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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Lizhi Cai Jin Wang Mingang Chen Jilong Wang School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

With the popularity of deep reinforcement learning(DRL), people have great interest in using deep reinforcement learning for application automated testing. However, most automated testing methods based on reinforcement learning ignore text information, use random sampling in experience replay and ignore the characteristics of Android automated testing. To solve above problem, this paper proposes ITPRTesting(Integrated Text feature information and Priority experience in Testing). It extracts the text information in the interface and uses the BERT algorithm to generate sentence vectors. It fuses the interactive control feature diagram(ICFD), which is mentioned in the previous work, and text information as the state required by reinforcement learning. And in reinforcement learning, the priority experience replay is combined, also the traditional priority experience replay is improved. This paper has carried out experiments on 10 open source applications. The experimental results show that ITPRTesting is superior to other methods in statement coverage and branch coverage.

关键词： Deep learning Fuses Bit error rate Reinforcement learning software quality Feature extraction software reliability

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Enhancing the Transferability of Adversarial Attacks with Stealth Preservation

Enhancing the Transferability of Adversarial Attacks with St...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Xinwei Zhang Tianyuan Zhang Yitong Zhang Shuangcheng Liu School of Computer Science and Engineering Beihang University Beijing China State Key Lab of Software Development Environment Beihang University Beijing China Shen Yuan Honors College Beihang University Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep neural networks are susceptible to attacks from adversarial examples in recent years. Especially, the black-box attacks cause a more serious threat to practical applications. However, while most existing black-box attacks have achieved a high success rate in deceiving models, they have not focused on the stealthiness of adversarial examples, often exhibiting suspicious visual appearances. To address this issue, this paper proposes the Mask Momentum Iterative Attack (MMIA), which introduces a masking mechanism and adopts an optimal perturbation strategy to identify regions of an image most vulnerable to attacks. This approach effectively ensures the transferability and stealthiness of adversarial examples. Simultaneously, by integrating image enhancement techniques and temporal and spatial momentum terms into the iterative process of the attack, we prevent the attack from getting stuck in local optima, further improving the transferability of adversarial examples. To enhance the success rate of black-box attacks, we apply MMIA to a model ensemble using a joint optimization strategy. We demonstrate that adversarially trained models with a strong defense ability are also susceptible to our black-box attacks. We conduct extensive experiments on classification tasks using common vision models, and our results significantly demonstrate the superiority of our method over state-of-the-art approaches when considering both transferability and stealthiness.

关键词： Visualization Fuses Computational modeling Perturbation methods Closed box Pattern recognition Iterative methods

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Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework

Enhanced Student Stress Prediction in Higher Education Using...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati K. Amudha D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Mullana Ambala India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance.

关键词： Training Accuracy Redundancy Human factors Machine learning Mental health Convolutional neural networks Integrated circuit modeling Stress Principal component analysis

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Three-Dimensional Numerical Study on the Effect of Radius of the Equal-Area Mixing Chamber on Auxiliary Entraining of the Two-Stage Ejector

SSRN

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SSRN 2023年

作者： Yan, Jia Gao, Haiping Wang, Zheng School of Civil Engineering and Architecture Southwest University of Science and Technology Mianyang City621010 China Tianfu Engineering Numerical Simulation and Software Innovation Center School of Computer Sciences Sichuan University Chengdu City610041 China

In this study, for the two-stage ejector under three inlet conditions of air-conditioning, refrigerating and freezing, a three-dimensional simulation is used to study the impact of the mixing chamber radius of the first and second stage on the minimum pressure in the ejector without auxiliary entraining first, and then, the impact of real auxiliary entraining effects on ejector performance for two-stage ejector with varied equal-area mixing chamber radius is evaluated accordingly. Results show that: (1) as the second stage equal-area mixing chamber radius (Ram2) increases, the minimum first stage ejector pressure (Pmin1) decreases and the minimum second stage ejector pressure (Pmin2) increases;(2) with the increase of radius of the first-stage equal-area mixing chamber (Ram1), the decreasing effect of Pmin1 and the increasing effect of Pmin2 are both weakened as Ram2 increases from 3.0 mm to 4.6 mm;(3) the minimum pressures of the two-stage have a great influence on the auxiliary entraining effect. © 2023, The Authors. All rights reserved.

关键词： Ejectors (pumps)

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Relationship between factors influencing the software development process and software defects

Relationship between factors influencing the software develo...

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IEEE International Conference on Dependable Systems, Services and Technologies (DESSERT)

作者： Oleksandr Gordieiev Daria Gordieieva Austen Rainer Olga Pishchukhina Department of Software engineering Lutsk National Technical University Lutsk Ukraine School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom Department of Computer Engineering and Cybersecurity Lutsk National Technical University Lutsk Ukraine

Poor software quality can lead to crashes, failures or downtime, the consequences of which may be lost profits, financial costs, leakage or loss of data, accidents, human casualties, material losses or environmental disasters. The total cost of poor software quality for IT companies in the USA is estimated at ${\$}$2.41 trillion per year and tends to increase. Among the identified costs, the cost of unsuccessful projects is estimated at ${\$}$260 billion, and the total cost of operational failures caused by poor quality software is estimated at ${\$}$1.81 trillion. Late finding and fixing of software defects directly affects the success of the project as a whole, since it significantly increases the cost and development time, and affects its quality. This paper discussed the relationship between factors affecting the software development process and various groups of defects. It has been found that the leading groups of defects affecting the software quality are the defects in software requirements and defects in the design of software interfaces for human-computer interaction with the total percentage of influence of 33.3%.

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Machine Learning and Deep Learning Methods on Breast Cancer Metastases Detection

Machine Learning and Deep Learning Methods on Breast Cancer ...

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作者： Wentao Zheng Kan Liu Fuqi Che Yichen Zheng School of Computer Science and Engineering Central South University School of Optics and Photonics Beijing Institute of Technology School of Software Dalian University of Technology

There is a lot of room for research in how to detect breast cancer metastases in whole slide images more quickly and *** work contrasts several machine learning and deep learning models on the breast cancer metastases detection problem and experiments the impacts of feature extraction,dimension reduction and the sizes of the datasets on these *** work also establishes a breast cancer metastases detection pipeline based on the most accurate model in the *** the models,Dense Model using features trained from DenseNet201 with weights trained by ImageNet is of the best *** work develops a computer aided method to examine the cancerous regions in the whole slide images of breast cancer more quickly and precisely than an experienced doctor does.

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Do bugs propagate? an empirical analysis of temporal correlations among software bugs 35

Do bugs propagate? an empirical analysis of temporal correla...

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35th European Conference on Object-Oriented Programming, ECOOP 2021

作者： Gu, Xiaodong Han, Yo-Sub Kim, Sunghun Zhang, Hongyu School of Software Shanghai Jiao Tong University China Department of Computer Science Yonsei University Seoul Korea Republic of Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The University of New Castle Australia

ISBN: (纸本)9783959771900

The occurrences of bugs are not isolated events, rather they may interact, affect each other, and trigger other latent bugs. Identifying and understanding bug correlations could help developers localize bug origins, predict potential bugs, and design better architectures of software artifacts to prevent bug affection. Many studies in the defect prediction and fault localization literature implied the dependence and interactions between multiple bugs, but few of them explicitly investigate the correlations of bugs across time steps and how bugs affect each other. In this paper, we perform social network analysis on the temporal correlations between bugs across time steps on software artifact ties, i.e., software graphs. Adopted from the correlation analysis methodology in social networks, we construct software graphs of three artifact ties such as function calls and type hierarchy and then perform longitudinal logistic regressions of time-lag bug correlations on these graphs. Our experiments on four open-source projects suggest that bugs can propagate as observed on certain artifact tie graphs. Based on our findings, we propose a hybrid artifact tie graph, a synthesis of a few well-known software graphs, that exhibits a higher degree of bug propagation. Our findings shed light on research for better bug prediction and localization models and help developers to perform maintenance actions to prevent consequential bugs. © 2021 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Open source software

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Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey

arXiv

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arXiv 2024年

作者： Ye, Mang Shen, Wei Du, Bo Snezhko, Eduard Kovalev, Vassili Yuen, Pong C. The National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China United Institute of Informatics Problems of Belarus National Academy of Sciences National Academy of Sciences of Belarus Minsk Belarus Department of Computer Science Hong Kong Baptist University Hong Kong

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm where different parties collaboratively learn models using partitioned features of shared samples, without leaking private data. Recent research has shown promising results addressing various challenges in VFL, highlighting its potential for practical applications in cross-domain collaboration. However, the corresponding research is scattered and lacks organization. To advance VFL research, this survey offers a systematic overview of recent developments. First, we provide a history and background introduction, along with a summary of the general training protocol of VFL. We then revisit the taxonomy in recent reviews and analyze limitations in-depth. For a comprehensive and structured discussion, we synthesize recent research from three fundamental perspectives: effectiveness, security, and applicability. Finally, we discuss several critical future research directions in VFL, which will facilitate the developments in this field. We provide a collection of research lists and periodically update them at https://***/shentt67/VFL_Survey. Copyright © 2024, The Authors. All rights reserved.

关键词： Privacy-preserving techniques

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Delving into the reversal curse: how far can large language models generalize? 24

Delving into the reversal curse: how far can large language ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhengkai Lin Zhihang Fu Kai Liu Liang Xie Binbin Lin Wenxiao Wang Deng Cai Yue Wu Jieping Ye State Key Lab of CAD & CG Zhejiang University and Alibaba Cloud Alibaba Cloud Alibaba Cloud and College of Biomedical Engineering & Instrument Science Zhejiang University College of Computer Science and Technology Zhejiang University of Technology School of Software Technology Zhejiang University and Fullong Inc. School of Software Technology Zhejiang University State Key Lab of CAD & CG Zhejiang University

ISBN: (纸本)9798331314385

While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks. A prime example is the recently debated "reversal curse", which surfaces when models, having been trained on the fact "A is B", struggle to generalize this knowledge to infer that "B is A". In this paper, we examine the manifestation of the reversal curse across various tasks and delve into both the generalization abilities and the problem-solving mechanisms of LLMs. This investigation leads to a series of significant insights: (1) LLMs are able to generalize to "B is A" when both A and B are presented in the context as in the case of a multiple-choice question. (2) This generalization ability is highly correlated to the structure of the fact "A is B" in the training documents. For example, this generalization only applies to biographies structured in "[Name] is [Description]" but not to "[Description] is [Name]". (3) We propose and verify the hypothesis that LLMs possess an inherent bias in fact recalling during knowledge application, which explains and underscores the importance of the document structure to successful learning. (4) The negative impact of this bias on the downstream performance of LLMs can hardly be mitigated through training alone. These findings offer a novel perspective on interpreting LLMs' generalization through their intrinsic mechanisms and provide insights for developing more effective learning methods. https://***/alibaba/thinking_***

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Correction: Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions

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Discover Sustainability 2025年第1期6卷 1-2页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Khan, Muhammad Amir Rehman, Ateeq Ur Saeed, Mamoon M. Hamam, Habib Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Pakistan School of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Computer Science School Education Department Government of Punjab Layyah Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia School of Computing Gachon University Seongnam Republic of Korea Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Sana’a Yemen Faculty of Engineering Uni de Moncton Moncton Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia

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