This paper outlines significant advances in developing solar cells and photovoltaics using green nanomaterials and AI, which provide exciting potential for improving energy harvesting capacity. This review also presents an overview of the effects of the potential commercialization of our strategy, its social and environmental benefits, and its pitfalls.
Efforts to improve nutritional knowledge in adolescent girls through contemporary video-based education

Adolescent girls belong to a vulnerable age group that experiences nutritional problems with various risks of health problems such as anemia, chronic energy deficiency, and overnutrition. Increasing the nutritional knowledge of adolescent girls can be an opportunity to improve their nutritional status throughout the initial 8000 days of life. This community service activity uses contemporary video media to expand the knowledge of adolescent girls related to nutrition. Community service is integrated with life-cycle nutrition courses. The target audience was 30 adolescent girls. This activity is located at SMA Negeri 4 Palembang for 1 month. The activity method was carried out by designing and making contemporary videos by 3rd-semester nutrition students, followed by distributing educational videos to participants. Feedback from participants as well as pre- and post-test results were used in the evaluation process. Results from community service projects show that 30% of targets have anemia based on Hb examination with the GC-Hb tool, and 30% of targets have malnutrition based on IMT/U assessment. The target's knowledge increased after providing education with contemporary videos (p0.0001). After completing this community service project, it was concluded that the knowledge of adolescent girls increases by providing contemporary video-based nutrition education.
Developing community technology: A profile website for SDI Miftahus Sholihin

The Community Service Program titled "Developing Community Technology: A Profile Website for SDI Miftahus Sholihin" aims to address various challenges associated with school accreditation through the utilization of information and communication technology (ICT). The program's objectives are to enhance the quality of education and facilitate better access to information. It entails the design, development, and management of a profile website for SDI Miftahus Sholihin, alongside training sessions focused on website management and promotion strategies. These efforts are geared towards achieving improvement targets in school accreditation. The program timeline spans six months, encompassing phases of preparation, coordination, website development, training, and evaluation. Anticipated outcomes include heightened information quality and accessibility pertaining to school-related matters, as well as the enhancement of website management skills among school staff. Ultimately, the program aspires to elevate the educational quality of SDI Miftahus Sholihin, foster greater community engagement in educational initiatives, and facilitate the attainment of higher accreditation standards for the school.
Microcontroller based food dehydrator for Rengginang crackers micro businesses in Prambontergayang Village, Tuban District

The micro-business of Rengginang crackers with the Urnika brand in Prambontergayang Village, Tuban District, is an industry in the food sector. The process of drying Rengginang crackers in partner’s still uses conventional methods or under sunlight to carry out the drying process. Hence, it takes quite a long time to dry Rengginang, namely around 6-7 days. This community service program aims to develop and train the use of alternative technology for drying Rengginang crackers to Rengginang crackers micro-entrepreneurs in Prambontergayang Village, Tuban District. The method used for implementing PKM is a participatory approach (participatory action research/PAR). The results of the PKM carried out have had a positive impact on partners, namely more optimal and efficient drying. Drying using a microcontroller-based food dehydrator can speed up drying up to 120 minutes with a capacity of 5000 pcs of wet Rengginang. With this technology, partners can increase their production capacity for Rengginang crackers so that they can increase the expected number of sales.
Online Learning Attitude, Engagement, and Academic Achievement of College Students in China

With the rapid development of online education, understanding the performance and experiences of college students in this emerging learning mode is of paramount importance for improving educational quality. This study aims to delve into the close relationship between Chinese university students’ learning attitudes, engagement, and academic performance in the context of online learning environments. Employing a questionnaire survey and quantitative analysis, this study explores the intricate relationships between learning attitudes, engagement, and academic performance among Chinese university students engaged in online learning.
A Study on the Priority Derivation of Implementation Strategies for Resource Management and Operation Technology Development in the Logistics Industry

Globally, the industrial structure is changing to respond to digital transformation and climate change, and the logistics industry is also experiencing unprecedented upheaval, away from the existing labor-intensive industry. Currently, 10 high-value-added convergence logistics and delivery infrastructure innovation technology development-related R&amp;D projects are underway under the leadership of the convergence logistics group of the Korea Agency For Infrastructure Technology Advancement to change the status of the domestic logistics industry’s technology development from follower to first mover. However, in this study, a basic study on the priority of implementation strategies for the development of detailed technologies (policy) was conducted based on a survey of logistics experts in relation to the “logistics industry resource management and operation technology development” research project to be additionally carried out. The Importance-Performance Analysis (IPA) results of the average of importance and preparedness were placed in four quadrants to analyze the meaning of each sector’s alternatives, and the need to improve items related to the basic foundation such as private data opening, infrastructure construction, legal system improvement, and standardization was derived in the areas of concentration where the importance was high but the preparation was insufficient. However, this study has limitations such as the composition and method of survey respondents due to limited time and budget, so it is inevitable that research will be advanced through follow-up studies in the future.
Empirical Study on the Impact of Smart Logistics Platform Utilization Capabilities in the Era of the Fourth Industrial Revolution on Data-Driven Demand Forecasting Performance

The rapid emergence of Industry 4.0 technologies, such as big data, IoT, and AI, has significantly transformed the distribution and logistics landscape. Smart logistics platforms are increasingly adopted in this sector, leading to improvements in various aspects, including demand forecasting accuracy. This study aims to investigate the mediating effects of data compatibility, real-time risk response, and product history tracking on the relationship between a company’s smart logistics platform utilization capability and its data-driven demand forecasting performance. Drawing upon theoretical insights from existing literature, this study develops a research model and corresponding hypotheses. Operationalization of key constructs is achieved through the development of a structured questionnaire based on established measurement scales. To ensure the representativeness of our sample, the survey is distributed to professionals utilizing smart logistics platforms in domestic distribution and logistics companies. Following a rigorous data collection process, a total of 280 valid responses are retained for further analysis. This study aims to highlight the growing importance of smart logistics platform utilization capability as smart technologies advance. Based on the research results, it presents practical situations in the industrial field and provides policy implications for supporting small and medium-sized enterprises (SMEs) to government agencies and local governments.
Priority and Weight Analysis of Factors for Selection of 3rd Party Logistics Companies by Manufacturing Companies: Focusing on Comparison between Efficient and Responsive Supply Chains

Using AHP, it is to clearly analyze that there is a difference in the importance and weight of the selection criteria factors that should be applied first in the selection of a third-party logistics company depending on the characteristics of the product and supply chain from the viewpoint of Korean manufacturing companies. For this purpose, previous studies that presented factors for selecting third-party logistics companies were reviewed. As a method of integrating the presented selection criteria, 4 items of ‘logistics service’, 3 items of ‘logistics information (IT) system’, 4 items of ‘logistics price (cost)’, 5 selection criteria of 3 items of ‘Corporate Reputation’ and 6 items of ‘Capacity and Infrastructure’ and 20 application items were selected. And using these contents, a survey was conducted on manufacturing companies.&#x0D; Survey results As a questionnaire that can be used for AHP analysis, 51 companies in the efficient supply chain group and 35 companies in the reactive supply chain group were collected. The priority and weight of the selection criteria and application items were determined by performing pairwise comparisons on Tier II selection criteria and Tier III application factors, and calculating overlapping weights.&#x0D; The priority and importance of the Tier II selection criteria and the Tier III application factors were confirmed, and the weights for the selection criteria and application items that should be applied preferentially were calculated by synthesizing them. In the efficient supply chain group, the importance of stability, service price, image, cost reduction, accuracy, financial stability, and compatibility was high. In the reactive supply chain group, the importance of stability, accuracy, image, service price, information capability, compatibility, and incentives was high. Through these research results, by reflecting the characteristics of product manufacturing and supply chain, selection criteria and application items that should be applied first when manufacturers select third-party logistics companies are clearly presented.
Development of the method for fuzz testing mail services based on an intelligent input parameter mutation

This article proposes a method of fuzz testing mail services based on an intelligent input parameter mutation, which is applicable not only to standard email protocols but also to their extended versions. The research examines the main extensions of POP, IMAP, and SMTP protocols. The described method was applied in laboratory conditions to evaluate its effectiveness compared to traditional fuzz testing methods. The results confirmed the possibility of using the method for fuzz testing not only email clients but also servers. Further research is planned to create intelligent methods for evaluating testing coverage in the absence of prior instrumentation of email client and server source code.
Choosing optimal distance between ground-based technical observation posts

The article develops an approach to rational video monitoring construction of spatially distributed objects territories. Target. Justification for choice optimal distance between ground technical observation posts (TOP) based on electro-optical surveillance equipment in the visible and thermal imaging ranges. Results. The developed model makes it possible to determine maximum distance between adjacent TOPs, ensuring guaranteed surface targets detection and recognition in standard conditions. Practical significance. The optimal distance between TOPs ensures not only guaranteed quality of monitoring of territory boundaries, but also minimum linear cost of the security line.
"Clustering Algorithm for the Connected Model of Repeated Measurements and Survival Data: Application to HIV Study"

Many clinical trials applications generate repeated measurements and time to event (survival data). In longitudinal studies the patients are followed over many occasions (repeated measurements) and their data indicates biomarkers. Sometimes these longitudinal data include time to event, for example time to death, Alzahrani [1].
Divisibility of spheres with measurable pieces

For an $r$-tuple $(\gamma\_1,\ldots,\gamma\_r)$ of special orthogonal $d\times d$ matrices, we say that the Euclidean $(d-1)$-dimensional sphere $\mathbb S^{d-1}$ is $(\gamma\_1,\ldots,\gamma\_r)$-divisible if there is a subset $A\subseteq \mathbb S^{d-1}$ such that its translations by the rotations $\gamma\_1,\ldots,\gamma\_r$ partition the sphere. Motivated by some old open questions of Mycielski and Wagon, we investigate the version of this notion where the set $A$ has to be measurable with respect to the spherical measure. Our main result shows that measurable divisibility is impossible for a "generic" (in various meanings) $r$-tuple of rotations. This is in stark contrast to the recent result of Conley, Marks and Unger which implies that, for every "generic" $r$-tuple, divisibility is possible with parts that have the property of Baire.