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Density-aware and Depth-aware Visual Representation for Zero-Shot Object Counting

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Density-aware and Depth-aware Visual Representation for Zero...

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

作者： Fang Nan Feng Tian Ni Zhang Nian Liu Haonan Miao Guang Dai Mengmeng Wang Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Automation Northwestern Polytechnical University Xi’an China Computer Vision Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi UAE SGIT AI Lab State Grid Corporation of China Zhejiang University of Technology Hang Zhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Previous methods often utilize CLIP semantic classifiers with class names for zero-shot object counting. However, they ignore crucial density and depth knowledge for counting tasks. Thus, we propose a density-aware and depth-aware prompt counting model, which captures density information via learning density-aware prompts based on density-aware contrastive loss and incorporates depth guidance with predefined depth-aware prompts. To facilitate the training process, we design two strategies for standard counting loss and the contrastive loss, where the former prioritizes larger and sparser objects initially, gradually focusing on smaller and denser objects, and the latter adopts coarse-to-fine density learning. Besides, we construct a dataset named LVIS-372 with more real-world scenarios and balanced instance distribution compared to existing ones. Finally, the experimental results demonstrate the effectiveness of our proposed method.

关键词： Training Visualization Semantics Focusing Signal processing Acoustics Power capacitors Speech processing Standards

HTtext: A TextCNN-based pre-silicon detection for hardware Trojans

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HTtext: A TextCNN-based pre-silicon detection for hardware T...

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yi Xu Zhenyi Chen Binhong Huang Ximeng Liu Chen Dong College of Computer and Data Science Fuzhou University Fuzhou China University of South Floride Tampa USA Key Lab of Information Security of Network Systems (Fuzhou University) Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University)

With the advent of the intelligence era, the usage and investment for chips have risen year by year, causing the demand for chip security. Machine learning (ML) analysis has made progress as a pre-silicon hardware Trojan (HT) detection technology. Whereas the performance of existing methods almost relies on the accuracy of multi-feature representation, moreover, it is difficult to extract features manually and easily cause unstable classifier performance (namely uncertainty). In this paper, an automatic feature extraction detecting model is first proposed, named HTtext, which generates simple path sentences from chip netlists and employs TextCNN, a deep learning algorithm, to distinguish HT circuits. The pre-training for TextCNN only uses the automatic single-feature calculation to avoid the uncertainty problem. Additionally, the model can obtain non-repetitive HT component information expression, which satisfies the stable detection performance. To measure the efficiency and balance of the model, the paper proposes the concept of the Stability Efficiency Index (SEI). In the experimental results for the benchmark netlists, not only the average accuracy (ACC) in TextCNN is as high as 99.26%, but also its SEI value ranks first in all comparison classifiers, which proves that the proposed HTtext model has high stability in generality.

关键词： Ports (computers) Semiconductor device measurement Uncertainty Stability criteria Feature extraction Hardware Circuit stability

A Compositional Approach to Safety-Critical Resilient Control for Systems with Coupled Dynamics

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arXiv 2022年

作者： Al Maruf, Abdullah Niu, Luyao Clark, Andrew Mertoguno, J. Sukarno Poovendran, Radha The Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA98195-2500 United States The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Information and Cyber Sciences Georgia Tech Research Institute AtlantaGA30332 United States

Complex, interconnected Cyber-physical Systems (CPS) are increasingly common in applications including smart grids and transportation. Ensuring safety of interconnected systems whose dynamics are coupled is challenging because the effects of faults and attacks in one sub-system can propagate to other sub-systems and lead to safety violations. In this paper, we study the problem of safety-critical control for CPS with coupled dynamics when some sub-systems are subject to failure or attack. We first propose resilient-safety indices (RSIs) for the faulty or compromised sub-systems that bound the worstcase impacts of faulty or compromised sub-systems on a set of specified safety constraints. By incorporating the RSIs, we provide a sufficient condition for the synthesis of control policies in each failure- and attack- free sub-systems. The synthesized control policies compensate for the impacts of the faulty or compromised sub-systems to guarantee safety. We formulate sum-of-square optimization programs to compute the RSIs and the safety-ensuring control policies. We present a case study that applies our proposed approach on the temperature regulation of three coupled rooms. The case study demonstrates that control policies obtained using our algorithm guarantee system's safety constraints. © 2022, CC BY.

关键词： Safety engineering

A Dynamic Demand-driven Smart Manufacturing for Mass Individualization Production

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A Dynamic Demand-driven Smart Manufacturing for Mass Individ...

IEEE International Conference on Systems, Man and Cybernetics

作者： Qiyu Hong Zhenyi Chen Chen Dong Qiancheng Xiong College of Computer and Data Science Fuzhou University Fuzhou China Key Lab of Information Security of Network Systems Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University University of South Florida Tampa United States

ISBN: (纸本)9781665442084

Industry 4.0 intends to realize mass individualization production driven by customers’ demands, thus generating several advanced manufacturing modes. However, the existing modes have disadvantages such as low customer participation, low response for modifications in real-time, and low utilization of distributed idle productivity resources. The above deficiencies would not cater to the idea of Industry 4.0 well, making the cost of individualization products too high or the degree of individualization too low. In this paper, a customer highly involved dynamic demand-driven smart manufacturing mode and a mathematical model are presented for mass individualization production. Under this mode, customers can be prosumers, participating in the whole production process and proposing modifications. Meanwhile, multiple distributed idle productivity resources are employed with dynamic demand-driven manufacturing. In consequence of the mathematical model and algorithms, the multiple task allocation among multiple factories problem is solved, maximizing the total profit of available factories with high efficiency and low resource waste. Experiments and comparisons are made about the feasibility and efficiency of the algorithms.

关键词： Productivity Heuristic algorithms Dynamic scheduling Real-time systems Production facilities Mathematical models Fourth Industrial Revolution

Zero-pole cancellation for identity-based aggregators:a constant-size designated verifier-set signature

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Frontiers of computer Science 2020年第4期14卷 197-210页

作者： E CHEN Yan ZHU Changlu LIN Kewei LV School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China College of Mathematics and Informatics Fujian Normal UniversityFuzhou350117China Fujian Provincial Key Lab of Network Security&Cryptology Fujian Normal UniversityFuzhou350007China Institute of Information Engineering DCS Research CenterChinese Academy of SciencesBeijing100093China

In this paper we present a designated verifier-set signature(DVSS),in which the signer allows to designate many verifiers rather than one verifier,and each designated verifier can verify the validity of signature by *** research starts from identity-based aggregator(IBA)that compresses a designated set of verifier’s identities to a constant-size random string in cryptographic *** IBA is constructed by mapping the hash of verifier’s identity into zero or pole of a target curve,and extracting one curve’s point as the result of aggregation according to a specific *** the different types of target curves,these two IBAs are called as zeros-based aggregator and poles-based aggregator,*** on them,we propose a practical DVSS scheme constructed from the zero-pole cancellation method which can eliminate the same elements between zeros-based aggregator and poles-based *** to this design,our DVSS scheme has some distinct advantages:(1)the signature supporting arbitrary dynamic verifiers extracted from a large number of users;and(2)the signature with short and constant *** rigorously prove that our DVSS scheme satisfies the security properties:correctness,consistency,unforgeability and *** is a preview of subscription content,log in to check access.

关键词： designated verifier-set signature aggregator unforgeability exclusivity

AsyncSC: An Asynchronous Sidechain for Multi-Domain Data Exchange in Internet of Things

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arXiv 2024年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Gao, Sheng Tong, Wei Lu, Di Shen, Yulong Du, Xiaojiang The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'An710071 China The Zhejiang Lab Hangzhou311121 China The School of Information Central University of Finance and Economics Beijing100081 China The School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China The School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi'An710071 China The School of Engineering and Science Stevens Institute of Technology Hoboken07030 United States

Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) schemes for IoT multi-domain data exchange are constrained by the need for synchronous networks, hindering efficient cross-chain interactions in discontinuous networks and leading to suboptimal data exchange. In this paper, we propose AsyncSC, a novel asynchronous sidechain construction. It employs a committee to provide Cross-Blockchain as a Service (C-BaaS) for data exchange in multi-domain IoT. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We analyze and prove the security of AsyncSC, simulate an asynchronous communication environment, and conduct a comprehensive evaluation. The results show that AsyncSC outperforms SOTA schemes, improving throughput by an average of 1.21 to 3.96 times, reducing transaction latency by 59.76% to 83.61%, and maintaining comparable resource overhead. Copyright © 2024, The Authors. All rights reserved.

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Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning

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arXiv 2024年

作者： Sun, Dongwei Yao, Jing Zhou, Changsheng Cao, Xiangyong Ghamisi, Pedram School of Computer Science and Technology the Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics and Statistics Guangdong University of Technology Guangzhou China Helmholtz-Zentrum Dresden-Rossendorf Freiberg09599 Germany Lancaster Environment Centre Lancaster University LancasterLA1 4YR United Kingdom

Remote sensing image change description represents an innovative multimodal task within the realm of remote sensing processing. This task not only facilitates the detection of alterations in surface conditions, but also provides comprehensive descriptions of these changes, thereby improving human interpretability and interactivity. Generally, existing deep-learning-based methods predominantly utilized a three-stage framework that successively perform feature extraction, feature fusion, and localization from bitemporal images before text generation. However, this reliance often leads to an excessive focus on the design of specific network architectures and restricts the feature distributions to the dataset at hand, which in turn results in limited generalizability and robustness during application. To address these limitations, this paper proposes a novel approach for remote sensing image change detection and description that incorporates diffusion models, aiming to transition the emphasis of modeling paradigms from conventional feature learning to data distribution learning. The proposed method primarily includes a simple multi-scale change detection module, whose output features are subsequently refined by an well-designed diffusion model. Furthermore, we introduce a frequency-guided complex filter module to boost the model performance by managing high-frequency noise throughout the diffusion process. We validate the effectiveness of our proposed method across several datasets for remote sensing change detection and description, showcasing its superior performance compared to existing techniques. The code will be available at MaskApproxNet after a possible publication. Copyright © 2024, The Authors. All rights reserved.

关键词： Change detection

Disentangled Noisy Correspondence Learning

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arXiv 2024年

作者： Dang, Zhuohang Luo, Minnan Wang, Jihong Jia, Chengyou Han, Haochen Wan, Herun Dai, Guang Chang, Xiaojun Wang, Jingdong The School of Computer Science and Technology The Ministry of Education Key Laboratory of Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China SGIT AI Lab State Grid Shaanxi Electric Power Company Limited State Grid Corporation of China Shaanxi China The School of Information Science and Technology University of Science and Technology China United Arab Emirates The Baidu Inc China

Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impractical as real-world data inevitably involves imperfect alignments, i.e., noisy correspondences. Although some works explore similarity-based strategies to address such noise, they suffer from sub-optimal similarity predictions influenced by modality-exclusive information (MEI), e.g., background noise in images and abstract definitions in texts. This issue arises as MEI is not shared across modalities, thus aligning it in training can markedly mislead similarity predictions. Moreover, although intuitive, directly applying previous cross-modal disentanglement methods suffers from limited noise tolerance and disentanglement efficacy. Inspired by the robustness of information bottlenecks against noise, we introduce DisNCL, a novel information-theoretic framework for feature Disentanglement in Noisy Correspondence Learning, to adaptively balance the extraction of MII and MEI with certifiable optimal cross-modal disentanglement efficacy. DisNCL then enhances similarity predictions in modality-invariant subspace, thereby greatly boosting similarity-based alleviation strategy for noisy correspondences. Furthermore, DisNCL introduces soft matching targets to model noisy many-to-many relationships inherent in multi-modal input for noise-robust and accurate cross-modal alignment. Extensive experiments confirm DisNCL's efficacy by 2% average recall improvement. Mutual information estimation and visualization results show that DisNCL learns meaningful MII/MEI subspaces, validating our theoretical analyses. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

Enhancing Deep Paraphrase Identification via Leveraging Word Alignment information

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Enhancing Deep Paraphrase Identification via Leveraging Word...

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Boxin Li Tingwen Liu Bin Wang Lihong Wang Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Xiaomi AI Lab National Computer Network Emergency Response Technical Team Coordination Center of China

Recent deep learning based methods have achieved impressive performance on paraphrase identification (PI), a fundamental NLP task, judging whether two sentences are semantically equivalent or not. However, their success heavily relies on massive labeled samples, which are time-consuming and expensive to obtain. To alleviate this problem, this study explores the effect of word alignment information (WAI), extracted by existing monolingual alignment tools, on deep PI baseline models. Apart from directly encoding WAI into fixed-size embeddings, we propose a novel auxiliary task so that the baselines can be pre-trained using a large amount of unlabeled in-domain data. Moreover, our proposed auxiliary task can also jointly train with the baselines, aiming to eliminate the overheads of preprocessing WAI at the test period. Experimental results verify that our methods can significantly outperform the deep PI baseline model.

关键词： Deep learning Conferences Tools Speech enhancement Signal processing Encoding Acoustics