Reviewerly/exHarmony huggingface dataset:
Delay-Aware Hierarchical Federated Learning

Federated learning has gained popularity as a means of training models distributed across the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL) to improve the efficiency of distributed machine learning (ML) model training by accounting for communication delays between edge and cloud. Different from traditional federated learning, DFL leverages multiple stochastic gradient descent iterations on local datasets within each global aggregation period and intermittently aggregates model parameters through edge servers in local subnetworks. During global synchronization, the cloud server consolidates local models with the outdated global model using a local-global combiner, thus preserving crucial elements of both, enhancing learning efficiency under the presence of delay. A set of conditions is obtained to achieve the sub-linear convergence rate of O(1/k) for strongly convex and smooth loss functions. Based on these findings, an adaptive control algorithm is developed for DFL, implementing policies to mitigate energy consumption and communication latency while aiming for sublinear convergenc. Numerical evaluations show DFL’s superior performance in terms of faster global model convergence, reduced resource consumption, and robustness against communication delays compared to existing FL algorithms. In summary, this proposed method offers improved efficiency and results when dealing with both convex and nonconvex loss functions.
Adaptive Decentralized Federated Learning in Resource-Constrained IoT Networks

Decentralized Federated Learning (DFL) is a novel distributed machine learning paradigm where participants collaborate to train machine learning models without the assistance of the central server. The decentralized framework can effectively overcome the communication bottleneck and single point of failure issues encountered in Federated Learning (FL). However, most existing DFL methods may ignore the communication resource constraints of the system. This may result in these methods unsuitable for many practical scenarios because the given resource constraints cannot be guaranteed. In this paper, we propose a novel DFL, called AdapCom-DFL, that can adaptively adjust the compression ratio of transmission data to keep the communication latency within the constraint. Furthermore, we propose a communication network topology pruning approach to reduce communication overhead by pruning poor links with low data rates while ensuring the convergence. Additionally, a power allocation approach is presented to improve the performance by reallocating the power of communication links while complying with the communication energy constraint. Extensive simulation results demonstrate that the proposed AdapCom-DFL with network pruning and power allocation approach achieves better performance and requires less bandwidth under the given resource constraints compared with some existing approaches.
MDDP: Making Decisions from Different Perspectives in Multi-Agent Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) has made remarkable progress in recent years. However, in most MARL methods, agents share a policy or value network, which is easy to result in similar behaviors of agents and thus limits the flexibility of the method to handle complex tasks. To enhance the diversity of agent behaviors, we propose a novel method, Making Decisions from Different Perspectives (MDDP). This method enables agents to switch flexibly between different policy roles and make decisions from different perspectives, which can improve the adaptability of policy learning in complex scenarios. Specifically, in MDDP, we design a new Self-attention and Gated Recurrent Unit (GRU) based Dueling Architecture Network (SG-DAN) to estimate the individual <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -values. SGDAN contains two components: the new Self-Attention based Role-switching network (SAR) and the capable GRU-based State value Estimation network (GSE). SAR takes charge of action advantage estimation and GSE is responsible for state value estimation. Experimental results on the challenging StarCraft II micromanagement benchmark not only verify the modeling reasonability of MDDP but also demonstrate its performance superiority over the related advanced approaches.
Machine Learning and Deep Learning Techniques

The classification of images is a multifaceted task influenced by numerous factors. This chapter looks at a variety of difficulties, challenges, machine learning algorithms, deep learning algorithms, and applications of image classification. The purpose of this chapter is to summarize the most important advanced classification methodologies as well as the techniques for improving classification accuracy. In addition to classification performance, other crucial issues are discussed. According to this review, developing an image-processing strategy that can classify remotely sensed data successfully is critical. Remote sensing data must be used effectively, and appropriate algorithms must be selected to improve classification accuracy. As multisource data classification has become increasingly complex, non-parametric classifiers such as artificial neural networks and decision trees have become more and more relevant. Other approaches include random forest classifiers, support vector machines, and object-based classification tree analysis. Identifying and reducing uncertainty in the image-processing chain is needed to improve classification accuracy. Finally, future research directions for improving image classification are discussed. Machine learning techniques and deep learning techniques have great potential, but they also face several challenges and issues. Addressing these challenges, such as data availability, interpretability, overfitting, scalability, fairness, and staying up to date, is crucial for the successful application and further development of these techniques in various domains.
Energo koncept Yaodong podzemnih zgrada u XXI veku na teritoriji Srbije

Due to their interesting architectural solution, Yaodong underground buildings seem to offer a balance between the natural and created parameters of the locations (on the one side) and ambient comfort (on the other side). In the first part of the paper, the Yaodong concept is defined (time of creation, geographical origin, categorization, the spatial organization of the interior, geometrical characteristics, etc.). In the second part of the work, the Authors develop a concrete model (in the Google SketchUp software) of the traditional Yaodong underground building, and then simulate (in the EnergyPlus software) the consumption of final and primary energy in the same, while using a weather file for the city of Kragujevac. Although this type of construction is characteristic of northwestern China, the authors think that the mentioned construction design can be of national importance, especially when taking into account the fact that the consumption of final (and primary) energy of the last year at its peak, which also applies to the residential sector.
Analisis Butir Soal Tema 3 “Peduli Terhadap Makhluk Hidup” Kelas IV SDN 2 Kertaraharja Tahun Pelajaran 2021/2022

This study aims to determine the feasibility of the question items compiled by researchers for multiple choice tests on theme 3 "Caring for Living Things" grade IV elementary school. The feasibility of the question item in question is in terms of the validity of the question, the reliability of the question, the difficulty index of the question, the differentiability, and distractor effectiveness. This research is a descriptive research that is analyzed by quantitative methods. The subjects are grade IV students of SDN 2 Kertaraharja for the 2021/2022 academic year, totaling 40 students. Data collection technique with multiple choice test amounting to 20 items. The data obtained are analyzed with the help of the SPSS 25.0 program. The results showed that (1) The quality of the material aspects and construction aspects in the grain showed that the overall question item was very good. However, the language aspect still does not meet the criteria for writing indicators (2) The quality of the uji validity shows 90% of the items of the question are valid. (3) The reliability tested has an value r11 more than 0.70 i.e. 0.763 meaning the question of having high reliability; (4) The difficulty rate of 95% of the questions includes medium question criteria, 5% of difficult questions; (5) Thedifferentiator of the questions shows 95% of the questions are good and 5% are not good; (6) Ethe fecundity of the extractor indicates that 100% of the option is declared to have functioned effectively; and (7) Overall the quality of the question items is good but the question items number 16 and 19 are not good items.
WEB-BASED EXPERT SYSTEM FOR IDENTIFYING PESTS AND DISEASE OF OYSTER MUSHROOM

Oyster Mushroom is a mushroom with a circular hood like an oyster shell. Oyster mushroom cultivators must understand the art and knowledge in the cultivation of oyster mushrooms. To care for oyster mushrooms oyster mushroom cultivators usually seek knowledge through sources such as books and articles as literacy. This is unsatisfactory because it is less efficient in diagnosing diseases or pests from oyster mushrooms. Therefore, it is necessary to have an expert system that represents expert knowledge and is able to provide diagnosis and solutions from oyster pests or fungal diseases. One of the methods in the expert system is Forward Chaining. Forward Chaining is a fact to get a conclusion from that fact. In this expert system, there are 5 pests of oyster mushrooms, including mites, caterpillars, slugs, termites, rats. Oyster fungus disease in this study also has 5 types of diseases including trichoderma spp, mucor, neurospora spp, bacteria, penicillium spp. The results showed that the accuracy of the system reached 90.1%. By implementing forward chaining in the oyster mushroom expert system, oyster mushroom cultivators, especially new cultivators, can conduct consultations for the identification of oyster fungus pests and diseases effectively and efficiently. The implementation of forward chaining in the oyster mushroom expert system begins with entering the symptoms in oyster mushrooms and then processing to find out the pests or diseases that are infected. The results of the consultation are supplemented with solutions to deal with pests or diseases present in oyster mushrooms.
Effectiveness of stego images pre-processing withspectral analysis methods

Early detection of sensitive data leakage during message transmission in communication systems is topical task today. This is complicated by applying of attackers to advanced steganographic methods. Feature of such methods is sensitive information embedding into innocuous (cover) files, such as digital images. This drastically reduces effectiveness of modern stegdetectors based on applying of signature and statistical steganalysis methods. There are proposed several approaches for improving detection accuracy of stegdetectors that are based on image pre-processing(calibration). These methods are aimed at estimation parameters either of stego, or coverimages from current analysed image.The first group of calibration methods requires prior information about features of used embedding methods to minimize detection error. In most cases, this information is limited that decrease effectiveness of such calibration methods. The second group of calibration methodsis of special interest today due to extensive set of proposed methods for advanced image denoising techniques. Nevertheless, practical usage of such methods requires carefully adjustment of parameters.Thisrestrictsfast re-training of stegdetector for revealing stego images formed according to unknown embedding methods.Thepromising approach for estimation cover image parameters from current (noisy) images is based on applying of novel methods of spectral analysis, namely sparse and redundant representationof signals. Featureof these methods is ability to adjust parameters of basicfunctions to statistical parameters of analysed set of image. This allows improving effectiveness of stegdetectors without necessity to re-tune theirs parameters for new set if images. The paper is aimed at performance analysis of stego images pre-processing with usage of advanced methods of spectral analysis. The analysis was performed for state-of-the-art HUGO embedding methods by usage of standard ALASKA dataset. Based on obtained results, it was revealed that applying of proposedmethods allows improving detection accuracyup to 6%even in case of absence prior information about used embedding methods and low cover image payload, e.g.less than 10%. Nevertheless, practical usage of thesemethods for image calibration requires further improvement of dictionary learningprocedure, namely decreasing its computation complexity byprocessing images with high resolution.
IMPROVING THE STUDENTS’ VOCABULARY MASTERY BY USING WORD SQUARE MEDIA

The purpose of this research is to find out whether using word square media can improve the vocabulary mastery of the eleventh grade students of SMK Negeri 3 Metro. In this study, researchers conducted classroom action research (CAR) which was carried out in three cycles. Each cycle consists of planning, acting, observing and reflecting. The subjects of this study were 32 students of eleventh grade at SMK Negeri 3 Metro. In collecting data, researchers used pre-test, post-test 1, post-test 2, post-test 3, observing, interview, and questionnaire. The results of the study show that using Word square media can improve stu-dents’ vocabulary mastery at eleventh grade of SMK Negeri 3 Metro, this can be seen in the progress of the pre-test and post-test cycle 1 to cycle 3. the average of pre-test score was 42.31% and the post-test in cycle 1 was 61.37%, the average value in post-test 2 was 75.40% and the average score in cycle 3 was 81.37%. pre-test and post-test cycle 1 to post-test cy-cle 3 there was an increase of 5.97%. it means that the results of the post test have reached indicators of success that can be achieved on the results of this post-test. Students responses in using word square media that have been done can be seen that 93.45% of students gave respones very positive in learning using word square learning media and word square media can be said to be practical and easy to use for students, this can be seen from the enthusiasm of students and their spirits at the first time in using word square media.
Multi-Class Support Vector Machine Based on Minimization of Reciprocal-Geometric-Margin Norms

Abstract In this paper, we propose a SVM algorithm for multi-class classification. It follows multi-objective multi-class SVM (MMSVM), which maximizes class-pair margins on a multi-class linear classifier. The proposed method, called reciprocal-geometric-margin-norm SVM (RGMNSVM) is derived by applying the ℓ p -norm scalarization and convex approximation to MMSVM. Additionally, we develop the margin theory for multi-class linear classification, in order to justify maximization of class-pair geometric margins. Experimental results on artificial datasets explain situations where the proposed RGMNSVM successfully works, while conventional multi-class SVMs fail in generalization. Results of classification performance evaluation using benchmark data sets show that RGMNSVM is generally comparable with one of the best multi-class SVMs proposed by Weston and Watkins (1999). However, we observe that the proposed approach to geometric margin maximization actually has better generalization capability for certain real-world data sets.
Improving Higher Education Supply Chain Performance Through Procurement Digital Transformation

Technological disruption has forced higher education institutions to re-adjust their governance model so that organization sustainability can be achieved in facing future challenges. The purpose of the implementation of governance digital transformation is to improve organizational performance, particularly in higher education institutions. Nowadays, almost every organization in the world is modifying their business processes by integrating technology into their operational activities, especially the procurement process as a supply chain support, to achieve established organizational strategic business goals. Through governance and digital transformation, flexibility in meeting market changes can be acquired. The purpose of this study is to find out the impact of procurement's digital transformation in higher education and improve its supply chain performance. The methodology used in this study is a literature review from several sources. As a conclusion, we aim to show that procurement digital transformation has significantly impacted supply chain performance, and supply chain innovation has also significantly impacted higher education institution performance
Readiness of UT Students in Supporting the Acceleration of SDGs Amid the Challenges of an Economic Recession

This study aims to investigate the readiness of Human Resources (HR) of Open University (UT) students in supporting the acceleration of Sustainable Development Goals (SDGs) amidst the challenges of an economic recession. The research methodology involves surveys and data analysis to evaluate students' understanding, views and knowledge of the role of HR in achieving the 2025 SDGs during a period of economic recession. The research results show that the majority of students have a diverse understanding of the concept of HR in the context of an economic recession and support investment in HR development. However, obstacles to access to the technology needed to support HR understanding were also identified. This research makes a new contribution by exploring the readiness of UT student human resources which was poorly understood in the previous literature. In conclusion, efforts to maximize the potential of UT students in supporting the SDGs in facing an economic recession require improved education, training, collaboration and greater technological support. These findings highlight the need for the role of higher education in preparing young people to contribute to sustainable development and overcome global economic challenges.
PEMBENTUKAN MINAT BACA SISWA SEKOLAH DASAR: ANALISIS PERAN GURU

Interest in reading in students is formed because of the relationship between the teacher's role and students' reading activities in the classroom. improved, and find out the obstacles faced by teachers in increasing reading interest in class IV students at SDIT Lukman Al-Hakim Surakarta. In this study using a qualitative approach with the case study method, the validity of the data in this study with the extension of the technique of observation, technical triangulation and source triangulation. The analysis in this study uses qualitative data analysis which can make clear understanding by other people and is presented in writing in the form of a research report, from three strands, namely data reduction, data review and conclusions. The results of this study indicate that teachers play many roles in increasing students' interest in reading. In realizing a good interest in reading, the teacher has his own strategy, namely: (1) providing a stimulus, (2) providing special additional hours for students who are still constrained in reading, (3) creating a new learning atmosphere. The teacher gives freedom during lessons to do learning in the library. (4) understand the character of each learner. (5) providing motivation that reading is a means to broaden knowledge. From the five strategies used by the teacher in increasing students' interest in reading, it can be seen that the teacher acts as an educator, teacher, motivator and innovator. After the teacher becomes the four roles, the teacher will become an evaluator for his students, evaluating students at the end of learning activities, in order to improve the quality of learning.
Curvature Coding in Human Vision: A Classical Review Across Psychophysics, Neurophysiology and Computer Vision. What’s Missing?

A study of curvature along the contour provides important sources of information about the shape of an object. Knowledge of contour curvature allows for the perception of and interaction with 3D objects. In this review, we will explore the value of contour curvature in the field of neurophysiology, psychophysics, computer vision, and psychology. The serial consolidation of disjoint edges, oriented in a particular manner, into composited curves allows building of identifiable shapes in occluded natural environments [Hess1999, Wertheimer1923, Zucker1989, Feldman2001]. Examples are our ability to pick objects, sort and manage shapes otherwise hidden or confounded by occlusion in messy natural environments. Contour curvature has been intensively studied in psychology as it forms the first steps in 2D shape building which eventually becomes 3D object recognition in the human brain.
Ontologies4Cat: Investigating the Landscape of Ontologies for Catalysis Research Data Management

Abstract As scientific digitization advances it is imperative ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) for machine-processable data. Ontologies play a vital role in enhancing data FAIRness by explicitly representing knowledge in a machine-understandable format. Research data in catalysis research often exhibits complexity and diversity, necessitating a respectively broad collection of ontologies. While ontology portals such as EBI OLS and BioPortal aid in ontology discovery, they lack deep classification, while quality metrics for ontology reusability and domains are absent. Thus, this work provides an approach for systematic collection of ontology metadata with focus on the catalysis research data value chain.By cassifying ontologies by subdomains of catalysis research, the approach is offering efficient comparison across ontologies.Furthermore, a workflow and codebase is presented, facilitating representation of the metadata on GitHub. Finally, a method is presented to automatically map the classes contained in the ontologies of the metadata collection against each other, providing further insights on relatedness of the ontologies listed.The presented methodology is designed for its reusability, enabling its adaptation to other ontology collections or domains of knowledge. The ontology metadata taken up for this work and the code developed and described in this work are available in a GitHub repository at: https://github.com/nfdi4cat/Ontology-Overview-of-NFDI4Cat/jcheminf
Case Method and Its Use in Chemistry

Case study is an educational technology consisting of a set of optimal methods and means of teaching, information, communication and management that ensure the guaranteed achievement of the predicted educational results in the process of implementing the educational goal and solving the described practical problem situation in the case.
Analisa Kinerja Metode Support Vector Machine untuk Analisa Sentimen Ulasan Pengguna Google Maps

Sentiment Analysis is a subset of data mining that analyzes, understands, processes, and extracts textual data in the form of opinions or reviews about a certain object. The advancement of technology, such as Google Maps, can make it easier to find information on the location of an object. Google Maps also gives an object review. These reviews provide information that can be used to examine the feelings expressed by visitors. A text mining methodology with the Support Vector Machine method is utilized to process customer review text data. This method is part of a classification strategy that recognizes and categorizes data into two portions separated by a hyperplane. The data used in this case study are visitors' reviews that is recorded on Google Maps Review of the Eternal Flame, a tourist attraction obejct located in Sumenep.