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

作者： Zhi Chen Lingxiao Jiang Centre for Research on Intelligent Software Engineering School of Computing and Information Systems Singapore Management University

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

In recent years, AI-based software engineering has progressed from pre-trained models to advanced agentic workflows, with software Development Agents representing the next major leap. These agents, capable of reasoning, planning, and interacting with external environments, offer promising solutions to complex software engineering tasks. However, while much research has evaluated code generated by large language models (LLMs), comprehensive studies on agent-generated patches, particularly in real-world settings, are lacking. This study addresses that gap by evaluating 4,892 patches from 10 top-ranked agents on 500 real-world GitHub issues from SWE-Bench Verified, focusing on their impact on code quality. Our analysis shows no single agent dominated, with 170 issues unresolved, indicating room for improvement. Even for patches that passed unit tests and resolved issues, agents made different file and function modifications compared to the gold patches from repository developers, revealing limitations in the benchmark's test case coverage. Most agents maintained code reliability and security, avoiding new bugs or vulnerabilities; while some agents increased code complexity, many reduced code duplication and minimized code smells. Finally, agents performed better on simpler codebases, suggesting that breaking complex tasks into smaller sub-tasks could improve effectiveness. This study provides the first comprehensive evaluation of agent-generated patches on real-world GitHub issues, offering insights to advance AI-driven software development.

关键词： Gold Codes Large language models Computer bugs Focusing Complexity theory Security Reliability software development management software engineering

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MuLAD: Multimodal Aggression Detection from Social Media Memes Exploiting Visual and Textual Features 27th

MuLAD: Multimodal Aggression Detection from Social Media Me...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hasan, Md. Maruf Ahsan, Shawly Hoque, Mohammed Moshiul Dewan, M. Ali Akber Chittagong University of Engineering and Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada

ISBN: (纸本)9783031781186

Aggression detection from memes is challenging due to their region-specific interpretation and multimodal nature. Detecting or classifying aggressive memes is complicated in low-resource languages (including Bengali) because benchmark datasets and primary language processing software are needed. This paper proposes an innovative meme classification technique that harnesses deep learning (DL) approaches to leverage memes’ visual and textual features in Bengali. Various DL frameworks, such as VGG16, VGG19, ResNet50, CNN, BiLSTM, and BiLSTM+CNN, extract visual and textual features from memes. A novel corpus named the Bengali Meme Dataset (AMemD) is also introduced, comprising a substantial amount of multimodal data, including text and image components. Experimental results on AMemD demonstrate the effectiveness of the proposed approach. The CNN combined with VGG16 obtained the highest f1-score of 0.738 among all multimodal techniques tested. This pioneering research offers valuable insights into the complex task of aggression detection from memes in Bengali and provides a foundation for future studies in this area. The dataset is available at https://***/Maruf089/Multimodal-Aggression-Detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection System

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Computer systems Science & Engineering 2023年第11期47卷 2497-2512页

作者： Mahmoud Ragab Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The Internet of Things(IoT)system has confronted dramatic growth in high dimensionality and data *** system named intrusion detection systems(IDS)is broadly utilized for the enhancement of security posture in an IT *** IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive ***,machine learning(ML)-related techniques were used for detecting intrusion in IoTs ***,the IoT IDS mechanism faces significant challenges because of physical and functional *** IoT features use every attribute and feature for IDS self-protection unrealistic and *** study develops a Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning(MM-WMVEDL)model for *** proposed MM-WMVEDL technique aims to discriminate distinct kinds of attacks in the IoT *** attain this,the presented MM-WMVEDL technique implements min-max normalization to scale the input *** feature selection purposes,the MM-WMVEDL technique exploits the Harris hawk optimization-based elite fractional derivative mutation(HHO-EFDM)*** the presented MM-WMVEDL technique,a Bi-directional long short-term memory(BiLSTM),extreme learning machine(ELM)and an ensemble of gated recurrent unit(GRU)models take place.A wide range of simulation analyses was performed on CICIDS-2017 dataset to exhibit the promising performance of the MM-WMVEDL *** comparison study pointed out the supremacy of the MM-WMVEDL method over other recent methods with accuracy of 99.67%.

关键词： Internet of Things intrusion detection system machine learning ensemble deep learning metaheuristics

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Wave Density Spectra Estimation with LSTM from Sentinel-1 SAR in the Baltic Sea 13

Wave Density Spectra Estimation with LSTM from Sentinel-1 SA...

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13th IEEE/OES Current, Waves and Turbulence Measurement, CWTM 2024

作者： Rikka, Sander Nomm, Sven Alari, Victor Simon, Martin Bjorkqvist, Jan-Victor Tallinn University of Technology School of Science Department of Marine Systems Tallinn Estonia Tallinn University of Technology School of Information Technology Department of Software Science Tallinn Estonia Norwegian Meteorological Institute Bergen Norway

ISBN: (纸本)9798350393910

Surface wave spectra estimation from Synthetic Aperture Radar (SAR) data has been a subject of many studies. In the open ocean, where high-resolution Wave Mode data capture predominately swell waves, various inverse methods have been developed to extract 2D wave information. However, these methods face limitations in wind-dominated regional seas such as the Baltic Sea, where swell waves are lacking, and ScanSAR (TOPS mode for Sentinel-1 (S1)) acquisitions with lower resolution are widespread. To overcome the problem, a long-short-term memory (LSTM) recurrent neural network is used to estimate 1D wave density spectra from S1 SAR image spectra. A total of 71,110 Sentinel-1 Interferometric Wide (IW) swath subimages along with the corresponding NORA3 WAM hindcast spectra are processed and used to train the LSTM model. The experiments show that the frequency-to-frequency correlations exceed 0.8 between 0.2 Hz and 0.4 Hz in the test dataset. The peak frequency of the estimated spectra differs from that of the baseline spectra by 0.13 Hz. The peak value of the spectra shows a difference of 0.38 m2/Hz. The bulk wave parameters calculated from the predicted spectra show a high correlation (0.78 for HS) and low RMSE values (0.39 m for HS ), which achieves similar performance than previous empirical methods. © 2024 IEEE.

关键词： Long short-term memory

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Data-centric-infrastructure Functional Architecture

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NTT Technical Review 2022年第5期20卷 24-31页

作者： Masutani, Hitoshi Schumacher, Christoph Takasugi, Koichi Network Service Platform Research Group Core Network Technology Research Project NTT Network Service Systems Laboratories Japan Computing Systems Group System Software Project NTT Software Innovation Center Japan Frontier Communication Laboratory NTT Network Innovation Laboratories Japan

Society is changing at a rapid pace due to technical innovations in Internet technologies and cloud computing. In the near future, ever-more refined artificial-intelligence applications are expected, working on ever-larger data sets. Implementing such applications requires increasing the computational capacity available to the providers of such newly emerging services. However, energy consumption of such services must be reduced to achieve ESG (environmental, social, and governance) and the sustainable development goals. To fulfill both of these objectives at the same time, a drastic rethink of computing infrastructure is required. With this in mind, the IOWN Global Forum is creating the data-centric-infrastructure (DCI) functional architecture. This article gives an overview of the first DCI functional architecture reference document about this next-generation computing foundation. © 2022 Nippon Telegraph and Telephone Corp.. All rights reserved.

关键词： Sustainable development

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Phase-bias-blind computational ghost imaging with optical phased array

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Optics Express 2025年第10期33卷 21132-21141页

作者： Hirata, Sota Matsuda, Naoki Komatsu, Kento Tanemura, Takuo Horisaki, Ryoichi Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan

We propose a method for computational ghost imaging (CGI) with an optical phased array (OPA), wherein phase biases on the phase shifters are intrinsically unknown. In our method, an object is illuminated with a number of light fields from the OPA using random phase shifts, and the light from the object is captured by a single-pixel detector. The object and the phase biases are simultaneously estimated from the measured intensity signals. We numerically demonstrate the proposed method. Furthermore, we present complex amplitude field acquisition in OPA-based CGI with intensity measurements performed without reference light by simply adding an iris to the detector. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Computational Imaging

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SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain Management 4

SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain ...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Amarasinghe, A.A.B.G. Gamagedara, G.M.H.S. Pitigala, K.K. Subawickrama, H.D.A.E. Abeywardena, Kavinga Yapa Yapa, Kanishka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. The rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. There is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. Throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. This situation is worse in developing countries. The unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. This solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. This can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application. © 2022 IEEE.

关键词： Smart contract

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Boosting Robustness in Automatic Modulation Recognition for Wireless Communications

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1635-1648页

作者： Zhao, Yuhang Wang, Yajie Zhang, Chuan Li, Chunhai Xiong, Zehui Zhu, Liehuang Niyato, Dusit Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Guilin University of Electronic Technology Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin541004 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Nanyang Technological University College of Computing and Data Science 639798 Singapore

In the radio frequency field, deep neural networks have been widely used for automatic modulation recognition tasks due to their superior accuracy. However, it has been shown that these models are susceptible to adversarial examples, which are the kinds of carefully crafted perturbations that can lead to model misclassification and raise security issues in applications. To solve this problem, we propose an Ultra-Fusion Adversarial Training method, which combines adversarial training and ensemble learning to enable the model robustness to withstand different attack strengths. We explore the number and distribution of ensembled attacks and introduce a Fermi-function-like distribution to optimally balance the performance of different attack strengths. Additionally, we investigate the effect of the signal-to-noise ratio (SNR) interval on the model accuracy and robustness, suggesting the effective SNR interval for training. Considering the demand for practical application scenarios of modulation recognition, we propose a comprehensive robustness metric based on weighted integral to evaluate the robustness of the trained models. Numerical experiments demonstrate that our method improves the model's robustness by 31.89% against white-box attacks, and achieves up to an 80.54% improvement in black-box scenarios. These results show that our method has the ability to resiliently resist potential attacks of various strengths and can be applied to spectrum application scenarios with high-security requirements. © 2015 IEEE.

关键词： Signal modulation

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Junior software Developers’ Perspectives on Adopting LLMs for software Engineering: a Systematic Literature Review

arXiv

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arXiv 2025年

作者： Ferino, Samuel Hoda, Rashina Grundy, John Treude, Christoph Department of Software Systems and Cybersecurity Faculty of IT Monash University Melbourne Australia School of Computing and Information Systems Singapore Management University Singapore

Context: Many studies exploring the adoption of Large Language Model-based tools for software development by junior developers have emerged in recent years. These studies have sought to understand developers’ perspectives about using those tools, a fundamental pillar for successfully adopting LLM-based tools in software Engineering. Objective: The aim of this paper is to provide an overview of junior software developers’ perspectives and use of LLM-based tools for software engineering (LLM4SE). Method: We conducted a systematic literature review (SLR) following guidelines by Kitchenham et al. on 56 primary studies, applying the definition for junior software developers as software developers with equal or less than five years of experience, including Computer Science/software Engineering students. Results: We found that the majority of the studies focused on comprehending the different aspects of integrating AI tools in SE. Only 8.9% of the studies provide a clear definition for junior software developers, and there is no uniformity. Searching for relevant information is the most common task using LLM tools. ChatGPT was the most common LLM tool present in the studies (and experiments). A majority of the studies (83.9%) report both positive and negative perceptions about the impact of adopting LLM tools. We also found and categorized advantages, challenges, and recommendations regarding LLM adoption. Conclusion: Our results indicate that developers are using LLMs not just for code generation, but also to improve their development skills. Critically, they are not just experiencing the benefits of adopting LLM tools, but they are also aware of at least a few LLM limitations, such as the generation of wrong suggestions, potential data leaking, and AI hallucination. Our findings offer implications for software engineering researchers, educators, and developers. © 2025, CC BY-NC-ND.

关键词： Computer aided software engineering

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Efficient Storage and Retrieval of Similar Data in Edge computing systems 29

Efficient Storage and Retrieval of Similar Data in Edge Comp...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Liu, Yuanfeng Xie, Junjie Liao, Hanlong Chen, Sheng Liu, Xiulong Guo, Deke Tianjin University College of Intelligence and Computing Tianjin300350 China AMS Institute of Systems Engineering Beijing100141 China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha410073 China

ISBN: (纸本)9798350330717

Edge computing is migrating services from remote clouds to the network edge, where a vast amount of data is also flowing into edge nodes. In this context, the Edge Data-Sharing System (EDSS) enhances service quality by enabling edge nodes to cooperate. However, the EDSS is suitable for precise search and faces the existing high overhead when many users retrieve similar data. To solve the obstacle, this paper proposes a similarity-based edge storage system, SESS, which leverages the software-defined edge network to realize efficient storage and retrieval of similarity data. We first design RealminHash, a core module of SESS, for efficient signature and and index for each data. Then, SESS calculates the storage strategy based on the similarity between data. Importantly, SESS adjusts this strategy using periodic network information to ensure load balancing. Experimental results demonstrate that SESS realizes the nearest-neighbor storage while maintaining load balancing. SESS outperforms the well-known k-means and spectral clustering methods in terms of accuracy and latency and supports millisecond similar queries. © 2023 IEEE.

关键词： Edge computing LSH minHash similarity-based storage software-defined edge network

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