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Efficient and Privacy-Preserving Ranking-Based Federated Learning 24th

Efficient and Privacy-Preserving Ranking-Based Federated Le...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Liu, Tao Ren, Xuhao Wang, Yajie Wu, Huishu Zhang, Chuan China Academy of Information and Communications Technology Beijing100081 China Beijing Institute of Technology Beijing100081 China China University of Political Science and Law Beijing100081 China

ISBN: (纸本)9789819615445

Recently, a lot of works have proposed privacy-preserving schemes to address the privacy issues in federated learning (FL). However, FL also faces the challenge of high communication overhead due to limited client resources (e.g., mobile phones and wearable devices), so minimizing the communication between FL servers and clients is necessary. Although some existing works have solved this problem, they mainly focus on reducing the upload communication from client to server, while the entire model is used in the download communication from server to client. In this paper, we propose EPRFL to address this issue. Specifically, the client uses local data to rank the neural network parameters provided by the server, and a voting mechanism and homomorphic encryption are leveraged to aggregate and encrypt the rankings. The server then aggregates the encrypted local rankings. In addition, we use super-increasing sequences to compress and package the local rankings efficiently, further reducing communication costs. Finally, we demonstrate the security of EPRFL through security analysis and its high communication efficiency by experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Numerical Analysis of Optimal Control of Assets and Liabilities by a Bank 4th

Numerical Analysis of Optimal Control of Assets and Liabi...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Zhukova, Aleksandra Flerova, Anna Chernov, Alexey Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Vavilov str 44 bld. 2 Moscow Russia Moscow Institute of Physics and Technology Institutki per. 9 Dolgoprudny141701 Russia

ISBN: (纸本)9783031812408

The formal description of the assets and liabilities management (ALM) by a bank within regulatory requirements leads to an optimal control problem with phase constraints. The phase constraints arise from the restrictions on banking activities, which are set by law to maintain the banking process. Solving such model explicitly is rarely possible, and numerical calculations enable to study its properties. The indirect method of the solution involves formulating the system of necessary optimality conditions and then computation of the ordinary differential equations system combined with specific equations for dual variables. This paper provides the details of application of the numerical methods and its specifics in the proposed economic model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Banking

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Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation 31

Distance-Adaptive Quaternion Knowledge Graph Embedding with ...

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31st international conference on Computational Linguistics, COLING 2025

作者： Wang, Weihua Liang, Qiuyu Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet through either semantic matching or geometric distance scoring functions. However, it appears that semantic matching diminishes the separability of entities, while the distance scoring function weakens the semantics of entities. To address this issue, we propose a novel quaternion knowledge graph embedding model. Our model combines semantic matching with the geometric distance of entities to better measure the plausibility of triplets. Specifically, in the quaternion space, we perform a right rotation on head entity and a reverse rotation on tail entity to learn rich semantic features. We then utilize distance-adaptive translations to learn geometric distance between entities. Furthermore, we provide mathematical proofs to demonstrate our model can handle complex logical relationships. Extensive experimental results and analyses show our model significantly outperforms previous models on well-known knowledge graph completion benchmark datasets. Our code is available at https://***/llqy123/DaBR. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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7th international conference on Educational technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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SafeHome: automation and Security System Using Internet of Things with Mobile Application 5

SafeHome: Automation and Security System Using Internet of T...

引用

5th international conference on Advanced Research in Computing, ICARC 2025

作者： Abas, Patricia A. Reyes, Bryll Christopher O. Serquina, Jazzy Niega, Juliet O. Medina, Oliver A. National University - Philippines Department of Computer Engineering Calamba City Philippines

ISBN: (纸本)9798331530983

The rapid advancements in Internet of Things (IoT) technology have transformed home automation and security, addressing the demand for convenience, comfort, and energy efficiency. Despite global market growth, barriers such as limited user understanding, power dependency, and lack of localized functionality hinder adoption in the Philippines. To address these challenges, this study developed SafeHome, an IoT-enabled smart home automation and security system. SafeHome integrates ESP32 and Arduino Nano microcontrollers with Firebase Realtime Database, enabling real-time device synchronization and monitoring through an Android mobile application. Key features include PIN-based security, push notifications, ultrasonic presence detection, and a safety breaker to prevent electrical hazards. The system also incorporates offline keypad access and backup power to ensure uninterrupted security operations. Agile Scrum methodology guided the system's iterative development, emphasizing user-centric design and continuous testing. A quantitative approach was applied to the descriptive research method using a five-point Likert scale in the evaluation. Data were collected from 12 evaluators, comprising six industry professionals and six homeowners, using a survey instrument based on the ISO/IEC 25010:2023 product quality model. The evaluation result from industry professionals is 4.76 out of 5, and 4.93 out of 5 from homeowners, corresponding to an "Excellent"descriptive interpretation. This result shows the developed system meets high security, usability, and user acceptance performance. Overall, the study demonstrates the potential of IoT-driven solutions to bridge gaps in home automation, offering an accessible and adaptable system tailored to local contexts. SafeHome advances smart home technology and addresses critical challenges, ensuring broader adoption and impact in modern residential settings. © 2025 IEEE.

关键词： ISO Standards

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Blockchain-Based Anomaly Detection and Intrusion Prevention in IoT Networks 8th

Blockchain-Based Anomaly Detection and Intrusion Prevention ...

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8th international conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Ganesh Kumar, G. Kanakaprabha, S. Venu Gopal, Gaddam Shaik, Riaz Senthil Kumar, T. Udhaya Kumar, T. Department of Information Technology Hindusthan Institute of Technology Tamilnadu Coimbatore641032 India Department of Computer Science and Engineering Rathinam Technical Campus Anna University Coimbatore India B V Raju Institute of Technology Telangana Narsapur Medak502313 India Department of Internet of Things KL Deemed to be University Vijayawada India Department of Information Technology Hindusthan Institute of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India

ISBN: (纸本)9789819757855

The increasing adoption of IoT devices has raised significant concerns about security and privacy. This paper proposes a blockchain-based approach for anomaly detection and intrusion prevention in IoT networks. The methodology combines blockchain technology with anomaly detection algorithms to enhance the security and trustworthiness of IoT systems. Through simulations and experiments using real-world IoT datasets, the effectiveness of the approach is evaluated. The results demonstrate improved accuracy in detecting anomalies and reduced false positives compared to traditional methods. The blockchain-based solution also provides tamper-proof and decentralized data storage, ensuring the integrity and availability of IoT network information. This research highlights the potential of blockchain technology as a robust solution for enhancing anomaly detection and intrusion prevention in IoT networks, addressing critical security challenges in the IoT ecosystem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Retrieval-Augmented Generation for Large Language Model based Few-shot Chinese Spell Checking 31

Retrieval-Augmented Generation for Large Language Model base...

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31st international conference on Computational Linguistics, COLING 2025

作者： Dong, Ming Cheng, Zhiwei Luo, Changyin He, Tingting Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning China National Language Resources Monitoring Research Center for Network Media China School of Computer Central China Normal University Wuhan China

ISBN: (纸本)9798891761964

Large language models (LLMs) are naturally suitable for Chinese spelling check (CSC) task in few-shot scenarios due to their powerful semantic understanding and few-shot learning capabilities. Recent CSC research has begun to use LLMs as foundational models. However, most current datasets are primarily focused on errors generated during the text generation process, with little attention given to errors occurring in the modal conversion process. Furthermore, existing LLM-based CSC methods often rely on fixed prompt samples, which limits the performance of LLMs. Therefore, we propose a framework named RagID (Retrieval-Augment Generation and Iterative Discriminator Strategy). By utilizing semantic-based similarity search and an iterative discriminator mechanism, RagID can provide well-chosen prompt samples and reduce over-correction issues in LLM-based CSC. RagID demonstrates excellent effectiveness in few-shot scenarios. We conducted comprehensive experiments, and the results show that RagID achieves the best performance on dataset that include data from multiple domains and dataset containing modal conversion spelling errors. The dataset and code are available online. © 2025 Association for Computational Linguistics.

关键词： Semantics

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Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models 24

Forecasting Influenza-like Illness in Compulsory Education S...

引用

13th international conference on Bioinformatics and Biomedical science, ICBBS 2024

作者： Chen, Ying Luo, Jinsong Liang, Deyi Xie, Yewei Zhou, Yi Wu, Dan Cheng, Weibin Tang, Weiming Jing, Fengshi Faculty of Data Science City University of Macau China School of Automation Central South University Hunan Changsha China Institute for Hospital Management Tsinghua University Guangdong Shenzhen China Guangdong Women and Children's Hospital Guangdong Guangzhou China Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Singapore Zhuhai Center for Disease Control and Prevention Guangdong Zhuhai China School of Public Health Nanjing Medical University Jiangsu Nanjing China School of Data Science City University of Hong Kong Hong Kong Hong Kong Guangdong Second Provincial General Hospital Guangdong Guangzhou China School of Medicine University of North Carolina at Chapel Hill Chapel HillNC United States

ISBN: (纸本)9798400717956

This study investigates the predictive utility of Google search queries for forecasting influenza-like illness (ILI) in compulsory education schools in Macau. The increasing availability of online data offers a novel approach to health surveillance, potentially improving the timeliness and accuracy of ILI predictions in educational settings. We employed three machine learning models: extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and ridge regression (Ridge), to forecast the ILI-caused absence rate in kindergartens, primary schools, and middle schools one and two weeks in advance in Macau. The covariates for these models include Google search queries and historical ILI data. This is Macau's first study to apply machine learning methodologies and Internet big data to the surveillance of ILI in educational institutions. Our approach offers a feasible and computationally efficient method for forecasting ILI in Macau's compulsory education schools. This methodology could be adapted for use in other regions with limited influenza data resources, providing a valuable tool for public health planning and response. © 2024 Copyright held by the owner/author(s).

关键词： Biomedical data analysis

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Heart Disease Identification Using Ensemble Learning and Deep Learning 3rd

Heart Disease Identification Using Ensemble Learning and Dee...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Siruvoru, Vahini Nampally, Vijay Kumar Thati, Deepak Devanaboyina, Sai Abhinav Sanagala, Sai Dheeraj Reddy Department of Computer Science and Business System B V Raju Institute of Technology Telangana Medak Narsapur India

ISBN: (纸本)9789819783540

technology in the field of health care has been constantly enhanced incorporating machine learning and various other technologies for clinical decision support systems or computer-aided healthcare systems which assist in examining the complications. Heart Disease is an issue of concern for any age group and gender, which depends on innumerable factors like cholesterol, blood pressure, chest pain and more. Furthermore, initial methods of getting a check-up from the doctor to determine whether the patient is suffering or will be suffering from the disease is time consuming as well as laborious. Considering this, by using Machine Learning and Deep Learning, it is possible to overcome the complications and produce an effective and accurate means of detecting heart disease. This is achieved by deploying ANN, performing feature reduction by PCA as well and creating an ensemble model of Random Forest, Decision Tree, and KNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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A Whale Falls, All Thrive: Mitigating Attention Gap to Improve Adversarial Transferability 27th

A Whale Falls, All Thrive: Mitigating Attention Gap to Impro...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Wan, Qiang Chen, Biwei Peng, Anjie Zeng, Hui Southwest University of Science and Technology Mianyang China Beijing Normal University Zhuhai China

ISBN: (纸本)9783031783111

Deep neural networks (DNNs) are deemed vulnerable to adversarial examples (AEs). Transfer-based attacks enable attackers to craft adversarial images based on local surrogate models without feedback from remote ones. One of the promising attacks is to distract the attention map of the surrogate model that is likely to be shared among remote models. However, we find that the attention maps calculated from a local model are usually over-focus on the most critical area, which limits the transferability of the attacks. In response to this challenge, we propose an enhanced image transformation method (EIT), which guides adversarial perturbations to distract not only the most critical area but also other relevant regions. The proposed EIT effectively mitigates the differences in attention maps between multiple models and better neutralizes model-specific features, thereby avoiding getting stuck in local optima specific to the surrogate model. Experiments confirm the superiority of our approach to the state-of-the-art benchmarks. Our implementation is available at: ***/britney-code/EIT-attack. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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