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International Conference on Advanced Communication Technology (ICACT)

作者： Tsetsentsengel Munkhbayar Zolzaya Dashdorj Zoljargal Jargalsaikhan Kang Tae-Koo Erdenebaatar Altangerel Department of Computer Science Mongolian University of Science and Technology Ulaanbaatar Mongolia Department of Human Intelligence and Robot Engineering Sangmyung University Seoul Republic of Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

Demand prediction in transportation systems plays a critical role in optimizing resources and improving service efficiency. This study explores demand prediction for Ulaanbaatar's public transportation network using Graph Attention Networks (GATs), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). GATs effectively capture spatial relationships, achieving the best performance while GANs struggle with stability and convergence issues. The findings emphasize the potential of using graph-based methods that incorporate key stations in the analysis of public transportation networks for predicting transit demand.

关键词： Deep learning Neural networks Demand forecasting Predictive models Generative adversarial networks Stability analysis Convolutional neural networks Resource management Public transportation Convergence

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Let's Influence Algorithms Together: How Millions of Fans Build Collective Understanding of Algorithms and Organize Coordinated Algorithmic Actions 25

Let's Influence Algorithms Together: How Millions of Fans Bu...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Qing Xiao Yuhang Zheng Xianzhe Fan Bingbing Zhang Zhicong Lu Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA University of Amsterdam Amsterdam Amsterdam Netherlands Tsinghua University Beijing China School of Journalism and Mass Communication University of Iowa iowa City Iowa USA Department of Computer Science George Mason University Fairfax Virginia USA

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Dialogue-to-Video Retrieval via Multi-Grained Attention Network

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IEEE Access 2025年 13卷 82302-82311页

作者： Yu, Yi Zhang, Jiwei Tang, Suhua Li, Wei Hiroshima University Graduate School of Advanced Science and Engineering Higashihiroshima739-0046 Japan KDDI Research Inc. Human-Centered AI Laboratories Fujimino356-8502 Japan Wakayama University Department of Systems Engineering Wakayama640-8441 Japan The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Chofu182-8585 Japan Fudan University School of Computer Science Shanghai200433 China

Dialogue-to-video retrieval is an interesting while challenging task, which exploits an AI agent to retrieve the video that matches and aligns with the conversational context between users. In particular, given a history of dialogue exchanges, the agent is expected to identify the most fitting video content that complements the ongoing conversation. The computational complexity posed by the processing of videos within deep neural networks encourages us to adapt CLIP, a cutting-edge large multi-modal models (LLMs), into the dialogue-video domain. On this basis, we propose a multi-grained attention network (MGAT), integrating query-scoring, dual-softmax and query-bank normalization techniques. We design a multi-grained attention module to optimize the inadequate modeling of conversation semantics in existing pre-trained models, dynamically assign weights during conversation feature extraction, and introduce conversation context features in multimodal alignment. Most importantly, fine-grained similarity by per-round query-frame scoring and coarse-grained similarity by high-level semantics of all rounds of dialogues for each video are respectively calculated, which are further learned via multi-grained attention mechanism. This approach effectively transfers CLIP's text-image multimodal knowledge into the dialogue-video retrieval system, alleviating the need for resource-intensive and costly fine-tuning dialogue-video procedures. Extensive experiments on multiple datasets demonstrate that our method outperforms state-of-the-art approaches. © 2013 IEEE.

关键词： Deep neural networks

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AI-based model for securing cognitive IoT devices in advance communication systems

International Journal of Cognitive Computing in Engineering

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International Journal of Cognitive Computing in Engineering 2025年 6卷 351-359页

作者： Gaurav, Akshat Arya, Varsha Chui, Kwok Tai Gupta, Brij B. Ronin Institute MontclairNJ United States Asia University Taichung 413 Taiwan Dehradun India UCRD Chandigarh University Chandigarh India Hong Kong Metropolitan University Hong Kong SAR China Department of Computer Science and Information Engineering Asia University Taichung Taiwan University of Economics and Human Science Warsaw Poland Symbiosis International University Pune India School of Cybersecurity Korea University Seoul Korea Republic of

The pervasive integration of the Cognitive Internet of Things (CIoT) in advanced communication systems has significantly heightened security concerns due to inherent vulnerabilities that cyber attackers exploit. These security challenges are exacerbated by IoT devices’ limited computational resources and storage capacities, rendering traditional security protocols ineffective. This paper introduces a AI- based security model tailored explicitly for IoT environments within advanced communication systems. Our approach leverages AI techniques for cyber attack detection and mitigation. The effectiveness of our model is demonstrated through its capability to identify different types of cyberattacks with a precision of more than 99%. This study advances the security mechanisms for IoT devices and offers a scalable and efficient solution to safeguard user privacy and system integrity in sophisticated communication networks. © 2025 The Authors

关键词： Cyber attacks

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SiamCircle: Trajectory Representation Learning in Free Settings 23rd

SiamCircle: Trajectory Representation Learning in Free Sett...

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23rd International Symposium on Intelligent Data Analysis, IDA 2025

作者： Nasri, Maedeh Baratchi, Mitra Koutamanis, Alexander Rieffe, Carolien Department of Developmental Psychology Leiden University Leiden Netherlands Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Faculty of Architecture and the Built Environment Delft University of Technology Delft Netherlands Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Department of Psychology and Human Development Institute of Education UCL London United Kingdom

ISBN: (纸本)9783031913976

Trajectory representation learning (TRL) is an intermediate step in handling trajectory data to realize various downstream machine-learning tasks. While most previous TRL research focuses on modeling structured movements in large-scale urban spaces (e.g., cars or pedestrians on streets), this paper focuses on a more challenging scenario of modeling free movement in small-scale social spaces (e.g., children playing in a schoolyard). We present a TRL model, SiamCircle, to process raw trajectories without additional feature extraction to prevent information loss. SiamCircle adopts a Siamese network with Circle Loss to learn trajectory embeddings. Furthermore, SiamCircle employs a data augmentation process to enable self-supervised learning and enrich the input data to address the limited access to high-quality data and ground truth. We evaluate the performance of SiamCircle in downstream tasks using trajectory ranking and clustering performance via seven evaluation metrics collectively. Using an ablation study, we explored the impact of different loss functions on the model’s performance. Accordingly, we selected a 2-D convolutional design with Circle Loss as the best-performing model. In a comparative study, we compared our model against three other baselines. We observed up to 19% improvements in trajectory ranking tasks and achieved the highest average rank in supervised clustering tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clustering Trajectory Representation Learning Triplet Loss

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An EEG Method to Identify Image Preference With an Explicit/Implicit Task Brain-computer Interface

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IEEE Transactions on Affective Computing 2025年

作者： Li, Yulei Li, Shuyi Qi, Hongzhi Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin300092 China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin300072 China

Accurately determining an individual's preference for images remains a major challenge in the field of emotional research. This study proposes a novel paradigm for identifying individual image preferences using electroencephalography (EEG) signals and brain-computer interface (BCI). The paradigm involves both explicit and implicit tasks, where participants perform a typical event-related potential-based brain-computer interface(ERP-BCI) operation and their subjective image preferences are identified, respectively. Two experiments with a total of 27 participants demonstrate that event-related potential (ERP) signals during explicit BCI tasks are significantly influenced by target image preferences, enabling high-accuracy image preference recognition. Online experiments selecting positive and negative preference images from a candidate pool show top-1 accuracy approaching 100% and top-3 accuracy exceeding 90%. These results indicate the effectiveness of the proposed EEG-based image preference recognition paradigm, laying the groundwork for preference analysis applications. © 2010-2012 IEEE.

关键词： Brain mapping

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I Can’t Share Code, but I need Translation - An Empirical Study on Code Translation through Federated LLM

arXiv

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arXiv 2025年

作者： Kumar, Jahnavi Janapati, Venkata Lakshmana Sasaank Tanguturi, Mokshith Reddy Chimalakonda, Sridhar Research in Intelligent Software & Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

Owing to the rapid evolution of technologies and project requirements, organizations need to upgrade the code base in their software projects to a new version of the programming language or even translating to an entirely new one. However, code translation is resource-intensive and requires expertise in both the source and target languages. While researchers have made progress in automating translations between legacy and modern languages, recent work has increasingly turned to pre-trained Large Language Models (LLMs) to translate efficiently. Given the proprietary nature of code, organizations prefer finetuning LLMs locally rather than relying on external APIs. This is one of the first empirical studies that proposes a Federated LLM-based approach for code translation. The proposed approach enables clients to jointly train a code translator without sharing sensitive data. This study demonstrates that participants can collaboratively develop a FedLLM for efficient code translation (particularly C# to Java and vice-versa) with superior results (more than 40% improvement in CodeLLaMA’s CodeBLEU score) compared to individual client models. Our findings indicate that FedLLM offers a collaborative approach to code translation and could serve as a promising direction for future research in this field. © 2025, CC BY.

关键词： Program translators

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Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Yin, Zhaoxia Zhu, Shaowei Su, Hang Peng, Jianteng Lyu, Wanli Luo, Bin East China Normal University School of Communication & Electronic Engineering Shanghai China Anhui University China Tsinghua University Department of Computer Science and Technology Beijing100084 China OPPO Intellisense and Interaction Research Department China

Deep Neural Networks (DNNs) have recently made significant strides in various fields;however, they are susceptible to adversarial examples-crafted inputs with imperceptible perturbations that can mislead these networks. Notably, even when adversaries lack access to the complete model parameters, they can still generate adversarial examples targeting a range of DNN-based task systems. Various defense mechanisms have been proposed, such as feature compression and gradient masking. Nevertheless, extensive research indicates that these methods often address only specific attacks, rendering them ineffective against novel and unknown attack strategies. Recent studies have highlighted the efficacy of identifying adversarial examples in the frequency domain;however, these approaches are limited to frequency-based analysis. In this study, we experimentally observe that adversarial examples possess significant characteristics in local regions. Specifically, adversarial perturbations exhibit localized randomness, whereas the high-frequency information in normal examples is both locally coherent and semantically relevant. This critical distinction enables effectively distinguishing adversarial examples from normal ones. To leverage this insight, we aim to enhance the high-frequency features of input examples to amplify their feature disparities. We propose an image enhancement method utilizing local histogram equalization. Our experimental results demonstrate that this method substantially improves detector performance without modifying the existing detection models. Furthermore, this technique can be seamlessly integrated with task models, effectively reducing deployment costs in practical applications. © 2004-2012 IEEE.

关键词： Frequency domain analysis

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Long-Term Effects of User Expertise and Application Design on Collision Anxiety in VR Games 25

Long-Term Effects of User Expertise and Application Design o...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Patrizia Ring Maic Masuch Faculty of Computer Science / Department of Human-centered Computing and Cognitive Science (HCCS) / Entertainment Computing Group University of Duisburg-Essen Duisburg Germany

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How Users Who are Blind or Low Vision Play Mobile Games: Perceptions, Challenges, and Strategies

arXiv

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arXiv 2025年

作者： Ran, Zihe Fan, Xianzhe Li, Xiyu Li, Franklin Mingzhe Lu, Zhicong Xiao, Qing Wang, Yanyun Communication University of China Beijing China Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Department of Computer Science George Mason University FairfaxVA United States College of Media Communication and Information University of Colorado Boulder BoulderCO United States

As blind and low-vision (BLV) players engage more deeply with games, accessibility features have become essential. While some research has explored tools and strategies to enhance game accessibility, the specific experiences of these players with mobile games remain underexamined. This study addresses this gap by investigating how BLV users experience mobile games with varying accessibility levels. Through interviews with 32 experienced BLV mobile players, we explore their perceptions, challenges, and strategies for engaging with mobile games. Our findings reveal that BLV players turn to mobile games to alleviate boredom, achieve a sense of accomplishment, and build social connections, but face barriers depending on the game’s accessibility level. We also compare mobile games to other forms of gaming, highlighting the relative advantages of mobile games, such as the inherent accessibility of smartphones. This study contributes to understanding BLV mobile gaming experiences and provides insights for enhancing accessible mobile game design. Copyright © 2025, The Authors. All rights reserved.

关键词： Game design

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