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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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A One-Dimensional Residual Network and Physical Fitness-Based Exercise Prescription Recommendation Method 10

A One-Dimensional Residual Network and Physical Fitness-Base...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Fan, Runqing Peng, Zhenlian Cao, Buqing Liu, Jianxun Che, Peng Chen, Tieping Hunan University of Science and Technology School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology School of Physical Education Xiangtan China

ISBN: (纸本)9798350312461

In the context of the national big data strategy, physical fitness test data has become one of the main influencing factors in guiding and promoting the participation of the population in sports and fitness. Recommending exercise prescriptions based on national physical fitness test data has become an important research topic. However, currently, there is little research on how to accurately use computer data processing technology to recommend exercise prescriptions based on physical fitness test data. In this study, we propose a ResNet-based Exercise Prescription (ResNet-EP) method that utilizes one-dimensional residual neural network technology to recommend exercise prescriptions based on physical fitness testing data. This method comprehensively analyzes physical fitness testing data and exercise prescription data and realizes the automatic recommendation of exercise prescriptions. Experimental results on a real dataset demonstrate that the ResNet-EP model outperforms other comparison models in terms of precision (79.98%), recall (83.73%), and F1 score (81.81%). This study provides novel insights into the combination of physical fitness testing and exercise. © 2023 IEEE.

关键词： Machine learning

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A New Class of Multiple Optical ZCZ Sequence Sets with Good Cross-Correlation 6

A New Class of Multiple Optical ZCZ Sequence Sets with Good ...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Yan, Yang Jin, Rize Wang, Qiuyan Sun, Shimin Tianjin University of Technology and Education School of Information Technology and Engineering Tianjin China Tiangong University School of Software Tianjin China Tiangong University School of Computer Science and Technology Tianjin China

ISBN: (纸本)9798350325485

Zero correlation zone (ZCZ) sequences have been widely applied to quasi-synchronous code division multiple access system (QS-CDMA) and therefore have been extensively studied in recent years. In this paper, ZCZ sequence sets are constructed. The presented ZCZ sequence sets have the following properties: 1) each sequence in the constructed sequence set is perfect;2) each set is an optimal ZCZ sequence set with parameters (N2,N,N);3) the maximum inter-set cross-correlation of the given multiple sequence sets meets the Sarwate bound. © 2023 IEEE.

关键词： Set theory

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Comparison of Standard and Modified Ant Colony Algorithm with Fuzzy system for Path Planning of Robot in Complex Environment 14

Comparison of Standard and Modified Ant Colony Algorithm wit...

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14th International Conference on Intelligent Systems: Theories and Applications, SITA 2023

作者： Sharan, Sudeep Minh, Anh Tong Ngoc Nauth, Peter Dominguez-Jimenez, Juan Jose Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt am Main Germany University of Cádiz Ucase Software Engineering Group School of Engineering Cádiz Spain

ISBN: (纸本)9798350308211

The requirements of optimizing path planning for the autonomous robot are in high demand in such industrial communities, especially in manufacturing and caring social support. The application of meta-heuristic methods in autonomous problems is considered because of their adaptivity and robustness. Ant Colony Optimization (ACO) is one of the researchers' approaches because of its effectiveness in the ant community. However, some constraints are making this method less productive. Therefore, this paper is to generate a modified ACO combined with Fuzzy logic (ACOFL) to minimize its drawbacks and maximize the robustness of path planning. This study presents the role of ACO algorithms in Path Planning, especially in the complex aspect, through the theoretical background of ACO and the other combination with other algorithms. The improved mathematics model evaluation demonstrates the upgrade steps in the path-finding process. Simulation results shows that the modified ACO algorithm is effective for complex environment compare to standard ACO algorithm. Moreover, the comparison results are presented in this paper between the standard ACO and modified ACO algorithm. © 2023 IEEE.

关键词： Motion planning

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CBBFT - A Byzantine Fault Tolerance Mechanism for Education Credit Banks

CBBFT - A Byzantine Fault Tolerance Mechanism for Education ...

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computer Information science and Application Technology (CISAT), International Conference on

作者： Xin Li Lianghaojie Zhou Ningtian Chen Jianyong Jiang Peng Liu School of Computer Science and Engineering / School of Software GuangXi Normal University Guilin China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

In the education sector, an increasing amount of research is beginning to explore the application of blockchain technology to credit banks. This paper proposes a consortium blockchain consensus mechanism tailored for credit bank applications, aiming to address issues such as single point of failure, large message volumes, and slowest node limitations inherent in existing BFT-like consensus protocols. This includes algorithms for secret random selection of block proposer nodes based on reputation scores and block selection, as well as introducing a reputation model. Subsequently, a detailed analysis of the security of this consensus protocol is conducted. Finally, the paper evaluates the performance of the proposed consensus protocol through experimentation.

关键词： Fault tolerance Information science Fault tolerant systems Education Throughput Data structures Consensus protocol Security

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Dynamic Language Refinement for Graceful Degradation in Scene Text Recognition 6

Dynamic Language Refinement for Graceful Degradation in Scen...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Tian, Shaocong Jin, Rize Paik, Joon-Young Fu, Chen Tiangong University School of Software Tianjin China Tiangong University School of Computer Science &technology Tianjin China Shenzhen University College of Physics and Optoelectronic Engineering Shenzhen China

ISBN: (纸本)9798350325485

Scene text recognition is a mature technology which can get a near-perfect result in regular scenes. However, there are still troubles in recognizing scene texts with image blur, characters missing, and occlusion. Recent end-To-end frameworks have investigated the importance of linguistic knowledge, utilizing either implicit or explicit language models. However, joint training of language and vision models tends to interfere the tunings of each other. In this paper, we present a novel gradient isolation method to train language and vision models separately, yet in an end-To-end architecture. In addition, we propose a dynamic iterative training procedure for graceful refining the language model. Extensive experiments confirm that the proposed method has superiority on regular and irregular scene text images and achieves state-of-The-Art results. © 2023 IEEE.

关键词： Iterative methods

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Dynamic Prompting of Frozen Text-to-Image Diffusion Models for Panoptic Narrative Grounding 24

Dynamic Prompting of Frozen Text-to-Image Diffusion Models f...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Hongyu Hui, Tianrui Ding, Zihan Zhang, Jing Ma, Bin Wei, Xiaoming Han, Jizhong Liu, Si School of Artificial Intelligence Beihang University Beijing China School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Software Beihang University Beijing China Meituan Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400706868

Panoptic narrative grounding (PNG), whose core target is fine-grained image-text alignment, requires a panoptic segmentation of referred objects given a narrative caption. Previous discriminative methods achieve only weak or coarse-grained alignment by panoptic segmentation pretraining or CLIP model adaptation. Given the recent progress of text-to-image Diffusion models, several works have shown their capability to achieve fine-grained image-text alignment through cross-attention maps and improved general segmentation performance. However, the direct use of phrase features as static prompts to apply frozen Diffusion models to the PNG task still suffers from a large task gap and insufficient vision-language interaction, yielding inferior performance. Therefore, we propose an Extractive-Injective Phrase Adapter (EIPA) bypass within the Diffusion UNet to dynamically update phrase prompts with image features and inject the multimodal cues back, which leverages the fine-grained image-text alignment capability of Diffusion models more sufficiently. In addition, we also design a Multi-Level Mutual Aggregation (MLMA) module to reciprocally fuse multi-level image and phrase features for segmentation refinement. Extensive experiments on the PNG benchmark show that our method achieves new state-of-the-art performance. © 2024 ACM.

关键词： Image segmentation

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Gland Segmentation in H&E Histopathological Images using U-Net with Attention Module 12

Gland Segmentation in H&E Histopathological Images using U-N...

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12th International Conference on Image Processing Theory, Tools and Applications, IPTA 2023

作者： Yildirim, Zeynep Samet, Refik Hancer, Emrah Nemati, Nooshin Mali, Mohamed Traore Ankara University Department of Computer Engineering Ankara06830 Turkey Mehmet Akif Ersoy University Department of Software Engineering Burdur15030 Turkey Victoria University of Wellington School of Engineering and Computer Science Wellington6140 New Zealand

ISBN: (纸本)9798350325416

Gland segmentation refers to the process of identifying and delineating glandular regions within histopathology images. However, gland segmentation in histopathology images is a challenging task due to several factors such as shape variations and overlapping structures. Despite numerous research efforts dedicated to addressing this challenge, it remains an unresolved problem. To address this issue, we introduce a deep gland segmentation method by designing an enhanced U-Net variant combined with an Attention module. By incorporating the Attention module into the U-Net architecture, the model becomes more adept at capturing contextual information and fine details within the histopathology images. To verify the effectiveness of the introduced method, we compare it with well-known semantic segmentation architectures on the GLAS and GRAG datasets. According to the results, our approach demonstrates a high success rate in gland segmentation, effectively competing with existing state-of-the-art methods. © 2023 IEEE.

关键词： Semantic Segmentation

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI 41

The Good, The Bad, and Why: Unveiling Emotions in Generative...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Cheng Wang, Jindong Zhang, Yixuan Zhu, Kaijie Wang, Xinyi Hou, Wenxin Lian, Jianxun Luo, Fang Yang, Qiang Xie, Xing Microsoft Research Beijing China CAS Institute of Software Beijing China Department of Computer Science William & Mary WilliamsburgVA United States School of Psychology Beijing Normal University Beijing China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Emotion significantly affects our daily behaviors and interactions. Although recent generative AI models, such as large language models, have shown impressive performance in various tasks, it remains unclear whether they truly comprehend emotions and why. This paper aims to address this gap by incorporating psychological theories to gain a holistic understanding of emotions in generative AI models. Specifically, we propose three approaches: 1) EmotionPrompt to enhance the performance of the AI model, 2) EmotionAttack to impair the performance of the AI model, and 3) EmotionDecode to explain the effects of emotional stimuli, both benign and malignant. Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. More importantly, EmotionDecode reveals that AI models can comprehend emotional stimuli similar to the dopamine mechanism in the human brain. Our work heralds a novel avenue for exploring psychology to enhance our understanding of generative AI models, thus boosting the research and development of human-AI collaboration and mitigating potential risks. Copyright 2024 by the author(s)

关键词： Generative adversarial networks

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