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作者： Xu, Xiaolong Chen, Yi Zhang, Xuyun Liu, Qingxiang Liu, Xihua Qi, Lianyong School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand School of Information Science and Engineering Qufu Normal University Qufu China

Edge computing (EC) emerges as a novel computing paradigm to offload computing tasks from user equipments (UEs) to edge notes (ENs) in fifth-generation networks, which definitely breaks the resource limitation of UEs to a certain degree. However, it is troublesome to guarantee the overall operating performance of ENs due to the uneven distributed resource demands of UEs, the resulting transmission delay and the data loss for computation offloading between the covered EN and the deployed destination EN. In view of this challenge, a blockchain-based computation offloading method, named BCO, is proposed in this paper. Technically, since blockchain is a promising technique for the decentralized system, a blockchain-based EC framework is designed to degrade the data loss possibility by integrating blockchain and EC. Then, the nondominated sorting genetic algorithm, the third version (NSGA-III), is leveraged to acquire the balanced offloading strategies. Furthermore, by taking advantage of Simple Additive Weighting and Multiple Criteria Decision Making, the optimal offloading strategy is identified. Finally, systematic experiments and analyses on the comparative experiment are conducted to verify the efficiency of our proposed method BCO. © 2019 John Wiley & Sons, Ltd.

关键词： Blockchain

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SigGuard: Hardening Vulnerable Signal Handling in Commodity Operating Systems

SigGuard: Hardening Vulnerable Signal Handling in Commodity ...

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Reliable Distributed Systems

作者： Miao Cai Junru Shen Tianning Zhang Hao Huang Baoliu Ye Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University School of Computer and Information Hohai University State Key Laboratory for Novel Software Technology Nanjing University

ISBN: (数字)9781665497534

ISBN: (纸本)9781665497541

Signal is a useful mechanism provided by many commodity operating systems. However, current signal handling has serious security concerns due to vulnerable design in missing integrity protections for signal handling control flow. Security weaknesses caused by vulnerable design are exploited by adversaries to mount dangerous control-flow attacks. To tackle these issues, this paper investigates root causes of signal-related attacks and proposes SigGuard to harden vulnerable signal handling mechanism. To protect unsafe signal handler execution flow, we design a customized signal handler CFI framework which supports low-cost, reentrant, online CFI analysis and enforcement. To secure signal handler return control flow, we propose an efficient, software-based, intra-process memory isolation method to ensure signal frame data integrity. We evaluate SigGuard with both security and performance experiments. In security experiments, SigGuard successfully thwarts four signal-based attacks, including two proof-of-concept exploits and two realistic attacks conducted in Nginx and Apache server programs, respectively. We also evaluate SigGuard key techniques with a series of microbenchmarks and real-world applications. Experimental results suggest that key defense techniques used in SigGuard introduce reasonable performance costs.

关键词： Costs Operating systems Data integrity Memory management Reliability engineering Security Servers

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Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy

arXiv

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arXiv 2024年

作者： Cai, Ruichu Huang, Siyang Qiao, Jie Chen, Wei Zeng, Yan Zhang, Keli Sun, Fuchun Yu, Yang Hao, Zhifeng School of Computer Science Guangdong University of Technology Guangzhou510006 China Guangzhou510555 China School of Mathematics and Statistics Beijing Technology and Business University Beijing102401 China Huawei Noah’s Ark Lab Shenzhen518116 China Department of Computer Science and Technology Tsinghua University Beijing100190 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China College of Science Shantou University Shantou515063 China

As a key component to intuitive cognition and reasoning solutions in human intelligence, causal knowledge provides great potential for reinforcement learning (RL) agents’ interpretability towards decision-making by helping reduce the searching space. However, there is still a considerable gap in discovering and incorporating causality into RL, which hinders the rapid development of causal RL. In this paper, we consider explicitly modeling the generation process of states with the causal graphical model, based on which we augment the policy. We formulate the causal structure updating into the RL interaction process with active intervention learning of the environment. To optimize the derived objective, we propose a framework with theoretical performance guarantees that alternates between two steps: using interventions for causal structure learning during exploration and using the learned causal structure for policy guidance during exploitation. Due to the lack of public benchmarks that allow direct intervention in the state space, we design the root cause localization task in our simulated fault alarm environment and then empirically show the effectiveness and robustness of the proposed method against state-of-the-art baselines. Theoretical analysis shows that our performance improvement attributes to the virtuous cycle of causal-guided policy learning and causal structure learning, which aligns with our experimental results. © 2024, CC BY.

关键词： Reinforcement learning

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Anomize: Better Open Vocabulary Video Anomaly Detection

arXiv

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arXiv 2025年

作者： Li, Fei Liu, Wenxuan Chen, Jingjing Zhang, Ruixu Wang, Yuran Zhong, Xian Wang, Zheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China State Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China

Open Vocabulary Video Anomaly Detection (OVVAD) seeks to detect and classify both base and novel anomalies. However, existing methods face two specific challenges related to novel anomalies. The first challenge is detection ambiguity, where the model struggles to assign accurate anomaly scores to unfamiliar anomalies. The second challenge is categorization confusion, where novel anomalies are often misclassified as visually similar base instances. To address these challenges, we explore supplementary information from multiple sources to mitigate detection ambiguity by leveraging multiple levels of visual data alongside matching textual information. Furthermore, we propose incorporating label relations to guide the encoding of new labels, thereby improving alignment between novel videos and their corresponding labels, which helps reduce categorization confusion. The resulting Anomize framework effectively tackles these issues, achieving superior performance on UCF-CRIME and XD-VIOLENCE datasets, demonstrating its effectiveness in OVVAD. Copyright © 2025, The Authors. All rights reserved.

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SGNet: Salient Geometric Network for Point Cloud Registration

SGNet: Salient Geometric Network for Point Cloud Registratio...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Qianliang Wu Yaqing Ding Lei Luo Haobo Jiang Shuo Gu Chuanwei Zhou Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Point Cloud Registration (PCR) is a critical and challenging task in computer vision and robotics. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. We introduce an anchor node selection strategy to optimize this high-order transformer further. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify critical correspondences for successful registration. The experiments conducted on the 3DMatch/3DLoMatch and KITTI datasets demonstrate the effectiveness of our method.

关键词： Point cloud compression computer vision Accuracy Shape Semantics Transformers Feature extraction Encoding Intelligent robots

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Adaptive Fake Audio Detection with Low-Rank Model Squeezing

Adaptive Fake Audio Detection with Low-Rank Model Squeezing

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2023 Workshop on Deepfake Audio Detection and Analysis, DADA 2023

作者： Zhang, Xiaohui Yi, Jiangyan Tao, Jianhua Wang, Chenglong Xu, Le Fu, Ruibo State Key Laboratory of Multimodal Artificial Intelligence System Institute of Automation Chinese Academy of Sciences China School of Computer and Information Technology Beijing Jiaotong University China University of Science and Technology of China China Department of Automation Tsinghua University China School of Artificial Intelligence University of Chinese Academy of Sciences China

The rapid advancement of spoofing algorithms necessitates the development of robust detection methods capable of accurately identifying emerging fake audio. Traditional approaches, such as finetuning on new datasets containing these novel spoofing algorithms, are computationally intensive and pose a risk of impairing the acquired knowledge of known fake audio types. To address these challenges, this paper proposes an innovative approach that mitigates the limitations associated with finetuning. We introduce the concept of training low-rank adaptation matrices tailored specifically to the newly emerging fake audio types. During the inference stage, these adaptation matrices are combined with the existing model to generate the final prediction output. Extensive experimentation is conducted to evaluate the efficacy of the proposed method. The results demonstrate that our approach effectively preserves the prediction accuracy of the existing model for known fake audio types. Furthermore, our approach offers several advantages, including reduced storage memory requirements and lower equal error rates compared to conventional finetuning methods, particularly on specific spoofing algorithms. © 2023 CEUR-WS. All rights reserved.

关键词： Fake detection

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Hallu-PI: Evaluating Hallucination in Multi-modal Large Language Models within Perturbed Inputs

arXiv

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arXiv 2024年

作者： Ding, Peng Wu, Jingyu Kuang, Jun Ma, Dan Cao, Xuezhi Cai, Xunliang Chen, Shi Chen, Jiajun Huang, Shujian National Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Computer Science and Technology Zhejiang University Hangzhou China Meituan Shanghai China Meituan Beijing China Zhejiang-Singapore Innovation and AI Joint Research Lab Zhejiang University Hangzhou China

Multi-modal Large Language Models (MLLMs) have demonstrated remarkable performance on various visual-language understanding and generation tasks. However, MLLMs occasionally generate content inconsistent with the given images, which is known as "hallucination". Prior works primarily center on evaluating hallucination using standard, unperturbed benchmarks, which overlook the prevalent occurrence of perturbed inputs in real-world scenarios-such as image cropping or blurring-that are critical for a comprehensive assessment of MLLMs' hallucination. In this paper, to bridge this gap, we propose Hallu-PI, the first benchmark designed to evaluate Hallucination in MLLMs within Perturbed Inputs. Specifically, Hallu-PI consists of seven perturbed scenarios, containing 1,260 perturbed images from 11 object types. Each image is accompanied by detailed annotations, which include fine-grained hallucination types, such as existence, attribute, and relation. We equip these annotations with a rich set of questions, making Hallu-PI suitable for both discriminative and generative tasks. Extensive experiments on 12 mainstream MLLMs, such as GPT-4V and Gemini-Pro Vision, demonstrate that these models exhibit significant hallucinations on Hallu-PI, which is not observed in unperturbed scenarios. Furthermore, our research reveals a severe bias in MLLMs' ability to handle different types of hallucinations. We also design two baselines specifically for perturbed scenarios, namely Perturbed-Reminder and Perturbed-ICL. We hope that our study will bring researchers' attention to the limitations of MLLMs when dealing with perturbed inputs, and spur further investigations to address this issue. Our code and datasets are publicly available at https://***/NJUNLP/Hallu-PI. © 2024, CC BY.

关键词： Visual languages

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Training Verification-Friendly Neural Networks via Neuron Behavior Consistency 39

Training Verification-Friendly Neural Networks via Neuron Be...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Zongxin Zhao, Zhe Song, Fu Sun, Jun Yang, Pengfei Huang, Xiaowei Zhang, Lijun Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China RealAI Beijing China Nanjing Institute of Software Technology Nanjing China Singapore Management University Singapore College of Computer and Information Science Software College Southwest University Chongqing China The University of Liverpool Liverpool United Kingdom

ISBN: (纸本)157735897X

Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel method for training verification-friendly neural networks, which are robust, easy to verify, and relatively accurate. Our method integrates neuron behavior consistency into the training process, making neuron activation states remain consistent across different inputs within a local neighborhood. This reduces the number of unstable neurons and tightens the bounds of neurons thereby enhancing the network’s verifiability. We evaluated our method using the MNIST, Fashion-MNIST, and CIFAR-10 datasets with various network architectures. The experimental results demonstrate that networks trained using our method are verification-friendly across different radii and architectures, whereas other tools fail to maintain verifiability as the radius increases. Additionally, we show that our method can be combined with existing approaches to further improve the verifiability of networks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Band Prompting Aided SAR and Multi-Spectral Data Fusion Framework for Local Climate Zone Classification

Band Prompting Aided SAR and Multi-Spectral Data Fusion Fram...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haiyan Lan Shujun Li Mingjie Xie Xuanjia Zhao Hongning Liu Pengming Feng Dongli Xu Guangjun He Jian Guan College of Computer Science and Technology Harbin Engineering University Harbin China School of Astronautics Beihang University Beijing China School of Software Dalian University of Technology Dalian China State Key Laboratory of Space-Ground Integrated Information Technology Beijing China Independent Researcher

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Local climate zone (LCZ) classification is of great value for understanding the complex interactions between urban development and local climate. Recent studies have increasingly focused on the fusion of synthetic aperture radar (SAR) and multi-spectral data to improve LCZ classification performance. However, it remains challenging due to the distinct physical properties of these two types of data and the absence of effective fusion guidance. In this paper, a novel band prompting aided data fusion framework is proposed for LCZ classification, namely BP-LCZ, which utilizes textual prompts associated with band groups to guide the model in learning the physical attributes of different bands and semantics of various categories inherent in SAR and multi-spectral data to augment the fused feature, thus enhancing LCZ classification performance. Specifically, a band group prompting (BGP) strategy is introduced to align the visual representation effectively at the level of band groups, which also facilitates a more adequate extraction of semantic information of different bands with textual information. In addition, a multivariate supervised matrix (MSM) based training strategy is proposed to alleviate the problem of positive and negative sample confusion by completing the supervised information. The experimental results demonstrate the effectiveness and superiority of the proposed data fusion framework.

关键词： Training Visualization Semantics Data integration Signal processing Robustness Data mining Speech processing Synthetic aperture radar Meteorology

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Online Neural Fiber Classification Method based on Lightweight Neural Network 7

Online Neural Fiber Classification Method based on Lightweig...

引用

7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Xu, Baolei Li, Xiaojie Shi, Canghong Sichuan University of Arts and Sciences Key Laboratory of Exploitation and Study of Distinctive Plants in Education Department of Sichuan Province Dazhou China Chengdu University of Information Technology School of Computer Science Chengdu China Xihua University School of Computer Science and Software Engineering Chengdu China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Traditional neural fiber classification methods are generally difficult to balance classification accuracy and algorithm complexity, and there is a problem of relying on manual intervention, mainly including manual drawing of region of interest (ROI) and manual screening of target fiber bundles, resulting in uncertainty of classification results and large data error. To reduce manual intervention, balance accuracy and algorithm complexity, a simple and effective BP neural network model combined with fast image processing algorithm is proposed. Based on the neural fiber map information, this method uses the fiber coordinate information to train the classification network structure and realize the online fiber classification. While improving the classification accuracy, it also reduces the complexity of the algorithm and does not need to know the distribution of fibers in advance. Compared with several typical classification algorithms, BP neural network model has strong robustness and can effectively prevent over-fitting. The experimental results show that the method performs well in several classifiers, and the classification accuracy is optimal. The experimental training accuracy is 0.9824, and the test accuracy is 0.9774. © 2022 IEEE.

关键词： Neural networks

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