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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Vahid Majdinasab Michael Joshua Bishop Shawn Rasheed Arghavan Moradidakhel Amjed Tahir Foutse Khomh Department of Computer and Software Engineering Polytechnique Montreal Canada School of Mathematical and Computational Sciences Massey University New Zealand Information & Communication Technology Group UCOL - Te Pukenga New Zealand

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

AI-powered code generation models have been developing rapidly, allowing developers to expedite code generation and thus improve their productivity. These models are trained on large corpora of code (primarily sourced from public repositories), which may contain bugs and vulnerabilities. Several concerns have been raised about the security of the code generated by these models. Recent studies have investigated security issues in AI-powered code generation tools such as GitHub Copilot and Amazon Code Whisperer, revealing several security weaknesses in the code generated by these tools. As these tools evolve, it is expected that they will improve their security protocols to prevent the suggestion of insecure code to developers. This paper replicates the study of Pearce et al., which investigated security weaknesses in Copilot and uncovered several weaknesses in the code suggested by Copilot across diverse scenarios and languages (Python, C, and Verilog). Our replication examines Copilot's security weaknesses using newer versions of Copilot and CodeQL (the security analysis framework). The replication focused on the presence of security vulnerabilities in Python code. Our results indicate that, with the improvements in newer versions of Copilot, the percentage of vulnerable code suggestions has reduced from 36.54% to 27.25%. Nonetheless, it remains evident that the model still suggests insecure code.

关键词： Productivity Codes Protocols Reviews Filtering software Security

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A Hybrid Blockchain-Edge Architecture for Electronic Health Record Management With Attribute-Based Cryptographic Mechanisms

A Hybrid Blockchain-Edge Architecture for Electronic Health ...

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作者： Guo, Hao Li, Wanxin Nejad, Mark Shen, Chien-Chung Northwestern Polytechnical University Research and Development Institute Shenzhen518057 China Northwestern Polytechnical University School of Software Xi'an710129 China Xi'an Jiaotong-Liverpool University Department of Communications and Networking Suzhou215123 China University of Delaware Department of Civil and Environmental Engineering NewarkDE19716 United States University of Delaware Department of Computer and Information Sciences NewarkDE19716 United States

This paper presents a hybrid blockchain-edge architecture for managing Electronic Health Records (EHRs) with attribute-based cryptographic mechanisms. The architecture introduces a novel attribute-based signature aggregation (ABSA) scheme and multi-authority attribute-based encryption (MA-ABE) integrated with Paillier homomorphic encryption (HE) to protect patients' anonymity and safeguard their EHRs. All the EHR activities and access control events are recorded permanently as blockchain transactions. We develop the ABSA module on Hyperledger Ursa cryptography library, MA-ABE module on OpenABE toolset, and blockchain network on Hyperledger Fabric. We measure the execution time of ABSA's signing and verification functions, MA-ABE with different access policies and homomorphic encryption schemes, and compare the results with other existing blockchain-based EHR systems. We validate the access activities and authentication events recorded in blockchain transactions and evaluate the transaction throughput and latency using Hyperledger Caliper. The results show that the performance meets real-world scenarios' requirements while safeguarding EHR and is robust against unauthorized retrievals. © 2004-2012 IEEE.

关键词： Blockchain

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Imbalanced Nodes Classification for Graph Neural Networks Based on Valuable Sample Mining 6

Imbalanced Nodes Classification for Graph Neural Networks Ba...

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6th International Conference on Electronic Information Technology and computer engineering, EITCE 2022

作者： Liu, Min Jin, Siwen Jin, Luo Wang, Shuohan Fang, Yu Shi, Yuliang School of Software Beijing University of Technology Beijing China School of Electronic Information Engineering Sias University Henan province Zhengzhou China Computer Department Beijing Technology and Business University Beijing China School of Mechanical and Marine Engineering Beibu Gulf University Guangxi Zhuang Autonomous Region Qinzhou City China Department of Computer Science Wenzhou-Kean University Zhejiang Province Wenzhou China

ISBN: (纸本)9781450397148

Node classification is an important task in graph neural networks, but most existing studies assume that samples from different classes are balanced. However, the class imbalance problem is widespread and can seriously affect the model's performance. Reducing the adverse effects of imbalanced datasets on model training is crucial to improve the model's performance. Therefore, a new loss function FD-Loss is reconstructed based on the traditional algorithm-level approach to the imbalance problem. Firstly, we propose sample mismeasurement distance to filter edge-hard samples and simple samples based on the distribution. Then, the weight coefficients are defined based on the mismeasurement distance and used in the loss function weighting term, so that the loss function focuses only on valuable samples. Experiments on several benchmarks demonstrate that our loss function can effectively solve the sample node imbalance problem and improve the classification accuracy by 4% compared to existing methods in the node classification task. © 2022 Association for Computing Machinery.

关键词： Classification (of information)

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Validating IP Reputation in Cloud Firewall Systems Using Machine Learning Driven Signature Generation and Detection Techniques

Validating IP Reputation in Cloud Firewall Systems Using Mac...

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IEEE Industrial Electronics and Applications Conference (IEACon)

作者： NW Chanka Lasantha Valmik Tilwari MWP Maduranga Ruvan Abeysekara Nilotpal Chakraborty Debashree Sharma Faculty of Graduate Studies IIC University of Technology Phnom Penh Cambodia Department of Electronics & Communication Engineering Indian Institute of Information Technology Guwahati India Department of Software Engineering Sri Lanka Technology Campus Padukka Sri Lanka Department of Computer Science Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798350382976

ISBN: (纸本)9798350382983

This paper offers a new approach to validation for IP address reputation (IPR) using dynamic signatures achieved through the application of machine learning (ML) to conduct an accurate detection of malicious IPs. The proposed solution connects to the Amazon Web Services (AWS) Web Application Firewall (WAF) to improve security by deploying artificial intelligence (AI) models and natural language (NL) processing for automation of information extraction from IP-related information saved in the AWS object storages. All these lead to minimization of the number of false positives, real-time feedback, and optimization of IP reputation checking. The experimental outcome proves the capability of the proposed system to mitigate the IP address of the attacker and improve the protection of web applications against cybercrimes. This new approach is much advanced and a major leap ahead of other conventional IP reputation techniques, which offer a good protection shield against spam, DDoS, and other such evils.

关键词： Radio frequency Accuracy Firewalls (computing) Web services Natural languages Real-time systems IP networks Protection computer crime Optimization

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An Efficient Deep Learning Model based Diagnosis System for Lung Cancer Disease

An Efficient Deep Learning Model based Diagnosis System for ...

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International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Gul Zaman Khan Ibrar Ali Shah Farhatullah Muhammad Ikram Ullah Inam Ullah Muhammad Ihtesham Hazrat Junaid Spogmay Yousafzai Fouzia Sardar Department of Computer software Engineering UET Mardan Pakistan School of Automation China University of Geosciences Wuhan China School of software Dalian Jiaotong University Dalian China Department of Computer science Abdul Wali Khan University Maradan Pakistan Department of Computer Science and Information Technology University of Malakand Dir Lower Pakistan Department of zoology University of Malakand Dir. Lower Pakistan

Lung cancer illness seriously impacts people's health. Medical history-based detection of lung cancers has been utilized but it has unsatisfactory results. Artificial intelligence algorithms are more precise and efficient in classifying lung cancer patients and healthy persons. Additionally, the medical history-based diagnosis of lung cancer disease is costly and time consuming. The life of lung cancer disease is very short after detection. Artificial intelligence-based diagnosis systems can detect the lung cancer disease early and efficiently. However, previous research work as several limitations, for example, some techniques computation time is very high but their accuracy is good while some techniques have less computation time but accuracy is not good. The proposed work suggests a deep convolutional neural network-based diagnosis system for lung cancer disease early and accurate detection. We made use of publically available dataset downloaded from Kaggle online repository and applied deep convolutional neural network for accurate lung cancer detection. Furthermore, we have applied some preprocessing and features selection techniques such as max, min, standard deviation and variance threshold. The proposed CNN model achieved 99.2% validation accuracy, 99.8% training accuracy, 99% precision, and 99% recall in minimum computation time of 6 sec which is acceptable.

关键词： Training Deep learning Computational modeling Lung cancer Lung Feature extraction Mathematical models

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Towards a global machine learning model to fill gaps in flood frequency: Random Forest to estimate MNDWI in Bangladesh

Towards a global machine learning model to fill gaps in floo...

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Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International Conference on

作者： James Houghton Carolynne Hultquist James Atlas Andrew Zimmer Computer Science and Software Engineering University of Canterbury Christchurch NZ School of Earth and Environment University of Canterbury Christchurch NZ Department of Earth Sciences Montana State University Bozeman MT USA

ISBN: (数字)9798350389678

ISBN: (纸本)9798350389685

An understanding of historical trends of flooding is essential to capture current risk as more people are exposed to flood hazards than ever before. Flood modeling relies on accurate and frequent remote sensing inputs to produce reliable flood risk results. However, remote sensing datasets are limited by temporal resolution which, in the case of Landsat, leaves 87% of days without observation. This study aims to address these gaps in observation using a Random Forest model to predict the Modified Normalized Difference Water Index (MNDWI) in Landsat images by fusing gridded precipitation and elevation data. The trained model is then used to estimate MNDWI for when there are no Landsat observations. The model was evaluated on a 50 by 50 pixel test patch achieving a residual squared error between the true average MNDWI and the estimated average MNDWI of 0.48.

关键词： Time-frequency analysis Precipitation Predictive models Market research Data models Floods Reliability

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze

arXiv

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arXiv 2022年

作者： Pourmemar, Majid Joshi, Yashas Poullis, Charalambos Immersive and Creative Technologies Lab Department of Computer Science and Software Engineering Concordia University Montreal Canada

There are currently limited guidelines on designing user interfaces (UI) for immersive augmented reality (AR) applications. Designers must reflect on their experience designing UI for desktop and mobile applications and conjecture how a UI will influence AR users' performance. In this work, we introduce a predictive model for determining users' performance for a target UI without the subsequent involvement of participants in user studies. The model is trained on participants' responses to objective performance measures such as consumed endurance (CE) and pointing time (PT) using hierarchical drop-down menus. Large variability in the depth and context of the menus is ensured by randomly and dynamically creating the hierarchical drop-down menus and associated user tasks from words contained in the lexical database WordNet. Subjective performance bias is reduced by incorporating the users' non-verbal standard performance WAIS-IV during the model training. The semantic information of the menu is encoded using the Universal Sentence Encoder. We present the results of a user study that demonstrates that the proposed predictive model achieves high accuracy in predicting the CE on hierarchical menus of users with various cognitive abilities. To the best of our knowledge, this is the first work on predicting CE in designing UI for immersive AR applications. © 2022, CC BY-NC-ND.

关键词： User interfaces

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An n-MOSFET Layout with Multi-Finger Z Gate for Radiation Tolerance 5

An n-MOSFET Layout with Multi-Finger Z Gate for Radiation To...

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5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Shan, Chan Liu, Ying Chen, Huan Huo, Dong-Yang Xu, Tian-Rong Jimei University College of Ocean Information Engineering Xiamen China Xiamen Institute of Software Technology Department of Software Technology Xiamen China Minjiang University School of Journalism and Communication Fuzhou China Universiti Malaya Faculty of Computer Science and Information Technology Department of Information Systems Kuala Lumpur Malaysia Universiti Malaya Planning Faculty of Built Environment Department of Urban and Regional Kuala Lumpur Malaysia

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

In this paper, a new n-MOSFET layout with multi-finger Z gate is proposed to reduce the total ionizing dose (TID) effect. In addition to the proposed layout, multi-finger single gate layout is also simulated using Sentaurus TCAD in 3D. Firstly, drain current - gate voltage curves of both layouts are compared in order to verify the radiation hardening ability of the proposed layout. Secondly, we compare TID responses by extracting threshold voltages and leakage currents from both layouts. Thirdly, the finger number is investigated for both layouts because it is an important factor for multi-finger layout. The results have indicated that the finger number significantly affects the multi-finger single gate layout, whereas the proposed multi-finger Z gate layout is relatively little affected by the finger number in radiation environment. © 2022 IEEE.

关键词： Drain current

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SSC- l0 : Sparse Subspace Clustering with the l0 Inequality Constraint 7th

SSC- l0 : Sparse Subspace Clustering with the l0 Inequality...

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Wang, Yangbo Zhou, Jie Lin, Qingshui Lu, Jianglin Gao, Can National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Basic Teaching Department Liaoning Technical University Huludao125105 China College of Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9783031476648

Self-expression learning methods often obtain a coefficient matrix to measure the similarity between pairs of samples. However, directly using all points to represent a fixed sample in a class under the self-expression framework may not be ideal, as points from other classes participate in the representing process. To alleviate this issue, this study attempts to achieve representation learning between points only coming from the same class. In practice, it is easier for data points from the same class to represent each other than that from different classes. So, when reconstructing a point, if the number of non-zero elements in the coefficient vector is limited, a model is more likely to select data points from the class where the reconstructed point lies to complete the reconstruction work. Based on this idea, we propose Sparse Subspace Clustering with the l0 inequality constraint (SSC- l0 ). In SSC- l0, the l0 inequality constraint determines the maximum number of non-zero elements in the coefficient vector, which helps SSC- l0 to conduct representation learning among the points in the same class. After introducing the simplex constraint to ensure the translation invariance of the model, an optimization method concerning l0 inequality constraint is formed to solve the proposed SSC- l0, and its convergence is theoretically analyzed. Extensive experiments on well-known datasets demonstrate the superiority of SSC- l0 compared to several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Learning systems

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