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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS)

作者： Huixuan Xiong Kai Jin Jingnian Liu Jiahong Cai Lijun Xiao School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China the College of Computer Science and Electronics Engineering Hunan University Changsha China Big Data Development and Research Center Guangzhou College of Technology and Business Guangzhou China

Deep learning is a powerful multi-layer architecture that has important applications in image processing and text classification. This paper first introduces the development of deep learning and two important algorithms of deep learning: convolutional neural networks and recurrent neural networks. The paper then introduces three applications of deep learning for image recognition, image detection, and image forensics, as well as three text classification methods based on convolutional neural networks, recurrent neural networks, and other text classification methods. Finally, the development trend of deep learning in the field of text and image processing and the difficulties to be further researched are summarised and prospected.

关键词： Deep learning Image forensics Recurrent neural networks Computational modeling Text categorization Feature extraction Data models

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Self-Supervised Teaching and Learning of Representations on Graphs 23

Self-Supervised Teaching and Learning of Representations on ...

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2023 World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Fu, Zhenqiang Sun, Lu Wang, Xianpeng Xu, Gang Yan, Xiaoran Xia, Feng Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China State Key Laboratory of Marine Resource Utilization in South China Sea School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification. © 2023 ACM.

关键词： Classification (of information)

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Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5445-5460页

作者： Lin, Hui Li, Nan Yao, Pengjuan Dong, Kexin Guo, Yuhan Hong, Danfeng Zhang, Ying Wen, Congcong China Academy of Electronics and Information Technology Beijing100846 China China Meteorological Administration National Satellite Meteorological Center Innovation Center for FengYun Meteorological Satellite Beijing100081 China Tsinghua University State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Beijing100084 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China New York University Abu Dhabi Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates University of Science and Technology of China Anhui China

Object detection is a fundamental task in computer vision that involves accurately locating and classifying objects within images or video frames. In remote sensing, this task is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. GE-FSOD demonstrates superior robustness and accuracy in remote sensing FSOD tasks through these enhancements. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our

关键词： Labeled data

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Towards Efficient Verification of Constant-Time Cryptographic Implementations

Proceedings of the ACM on Software Engineering

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Proceedings of the ACM on software Engineering 2024年第FSE期1卷 1019-1042页

作者： Luwei Cai Fu Song Taolue Chen ShanghaiTech University Shanghai China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China / University of Chinese Academy of Sciences Beijing China / Nanjing Institute of Software Technology Nanjing China Birkbeck University of London London United Kingdom

Timing side-channel attacks exploit secret-dependent execution time to fully or partially recover secrets of cryptographic implementations, posing a severe threat to software security. Constant-time programming discipline is an effective software-based countermeasure against timing side-channel attacks, but developing constant-time implementations turns out to be challenging and error-prone. Current verification approaches/tools suffer from scalability and precision issues when applied to production software in practice. In this paper, we put forward practical verification approaches based on a novel synergy of taint analysis and safety verification of self-composed programs. Specifically, we first use an IFDS-based lightweight taint analysis to prove that a large number of potential (timing) side-channel sources do not actually leak secrets. We then resort to a precise taint analysis and a safety verification approach to determine whether the remaining potential side-channel sources can actually leak secrets. These include novel constructions of taint-directed semi-cross-product of the original program and its Boolean abstraction, and a taint-directed self-composition of the program. Our approach is implemented as a cross-platform and fully automated tool CT-Prover. The experiments confirm its efficiency and effectiveness in verifying real-world benchmarks from modern cryptographic and SSL/TLS libraries. In particular, CT-Prover identify new, confirmed vulnerabilities of open-source SSL libraries (e.g., Mbed SSL, BearSSL) and significantly outperforms the state-of-the-art tools.

关键词： Timing side-channel constant-time cryptographic implementation formal verification taint analysis

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FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants

arXiv

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arXiv 2023年

作者： Tan, Shanli Cheng, Hao Wu, Xiaohu Yu, Han He, Tiantian Ong, Yew-Soon Wang, Chongjun Tao, Xiaofeng National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications China State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Nanyang Technological University Singapore Agency for Science Technology and Research Singapore

Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models. Given the potential data heterogeneity, it is crucial to select appropriate collaborators for each FL participant (FL-PT) based on data complementarity. Recent studies have addressed this challenge. Similarly, it is imperative to consider the inter-individual relationships among FL-PTs where some FL-PTs engage in competition. Although FL literature has acknowledged the significance of this scenario, practical methods for establishing FL ecosystems remain largely unexplored. In this paper, we extend a principle from the balance theory, namely "the friend of my enemy is my enemy", to ensure the absence of conflicting interests within an FL ecosystem. The extended principle and the resulting problem are formulated via graph theory and integer linear programming. A polynomial-time algorithm is proposed to determine the collaborators of each FL-PT. The solution guarantees high scalability, allowing even competing FL-PTs to smoothly join the ecosystem without conflict of interest. The proposed framework jointly considers competition and data heterogeneity. Extensive experiments on real-world and synthetic data demonstrate its efficacy compared to five alternative approaches, and its ability to establish efficient collaboration networks among FL-PTs. Copyright © 2023, The Authors. All rights reserved.

关键词： Ecosystems

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Detecting PLC Intrusions Using Control Invariants

Detecting PLC Intrusions Using Control Invariants

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作者： Yang, Zeyu He, Liang Yu, Hua Zhao, Chengcheng Cheng, Peng Chen, Jiming Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China University of Colorado Department of Computer Science and Engineering DenverCO80204 United States

Programmable logic controllers (PLCs), i.e., the core of control systems, are well-known to be vulnerable to a variety of cyber attacks. To mitigate this issue, we design PLC-Sleuth, a novel noninvasive intrusion detection/localization system for PLCs, which is built on a set of control invariants - i.e., the correlations between sensor readings and the concomitantly triggered PLC commands - that exist pervasively in all control systems. Specifically, taking the system's supervisory control and data acquisition log as input, PLC-Sleuth abstracts/identifies the system's control invariants as a control graph using data-driven structure learning, and then monitors the weights of graph edges to detect anomalies thereof, which is in turn, a sign of intrusion. We have implemented and evaluated PLC-Sleuth using both a platform of ethanol distillation system (EDS) and a realistically simulated Tennessee Eastman (TE) process. The results show that PLC-Sleuth can: 1) identify control invariants with 100%/98.11% accuracy for EDS/TE;2) detect PLC intrusions with 98.33%/0.85 ‰ true/false positives (TPs/FPs) for EDS and 100%/0% TP/FP for TE;and 3) localize intrusions with 93.22%/96.76% accuracy for EDS/TE. © 2014 IEEE.

关键词： Ethanol

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UltraEdit: instruction-based fine-grained image editing at scale 24

UltraEdit: instruction-based fine-grained image editing at s...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haozhe Zhao Xiaojian Ma Liang Chen Shuzheng Si Rujie Wu Kaikai An Peiyu Yu Minjia Zhang Qing Li Baobao Chang National Key Laboratory for Multimedia Information Processing Peking University and School of Software and Microelectronics Peking University State Key Laboratory of General Artificial Intelligence BIGAI National Key Laboratory for Multimedia Information Processing Peking University and School of Computer Science Peking University China Department of Computer Science and Technology Tsinghua University School of Computer Science Peking University China UCLA UIUC National Key Laboratory for Multimedia Information Processing Peking University

ISBN: (纸本)9798331314385

This paper presents UltraEdit, a large-scale (~4M editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to address the drawbacks in existing image editing datasets like InstructPix2Pix [10] and MagicBrush [71], and provide a systematic approach to producing massive and high-quality image editing samples. UltraEdit offers several distinct advantages: 1) It features a broader range of editing instructions by leveraging the creativity of large language models (LLMs) alongside in-context editing examples from human raters; 2) Its data sources are based on real images, including photographs and artworks, which provide greater diversity and reduced bias compared to datasets solely generated by text-to-image models; 3) It also supports region-based editing, enhanced by high-quality, automatically produced region annotations. Our experiments show that canonical diffusion-based editing baselines trained on UltraEdit set new records on MagicBrush and Emu-Edit benchmarks. Our analysis further confirms the crucial role of real image anchors and region-based editing data. The dataset, code, and models are available in ***/pkunlp-icler/UltraEdit.

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GCMNet: Gated Cascade Multi-scale Network for Crowd Counting 18th

GCMNet: Gated Cascade Multi-scale Network for Crowd Counting

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Zheng, Jinfang Zhao, Panpan Xie, Jinyang Lyu, Chen Lyu, Lei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China

ISBN: (纸本)9783030893620

With the rapid development of convolutional neural networks, many CNN-based methods have emerged and made promising progress in the field of crowd counting. However, dealing with extremely scale variation remains a challenging but attractive issue. In this paper, we propose an innovative Gated Cascade Multi-scale Network (GCMNet) to tackle with the issue by taking full advantage of the representation of multi-scale features in a multi-level network. First of all, we implement such an idea by obtaining rich contextual information with a multi-scale contextual information enhancement module. Then, considering the pixel-level image detail information that is lost during the successive feature extraction process, we propose a hopping cascade module to refine this detail information. However, naively refining all the detail information is sub-optimal. Therefore, a gated information selection delivery module is designed to adaptively control the delivery of information between multi-level features. Combined with our proposed module, our method can effectively generate high-quality crowd density maps. The superiority of our method over current methods is demonstrated through extensive experiments on four challenging datasets. © 2021, Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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YOLOO: You Only Learn from Others Once

arXiv

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arXiv 2024年

作者： Gu, Lipeng Wei, Mingqiang Yan, Xuefeng Zhu, Dingkun Zhao, Wei Xie, Haoran Liu, Yong-Jin School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Shenzhen Institute of Research Nanjing University of Aeronautics and Astronautics Shenzhen China The School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China The School of Computer Science Jiangsu University of Technology Changzhou China The Division of Artificial Intelligence School of Data Science Lingnan University New Territories Hong Kong The MOE-Key Laboratory of Pervasive Computing Department of Computer Science and Technology Tsinghua University Beijing China

Multi-modal 3D multi-object tracking (MOT) typically necessitates extensive computational costs of deep neural networks (DNNs) to extract multi-modal representations. In this paper, we propose an intriguing question: May we learn from multiple modalities only during training to avoid multi-modal input in the inference phase? To answer it, we propose YOLOO, a novel multi-modal 3D MOT paradigm: You Only Learn from Others Once. YOLOO empowers the point cloud encoder to learn a unified tri-modal representation (UTR) from point clouds and other modalities, such as images and textual cues, all at once. Leveraging this UTR, YOLOO achieves efficient tracking solely using the point cloud encoder without compromising its performance, fundamentally obviating the need for computationally intensive DNNs. Specifically, YOLOO includes two core components: a unified tri-modal encoder (UTEnc) and a flexible geometric constraint (F-GC) module. UTEnc integrates a point cloud encoder with image and text encoders adapted from pretrained CLIP. It seamlessly fuses point cloud information with rich visual-textual knowledge from CLIP into the point cloud encoder, yielding highly discriminative UTRs that facilitate the association between trajectories and detections. Additionally, FGC filters out mismatched associations with similar representations but significant positional discrepancies. It further enhances the robustness of UTRs without requiring any scene-specific tuning, addressing a key limitation of customized geometric constraints (e.g., 3D IoU). Lastly, high-quality 3D trajectories are generated by a traditional data association component. By integrating these advancements into a multi-modal 3D MOT scheme, our YOLOO achieves substantial gains in both robustness and efficiency. Comprehensive experiments on KITTI and Waymo demonstrate the superior performance of our YOLOO over its twenty competitors. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Research on the Method of Emotion Contagion in Virtual Space Based on SIRS 15th

Research on the Method of Emotion Contagion in Virtual Space...

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15th CCF Conference on computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Liu, Heng Hong, Xiao Lu, Dianjie Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9789811625398

There is emotional contagion in virtual space, and the process of emotional contagion is also affected by emotional recurrence. However, the existing emotional contagion model does not consider the impact of individual emotional recurrence on emotional contagion in the virtual space. Therefore, the emotional contagion process cannot be simulated. The paper proposes a method of recurrent emotional contagion in virtual space. First, we construct an individual emotional recurrence contagion model based on SIRS (Susceptible-Infected-Recovered-Susceptible), we propose emotional recurrence contagion rules, and consider the impact of emotional recurrence in the virtual space on the emotional contagion process;secondly, we construct a virtual space emotional recurrence contagion model (REC) and solve it to simulate the process of emotional recurrence contagion. Third, we build a WS small-world network based on REC simulation. Provide REC (Recurrent emotional contagion model) simulation process in WS small-world network to verify the rationality of the REC. Finally, the REC is simulated to show the simulation effect of emotional recurrence contagion. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Small-world networks

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