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24th IEEE International Conference on Source Code Analysis and Manipulation, SCAM 2024

作者： Alrashedy, Kamel Binjahlan, Ahmed Georgia Institute of Technology School of Computer Science AtlantaGA United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9798331528508

Millions of developers share their code on open-source platforms like GitHub, which offer social coding opportunities such as distributed collaboration and popularity-based ranking. Software engineering researchers have joined in as well, hosting their research artifacts (tools, replication package & datasets) in repositories, an action often marked as part of the publication's contribution. Yet a decade after the first such paper-with-GitHub-link, little is known about the fate of such repositories in practice. Do research repositories ever gain the interest of the developer community, or other researchers? If so, how often and why (not)? Does effort invested on GitHub pay off with research impact? In short: we ask whether and how authors engage in social coding related to their research. We conduct a broad empirical investigation of repositories from published work, starting with ten thousand papers in top SE research venues, hand-Annotating their 3449 GitHub (and Zenodo) links, and studying 309 paper-related repositories in detail. We find a wide distribution in popularity and impact, some strongly correlated with publication venue. These were often heavily informed by the authors' investment in terms of timely responsiveness and upkeep, which was often remarkably subpar by GitHub's standards, if not absent altogether. Yet we also offer hope: popular repositories often go hand-in-hand with well-cited papers and achieve broad impact. Our findings suggest the need to rethink the research incentives and reward structure around research products requiring such sustained contributions. © 2024 IEEE.

关键词： Software engineering

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Ensemble-based Machine Learning Classification for Early Detection of Autism Spectrum Disorder 3

Ensemble-based Machine Learning Classification for Early Det...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Subhash, Ambika Rani Ashwin Kumar, U.M. School of Computer Science and Engineering REVA University Bengaluru India Department of Information Science and Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (纸本)9798331527549

A complicated neurological illness, autism spectrum disorder (ASD) impacts social skills, communication, and other behavioral elements. Because the symptoms of ASD might be confused with those of other mental health conditions, diagnosing it can take a long time. The goal of this study is to develop a categorization model that can forecast the risk of ASD in toddlers, kids, teens, and adults. To improve speed, we used a variety of machine learning methods and ensembled them. Four publicly accessible non-clinical ASD screening datasets from Kaggle and the UCI library were used to assess the models. These datasets included 1,054 cases with 19 features for toddlers and different characteristics for older age groups. The classifiers' accuracy was almost 99%, according to the results. © 2025 IEEE.

关键词： Adversarial machine learning

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Intrusion Detection System Focused on Deep Learning for Mobile IoT Networks 24

Intrusion Detection System Focused on Deep Learning for Mobi...

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10th International Conference on Computing and Artificial Intelligence, ICCAI 2024

作者： Aljumah, Abdullah Ahanger, Tariq Ahamed College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Saudi Arabia School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW Australia

ISBN: (纸本)9798400717055

Detection mechanisms for intrusion play a key role in the identification of vulnerable behaviors that denigrate IoT networks. Mobile Adhoc Networks (MANETs) are wireless networks with the ability to transmit data without the need for infrastructure to run them. Recently, the IoT networking model has arisen, which is a superset of the above conceptualization. Its fragmented existence and minimal availability of resources pose one of the major issues to provide secure data communication. Intrusion Detection System (IDS) is indispensable for acclimatizing with difficulties. Researchers have traditionally used a heuristic approach focused on the heterogeneity of appropriately classified cases (CCIs), with a cumulative estimate of fluctuation (AMoF). It consists of 2 phases;phase-1 accumulates values by dedicated sniffers (DSs) to produces the CCI which is further transmitted to the supernode (SN). SN conducts the linear regression procedure over the CCIs acquired from DSs in phase-2 to separate the benevolent and vulnerable nodes. For various extreme network situations, the identification characterization is provided in this work using Gauss Markov (GM), and Random waypoint (RWP). In the enhanced velocity environment, detection rates surpass 98%, whereas, 90% is registered for limited speed. © 2024 ACM.

关键词： Network intrusion

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Advancing Horizons: Exploring the Aerospace IoT Revolution - Analysis, Advancements, and Prospects for the Future 3

Advancing Horizons: Exploring the Aerospace IoT Revolution -...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Rajora, Ricky Rajora, Aarju Sharma, Bhanu Kumar, Gotte Ranjith Aggarwal, Priyanshi Thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140401 India Chandigarh Engineering College Department of Computer Science & Engineering Mohali Jhanjeri India Sr University School of Computer Science & Artificial Intelligence Warangal India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350381931

This study explores the profound influence of the Internet of Things (IoT) on the aerospace sector, examining the complex interplay between aircraft and IoT technology. This research examines the potential advantages and obstacles associated with the use of IoT technology in the aerospace industry. The primary areas of interest are improved operational effectiveness, heightened safety measures, and more scope for creativity. The use of IoT technology in the aviation industry has seen a notable rise. This has led to the widespread deployment of sensors that collect real-time data pertaining to diverse factors, including engine functionality and the in-flight experiences of passengers. The paper presents a thorough examination of Aerospace IoT, highlighting its significant impact on several areas within the aerospace industry. The objective of this project is to provide significant contributions to scholars, industry experts, policymakers, and technologists by offering important insights on the transformative capabilities of Aerospace IoT. © 2024 IEEE.

关键词： Internet of things

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Low Field MRI Image Synthesis 4

Low Field MRI Image Synthesis

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Wang, Kun Jin, Aobo Guo, Hongchun Wan, Yun Deng, Qixin Chu, Wenhui Raval, Maulin Gohel, Hardik University of Houston Computer Science Houston United States University of Houston Victoria Computer Science Victoria United States Chaoyang Engineering Technology School Basic English Chaoyang China Mathematics and Computer Science Wabash College Crawfordsville United States Computer Science Texas A&M University College Station United States

ISBN: (纸本)9798331518882

Low-field MRI image creation attracts attention because it is critical for advancing medical imaging research. Synthesized low-field MRI images can be automatically paired with relative high-field MRI images to form the dataset to train super-resolution deep learning models. In this work, we provide a detailed comparison of two different approaches to low-field MRI image synthesis, each aimed at simulating the various imaging properties of low-field systems. We propose a series of quantitative and qualitative metrics to evaluate the performance of each method, assessing its ability to replicate critical attributes such as noise levels, spatial resolution, dynamic range, and preservation of anatomical structures. Through the analysis of these variables, we want to highlight the merits and drawbacks of each technique and provide valuable insights into their appropriateness for various clinical and research applications. This review aims to offer suggestions for developing more reliable and accurate low-field MRI synthesis procedures, particularly in resource-constrained contexts where high-field MRI systems may be inaccessible. © 2025 IEEE.

关键词： Magnetic resonance imaging

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MedBot - An NLP based ChatBot for Diabetes Prediction 15

MedBot - An NLP based ChatBot for Diabetes Prediction

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sreekar, K. Aswin, V. Narayanan, Vishnu Thangavel, Senthil Kumar Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350370249

In this age of technology, with digital devices being accessible by almost everyone, have made our lives much better. With the advent of Machine Learning and advanced algorithms at our tips, the applications are immensurate. Such an application can be found in the health-care sector. The need for better and accessible health-care is of immense precedence. In this paper, we employed Machine Learning algorithms like Logistic Regression and other state of the art techniques to predict diabetes, given patient data. Diabetes is an incessant condition that is affecting millions worldwide, every year. Early detection of diabetes and prompt diagnosis is crucial. We have addressed that using a chat-bot to predict diabetes, which we believe is a portable and accessible solution for first-level assessment. We have employed PIMA Indian Diabetes Dataset into our chat-bot, providing interactive medical advice, and enhancing patient engagement, using Telegram API, resulting in an accuracy of 77%. © 2024 IEEE.

关键词： Electronic health record

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Learning a Provably Good Wirelength Model for Analytical Placement

Learning a Provably Good Wirelength Model for Analytical Pla...

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2023 International Symposium of Electronics Design Automation, ISEDA 2023

作者： Chen, Zhiyang Yao, Hailong Yin, Xia Tsinghua University Department of Computer Science and Technology China School of Computer and Communication Engineering University of Science and Technology Beijing China

ISBN: (纸本)9798350304510

Placement is one of the most critical design stages for very large-scale integrated (VLSI) circuits. Modern VLSI placers usually adopt the analytical placement framework, which formulates placement into a non-linear optimization problem. A key ingredient of analytical placers is the wirelength model. Previous works propose various models to approximate the half-perimeter wirelength (HPWL). However, these models rely on human experiences and may not be fully optimized on realworld designs. In this work, we propose a novel data-driven parameterized wirelength model, which learns the best configuration from placement instances in the training set via Bayesian optimization. Our model is effective and efficient, together with a provable upper bound of the generalization gap. Therefore, the model can generalize to testing data that is unseen during training. Experiments show that our model improves HPWL on average by 1.9% compared with the classic weighted average model on ISPD-2005 benchmarks using DREAMPlace. © 2023 IEEE.

关键词： Nonlinear programming

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CHIE: Generative MRC Evaluation for in-context QA with Correctness, Helpfulness, Irrelevancy, and Extraneousness Aspects 2

CHIE: Generative MRC Evaluation for in-context QA with Corre...

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2nd Workshop on Generalisation (Benchmarking) in NLP, GenBench 2024

作者： Phatthiyaphaibun, Wannaphong Nonesung, Surapon Limkonchotiwat, Peerat Udomcharoenchaikit, Can Sawatphol, Jitkapat Chuangsuwanich, Ekapol Nutanong, Sarana School of Information Science and Technology VISTEC Thailand Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand

ISBN: (纸本)9798891761827

The evaluation of generative models in Machine Reading Comprehension (MRC) presents distinct difficulties, as traditional metrics like BLEU, ROUGE, METEOR, Exact Match, and F1 score often struggle to capture the nuanced and diverse responses. While embedding-based metrics such as BERTScore and BARTScore focus on semantic similarity, they still fail to fully address aspects such as recognizing additional helpful information and rewarding contextual faithfulness. Recent advances in large language model (LLM) based metrics offer more fine-grained evaluations, but challenges such as score clustering remain. This paper introduces a multi-aspect evaluation framework, CHIE, incorporating aspects of Correctness, Helpfulness, Irrelevance, and Extraneousness. Our approach, which uses binary categorical values rather than continuous rating scales, aligns well with human judgments, indicating its potential as a comprehensive and effective evaluation method. © 2024 Association for Computational Linguistics.

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Multi-Domain Graph-Enhanced Joint Intent and Slot Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第2期52卷 374-382页

作者： Shi, Wu Yuan, Weihao Wang, Yang Dong, Yindong Xu, Gaojian School of Information and Artificial Intelligence Anhui Agricultural University Hefei230036 China School of Computer Science and Technology Xidian University Xi’an710126 China School of Engineering Anhui Agricultural University Hefei230036 China

Comprehension of spoken language is essential in dialog systems, as it supports two fundamental tasks: intent classification and slot filling. At present, federated modeling methodologies prevail in the domain of spoken language comprehension. Nevertheless, current models encounter constraints in accurately representing the interrelations among activities and utilizing cross-domain semantic data. This study introduces the Multi-Domain Graph-Enhanced Joint Intent and Slot Learning (MDG-JISL) paradigm to tackle these difficulties. MDG-JISL amalgamates a pre-trained BERT model with a self-trained FastText model to create superior sentence-level and word-level representations. A syntactic dependency tree is employed to create graph structures among words, which are subsequently refined by the implementation of a Graph Convolutional Network (GCN) to more effectively capture relationship properties. A Conditional Random Field (CRF) model is utilized for decoding, enhancing the model’s efficacy in natural language processing tasks. Experimental findings indicate that MDG-JISL attains remarkable performance on cross-domain datasets, with Slot(F1), Intent (Acc), and Overall (Acc) metrics of 98.7%, 98.4%, and 92.4%, respectively. The results validate the model’s efficacy in intent classification and slot filling. © (2025), (International Association of Engineers). All rights reserved.

关键词： Syntactics

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MULTISOURCE DUAL UNSUPERVISED DOMAIN ADAPTATION METHOD FOR REMAINING LIFE PREDICTION UNDER MULTIPLE OPERATING CONDITIONS 14

MULTISOURCE DUAL UNSUPERVISED DOMAIN ADAPTATION METHOD FOR R...

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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2024

作者： Zhou, Ningxu Lyu, Yi Shen, Zaichen Xiao, Xu Fan, Ruhui School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer University of Electronic Science and Technology of China Zhongshan Institutes Zhongshan China School of Automation Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781837242672

Aero engines, with their complex structures and intricate gas paths, are a crucial component of the aviation system. Traditional machine learning-based methods for predicting the remaining useful life (RUL) of rolling bearings often struggle to provide adequate feature representation and adaptive extraction due to complex operating conditions and challenges in obtaining degradation data. Given that traditional machine learning methods require substantial amounts of labeled data, they frequently fail to yield satisfactory RUL prediction results. To overcome these limitations, domain adaptation methods have been employed, facilitating the transfer of knowledge from a well-labeled source domain to an unlabeled target domain. Additionally, to augment the available data, a multisource domain transfer approach is proposed, which transfers data from multiple source domains to the target domain intended for prediction. To enhance the effectiveness of this multisource domain transfer method, a dual alignment strategy is introduced for aligning features at both the feature and semantic levels, thereby improving predictive performance. The model incorporates a convolutional neural network (CNN) and long short-term memory (LSTM) as its backbone, leveraging CNN's robust feature extraction capabilities combined with LSTM’s proficiency in processing time-dependent data. Experiments conducted on the CMAPSS dataset, along with comparisons and ablation studies against current popular multisource domain adaptation methods, demonstrate the superiority of the proposed approach. © The Institution of engineering & Technology 2024.

关键词： Convolutional neural networks

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