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arXiv 2023年

作者： Lu, Zhichao Ding, Chuntao Wang, Shangguang Cheng, Ran Juefei-Xu, Felix Boddeti, Vishnu Naresh School of Software Engineering Sun Yat-Sen University Zhuhai China School of Computer and Information Technology Beijing Jiaotong University Beijing China Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China New York University New YorkNY United States Department of Computer Science and Engineering Michigan State University East LansingMI United States

Deploying high-performance convolutional neural network (CNN) models on low-earth orbit (LEO) satellites for rapid remote sensing image processing has attracted significant interest from industry and academia. However, the limited resources available on LEO satellites contrast with the demands of resource-intensive CNN models, necessitating the adoption of ground-station server assistance for training and updating these models. Existing approaches often require large floating-point operations (FLOPs) and substantial model parameter transmissions, presenting considerable challenges. To address these issues, this paper introduces a ground-station server-assisted framework. With the proposed framework, each layer of the CNN model contains only one learnable feature map (called the seed feature map) from which other feature maps are generated based on specific rules. The hyperparameters of these rules are randomly generated instead of being trained, thus enabling the generation of multiple feature maps from the seed feature map and significantly reducing FLOPs. Furthermore, since the random hyperparameters can be saved using a few random seeds, the ground station server assistance can be facilitated in updating the CNN model deployed on the LEO satellite. Experimental results on the ISPRS Vaihingen, ISPRS Potsdam, UAVid, and LoveDA datasets for semantic segmentation services demonstrate that the proposed framework outperforms existing state-of-the-art approaches. In particular, the SineFM-based model achieves a higher mIoU than the UNetFormer on the UAVid dataset, with 3.3× fewer parameters and 2.2× fewer FLOPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Neural network models

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Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services

Seed Feature Maps-based CNN Models for LEO Satellite Remote ...

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IEEE International Conference on Web Services (ICWS)

作者： Zhichao Lu Chuntao Ding Shangguang Wang Ran Cheng Felix Juefei-Xu Vishnu Naresh Boddeti School of Software Engineering Sun Yat-sen University Zhuhai China School of Computer and Information Technology Beijing Jiaotong University Beijing China Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China New York University New York NY USA Department of Computer Science and Engineering Michigan State University East Lansing MI USA

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OblivGM: Oblivious Attributed Subgraph Matching as a Cloud Service

arXiv

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arXiv 2022年

作者： Wang, Songlei Zheng, Yifeng Jia, Xiaohua Huang, Hejiao Wang, Cong The School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China The Department of Computer Science City University of Hong Kong Hong Kong The Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China

In recent years there has been growing popularity of leveraging cloud computing for storing and querying attributed graphs, which have been widely used to model complex structured data in various applications. Such trend of outsourced graph analytics, however, is accompanied with critical privacy concerns regarding the information-rich and proprietary attributed graph data. In light of this, we design, implement, and evaluate OblivGM, a new system aimed at oblivious graph analytics services outsourced to the cloud. OblivGM focuses on the support for attributed subgraph matching, one popular and fundamental graph query functionality aiming to retrieve from a large attributed graph subgraphs isomorphic to a small query graph. Built from a delicate synergy of insights from attributed graph modelling and advanced lightweight cryptography, OblivGM protects the confidentiality of data content associated with attributed graphs and queries, conceals the connections among vertices in attributed graphs, and hides search access patterns. Meanwhile, OblivGM flexibly supports oblivious evaluation of varying subgraph queries, which may contain equality and/or range predicates. Extensive experiments over a real-world attributed graph dataset demonstrate that while providing strong security guarantees, OblivGM achieves practically affordable performance (with query latency on the order of a few seconds). Copyright © 2022, The Authors. All rights reserved.

关键词： Cloud analytics

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Dynamic Modeling of Strip Rolling Process Using Probabilistic Neural Network

Dynamic Modeling of Strip Rolling Process Using Probabilisti...

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International Workshop on Advanced Motion Control (AMC)

作者： Jifei Deng Seppo Sierla Jie Sun Valeriy Vyatkin Department of Electrical Engineering and Automation Aalto University Espoo Finland State Key Laboratory of Rolling and Automation Northeastern University Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

This paper proposes an intelligent dynamic modeling method for strip rolling process. Actuators of a cold rolling mill perform actions, including work roll bending, intermediate roll bending, and roll gap tilting, to regulate the product quality. Conventional first-principles modeling necessitates intricate mathematical equations derived from process analysis and action evaluation. However, the complexity of the model increases the difficulty of upgrading the model. To address this limitation, a novel approach involving a deep neural network with a Gaussian distribution layer is proposed to effectively capture the process dynamics. A negative log likelihood is established as the loss function for the model using real-world industrial data. Experiment outcomes demonstrate the efficacy of the proposed method in achieving an accurate dynamic model.

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Defense Against Adversarial Faces at the Source: Strengthened Faces Based on Hidden Disturbances

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Li, Shuangliang Wang, Jinwei Wu, Hao Zhang, Jiawei Cheng, Xin Luo, Xiangyang Ma, Bin School of Software Nanjing Universityof Information Science and Technology Nanjing210044 China College of Cryptology and Cyber Science Nankai University Tianjing300073 China State Key Laboratory of Mathemat-ical Engineering and Advanced Computing Zhengzhou450002 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of Cyber Security Qilu University of Technology Shandong Academy of Sciences Shandong250353 China

Face Recognition (FR) systems, while widely used across various sectors, are vulnerable to adversarial attacks, particularly those based on deep neural networks. Despite existing efforts to enhance the robustness of FR models, they still face the risk of secondary adversarial attacks. To address this, we propose a novel approach employing "strengthened face" with preemptive defensive perturbations. Strengthened face ensures original recognition accuracy while safeguarding FR systems against secondary attacks. In the white-box scenario, the strengthened face utilizes gradient-based and optimization-based methods to minimize feature representation differences between face pairs. For the black-box scenario, we propose Shielded Gradient Sign Descent (SGSD) to optimize the gradient update direction of strengthened faces, ensuring the transferability and effectiveness against unknown adversarial attacks. Experimental results demonstrate the efficacy of strengthened faces in defending against adversarial faces without compromising the performance of FR models or face image visual quality. Moreover, SGSD outperforms conventional methods, achieving an average performance improvement of 4% in transferability across different attack intensities. © 2020 IEEE.

关键词： adversarial attacks face recognition facial protection

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Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping

arXiv

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arXiv 2022年

作者： Zheng, Zhuoxun Zhou, Baifan Zhou, Dongzhuoran Cheng, Gong Jiménez-Ruiz, Ernesto Soylu, Ahmet Kharlamov, Evgeny Bosch Center for Artificial Intelligence Renningen Germany Department of Computer Science Oslo Metropolitan University Oslo Norway SIRIUS Centre University of Oslo Oslo Norway State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science City University of London London United Kingdom

Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the unified data schemata are a prominent solution that offers high quality data integration and a convenient and standardised way to exchange data and to layer analytical applications over it. However, poor design of ontologies of high degree of mismatch between them and industrial data naturally lead to KGs of low quality that impede the adoption and scalability of industrial analytics. Indeed, such KGs substantially increase the training time of writing queries for users, consume high volume of storage for redundant information, and are hard to maintain and update. To address this problem we propose an ontology reshaping approach to transform ontologies into KG schemata that better reflect the underlying data and thus help to construct better KGs. In this poster we present a preliminary discussion of our on-going research, evaluate our approach with a rich set of SPARQL queries on real-world industry data at Bosch and discuss our findings. Copyright © 2022, The Authors. All rights reserved.

关键词： Digital storage

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A Survey on Extreme Multi-label Learning

arXiv

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arXiv 2022年

作者： Wei, Tong Mao, Zhen Shi, Jiang-Xin Li, Yu-Feng Zhang, Min-Ling The School of Computer Science and Engineering Southeast University Nanjing210096 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The National Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210023 China

Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good performance in various tasks, they implicitly assume the size of target label space is not huge, which can be restrictive for real-world scenarios. Moreover, it is infeasible to directly adapt them to extremely large label space because of the compute and memory overhead. Therefore, eXtreme Multi-label Learning (XML) is becoming an important task and many effective approaches are proposed. To fully understand XML, we conduct a survey study in this paper. We first clarify a formal definition for XML from the perspective of supervised learning. Then, based on different model architectures and challenges of the problem, we provide a thorough discussion of the advantages and disadvantages of each category of methods. For the benefit of conducting empirical studies, we collect abundant resources regarding XML, including code implementations, and useful tools. Lastly, we propose possible research directions in XML, such as new evaluation metrics, the tail label problem, and weakly supervised XML. © 2022, CC BY.

关键词： XML

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3D Human Pose Lifting with Grid Convolution

arXiv

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arXiv 2023年

作者： Kang, Yangyuxuan Liu, Yuyang Yao, Anbang Wang, Shandong Wu, Enhua State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China Tsinghua University China Intel Labs China China Faculty of Science and Technology University of Macau China

Existing lifting networks for regressing 3D human poses from 2D single-view poses are typically constructed with linear layers based on graph-structured representation learning. In sharp contrast to them, this paper presents Grid Convolution (GridConv), mimicking the wisdom of regular convolution operations in image space. GridConv is based on a novel Semantic Grid Transformation (SGT) which leverages a binary assignment matrix to map the irregular graph-structured human pose onto a regular weave-like grid pose representation joint by joint, enabling layer-wise feature learning with GridConv operations. We provide two ways to implement SGT, including handcrafted and learnable designs. Surprisingly, both designs turn out to achieve promising results and the learnable one is better, demonstrating the great potential of this new lifting representation learning formulation. To improve the ability of GridConv to encode contextual cues, we introduce an attention module over the convolutional kernel, making grid convolution operations input-dependent, spatial-aware and grid-specific. We show that our fully convolutional grid lifting network outperforms state-of-the-art methods with noticeable margins under (1) conventional evaluation on Human3.6M and (2) cross-evaluation on MPI-INF-3DHP. Code is available at https://***/OSVAI/GridConv. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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SecSkyline: Fast Privacy-Preserving Skyline Queries over Encrypted Cloud Databases

arXiv

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arXiv 2022年

作者： Zheng, Yifeng Wang, Weibo Wang, Songlei Jia, Xiaohua Huang, Hejiao Wang, Cong The School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China The Department of Computer Science City University of Hong Kong Hong Kong The Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China

The well-known benefits of cloud computing have spurred the popularity of database service outsourcing, where one can resort to the cloud to conveniently store and query databases. Coming with such popular trend is the threat to data privacy, as the cloud gains access to the databases and queries which may contain sensitive information, like medical or financial data. A large body of work has been presented for querying encrypted databases, which has been mostly focused on secure keyword search. In this paper, we instead focus on the support for secure skyline query processing over encrypted outsourced databases, where little work has been done. Skyline query is an advanced kind of database query which is important for multi-criteria decision-making systems and applications. We propose SecSkyline, a new system framework building on lightweight cryptography for fast privacy-preserving skyline queries. SecSkyline ambitiously provides strong protection for not only the content confidentiality of the outsourced database, the query, and the result, but also for data patterns that may incur indirect data leakages, such as dominance relationships among data points and search access patterns. Extensive experiments demonstrate that SecSkyline is substantially superior to the state-of-the-art in query latency, with up to 813× improvement. Copyright © 2022, The Authors. All rights reserved.

关键词： Outsourcing

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SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness

arXiv

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arXiv 2023年

作者： Huang, Zeyuan He, Qiang Maher, Kevin Deng, Xiaoming Lai, Yu-Kun Ma, Cuixia Qin, Sheng-Feng Liu, Yong-Jin Wang, Hongan The Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences and also with the School of Computer Science and Technology University of Chinese Academy of Sciences China Diatom Design Limited Liability Company United States The School of Computer Science and Informatics Cardiff University United Kingdom The School of Design Northumbria University United Kingdom The Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China

As communications are increasingly taking place virtually, the ability to present well online is becoming an indispensable skill. Online speakers are facing unique challenges in engaging with remote audiences. However, there has been a lack of evidence-based analytical systems for people to comprehensively evaluate online speeches and further discover possibilities for improvement. This paper introduces SpeechMirror, a visual analytics system facilitating reflection on a speech based on insights from a collection of online speeches. The system estimates the impact of different speech techniques on effectiveness and applies them to a speech to give users awareness of the performance of speech techniques. A similarity recommendation approach based on speech factors or script content supports guided exploration to expand knowledge of presentation evidence and accelerate the discovery of speech delivery possibilities. SpeechMirror provides intuitive visualizations and interactions for users to understand speech factors. Among them, SpeechTwin, a novel multimodal visual summary of speech, supports rapid understanding of critical speech factors and comparison of different speech samples, and SpeechPlayer augments the speech video by integrating visualization of the speaker's body language with interaction, for focused analysis. The system utilizes visualizations suited to the distinct nature of different speech factors for user comprehension. The proposed system and visualization techniques were evaluated with domain experts and amateurs, demonstrating usability for users with low visualization literacy and its efficacy in assisting users to develop insights for potential improvement. Copyright © 2023, The Authors. All rights reserved.

关键词： Modal analysis

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