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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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Unsupervised Time Series Anomaly Detection Based on Adversarial Interpolation and Pseudo-anomaly Calibration

Unsupervised Time Series Anomaly Detection Based on Adversar...

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International Conference on Control, Automation and Robotics (ICCAR)

作者： Xinwei Chen Xiaohui Lin Zuoyong Li Haoyi Fan Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Existing work on unsupervised time series anomaly detection relies on clean training datasets (free of anomalies), which is often violated in practice. In this work, we propose an unsupervised time series anomaly detection method based on adversarial interpolation and pseudo-anomaly calibration for training an anomaly detector under data con-tamination. Specifically, the normality description learned from the model is improved by using the following two proposed calibration methods: (1) by enforcing adversarial interpolation to learn an effective normality description from the typical samples rather than the edge or abnormal samples, and (2) by discriminating the original samples from the simulated pseudo-anomaly samples to learn the exact normality description boundary. These two calibration ways result in the normality description that prevents data contamination. Experimental results compared with baselines on public time series datasets demonstrate the effectiveness of our proposed method.

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Intelligent control of mode-locked femtosecond pulses by time-stretch-assisted real-time spectral analysis

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Light(Science & Applications) 2020年第1期9卷 1881-1888页

作者： Guoqing Pu Lilin Yi Li Zhang Chao Luo Zhaohui Li Weisheng Hu State Key Lab of Advanced Communication Systems and Networks Shanghai Institute for Advanced Communication and Data ScienceShanghai Jiao Tong UniversityShanghai 200240China Sun Yat-sen University/Southern Marine Science and Engineering Guangdong Laboratory ZhuhaiChina

Mode-locked fiber lasers based on nonlinear polarization evolution can generate femtosecond pulses with different pulse widths and rich spectral distributions for versatile applications through polarization ***,a precise and repeatable location of a specific pulsation regime is extremely ***,by using fast spectral analysis based on a time-stretched dispersion Fourier transform as the spectral discrimination criterion,along with an intelligent polarization search algorithm,for the first time,we achieved real-time control of the spectral width and shape of mode-locked femtosecond pulses;the spectral width can be tuned from 10 to 40 nm with a resolution of ~1.47 nm,and the spectral shape can be programmed to be hyperbolic secant or ***,we reveal the complex,repeatable transition dynamics of the spectrum broadening of femtosecond pulses,including five middle phases,which provides deep insight into ultrashort pulse formation that cannot be observed with traditional mode-locked lasers.

关键词： polarization tuning spectral

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BERT-based Chinese text classification for emergency domain with a novel loss function

arXiv

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arXiv 2021年

作者： Wang, Zhongju Wang, Long Huang, Chao Luo, Xiong School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China

This paper proposes an automatic Chinese text categorization method for solving the emergency event report classification problem. Since bidirectional encoder representations from transformers (BERT) has achieved great success in natural language processing domain, it is employed to derive emergency text features in this study. To overcome the data imbalance problem in the distribution of emergency event categories, a novel loss function is proposed to improve the performance of the BERT-based model. Meanwhile, to avoid the impact of the extreme learning rate, the Adabound optimization algorithm that achieves a gradual smooth transition from Adam to SGD is employed to learn parameters of the model. To verify the feasibility and effectiveness of the proposed method, a Chinese emergency text dataset collected from the Internet is employed. Compared with benchmarking methods, the proposed method has achieved the best performance in terms of accuracy, weighted-precision, weighted-recall, and weighted-F1 values. Therefore, it is promising to employ the proposed method for real applications in smart emergency management *** Codes 68T50 Copyright © 2021, The Authors. All rights reserved.

关键词： Disasters

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Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning

arXiv

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arXiv 2023年

作者： Wen, Xin-Cheng Chen, Yupan Gao, Cuiyun Zhang, Hongyu Zhang, Jie M. Liao, Qing School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Big Data and Software Engineering Chongqing University China Department of Informatics King’s College London United Kingdom Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China Peng Cheng Laboratory China

Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in learning the graph representations of source code and are commonly adopted by existing DL-based vulnerability detection methods. However, the existing methods are still limited by the fact that GNNs are essentially difficult to handle the connections between long-distance nodes in a code structure graph. Besides, they do not well exploit the multiple types of edges in a code structure graph (such as edges representing data flow and control flow). Consequently, despite achieving state-of-the-art performance, the existing GNN-based methods tend to fail to capture global information (i.e., long-range dependencies among nodes) of code graphs. To mitigate these issues, in this paper, we propose a novel vulnerability detection framework with grAph siMplification and enhanced graph rePresentation LEarning, named AMPLE. AMPLE mainly contains two parts: 1) graph simplification, which aims at reducing the distances between nodes by shrinking the node sizes of code structure graphs;2) enhanced graph representation learning, which involves one edge-aware graph convolutional network module for fusing heterogeneous edge information into node representations and one kernel-scaled representation module for well capturing the relations between distant graph nodes. Experiments on three public benchmark datasets show that AMPLE outperforms the state-of-the-art methods by 0.39%-35.32% and 7.64%-199.81% with respect to the accuracy and F1 score metrics, respectively. The results demonstrate the effectiveness of AMPLE in learning global information of code graphs for vulnerability detection. © 2023, CC BY.

关键词： Deep learning

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An ontology automation construction scheme for Chinese e-government thesaurus optimizing

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Proceedings of the Association for Information Science and Technology 2020年第1期57卷

作者： Wang, Hao Zhang, Wei Deng, Sanhong Zhang, Baolong School of Information Management Nanjing University Nanjing China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing China

To optimize the term hierarchy in the manual e-government thesaurus, we combine the mainstream knowledge organization technology to form a complete set of ontology automation construction scheme. We build an e-government knowledge base by using subject words in the Comprehensive E-government Thesaurus as the term set and encyclopedia text as the corpus. The specific work includes the extraction of semantic features from the bag-of-words model, determination of the number of clusters by linear and nonlinear dimensionality reduction, division of terms by spectral clustering, social network analysis to determine the class label, and storing knowledge ontology via OWL. The recall rate of term hierarchy in the ontology is excellent, indicating the ontology has good knowledge extensibility, and also proving the efficiency of the scheme proposed in this work. Besides, the application model of a term hierarchy in information retrieval can show a richer semantic relation than the original thesaurus to guide the retrieval extension of government information resources. 83rd Annual Meeting of the Association for Information Science & Technology October 25-29, 2020. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

关键词： Ontology

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Secure two-party SM9 signing

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Science China(Information Sciences) 2020年第8期63卷 239-241页

作者： Yongheng MU Haixia XU Peili LI Tianjun MA State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences Data Assurance and Communication Security Research Center Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences

Dear editor,With the rapid development of digital currencies and blockchain technologies, an increasing number of countries are planning to launch or have already launched their own central bank-issued digital currency (CBDC). In addition to drawing on advanced technologies such as blockchain, the ideal CBDC should also reconcile the functional con-

关键词： SM9 Identity-based Two-party signing key protection CBDC Elliptic Curve Paillier encryption

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Adversarial Attacks on Large Language Model-Based System and Mitigating Strategies: A Case Study on ChatGPT

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Security and Communication Networks 2023年第1期2023卷

作者： Liu, Bowen Xiao, Boao Jiang, Xutong Cen, Siyuan He, Xin Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China School of Computer Science & Technology Nanjing University of Posts and Telecommunications Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

Machine learning algorithms are at the forefront of the development of advanced information systems. The rapid progress in machine learning technology has enabled cutting-edge large language models (LLMs), represented by GPT-3 and ChatGPT, to perform a wide range of NLP tasks with a stunning performance. However, research on adversarial machine learning highlights the need for these intelligent systems to be more robust. Adversarial machine learning aims to evaluate attack and defense mechanisms to prevent the malicious exploitation of these systems. In the case of ChatGPT, adversarial induction prompt can cause the model to generate toxic texts that could pose serious security risks or propagate false information. To address this challenge, we first analyze the effectiveness of inducing attacks on ChatGPT. Then, two effective mitigating mechanisms are proposed. The first is a training-free prefix prompt mechanism to detect and prevent the generation of toxic texts. The second is a RoBERTa-based mechanism that identifies manipulative or misleading input text via external detection models. The availability of this method is demonstrated through experiments. © 2023 Bowen Liu et al.

关键词： Machine learning

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Distributed join algorithms on multi-CPU clusters with GPUDirect RDMA 19

Distributed join algorithms on multi-CPU clusters with GPUDi...

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48th International Conference on Parallel Processing, ICPP 2019

作者： Guo, Chengxin Chen, Hong Zhang, Feng Li, Cuiping Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE School of Information Renmin University of China China

ISBN: (纸本)9781450362955

In data management systems, query processing on GPUs or distributed clusters have proven to be an effective method for high efficiency. However, the high PCIe data transfer overhead between CPUs and GPUs, and the communication cost between nodes in distributed systems are usually bottleneck for improving system performance. Recently, GPUDirect RDMA has been developed and has received a lot of attention. It contains the features of the RDMA and GPUDirect technologies, which provides new opportunities for optimizing query processing. In this paper, we revisit the join algorithm, one of the most important operators in query processing, with GPUDirect RDMA. Specifically, we explore the performance of the hash join and sort merge join with GPUDirect RDMA. We present a new design using GPUDirect RDMA to improve the data communication in distributed join algorithms on multi-GPU clusters. We propose a series of techniques, including multi-layer data partitioning, and adaptive data communication path selection for various transmission channels. Experiments show that the proposed distributed join algorithms using GPUDirect RDMA achieve up to 1.83x performance speedup compared to the state-of-the-art distributed join algorithms. To the best of our knowledge, this is the first work for distributed GPU join algorithms. We believe that the insights and implications in this study shall shed lights on future researches using GPUDirect RDMA. © 2019 ACM.

关键词： Graphics processing unit

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Fs-Woa-Stacking: A Novel Ensemble Model for Early Diagnosis of Breast Cancer

SSRN

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SSRN 2024年

作者： Xiao, Tianyun Kong, Shanshan Zhang, Zichen Liu, Fengchun Yang, Aimin Hua, Dianbo Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing101149 China

In this paper, a new integrated scheme is proposed to accurately predict breast cancer, help doctors make early diagnosis and treatment plans, and improve the prognosis of patients. We selects five mainstream machine learning models: support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost). The Wisconsin Breast Cancer database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) are used as datasets to investigate the predictive performance of these single and ensemble models. Then, we use multiple linear regression method for feature selection (FS), the experimental results show that the change of feature subset will significantly affect the performance of the model. After that, we apply whale optimization algorithm (WOA) to optimize the hyperparameters of the model to improve their prediction performance. In addition, we ensemble these models by stacking, investigate the performance changes of the ensemble model when different models are used as meta learners. Finally, the FS-WOA-Stacking model achieves 99.56% accuracy on WBCD and 99.65% accuracy on WDBC. Compared with the existing breast cancer prediction models, the performance of the proposed model is at an excellent level. © 2024, The Authors. All rights reserved.

关键词： Adaptive boosting

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