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Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

Computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

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Class-Dynamic and Hierarchy-Constrained Network for Entity Linking 28th

Class-Dynamic and Hierarchy-Constrained Network for Entity...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Kehang Liu, Qi Zhang, Kai Liu, Ye Tao, Hanqing Huang, Zhenya Chen, Enhong Auhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9783031306716

Entity Linking (EL) aims to map mentions in a text to corresponding entities in a knowledge base. Existing EL methods usually rely on sufficient labeled data to achieve the best performance. However, the massive investment in data makes EL systems viable only to a limited audience. There is ample evidence that introducing entity types can provide the model prior knowledge to maintain the model performance in low-data regimes. Unfortunately, current low-data EL methods usually employ entity types by rule constraints, which are in a shallow manner. Furthermore, they usually ignore fine-grained interaction between mention and its context, resulting in insufficient semantic information of mention representation in low-data regimes. To this end, we propose a Class-Dynamic and Hierarchy-Constrained Network (CDHCN) for entity linking. Specifically, we propose a dynamic class scheme to learn a more effective representation for each entity type. Besides, we formulate a hierarchical constraint scheme to reduce the matching difficulty of the given mention and corresponding candidate entities by utilizing entity types. In addition, we propose an auxiliary task called mention position prediction (MPP) to obtain an informative mention representation in low-data regimes. Finally, extensive in-domain and out-of-domain experiments demonstrate the effectiveness of our method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge based systems

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Learning Subpocket Prototypes for Generalizable Structure-based Drug Design

arXiv

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arXiv 2023年

作者： Zhang, Zaixi Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China

Generating molecules with high binding affinities to target proteins (a.k.a. structure-based drug design) is a fundamental and challenging task in drug discovery. Recently, deep generative models have achieved remarkable success in generating 3D molecules conditioned on the protein pocket. However, most existing methods consider molecular generation for protein pockets independently while neglecting the underlying connections such as subpocket-level similarities. Subpockets are the local protein environments of lig- and fragments and pockets with similar subpockets may bind the same molecular fragment (motif) even though their overall structures are different. Therefore, the trained models can hardly generalize to unseen protein pockets in real-world applications. In this paper, we propose a novel method DrugGPS for generalizable structure-based drug design. With the biochemical priors, we propose to learn subpocket prototypes and construct a global interaction graph to model the interactions between subpocket prototypes and molecular motifs. Moreover, a hierarchical graph transformer encoder and motif-based 3D molecule generation scheme are used to improve the model’s performance. The experimental results show that our model consistently outperforms baselines in generating realistic drug candidates with high affinities in challenging out-of-distribution settings. © 2023, CC BY-NC-SA.

关键词： Proteins

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Cooperative Classification and Rationalization for Graph Generalization

arXiv

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arXiv 2024年

作者： Yue, Linan Liu, Qi Liu, Ye Gao, Weibo Yao, Fangzhou Li, Wenfeng Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Douyin Vision Co. Ltd Beijing China

Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) data. Several approaches have been proposed to address this problem. Among them, one solution is to diversify training distributions in vanilla classification by modifying the data environment, yet accessing the environment information is complex. Besides, another promising approach involves rationalization, extracting invariant rationales for predictions. However, extracting rationales is difficult due to limited learning signals, resulting in less accurate rationales and diminished predictions. To address these challenges, in this paper, we propose a Cooperative Classification and Rationalization (C2R) method, consisting of the classification and the rationalization module. Specifically, we first assume that multiple environments are available in the classification module. Then, we introduce diverse training distributions using an environment-conditional generative network, enabling robust graph representations. Meanwhile, the rationalization module employs a separator to identify relevant rationale subgraphs while the remaining non-rationale subgraphs are de-correlated with labels. Next, we align graph representations from the classification module with rationale subgraph representations using the knowledge distillation methods, enhancing the learning signal for rationales. Finally, we infer multiple environments by gathering non-rationale representations and incorporate them into the classification module for cooperative learning. Extensive experimental results on both benchmarks and synthetic datasets demonstrate the effectiveness of C2R. Code is available at https://***/yuelinan/Codes-of-C2R. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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AdaptSSR: pre-training user model with augmentation-adaptive self-supervised ranking 23

AdaptSSR: pre-training user model with augmentation-adaptive...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yang Yu Qi Liu Kai Zhang Yuren Zhang Chao Song Min Hou Yuqing Yuan Zhihao Ye Zaixi Zhang Sanshi Lei Yu Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence OPPO Research Institute Hefei University of Technology

User modeling, which aims to capture users' characteristics or interests, heavily relies on task-specific labeled data and suffers from the data sparsity issue. Several recent studies tackled this problem by pre-training the user model on massive user behavior sequences with a contrastive learning task. Generally, these methods assume different views of the same behavior sequence constructed via data augmentation are semantically consistent, i.e., reflecting similar characteristics or interests of the user, and thus maximizing their agreement in the feature space. However, due to the diverse interests and heavy noise in user behaviors, existing augmentation methods tend to lose certain characteristics of the user or introduce noisy behaviors. Thus, forcing the user model to directly maximize the similarity between the augmented views may result in a negative transfer. To this end, we propose to replace the contrastive learning task with a new pretext task: Augmentation-Adaptive Self-Supervised Ranking (AdaptSSR), which alleviates the requirement of semantic consistency between the augmented views while pre-training a discriminative user model. Specifically, we adopt a multiple pairwise ranking loss which trains the user model to capture the similarity orders between the implicitly augmented view, the explicitly augmented view, and views from other users. We further employ an in-batch hard negative sampling strategy to facilitate model training. Moreover, considering the distinct impacts of data augmentation on different behavior sequences, we design an augmentation-adaptive fusion mechanism to automatically adjust the similarity order constraint applied to each sample based on the estimated similarity between the augmented views. Extensive experiments on both public and industrial datasets with six downstream tasks verify the effectiveness of AdaptSSR.

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PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PARTIAL OBSERVATIONS 11

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PART...

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11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Interference Characterization and Mitigation for Multi-Beam ISAC Systems in Vehicular Networks

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IEEE Transactions on Wireless Communications 2024年第10期23卷 14729-14742页

作者： Wang, Yi Zhang, Qixun Zhang, J. Andrew Wei, Zhiqing Feng, Zhiyong Peng, Jinlin The School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Electronic Science and Technology of China Quzhou324003 China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China The Global Big Data Technologies Centre University of Technology Sydney SydneyNSW2007 Australia The Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing100071 China

— Millimeter-wave Integrated Sensing and Communications (ISAC) with multi-beam design holds significant promise for vehicular networks, offering multi-target omnidirectional sensing and high-capacity communication services concurrently. Nonetheless, the considerable challenge of potential mutual interference arises due to the high mobility and density of transmitters in such networks. To address this challenge effectively, we propose leveraging inter-vehicle communication to schedule communication and sensing signals for vehicles, thereby enhancing networked sensing capabilities. We first introduce an analytical framework to characterize the mutual interference among multiple vehicles. Subsequently, we evaluate the effectiveness of our proposed interference mitigation method in terms of interference probability, duration, and the achievable detectable density. Additionally, recognizing the different performance requirements of communication and sensing functions, we investigate a joint resource allocation problem catering to both aspects. Simulation results demonstrate a notable enhancement in the proposed ISAC-based interference mitigation, with a 58% reduction in interference probability compared to benchmarking schemes. © 2002-2012 IEEE.

关键词： Resource allocation

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Screening of Key Genes in Retinoblastoma and Construction of ceRNA Regulatory Network 1

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2nd International Symposium on Biomedical and Computational Biology, BECB 2022

作者： Guo, Jiaxin Liu, Yize Li, Fu Qin, Rong Zhang, Langlang Gao, Chao He, Xiaohong Chongqing Key Laboratory on Big Data for Bio Intelligence College of Bioinformation Chongqing University of Posts and Telecommunications Chongqing400065 China School of Medicine University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (数字)9783031251917

ISBN: (纸本)9783031251900

Retinoblastoma (RB) is an intraocular malignancy with a high incidence and very severe symptoms in children and is a rare life-threatening ophthalmic disease. Screening for key genes in retinoblastoma to identify the main molecular mechanisms of RB pathogenesis. The high-throughput sequencing data GSE111168, gene expression microarray data sets GSE24673 and GSE41321 were obtained from the GEO database. Differential genes were screened using the "limma" package, the threshold was set "padj © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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Label Inference Attack Based on Soft Label Towards Two-Party Split Learning 14

Label Inference Attack Based on Soft Label Towards Two-Party...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Zhou, Chenchen Cao, Hongbo Zhao, Yingying Zhao, Sicong Sun, Yongqi Ministry of Education Key Laboratory of Big Data & Artificial Intelligence in Transportation China Beijing Jiaotong University School of Computer and Lnformation Technology Beijing100044 China Beijing Aerocim Technology Co. Ltd Beijing102308 China

ISBN: (纸本)9798350371024

In Vertical Federated Learning, Split Learning has gained popularity as a technique where a neural network is split into two parts held by two parties. One party holds the input data, and another party holds the corresponding labels. The two parties exchange intermediate features and gradients of features to update the network parameters to preserve the privacy of the private data and network parameters. However, recent studies just utilized a small amount of auxiliary data to infer private labels. In the paper, we propose a label inference attack method based on soft label by leveraging the auxiliary data. To further enhance the performance of the attack method, we design a loss function that combines a gradient-matching loss and a model loss based on soft label. Our experimental evaluation is conducted on classification datasets, and the results demonstrate that our model has superior attack effectiveness and generalization capabilities. © 2023 IEEE.

关键词： Classification (of information)

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Attention-Based Residual Dense Shrinkage Network for ECG Denoising

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial Intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control Center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

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