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arXiv 2022年

作者： Rajbahadur, Gopi Krishnan Wang, Shaowei Oliva, Gustavo A. Kamei, Yasutaka Hassan, Ahmed E. The Centre for Software Excellence Huawei Canada School of Computing Queen’s University Canada The department of computer science University of Manitoba Canada Lab Graduate School and Faulty of Information Science and Electrical Engineering Kyushu University Japan

Classifier specific (CS) and classifier agnostic (CA) feature importance methods are widely used (often interchangeably) by prior studies to derive feature importance ranks from a defect classifier. However, different feature importance methods are likely to compute different feature importance ranks even for the same dataset and classifier. Hence such interchangeable use of feature importance methods can lead to conclusion instabilities unless there is a strong agreement among different methods. Therefore, in this paper, we evaluate the agreement between the feature importance ranks associated with the studied classifiers through a case study of 18 software projects and six commonly used classifiers. We find that: 1) The computed feature importance ranks by CA and CS methods do not always strongly agree with each other. 2) The computed feature importance ranks by the studied CA methods exhibit a strong agreement including the features reported at top-1 and top-3 ranks for a given dataset and classifier, while even the commonly used CS methods yield vastly different feature importance ranks. Such findings raise concerns about the stability of conclusions across replicated studies. We further observe that the commonly used defect datasets are rife with feature interactions and these feature interactions impact the computed feature importance ranks of the CS methods (not the CA methods). We demonstrate that removing these feature interactions, even with simple methods like CFS improves agreement between the computed feature importance ranks of CA and CS methods. In light of our findings, we provide guidelines for stakeholders and practitioners when performing model interpretation and directions for future research, e.g., future research is needed to investigate the impact of advanced feature interaction removal methods on computed feature importance ranks of different CS methods. © 2022, CC BY-NC-ND.

关键词： Defects

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SCALABLE EDGE BLOCKING ALGORITHMS FOR DEFENDING ACTIVE DIRECTORY STYLE ATTACK GRAPHS :A PREPRINT

arXiv

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arXiv 2022年

作者： Guo, Mingyu Ward, Max Neumann, Aneta Neumann, Frank Nguyen, Hung School of Computer Science University of Adelaide Australia School of Physics Maths and Computing Computer Science and Software Engineering University of Western Australia Australia Department of Molecular and Cellular Biology Harvard University CambridgeMA United States

Active Directory (AD) is the default security management system for Windows domain networks. An AD environment naturally describes an attack graph where nodes represent computers/accounts/security groups, and edges represent existing accesses/known exploits that allow the attacker to gain access from one node to another. Motivated by practical AD use cases, we study a Stackelberg game between one attacker and one defender. There are multiple entry nodes for the attacker to choose from and there is a single target (Domain Admin). Every edge has a failure rate. The attacker chooses the attack path with the maximum success rate. The defender can block a limited number of edges (i.e., revoke accesses) from a set of blockable edges, limited by budget. The defender’s aim is to minimize the attacker’s success rate. We exploit the tree-likeness of practical AD graphs to design scalable algorithms. We propose two novel methods that combine theoretical fixed parameter analysis and practical optimisation techniques. For graphs with small tree widths, we propose a tree decomposition based dynamic program. We then propose a general method for converting tree decomposition based dynamic programs to reinforcement learning environments, which leads to an anytime algorithm that scales better, but loses the optimality guarantee. For graphs with small numbers of non-splitting paths (a parameter we invent specifically for AD graphs), we propose a kernelization technique that significantly downsizes the model, which is then solved via mixed-integer programming. Experimentally, our algorithms scale to handle synthetic AD graphs with tens of thousands of nodes. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Systematic Literature Review on Global software Development Risks in Agile Methodology 8

Systematic Literature Review on Global Software Development ...

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8th International Conference on Information Technology and Multimedia, ICIMU 2020

作者： Podari, Zuriyaninatasa Arbain, Adila Firdaus Ibrahim, Noraini Abang Jawawi, Dayang Norhayati Nasir Wan Kadir, Wan Mohd Fahmi, Azim Muhammad Universiti Tun Hussein Onn Malaysia Software Engineering Department Parit Raja86400 Malaysia School of Computing Universiti Teknologi Malaysia Skudai81310 Malaysia

ISBN: (纸本)9781728173108

Background: The word 'Global software Development' can be described as the development of software, with development teams spread across different geographical locations. Problem statement: The issues arise when there are gaps in information, workflows or processes, policies and others in the world. Objective: This paper aims to build an understanding of the risk in Global software Development. Then, to identify category risk in Global software Development and how Agile can reduce or mitigate their challenges. Method: This review paper using the standard systematic literature review method by Kitchehamm by reviewing and analyzing the relevant state-of -art techniques and approaches in the journal libraries based on the research questions. Results: The findings show that communication in Agile Global software development is the main risk challenge. Contribution: The contributions of this paper may support the academician to propose and validate an enhanced approach to solve the issues if risk management in Agile GSD environment and assist the practitioners in choosing the most suitable and relevant method based on the requirement of Agile GSD project. This review paper is also focusing on the contribution to the software engineering Management and software engineering Models and Methods Knowledge Area. © 2020 IEEE.

关键词： Risk management

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Application of Transfer Learning for Fruits and Vegetable Quality Assessment 14

Application of Transfer Learning for Fruits and Vegetable Qu...

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14th International Conference on Innovations in Information Technology, IIT 2020

作者： Turaev, Sherzod Almisreb, Ali Abd Saleh, Mohammed A. United Arab Emirates University Department of Computer Science and Software Engineering Al Ain15551 United Arab Emirates International University of Sarajevo Department of Computer Science and Engineering and Software Engineering Sarajevo71210 Bosnia and Herzegovina University College of Technology School of Computing and Creative Media Sarawak Sarawak Malaysia

ISBN: (纸本)9781728181844

In this paper, we utilize the concept of transfer learning in fruits and vegetable quality assessment. The transfer learning concept applies the idea of reuse the pre-trained Convolutional Neural Network to solve a new problem without the need for large-scale datasets for training. Eight pre-trained deep learning models namely AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, Vgg16, Vgg19, and NasNetMobile are fine-tuned accordingly to evaluate the quality of fruits and vegetable. To evaluate the training and validation performance of each fine-tuned model, we collect a dataset consists of images from 12 fruits and vegetable samples. The dataset builds over five weeks. For every week 70 images collected therefore the total number of images over five weeks is 350 and the total number of images in the dataset is (12∗350) 4200 images. The overall number of classes in the dataset is (12∗5) 60 classes. The evaluation of the models was conducted based on this dataset and also based on an augmented version. The model's outcome shows that the Vgg19 model achieved the highest validation accuracy over the original dataset with 91.50% accuracy and the ResNet18 model scored the highest validation accuracy based on the augmented dataset with 91.37% accuracy. © 2020 IEEE.

关键词： Vegetables

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Unsupervised software repositories mining and its application to code search

Unsupervised software repositories mining and its applicatio...

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作者： Hu, Gang Peng, Min Zhang, Yihan Xie, Qianqian Gao, Wang Yuan, Mengting Department of National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Computing National University of Singapore Singapore

software repositories are crucial resources for many software tasks, including code retrieval and annotation. Programming forums provide questions and answers (Q&A) from software developers, containing abundant code-description posts for exchanging knowledge about programming issues. However, most posts provide personal opinions of users that are often not adequately confirmed or outdated. Mining software repositories in such open and unrestricted forums is challenging. Since the posts can be arbitrary and noisy, it is difficult to get unified labels for supervised noise elimination. Different from existing mining approaches, this paper proposes Code-Description Mining Framework (CodeMF), an unsupervised framework to eliminate noisy posts and extract high quality software repositories from programming forums. CodeMF treats all social features of the posts as discrete-time signals for kernel principal component analysis and further performs wavelet transform feature fusion to find the delicate changes (noises in temporal signals). We conduct comprehensive experiments on StackOverflow. Experimental results demonstrate that CodeMF can effectively reduce running time and improve precision via mining high-quality software repositories for various programming languages, especially for the large-scale codebases. To further illustrate the effect of CodeMF applied in software tasks, we introduce it to improve the performance of query-expansion code search. Meanwhile, for SQL and C# programs, compared to the state-of-the-art query-expansion method QECK, the improvement of QECKCodeMF is 2% and 6% on Recall@10, and 4% and 14% on mean reciprocal rank, respectively. © 2019 John Wiley & Sons, Ltd.

关键词： Knowledge management

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Learning Signed Hyper Surfaces for Oriented Point Cloud Normal Estimation

arXiv

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arXiv 2023年

作者： Li, Qing Feng, Huifang Shi, Kanle Gao, Yue Fang, Yi Liu, Yu-Shen Han, Zhizhong School of Software Tsinghua University Beijing China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Kuaishou Technology Beijing China Center for Artificial Intelligence and Robotics New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wayne State University Detroit United States

We propose a novel method called SHS-Net for point cloud normal estimation by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods estimate oriented normals through a two-stage pipeline, i.e., unoriented normal estimation and normal orientation, and each step is implemented by a separate algorithm. However, previous methods are sensitive to parameter settings, resulting in poor results from point clouds with noise, density variations and complex geometries. In this work, we introduce signed hyper surfaces (SHS), which are parameterized by multi-layer perceptron (MLP) layers, to learn to estimate oriented normals from point clouds in an end-to-end manner. The signed hyper surfaces are implicitly learned in a high-dimensional feature space where the local and global information is aggregated. Specifically, we introduce a patch encoding module and a shape encoding module to encode a 3D point cloud into a local latent code and a global latent code, respectively. Then, an attention-weighted normal prediction module is proposed as a decoder, which takes the local and global latent codes as input to predict oriented normals. Experimental results show that our algorithm outperforms the state-of-the-art methods in both unoriented and oriented normal estimation. © 2023, CC BY-NC-SA.

关键词： Surface reconstruction

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Joint Multimodal Entity-Relation Extraction Based on Edge-enhanced Graph Alignment Network and Word-pair Relation Tagging

arXiv

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arXiv 2022年

作者： Yuan, Li Cai, Yi Wang, Jin Li, Qing School of Software Engineering South China University of Technology Guangzhou China MOE of China China The Peng Cheng Laboratory Shenzhen China School of Information Science and Engineering Yunnan University Yunnan China Department of Computing The Hong Kong Polytechnic University Hong Kong

Multimodal named entity recognition (MNER) and multimodal relation extraction (MRE) are two fundamental sub-tasks in the multimodal knowledge graph construction task. However, the existing methods usually handle two tasks independently, which ignores the bidirectional interaction between them. This paper is the first to propose jointly performing MNER and MRE as a joint multimodal entity-relation extraction task (JMERE). Besides, the current MNER and MRE models only consider aligning the visual objects with textual entities in visual and textual graphs but ignore the entity-entity relationships and object-object relationships. To address the above challenges, we propose an edge-enhanced graph alignment network and a word-pair relation tagging (EEGA) for the JMERE task. Specifically, we first design a word-pair relation tagging to exploit the bidirectional interaction between MNER and MRE and avoid error propagation. Then, we propose an edge-enhanced graph alignment network to enhance the JMERE task by aligning nodes and edges in the cross-graph. Compared with previous methods, the proposed method can leverage the edge information to auxiliary alignment between objects and entities and find the correlations between entity-entity relationships and object-object relationships. Experiments are conducted to show the effectiveness of our model © 2022, CC BY.

关键词： Alignment

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Controllable Abstractive Sentence Summarization with Guiding Entities 28

Controllable Abstractive Sentence Summarization with Guiding...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Zheng, Changmeng Cai, Yi Zhang, Guanjie Li, Qing School of Software Engineering South China University of Technology Guangzhou China Ministry of Education China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781952148279

Entities are the major proportion and build up the topic of text summaries. Although existing text summarization models can produce promising results of automatic metrics, for example, ROUGE, it is difficult to guarantee that an entity is contained in generated summaries. In this paper, we propose a controllable abstractive sentence summarization model which generates summaries with guiding entities. Instead of generating summaries from left to right, we start with a selected entity, generate the left part first, then the right part of a complete summary. Compared to previous entity-based text summarization models, our method can ensure that entities appear in final output summaries rather than generating the complete sentence with implicit entity and article representations. Our model can also generate more novel entities with them incorporated into outputs directly. To evaluate the informativeness of the proposed model, we develop a fine-grained informativeness metrics in the relevance, extraness and omission perspectives. We conduct experiments in two widely-used sentence summarization datasets and experimental results show that our model outperforms the state-of-the-art methods in both automatic evaluation scores and informativeness metrics. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Text processing

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FLOW: Fusing and Shuffling Global and Local Views for Cross-User Human Activity Recognition with IMUs

arXiv

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arXiv 2024年

作者： Qiu, Qi Zhu, Tao Duan, Furong Wang, Kevin I-Kai Chen, Liming Nie, Mingxing Wan, Yaping School of Computer Science University of South China Hunan Hengyang430001 China School of Computing University of Ulster NewtownabbeyBT37 0QB United Kingdom Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand School of Computer Science and Technology Dalian University of Technology Liaoning Dalian116000 China

Inertial Measurement Unit (IMU) sensors are widely employed for Human Activity Recognition (HAR) due to their portability, energy efficiency, and growing research interest. However, a significant challenge for IMU-HAR models is achieving robust generalization performance across diverse users. This limitation stems from substantial variations in data distribution among individual users. One primary reason for this distribution disparity lies in the representation of IMU sensor data in the local coordinate system, which is susceptible to subtle user variations during IMU wearing. To address this issue, we propose a novel approach that extracts a global view representation based on the characteristics of IMU data, effectively alleviating the data distribution discrepancies induced by wearing styles. To validate the efficacy of the global view representation, we fed both global and local view data into model for experiments. The results demonstrate that global view data significantly outperforms local view data in cross-user experiments. Furthermore, we propose a Multi-view Supervised Network (MVFNet) based on Shuffling to effectively fuse local view and global view data. It supervises the feature extraction of each view through view division and view shuffling, so as to avoid the model ignoring important features as much as possible. Extensive experiments conducted on OPPORTUNITY and PAMAP2 datasets demonstrate that the proposed algorithm outperforms the current state-of-the-art methods in cross-user HAR. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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A Convolution-Based System for Malicious URLs Detection

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Computers, Materials & Continua 2020年第1期62卷 399-411页

作者： Chaochao Luo Shen Su Yanbin Sun Qingji Tan Meng Han Zhihong Tian Institute of Computer Application China Academy of Engineering PhysicsMianyang621054China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Mechanical and Electrical Engineering Heilongjiang State Farm Science Technology Vocational CollegeHarbin150431China Department of Computing and Software Engineering Kennesaw State UniversityKennesawGA 30144USA

Since the web service is essential in daily lives,cyber security becomes more and more important in this digital *** Uniform Resource Locator(URL)is a common and serious threat to *** hosts unsolicited content and lure unsuspecting users to become victim of scams,such as theft of private information,monetary loss,and malware ***,it is imperative to detect such ***,traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious *** this paper,we propose a novel malicious URL detection method based on deep learning model to protect against web ***,we firstly use auto-encoder to represent ***,the represented URLs will be input into a proposed composite neural network for *** order to evaluate the proposed system,we made extensive experiments on HTTP CSIC2010 dataset and a dataset we collected,and the experimental results show the effectiveness of the proposed approach.

关键词： CNN anomaly detection web security auto-encoder deep learning

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