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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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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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Evaluating deep graph neural networks

arXiv

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arXiv 2021年

作者： Zhang, Wentao Sheng, Zeang Jiang, Yuezihan Xia, Yikuan Gao, Jun Yang, Zhi Cui, Bin School of EECS Key Laboratory of High Confidence Software Technologies Peking University Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications

Graph Neural Networks (GNNs) have already been widely applied in various graph mining tasks. However, they suffer from the shallow architecture issue, which is the key impediment that hinders the model performance improvement. Although several relevant approaches have been proposed, none of the existing studies provides an in-depth understanding of the root causes of performance degradation in deep GNNs. In this paper, we conduct the first systematic experimental evaluation to present the fundamental limitations of shallow architectures. Based on the experimental results, we answer the following two essential questions: (1) what actually leads to the compromised performance of deep GNNs;(2) when we need and how to build deep GNNs. The answers to the above questions provide empirical insights and guidelines for researchers to design deep and well-performed GNNs. To show the effectiveness of our proposed guidelines, we present Deep Graph Multi-Layer Perceptron (DGMLP), a powerful approach (a paradigm in its own right) that helps guide deep GNN designs. Experimental results demonstrate three advantages of DGMLP: 1) high accuracy - it achieves state-of-the-art node classification performance on various datasets;2) high flexibility - it can flexibly choose different propagation and transformation depths according to graph size and sparsity;3) high scalability and efficiency - it supports fast training on large-scale graphs. Our code is available in https://***/zwt233/DGMLP. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning

Vulnerability Detection with Graph Simplification and Enhanc...

引用

International Conference on software engineering (ICSE)

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

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.

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An advanced algorithm for discrimination prevention in data mining

An advanced algorithm for discrimination prevention in data ...

引用

2022 IEEE Conference on Telecommunications, Optics and Computer Science, TOCS 2022

作者： Wang, Shuoyu Ren, Jie Fang, Hao Pan, Jun Hu, Xin Zhao, Tingyun Tibet University College of Information Science and Technology Lhasa China Lyuliang University Department of Computer Science and Technology Lvliang China Liaoning University of Traditional Chinese Medicine College of Foreign Languages Shenyang China Beihang University School of Electronic and Information Engineering Beijing China Yangtze Normal University School of Mathematics and Statistics Chongqing China Changzhou College of Information Technology School of Software and Big Data Changzhou China

ISBN: (纸本)9781665470537

data mining has been effectively utilized in huge expanded application areas. Associations overall go with significant business choices in light of mining of data. Discrimination in navigation and preservation of protection of data are a couple of significant difficulties in data mining. Discrimination is of two sorts, Direct Discrimination and Indirect Discrimination. Discrimination prevention includes two significant angles disclosure of direct and additionally roundabout discrimination and data transformation as alteration of separating rules without influencing data quality. Existing strategies for discrimination prevention utilize one of the three methodologies, in particular, the pre-process, in-process or post-process approach. Tests for the exhibition assessment of all calculations by fluctuating these info boundaries, each in turn to review and break down impact of these boundaries. © 2022 IEEE.

关键词： data mining

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Transfer learning architectures with fine-tuning for brain tumor classification using magnetic resonance imaging

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Healthcare Analytics 2023年 4卷 100270-100270页

作者： Islam, Md. Monirul Barua, Prema Rahman, Moshiur Ahammed, Tanvir Akter, Laboni Uddin, Jia Department of Software Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh Department of Computer Science and Engineering University of Information Technology and Sciences (UITS) Dhaka 1212 Bangladesh AI and Big Data Department Endicott College Woosong University South Korea

Deep learning methods in artificial intelligence are used for brain tumor diagnosis as they handle a huge amount of data. Compared to computerized tomography (CT), Ultrasound, and X-ray imaging, Magnetic Resonance Imaging (MRI) is effectively used for machine vision-based brain tumor diagnosis. However, due to the complex nature of the brain, brain tumor diagnosis is always challenging. This research aims to study the effectiveness of deep transfer learning architectures in brain tumor diagnosis. This paper applies four transfer learning architectures- InceptionV3, VGG19, DenseNet121, and MobileNet. We used a dataset with data from three benchmark databases of figshare, SARTAJ, and Br35H to validate the models. These databases have four classes: pituitary, no tumor, meningioma, and glioma. Image augmentation is applied to make the classes balanced. Experimental results demonstrate that the MobileNet outperforms competing methods by exhibiting an accuracy of 99.60%. © 2023 The Author(s)

关键词： Artificial intelligence Brain tumor Computerized tomography Deep learning Magnetic resonance imaging Transfer learning

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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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WATSON: A Workflow-based data Storage Optimizer for Analytics 36

WATSON: A Workflow-based Data Storage Optimizer for Analytic...

引用

36th International Conference on Massive Storage Systems and Technology, MSST 2020

作者： Zou, Jia Zhao, Ming Shi, Juwei Wang, Chen Computer Science and Engineering Arizona State University Tempe United States Search Technology Center Asia Microsoft Beijing China School of Software EIRI Tsinghua University National Engineering Lab for Big Data Software Beijing China

This paper studies the automatic optimization of data placement parameters for the inter-job write once read many (WORM) scenario where data is first materialized to storage by a producer job, and then accessed for many times by one or more consumer jobs. Such scenario is ubiquitous in big data analytics applications but existing big data auto-tuning techniques are often focused on single job performance. To address the shortcomings in existing works, this paper investigates data placement parameters regarding blocking, partitioning and replication and models the trade-offs caused by different configurations of these parameters through a producer-consumer model. We then present a novel cross-layer solution, WATSON, which can automatically predict future workloads’ data access patterns and tune data placement parameters accordingly to optimize the performance for an inter-job WORM scenario. WATSON can achieve up to eight times performance speedup on various analytics workloads. © 2020 36th International Conference on Massive Storage Systems and Technology, MSST 2020. All Rights Reserved.

关键词： data Analytics

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KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

arXiv

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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

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ToFM: Topic-specific Facet Mining by Facet Propagation within Clusters 12

ToFM: Topic-specific Facet Mining by Facet Propagation withi...

引用

12th IEEE International Conference on big Knowledge, ICBK 2021

作者： Li, Hongxuan Wei, Bifan Liu, Jun Guo, Zhaotong Qi, Jingchao Wu, Bei Liu, Yong Shi, Yuanyuan Xi'An Jiaotong University School of Computer Science and Technology Xi'an China Xi'An Jiaotong University National Engineering Lab For Big Data Analytics Xi'an China School of Continuous Education Xi'An Jiaotong University Xi'an China Servyou Software Group Co. Ltd. China

ISBN: (纸本)9781665438582

Mining the facets of topics is an essential task for information retrieval, information extraction and knowledge base construction. For the topics in courses, there are three challenges: different topics have different facet, the labels of facets rarely appear in the topic description text and not all topics have enough textural information to mine facets. In this paper we propose a weakly-supervised algorithm for topic-specific facet mining (ToFM for short) based on our finding that similar topics in a cluster have similar facet sets. For example, topics Binary Search Tree, Suffix Tree and AVL tree in Tree cluster have example, insertion, deletion, traversal and other similar facets. ToFM first splits topics in a domain into several topic clusters based on the topic description text. Then ToFM extracts initial facet sets for all topics from the corresponding Wikipedia article pages. Finally, ToFM performs a normalized facet propagation within each topic cluster to acquire final facet sets of every topic. We evaluate the performance of ToFM on six real-world datasets and experimental results show that ToFM achieves better performance than the existing facet mining algorithms. © 2021 IEEE.

关键词： Information retrieval

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