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arXiv 2024年

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Jinan250353 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices

arXiv

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arXiv 2024年

作者： Ding, Chuntao Cao, Xu Xie, Jianhang Fan, Linlin Wang, Shangguang Lu, Zhichao School of Artificial Intelligence Beijing Normal University Beijing China Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education with School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Computer and Information Engineering Henan Normal University Henan453007 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computer Science City University of Hong Kong HKSAR Hong Kong

Efficient fine-tuning of pre-trained convolutional neural network (CNN) models using local data is essential for providing high-quality services to users using ubiquitous and resource-limited Internet of Things (IoT) devices. Low-Rank Adaptation (LoRA) fine-tuning has attracted widespread attention from industry and academia because it is simple, efficient, and does not incur any additional reasoning burden. However, most of the existing advanced methods use LoRA to fine-tune Transformer, and there are few studies on using LoRA to fine-tune CNN. The CNN model is widely deployed on IoT devices for application due to its advantages in comprehensive resource occupancy and performance. Moreover, IoT devices are widely deployed outdoors and usually process data affected by the environment (such as fog, snow, rain, etc.). The goal of this paper is to use LoRA technology to efficiently improve the robustness of the CNN model. To this end, this paper first proposes a strong, robust CNN fine-tuning method for IoT devices, LoRA-C, which performs low-rank decomposition in convolutional layers rather than kernel units to reduce the number of fine-tuning parameters. Then, this paper analyzes two different rank settings in detail and observes that the best performance is usually achieved when α/r is a constant in either standard data or corrupted data. This discovery provides experience for the widespread application of LoRA-C. Finally, this paper conducts many experiments based on pre-trained models. Experimental results on CIFAR-10, CIFAR-100, CIFAR-10-C, and Icons50 datasets show that the proposed LoRA-Cs outperforms standard ResNets. Specifically, on the CIFAR-10-C dataset, the accuracy of LoRA-C-ResNet-101 achieves 83.44% accuracy, surpassing the standard ResNet-101 result by +9.5%. On the Icons-50 dataset, the accuracy of LoRA-C-ResNet-34 achieves 96.9% accuracy, surpassing the standard ResNet-34 result by +8.48%. In addition, compared with full parameter fine-tuning, LoRA-

关键词： Quality of service

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Decentralized accelerated proximal gradient descent 34

Decentralized accelerated proximal gradient descent

引用

34th Conference on Neural Information Processing Systems, NeurIPS 2020

作者： Ye, Haishan Zhou, Ziang Luo, Luo Zhang, Tong Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Mathematics The Hong Kong University of Science and Technology Hong Kong

Decentralized optimization has wide applications in machine learning, signal processing, and control. In this paper, we study the decentralized composite optimization problem with a non-smooth regularization term. Many proximal gradient based decentralized algorithms have been proposed in the past. However, these algorithms do not achieve near optimal computational complexity and communication complexity. In this paper, we propose a new method which establishes the optimal computational complexity and a near optimal communication complexity. Our empirical study shows that the proposed algorithm outperforms existing state-of-the-art algorithms. © 2020 Neural information processing systems foundation. All rights reserved.

关键词： Signal processing

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Simulation study on consequence of leakage accident of liquid chlorine storage tank 2

Simulation study on consequence of leakage accident of liqui...

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2nd International Conference on Artificial Intelligence and Advanced Manufacture, AIAM 2020

作者： Fan, Xiaohua Ding, Yunchao Tang, Jiangtao Chongqing University of Science and Technology School of Safety Engineering Chongqing China Chongqing City Management College School of Big Data and Information Industry Chongqing China

ISBN: (纸本)9781728199863

In this paper, Aloha accident consequence simulation software was used to simulate the accident consequence of liquid chlorine tank leakage. The influence of wind speed, leakage aperture, ambient temperature and humidity on the hazard range of liquid chlorine leakage was discussed. The results showed that when the temperature increases, the cloud coverage decreases, the area of leakage hole increases, the height of leakage hole decreases and the roughness of diffusion terrain decreases, the dangerous diffusion area of liquid chlorine leakage will increase, otherwise it will decrease. When the wind speed is 1-3m / s, the dangerous concentration area of liquid chlorine leakage increases with the increase of wind speed, while when the wind speed is 3-10m / s, the dangerous concentration area of liquid chlorine leakage decreases with the increase of wind speed. © 2020 IEEE.

关键词： Computer software

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scMonica: Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis

scMonica: Single-cell Mosaic Omics Nonlinear Integration and...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoli Li Rui Zhang Saba Aslam Huijun Li Yuxi Chen Zequn Zhang Ruey-Song Huang Hongyan Wu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Centre for Cognitive and Brain Sciences University of Macau Macau SAR China Faculty of Science and Technology University of Macau Macau SAR China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China Faculty of Health Sciences University of Macau Macau SAR China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Single-cell mosaic integration has revolutionized our understanding of cellular heterogeneity, offering unprecedented resolution into cellular states and contexts. While large language models (LLMs) have achieved success in the analysis of single-cell omics data, their application specifically focused on the integration of mosaic data remains limited. Current computational approaches often fall short in addressing these challenges. They frequently depend on assumptions of data completeness and uniform quality, failing to manage the variability and noise effectively introduced by missing data. Moreover, these models might not efficiently process large, diverse datasets or account for biological heterogeneity and long-ranging dependencies across various data types. To address these shortcomings, we introduce the Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis (scMonica) framework, which employs a LSTM-transformer hybrid architecture. This innovative model combines the strengths of Long Short-Term Memory (LSTM) networks, which excel at capturing long-range dependencies within sequential gene expression patterns, with transformers, renowned for their attention mechanisms that handle the complex, non-linear interactions characteristic of multi-layered datasets. By leveraging these complementary strengths, our approach enhances the integration process significantly, allowing for nuanced management of the intrinsic heterogeneity and sparsity of mosaic datasets. Comprehensive evaluations demonstrate the robustness and effectiveness of our approach, offering unparalleled versatility and accuracy in multi-omics data analysis. These advancements underscore scMonica’s potential to drive significant insights in single-cell developmental biology, oncology, and beyond. We discuss the underlying technologies, analyze their applications, and contemplate future directions that promise to extend the boundaries of both research and clinical domains.

关键词： Biological system modeling Microprocessors Large language models Noise Computer architecture Transformers Oncology Robustness Gene expression Long short term memory

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Distributed Power Dispatching Solution for A Future Economic and Environment-friendly Energy Internet 2020

Distributed Power Dispatching Solution for A Future Economic...

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9th International Conference on Informatics, Environment, Energy and Applications, IEEA 2020

作者： Li, Yang Qin, Zhaoming Zhang, Fan Qin, Yuchao Hua, Haochen Cao, Junwei Big Data Center of State Grid Corporation of China Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781450376891

In recent years, the research of power dispatch has been popular in the field of energy Internet (EI) system operation and management. In this paper, a distributed algorithm considering multiple objectives is applied to solve power dispatching problem within the scenario of EI. The power dispatching problem is formulated as a convex optimization problem, and the distributed subgradient-push strategy is adopted in each agent. In this manner, the decentralized scheme can be more scalable for the power dispatching tasks in large EI systems. Case study is conducted to evaluate the feasibility of the proposed method. © 2020 ACM.

关键词： Convex optimization

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A new method for measuring the similarity of vague sets and its application in fault diagnosis 6th

A new method for measuring the similarity of vague sets and ...

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Liu, Yongzhi Pi, Dechang Alibaba Big Data School of Fuzhou Polytechnic Fuzhou350108 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9789811581007

A new similarity measurement method of vague value is proposed which compare with the existing similarity measurement methods. The proposed method has good distinguishing degree and less computation steps and times which possess the basic properties and satisfying similarity measurement. By the data analysis of data membership degree investigate the pattern recognition to achieve the measuring. Moreover, comparison with the results of both methods has proved that proposed method is a more reasonable way to measure the similarity of Vague value, Finally, the method is proved to be effective in fault diagnosis. © Springer Nature Singapore Pte Ltd 2020.

关键词： Failure analysis

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Empirical Research on Cluster Analysis of Spectral Information of Hyperspectral Remote Sensing data 13

Empirical Research on Cluster Analysis of Spectral Informati...

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2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International Conference on Smart data, Smartdata 2020

作者： Dong, Yueyu Dai, Fei Xu, Xiaolong Qin, Mingming Qiang, Zhenping School of Big Data and Intellegent Engineering Southwest Forestry University Kunming China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9781728176475

The most notable features of hyperspectral remote sensing data are high spectral resolution and high dimensionality. Analyzing the spectral information of the data with clustering requires proper spectral measurements. And clustering results validating methods satisfy high-dimensional data are required. In this paper, three clustering algorithms of K-means, K-means++, and Chameleon were used in the empirical research, based on the famous data set of IndianPine. In the experiments, four spectral measures of SBD, SID, SSD and SPM were used as spectral similarity measures, and S-Dbw was chosen as clustering validating method. As S-Dbw is a relative validating index, the algorithms with different parameter combinations had been experimented many times, verified the effectiveness of the solution composed with selected clustering methods, spectral measures, and clustering validating method. Experimental results showed that hyperspectral information enhanced the ability to distinguish ground objects;Chameleon's performance was best, and S-Dbw could effectively analyze hyperspectral data;the performance of the spectral measurements showed a certain correlation with the clustering method. © 2020 IEEE.

关键词： Cluster analysis

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A Hybrid MTS Anomaly Detection Method Based on Reconstruction and Adaptive Spatial-Temporal Graph Network

A Hybrid MTS Anomaly Detection Method Based on Reconstructio...

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data Driven Control and Learning Systems (DDCLS)

作者： X. Yuan Ke Ning K. Wei Yan-Lin He Qun-Xiong Zhu Ming-Qing Zhang Yi Luo Yang Zhang College of Information Science and Technology Beijing University of Chemical Technology Beijing P. R. China Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing P. R. China Faculty of Applied Sciences Macao Polytechnic University Macao SAR P. R. China Research Institute of Mine Big Data Chinese Institute of Coal Science Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Effective anomaly detection of multivariate time series data is crucial for modern industrial applications. However, existing works usually ignore spatial and temporal dependencies of series. The data with complicated spatial-temporal feature put forward higher requirements for accurate detection of anomalies. In this paper, we propose an anomaly detection method for multivariate time series based on spatial-temporal network (STN). Firstly, the time series are reconstructed by Generative Adversarial Networks (GAN). The reconstruction block is used to amplify the influence of the abnormal state and prompt the anomalies more detectable, as poorly reconstructed data of abnormal state further worsen the quality of the final predicted value. Secondly, an adaptive learning graph structure layer is proposed. The graph structure is learned by adaptive method without any prior knowledge and is used for reconstructed data prediction. Thirdly, we use STN to extract spatial and temporal dependencies of series. Especially, attention mechanism is introduced into graph convolution network to enhance the prediction performance by focusing on more important neighbor node message. Finally, our model is tested on 3 public datasets to evaluate performance. The results demonstrate that our method performs better than baselines.

关键词： Learning systems Attention mechanisms Convolution Time series analysis Neural networks Focusing Generative adversarial networks

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

arXiv

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arXiv 2024年

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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