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2nd International Conference on Networks, Communications and Information technology, CNCIT 2023

作者： Li, Shaofeng Wang, Tingyu Wan, Bo School of Computer Science and Technology Xidian University Xi'an China

ISBN: (纸本)9798400700620

To enhance the efficiency of different subsystems within embedded devices, thereby augmenting their capabilities, we incorporate cloud computing architecture within these devices to create an in-device micro-cloud platform. This allows subsystems, which were originally independent, to collaborate. Tasks on the device can be managed within a unified scheduling framework, thus increasing task flexibility and reducing task interaction overhead. Simultaneously, tasks on embedded devices often possess real-time requirements, an aspect often overlooked in previous cloud computing research. Accordingly, this paper prioritizes the real-time nature of tasks, studying a fair scheduling algorithm for tasks within the micro-cloud platform to address this hitherto neglected issue. © 2023 ACM.

关键词： Cloud computing architecture

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Logisim Circuit Similarity Algorithm Based on Maximum Connected Subgraph

Logisim Circuit Similarity Algorithm Based on Maximum Connec...

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2023 International Conference on computer, Artificial Intelligence, and Control Engineering, CAICE 2023

作者： Fang, Kengyi School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9781510665040

Logisim is a cross-platform educational tool for designing and simulating digital logic circuits, and it is widely used in experiments for courses such as computer organization in universities. As Logisim is widely used, tasks such as plagiarism detection require the calculation of Logisim circuit similarity, and the Logisim circuit similarity algorithm becomes very important. Logisim can represent circuits as text in XML format or render circuits as RGB images, which means that Logisim circuit similarity can be calculated using both text similarity algorithms and image similarity algorithms. However, due to the characteristics of Logisim circuits, text similarity algorithms and image similarity algorithms cannot achieve good results. In this paper, we propose a Logisim circuit similarity algorithm based on the maximum connected subgraph. We first represent the Logisim circuit as a collection of maximum connected subgraphs, then calculate the proportion of nodes with the same type, similar location and isomorphic local subgraphs in maximum connected subgraphs as the similarity of maximum connected subgraphs, and finally calculate the proportion of maximum connected subgraphs whose maximum similarity exceeds a threshold as the similarity of the collection of maximum connected subgraphs (i.e., Logisim circuits). We construct two Logisim similar circuit datasets based on real-world data. The experimental results on these datasets show that our method outperforms well-known text similarity algorithms and image similarity algorithms. © 2023 SPIE.

关键词： Timing circuits

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Tu, Longchuan Meng, Shunmei Liu, Xiao Liu, Guanfeng Beheshti, Amin Zhang, Xuyun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjin China State Key Laboratory for Novel Software Technology Nanjing University Nanjin China School of Computing Macquarie University Sydney Australia

ISBN: (纸本)9789819605699

Recently, graph neural networks(GNNs) have played a key crucial in many recommendation situations. In particular, contrastive learning-based hypergraph neural networks (HGNNs) are gradually becoming a research focus for addressing issues of data sparsity and noise. Despite many studies proving their outstanding performance, there are still some shortcomings: i) Most contrastive learning-based HGNNs primarily rely on cross-view contrastive learning, while neglecting contrastive learning on the interaction graph. ii) Utilizing node embedding for hypergraph structure learning is susceptible to the influence of low-quality representations, thereby constraining the learning capability of HGNNs. To address these issues, we offer a Semantic Similarity-based Graph Contrastive Learning framework (SSGCL), which aims to jointly learn representations with rich semantic information through within-view and cross-view contrastive learning. Specifically, we first introduce consistency contrastive learning, which captures self-supervised signals through semantic similarity between nodes and their neighborhoods. It better captures the unique features of nodes through the connections between nodes and their neighborhoods. Then, we utilize interaction graph learning hypergraph structure, promoting it to extract potential node connections and thus improving its ability to describe homogeneous node relationships. Experimental evaluations on three actual datasets show that SSGCL performs much better than the current baseline models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Minimizing Cost and Delay for Video Datacenters Using Efficient Fuzzy Dominance-Based Particle Swarm Optimization 29

Minimizing Cost and Delay for Video Datacenters Using Effici...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yang, Zi Wang, Jin Zhang, Jingwen Zhang, Yi Sun, Jin Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Nantong University School of Information Science and Technology Nantong226000 China

ISBN: (纸本)9798350330717

In multi-datacenter video forwarding environments, monetary cost and forwarding delay are both important concerns. This paper studies the problem of scheduling end user requests onto the media servers of multiple video datacenters (VDCs), with the objective of joint optimization of cost and delay. The studied scheduling problem is formulated as a multi-objective optimization problem (MOP) with a set of binary decision variables. We introduce a new metric of efficient fuzzy dominance (EFD) to evaluate the quality of MOP solutions with low computational complexity. With this new EFD metric, we propose a EFD-based particle swarm optimization (EFDPSO) algorithm to seek for the optimized scheduling solutions. EFDPSO employs a position-based mapping mechanism to convert each particle into a scheduling solution, represented by a request sequence, and develops a shortest path-based strategy to schedule the requests in the sequence such that cost and delay can be calculated. By using the EFD metric to determine the dominance among all the solutions mapped from the particles, EFDPSO identify a set of non-dominated MOP solutions and relies on PSO's update rule to iteratively improve the non-dominated solution set. We perform extensive experiments to verify that the EFD metric can substantially reduce the computation time in solution evaluation and the proposed EFDPSO leads to higher quality of MOP solutions compared with baseline metaheuristic algorithms. © 2023 IEEE.

关键词： fuzzy dominance multi-objective optimization particle swarm optimization video datacenter

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Exponential Synchronization for Mobile Robotic Formation: A Hybrid Dynamic Event-Triggered Intermittent Control Method

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-10页

作者： Chang, Zhenyu Guo, Ying Zong, Guangdeng Qi, Wenhai Zhao, Xudong School of Control Science and Engineering Tiangong University Tianjin China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Engineering Qufu Normal University Rizhao China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China

To address the formation tracking issue of mobile robotic systems, this paper constructs a novel hybrid dynamic event-triggered intermittent control strategy, which can achieve the exponential synchronization of the systems. Considering the limited control resources, the intermittent control method is introduced into the distributed control strategy to save resources, and the existing intermittent control model is reconstructed to describe the system model better. A hybrid dynamic event-triggered mechanism is developed by combining the dynamic event-triggered method with the time sampling strategy, eliminating the Zeno phenomenon. The control time sequences in the developed intermittent control strategy are automatically selected by the hybrid dynamic event-triggered sequences, rather than artificially designed in advance, which reduces a certain degree of design complexity. The developed control strategy effectively saves control resources while alleviating the burden of network communication. Sufficient conditions for achieving exponential synchronization formation tracking are provided, and the exponential convergence of the formation error is demonstrated through the proposed lemma. Finally, a control task of multi-mobile robots formation is presented to verify the effectiveness of the theoretical analysis. IEEE

关键词： Synchronization

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An Efficient Device Placement Method for Distributed Training of Multi-branch Neural Network-Based Remote Sensing Interpretation 25th

An Efficient Device Placement Method for Distributed Traini...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Long, Ao Wang, Yuewei Huang, Xiaohui Han, Wei Fan, Runyu Chen, Yunliang Li, Jianxin School of Computer Science China University of Geosciences Wuhan China School of Information Technology Deakin University Melbourne Australia

ISBN: (纸本)9789819605699

Multi-branch neural networks are widely used in remote sensing intelligent interpretation because they can fuse multi-modal remote sensing data to improve interpretation accuracy. Meanwhile, in the current "big data era" of remote sensing, the application of distributed deep learning has been steadily growing to improve the training efficiency of remote sensing interpretation models. However, prevailing distributed training systems necessitate users to manually devise a parallelization plan or automatically generate one from a restricted space of model parallelism configurations. At the same time, these techniques are not suitable for extending the application of multi-branch neural network-based remote sensing interpretation on computing devices. In this paper, considering both the data and the model parallelism, we design and implement a fast and near-optimal device placement method for automatically placing branches in multi-branch neural networks across multiple devices. The core idea is to consider the trade-off between the computation and the inter-device communication to minimize the total training time. Through careful formulation of the problem as an integer program, our method can offer optimal placement and scheduling of the branches. We conduct experiments to deploy and evaluate the proposed method on two multi-branch networks. The results indicate that the method finds non-trivial device placements outperforming baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mixed-integer linear programming

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Implementing a Secure Blockchain-Based Wallet System with Multi-Factor Authentication 19

Implementing a Secure Blockchain-Based Wallet System with Mu...

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19th IEEE International Conference on e-Business Engineering, ICEBE 2023

作者： Almadani, Mwaheb S. Hussain, Farookh Khadeer University of Technology Sydney School of Computer Science Sydney Australia

ISBN: (纸本)9798350325553

With the increased use of blockchain technology, the significance of digital currencies has grown, making it crucial to ensure the security of blockchain wallets. A blockchain wallets private key serves as proof of digital asset ownership, thus, it must be protected at all times. Security threats such as phishing attacks can result in severe digital asset losses. Therefore, implementing rigorous authentication protocols is of vital importance to prevent unauthorized access. This paper proposes a secure implementation of a blockchain wallet website by integrating four different multifactor authentication settings to enhance cryptographic keys and digital asset management. We developed a comprehensive system model to visualize the system interactions and provide an adequate understanding of its architecture. Furthermore, the solution was implemented using Python and Ethereum libraries, followed by extensive evaluation and testing. The results show that system models enhance and facilitate the implementation and validation of blockchain wallets, thereby contributing to the development of robust security measures. © 2023 IEEE.

关键词： Blockchain

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Generating Secure Fingerprint Templates via Random Dropout and Local Bin-Wise Max out Hashing 7

Generating Secure Fingerprint Templates via Random Dropout a...

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Li, Yuxing Hainan University School of Computer Science And Technology Haikou China

ISBN: (纸本)9798350359145

Cancelable biometrics is an effective measure to combat malicious attacks. Existing methods generally suffer from redundant feature magnitude or deteriorated accuracy, which may limit the usage of biometric data. In this paper, we propose a secure fingerprint template generation scheme to transform the unprotected fingerprint feature into a compact (only 64bytes) and cancelable fingerprint binary codes via random dropout and local bin-wise max out hashing. This representation enables efficient matching, and high security and can also combined with advanced encryption schemes (e.g., fuzzy commitment). Extensive experiments conducted on FVC2002 and FVC2004 fingerprint datasets coupled with comprehensive theoretical analyses attested that the proposed method offers high matching accuracy and strong anti-attack resilience. Besides, the requirements of revocability, unlinkability, and non-invertibility are also well guaranteed. © 2023 IEEE.

关键词： Signal encoding

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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Empowering Diffusion Models on the Embedding Space for Text Generation

Empowering Diffusion Models on the Embedding Space for Text ...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Gao, Zhujin Guo, Junliang Tan, Xu Zhu, Yongxin Zhang, Fang Bian, Jiang Xu, Linli School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Microsoft Research Asia China

ISBN: (纸本)9798891761148

Diffusion models have achieved state-of-the-art synthesis quality on both visual and audio tasks, and recent works further adapt them to textual data by diffusing on the embedding space. In this paper, we conduct systematic studies of the optimization challenges encountered with both the embedding space and the denoising model, which have not been carefully explored. Firstly, the data distribution is learnable for embeddings, which may lead to the collapse of the embedding space and unstable training. To alleviate this problem, we propose a new objective called the anchor loss which is more efficient than previous methods. Secondly, we find the noise levels of conventional schedules are insufficient for training a desirable denoising model while introducing varying degrees of degeneration in consequence. To address this challenge, we propose a novel framework called noise rescaling. Based on the above analysis, we propose Difformer, an embedding diffusion model based on Transformer. Experiments on varieties of seminal text generation tasks show the effectiveness of the proposed methods and the superiority of Difformer over previous state-of-the-art embedding diffusion baselines. ©2024 Association for Computational Linguistics.

关键词： Embeddings

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