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An Extended Euclidean Algorithm Implementation over Ciphertext Space 2nd

An Extended Euclidean Algorithm Implementation over Ciphert...

2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Chi, Po-Wen Lin, Yuan-Pang Wang, Ming-Hung Chuang, Yun-Hsin Department of Computer Science and Information Engineering National Taiwan Normal University Taiwan Taipei Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Taiwan Chiayi Taiwan Engineering Management Graduate Program College of Engineering National Cheng Kung University Taiwan Tainan Taiwan

ISBN: (纸本)9789819777853

Fully homomorphic encryption (FHE) is a useful cryptographic primitive. It makes a third-party manipulate data in the ciphertext space and therefore is a key technology for privacy-preserving outsourcing services. Theoretically, FHE can evaluate any circuits. However, the implementation is not trivial. The reason it lacks lots of basic operations like conditional check and branch control. In this paper, we focus on extended Euclidean algorithm operation over FHE. We introduce the problems we encounter and show how we solve these issues. We implement our design and evaluate its performance on a real computer. Finally, we propose some research directions for our future work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ciphertext

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Deep-Learning Approach for An Analysis of Real-estate Prices and Transactions

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IEEE Access 2025年 13卷 89248-89265页

作者： Yang, Cheng-Hong Lee, Borcy Lin, Yu-Da Tainan University of Technology Department of Information Management Tainan Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung Taiwan Kaohsiung Medical University Ph.D. Program in Biomedical Engineering Kaohsiung Taiwan Kaohsiung Medical University School of Dentistry Kaohsiung Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Kaohsiung Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

Research on the study of houses, condominiums and buildings in Taiwan’s metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. Data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (DBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results of this study indicate that three key factors—the number of real estate transactions, the average unit price of real estate transactions, and the building material and construction index—significantly affected real estate prices in Taiwan. Our results could help researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors. In addition, the relationships between real estate trends and the aforementioned three key factors were determined to obtain valuable information that can enable the Taiwanese government to regulate the property market and prevent excessive growth. The framework proposed in this paper allows researchers and governments to focus on specific aspects of real estate development without being influenced b

关键词： Houses

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Efficient RGBT Tracking via Multi-Path Mamba Fusion Network

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IEEE Signal Processing Letters 2025年 32卷 1790-1794页

作者： Hong, Fanghua Wang, Wanyu Lu, Andong Liu, Lei Wang, Qunjing Anhui University School of Electronic Information Engineering Hefei230601 China Anhui University School of Artificial Intelligence Hefei230601 China Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei230601 China Anhui University of Science and Technology School of Artificial Intelligence Hefei230601 China

RGBT tracking aims to fully exploit the complementary advantages of visible and infrared modalities to achieve robust tracking, thus the design of multimodal fusion network is crucial. However, existing methods typically adopt CNNs or Transformer networks to construct the fusion network, which poses a challenge in achieving a balance between performance and efficiency. To overcome this issue, we introduce an innovative visual state space (VSS) model, represented by Mamba, for RGBT tracking. In particular, we design a novel multi-path Mamba fusion network that achieves robust multimodal fusion capability while maintaining a linear overhead. First, we design a multi-path Mamba layer to sufficiently fuse two modalities in both global and local perspectives. Second, to alleviate the issue of inadequate VSS modeling in the channel dimension, we introduce a simple yet effective channel swapping layer. Extensive experiments conducted on four public RGBT tracking datasets demonstrate that our method surpasses existing state-of-the-art trackers. Notably, our fusion method achieves higher tracking performance compared to the well-known Transformer-based fusion approach (TBSI), while also achieving 92.8% and 80.5% reductions in parameter count and computational cost, respectively. © 1994-2012 IEEE.

关键词： Alignment

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Offsets Reconstruction Method for Clock Skew Measurement Over High-Jitter Communication

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SN computer science 2025年第5期6卷 1-14页

作者： Sastra, Nyoman Putra Saputra, Komang Oka Teng, Wei-Chung Study Program of Electrical Engineering Faculty of Engineering Udayana University Jalan Raya Kampus Bukit Jimbaran Bali Badung 80361 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Taipei 106 China

Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the clock skew sought. When some outliers separate from the thick line due to higher delay jitter, current advanced approaches use the lower bound or the majority of offsets to derive accurate clock skew. However, outliers in offsets that shape parallel dotted lines, reduce the availability of offsets in the lower region, and scatter widely the offsets from their majority zone. These conditions drive the current skew estimation approaches to lose their accuracy. This paper then developed a new method, which reconstructs back the outliers into their origin positions, in order to provide ideal offsets for clock skew measurement. The experimental evaluations have demonstrated that the clock skew accuracy of dotted-lines offsets tampered by outliers was improved from 2.1 to 0.9 ppm by the proposed reconstruction method. For a broader purpose, the reconstruction method has been adjusted to be implemented also on non-dotted-lines offsets. The robustness of the proposed method for this case has also been exhibited by the very ideal reconstructed offsets that shape like a thick line. © The Author(s) 2025.

关键词： Clock skew Delay jitter Dotted lines Hough transform

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Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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FPGA implementation of low cost and low power chaotic encryption scheme based on a discrete-space chaotic map

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Multimedia Tools and Applications 2025年 1-22页

作者： Moreira Bezerra, João Inácio Machado, Gustavo Iankowski Soares, Rafael Valduga de Almeida Camargo, Vinícius Molter, Alexandre Post Graduate Program in Computer Science Universidade Federal de Pelotas Rio Grande do Sul Pelotas96010610 Brazil Graduate Program in Computer Engineering Universidade Federal de Pelotas Rio Grande do Sul Pelotas96010610 Brazil Post Graduate Program in Mathematical Modeling Universidade Federal de Pelotas Rio Grande do Sul Capão do Leão96160000 Brazil

The exponential growth of the number of devices connected to the Internet and the use of IoT applications increases the amount of data exchange over public channels in low-cost and low-power embedded systems. Images are the most widely shared files, and standard symmetric algorithms such as the Advanced Encryption Standard (AES) are optimized for text but not image encryption. Consequently, new approaches like chaotic encryption have attracted researchers’ interest. Chaotic encryption schemes have been analyzed mostly from a software standpoint, but recent advancements in the field, through the use of discrete chaotic maps, enabled the development of simpler hardware implementations of ciphers with promising reductions in power and cost and increased performance. This work presents the Field-programmable Gate Array (FPGA) implementation of a simultaneous permutation-diffusion chaotic encryption scheme utilizing a discrete-space chaotic map. The FPGA implementation of the cipher is analyzed considering area, maximum frequency, and power consumption, and comparisons are made with the AES. The proposed algorithm has a satisfactory trade-off between area, power and performance, obtaining a peak throughput 59% higher than state-of-the-art symmetric ciphers in the literature. Thus, the proposed cipher shows that chaotic ciphers are viable for both high-performance and low-power embedded devices. Tests verified the security of the proposed cipher against statistical, differential, brute-force, and chosen plain-text attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cryptography

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Asynchronous I/O Persistence for In-Memory Database Servers: Leveraging io_uring to Optimize Redis Persistence 17th

Asynchronous I/O Persistence for In-Memory Database Servers:...

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17th International Conference on Cloud Computing, CLOUD 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Le-Gao Li, Yanzhi Laohakangvalvit, Tipporn Sugaya, Midori Innovative Global Program College of Engineering Shibaura Institute of Technology Tokyo Koto City Japan Department of Computer Science and Engineering College of Engineering Shibaura Institute of Technology Tokyo Koto City Japan

ISBN: (纸本)9783031771521

With constant hardware improvements allowing for increasingly large memory sizes, in-memory database servers have become an attractive option for various cloud applications. Even though in-memory database servers store all data in memory, there is still a need to persist data onto an I/O device for maintenance, updates, or protection against server crashes. In high throughput environments, persistence happens often, significantly hindering server throughput performance. Although popular in-memory database servers such as Redis or Dragonfly DB have adopted asynchronous I/O for socket operations, none have extended this approach for persistence. This results in significantly longer time taken for persistence, leading to prolonged reduced throughput. Given the recent introduction of io_uring, which has demonstrated significant performance improvements over past asynchronous I/O APIs, we have decided to implement it into Redis to evaluate its effectiveness in reducing these performance overheads. Our findings indicate that io_uring can significantly enhance performance in use cases with medium to large payload sizes but may hinder performances in use cases involving small payload sizes due to higher complexity and higher operation cost per datapoint. This method can likely be applied to other servers, such as Dragonfly DB and Memcached, to achieve similar benefits in appropriate scenarios. Additionally, in-memory database server persistence can serve as valuable benchmarks for further io_uring development and optimization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Benchmarking

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IKT-BT: Indirect Knowledge Transfer Behavior Tree Framework for Multirobot Systems Through Communication Eavesdropping

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IEEE Transactions on Cybernetics 2025年第6期55卷 2558-2570页

作者： Venkata, Sanjay Sarma Oruganti Parasuraman, Ramviyas Pidaparti, Ramana Rensselaer Polytechnic Institute Cognitive Science Department TroyNY12180 United States University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States University of Georgia School of Computing AthensGA30602 United States University of Georgia School of Environmental Civil Agricultural and Mechanical Engineering AthensGA30602 United States

Multiagent and multirobot systems (MRS) often rely on direct communication for information sharing. This work explores an alternative approach inspired by eavesdropping mechanisms in nature that involves casual observation of agent interactions to enhance decentralized knowledge dissemination. We achieve this through a novel indirect knowledge transfer through behavior trees (IKT-BT) framework tailored for a behavior-based MRS, encapsulating knowledge and control actions in behavior trees (BT). We present two new BT-based modalities—eavesdrop-update (EU) and eavesdrop-buffer-update (EBU)—incorporating unique eavesdropping strategies and efficient episodic memory management suited for resource-limited swarm robots. We theoretically analyze the IKT-BT framework for an MRS and validate the performance of the proposed modalities through extensive experiments simulating a search and rescue mission. Our results reveal improvements in both global mission performance outcomes and agent-level knowledge dissemination with a reduced need for direct communication. © 2013 IEEE.

关键词： Knowledge transfer

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Can ChatGPT Be Addictive? A Call to Examine the Shift from Support to Dependence in AI Conversational Large Language Models

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Human-Centric Intelligent Systems 2025年第1期5卷 77-89页

作者： Yankouskaya, Ala Liebherr, Magnus Ali, Raian Department of Psychology Faculty Science and Technology Bournemouth University Poole UK Department of Computer Science and Applied Cognitive Science Faculty of Engineering University of Duisburg-Essen Duisburg Germany College of Science and Engineering Hamad Bin Khalifa University Ar-Rayyan Qatar

The rapid rise of ChatGPT has introduced a transformative tool that enhances productivity, communication, and task automation across industries. However, concerns are emerging regarding the addictive potential of AI large language models. This paper explores how ChatGPT fosters dependency through key features such as personalised responses, emotional validation, and continuous engagement. By offering instant gratification and adaptive dialogue, ChatGPT may blur the line between AI and human interaction, creating pseudosocial bonds that can replace genuine human relationships. Additionally, its ability to streamline decision-making and boost productivity may lead to over-reliance, reducing users' critical thinking skills and contributing to compulsive usage patterns. These behavioural tendencies align with known features of addiction, such as increased tolerance and conflict with daily life priorities. This viewpoint paper highlights the need for further research into the psychological and social impacts of prolonged interaction with AI tools like ChatGPT.

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Government Digital Transformation: A Tailor-Made Digital Maturity Assessment Framework

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IEEE Access 2025年 13卷 71120-71132页

作者： Alfadhli, Muna Kucukvar, Murat Onat, Nuri C. Al-Maadeed, Somaya Abdessadok, Abdelhamid Engineering Management Program College of Engineering Qatar University Doha Qatar Daniels College of Business University of Denver DenverCO80208 United States Qatar Transportation and Traffic Safety Center College of Engineering Qatar University Doha Qatar Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Ministry of Social Development and Family Doha Qatar

Governments seek to implement digital transformation initiatives to improve the effectiveness of their public services. To achieve this, governments need to assess their IT environment and evaluate their digital maturity. The conventional digital maturity assessment frameworks, mainly constructed by consulting firms, often fail when applied to the public sector due to the differences between the business and government sectors in terms of their operational missions and activities. Therefore, a maturity assessment framework that accommodates the unique requirements of governments and their operating environments is necessary. The novelty of this research lies in its contribution to digital transformation in the public sector by presenting a framework specifically designed to assess the digital transformation maturity of government organizations. This research is based on analyzing the factors of maturity assessment models developed by industry leaders from three leading global consulting firms. Insights from the academic literature on these factors were extracted, and a comprehensive survey was conducted with 88 IT experts from government institutes across six countries to ensure their relevance and applicability in real-world government settings. In this research, we tested our hypothesis on whether there is a significant relationship between the importance of digital transformation factors and their implementation. The analysis indicated that there is no strong statistical relationship between the importance of digital transformation maturity factors and their implementation. The framework helps assess digital maturity and supports the formulation of effective strategies to drive successful digital transformation initiatives in government. © 2013 IEEE.

关键词： Digital transformation Organizations Government Capability maturity model Industries Biological system modeling Technological innovation Stakeholders Planning Current measurement

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