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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Yuxuan Zhang Miaojing Shi Taiyi Su Hanli Wang Department of Computer Science & Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai P. R. China Department of Control Science & Engineering Tongji University Shanghai P. R. China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Incremental few-shot semantic segmentation (IFSS) aims to incrementally expand a semantic segmentation model’s ability to identify new classes based on few samples. However, it grapples with the dual challenges of catastrophic forgetting (due to feature drift in old classes) and overfitting (triggered by inadequate samples in new classes). To address these issues, a novel approach is proposed to integrate pixel-wise and region-wise contrastive learning, complemented by an optimized example and anchor sampling strategy. The proposed method incorporates a region memory and pixel memory designed to explore the high-dimensional embedding space more effectively. The memory, retaining the feature embeddings of known classes, facilitates the calibration and alignment of seen class features during the learning process of new classes. To further mitigate overfitting, the proposed approach implements an optimized example and anchor sampling strategy. Extensive experiments show the competitive performance of the proposed method. The source code of this work can be found in https://***.

关键词： Circuits and systems Semantic segmentation Source coding Semantics Calibration

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Cross-Modal Representation Reconstruction for Zero-Shot Classification

Cross-Modal Representation Reconstruction for Zero-Shot Clas...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yu Wang Shenjie Zhao Key Laboratory of Embedded System and Service Computing Ministry of Education P. R. China Mojie Technology (Ningbo)

ISBN: (纸本)9781728176055;9781728176062

Zero-shot learning (ZSL) aims to recognize novel classes without training samples through transferring knowledge from seen classes, based on the assumption that both the seen and unseen classes share a latent semantic space. Previous works either focus on directly learning various mapping functions between visual space and semantic space, or searching a latent common subspace to alleviate semantic gap between different modalities. However, few methods directly learn modality-invariant representations for ZSL. In this paper, we propose a Cross-Modal Representation Reconstruction (CM-RR) framework to bridge the semantic gap between visual features and semantic attributes, as well as introducing a novel regularizer for automatically feature selection. Moreover, an iterative optimization process based on the ALM (Augmented Lagrangian Method) algorithm with the alternating direction strategy is developed to solve the proposed formulation. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach, and even the performance surpasses some deep learning based methods.

关键词： Training Visualization Semantics Signal processing algorithms Signal processing Feature extraction Iterative algorithms

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Cross-Domain Semantic Segmentation of Urban Scenes via Multi-Level Feature Alignment

Cross-Domain Semantic Segmentation of Urban Scenes via Multi...

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International Conference on Pattern Recognition

作者： Bin Zhang Shengjie Zhao Rongqing Zhang Key Laboratory of Embedded System and Service Computing Ministry of Education China School of Software Engineering Tongji University Shanghai China

Semantic segmentation is an essential task in plenty of real-life applications such as virtual reality, video analysis, autonomous driving, etc. Recent advancements in fundamental vision-based tasks ranging from image classification to semantic segmentation have demonstrated deep learning-based models' high capability in learning complicated representation on large datasets. Nevertheless, manually labeling semantic segmentation dataset with pixel-level annotation is extremely labor-intensive. To address this problem, we propose a novel multi-level feature alignment framework for cross-domain semantic segmentation of urban scenes by exploiting generative adversarial networks. In the proposed multi-level feature alignment method, we first translate images from one domain to another one. Then the discriminative feature representations extracted by the deep neural network are concatenated, followed by domain adversarial learning to make the intermediate feature distribution of the target domain images close to those in the source domain. With these domain adaptation techniques, models trained with images in the source domain where the labels are easy to acquire can be deployed to the target domain where the labels are scarce. Experimental evaluations on various mainstream benchmarks confirm the effectiveness as well as robustness of our approach.

关键词： Image segmentation Solid modeling Semantics Neural networks Virtual reality Feature extraction Robustness

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Design and Implementation of DSP Cache 21

Design and Implementation of DSP Cache

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Liang, Kunkun Wu, Jun Ren, Haoqi Zhang, Zhifeng Tan, Bin Tongji University Electronics and Information Engineering College Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China School of Computer Science Fudan University Shanghai200438 China College of Electronics and Information Engineering Jinggangshan University Ji'an343009 China

ISBN: (纸本)9781665432061

The cache can improve the DSP processor's access speed to external memory and solve the 'Storage Wall' problem. Designing an efficient and flexible cache module plays an vital role in improving the memory access efficiency and overall performance of the DSP. Based on the analysis of DSP storage level design requirements, combined with the actual pipeline structure of the DSP processor SWIFT independently developed by our laboratory, in this paper, we design and implement first- level instruction and data cache. For L1 D-Cache, the relevant conflict detection and processing module is designed to support four parallel Load/Store access requests. The size of the cache can be flexibly configured according to actual application requirements to achieve a balance between power consumption and latency. To write back the modified data left in the cache before the cache size is changed, the Cacheclean instruction is designed. Finally, module-level functional verification and logic synthesis are carried out. The results show that the cache function meets expectations, and the critical path after logic synthesis optimization meets the requirement of 1GHz frenuency. © 2021 IEEE.

关键词： Pipelines

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HeavyTracker: An Efficient Algorithm for Heavy-Hitter Detection in High-Speed Networks

HeavyTracker: An Efficient Algorithm for Heavy-Hitter Detect...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Jiakun Yu Yu-E Sun He Huang Yang Du Guoju Gao Hongli Xu Shigang Chen School of Computer Science and Technology Soochow University Suzhou China School of Rail Transportation Soochow University Suzhou China Ministry of Education Key Laboratory of Embedded System and Service Computing (Tongji University) Shanghai China School of Computer Science and Technology University of Science and Technology of China Hefei China

Detecting heavy hitters that constitute the majority of network traffic is a critical task in network measurement. However, the highly skewed network traffic and the size-limited on-chip memory pose great challenges for heavy-hitter detection. The prior arts either use sketches to track all flows or maintain a fix-sized tracking list for recording elephant flows, resulting in limited detection precision or time-consuming tracking list exchanges. This paper presents HeavyTracker, an efficient algorithm that detects heavy hitters based on a count-with-threshold strategy. A two-dimensional tracker unit array is employed in our design to capture the heavy hitters, where each flow is randomly mapped to one unit in all rows. The tracker unit is designed to accept many flows and precisely report the largest two with frequencies that reach a predefined threshold. This design eliminates the frequent and time-consuming exchanges for maintaining the tracking list, allowing us to use a hash table to track the reported elephant flows. Experimental results based on real Internet traces show that HeavyTracker achieves a high $\mathbf{F}_{\beta}$-Score of threshold-t detection (0.97) and over 99% precision of top-k detection under a tight memory size. Besides, it reduces the frequency estimation error by 97.4% compared to the state-of-the-art.

关键词： Art High-speed networks Measurement uncertainty Telecommunication traffic Size measurement Frequency estimation Internet

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Synchronization and control for multi-weighted and directed complex networks

arXiv

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arXiv 2021年

作者： Liu, Xiwei Department of Computer Science and Technology Tongji University China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

The study of complex networks with multi-weights has been a hot topic recently. For a network with a single weight, previous studies have shown that they can promote synchronization. But for complex networks with multi-weights, there are no rigorous analysis to show that synchronization can be reached faster. In this paper, the complex network is allowed to be directed, which will make the synchronization analysis difficult for multiple couplings. In virtue of the normalized left eigenvectors (NLEVec) corresponding to the zero eigenvalue of coupling matrices, we prove that if the Chebyshev distance between NLEVec is less than some value, which is defined as the allowable deviation bound, then the synchronization and control will be realized with sufficiently large coupling strengths, i.e., all coupling matrices do accelerate synchronization. Moreover, adaptive rules are also designed for the coupling strength. © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

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GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning

arXiv

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arXiv 2024年

作者： Qu, Sanqing Zou, Tianpei Röhrbein, Florian Lu, Cewu Chen, Guang Tao, Dacheng Jiang, Changjun The School of Automotive Engineering Department of Computer Science Tongji University China The Faculty of Computer Science Chemnitz University of Technology Germany The Department of Computer Science Shanghai Jiao Tong University China The School of Computer Science Faculty of Engineering The University of Sydney Australia The Department of Computer Science Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China

Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known" data belonging to common categories and segregate them from target-private "unknown" data. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm to discern between target classes, complemented by a local k-NN clustering strategy to mitigate negative transfer. Despite the effectiveness, the inherent closed-set source architecture leads to uniform treatment of "unknown" data, impeding the identification of distinct "unknown" categories. To address this, we evolve GLC to GLC++, integrating a contrastive affinity learning strategy. We examine the superiority of GLC and GLC++ across multiple benchmarks and category shift scenarios. Remarkably, in the most challenging open-partial-set scenarios, GLC and GLC++ surpass GATE by 16.7% and 18.6% in H-score on VisDA, respectively. GLC++ enhances the novel category clustering accuracy of GLC by 4.3% in open-set scenarios on Office-Home. Furthermore, the introduced contrastive learning strategy not only enhances GLC but also significantly facilitates existing methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Effcient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

Security and Safety

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Security and Safety 2022年第1期1卷 145-165页

作者： Zhong Li Xianke Wu Changjun Jiang College of Information Science and Technology Donghua UniversityShanghai 201620China Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer ScienceTongji UniversityShanghai 201804China Shanghai Network Financial Security Collaborative Innovation Center Tongji UniversityShanghai 201804China

Nowadays,large numbers of smart sensors(e.g.,road-side cameras)which com-municate with nearby base stations could launch distributed denial of services(DDoS)attack storms in intelligent transportation *** attacks disable the services provided by base *** in this paper,considering the uneven communication traffic ows and privacy preserving,we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the ***,in the federated learning,we need to consider the problem of poisoning attacks due to malicious *** poisoning attacks will lead to the intelligent transportation systems paralysis without security *** poisoning attacks mainly apply to the classi cation model with labeled *** this paper,we propose a reinforcement learning-based poisoningmethod speci cally for poisoning the prediction model with unlabeled ***,previous related defense strategies rely on validation datasets with labeled data in the ***,it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving,and our datasets are also ***,we give a validation dataset-free defense strategy based on Dempster-Shafer(D-S)evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS *** our experiments,we simulate 3000 points in combination with DARPA2000 dataset to carry out *** results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short ***,we compare our proposed defense algorithm with three popularly used defense *** results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

关键词： Poisoning attacks Defenses Multi-step DDoS prediction Unlabeled data Intel-ligent transportation systems

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A Blockchain Based Automatic Access Scheme Design and Implement for Small Cell Base Station 21

A Blockchain Based Automatic Access Scheme Design and Implem...

引用

21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Liang, Jingchao Wu, Jun Tan, Bin Ren, Haoqi Zhang, Zhifeng School of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Fudan University School of Computer Science Shanghai China Jinggangshan University Electronics and Information Engineering Ji'an China Tongji University Electronics and Information Engineering Shanghai China

ISBN: (纸本)9781665432061

5G technology is constrained by its higher frequency band and smaller coverage area, which leads to the need for operators to use technologies such as small cell base stations to increase the density of base station deployment to ensure signal coverage quality, which leads to more enormous construction costs. Therefore, it is urgent to find a safe and reliable solution that can use the existing public network to realize small cell base stations with automatic access. Based on this demand, we summarize various existing small cell base station automatic access technology solutions and their advantages and disadvantages while combing the characteristics of blockchain technology and its application solutions in similar scenarios. And then, we propose a new blockchain-based base station automatic access solution and implements the system in a practical scenario. In our solution, we innovatively introduce blockchain as an intermediate manager for small base stations and core networks, which solves traditional solutions requiring customized equipment while ensuring safety and reliability. It reduces costs and improves efficiency, but some problems are brought by the 'decentralization' of blockchain waiting to be solved. © 2021 IEEE.

关键词： Blockchain

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Low-Rank Subspace Representation from Optimal Coded-Aperture for Unsupervised Classification of Hypersepctral Imagery

Low-Rank Subspace Representation from Optimal Coded-Aperture...

引用

IEEE International Conference on Image Processing

作者： Jianchen Zhu Tong Zhang Shengjie Zhao Key Laboratory of Embedded System and Service Computing Ministry of Education

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

This paper aims at developing a clustering approach with spectral images directly from the compressive measurements of coded aperture snapshot spectral imager (CASSI). Assuming that compressed measurements often lie approximately in low dimensional subspaces corresponding to multiple classes, state of the art methods generally obtains optimal solution for each step separately but cannot guarantee that it will achieve the globally optimal clustering results. In this paper, a low-rank subspace representation (LRSR) algorithm is proposed to perform clustering on the compressed measurements. In addition, a subspace structured norm is added into the objective of low-rank representation problem exploiting the fact that each point in a union of subspaces can be expressed as a sparse linear combination of all other points and that the matrix of the points within each subspace is low rank. Simulation with real dataset illustrates the accuracy of the proposed spectral image clustering approach.

关键词： Image coding Encoding Apertures Clustering algorithms Imaging Three-dimensional displays Sensors

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