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IEEE International Conference on computer-Aided Design

作者： Haichuan Hu Zichen Xu Yuhao Wang Fangming Liu Services Computing Technology and System Lab Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Mathematics and Computer Science Nanchang University Nanchang China Pengcheng Laboratory Shenzhen China

The natural bijection between a proposed circuit design and its graph representation shall allow any graph optimization algorithm deploying into many-core systems efficiently. However, this process suffers from the exponentially growing overhead and heavy memory footprint with the signal propagation. To conquer the unique challenge, we systematically study the simulation with millions of gates, and identify that the processing complexity could grow exponentially from the signal inputs, the skewness of the computational graph stays. Thus, we present ZhouBi, a fast and scalable gate-level simulation framework to fully exploit the parallelism from many-core systems. ZhouBi contributes in threefolds, (I) a graph representation that colors gate-level netlists and identifies skew partitions based on the graph skewness; (II) A set of heuristic algorithms that picks opportunistic and conservative algorithms to accelerate the simulation; (III) A system facility that supports selective mapping between simulation and many-core, providing a tradeoff between the risk of concurrent simulation fail and performance gain. We have prototyped ZhouBi and evaluated it with practical baselines. ZhouBi can achieve a 27.6× performance gain, as compared to the state-of-the-practice Veriwell without compromising any correctness. Our framework supports large graphs enabling scale-out gate-level simulations for chip design.

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Improving multi-view document clustering: leveraging multi-structure processor and hybrid ensemble clustering module 30

Improving multi-view document clustering: leveraging multi-s...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Bai, Ruina Bai, Qi Huang, Ruizhang Chen, Yanping Qin, Yongbin Text Computing & Cognitive Intelligence Engineering Research Center National Education Ministry College of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Mathematical Sciences Zhejiang University Hangzhou310058 China

ISBN: (纸本)9782493814104

We introduce a multi-view document clustering model called DMsECN (Deep Multi-structure Ensemble Clustering Network), comprising a multi-structure processor and a hybrid ensemble clustering module. Unlike existing models, DMsECN distinguishes itself by creating a consensus structure from multiple clustering structures. The multi-structure processor comprises two stages, each contributing to the extraction of clustering structures that preserve both consistency and complementarity across multiple views. Representation learning extracts both view and view-fused representations from multi-views through the use of contrastive learning. Subsequently, multi-structure learning employs distinct view clustering guidance to generate the corresponding clustering structures. The hybrid ensemble clustering module merges two ensemble methods to amalgamate multiple structures, producing a consensus structure that guarantees both the separability and compactness of clusters within the clustering results. The attention-based ensemble primarily concentrates on learning the contribution weights of diverse clustering structures, while the similarity-based ensemble employs cluster assignment similarity and cluster classification dissimilarity to guide the refinement of the consensus structure. Experimental results demonstrate that DMsECN outperforms other models, achieving new state-of-the-art results on four multi-view document clustering datasets. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Structure (composition)

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FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training

FedCST: Federated Learning on Heterogeneous Resource-constra...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Zhilu Wang Haiyang Lin Qi Liu Yonghong Zhang Xiaodong Liu Nanjing University of Information Science and Technology School of Computer Science Nanjing Jiangsu China Nanjing University of Information Science and Technology School of Software Nanjing Jiangsu China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing Jiangsu China Nanjing University of Information Science and Technology School of Automation Nanjing Jiangsu China Edinburgh Napier University School of Computing Edinburgh UK

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

With the rapid development of 5G and Internet of Things (IoT) technologies, edge devices such as sensors, smartphones, and wearable devices have become increasingly prevalent. The massive amount of distributed data generated by these devices offers unprecedented opportunities for deep learning, especially in fields like computer vision and edge computing. However, this data often exists in isolated data silos, and due to its privacy-sensitive nature, it cannot be directly processed in a centralized manner. Additionally, the data heterogeneity and devices heterogeneity further exacer-bates the challenges in federated learning (FL). Moreover, with the implementation of data privacy protection regulations, user demand for privacy protection is growing significantly. Therefore, how to effectively utilize dispersed data for distributed collaborative learning while ensuring data privacy is a pressing issue that needs to be addressed in the field of FL. In response to these challenges, this work has conducted research on clustering and segmentation FL algorithms for heterogeneous resource-constrained devices. A lightweight homogeneous device clustering strategy has been designed, which incorporates a split learning (SL) mechanism to enhance the accuracy and training efficiency of FL models. This approach reduces the load on resource-constrained devices and improves privacy security. Consequently, this method can meet the dual requirements of privacy protection and computational efficiency in real-world scenarios.

关键词： Training Data privacy Privacy Federated learning Computational modeling Distributed databases Security Internet of Things Protection Edge computing

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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An IoT-Based Microservice Platform for Virtual Puppetry Performance

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IEEE Access 2023年 11卷 103014-103032页

作者： Luo, Helin Lin, Yi-Bing Liao, Chen-Chi Huang, Yung-Hui Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan Tainan National University of the Arts Graduate Institute of Animation and Film Art Tainan720 Taiwan National Yang Ming Chiao Tung University Department of Computer Science and Engineering Hsinchu300 Taiwan China Medical University College of Humanities and Sciences Taichung406 Taiwan National Cheng Kung University Miin Wu School of Computing Tainan701 Taiwan Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan

This study proposes a web-based real-time remote-controlled virtual puppetry (avatar) performance platform called AvatarTalk to combine traditional Taiwanese puppetry, the Internet of Things (IoT), and 3D avatar models. AvatarTalk enables puppeteers to control avatar puppets on the web using either motion capture gloves or camera-based image recognition. This transformative performance approach not only facilitates remote performances and multi-screen presentations across various devices but also introduces a fresh gesture interpretation technique for dance artists. AvatarTalk supports a mechanism that can accommodate new control and puppet devices from other approaches through the IoT-based microservice concept. We develop a calibration procedure to enable the accurate capture of hand gestures to manipulate the movements of virtual puppets, empowering them to perform fundamental traditional puppetry poses such as nodding, bowing, and synchronized hand movements. We have conducted experiments to show the accuracy of AvatarTalk calibration. Our study indicates that AvatarTalk can almost detect the right gestures (98.75%-100% recall) and very seldom mistake the wrong gestures (90.8%-100% precision). Additionally, we also provide the mechanism to measure the delays of controlling the puppets. An analytic model is proposed to design the delay times of the messages between the control device of a puppeteer and the AvatarTalk server. In the current AvatarTalk implementation, if the message delay does not exceed 0.1 seconds, four puppeteers can synchronize their actions if the elapsed time between two actions of a puppeteer is longer than 0.3 second. © 2013 IEEE.

关键词： Internet of things

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An Improved Lightweight CNN_BiLSTM Model for Network Intrusion Detection Using the UNSW-NB15 Dataset

An Improved Lightweight CNN_BiLSTM Model for Network Intrusi...

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computer, Vision and Intelligent technology (ICCVIT), International Conference on

作者： Licheng Zhuang Jun Hu Qingqing Wang Yanyan Wang Kaikai Zhang Sheng Liu School of Computer Science and Technology Huaibei Normal University Huaibei China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

The attack behavior in the network poses a threat to security and privacy issues. In order to improve the effectiveness of IoT network attack detection and compensate for the shortcomings of traditional machine learning methods in detection capabilities, a network intrusion detection method based on lightweight convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) is proposed. This method preprocesses data through normalization techniques, extracts data features from both spatial and temporal dimensions using CNN and BiLSTM models, and inputs the extracted spatiotemporal feature vectors into the CNN BiLSTM upper classifier for training, achieving intrusion detection. The proposed model was validated on the dataset UNSW-NB15, and the results showed that the binary and multi classification accuracy of the CNN_BiLSTM model was around 93%. Compared with other methods, the proposed method significantly improves the accuracy of network intrusion detection, and also demonstrates better performance in detection accuracy and false positive rate.

关键词： Training Accuracy Bidirectional long short term memory Network intrusion detection Feature extraction Vectors Spatiotemporal phenomena Convolutional neural networks Data mining Security

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Efficient FPGA-based graph processing with hybrid pull-push computational model

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Frontiers of computer science 2020年第4期14卷 13-28页

作者： Chengbo YANG Long ZHENG Chuangyi GUI Hai JIN National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China

Hybrid pull-push computational model can provide compelling results over either of single one for processing real-world *** and pipeline parallelism of FPGAs make it potential to process different stages of graph ***,considering the limited on-chip resources and streamline pipeline computation,the efficiency of hybrid model on FPGAs often suffers due to well-known random access feature of graph *** this paper,we present a hybrid graph processing system on FPGAs,which can achieve the best of both *** approach on FPGAs is unique and novel as ***,we propose to use edge block(consisting of edges with the same destination vertex set),which allows to sequentially access edges at block granularity for locality while still preserving the *** to the independence of blocks in the sense that all edges in an inactive block are associated with inactive vertices,this also enables to skip invalid blocks for reducing redundant ***,we consider a large number of vertices and their associated edge-blocks to maintain a predictable execution *** also present to switch models in advance with few stalls using their state *** evaluation on a wide variety of graph algorithms for many real-world graphs shows that our approach achieves up to 3.69x speedup over state-of-the-art FPGA-based graph processing systems.

关键词： graph processing efficiency computational model FPGAs

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Adaptive ELM-Based Time-Constrained Control of Uncertain Robotic Manipulators With Minimum Learning Computation

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IEEE Transactions on Emerging Topics in computing 2025年

作者： Jin, Xiaozheng Hou, Yuhan Wu, Xiaoming Chi, Jing Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Jinan250013 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Jinan250014 China

This paper studies the extreme learning machine (ELM)-based time-constrained trajectory tracking control of a class of robotic manipulator systems with model/input uncertainties, matched and mismatched disturbances. A uniform robust exact differentiator (URED) is developed to construct a multi-layer virtual control framework of the robotic manipulator system. Under the control framework, adaptive ELM-based control strategies are proposed to suppress the influence of the uncertainties and disturbances layer-by-layer, combining with the minimum learning parameter (MLP) technique which is employed to reduce the calculation complexity of ELM parameters. The significance of this study is that general model/input uncertainties, matched and mismatched disturbances can be effectively compensated within a limited time, so that bounded trajectory tracking of the robotic manipulator can be achieved avoiding the complexity explosion issue. The time-constrained stability of the closed-loop robotic error systems is proved through Lyapunov stability theory. Finally, comparative simulations are employed to display the feasibility and superiority of the designed robust adaptive ELM-based control schemes of a robotic manipulator system. © 2013 IEEE.

关键词： Robust control

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Predicting CircRNA-Disease Associations Through Non-negative Matrix Factorization and Adversarially Regularized Variational Graph Autoencoder

Predicting CircRNA-Disease Associations Through Non-negative...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mianshuo Lu Lei Wang Jinzhu Sun Yang Li Mengmeng Wei Yu Wei Changchun Liu Zhengwei Li School of Computer Science and Technology China University of Mining and Technology Xuzhou China Big Data and Intelligent Computing Research Center Guangxi Academy of Sciences Nanning China Zaozhuang Mental Health Center Zaozhuang China School of Computer Science and Information Engineering Hefei University of Technology Hefei China College of Information Science and Enginnering Zaozhuang Univerity China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Circular RNA (circRNA) is an RNA molecule that plays an important role in both pathology and physiology. Accurate identification of associations between circRNAs and diseases is crucial for further physiology research. However, verifying the circRNA-disease associations (CDA) through biological experimental methods is time-consuming. Here, we propose a novel method combines Non-negative Matrix Factorization (NMF) and Adversarially Regularized Variational Graph Autoencoder (ARVGA) to accurately predict CDA. Our model first fuses multi-source information in order to build circRNA similarity, disease similarity and circRNA-disease association matrices. Thereby our model constructs graphs for circRNA and disease respectively and optimize them using a K-means clustering algorithm. We obtain linear features using NMF and non-linear features using ARVGA. Finally, an Extremely Randomized Trees classifier is employed to predict CDA. On the gold standard dataset CircR2Disease, our model achieved a prediction accuracy of 94.8% and an AUC of 0.984 under 5-fold cross-validation. In case study, 19 of top 20 predicted circRNAs associated with Hepatocellular Carcinoma were confirmed in relevant literature. Furthermore, ablation experiment, classifier experiment and independent datasets test fully demonstrate the effectiveness and robustness of our model.

关键词： Biological system modeling RNA Autoencoders Clustering algorithms Predictive models Feature extraction Prediction algorithms Robustness Physiology Diseases

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Optimized MARL for Latency-Sensitive Collaborative Service Placement in Edge computing

Optimized MARL for Latency-Sensitive Collaborative Service P...

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IEEE International Conference on Web Services (ICWS)

作者： Guoqiang Liu Xiaolong Xu Xiyuan Xu Xinyue Ji Lianyong Qi Xuyun Zhang School of Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing University of Information Science and Technology Nanjing China College of Computer Science and Technology China University of Petroleum (East China) Qingdao China School of Computing Macquarie University Sydney Australia

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

By distributing computing and storage resources to edge servers (ESs), edge computing enables service execution closer to requests, alleviating high latency caused by long-distance data transmission. The communication between ESs facilitates collaborative service placement (CSP) on multiple ESs, leading to latency reduction and thereby improving the quality of service. However, the discrepancies in service request distributions among ESs pose challenge to effective CSP, while limited prior knowledge further increases the difficulty in policy formation. Additionally, the delayed awareness of placement strategies among other ESs exacerbates the influence of non-Markovian nature. To overcome above challenges, a leader-follower based multi-agent reinforcement learning for CSP scheme, named LFMCSP, is proposed in this paper. Specifically, a CSP model is formulated, with ESs abstracted as intelligent agents within it. Then, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is utilized to explore potential patterns in complex requests by experience sharing. To achieve transparency of global states for all agents, the leader-follower architecture is integrated into MADDPG. Through rigorous experiments and comparisons with various baseline schemes, LFMCSP demonstrates effectiveness in reducing service execution delays.

关键词： Collaboration computer architecture Quality of service Reinforcement learning Space exploration Service-oriented architecture Delays Servers Intelligent agents Edge computing

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