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Implicit privacy preservation:a framework based on data generation

Security and Safety 2022年第1期1卷 166-183页

作者： Qing Yang Cheng Wang Teng Hu Xue Chen Changjun Jiang Key Laboratory of Embedded System and Service Computing(Tongji University) Ministry of EducationShanghai 201804China National(Province-Ministry Joint)Collaborative Innovation Center for Financial Network Security Tongji UniversityShanghai 201804China

This paper addresses a special and imperceptible class of privacy,called implicit *** contrast to traditional(explicit)privacy,implicit privacy has two essential properties:(1)It is not initially de ned as a privacy attribute;(2)it is strongly associated with privacy *** other words,attackers could utilize it to infer privacy attributes with a certain probability,indirectly resulting in the disclosure of private *** deal with the implicit privacy disclosure problem,we give a measurable de nition of implicit privacy,and propose an ex-ante implicit privacy-preserving framework based on data generation,called *** framework consists of an implicit privacy detection module and an implicit privacy protection *** former uses normalized mutual information to detect implicit privacy attributes that are strongly related to traditional privacy *** on the idea of data generation,the latter equips the Generative Adversarial Network(GAN)framework with an additional discriminator,which is used to eliminate the association between traditional privacy attributes and implicit *** elaborate a theoretical analysis for the convergence of the *** demonstrate that with the learned generator,IMPOSTER can alleviate the disclosure of implicit privacy while maintaining good data utility.

关键词： Privacy preservation Implicit privacy Generative adversarial network Data utility Data generation

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DSP Assembler Auto-Generation Technique and ToolChain Integration 21

DSP Assembler Auto-Generation Technique and ToolChain Integr...

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Wang, Wanli Wu, Jun Ren, Haoqi Zhang, Zhifeng Tongji University Electronics and Information Engineering College Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Fudan University School of Computer Science Shanghai200438 China

ISBN: (纸本)9781665432061

To promote the application of DSP, a fast and accurate toolchain generation method must be realized. Unlike the traditional manual method, in this paper, we propose a toolchain generation algorithm based on the architecture description language, and migrate the toolchain to adapt to the specified processor architecture. The algorithm uses the DSP architecture description as input to generate intermediate expressions, then uses lexical analysis and syntax analysis tools to migrate the assembly toolchain. In addition, an integrated development environment is developed based on Eclipse, which creates a friendly human-computer interface. Experiments show that this toolchain generation algorithm runs fast and accurately, which solves problems of the development and application of the DSP toolchain, and creates favorable conditions for the promotion of DSP. © 2021 IEEE.

关键词： Syntactics

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Efficient Routing Strategy for Sdn Microservices Based on 5g Cloud Native Scenario

SSRN

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SSRN 2022年

作者： Sheng, Shuo Yan, Chungang Key Laboratory ofEmbedded System and Service Computing Ministry of Education Tongji University Shanghai China

In the 5G cloud native scenario, a variety of heterogeneous networks are combined and decoupled based on the SDN multi-dimensional splitting method to find a way to restructure SDN within the microservice framework. In order to solve this problem, this paper proposes a microservice splitting idea within the cloud native framework system to conduct multi-dimensional restructuring of SDN, and proposes a new algorithm to improve the QOS of SDN under the microservice framework, further reducing end-to-end delay Cost, load balancing, energy consumption and other indicators, experiments prove that the SDN + micro service splitting idea in this paper is progressiveness, and multi-objective optimization is carried out based on the optimization algorithm, and the performance is significantly improved. © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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A Better Cardinality Estimator with Fewer Bits, Constant Update Time, and Mergeability

A Better Cardinality Estimator with Fewer Bits, Constant Upd...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yang Du He Huang Yu-E Sun Kejian Li Boyu Zhang Guoju Gao School of Computer Science and Technology Soochow University Suzhou China School of Rail Transportation Soochow University Suzhou China Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China

Cardinality estimation is a fundamental problem with diverse practical applications. HyperLogLog (HLL) has become a standard in practice because it offers good memory efficiency, constant update time, and mergeability. Some recent work achieved better memory efficiency, but typically at the cost of impractical update time or losing mergeability, making them incompatible with applications like network-wide traffic measurement. This work presents SpikeSketch, a better cardinality estimator that reduces memory usage of HLL by 37% without sacrificing other crucial metrics. We adopt a bucket-based data structure to promise constant update time, design a smoothed log 4 ranking and a spike coding scheme to compress cardinality observables into buckets, and propose a lightweight mergeable lossy compression to balance memory usage, information loss, and mergeability. Then we derive an unbiased estimator for recovering cardinality from the lossy-compressed sketch. Theoretical and empirical results show that SpikeSketch can work as a drop-in replacement for HLL because it achieves a near-optimal MVP (memory-variance-product) of 4.08 (37% smaller than HLL) with constant update time and mergeability. Its memory efficiency even defeats ACPC and HLLL, the state-of-the-art lossless-compressed sketches using linear-time compression to reduce memory usage.

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E2DAS: An Efficient Equivariant Dynamic Aggregation Saliency Model for Omnidirectional Images 27th

E2DAS: An Efficient Equivariant Dynamic Aggregation Saliency...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Nana Liu, Qian Zhu, Dandan Zhu, Kun Zhai, Guangtao Yang, Xiaokang School of Computer Science and Technology Donghua University No.2999 Renmin North Road Songjiang District Shanghai201620 China Institute of AI Education Shanghai East China Normal University No.3663 Zhongshan North Road Putuo District Shanghai200333 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University No.4800 Cao’an Highway Jiading District Shanghai201804 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9783031781216

Recent years have witnessed rapid progress of convolutional neural networks (CNNs) and their successful application in the task of saliency prediction for omnidirectional images (ODIs). Albeit achieving tremendous performance improvements, these CNNs-based saliency models are plagued by two major shortcomings: spatial content-agnostic and computationally intensive. Inspired by the effectiveness of equivariant network in the majority of computer vision tasks, we propose a novel efficient equivariant dynamic aggregation saliency (E2DAS) model to efficiently tackle the issue of human fixation prediction in ODIs. To be specific, our proposed model consists of an efficient equivariant module, a dynamic convolutional aggregation module, and an optimization computation module. Different from existing saliency models for ODIs, we are the first attempt to introduce an efficient equivariant dynamic convolutional aggregation operation into the saliency prediction task, which can fundamentally alleviate the projection distortion problem and can effectively learn spatial content-adaptive features. Moreover, we clearly observe a considerable decrease in the number of parameters resulting from the replacement of standard convolution with dynamic convolution aggregation. Extensive experiments on several benchmark datasets show the proposed model’s superiority over other state-of-the-art methods in terms of performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Equivariant dynamic aggregation light-weight model omnidirectional images saliency prediction spatial content-adaptive

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Heuristic Elastic Scaling for Kubernetes Heterogeneous Microservices

Heuristic Elastic Scaling for Kubernetes Heterogeneous Micro...

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IEEE International Conference on Networking, Sensing and Control

作者： Song Wang Sheng He Yishuang Ning Xiang Gao Zhijun Ding Department of Computer Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Tongji University Shanghai China Kingdee Research Kingdee International Software Group Co. Ltd Shenzhen China Intelligent Computing Facility Innovation Center ZhejiangLab Hangzhou China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Kubernetes has become the basic platform for building cloud native applications. However, existing horizontal scaling methods based on Kubernetes have problems with resource redundancy. Furthermore, the combined horizontal and vertical scaling based on Kubernetes takes a long time. Regarding the issue above, first, we describe the execution processes of three scaling techniques and compare them in different dimensions; second, we construct a formal elastic scaling model for expansion scenarios, with the goal of minimizing the total cost; and third, we introduce a heuristic expansion algorithm after balancing resource redundancy and execution time as the heuristic information. The experiment results show that the elastic scaling algorithm proposed in this paper achieves better performance than other algorithms.

关键词： Costs Heuristic algorithms Redundancy Buildings Microservice architectures Sensors

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Mining World Indicators for Analyzing and Modeling the Development of Countries

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ACM/IMS Transactions on Data Science 2021年第4期2卷 1–27页

作者： Huang, Hong Chi, Mingyuan Song, Yu Jin, Hai The National Engineering Research Center for Big Data Technology and System Key Laboratory of Service Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

The world indicators released by the World Bank or other organizations usually give the basic public knowledge about the world. However, separate and static index lacks the complex interplay among different indicators and thus cannot help us have an overall understanding of the world. To this end, we study the world indicators from a different angle. Firstly, we discover that there exist correlations between indicators either from a static view or from a dynamic view. Moreover, taking the trade and diplomatic relationships into consideration, we construct a multi-relational network to depict the interactions between different countries, and propose a Multiple Relations to Vector (MR2vec) model to study world indicators from a network perspective. The experimental results show the changes of world indicators are predictable with the proposed model, and our proposed MR2vec has wide adaptability in predicting multi-relation networks. © 2022 Copyright held by the owner/author(s).

关键词： Data mining

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TDiffSal: Text-Guided Diffusion Saliency Prediction Model for Images 27th

TDiffSal: Text-Guided Diffusion Saliency Prediction Model fo...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Nana Xiong, Min Zhu, Dandan Zhu, Kun Zhai, Guangtao School of Computer Science and Technology Donghua University No. 2999 Renmin North Road Songjiang District Shanghai201620 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China Institute of AI Education Shanghai East China Normal University No. 3663 Zhongshan North Road Putuo District Shanghai200333 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University No. 4800 Cao’an Highway Jiading District Shanghai201804 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9783031781858

Existing visual saliency prediction methods mainly focus on single-modal visual saliency prediction, while ignoring the significant impact of text on visual saliency. To more comprehensively explore the influence of text on human attention in images, we propose a text-guided diffusion saliency prediction model, named TDiffSal. In specific, recent studies on stable diffusion models have shown promising performance in unifying tasks due to their inherent generalization ability. Inspired by this, a novel diffusion model for generalized visual-text saliency prediction is proposed, which formulates the prediction issue as a conditional generative task of the saliency map by employing input visual and text as the conditions. Meanwhile, we introduce a multi-head fusion module to effectively integrate text features and image features, which can efficiently guide the image denoising process and progressively refine the generated saliency map to make it semantically relevant to the text. Additionally, we employ an efficient pre-training strategy to enhance the robustness and generalization of the proposed model. We conduct extensive experiments on benchmark datasets to demonstrate its superior performance compared to other state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Structure-Aware Generative Adversarial Network for Text-to-Image Generation

Structure-Aware Generative Adversarial Network for Text-to-I...

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IEEE International Conference on Image Processing

作者： Wenjie Chen Zhangkai Ni Hanli Wang Department of Computer Science and Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai P. R. China

Text-to-image generation aims at synthesizing photo-realistic images from textual descriptions. Existing methods typically align images with the corresponding texts in a joint semantic space. However, the presence of the modality gap in the joint semantic space leads to misalignment. Meanwhile, the limited receptive field of the convolutional neural network leads to structural distortions of generated images. In this work, a structure-aware generative adversarial network (SaGAN) is proposed for (1) semantically aligning multimodal features in the joint semantic space in a learnable manner; and (2) improving the structure and contour of generated images by the designed content-invariant negative samples. Experimental results show that SaGAN achieves over 30.1% and 8.2% improvements in terms of FID on the datasets of CUB and COCO when compared with the state-of-the-art approaches.

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Feedback-based Coarse Time-Granularity POI Recommendation and Itinerary Planning

Feedback-based Coarse Time-Granularity POI Recommendation an...

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Chinese Automation Congress (CAC)

作者： Jie Zong Zhijun Ding The Key Laboratory of Embedded System and Service Computing Ministry of Education TONGJI University Shanghai China

ISBN: (纸本)9781665426480

For tourists, planning their own travel itinerary to a strange city is really challenging. Although there are many researches on Point of Interests (POIs) recommendation and itinerary planning, two problems occur in currently proposed recommendation algorithms. First, the time granularity of the algorithms is too fine. POI recommendation should be considered within a more coarse-grained time factor. For example, a visitor wouldn’t like to visit a water park in winter, although he appears to love such places. However, seldom researches have taken this into consideration. Second, most of the algorithms build their models based on the tourists’ trajectory, which means, they did not consider users’ feedback. In fact, quite a number of tourists do not like their travel experience and give negative feedbacks after their visits. In this situation, we shouldn’t recommend similar POIs to this user because he may not like such kind of scenic spots at all. To address these two problems, this paper proposes an algorithm called Feedback-based Coarse Time-Granularity POI Recommendation, which can extract information from comments and distinguish tourists’ attitudes towards the POIs they have visited. At the same time, the coarse-grained time factor is considered while making recommendations. In addition, an itinerary planning algorithm is proposed. Experiments show that our algorithm can reach SOTA.

关键词： Negative feedback Automation Urban areas Planning Trajectory Time factors Data mining

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