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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Nasari, Maulin Zakiyyah, Alfi Yusrotis Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

Node classification is a crucial task in many applications, where the objective is to accurately classify nodes based on their features. Unlike traditional machine learning methods, which often fall short in handling graph-structured data, Graph Neural Networks (GNNs) offer a robust solution to this problem. This research aims to identify the most effective GNN model for node classification by evaluating several methods, including Graph Convolutional Networks (GCN), GraphSAGE, Graph Attention Networks (GAT), and ChebNet. Through careful tuning of key hyperparameters, specifically the number of hidden units per layer and the learning rate, the study achieved optimal model performance. The best results were obtained using the GraphSAGE model with 64 hidden units per layer and a learning rate of 0.01, leading to exceptional accuracy and F1 scores of 0.999823 and 0.999852, respectively. These findings demonstrate the effectiveness of hyperparameter optimization in enhancing GNN performance for node classification tasks. © 2024 IEEE.

关键词： Graph neural networks

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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Standard Multi-Layer Perceptron on Positive - Unlabeled Glycosylation Site dataset 8

Standard Multi-Layer Perceptron on Positive - Unlabeled Glyc...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Mahesworo, Bharuno Cenggoro, Tjeng Wawan Lumbanraja, Favorisen Rosyking Pardamean, Bens Statistics Department School of Computer Science Bina Nusantara University Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia Computer Science Department School of Computer Science Bina Nusantara University Indonesia Computer Science Department School of Matematics and Natural Science University of Lampung Indonesia

The implementation of computational approaches for protein glycosylation site prediction is becoming popular since the experimental-validated glycosylation data became more abundant. Some of the data were found to be wrong after the experiment was again carried out with more sophisticated technology. To solve this issue, the latest state-of-the-art model trained the model based on a positive-unlabelled algorithm. The aim of this research is to explore the possibility of an approach applying a simple neural network algorithm and still achieve high classification performance. The model proposed in this research gave competitive results with fewer preprocessing steps. Increasing the accuracy of glycosylation site prediction can complement laboratory-based methods and is very useful for understanding the role of glycosylation. © 2023 The Authors. Published by Elsevier B.V.

关键词： Glycosylation PTM Site Prediction Standard Multi-Layer Perceptron

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ABSE: Adaptive Baseline Score-based Election for Leader-based BFT Systems

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IEEE Transactions on Parallel and Distributed Systems 2025年第08期36卷 1634-1650页

作者： Liu, Xuyang Zhang, Zijian Li, Zhen Yin, Hao Li, Meng Liu, Jiamou Conti, Mauro Zhu, Liehuang Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China The University of Auckland School of Computer Science New Zealand Beijing Institute of Technology School of Computer Science and Technology Beijing China Peking University Changsha Institute for Computing and Digital Economy Changsha China Hefei University of Technology School of Computer Science and Information En gineering Hefei China Department of Mathematics HIT Center Padua of University Padua Italy

Leader-based BFT systems face potential disruption and performance degradation from malicious leaders, with current solutions often lacking scalability or greatly increasing complexity. In this paper, we introduce ABSE, an Adaptive Baseline Score-based Election approach to mitigate the negative impact of malicious leaders on leader-based BFT systems. ABSE is fully localized and proposes to accumulate scores for processes based on their contribution to consensus advancement, aiming to bypass less reliable participants when electing leaders. We present a formal treatment of ABSE, addressing the primary design and implementation challenges, defining its generic components and rules for adherence to ensure global consistency. We also apply ABSE to two different BFT protocols, demonstrating its scalability and negligible impact on protocol complexity. Finally, by building a system prototype and conducting experiments on it, we demonstrate that ABSE-enhanced protocols can effectively minimize the disruptions caused by malicious leaders, whilst incurring minimal additional resource overhead and maintaining base performance. © 1990-2012 IEEE.

关键词： Protocols Proposals Complexity Theory Voting Reliability Scalability Synchronization Electronic Mail Leadership computer science Distributed Consensus Byzantine Fault Tolerance Leader Election Score Based Mechanism Byzantine Fault Tolerance Scalable System Performance Impact Of Leadership Global Consistency Throughput Health Related Quality Of Life data Structure Batch Size Leadership Roles Baseline Scores Multiple Phases Specific Scenarios Correction Process Original Protocol Effective Leadership Directed Acyclic Graph Public Key Complex Communication Leaderless Strong Edges Leader Election Current Round Consensus In Round Previous Rounds Previous Round Network Delay Baseline Performance Communication Overhead Increased System Complexity

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Automated Product Description Generator Using GPT-Neo: Leveraging Transformer-Based Language Models on Amazon Review dataset 9

Automated Product Description Generator Using GPT-Neo: Lever...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Felix, Bryan Gunawan, Alexander Agung Santoso Suhartono, Derwin School of Computer Science Bina Nusantara University Mathematics Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In this modern era, technological advancements are progressing rapidly, leading to many activities being conducted online. One of the sectors most significantly impacted is the trade sector, where one contributing factor is the emergence of e-commerce. This presents a unique challenge, especially for sellers who wish to conduct business on such an e-commerce platform, where the time required to upload a product is not insignificant. Therefore, this research aims to develop a transformer-based model using the Amazon review dataset. This study compares baseline GPT-2 model with the proposed GPT-Neo model. The results indicate that the proposed GPT-Neo model performs better. The GPT-Neo model achieved the best scores on the test data using a temperature parameter of 0.7 and a top_k of 50, with an average BLEU score of 43.10% and a ROUGE-L score of 11.93%, showcasing its capacity to create more coherent and contextually correct product descriptions. Furthermore, it improves businesses' overall e-commerce experience. © 2024 IEEE.

关键词： Natural language processing systems

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Transfer Learning Method for Malaria Identification 8

Transfer Learning Method for Malaria Identification

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Herli, Cecilia Audrey Wijaya, Daniel Zerge Hansen Angela, Michelle Pragusma, Zakaria Berlam Wulandari, Ajeng Anggreainy, Maria Susan Bina Nusantara University Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta11530 Indonesia

ISBN: (纸本)9798350342604

Malaria is a severe disease caused by parasites of the genus Plasmodium, which are transmitted to humans through the bite of an infected female Anopheles mosquito. Symptoms of malaria begin to appear at least within 10 to 15 days. If malaria is not treated immediately, it is feared that it will cause respiratory problems, shortness of breath, and death. To avoid the occurrence of these events, the idea arose to create an AI (Artificial Intelligence) project that can recognize the presence of malaria parasites in blood cells. Thus, the main objective of this project is to find out how to create a Machine Learning model that can efficiently identify malaria parasites in the human body. The AI project uses CNN (Convolutional Neural network) as an algorithm to recognize the presence or absence of parasites in blood cell images that will be inputted by the user. Process of implementing CNN, using VGG19 which is an advanced CNN that has pre-trained layers and a good understanding of describing an image, both the shape, color, and structure of the image. After implementing the Transfer Learning algorithm on the dataset, the result is a Transfer Learning algorithm that can detect the presence of Malaria parasites in blood cells with an accuracy rate of 92 percent a specificity of 95 percent, and a sensitivity of 89 percent. The accuracy can still increase depending on the diversity of the data provided. The more often we train and input test data as train data, the accuracy of AI will also increase. © 2023 IEEE.

关键词： Convolutional neural networks

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A Study of data Leakage Prevention Techniques in Remote Collaborative Work Application 24

A Study of Data Leakage Prevention Techniques in Remote Coll...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Xu, Wenfang Zhao, Shusheng School of Data Science and Computer Engineering Xiamen Institute of Technology China

ISBN: (纸本)9798400718137

Under the wave of digital transformation, remote collaboration has become the new normal in the daily operation of enterprises. But at the same time, the risk of sensitive data leakage and the insufficiency of protection measures have increasingly become the focus of attention, posing a severe challenge to enterprise data security. To address this challenge, this paper proposes an innovative comprehensive data leakage prevention technical solution. This solution integrates cloud services and kernel technology and creates a set of data security protection systems for the remote collaboration working environment. This technical solution adopts a series of effective technical means, including asymmetric encryption, one-way data flow control, dynamic digital watermarking technology, injection attack defense, and program anti-hanging mechanism, significantly enhancing the security of data in the collaborative office system. The constructed framework includes multiple modules such as user management, service support, kernel driver, and user terminal. This technical solution can be integrated with enterprise remote collaboration office software, providing a higher level of security guarantee for the remote office environment, effectively curbing the risk of sensitive data leakage, and also providing solid technical support for the healthy and stable development of remote collaboration work. © 2024 Copyright held by the owner/author(s).

关键词： Information leakage

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Ontology-Based Semantic Reasoning for Multi-Source Heterogeneous Industrial Devices Using OPC UA

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IEEE Internet of Things Journal 2025年第13期12卷 25020-25032页

作者： Bi, Jing Wu, Rina Yuan, Haitao Wang, Ziqi Zhang, Jia Zhou, MengChu Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

The advent of smart manufacturing in Industry 4.0 signifies the era of connections. As a communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, an integration framework named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. QOMOU extracts information models of OPC UA servers into resource description framework triples and utilizes web ontology language for semantic enrichment and inference. Then, an Event Class Semantic Similarity Calculation (ECSSC) method is proposed for device type identification, enabling the classification of semantically heterogeneous OPC UA devices. The effectiveness of ECSSC is validated through queries with the RDF query language (SPARQL) protocol in Apache Jena. Experimental results demonstrate that ECSSC improves the accuracy of device identification by approximately 7% compared to benchmark device identification models. Specifically, compared with graph embedding-based methods, QOMOU’s query performance is approximately 13% higher, and its query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture. © 2014 IEEE.

关键词： Structured Query Language

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Knowledge Graph-Enhanced Semantic Cache for Low-Latency and Cost-Effective Inference in Large Language Models 9

Knowledge Graph-Enhanced Semantic Cache for Low-Latency and ...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Dominic, Nicholas Pardamean, Bens Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In organizational knowledge management, Large Language Model (LLM) caches act as a semantic repository gathered from previous LLM responses. Due to intensive calls from multiple users, LLM may suffer from high inference latency. While there are many prior available approaches to solve this problem, most of them are inherently complex. This paper introduced a Knowledge Graph-enhanced Semantic Cache mechanism as an alternative, lightweight technique to boost retrieval for similar prompts. The latest state-of-the-art open-source LLM, named Google's Gemma-2B-it, was used to generate sample prompts and responses as a draft, while a knowledge graph (KG) was built from Wikipedia sentences. To create embeddings of prompts and KG, all-MiniLM-L6-v2 from SentenceTransformer was used. This new cache system resulted in up to 28% improvement over a standard model. In particular, reinforcement with KG cache embeddings yielded more than 85% semantic cache accuracy. To map the next trajectory of this pilot study, an overview of the extended framework for LLM knowledge management was also presented in this paper. The framework includes the new KG- enhanced cache system equipped with scalable security and fallback mechanisms that can promote green technology through substantial improvements in latency, throughput, and overall LLM costs. © 2024 IEEE.

关键词： Knowledge graph

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Facility Location Games with Optional Preferences: A Revisit 39

Facility Location Games with Optional Preferences: A Revisit

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sha, Xingchen Bao, Shuyu Chan, Hau Chau, Vincent Fong, Ken C.K. Li, Minming Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Computer Science and Engineering University of Nebraska-Lincoln United States School of Computer Science and Engineering Southeast University China Division of Artificial Intelligence School of Data Science Lingnan University Hong Kong Hong Kong

ISBN: (纸本)157735897X

We study the k-facility location games with optional preferences on the line. In the games, each strategic agent has a public location preference on the k facility locations and a private optional preference on the preferred/acceptable set of facilities out of the k facilities. Our goal is to design strategyproof mechanisms to elicit agents’ optional preferences and locate k facilities to minimize the social or maximum cost of agents based on their facility preferences and public agent locations. We consider two variants of the facility location games with optional preferences: the Min variant and the Max variant where the agent’s cost is defined as their distance to the closest acceptable facility and the farthest acceptable facility, respectively. For the Min variant, we present two deterministic strategyproof mechanisms to minimize the maximum cost and social cost with k ≥ 3 facilities and well-separated n agents, achieving approximation ratios of 3 and 2n + 1 respectively. We complement the results by establishing lower bounds of 23 and n4 for the approximation ratios achievable by any deterministic strategyproof mechanisms for the maximum cost and social cost, respectively. We then improve our results in a special setting of the Min variant where there are exactly three facilities and present two deterministic strategyproof mechanisms to minimize the maximum cost and social cost. For the Max variant, we present an optimal deterministic strategyproof mechanism for the maximum cost and a k-approximation deterministic strategyproof mechanism for the social cost. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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