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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Deng, Chao Li, Hongdong Wang, Jianxin School of Computer Science and Engineering Central South University Changsha410083 China Hunan Provincial Key Lab on Bioinformatics Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi830091 China

ISBN: (纸本)157735897X

Identifying cancer genes is crucial for treatment and understanding pathogenesis. Recent methods typically leverage protein-protein interaction (PPI) networks or gene functional association data from annotated gene sets. There may be some shared neighborhood structure information between these two types of gene association data. While this common information may contain more accurate gene association information, existing methods often overlook this potential. To address this gap, we introduce DISFusion, which integrates multi-omics cancer data, PPI networks, and gene functional associations to identify cancer genes. A key innovation of DISFusion is the cross-view decorrelation loss, which enhances the common information between PPI networks and gene functional associations, thereby improving prediction accuracy. Extensive experiments indicate that DISFusion outperforms state-of-the-art methods and exhibits greater generalization ability. Moreover, analysis of CPTAC pan-cancer proteomic data highlights significant associations between the 30 novel cancer genes predicted by DISFusion and multiple cancer types, underscoring its practical utility. These findings validate the effectiveness of enhancing common information and provide new insights into cancer gene identification. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection

arXiv

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arXiv 2022年

作者： Wan, Wei Hu, Shengshan Lu, Jianrong Yu Zhang, Leo Jin, Hai He, Yuanyuan School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab School of Information Technology Deakin University Australia

Federated learning (FL) enables multiple clients to collaboratively train an accurate global model while protecting clients’ data privacy. However, FL is susceptible to Byzantine attacks from malicious participants. Although the problem has gained significant attention, existing defenses have several flaws: the server irrationally chooses malicious clients for aggregation even after they have been detected in previous rounds;the defenses perform ineffectively against sybil attacks or in the heterogeneous data setting. To overcome these issues, we propose MAB-RFL, a new method for robust aggregation in FL. By modelling the client selection as an extended multi-armed bandit (MAB) problem, we propose an adaptive client selection strategy to choose honest clients that are more likely to contribute high-quality updates. We then propose two approaches to identify malicious updates from sybil and non-sybil attacks, based on which rewards for each client selection decision can be accurately evaluated to discourage malicious behaviors. MAB-RFL achieves a satisfying balance between exploration and exploitation on the potential benign clients. Extensive experimental results show that MAB-RFL outperforms existing defenses in three attack scenarios under different percentages of attackers. Copyright © 2022, The Authors. All rights reserved.

关键词： data privacy

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Automatic combination strategy search in segmentation of Cardiac T1 mapping image 4

Automatic combination strategy search in segmentation of Car...

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4th International Conference on Advanced Electronic Materials, Computers and Software engineering, AEMCSE 2021

作者： Wangchen, Xingyu Shen, Wenfeng Takasumi, Hideaki Seino, Shinya Zhu, Xin Shanghai University School of Computer Engineering and Science Shanghai China Shanghai University School of Computer Engineering and Science Shanghai Institute for Advanced Communication and Data Science Shanghai China Fukushima Medical University Hospital Department of Radiology Fukushima Japan The University of Aizu Biomedical Information Engineering Lab Fukushima9658580 Japan

ISBN: (纸本)9781665415965

Fusion of multiple medical images may improve image quality and ease feature extraction for the diagnosis and therapy of diseases. In cardiac magnetic resonance (MR) images T1 mapping, multiple MR images are reconstructed for the estimation of T1 mapping;therefore, heart myocardium should be segmented from images for further analysis using T1 values. Traditionally, segmentation is performed for each image separately, and the per-image segmentation results are combined in a complicated way to get a final segmentation result. Traditional combination methods require the segmentation results of all images satisfy certain criteria, and therefore often fail because of low image quality. However, it is very difficult to design a combination strategy to integrate the information of multiple images for segmentation. In this study, we designed the Segfuse network based on convolutional neural networks to get strategies for improving the segmentation performance of T1 mapping images even when some images were of low quality. After validating with 112 images from 42 subjects, the segmentation accuracy based on the Segfuse had a mean dice of 80.2%, nearly equivalent to that of a traditional method. © 2021 IEEE.

关键词： Image segmentation

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A Surrogate-Assist Quasi-Affine Transformation Evolutionary for Multi-Objective Optimization of Empty Train Deployment on Heavy-Haul Railways

SSRN

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SSRN 2023年

作者： Du, Zhi-Gang Pan, Jeng-Shyang Xu, He-Ying Chu, Shu-Chuan Ni, Shao-Quan School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu610031 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031 China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031 China

In recent years, surrogate-assisted evolutionary algorithms have become increasingly popular for solving expensive and time-consuming single-objective and multi-objective problems. However, in multi-objective optimization, the Pareto non-dominated solution set space can quickly become computationally intractable, making it challenging to select which samples to evaluate using the expensive fitness function. In this paper, we propose a novel surrogate-assist QUasi-Affine TRansformation Evolutionary algorithm (SA-QUATRE/MO) for solving multi-objective optimization problems. The SA-QUATRE/MO algorithm uses a radial basis neural network as a surrogate model to improve the speed of the algorithm operation by replacing the expensive fitness evaluation with the surrogate model. To ensure the excellence and diversity of the selected samples while keeping the archived sample space fixed, we propose a technique called Vector Space Sampling, which samples objective points in the current set of non-dominated solutions by dividing several sub-vector spaces. Additionally, we propose an uncertain sample infilling strategy to select samples for real fitness evaluation using a designed uncertainty function. We compare the SA-QUATRE/MO algorithm with three state-of-the-art algorithms for multi-objective problems in the DTLZ, MaF, and ZDT test function suites. Finally, we apply the SA-QUATRE/MO algorithm to solve the optimization problem of the empty train deployment at the loading end of a heavy-haul railway. We build a model based on the S12 section of a certain railway and optimize it using the SA-QUATRE/MO algorithm, demonstrating the effectiveness and practicality of our proposed approach. © 2023, The Authors. All rights reserved.

关键词： Vector spaces

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Robust Zero Watermarking Algorithm for Medical Volume data Based on LBP 10th

Robust Zero Watermarking Algorithm for Medical Volume Data B...

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10th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2022

作者： Liu, Wenyi Li, Jingbing Liu, Jing Ma, Jixin School of Information and Communication Engineering Hainan University Haikou570228 China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou310058 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom

ISBN: (纸本)9789811934391

With the gradual rise of telemedicine, a large number of images will be transmitted on the network during network consultation, which may cause privacy disclosure. How to more effectively ensure the information security of patients and the copyright of medical images has attracted great attention of scholars in relevant fields. This paper proposed a robust watermarking method for medical volume data based on local binary pattern (LBP) transformation. Firstly, taking the Medical Volume data as the research object, the low frequency feature vector of the Medical Volume data is extracted by combining local binary mode transform and discrete cosine transform. Then hash perception was performed. Finally, we use zero watermark technology to embed the hash sequence and chaotic technology to encrypt watermark. Experimental prove that the proposed algorithm has strong anti-attack abilities. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining

PSMiner: A Pattern-Aware Accelerator for High-Performance St...

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Design Automation Conference

作者： Hao Qi Yu Zhang Ligang He Kang Luo Jun Huang Haoyu Lu Jin Zhao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Zhejiang Lab Zhejiang-HUST Joint Research Center for Graph Processing China Department of Computer Science University of Warwick United Kingdom

Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.

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Dual-Scale Enhanced and Cross-Generative Consistency Learning for Semi-Supervised Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Gu, Yunqi Zhou, Tao Zhang, Yizhe Zhou, Yi He, Kelei Gong, Chen Zhu, Huafu PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Information Science and Technology Nanjing210094 China School of Computer Science and Engineering Southeast University Nanjing211189 China Medical School Nanjing University National Institute of Healthcare Data Science Nanjing210023 China Institute of High Performance Computing A*STAR Singapore

Medical image segmentation plays a crucial role in computer-aided diagnosis. However, existing methods heavily rely on fully supervised training, which requires a large amount of labeled data with time-consuming pixel-wise annotations. Moreover, accurately segmenting lesions poses challenges due to variations in shape, size, and location. To address these issues, we propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image Segmentation (DEC-Seg). First, we propose a Cross-level Feature Aggregation (CFA) module that integrates cross-level adjacent layers to enhance the feature representation ability across different resolutions. To address scale variation, we present a scale-enhanced consistency constraint, which ensures consistency in the segmentation maps generated from the same input image at different scales. This constraint helps handle variations in lesion sizes and improves the robustness of the model. Furthermore, we propose a cross-generative consistency scheme, in which the original and perturbed images can be reconstructed using cross-segmentation maps. This consistency constraint allows us to mine effective feature representations and boost the segmentation performance. To produce more accurate segmentation maps, we propose a Dual-scale Complementary Fusion (DCF) module that integrates features from two scale-specific decoders operating at different scales. Extensive experimental results on polyp segmentation from colonoscopy images, skin lesion segmentation from dermoscopic images, and Brain glioma segmentation from MRI images demonstrate the effectiveness of our DEC-Seg against other state-of-the-art semi-supervised segmentation approaches. © 2024, The Authors. All rights reserved.

关键词： Computer aided diagnosis

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A Low-Code Development Framework for Constructing Industrial Apps 15th

A Low-Code Development Framework for Constructing Industrial...

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15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Wang, Jingyue Qi, Binhang Zhang, Wentao Sun, Hailong SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China

ISBN: (纸本)9789811625398

With the advent of the Industry 4.0, intelligent manufacturing has become a technological highland to conquer in the process of enterprise digitalization. As the core competitiveness of intelligent manufacturing, industrial apps, with new features such as customization and lightweight, has emerged as a new type of industrial software. Traditional development methods and tools can hardly meet the large demand of industrial software on account of its long development cycles while low-code development can greatly improve the productivity of industrial software, lower the barriers and reduce costs for development. Therefore, the research and application of low-code development for industrial apps has received much attention. Industrial Internet platforms such as Siemens, OutSystems have successively launched low-code tools. However, there is still a lack of an open, unified low-code development framework in industry. In response to the above problems, we propose a low-code framework to develop industrial apps quickly and easily, which paves the way for leveraging the crowd intelligence of worldwide developers to improve the productivity of developing industrial apps. Based on BPMN2.0 and Apache Activiti engine, this framework provides drag-and-drop process design, one-click process deployment and operation, data monitoring and other functions. In this paper, we present a prototype system of a low-code development framework and demonstrate its functions through a use case of developing a predictive maintenance application. Finally, the aircraft turbine life data is used to verify the effectiveness of the system. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Productivity

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Unsupervised Multi-shot Person Re-identification via Dynamic Bi-directional Normalized Sparse Representation 27th

Unsupervised Multi-shot Person Re-identification via Dynamic...

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Li, Xiaobao Wang, Wen Li, Qingyong Guo, Lijun Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Zhejiang China

ISBN: (纸本)9783030678319

Due to the abundant priori information and the widespread applications, multi-shot based person re-identification has drawn increasing attention in recent years. In this paper, the high labeling cost and the huge unlabeled data motivate us to focus on the unsupervised scenario and a unified coarse-to-fine framework is proposed, named by Dynamic Bi-directional Normalized Sparse Representation (DBNSR). Specifically, the method designs an alternatively iterative mechanism to dynamically update the estimated cross-camera labels and progressively learn a discriminative metric. In each iteration, a Bi-directional Normalized Sparse Representation (BNSR) model is applied to estimate the label for each pair of cross-camera person sequences with bi-directional probabilities defined to encode their mutual correlation. With the estimated labels, we learn a keep-it-simple-and-straight-forward metric (KISSME) to further make BNSR model available in a more discriminative and lower-dimensional subspace. Extensive experiments on PRID 2011, iLIDS-VID and MARS three benchmarks show that our method performs favorably against state-of-the-arts. © 2021, Springer Nature Switzerland AG.

关键词： Cameras

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