IEEE Transactions on Emerging Topics in Computing最新文献_第8页

A Virtual Reality Perceptual Study of Multi-Technique Redirected Walking Method 多技术重定向行走方法的虚拟现实感知研究

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-10-09 DOI: 10.1109/TETC.2024.3471249

Jesus Mayor;Laura Raya;Sofia Bayona;Alberto Sanchez

{"title":"A Virtual Reality Perceptual Study of Multi-Technique Redirected Walking Method","authors":"Jesus Mayor;Laura Raya;Sofia Bayona;Alberto Sanchez","doi":"10.1109/TETC.2024.3471249","DOIUrl":"https://doi.org/10.1109/TETC.2024.3471249","url":null,"abstract":"Within virtual reality experiences, locomotion methods manage the user’s movement within the virtual environment. The use of natural locomotion, common in virtual reality, can be limited in video games with large scenarios. Thus, video games with gamepad or teleport-based locomotion methods are gaining importance. Redirected walking methods focus on maximizing the exploitation of the real workspace. As the user moves in the real environment, subtle modifications are applied to that movement within the virtual environment. Although the results of the Multi-Technique Redirected Walking (MTRW) method that combines the application of four gain algorithms are promising, a perceptual evaluation with users is needed to determine its suitability. This article presents the perceptual evaluation of the presence and cybersickness factors for the MTRW method, comparing it with a Fully Natural Walking (FNW) method. The presence factor was measured with the Igroup Presence Questionnaire (IPQ), and no significant differences in the overall presence score were detected between the FNW and the MTRW methods. The cybersickness factor was measured using the Simulator Sickness Questionnaire (SSQ) and, this time, significant differences in cybersickness between the two locomotion methods were obtained. The potential increase in cybersickness should be weighed against the benefit of maximizing workspace utilization.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"604-613"},"PeriodicalIF":5.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10713064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NGQR: A Novel Generalized Quantum Image Representation NGQR：一种新的广义量子图像表示

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-10-07 DOI: 10.1109/TETC.2024.3471086

Zheng Xing;Xiaochen Yuan;Chan-Tong Lam;Penousal Machado

{"title":"NGQR: A Novel Generalized Quantum Image Representation","authors":"Zheng Xing;Xiaochen Yuan;Chan-Tong Lam;Penousal Machado","doi":"10.1109/TETC.2024.3471086","DOIUrl":"https://doi.org/10.1109/TETC.2024.3471086","url":null,"abstract":"To address the size limitations of existing quantum image models in terms of accurate image representation, as well as inaccurate image operation and retrieval, we propose a Novel Generalized Quantum Image Representation (NGQR) for images of arbitrary size and type. For generalizing the size model, we first propose the Perception-Aided Encoding (PE) method to perceive the target qubits in the quantum information. Based on PE, we propose the quantum image representation PE-NGQR, which accurately ignores redundant information thereby targeting valid pixels for operations and retrieval. Then, to accurately represent the needed pixel information without redundancy, we propose the Coherent-Size Encoding (CE) method. The CE can encode an arbitrary number of quantum states. Based on CE, we propose CE-NGQR, a quantum image model capable of accurate image representation, processing and retrieval. Specifically, we describe in detail the concept, representation and quantum circuits of NGQR. We provide detailed quantum circuits and simulations of NGQR-based operations and geometric transformations. Moreover, NGQR enables flexible quantum image scaling. We illustrate the complementarity of the proposed PE-NGQR and CE-NGQR through complexity simulations and clarify the respective applicability scenarios. Finally, comparisons and analyses with existing quantum image models demonstrate the versatility and flexibility advantages of NGQR.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"591-603"},"PeriodicalIF":5.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Analysis of Software Aging in Relational Database System Environments 关系型数据库系统环境下软件老化的比较分析

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-10-07 DOI: 10.1109/TETC.2024.3471684

Herderson Couto;Fumio Machida;Gustavo Callou;Ermeson Andrade

引用次数: 0

IEEE Transactions on Emerging Topics in Computing Information for Authors 电气和电子工程师学会（IEEE）《计算领域新兴专题论文》（IEEE Transactions on Emerging Topics in Computing）供作者参考的信息

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-09-05 DOI: 10.1109/TETC.2024.3449211

引用次数: 0

Special Section on Emerging Social Computing 新兴社交计算专栏

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-09-05 DOI: 10.1109/TETC.2024.3447428

Yuan-Hao Chang;Paloma Díaz;Yunpeng Xiao

引用次数: 0

DALTON - Deep Local Learning in SNNs via Local Weights and Surrogate-Derivative Transfer DALTON - 通过本地权重和代理-衍生转移在 SNN 中进行深度本地学习

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-08-27 DOI: 10.1109/TETC.2024.3440932

Ramashish Gaurav;Duy Anh Do;Thinh T. Doan;Yang Yi

{"title":"DALTON - Deep Local Learning in SNNs via Local Weights and Surrogate-Derivative Transfer","authors":"Ramashish Gaurav;Duy Anh Do;Thinh T. Doan;Yang Yi","doi":"10.1109/TETC.2024.3440932","DOIUrl":"10.1109/TETC.2024.3440932","url":null,"abstract":"Direct training of Spiking Neural Networks (SNNs) is a challenging task because of their inherent temporality. Added to it, the vanilla Back-propagation based methods are not applicable either, due to the non-differentiability of the spikes in SNNs. Surrogate-Derivative based methods with Back-propagation Through Time (BPTT) address these direct training challenges quite well; however, such methods are not neuromorphic-hardware friendly for the On-chip training of SNNs. Recently formalized Three-Factor based Rules (TFR) for direct local-training of SNNs are neuromorphic-hardware friendly; however, they do not effectively leverage the depth of the SNN architectures (we show it empirically here), thus, are limited. In this work, we present an <italic>improved version of a conventional three-factor rule, for local learning in SNNs which effectively leverages depth – in the context of learning features hierarchically. Taking inspiration from the Back-propagation algorithm, we theoretically derive our improved, local, three-factor based learning method, named DALTON (<underline>Deep Loc<underline>Al <underline>Learning via local Weigh<underline>Ts and Surr<underline>Ogate-Derivative Tra<underline>Nsfer), which employs <italic>weights and <italic>surrogate-derivative transfer from the local layers. Along the lines of TFR, our proposed method DALTON is also amenable to the neuromorphic-hardware implementation. Through extensive experiments on static (MNIST, FMNIST, & CIFAR10) and event-based (N-MNIST, DVS128-Gesture, & DVS-CIFAR10) datasets, we show that our proposed local-learning method DALTON makes <italic>effective use of the depth in Convolutional SNNs, compared to the vanilla TFR implementation.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"578-590"},"PeriodicalIF":5.4,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DP-PartFIM: Frequent Itemset Mining Using Differential Privacy and Partition DP-PartFIM：利用差异隐私和分区挖掘常项集

IF 5.4 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-08-26 DOI: 10.1109/TETC.2024.3443060

Xinyu Liu;Wensheng Gan;Lele Yu;Yining Liu

引用次数: 0

TampML: Tampering Attack Detection and Malicious Nodes Localization in NoC-Based MPSoC TampML：基于 NoC 的 MPSoC 中的篡改攻击检测和恶意节点定位

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-08-26 DOI: 10.1109/TETC.2024.3434663

Haoyu Wang;Basel Halak

{"title":"TampML: Tampering Attack Detection and Malicious Nodes Localization in NoC-Based MPSoC","authors":"Haoyu Wang;Basel Halak","doi":"10.1109/TETC.2024.3434663","DOIUrl":"10.1109/TETC.2024.3434663","url":null,"abstract":"The relentless growth in demand for computing resources has spurred the development of large-scale, high-performance chips with diverse, innovative architectures. The Network-on-Chip (NoC) paradigm has become a predominant system for on-chip communication within Multi-Processor System-on-Chip (MPSoC) designs. However, the increasing complexity and the reliance on outsourced Third-Party Intellectual Properties (3PIPs) introduce non-negligible risks of Hardware Trojan (HT) insertions by untrusted IP vendors. One of the most critical threats posed by HTs is the tampering with communication data packets. In this article, we introduce a comprehensive framework for the detection of tampering attacks and localization of HTs within NoCs. This framework is incorporated into a novel distributed monitoring architecture that leverages the NoC structure. Utilizing a machine learning model for malicious flit detection and a high-precision algorithm for HT node localization, the framework's efficacy has been substantiated through tests with real PARSEC benchmark workloads. Achieving an impressive detection accuracy and precision of 99.8% and 99.5% respectively, the framework can localize HT nodes with up to 100% precision and recall in most cases. Furthermore, the data cost of localization is on average only 3.7% of tampered flits, which is significantly more efficient—up to 11 times faster—than our initial methods. As a comprehensive and cutting-edge security solution for combating communication data tampering attacks, it accomplishes the expected performance while maintaining minimal power and hardware overhead.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 2","pages":"551-562"},"PeriodicalIF":5.1,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling Obfuscation Detection in Binary Software through eXplainable AI 通过可解释人工智能实现二进制软件中的混淆检测

IF 5.9 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-08-13 DOI: 10.1109/tetc.2024.3439884

Claudia Greco, Michele Ianni, Antonella Guzzo, Giancarlo Fortino

引用次数: 0

A Novel Adaptive $360^{circ }$360∘ Livestreaming With Graph Representation Learning Based FoV Prediction 利用基于图表示学习的 FoV 预测进行 360° 自适应直播的新方法

IF 5.1 2区计算机科学

IEEE Transactions on Emerging Topics in Computing Pub Date : 2024-08-09 DOI: 10.1109/TETC.2024.3435002

Xingyan Chen;Huaming Du;Mu Wang;Yu Zhao;Xiaoyang Shu;Changqiao Xu;Gabriel-Miro Muntean

{"title":"A Novel Adaptive $360^{circ }$360∘ Livestreaming With Graph Representation Learning Based FoV Prediction","authors":"Xingyan Chen;Huaming Du;Mu Wang;Yu Zhao;Xiaoyang Shu;Changqiao Xu;Gabriel-Miro Muntean","doi":"10.1109/TETC.2024.3435002","DOIUrl":"10.1109/TETC.2024.3435002","url":null,"abstract":"The exceptionally high bandwidth requirements associated with the delivery of live <inline-formula><tex-math>$360^{circ }$</tex-math></inline-formula> video content pose significant challenges in the current network context. An avenue for addressing this bandwidth challenge is to use the limited network resources for sending the user's Field-of-View (FoV) tiles at a high resolution, instead of transmitting all frame components at high quality. However, precisely forecasting the FoV for <inline-formula><tex-math>$360^{circ }$</tex-math></inline-formula> live video content distribution remains a complex endeavor due to the lack of pre-knowledge on user viewing behaviors. In this paper, we present GL360, a novel <inline-formula><tex-math>$360^{circ }$</tex-math></inline-formula> transmission framework, which employs Graph Representation Learning for FoV prediction. First, we analyze the interaction between users and tiles in panoramic videos utilizing a dynamic heterogeneous Relational Graph Convolutional Network (RGCN), which facilitates efficient user and tile embedding representation learning. Second, we propose an online dynamic heterogeneous graph learning (DHGL)-based algorithm to dynamically capture the time-varying features of the user's viewing behaviors with limited prior knowledge. Further, we design a FoV-aware content delivery algorithm that allows the edge servers to determine the video tiles’ resolution for each accessed user. Experimental results based on real traces demonstrate how our solution outperforms four other solutions in terms of FoV prediction and network performance.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 2","pages":"537-550"},"PeriodicalIF":5.1,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0