Reviewerly/exHarmony huggingface dataset:
Self-attention Hypergraph Pooling Network

PDF HTML XML Export Cite reminder Self-attention Hypergraph Pooling Network DOI: 10.21655/ijsi.1673-7288.00306 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Recently, Graph Convolutional neural Networks (GCNs) have attracted much attention by generalizing convolutional neural networks to graph data, which includes redefining convolution and pooling operations on graphs. Due to the limitation that graph data can only focus on dyadic relations, it cannot perform well in real practice. In contrast, a hypergraph can capture high-order data interaction and is easy to deal with complex data representation using its flexible hyperedges. However, the existing methods for hypergraph convolutional networks are still not mature, and there is no effective operation for hypergraph pooling currently. Therefore, a hypergraph pooling network with a self-attention mechanism is proposed. Using a hypergraph structure for data modeling, this model can learn node hidden features with high-order data information through hypergraph convolution operation which introduces a self-attention mechanism, select important nodes both on structure and content through hypergraph pooling operation, and then obtain more accurate hypergraph representation. Experiments on text classification, dish classification, and protein classification tasks show that the proposed method outperforms recent state-of-the-art methods. Reference Related Cited by
Revolutionising Food Safety Management: The Role of Blockchain Technology in Ensuring Safe and High-Quality Food Products

Ensuring food safety and quality is of utmost importance for public health and consumer trust. In recent years, blockchain technology (BCT) has been increasingly explored as a potential solution to revolutionise food safety management systems. This paper provides an inclusive overview of the role of BCT in ensuring safe and high-quality food products. Drawing on a review of relevant literature and a case study analysis, the paper evaluates the potential benefits and limitations of BCT in the food industry. The results show that blockchain-based food safety management systems offer several advantages, including increased transparency, traceability, and efficiency. However, there are also challenges related to implementation, such as the need for standardisation and interoperability. The paper concludes by offering recommendations for the adoption and implementation of BCT in the food industry and identifying opportunities for further research. The findings of this study have implications for policymakers, food safety regulators, and food industry stakeholders who seek to improve food safety and quality through the adoption of innovative technologies.
Blockchain-Based Gradient Inversion and Poisoning Defense for Federated Learning

Federated learning has emerged as a promising privacy-preserving machine learning technology, enabling multiple clients to collaboratively train a global model without sharing raw data. With the increasing adoption of federated learning in Internet of Things (IoT) scenarios, concerns about security and privacy have become critical. In particular, gradient inversion attacks and poisoning attacks pose significant threats to the integrity and effectiveness of the global model. In response, we propose a comprehensive blockchain-based defense mechanism that effectively protects federated learning systems from such attacks. We develop a novel combination of techniques, including public blockchain level protection and private blockchain level protection, which work in tandem to prevent attackers from reconstructing figures using the obtained gradients. This unique combination of methods provides a robust defense against gradient inversion attacks in federated learning IoT scenarios. We conduct extensive experiments to validate the effectiveness of our proposed approach against gradient inversion and poisoning attacks. Our results demonstrate improved accuracy and stable convergence of training loss under poisoning attacks, indicating that our method can be applied to a wide range of federated learning IoT scenarios, enhancing both the security and privacy of distributed machine learning systems.
PERANCANGAN SISTEM WEB FILTERING DENGAN METODE DNS FORWARDING PADA JARINGAN KOMPUTER BERBASIS MIKROTIK ROUTEROS

Internet services in the age of technology like today are certainly helpful in our daily lives. There are so many benefits that can be obtained from this Internet services, such as finding sources of information, sending various information, online communicating, etc. However, it is undeniable that Internet services has negative websites. One of solution to prevent network users from accessing these negative websites is by implementing web filtering system. Therefore, the purpose of this research is to design a network security system that is used as web filtering to minimize access to negative websites on the Internet. To achieve this, the author used DNS Forwarding method to design the prevention system on a computer network. DNS Forwarding is a useful method for filtering out websites that have negative content and also useful for forwarding those websites to other websites that healthier and safer. The design of this network security system is applied to a computer network based on MikroTik RouterOS that utilizes the MikroTik RB941-2nD-TC hAP-Lite as the main device. The results of the web filtering system design with the DNS Forwarding method are proven to be able to minimize the access to negative websites on the user's network system when accessing the Internet.
The tropical version of El Gamal Encryption

In this paper, we consider the new version of tropical cryptography protocol, i.e the tropical version of El Gamal encryption. We follow the ideas and modify the clasical El Gamal encryption using tropical matrices and matrix power in tropical algebra. Then we also provide a toy example for the reader’s understanding.
Optimization of SM4 Encryption Algorithm for Power Metering Data Transmission

This study focuses on enhancing the security of the SM4 encryption algorithm for power metering data transmission by employing hybrid algorithms to optimize its substitution box (S-box). A multi-objective fitness function is constructed to evaluate the S-box structure, aiming to identify design solutions that satisfy differential probability, linear probability, and non-linearity balance. To achieve global optimization and local search for the S-box, a hybrid algorithm model that combines genetic algorithm and simulated annealing is introduced. This approach yields significant improvements in optimization effects and increased non-linearity. Experimental results demonstrate that the optimized S-box significantly reduces differential probability and linear probability while increasing non-linearity to 112. Furthermore, a comparison of the ciphertext entropy demonstrates enhanced encryption security with the optimized S-box. This research provides an effective method for improving the performance of the SM4 encryption algorithm.
Aligning Large Language Models with Human Preferences through Representation Engineering

Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness. Existing methods for achieving this alignment often involves employing reinforcement learning from human feedback (RLHF) to fine-tune LLMs based on human labels assessing the relative quality of model responses. Nevertheless, RLHF is susceptible to instability during fine-tuning and presents challenges in implementation.Drawing inspiration from the emerging field of representation engineering (RepE), this study aims to identify relevant representations for high-level human preferences embedded in patterns of activity within an LLM, and achieve precise control of model behavior by transforming its representations. This novel approach, denoted as Representation Alignment from Human Feedback (RAHF), proves to be effective, computationally efficient, and easy to implement.Extensive experiments demonstrate the efficacy of RAHF in not only capturing but also manipulating representations to align with a broad spectrum of human preferences or values, rather than being confined to a singular concept or function (e.g. honesty or bias). RAHF's versatility in accommodating diverse human preferences shows its potential for advancing LLM performance.
Extracting Multiple Relations between Entities from Unstructured Threat Intelligence Reports

Abstract— Cyber threats are becoming an ever-increasing concern for organizations and even countries. Attackers are constantly in search of new and sophisticated attack vectors. On the other side, security defenders need to gather as much as possible information about the threats on the Internet and analyze them to understand current and emerging attack trends for effectively detecting and mitigating potential threats with fast response. This paper addresses the problem of automatically extracting threat intelligence from unstructured text sources. We focus specifically on the possibility of multiple relations between two entities and propose a two-stage process that allows any binary classifier to be used for multi-class classification without interfering with the binary algorithm used. The experimental results illustrate the efficiency of our proposed approach.
performance analysis of a cloud database on mobile devices

The article presents a performance analysis of Firebase cloud database. Two services, namely Realtime Database and Cloud Firestore, are examined, and their query speed are compared to those of the local SQLite database. Basic CRUD operations were examined, taking into account the number of records in the database, the size of individual records and the complexity of the database structure. Upon completion of the research, it was concluded that Realtime Database outperforms Cloud Firestore and cloud databases are slower than the local database when it comes to operations on a single record. However, when working with a larger volume of data, cloud database can achieve better results than SQLite. The accuracy of the outcome is also influenced by the stability of the network connection and the distance from the cloud server.
LSR-RIBNet: A Novel LiDAR Super-Resolution Model for Scene Semantic Segmentation in Outdoor Environments

LiDAR-based scene semantic segmentation is crucial for unmanned ground vehicles working in outdoor environments. However, the performance of scene semantic segmentation is often poor due to the low vertical resolution of commonly used LiDAR sensors. To achieve high-resolution LiDAR data for semantic segmentation tasks, this paper proposes a novel LiDAR super-resolution network named LSR-RIBNet. To make full use of the range, intensity, and azimuth information of raw LiDAR measurements, LSR-RIBNet takes the 2D projection images of LiDAR points as inputs and predicts a high-resolution 3D LiDAR point cloud. Comparative experiments on public datasets not only show the effectiveness of the proposed LSR-RIBNet model, but also verify the performance improvement of scene semantic segmentation tasks with the LiDAR super-resolution results.
Graph Convolutional Networks Based Muti-Label Deep Cross-Modal Hashing

Recently, multi-label deep cross-modal hashing (MDCH), which incorporates deep neural networks, hashing and multi-label learning for cross-modal retrieval tasks, has achieved excellent cross-modal retrieval results and thus became a highly popular area of research. Nevertheless, many existing MDCH methods concentrate on extracting information from multi-modal data, while neglecting the abundant semantic information in multiple labels. Few MDCH methods incorporate multi-label information, but they often treat labels as independent entities, ignoring the relationships between categories, which hinders the establishment of semantic connections among multi-modal data. In order to tackle the aforementioned challenges, we propose a graph convolutional networks based multi-label deep cross-modal hashing method (GMCH) in this paper. GMCH leverages two deep neural networks to generate hash representations from the original image-text pairs, during this process, a graph convolutional network is introduced to capture the category correlations of multi-labels and supervise the training of hash mapping. Experimental results on two commonly employed datasets validate the efficacy of the proposed GMCH method. You can find the code for our proposed GCMH at https://github.com/licher12/GMCH.git.
From Grey to Great?

Abstract In recent years, the possibilities for aspiring writers who wish to invest in publishing their work without the involvement of an established and external publishing house have grown tremendously. The aim of this article is to look into the global trend of ‘hybrid publishing services’ that operate in the grey zone between traditional publishing and self- publishing. Particular attention is given to the case of Hoi Publishing in Sweden. On the basis of empirical findings on the company’s legal and financial status, company turnover, and symbolic capital, the article examines the status and position of Hoi in relation to similar-sized royalty publishers in order to analyse how its business model fits into the contemporary Swedish literary publishing field. The findings of the case study are supplemented with survey data from 715 authors, publishing professionals, and literary critics, gathered in 2020.
Modification of the ontology using the teacher’s personal materials to increase the degree of automation of EC preparation

The object of the research is the technology of constructing a computer ontology (CO) for electronic textbooks of the subject area (KSA). As an example, the industry is considered-information and communication technologies (ICT) and the topic "databases". The subject of the study are models and methods of automated construction of a CO of the KSA. The methodology of modification of CO in the field of ICT is proposed by introducing into the formal computer ontology a description of the technologies of the learning process for secondary schools or extracurricular institutions, as well as for professional retraining of specialists, which allows preserving and reusing the collective experience of learning in an educational institution. The proposed solution differs from the existing ones in that the system of ontologized design of electronic courses (SODEC). It allows to automate the development of electronic courses (EC) and / or electronic textbooks (ET), reduces the cost and time of preparing ET, ensures compliance of EC with the current state in the field of ICT. As an example, the design section of ET for the topic "databases" is considered. The proposed solution allows to accumulate personal materials of the teacher and contributes to the rapid updating of the content and content of ET, if necessary. It is concluded that for more effective use of the proposed methodology, it is necessary to develop a similar SODEC, taking into account the language of description of CO – OWL, which will allow to accumulate collective experience in the methods of submitting material to teachers of educational institutions or extracurricular institutions, as well as during professional retraining of specialists
THE LITERACY AND NUMERACY ABILITY PROFILE WHICH ARE VIEWED FROM MINIMUM ASSESSMENT COMPONENTS (AKM)

Literacy and numeracy skills are cognitive learning outcomes measured in the Minimum Competency Assessment (AKM) and are part of the National Assessment (AN). This study aims to describe the literacy and numeracy achievement profile of junior high school students in terms of three aspects, namely content, cognitive processes, and context The descriptive quantitative approach method used in this research involved 141 class VIII junior high school students in North Aceh Regency and Lhokseumawe City through random sampling. Respondents are participants who took part in the 2021 National Assessment. The research instrument used was a question on literacy with 26 questions and numeracy with as many as 30 questions according to the grid and aspects of the AN that had been validated. The context of the questions is arranged in the form of personal, socio-cultural, and scientific. The distribution of the questions is in the form of 20% multiple-choice, 60% complex multiple-choice, 10% matching, 5% short answer or short answer, and 5% description. The results showedthe average percentage score of literacy ability was 43.87% with low criteria, meanwhile for numeracy skills, the average percentage score was 33.91% with very low criteria. These results indicate that students' literacy and numeracy skills in terms of each content need to be improved through the implementation of learning programs and the use of textbooks that lead to the improvement and development of literacy and numeracy skills.
CNN combined with attention module for facial expression recognition

Abstract In contemporary times, the proliferation of information technology has prompted an increased interest among scholars towards artificial intelligence, particularly its constituent algorithms, such as deep learning, multilayer perceptron and convolutional neural networks. Of specific interest is the analysis of facial expressions, which has emerged as a popular research topic. However, classifying facial expressions presents a significant challenge due to variations in expressions associated with different emotions, as well as similarities between the various emotions. The task of expression classification is further complicated by the abundance of facial features that must be considered. In this paper, the basic Convolutional Neural Network (CNN) model was first trained on the FRE-2013 face expression dataset and optimized by adding an attention module to learn and analyse the key features of faces, thereby improving the classification accuracy of the model. In short, this model achieved an accuracy of 71.65% and an F1-Score of 0.66 while the model without the attention module only achieved a model accuracy of 69.86% and an F1-Score of 0.65, an improvement of about 2%. The analysis demonstrated that by adding the attention mechanism, some important features e.g. the eyes and mouth, are given more weight, thus improving the classification accuracy.
Timeline reminder system bot and telegram assistant chatbot for a university student and lecturer

This research relates to the development of a chatbot application to help lecturers and students in the D3 Informatics Engineering study program remember schedules of activities and answer questions related to the study program. The background of this research is due to the difficulties of lecturers in managing activity schedules and answering questions that come from D3 Informatics Engineering study program students effectively and efficiently. This shows that having chatbot reminder and assistant applications will be very useful for study programs. The purpose of this research is to develop a bot and chatbot application that can help lecturers and students at Diploma 3 Informatics Engineering in managing activity schedules and answering questions related to study programs. The research method used is the Waterfall system development method, which is a type of System Development Life Cycle (SDLC). This method follows the sequential stages of system development starting from requirements analysis, system design, implementation, testing and maintenance. In this study, the chatbot application was developed using the Golang programming language, Codeigniter4 as the dashboard platform.
Penerapan Model Pembelajaran Role Playing Untuk Meningkatkan Percaya Diri Siswa (PTK pada Kelas V di Sekolah Dasar Negeri 52 Kota Bengkulu)

This study aims to determine the application of the Role Playing learning model to increase the confidence of students in class V B of SDN 52 Bengkulu City. The research subjects were teachers and students of class V B SDN 52 Bengkulu City. The research instrument was in the form of an observation sheet, which consisted of an observation sheet applying the Role Playing learning model and a student's confidence observation sheet. The data analysis technique uses the average score formula, highest score, lowest score, difference in score and range of values for each criterion. The average results of the implementation of the first cycle Role Playing learning model were obtained at 29.5 with sufficient categories and the second cycle obtained at 38.5 with a very good category. The results of the average self-confidence of the first cycle students were obtained at 19.03 with sufficient categories and the second cycle was obtained at 26.15 with very good categories. Based on the results of the t test obtained t0 6.25 is greater than t table 1.706. Then it can be concluded that the application of the Role Playing learning model can increase the confidence of students in class V B SDN 52 Bengkulu City.Keywords: Role Playing Learning Model, Confidence
Anonymous and Unlinkable Identity Scheme in Open Data Environment

In the 21st century, the massive spread of information on the internet has led to the development of social media as we know it. However, with the vast amount of information being transmitted every day, information misuse by hackers or unknown adversaries has become a major problem. The accessibility of information to a larger audience makes it easier to expose society to the threat of misinformation and fake news. False information is the most common example of information misuse and can cause social harm by leading to misunderstandings or creating a false sense of reality. These information security problems have motivated us to develop a scheme that not only identifies whether the information is fake or not but also protects the privacy and personal information of the information provider. By implementing our proposed scheme into the existing social media platform, we aim to reduce the influence of disinformation on the government. The key concept of this platform is to prevent information abuse, tampering, and retrieval, and to defend the information and news that the publisher wants to convey while protecting their sensitive identity. Ultimately, our proposed scheme aims to facilitate the communication of information and the publication of laws and regulations by ensuring that the information being transmitted is accurate and reliable.
THE INFLUENCE OF LEARNING MEDIA USABILITY AND LEARNING MOTIVATION ON STUDENT LEARNING OUTCOMES IN ECONOMICS SUBJECT IN THE 2022/2023 ACADEMIC YEAR

Conducted this research to analyze the effect of the usability of learning media on student learning outcomes in Economics subjects with learning motivation as an intervening variable. This research was conducted for 3 (three) months, from July to October 2022. The method used is a survey method using a quantitative approach. The study of the population was all Class XI IPS students in private high schools in the Sanggar 16 East Jakarta area, totaling 223 students. Determination of the number of samples using the Slovin formula and obtained a total sample of 143 students selected using the proportional random sampling technique. The collection technique uses the survey method with a closed questionnaire via Google Forms. The data used in this study are primary data for the independent variable (X) and secondary data for the dependent variable (Y). Data analysis used path analysis techniques with IBM SPSS Statistics 25. The results of this study indicate that: 1) the usability of learning media has a positive and significant effect on learning motivation, 2) the usability of learning media has a positive and significant effect on learning outcomes, 3) learning motivation has a positive and significant effect on learning outcomes, and 4) the usability of learning media has a positive and significant effect on student learning outcomes in class XI economics with learning motivation as an intervening variable