Reviewerly/exHarmony huggingface dataset:
Split Federated Learning: Speed up Model Training in Resource-Limited Wireless Networks

In this paper, we propose a novel distributed learning scheme, named group-based split federated learning (GSFL), to speed up artificial intelligence (AI) model training. Specifically, the GSFL operates in a split-then-federated manner, which consists of three steps: 1) Model distribution, in which the access point (AP) splits the AI models and distributes the client-side models to clients; 2) Model training, in which each client executes forward propagation and transmit the smashed data to the edge server. The edge server executes forward and backward propagation and then returns the gradient to the clients for updating local client-side models; and 3) Model aggregation, in which edge servers aggregate the server-side and client-side models. Simulation results show that the GSFL outperforms vanilla split learning and federated learning schemes in terms of overall training latency while achieving satisfactory accuracy.
Construction and Weight Distributions of Binary Linear Codes Based on Deep Holes

Deep holes are very important in the decoding of generalized RS codes, and deep holes of RS codes have been widely studied, but there are few works on constructing general linear codes based on deep holes. Therefore, we consider constructing binary linear codes by combining deep holes with binary BCH codes. In this article, we consider the 2-error-correcting binary primitive BCH codes and the extended codes to construct new binary linear codes by combining them with deep holes, respectively. Furthermore, three classes of binary linear codes are constructed, and then we determine the parameters and the weight distributions of these new binary linear codes.
Towards Serverless Data Exchange Within Federations

In this paper, we propose a novel approach for sharing privacy-sensitive data across federations of independent organizations, taking particular regard to flexibility and efficiency. Our approach benefits from data meshes and serverless computing – such as flexible ad-hoc composability or minimal operational overheads – to streamline data sharing phases, and to effectively and flexibly address the specific requirements of highly variable data sharing constellations. Based on a realistic scenario of data sharing for medical studies in a federation of hospitals, we propose a five-phase data product lifecycle and identify the challenges that each phase poses. On this basis, we delineate how our approach of serverless data exchange addresses the identified challenges. In particular, we argue that serverless data exchange facilitates low-friction data sharing processes through easily usable, customizable, and composable functions. In addition, the serverless paradigm provides high scalability while avoiding baseline costs in non-usage times. Altogether, we thus argue that the serverless data exchange paradigm perfectly fits federated data sharing platforms.
Nuclear Norm Maximization Based Curiosity-Driven Reinforcement Learning

Reinforcement learning (RL) has achieved promising results in solving numerous challenging sequential decision problems. To address the issue of sparse extrinsic rewards, researchers have proposed intrinsic rewards, enabling the agent to acquire skills that may prove valuable in the pursuit of future rewards. One representative approach for generating intrinsic rewards involves constructing a predictive model to assess the novelty of states. However, due to the stochastic nature of complex environments, intrinsic rewards can be noisy. Directly employing noisy forward predictions to supervise policies can be detrimental to learning performance and efficiency. Many recent studies utilize the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> norm or variance to measure novelty, which further amplifies the noise through squaring operations. In this paper, we aim to tackle these challenges by leveraging Nuclear Norm Maximization (NNM). Specifically, we propose a novel curiosity reward that accurately quantifies the novelty of the exploration environment while exhibiting a high tolerance for noise and outliers. Our extensive experiments in various benchmark environments demonstrate that NNM achieves state-of-the-art performance compared to previous curiosity-based methods. When trained solely with intrinsic rewards, NNM achieves a human-normalized score of 1.09 on a subset of 26 Atari games, twice the performance of methods based on competitive intrinsic rewards.
A Custom Bio-Inspired Algorithm for the Molecular Distance Geometry Problem

Protein structure allows for an understanding of its function and enables the evaluation of possible interactions with other proteins. The molecular distance geometry problem (MDGP) regards determining a molecule's three-dimensional (3D) structure based on the known distances between some pairs of atoms. An important application consists in finding 3D protein arrangements through data obtained by nuclear magnetic resonance (NMR). This work presents a study concerning the discretized version of the MDGP and the viability of employing genetic algorithms (GAs) to look for optimal solutions. We present computational results for input instances whose sizes varied from 10 to $$10^3$$ atoms. The results obtained show that approaches to solving the discrete version of the MDGP based on GAs are promising.
Identification of Hoax News in the Using Community TF-RF and C5.0 Tree Decision Algorithm

News has a great influence on social and political conditions, and news can drive the economy of a country. Identifying hoax news is very important to ensure that the information circulating in society is true and reliable, and helps limit the spread of false information. In the process of reading news spread on social media, people do not know whether it is fact or hoax news because they cannot distinguish whether the news circulating is real news or fake news which if left unchecked can result in the public being misinformed. Therefore, this research process is to create a sistem for identifying hoax news using Decision Tree C5.0, which is an algorithm for the development of the C4.5 algorithm which in a process is almost similar, but the C5.0 algorithm has more value than the C4.5 algorithm which is used for the data mining process with a classification method for 1000 data obtained by web scraping using the keywords "election 2024", "politics" and "checkfaktapilkadamafindo" on the Turnbackhoax.id and Detik.com sites. In this study, what distinguishes it from several previous studies is its existence in several test scenarios, namely classification using feature weighting, which in classification using feature weighting is TF.RF. After testing the confusion matrix on the C5.0 algorithm, it produces accuracy, precision, and recall on each training / test data (70/30) resulting in accuracy 79.33%, precision 80.00%, recall 97.00%, then training / test data (80/20) resulting in accruracy 79.50%, precision 81.00%, recall 95.00%, then training and test data (90/10) resulting in accuracy 72.00%, precision 74.00%, recall 89.00%.
A Short Video Classification Framework Based on Cross-Modal Fusion

The explosive growth of online short videos has brought great challenges to the efficient management of video content classification, retrieval, and recommendation. Video features for video management can be extracted from video image frames by various algorithms, and they have been proven to be effective in the video classification of sensor systems. However, frame-by-frame processing of video image frames not only requires huge computing power, but also classification algorithms based on a single modality of video features cannot meet the accuracy requirements in specific scenarios. In response to these concerns, we introduce a short video categorization architecture centered around cross-modal fusion in visual sensor systems which jointly utilizes video features and text features to classify short videos, avoiding processing a large number of image frames during classification. Firstly, the image space is extended to three-dimensional space–time by a self-attention mechanism, and a series of patches are extracted from a single image frame. Each patch is linearly mapped into the embedding layer of the Timesformer network and augmented with positional information to extract video features. Second, the text features of subtitles are extracted through the bidirectional encoder representation from the Transformers (BERT) pre-training model. Finally, cross-modal fusion is performed based on the extracted video and text features, resulting in improved accuracy for short video classification tasks. The outcomes of our experiments showcase a substantial superiority of our introduced classification framework compared to alternative baseline video classification methodologies. This framework can be applied in sensor systems for potential video classification.
Design of Music Recommendation Algorithm Based on Big Data Analysis and Cloud Computing

Based on practical application, this paper develops the design of music recommendation algorithm based on BD (big data) analysis and cloud computing. Therefore, this paper proposes a hybrid music recommendation algorithm. Specifically, the model can be divided into two layers, and the predicted output value of the first layer model is trained as a new feature of the second layer. In the first layer, the combination features can be automatically mined by k-means algorithm and used as the input of the faster training logistic regression algorithm. In the second layer, Canopy clustering is used to train the predicted values of the first layer as features, and finally the generated recommendation list is provided to users. The research results show that the hybrid music recommendation algorithm is more accurate than the traditional user-based CF (collaborative filtering) recommendation algorithm. The accuracy rate increased by 0.157. Through comparative experiments, it is verified that the music recommendation algorithm based on BD analysis and cloud computing proposed in this paper is superior to other song recommendation algorithms in accuracy.
Warehouse Management System Design at PT XYZ

The warehouse is one of the most important parts of a company. Ineffective warehousing processes can disrupt warehouse operations. Recording warehouse transactions that are done manually will require checking one by one on the hardcopy form that has been stored. Therefore, this internship program aims to improve the performance of warehousing operations at PT XYZ. The method used in the analysis is data collection and application system design using the prototype method. The data is taken based on the results of interviews and observations in the warehouse. This activity creates a web-based warehouse management system application to help control incoming and outgoing inventory. The system built makes it easy for administrators to see the number of items made for each event. The results of black box testing show that the application has 100% functionality. The test results illustrate that the application functions properly according to user needs and has been accepted by PT XYZ
Effective data quality management for electronic medical record data using SMART DATA

In the medical field, we face many challenges, including the high cost of data collection and processing, difficult standards issues, and complex preprocessing techniques. It is necessary to establish an objective and systematic data quality management system that ensures data reliability, mitigates risks caused by incorrect data, reduces data management costs, and increases data utilization. We introduce the concept of SMART data in a data quality management system and conducted a case study using real-world data on colorectal cancer. We defined the data quality management system from three aspects (Construction - Operation - Utilization) based on the life cycle of medical data. Based on this, we proposed the “SMART DATA” concept and tested it on colorectal cancer data, which is actual real-world data. We define “SMART DATA” as systematized, high-quality data collected based on the life cycle of data construction, operation, and utilization through quality control activities for medical data. In this study, we selected a scenario using data on colorectal cancer patients from a single medical institution provided by the Clinical Oncology Network (CONNECT). As SMART DATA, we curated 1,724 learning data and 27 Clinically Critical Set (CCS) data for colorectal cancer prediction. These datasets contributed to the development and fine-tuning of the colorectal cancer prediction model, and it was determined that CCS cases had unique characteristics and patterns that warranted additional clinical review and consideration in the context of colorectal cancer prediction. In this study, we conducted primary research to develop a medical data quality management system. This will standardize medical data extraction and quality control methods and increase the utilization of medical data. Ultimately, we aim to provide an opportunity to develop a medical data quality management methodology and contribute to the establishment of a medical data quality management system.
Impact of machine learning and deep learning in medical image analysis

In the whole world, people are suffering from a variety of diseases. In order to detect these diseases, several medical imaging procedures are used in which images from different parts of the body are captured through advanced sensors and well-designed machines. These medical imaging procedures increase the expectations of patients in achieving better healthcare services from medical experts. Till now, various image processing algorithms such as neural networks (NN), convolutional neural networks (CNN), and deep learning are used for image analysis, image representation, and image segmentation. Yet, these approaches are not giving promising results in some applications of the healthcare sector. So, this chapter gives an overview of state-of-the-art image processing algorithms as well as highlights its limitations. Most deep learning algorithm implementations focus on the images of digital histopathology, computerized tomography, mammography, and X-rays. This work offers a thorough analysis of the literature on the classification, detection, and segmentation of medical image data. This review aids the researchers in considering necessary adjustments to deep learning algorithm-based medical image analysis. Further, the applications of medical image processing using Artificial Intelligence (AI), machine learning (ML), and deep learning in the healthcare sector are discussed in this chapter.
Fundamentals Of Digital Forensics

Developed in tandem with law enforcement to guarantee applicability to real-world practise, this textbook/reference covers the theory and methods of digital forensic investigations via the use of examples. Forensic artefacts and limitations are discussed, along with forensic instruments used by law enforcement and private industry. Hands-on practise with typical tasks and the reinforcement of solid forensic thinking are emphasised. New sections on incident response duties and computer memory analysis have been included to this revised and updated second version. Contents and highlights: Describes what computer forensics is, its capabilities, and its limits. Covers the theoretical underpinnings as well as the basics of forensic technique.
Video Gim Sopan (Keselamatan Transportasi Jalan) untuk Siswa Sekolah Dasar

The behavior of motorized vehicle drivers who violate traffic rules is one of the most significant factors causing traffic accidents on the road. This is due to low traffic awareness. Low traffic awareness due to a lack of understanding of traffic rules. An Android-based polite game application was built to increase awareness of motorized vehicle drivers. Polite game development uses the waterfall method with supporting software such as Unity, Photoshop, and Visual Studio. Polite games can help you understand and be aware of the dangers that are on the road. The development of polite games can be done by adding game levels with other road safety theories.
APPLICATION OF FACENET MACHINE LEARNING MODEL AND HAAR CASCADE CLASSIFIER FOR BIOMETRIC IDENTIFICATION

The paper analyzes the principles of Haar cascade classifier and FaceNET machine learning simulation program for biometric identification. As an experiment, a recognition system was created that will allow human face recognition, which was developed in the Python programming language. Some libraries covered in the research process include numpy, OpenCV, pip, matplotlib, virtualenv and pickle. The Haar cascade classifier is used to detect objects in images and videos. For the process of generating a facial signature, the two programs used the FaceNET machine learning model, which uses convolutional neural networks in the process of extracting features from facial images and conducting a comparative analysis of them between the identification data and those stored in the database. Due to this, FaceNET identifies faces in images with high accuracy and security, which is useful for use in access control systems, automation of visitor accounting processes and other applications where facial recognition is necessary. The created system will provide an opportunity to recognize unique facial features, store them and link them with the user's documentary information.
Creation of the Moscow department of the Society of Zealots of Russian Historical Enlightenment (based on letters from I.F. Tyutchev to S.D. Sheremetev)

The article discusses the creation of a local department in Moscow of the Society of Zealots of Russian Historical Enlightenment in memory of Emperor Alexander III. For the first time, the materials of personal correspondence of the chairman of the Society, Count S.D. Sheremetev and I.F. Tyutchev, who was to form a local department, were involved. The Moscow department was given paramount importance. The initiative was supported at the level of the Moscow administration. However, to implement this initiative promptly it didn't work out.
STUDENTS’ SATISFACTION ON ENGLSH LEARNING DURING COVID-19 PANDEMIC AT SMP N 1 WONGSOREJO

This research aimed to know the satisfaction of the third grade students in English online learning during pandemic at SMPN 1 Wongsorejo. The scope of this study is to analyze the students satisfaction in learning English. The limitation of this study is students satisfaction in learning English during covid-19 pandemic of the third grade students at SMPN 1 Wongsorejo. The indicators of the research are tangible, reliability, responsiveness, assurance and empathy. This study used quantitative research. The subjects of the research are the 30 students of the third “C” grade at SMPN 1 Wongsorejo. The instruments used to collect the data is an questionnaire. The researcher made questionnaire in the form of positive statements for each stage and there were 25 statements (5 statements in tangible (facilities), 5 statements in reliability, 5 statements in responsiveness (willingness to help), 5 statements in assurance (ability to convey trust and confidence), and 5 statements in empathy). The students’ satisfaction would be calculated based on descriptive data analysis percentages and benchmarks. The finding of the research showed the satisfaction of the third grade students in English online learning during pandemic at SMPN 1 Wongsorejo is categorized as very high satisfaction with satisfaction score 86,27%.
UI/UX Design on Digilearn Application with the Iterative Design Thinking Methodology

Innovations in education and learning today are followed by advances in technology. The increasing use of online information technology has encouraged educational institutions to invest in new learning technologies such as E-Learning. E-Learning is teaching and learning that is supported and developed through technology and digital media, and is also a form of the concept of distance learning or distance learning. By understanding what they are complaining about, researchers as product designers can empathize with them, so that they can define their problems perfectly, create brilliant ideas, design solutions based on ideas, and try the results of these designs on target users. Therefore, researchers learn that empathy is the key to the success of a product because in the end, the product will be used by the users that the designer has expected.
Operational Sustainability and Digital Leadership for Cybercrime Prevention

The digital world of work and social media, despite its challenges, is here to stay as an integrated part of our day-to-day operational norms. Therefore, we must make the best of it on a proactive basis before the private data of our employees and consumers becomes hacked remotely by criminals. Privacy violations and hacking of data cannot be sustained since they can be very costly and may even lead to bankruptcy. As such, today’s leaders, managers, and educators have the responsibility of preparing their future replacements for the modern digital economy, so their organizations’ operational processes can remain competitive, safe, and sustainable. Operational sustainability, in this paper, is proposed as a tripod or “three-legged stool” of environmental, social, and digital responsibility. With more employees and entrepreneurs accessing digital data remotely through vulnerable or unsecure online platforms, the opportunities for cybercrimes rise. Therefore, this article focuses more on the often-neglected digitalization element of operational sustainability. All leaders must be aware of the legal, social, and environmental expectations of a digital society by doing what is good for the world while also being efficient and safe from cybercriminals. The paper proposes that future leaders must be socialized with a sustainability mindset about data privacy and safety measures that are necessary for this fast-changing digital economy where hackers and artificial intelligence (AI) tools can make the process more challenging. With AI being used by some actors to generate false yet realistic content, companies will have to do more to make sure their brands are not defamed or tarnished. As such, this conceptual article discusses a model for operational sustainability, which includes the privacy and safety of data that can be used by managers, educators and other leaders for training and development purposes in today’s digital world of work.
Robust clustering based on the most frequent value method

Assigning observations to highly separable although relatively homogeneous groups is still a challenging task despite the abundance of well-elaborated theories and effective, practical algorithms. Not just the aim of clustering then the underlying data itself influences the choice of method and the way of assessing the results. Outliers and non-normal data distribution can lead to surprising, unstable and many times undesirable clustering results especially in higher dimensions. This implies the importance of some human supervision in case of such unsupervised algorithms as well. In this paper a robust clustering alternative is presented based on the Most Frequent Value Method for crisp-type clustering in case of real-life data. The proposed approach is compared with the k-Medians algorithm. A favourable attribute of the applied procedure is its ease of application on multidimensional data sets where critical judgment of formed groups is particularly troublesome.