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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zhang, Zhiyao Gunter, George Quinones-Grueiro, Marcos Zhang, Yuhang Barbour, William Biswas, Gautam Work, Daniel Institute for Software Integrated Systems Vanderbilt University Department of Civil and Environmental Engineering United States Institute for Software Integrated Systems Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University Department of Computer Science United States

ISBN: (纸本)9798331505929

This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent's decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation. © 2024 IEEE.

关键词： Markov processes

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P3DC:Reducing DRAM Cache Hit Latency by Hybrid Mappings

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Journal of computer science & Technology 2024年第6期39卷 1341-1360页

作者： Ye Chi Ren-Tong Guo Xiao-Fei Liao Hai-Kun Liu Jianhui Yue National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Laboratory Wuhan 430074China Cluster and Grid Computing Laboratory Wuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Department of Computer Science Michigan Technological UniversityHoughton 49931-1295U.S.A.

Die-stacked dynamic random access memory(DRAM)caches are increasingly advocated to bridge the performance gap between the on-chip cache and the main *** fully realize their potential,it is essential to improve DRAM cache hit rate and lower its cache hit *** order to take advantage of the high hit-rate of set-association and the low hit latency of direct-mapping at the same time,we propose a partial direct-mapped die-stacked DRAM cache called *** design is motivated by a key observation,i.e.,applying a unified mapping policy to different types of blocks cannot achieve a high cache hit rate and low hit latency *** address this problem,P3DC classifies data blocks into leading blocks and following blocks,and places them at static positions and dynamic positions,respectively,in a unified set-associative *** also propose a replacement policy to balance the miss penalty and the temporal locality of different *** addition,P3DC provides a policy to mitigate cache thrashing due to block type *** results demonstrate that P3DC can reduce the cache hit latency by 20.5%while achieving a similar cache hit rate compared with typical set-associative caches.P3DC improves the instructions per cycle(IPC)by up to 66%(12%on average)compared with the state-of-the-art direct-mapped cache—BEAR,and by up to 19%(6%on average)compared with the tag-data decoupled set-associative cache—DEC-A8.

关键词： die-stacked dynamic random access memory(DRAM) cache set-associative direct-mapped hit latency

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Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4290-4302页

作者： Pang, Yan Li, Yunhao Huang, Teng Liang, Jiaming Wang, Zhen Dong, Changyu Kuang, Dongyang Hu, Ying Chen, Hao Lei, Tim Wang, Qiong The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The School of Artificial Intelligence Guangzhou University China The Zhejiang Lab Hangzhou China Sun Yat-sen University China The Department of Computer Science and Engineering The Department of Chemical and Biological Engineering Hong Kong University of Science and Technology China The Department of Electrical Engineering University of Colorado Denver United States

In the specialized domain of brain tumor segmentation, supervised segmentation approaches are hindered by the limited availability of high-quality labeled data, a condition arising from data privacy concerns, significant costs, and ethical issues. In response to this challenge, this paper presents a training framework that adeptly integrates a plug-and-play component, MOD, into current supervised learning models, boosting their efficacy in scenarios with limited data. The MOD consists of an Online Tokenizer and a Dense Predictor, which employs self-distillation and self-modeling on masked patches, promoting swift convergence and efficient representation learning. During the inference phase, the plug-and-play MOD component is excluded, preserving the computational efficiency of the original model without incurring extra processing costs. We substantiated the value of our approach through experiments on leading 3D brain tumor segmentation baselines. Remarkably, models augmented with the MOD consistently showcased superior results, achieving elevated Dice coefficients and HD95 scores on two datasets: BraTS 2021 and MSD 2019 Task-01 Brain Tumor. Code: https://***/aigzhusmart/MOD © 2013 IEEE.

关键词： Self-supervised learning

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Counterfactual discriminative micro-expression recognition

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Visual Intelligence 2024年第1期2卷 350-359页

作者： Yong Li Menglin Liu Lingjie Lao Yuanzhi Wang Zhen Cui Department of Computer Science City University of Hong KongHong Kong999077China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China

Micro-expressions are spontaneous,rapid and subtle facial movements that can hardly be suppressed or ***-expression recognition(MER)is one of the most challenging topics in affective *** aims to recognize subtle facial movements which are quite difficult for humans to perceive in a fleeting ***,many deep learning-based MER methods have been ***,how to effectively capture subtle temporal variations for robust MER still perplexes *** propose a counterfactual discriminative micro-expression recognition(CoDER)method to effectively learn the slight temporal variations for video-based *** explicitly capture the causality from temporal dynamics hidden in the micro-expression(ME)sequence,we propose ME counterfactual reasoning by comparing the effects of the facts *** ME sequences and the counterfactuals ***-revised ME sequences,and then perform causality-aware prediction to encourage the model to learn those latent ME temporal *** experiments on four widely-used ME databases demonstrate the effectiveness of CoDER,which results in comparable and superior MER performance compared with that of the state-of-the-art *** visualization results show that CoDER successfully perceives the meaningful temporal variations in sequential faces.

关键词： Affective computing Micro-expression recognition Temporal variation Counterfactual reasoning Causal graph

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Recoverable Facial Identity Protection via Adaptive Makeup Transfer Adversarial Attacks 39

Recoverable Facial Identity Protection via Adaptive Makeup T...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Xiyao Ma, Junxing Wang, Xinda Lin, Qianyu Zhang, Jian Schaefer, Gerald Turkay, Cagatay Fang, Hui School of Computer Science and Engineering Central South University China School of Software and Microelectronics Peking University China Department of Computer Science Loughborough University United Kingdom Centre for Interdisciplinary Methodologies University of Warwick United Kingdom

ISBN: (纸本)157735897X

Unauthorised face recognition (FR) systems have posed significant threats to digital identity and privacy protection. To alleviate the risk of compromised identities, recent makeup transfer-based attack methods embed adversarial signals in order to confuse unauthorised FR systems. However, their major weakness is that they set up a fixed image unrelated to both the protected and the makeup reference images as the confusion identity, which in turn has a negative impact on both attack success rate and visual quality of transferred photos. In addition, the generated images cannot be recognised by authorised FR systems once attacks are triggered. To address these challenges, in this paper, we propose a Recoverable Makeup Transferred Generative Adversarial Network (RMT-GAN) which has the distinctive feature of improving its image-transfer quality by selecting a suitable transfer reference photo as the target identity. Moreover, our method offers a solution to recover the protected photos to their original counterparts that can be recognised by authorised systems. Experimental results demonstrate that our method provides significantly improved attack success rates while maintaining higher visual quality compared to state-of-the-art makeup transfer-based adversarial attack methods. Our code and supplementary materials are available on Github. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Generative adversarial networks

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Dynamics-incorporated Modeling Framework for Stability Constrained Scheduling under High-penetration of Renewable Energy

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IEEE Transactions on Sustainable Energy 2025年第3期16卷 1673-1685页

作者： Wang, Jinning Li, Fangxing Fang, Xin Cui, Hantao She, Buxin Shuai, Hang Zhang, Qiwei Tomsovic, Kevin University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States NC State University Department of Electrical and Computer Engineering RaleighNC27606 United States Pacific Northwest National Laboratory RichlandWA99352 United States Johns Hopkins University Department of Environmental Health and Eng. BaltimoreMD21205 United States

In this paper, a modularized modeling framework is designed to enable a dynamics-incorporated power system scheduling under high-penetration of renewable energy. This unique framework incorporates an adapted hybrid symbolic-numeric approach to scheduling models, effectively bridging the gap between device- and system-level optimization models and streamlining the scheduling modeling effort. The adaptability of the proposed framework stems from four key aspects: extensible scheduling formulations through modeling blocks, scalable performance via effective vectorization and sparsity-aware techniques, compatible data structure aligned with dynamic simulators by common power flow data, and interoperable dynamic interface for bi-direction data exchange between steady-state generation scheduling and time-domain dynamic simulation. Through extensive benchmarks with various usage scenarios, the framework's accuracy and scalability are validated. The case studies also demonstrate the efficient interoperation of generation scheduling and dynamics, significantly reducing the modeling conversion work in stability-constrained grid operation towards high-penetration of renewable energy. © 2025 IEEE.

关键词： Adaptation models Dynamic scheduling Numerical models Power system stability Power system dynamics Stability analysis Aerodynamics Transient analysis Computational modeling Renewable energy sources

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A Secured and Optimized Clustering Approch in Vehicular Ad-hoc Network 4

A Secured and Optimized Clustering Approch in Vehicular Ad-h...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Khan, Imran Khan, Fazlullah Ali, Asad Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Computer Science Department KPK Mardan Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang315104 China University of Engineering and Technology Department of Computer Science KPK Peshawar Pakistan College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

Vehicular ad hoc networks (VANETs) take vital role in intelligent transportation systems, but they face challenges due to dynamic network topology, impacting communication efficiency, especially with increasing active nodes. Security and privacy concerns are paramount, necessitating authentication to ensure only legitimate vehicles access the network. Secure vehicular clustering offers a solution, yet the number of clusters affects network dependability and efficiency. To tackle this, we present a secure clustering mechanism using Bat optimization algorithm, mimicking echolocation behavior to optimize clusters. We introduce an authentication technique for cluster heads to admit approved members, enhancing vehicular authentication. Simulations using MATLAB evaluate our approach, showing improved performance in end-to-end delay, overhead, and cluster head count compared to similar methods. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Student Perceptions of Authentic Learning to Learn White-box Testing 2024

Student Perceptions of Authentic Learning to Learn White-box...

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55th ACM Technical Symposium on computer science Education, SIGCSE 2024

作者： Rahman, Akond Zhang, Yue Wu, Fan Shahriar, Hossain Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Computer Science Tuskegee University TuskegeeAL United States Center for Cybersecurity University of West Florida PensacolaFL United States

ISBN: (纸本)9798400704246

The pivotal role of white-box testing with respect to software quality assurance, necessitates dissemination of education materials related to white-box testing in the course curriculum. In this poster, we describe our experiences in conducting an authentic learning-based exercise related to white-box testing. From a conducted survey with 124 students, we observe the authentic learning-based exercise to be helpful for students to learn about white-box testing. © 2024 Owner/Author.

关键词： Students

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Blockchain-Based Forensic Investigation Metamodel (BBFIM) for Investigating IoT Cybercrimes 4

Blockchain-Based Forensic Investigation Metamodel (BBFIM) fo...

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4th International Conference on Computing and Information Technology, ICCIT 2025

作者： Alotaibe, Daifallah Zaid College of Computer Science and Engineering Department of Software Engineering University of Hafr Al Batin Hafr Al Batin Saudi Arabia

ISBN: (纸本)9798350353839

As smart devices are rapidly being developed, and people trust data transfer mechanisms such as Wi-Fi and the way they use them, as well as the fact that they rely heavily on data transfer mechanics, there has been an increase in cyber-attacks. Therefore, examining IoT safety threats and potential countermeasures can support investigators in determining how to deal with a range of stressful scenarios encountered when conducting cyber-crime studies to develop acceptable methods for dealing with them. The goal of this paper is to develop Blockchain-Based Forensic Investigation Metamodel for Investigating IoT Cybercrimes called (BBFIM). The purpose of the developed BBFIM is to improve the productivity and accuracy of examinations by providing a safe and decentralized environment where digital evidence, related to IoT-related crimes, can be stored, and analyzed to improve the productivity and accuracy of examinations. Through the integration of blockchain technologies the developed BBFIM ensures that the reliability of the evidence is retained throughout the investigation. It also provides transparency throughout the process. This study utilizes design science methods for development and validation purposes. Therefore, 889 IoT forensic models and frameworks have been reviewed in this study, and 15 models have been selected to develop the BBFIM. The developed BBFIM consists of three main IoT forensic practices: Forensic investigation phases, Forensic Tools and Techniques, and Blockchain Infrastructure. Compared to the existing IoT forensics models the developed BBFIM is fully decentralized which can guarantee the authority, consistency, and privacy of the data. © 2025 IEEE.

关键词： blockchain design science IoT IoT cybercrimes method component

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Computational Approaches for Traditional Chinese Painting: From the “Six Principles of Painting” Perspective

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Journal of computer science & Technology 2024年第2期39卷 269-285页

作者：张玮张建伟黄锦畦王懿芳封颖超杰王璐玮陈为 State Key Laboratory of CAD&CG Zhejiang UniversityHangzhou 310058China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong 999077China Kellogg School of Management Northwestern UniversityEvanston 60208U.S.A Laboratory of Art and Archaeology Image Zhejiang UniversityHangzhou 310058China

Traditional Chinese painting(TCP)is an invaluable cultural heritage resource and a unique visual art *** recent years,there has been a growing emphasis on the digitalization of TCP for cultural preservation and *** resulting digital copies have enabled the advancement of computational methods for a structured and systematic understanding of *** explore this topic,we conduct an in-depth analysis of 94 pieces of *** examine the current use of computer technologies on TCP from three perspectives,based on numerous conversations with ***,in light of the“Six Principles of Painting”theory,we categorize the articles according to their research focus on artistic ***,we create a four-stage framework to illustrate the purposes of TCP ***,we summarize the popular computational techniques applied to *** work also provides insights into potential applications and prospects,with professional opinion.

关键词： traditional Chinese painting(TCP) digital humanities cultural heritage computer vision deep learning

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