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32nd ACM International Conference on Multimedia, MM 2024

作者： Han, Zheng Zhu, Xiaobin Yang, Chun Zhou, Hongyang Qin, Jingyan Yin, Xu-Cheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798400706868

Existing few-shot learning methods generally focus on designing exquisite structures of meta-learners for learning task-specific prior to improve the discriminative ability of global embeddings. However, they often ignore the importance of learning stability in meta-training, making it difficult to obtain a relatively optimal model. From this key observation, we propose an innovative generic differentiable Reinforcement Learning (RL) strategy for few-shot classification. It aims to explore stable meta-optimization patterns in meta-training by learning generalizable optimizations for producing task-adaptive embeddings. Accordingly, our differentiable RL strategy models the embedding procedure of feature transformation layers in meta-learner to optimize the gradient flow implicitly. Also, we propose a memory module to associate historical and current task states and actions for exploring inter-task similarity. Notably, our RL-based strategy can be easily extended to various backbones. In addition, we propose a novel task state encoder to encode task representation, which fully explores inner-task similarities between support set and query set. Extensive experiments verify that our approach can improve the performance of different backbones and achieve promising results against state-of-the-art methods in few-shot classification. © 2024 ACM.

关键词： Contrastive Learning

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Exploration of Quantum Computing in Multi-Die FPGA Routing Problems 9

Exploration of Quantum Computing in Multi-Die FPGA Routing P...

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Tian, Haochang Zhang, Xu Wang, Chengkai Ji, Weiqing Kou, Mingyang Yao, Hailong School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798331509453

This paper delves into the innovative application of quantum computing to solve complex multi-die FPGA (FieldProgrammable Gate Array) routing challenges. Leveraging the Quantum Approximate Optimization Algorithm (QAOA), we propose a novel approach that integrates classical routing techniques with quantum computing methodologies. This integration specifically utilizes QAOA to enhance the efficiency and effectiveness of the routing process. This hybrid approach partitions the routing task into intra-FPGA and inter-FPGA components, optimizing each segment through the appropriate computational techniques. Extensive experiments conducted on quantum computing simulators, alongside trials on actual quantum hardware, demonstrate the theoretical potential of quantum computing in enhancing the efficiency of multi-die FPGA routing solutions. Our findings suggest that despite the current limitations of quantum hardware, such as noise and error rates, quantum computing holds promise for advancing EDA methodologies. This paper contributes to the field by pioneering the integration of quantum algorithms with EDA techniques, offering a novel perspective on future applications of quantum computing in complex routing problems. © 2024 IEEE.

关键词： Quantum computers

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A Novel and Efficient Web Service Discovery Method Based on Threaded Index

A Novel and Efficient Web Service Discovery Method Based on ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Liu, Jiamei School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781665410205

As the number of web services proliferates, service discovery has emerged as a burgeoning area of research. Conventional service discovery methods hinge on index creation to enhance the efficiency of service identification. Moreover, service discovery and composition techniques can be distributed across various edge services. Owing to the multi-level indexing mechanism, determining the hash range for distinct services is crucial. Additionally, heterogeneity between service compositions results in differing indexing structures. In this study, we present a novel thread-based indexing method explicitly devised for service discovery in web service environments. Our approach commences with employing indexing to distinguish partitioning intervals of web service repositories. Subsequently, we assign a corresponding index hash value to each web service. Each index is then threaded and registered within a thread-based index pool. By allocating threads to a central service discovery server, the pertinent search thread undertakes the service retrieval task initiated by the service consumer, enabling asynchronous processing and result delivery to the client. To corroborate our method, we performed comprehensive experiments in real-world web service discovery scenarios. The outcomes underscore the effectiveness of our approach, emphasizing its potential to augment service discovery in web service environments. © 2024 IEEE.

关键词： Web services

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Fatigue EEG signal detection based on Ensemble learning 4

Fatigue EEG signal detection based on Ensemble learning

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Chen, Jie Liu, Jian University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350349252

In recent years, researchers have proposed a variety of machine learning and deep learning-based techniques to process EEG signals, such as support vector machines, deep learning algorithms, and graph neural networks (GNNs). Particularly, graph neural networks can effectively learn the spatial connectivity relationships between different channels in EEG signals, thereby enhancing the accuracy and robustness of fatigue driving detection. This paper introduces an integrated architecture that combines an information-enhanced network (IENet) with a graph convolutional network (GCN) to augment feature extraction capabilities and improve model classification performance. Through preprocessing EEG data and computing brain connectivity metrics, we trained the graph neural network and validated the method's superior performance in fatigue driving detection. This research introduces novel methods and concepts aimed at advancing road traffic safety, particularly in mitigating the risks associated with fatigued driving. © 2024 IEEE.

关键词： Convolutional neural networks

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Real-World Super-Resolution via Multi-degradation Based Texture Feature Reconstruction 7

Real-World Super-Resolution via Multi-degradation Based Text...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Qiao, Ning Zhou, Hongyang Kou, Hailong Zhu, Xiaobin School of Computer and Communication Engineering University of Science and Technology Beijing BeiJing China

ISBN: (纸本)9798350350890

In the field of real-world image super-resolution(SR), collecting paired training data is extremely challenging. As a result, some SR methods are trained on synthetic datasets. But there remains a gap when applied to real-world data. To address this issue, this paper proposes a robust multi-degradation real-world reconstruction method for real-world SR based on a texture degradation reconstruction network. This method cleverly overcomes the inadaptability issue associated with solely using synthetic datasets. Specifically, we first train the encoder, decoder, and the proposed SR network using real data. The encoder learns to extract the genuine texture features of input images through training, while the decoder significantly enhances the model's adaptability by incorporating the multi-degradation information. Then, leveraging the trained encoder, decoder, and real LR data, we retrain the SR network. Through this approach, our method can be trained in the absence of paired data and can improve the SR network's adaptability to LR images with unknown degradation. Experimental results indicate that this method outperforms existing real-world SR methods in generating SR outputs with finer textures. © 2024 IEEE.

关键词： Decoding

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A Scalable and Timely Data Auditing Scheme for Metaverse 10

A Scalable and Timely Data Auditing Scheme for Metaverse

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10th IEEE Smart World Congress, SWC 2024

作者： Chen, Chen Wu, Tong Ren, Chao Sun, Changai University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331520861

The metaverse is a new trend in virtual reality applications, and data storage and management commonly rely on distributed storage systems. The integrity of the stored data has become a key concern in the metaverse. Furthermore, the scalability of data storage is a significant characteristic of the metaverse. Therefore, it is essential to provide a scalable data auditing approach for distributed outsourcing storage systems. However, the existing data auditing schemes do not fully consider the timeliness of auditing, which is a shred of important evidence for users to ensure the storage status. In this paper, we propose a scalable data auditing scheme with timeliness, which supports scalable data storage and timestamp updates. By leveraging the blockchain infrastructure, our scheme ensures the fairness and transparency of the data auditing process in a distributed storage system. To achieve efficiency in scalable data auditing, our scheme uses pseudo-indexed linked lists by ordinary arrays to store the relevant information needed for data auditing, which can effectively support resource-constrained blockchain for on-chain auditing. Finally, we analyze the computational cost, communication overhead, and storage overhead of the scheme theoretically, verify the correctness of the scheme and compare the efficiency of the key algorithms experimentally. © 2024 IEEE.

关键词： Storage efficiency

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Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding 30

Arbitrary Time Information Modeling via Polynomial Approxima...

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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： Fang, Zhiyu Qin, Jingyan Zhu, Xiaobin Yang, Chun Yin, Xu-Cheng School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9782493814104

Distinguished from traditional knowledge graphs (KGs), temporal knowledge graphs (TKGs) must explore and reason over temporally evolving facts adequately. However, existing TKG approaches still face two main challenges, i.e., the limited capability to model arbitrary timestamps continuously and the lack of rich inference patterns under temporal constraints. In this paper, we propose an innovative TKGE method (PTBox) via polynomial decomposition-based temporal representation and box embedding-based entity representation to tackle the above-mentioned problems. Specifically, we decompose time information by polynomials and then enhance the model's capability to represent arbitrary timestamps flexibly by incorporating the learnable temporal basis tensor. In addition, we model every entity as a hyperrectangle box and define each relation as a transformation on the head and tail entity boxes. The entity boxes can capture complex geometric structures and learn robust representations, improving the model's inductive capability for rich inference patterns. Theoretically, our PTBox can encode arbitrary time information or even unseen timestamps while capturing rich inference patterns and higher-arity relations of the knowledge base. Extensive experiments on real-world datasets demonstrate the effectiveness of our method. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Polynomial approximation

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A Lightweight Multiscale Feature Fusion Network for Solar Cell Defect Detection

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computers, Materials & Continua 2025年第1期82卷 521-542页

作者： Xiaoyun Chen Lanyao Zhang Xiaoling Chen Yigang Cen Linna Zhang Fugui Zhang School of Mechanical Engineering Guizhou UniversityGuiyang550025China School of Computer Science and Technology Beijing Jiaotong UniversityBeijing100044China

Solar cell defect detection is crucial for quality inspection in photovoltaic power generation *** the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective ***,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective *** paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these *** network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation ***,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information *** order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target ***,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter ***,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and *** and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon *** results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.

关键词： Defect segmentation multi-scale feature fusion multi-scale attention depthwise separable residual block

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Adaptive Dispersion Estimation for Linearized Electronic Dispersion Compensation in IM-DD Systems

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China communications 2025年第4期22卷 55-64页

作者： Huo Jiahao Tao Jianlong Zhang Xiaoying Zhu Jin Qin Peng Wei Huangfu School of Computer and Communication Engineering University of Science and TechnologyBeijing 100083China

An adaptive dispersion estimation(ADE)is proposed to compensate dispersion and estimate the transfer function of the fiber channel with GerchbergSaxton(G-S)algorithm,using the stochastic gradient descent(SGD)method in the intensity-modulation and direct-detection(IM-DD)system,improving the tolerance of the algorithm to chromatic dispersion(CD).In order to address the divergence arising from the perturbation in the amplitude of the received signal caused by the filtering effect of the non-ideal channels,a channel-compensation equalizer(CCE)derived from the back-to-back(BTB)scenario is employed at the transmitter to make the amplitude of the received signal depicting the CD effect more *** simulation results demonstrate the essentiality of CCE for the convergence and performance improvement of the G-S *** show that it supports 112Gb/s four-level pulse amplitude modulation(PAM4)over 100 km standard single-mode fiber(SSMF)transmission under the 7%forward error correction(FEC)threshold of ***,ADE improves the tolerance to wavelength drift from about 4 nm to 42 nm,and there is a better tolerance for fiber distance perturbation,indicating the G-S algorithm and its derived algorithms with the ADE scheme exhibit superior robustness to the perturbation in the system.

关键词： chromatic dispersion digital signal process intensity modulation and direct detection

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A privacy-preserving method for publishing data with multiple sensitive attributes

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CAAI Transactions on Intelligence technology 2024年第1期9卷 222-238页

作者： Tong Yi Minyong Shi Wenqian Shang Haibin Zhu School of Computer Science and Engineering Guangxi Normal UniversityGuilinChina School of Computer Science Communication University of ChinaBeijingChina Department of Computer Science and Mathematics Nipissing UniversityNorth BayOntarioCanada

The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes(SAs)have not considered the personalised privacy ***,sensitive information disclosure may also be caused by these personalised *** address the matter,this article develops a personalised data publishing method for multiple *** to the requirements of individuals,the new method partitions SAs values into two categories:private values and public values,and breaks the association between them for privacy *** the private values,this paper takes the process of anonymisation,while the public values are released without this *** algorithm is designed to achieve the privacy mode,where the selectivity is determined by the sensitive value frequency and undesirable *** experimental results show that the proposed method can provide more information utility when compared with previous *** theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an *** overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.

关键词： data privacy data publishing

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