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arXiv 2022年

作者： Wang, Qiuli Tan, Xin Liu, Chen School of Big Data and Software Engineering Chongqing University China Department of Computer Science and Engineering Shanghai Jiao Tong University China China

Since the pandemic of COVID-19, several deep learning methods were proposed to analyze the chest Computed Tomography (CT) for diagnosis. In the current situation, the disease course classification is significant for medical personnel to decide the treatment. Most previous deep-learning-based methods extract features observed from the lung window. However, it has been proved that some appearances related to diagnosis can be observed better from the mediastinal window rather than the lung window, e.g., the pulmonary consolidation happens more in severe symptoms. In this paper, we propose a novel Dual Window RCNN Network (DWRNet), which mainly learns the distinctive features from the successive mediastinal window. Regarding the features extracted from the lung window, we introduce the Lung Window Attention Block (LWA Block) to pay additional attention to them for enhancing the mediastinal-window features. Moreover, instead of picking up specific slices from the whole CT slices, we use a Recurrent CNN and analyze successive slices as videos. Experimental results show that the fused and representative features improve the predictions of disease course by reaching the accuracy of 90.57%, against the baseline with an accuracy of 84.86%. Ablation studies demonstrate that combined dual window features are more efficient than lung-window features alone, while paying attention to lung-window features can improve the model’s stability. © 2022, CC BY.

关键词： COVID-19

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PSO-ACSC:a large-scale evolutionary algorithm for image matting

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Frontiers of computer science 2020年第6期14卷 31-42页

作者： Yihui LIANG Han HUANG Zhaoquan CAI School of Computer Science University of Electronic Science and Technology of ChinaZhongshan InstituteZhongshan 528400China School of Software Engineering South China University of TechnologyGuangzhou 510006China Research Department Huizhou UniversityHuizhou 516007China

Image matting is an essential image processing technology due to its wide range of ***-based image matting is one of the main branches of image matting research that estimates alpha mattes by selecting the best pixel *** is essentially a large-scale multi-peak optimization problem of pixel *** study shows that particle swarm optimization(PSO)can effectively optimize the pixel ***,it still suffers from premature convergence problem which often occurs in pixel pair optimization that involves a large number of local *** address this problem,this work presents a parameter-free strategy for PSO called adaptive convergence speed controller(ACSC).ACSC monitors and conditionally controls the particles by competitive pixel pair recombination operator(CPPRO)and pixel pair reset operator(PPRO)during the *** performs CPPRO to improve the competitiveness of a particle when the performance of most of the pixel pairs is worse than that of the best-so-far *** is performed to avoid premature convergence when the alpha mattes regarding two selecleu particles ae liglly *** results show that ACSC significantly enhances the performance of PSO for image matting and provides competitive alpha mattes comparing with state-of-the-art evolutionary algorithms.

关键词： evolutionary computing particle swarm optimization large-scale optimization image matting

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Bc-Sse: A Blockchain-Aided, Secure and Practical Multi-Keyword Ranked Searchable Encryption Scheme

SSRN

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SSRN 2023年

作者： Zheng, Lixiang Zhang, Hanlin Lin, Jie Ge, Xinrui Kong, Fanyu College of Computer Science and Technology Qingdao University Qingdao266071 China School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China School of Software Shandong University Jinan250100 China

Searchable encryption facilitates the retrieval of encrypted data by cloud servers, ensuring both user privacy and data usability. While several techniques exist that integrate blockchain with searchable encryption to counteract potential data alteration by malicious servers and to affirm the integrity of search results, a significant limitation persists: many of these techniques store user data directly on the blockchain. This approach escalates storage costs and results in increased gas consumption. In this paper, we present BC-SSE: a blockchainaugmented, efficient multi-keyword ranked searchable encryption system. This scheme vectorizes index data and query data with the vector space model and the TF-IDF model. Besides, it calculates the correlation score between documents to enable ranking functionality. Moreover, this scheme adopts the balanced binary tree structure to build an index and enhance retrieval efficiency. To avoid exceeding GasLimit, we devised a "chunk T transaction" method and employed the InterPlanetary File System (IPFS) to store ciphertext data and avoid failure risks while ensuring data storage availability. A comprehensive theoretical analysis of BC-SSE is provided, and we further validate our system’s efficacy through a series of experiments on real-world datasets, demonstrating the robustness and practicality of the BC-SSE framework. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Smooth Representation Learning of Infrared and Visible Images

SSRN

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SSRN 2022年

作者： Fang, Aiqing Wu, Junsheng Ying, Li School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China School of Software Northwestern Polytechnical University Xi’an710072 China

Deep learning-based methods generally adopt an unsupervised similarity fusion optimization mechanism (UFM) or Generative Adversarial Network-based fusion optimization mechanism (GFM) toward image fusion tasks without ground truth (GT) labels, which leads to the problem that the fused image is over smooth or under smooth. This paper adopts a simple-yet-effective self-supervised fusion representation learning optimization mechanism (FLM) by directly maximizing the mutual information between the fused image and the samples (i.e., positive and negative samples), which aims to overcome over- and under-smoothing problems at intra-image level. Following InfoNCE, we further propose a smooth representation learning optimization mechanism (SFLM), which balances the fused image under-smoothing and over-smoothing at the inter-image level. Specifically, the SFLM is optimized by minimizing the total variation loss in the pixel space and maximizing the total variation loss in the gradient space. To show the superiority of SFLM, quantitative and qualitative experiments are carried out on four public infrared and visible image fusion datasets, including RoadScene, TNO, VIFB, and MF. Experimental results and comparisons with the state-of-the-art on four public datasets validate the effectiveness, robustness, and overall superiority of our method. © 2022, The Authors. All rights reserved.

关键词： Image fusion

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Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

arXiv

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arXiv 2024年

作者： Zhou, Ziqi Li, Minghui Liu, Wei Hu, Shengshan Zhang, Yechao Wan, Wei Xue, Lulu Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab. China Cluster and Grid Computing Lab. China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six

关键词： Topology

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The functionality on netpad 16

The functionality on netpad

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16th IEEE International Conference on computer science and Education, ICCSE 2021

作者： Li, Ning Li, Chuangzhong Gao, Ying Wang, Ying Zhao, Jietong Zhang, Linjing Guangzhou University School of Computer Science and Cyber Engineering Guangzhou China Institute of Guangzhou Software Engineering Guangzhou China Institute of Computing Science and Technology Guangzhou University Guangzhou China Fuzhou Administration Institute Fuzhou China

ISBN: (纸本)9781665414685

Modern information technology-assisted teaching is an important teaching means. In mathematics teaching activities, graphical changes can be vividly displayed through dynamic geometry software (DGS). Function is the basic knowledge in mathematics, and the functionality is the basic component of DGS. NetPad, one of the DGS, has more performance and simpler operation compared with other DGS. In this paper, we introduce the functionality of NetPad. It includes user-defined coordinate system, prompt function, expression input, implicit function, polar equation, parameter equation, function surface etc.. And its operation procedure is simpler to drawing curves dynamic trajectory and exploring function curves intersection. Through a teaching case, the superiority of NetPad in functionality is verified. © 2021 IEEE.

关键词： Functions

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E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and Generation

arXiv

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arXiv 2022年

作者： Zhong, Qihuang Ding, Liang Liu, Juhua Du, Bo Tao, Dacheng The School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China The School of Computer Science Faculty of Engineering The University of Sydney Australia

Sequence-to-sequence (seq2seq) learning is a popular fashion for large-scale pretraining language models. However, the previous seq2seq pretraining models generally focus on reconstructive objectives on the decoder side and neglect the effect of encoder-side supervision, which we argue may lead to sub-optimal performance. To verify our hypothesis, we first empirically study the functionalities of the encoder and decoder in seq2seq pretrained language models, and find that the encoder takes an important but under-exploitation role than the decoder regarding the downstream performance and neuron activation. Therefore, we propose an encoding-enhanced seq2seq pretraining strategy, namely E2S2, which improves the seq2seq models via integrating more efficient self-supervised information into the encoders. Specifically, E2S2 adopts two self-supervised objectives on the encoder side from two aspects: 1) locally denoising the corrupted sentence (denoising objective);and 2) globally learning better sentence representations (contrastive objective). With the help of both objectives, the encoder can effectively distinguish the noise tokens and capture high-level (i.e., syntactic and semantic) knowledge, thus strengthening the ability of seq2seq model to accurately achieve the conditional generation. On a large diversity of downstream natural language understanding and generation tasks, E2S2 dominantly improves the performance of its powerful backbone models, e.g., BART and T5. For example, upon BART backbone, we achieve +1.1% averaged gain on the general language understanding evaluation (GLUE) benchmark and +1.75% F0.5 score improvement on CoNLL2014 dataset. We also provide in-depth analyses to show the improvement stems from better linguistic representation. We hope that our work will foster future self-supervision research on seq2seq language model pretraining. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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VS-Net: A Voxel Encoding and Sparse Convolution Embedded Network for LiDAR 3D Object Detection

VS-Net: A Voxel Encoding and Sparse Convolution Embedded Net...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Liu, Meng Niu, Jianwei Liu, Yu The China North Artificial Intelligence and Innovation Research Institute Beijing100072 China The China North Vehicle Research Institute Beijing100072 China The State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Hangzhou Innovation Institute Beihang University Hangzhou310051 China The Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9781665481090

Accurate and efficient 3D object detection in LiDAR point cloud is critical for autonomous driving. In this paper, we provide a solution for LiDAR-only detection which employs voxel features and a sparse convolution network. Specifically, we utilized voxel centers to encode point clouds' voxel features. Then, combining the sparse convolution method, we leveraged residual learning to design the backbone network. The detector output predicted results for multi-category and multi-object detection. Our method was evaluated on three popular and challenging datasets (KITTI, nuScence and Waymo). Experimental results demonstrated that the accuracy and speed (27.8 FPS) of our model could effectively support object detection for autonomous driving in various scenarios. © 2022 IEEE.

关键词： Object detection

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An Unsupervised Convolutional Adversarial Anomaly Detection Model for IoT Data Infrastructure

An Unsupervised Convolutional Adversarial Anomaly Detection ...

引用

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Peian Wen Zhenyu Yang Lei Wu Sibo Qi Juan Chen School of Computer and Software Engineering Xihua University Chengdu China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China

Anomaly detection is critical to ensure the IoT (Internet of Things) data infrastructures' Quality of Service. However, due to the complexity of incon-spicuous(indistinct) anomalies, high dynamicity, and lack of anomaly labels in the operational IoT systems and cloud infrastructures, multivariate time series anomaly detection becomes more difficult. Existing approaches are rarely effective in meeting these challenges. In this paper, We propose a novel convolutional adversarial model based on CNN adversarial training with POT (Peak Over Threshold) dynamic threshold selection (CAT-POT) for multivariate time series anomaly detection in the IoT data. We build on the original autoencoder, replacing the fundamental linear changes with a convolutional neural network and adding a convolutional decoder to make an adversarial training architecture. The ability of the model to identify “slight” anomalies is enhanced through adversarial training, combined with POT dynamically selected thresholds to detect anomalies. We conduct ex-periments on five subsets of various open datasets, and the overall performance of CAT-POT is better than that of the baseline method. The average F1 value of CAT-POT on all datasets is 0.889, which is 5.1% higher than the suboptimal model, with the best improvement reaching 21.5%.

关键词： Training Time series analysis Quality of service Data models Real-time systems Decoding Complexity theory

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Analysis and Verification of Bisimulation Relationship for Learning Time-Behavior Sequence 9

Analysis and Verification of Bisimulation Relationship for L...

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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Feng, Shu Zhu, Yi Song, Mei Gao, Yuxiang School of Computer Science and Technology Jiangsu Normal University Xuzhou China Jiangsu Education Information Engineering Technology Research Center Xuzhou China Nanjing University of Aeronautics and Astronautics Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Similar learner determination has become a research hotspot in the field of personalized recommendation due to its lightweight method. At present, similar learner determination mainly adopts algorithms such as collaborative filtering, but such methods lack interpretability and cannot guarantee the reliability of the determination results. Aiming at this problem, this paper proposes a method for determining and verifying the similarity of learning time behavior sequences based on bisimulation, which uses logical reasoning to determine the similarity of learners, and uses tools to verify the correctness of the determination results. Firstly, the behavior properties of Temporal Calculus of Communication System (TCCS) are extended, and Learning Resources-Temporal Calculus of Communication System (LR-TCCS) is proposed to model the learning time-behavior sequence of learners. Secondly, we determine the similarity of learning time-behavior sequence through bisimulation. Thirdly, the bisimulation verification tool MWB is used to verify the similarity of learning time-behavior sequence, and to determine the optimal learner among similar learners through time allocation algorithm and behavior frequency analysis algorithm;Finally, the effectiveness of the method is verified by an example. © 2022 IEEE.

关键词： Calculations

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