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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Jonathan Ehret Valentin Dasbach Jan-Nikjas Hartmann Janina Fels Torsten W. Kuhlen Andrea Bönsch Visual Computing Institute RWTH Aachen University Germany Department of Computer Science RWTH Aachen University Germany Institute for Hearing Technology and Acoustics RWTH Aachen University Germany

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This work-in-progress paper investigates how virtual listening bystanders influence participants’ gaze behavior and their perception of turn-taking during scripted conversations with embodied conversational agents (ECAs). 25 participants interacted with five ECAs – two speakers and three bystanders – across three conditions: no bystanders, bystanders exhibiting random gazing behavior, and social bystanders engaging in mutual gaze and backchanneling. Participants either observed the conversation or actively participated as speakers by reciting prompted *** results indicated that bystanders reduced the participants’ attention to speakers, hindering their ability to anticipate turn changes and resulting in longer delays in shifting their gaze to the new speaker after an ECA yielded the turn. Random gazing bystanders were particularly noted for obscuring conversational flow. These findings underscore the challenges of designing effective and natural conversational environments, highlighting the need for careful consideration of ECA behaviors to enhance user engagement.

关键词： visualization Three-dimensional displays Social groups Conferences Oral communication Virtual reality Gaze tracking User interfaces Delays Complexity theory

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Exploring Gaze Dynamics: Initial Findings on the Role of Listening Bystanders in Conversational Interactions

Exploring Gaze Dynamics: Initial Findings on the Role of Lis...

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Ehret, Jonathan Dasbach, Valentin Hartmann, Jan-Nikjas Fels, Janina Kuhlen, Torsten W. Bönsch, Andrea Visual Computing Institute Rwth Aachen University Germany Rwth Aachen University Department of Computer Science Germany Rwth Aachen University Institute for Hearing Technology and Acoustics Germany

ISBN: (纸本)9798331514846

This work-in-progress paper investigates how virtual listening bystanders influence participants' gaze behavior and their perception of turn-taking during scripted conversations with embodied conversational agents (ECAs). 25 participants interacted with five ECAs - two speakers and three bystanders - across three conditions: no bystanders, bystanders exhibiting random gazing behavior, and social bystanders engaging in mutual gaze and backchanneling. Participants either observed the conversation or actively participated as speakers by reciting prompted *** results indicated that bystanders reduced the participants' attention to speakers, hindering their ability to anticipate turn changes and resulting in longer delays in shifting their gaze to the new speaker after an ECA yielded the turn. Random gazing bystanders were particularly noted for obscuring conversational flow. These findings underscore the challenges of designing effective and natural conversational environments, highlighting the need for careful consideration of ECA behaviors to enhance user engagement. © 2025 IEEE.

关键词： bystanders conversations eye tracking social groups turn taking Virtual reality

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Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation 22

Securing Consumer IoT Swarms Using Graph Transformers and SR...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Kohli, Varun Kohli, Bhavya Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln United States

ISBN: (纸本)9798331508050

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a ∼ 10-4 second inference latency on a laptop CPU. © 2025 IEEE.

关键词： computer viruses

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Translation-Invariant Quantum Algorithms for Ordered Search are Optimal

arXiv

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arXiv 2025年

作者： Carolan, Joseph Childs, Andrew M. Kovacs-Deak, Matt Schaeffer, Luke Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland United States David R. Cheriton School of Computer Science Institute for Quantum Computing University of Waterloo Canada

Ordered search is the task of finding an item in an ordered list using comparison queries. The best exact classical algorithm for this fundamental problem uses [log2 n] queries for a list of length n. Quantum computers can achieve a constant-factor speedup, but the best possible coefficient of log2 n for exact quantum algorithms is only known to lie between (ln 2)/π ≈ 0.221 and 4/log2 605 ≈ 0.433. We consider a special class of translation-invariant algorithms with no workspace, introduced by Farhi, Goldstone, Gutmann, and Sipser, that has been used to find the best known upper bounds. First, we show that any exact, k-query quantum algorithm for ordered search can be implemented by a k-query algorithm in this special class. Second, we use linear programming to show that the best exact 5-query quantum algorithm can search a list of length 7265, giving an ordered search algorithm that asymptotically uses 5 log7265 n ≈ 0.390 log2 n quantum queries. © 2025, CC BY.

关键词： Consensus algorithm

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Large Language Model Based Multi-Agent System Augmented Complex Event Processing Pipeline for Internet of Multimedia Things

arXiv

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arXiv 2025年

作者： Zeeshan, Talha Kumar, Abhishek Lovén, Lauri Pirttikangas, Susanna Tarkoma, Sasu Center for Ubiquitous Computing University of Oulu Oulu90570 Finland Department of Computer Science University of Helsinki Helsinki00560 Finland

This paper presents the development and evaluation of a Large Language Model (LLM), also known as foundation models, based multi-agent system framework for complex event processing (CEP) with a focus on video query processing use cases. The primary goal is to create a proof-of-concept (POC) that integrates state-of-the-art LLM orchestration frameworks with publish/subscribe (pub/sub) tools to address the integration of LLMs with current CEP systems. Utilizing the Autogen framework in conjunction with Kafka message brokers, the system demonstrates an autonomous CEP pipeline capable of handling complex workflows. Extensive experiments evaluate the system's performance across varying configurations, complexities, and video resolutions, revealing the trade-offs between functionality and latency. The results show that while higher agent count and video complexities increase latency, the system maintains high consistency in narrative coherence. This research builds upon and contributes to, existing novel approaches to distributed AI systems, offering detailed insights into integrating such systems into existing infrastructures. © 2025, CC BY-NC-SA.

关键词： Pipeline processing systems

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Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, and Challenges

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IEEE Communications Surveys and Tutorials 2025年

作者： Satpathy, Anurag Sahoo, Manmath Narayan Swain, Chittaranjan Bellavista, Paolo Guizani, Mohsen Muhammad, Khan Bakshi, Sambit Missouri University of Science and Technology Department of Computer Science RollaMO United States National Institute of Technology Visual Surveillance Laboratory Department of Computer Science and Engineering Odisha Rourkela769008 India Atal Bihari Vajpayee Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India Department of Computer Science and Engineering Italy Machine Learning Department Abu Dhabi United Arab Emirates Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Network virtualization (NV) allows service providers (SPs) to instantiate logically isolated entities called virtual networks (VNs) on top of a substrate network (SN). Though VNs bring about multiple benefits, particularly in terms of economic costs and elasticity, they also force various technical challenges to be addressed. The primary one is the issue of optimally allocating resources to VNs, also termed virtual network embedding (VNE). This paper presents an exhaustive survey of VNE by extensively covering the state-of-the-art research field in this very active field and focusing on the emerging research trends in industry and academia over the last decade. In addition, this survey originally contributes to the literature by proposing a novel taxonomy for existing VNE solutions and providing a thorough comparative study of their strategies. © 1998-2012 IEEE.

关键词： Virtual machine

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Parallel fault diagnosis using hierarchical fuzzy Petri net by reversible and dynamic decomposition mechanism

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Frontiers of Information Technology & Electronic Engineering 2025年第1期26卷 93-108页

作者： Yinhong XIANG Kaiqing ZHOU Arezoo SARKHEYLI-HÄGELE Yusliza YUSOFF Diwen KANG Azlan Mohd ZAIN School of Communication and Electronics Engineering Jishou UniversityJishou 416000China Internet of Things and People Research Center Department of Computer Science and Media TechnologyMalmöUniversityMalmö20506Sweden Faculty of Computing Universiti Teknologi MalaysiaSkudai 81310Malaysia

The state space explosion,a challenge analogous to that encountered in a Petri net(PN),has constrained the extensivestudy of fuzzy Petri nets(FPNs).Current reasoning algorithms employing FPNs,which operate through forward,backward,andbidirectional mechanisms,are *** algorithms streamline the inference process by eliminating irrelevant componentsof the ***,as the scale of the FPN grows,the complexity of these algorithms escalates sharply,posing a significantchallenge for practical *** address the state explosion issue,this work introduces a parallel bidirectional reasoningalgorithm for an FPN that utilizes reverse and decomposition strategies to optimize the implementation *** algorithminvolves hierarchically dividing a large-scale FPN into two sub-FPNs,followed by a converse operation to generate the reversalsub-FPN for the *** detailed mapping between the original and reversed FPNs is thoroughly *** operations are then conducted on the left-sub-FPN and the resulting reversal right-sub-FPN,with the final resultderived by computing the Euclidean distance between the outcomes from the output places of the two sub-FPNs.A case study ispresented to illustrate the implementation process,demonstrating the algorithm’s significant enhancement of inference efficiencyand substantial reduction in execution time.

关键词： Fuzzy Petri net(FPN) State explosion Decomposition Parallel Bidirectional reasoning

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MAD-ANET:Malware Detection Using Attention-Based Deep Neural Networks

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computer Modeling in Engineering & sciences 2025年第4期143卷 1009-1027页

作者： Waleed Khalid Al-Ghanem Emad Ul Haq Qazi Tanveer Zia Muhammad Hamza Faheem Muhammad Imran Iftikhar Ahmad Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia Centre of Artificial Intelligence Naif Arab University for Security SciencesRiyadh14812Saudi Arabia School of Arts and Sciences The University of Notre DameSydneyNSW 2007Australia Center for Smart Analytics Institute of InnovationScience and SustainabilityFederation University AustraliaBerwickVIC 3806Australia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the current digital era,new technologies are becoming an essential part of our ***,the number ofmalicious software ormalware attacks is rapidly *** is no doubt,themajority ofmalware attacks can be detected by most antivirus ***,such types of antivirus programs are one step behind malicious *** to these dilemmas,deep learning become popular in the detection and classification of malicious ***,researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and *** this research,we presented a lightweight attention-based novel deep Convolutional Neural Network(DNN-CNN)model for binary and multi-class malware classification,including benign,trojan horse,ransomware,and *** applied the Principal Component Analysis(PCA)technique for feature extraction for binary *** used the Synthetic Minority Oversampling Technique(SMOTE)to handle the imbalanced data during multi-class *** proposed attention-based malware detectionmodel is trained on the benchmarkmalware memory dataset named *** indicate that our model obtained high accuracy for binary and multi-class classification,99.5% and 97.9%,respectively.

关键词： Attention-based CNN malware detection machine learning deep learning classification

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Adaptive AI-Based Personalized Learning for Accelerated Vocabulary and Syntax Mastery in Young English Learners

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International Journal of Advanced computer science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient Learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (science and Information Organization). All rights reserved.

关键词： Students

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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