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arXiv 2024年

作者： Blom, Michelle Singh, Ronal Miller, Tim Sonenberg, Liz Trentelman, Kerry Saulwick, Adam School of Computing and Information Systems The University of Melbourne Melbourne Australia Defence Science and Technology Group Adelaide Australia CSIRO Data61 Melbourne Australia School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

Research in cognitive psychology has established that whether people prefer simpler explanations to complex ones is context dependent, but the question of ‘simple vs. complex’ becomes critical when an artificial agent seeks to explain its decisions or predictions to humans. We present a model for abstracting causal reasoning chains for the purpose of explanation. This model uses a set of rules to progressively abstract different types of causal information in causal proof traces. We perform online studies using 123 Amazon MTurk participants and with five industry experts over two domains: maritime patrol and weather prediction. We found participants’ satisfaction with generated explanations was based on the consistency of relationships among the causes (coherence) that explain an event;and that the important question is not whether people prefer simple or complex explanations, but what types of causal information are relevant to individuals in specific contexts. Copyright © 2024, The Authors. All rights reserved.

关键词： Abstracting

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Exploring the Problems, their Causes and Solutions of AI Pair Programming: A Study on GitHub and Stack Overflow

arXiv

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arXiv 2023年

作者： Zhou, Xiyu Liang, Peng Zhang, Beiqi Li, Zengyang Ahmad, Aakash Shahin, Mojtaba Waseem, Muhammad School of Computer Science Wuhan University Wuhan China School of Computer Science Central China Normal University Wuhan China School of Computing and Communications Lancaster University Leipzig Leipzig Germany School of Computing Technologies RMIT University Melbourne Australia Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

With the recent advancement of Artificial Intelligence (AI) and Large Language Models (LLMs), AI-based code generation tools become a practical solution for software development. GitHub Copilot, the AI pair programmer, utilizes machine learning models trained on a large corpus of code snippets to generate code suggestions using natural language processing. Despite its popularity in software development, there is limited empirical evidence on the actual experiences of practitioners who work with Copilot. To this end, we conducted an empirical study to understand the problems that practitioners face when using Copilot, as well as their underlying causes and potential solutions. We collected data from 473 GitHub issues, 706 GitHub discussions, and 142 Stack Overflow posts. Our results reveal that (1) Operation Issue and Compatibility Issue are the most common problems faced by Copilot users, (2) Copilot Internal Error, Network Connection Error, and Editor/IDE Compatibility Issue are identified as the most frequent causes, and (3) Bug Fixed by Copilot, Modify Configuration/Setting, and Use Suitable Version are the predominant solutions. Based on the results, we discuss the potential areas of Copilot for enhancement, and provide the implications for the Copilot users, the Copilot team, and researchers. © 2023, CC BY.

关键词： Software design

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Heart Disease Forecast: A Comparative Analysis of Recurrent Neural Network and Long Short Term Memory

Heart Disease Forecast: A Comparative Analysis of Recurrent ...

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Delhi Section Conference (DELCON), IEEE

作者： Bollapalli Althaph Nagendra Panini Challa Jajam Nagaraju Kamepalli S. L. Prasanna School of Computer Science and Engineering (SCOPE) VIT-AP University India Department of Information Technology Tirumala Engineering College Narasaraopet India Department of School of Computing Amaravati Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Currently, the healthcare industry faces numerous significant challenges, being the need to improve the prognosis accuracy for cardiac illnesses. Using Long Short-Term Memory technique during this test to contribute to the research and development of an intelligent system that can predict cardiac illness. This paper presents a comparison of the RNN and LSTM techniques for forecasting cardiac disease. This comparison prioritizes accuracy and simultaneously considers other prognostic factors. After analysis this research concludes that LSTM algorithm is the most preferable method when compared to the RNN methodology.

关键词： Accuracy Recurrent neural networks Time series analysis Medical services Predictive models Prediction algorithms Prognostics and health management Long short term memory Research and development Investment

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HRPDF: A Software-Based Heterogeneous Redundant Proactive Defense Framework for Programmable Logic Controller

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Journal of computer science & technology 2021年第6期36卷 1307-1324页

作者： Ke Liu Jing-Yi Wang Qiang Wei Zhen-Yong Zhang Jun Sun Rong-Kuan Ma Rui-Long Deng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Guizhou UniversityGuiyang 550025China School of Information Systems Singapore Management UniversitySingapore 689867Singapore

Programmable logic controllers(PLCs)play a critical role in many industrial control systems,yet face increasingly serious cyber *** this paper,we propose a novel PLC-compatible software-based defense mechanism,called Heterogeneous Redundant Proactive Defense Framework(HRPDF).We propose a heterogeneous PLC architecture in HRPDF,including multiple heterogeneous,equivalent,and synchronous runtimes,which can thwart multiple types of attacks against PLC without the need of external *** ensure the availability of PLC,we also design an inter-process communication algorithm that minimizes the overhead of *** implement a prototype system of HRPDF and test it in a real-world PLC and an OpenPLC-based device,*** results show that HRPDF can defend against multiple types of attacks with 10.22%additional CPU and 5.56%additional memory overhead,and about 0.6 ms additional time overhead.

关键词： industrial control system programmable logic controller proactive defense heterogeneous redundant architecture

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Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

Detecting Backdoors During the Inference Stage Based on Corr...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiaogeng Liu Minghui Li Haoyu Wang Shengshan Hu Dengpan Ye Hai Jin Libing Wu Chaowei Xiao School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Software Engineering Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan University School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab Arizona State University

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo) 1 1 https://***/CGCL-codes/TeCo, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability.

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Machine Learning-Based Screening Solution for COVID-19 Cases Investigation: Socio-Demographic and Behavioral Factors Analysis and COVID-19 Detection

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Human-Centric Intelligent Systems 2023年第4期3卷 441-460页

作者： Uddin, K. M. Aslam Prity, Farida Siddiqi Tasnim, Maisha Jannat, Sumiya Nur Faruk, Mohammad Omar Islam, Jahirul Murad, Saydul Akbar Adhikary, Apurba Bairagi, Anupam Kumar Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali Bangladesh Department of Statistics Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Engineering New Mexico Institute of Mining and Technology Socorro USA School of Computing Sciences and Engineering University of Southern Mississippi Hattiesburg USA Computing Sciences and Engineering Discipline Khulna University Khulna Bangladesh

The COVID-19 pandemic has unleashed an unprecedented global crisis, releasing a wave of illness, mortality, and economic disarray of unparalleled proportions. Numerous societal and behavioral aspects have conspired to fuel the rampant spread of COVID-19 across the globe. These factors encompass densely populated areas, adherence to mask-wearing protocols, inadequate awareness levels, and various behavioral and social practices. Despite the extensive research surrounding COVID-19 detection, an unfortunate dearth of studies has emerged to meticulously evaluate the intricate interplay between socio-demographic and behavioral factors and the likelihood of COVID-19 infection. Thus, a comprehensive online-based cross-sectional survey was methodically orchestrated, amassing data from a substantial sample size of 500 respondents. The precisely designed survey questionnaire encompassed various variables encompassing socio-demographics, behaviors, and social factors. The Bivariate Pearson’s Chi-square association test was deftly employed to unravel the complex associations between the explanatory variables and COVID-19 infection. The feature importance approach was also introduced to discern the utmost critical features underpinning this infectious predicament. Four distinct Machine Learning (ML) algorithms, specifically Decision Tree, Random Forest, CatBoost, and XGBoost, were employed to accurately predict COVID-19 infection based on a comprehensive analysis of socio-demographic and behavioral factors. The performance of these models was rigorously assessed using a range of evaluation metrics, including accuracy, recall, precision, ROC-AUC score, and F1 score. Pearson’s Chi-square test revealed a statistically significant association between vaccination status and COVID-19 infection. The use of sanitizer and masks, the timing of infection, and the interval between the first and second vaccine doses were significantly correlated with the likelihood of contracting the COVID-1

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Wireless Rechargeable Sensor Networks: Energy Provisioning Technologies, Charging Scheduling Schemes, and Challenges

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2025年

作者： Aziz, Samah Abdel Wang, Xingfu Hawbani, Ammar Qureshi, Bushra Alsamhi, Saeed H. Alabsi, Aisha Zhao, Liang Al-Dubai, Ahmed Ismail, A.S. University of Science and Technology of China School of Computer Science and Technology Hefei23002 China Zagazig University Zagazig44519 Egypt SRI AIT Technological University of the Shannon Midlands Midwest Ireland Al- Hikma University Sana'a Yemen Shenyang Aerospace University Shenyang110136 China Computing School Edinburgh Napier University United Kingdom

Recently, a plethora of promising green energy provisioning technologies has been discussed in the orientation of prolonging the lifetime of energy-limited devices (e.g., sensor nodes). Wireless rechargeable sensor networks (WRSNs) have emerged among other fields that could greatly benefit from such technologies. Such an ad-hoc network comprises a base station(s) and multiple sensor nodes, which are primarily deployed in harsh environments, meeting the requirements of transmitting, receiving, collecting, and processing data. Unlike existing works, this survey paper focuses on energy provisioning technologies within the context of WRSNs by reviewing two interrelated domains. First, we introduce various energy provisioning techniques and their associated challenges, including conventional energy harvesting methods (e.g., solar, thermal, and mechanical). We highlight wireless power transfer (WPT) as one of the most applicable technologies for WRSNs, covering both radiative and non-radiative WPT. Additionally, we present radio frequency (RF) energy harvesting, including simultaneous wireless information and power transfer (SWIPT) and wireless powered communication networks (WPCNs), as well as backscatter communications. Furthermore, we compare hybrid energy harvesting techniques (e.g., solar-RF, vibro-acoustic, solar-thermal, etc.). Second, we introduce the fundamentals of wireless charging, reviewing various charger types (static and mobile), charging policies (including full and partial charging), charging modes (offline and online), and charging schemes (periodic and on-demand). We also present the collaborative charging mechanisms. Additionally, we address several key challenges facing WRSNs, such as energy consumption, multi-charger coordination, dynamic network recharging, monitoring & security threats, vehicle-to-vehicle (V2V) charging, and hybrid WRSNs Finally, we highlight trends and future directions for integrating advanced artificial intelligence (AI) techno

关键词： Charging stations

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On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction

arXiv

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arXiv 2024年

作者： Jing, Xin Jing, Yichen Lu, Yuhuan Deng, Bangchao Yang, Sikun Yang, Dingqi The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China The School of Computing and Information Technology Great Bay University China

Information popularity prediction is important yet challenging in various domains, including viral marketing and news recommendations. The key to accurately predicting information popularity lies in subtly modeling the underlying temporal information diffusion process behind observed events of an information cascade, such as the retweets of a tweet. To this end, most existing methods either adopt recurrent networks to capture the temporal dynamics from the first to the last observed event or develop a statistical model based on self-exciting point processes to make predictions. However, information diffusion is intrinsically a complex continuous-time process with irregularly observed discrete events, which is oversimplified using recurrent networks as they fail to capture the irregular time intervals between events, or using self-exciting point processes as they lack flexibility to capture the complex diffusion process. Against this background, we propose ConCat, modeling the Continuous-time dynamics of Cascades for information popularity prediction. On the one hand, it leverages neural Ordinary Differential Equations (ODEs) to model irregular events of a cascade in continuous time based on the cascade graph and sequential event information. On the other hand, it considers cascade events as neural temporal point processes (TPPs) parameterized by a conditional intensity function which can also benefit the popularity prediction task. We conduct extensive experiments to evaluate ConCat on three real-world datasets. Results show that ConCat achieves superior performance compared to state-of-the-art baselines, yielding 2.3%-33.2% improvement over the best-performing baselines across the three datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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Enhancing Apple Quality Identification: A Multi-Layer Visual Feature Fusion Approach with Convolutional Neural Networks

Enhancing Apple Quality Identification: A Multi-Layer Visual...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Naresh Kumar Trivedi Himani Maheshwari Raj Gaurang Tiwari Vinay Gautam Ambuj Kumar Agarwal Suresh Kaswan Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Dehradun Uttrakhand India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India Department of Computer Science and Engineering Chandigarh University Punjab India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

This research uses real-world images with complicated disturbance information (the backdrop was identical to the apples’ surfaces) to identify and categorize apple quality. This research introduces a new model for the quick and accurate evaluation of apple quality based on convolutional neural networks (CNNs). The proposed model captures detailed, unique, and valuable image features for detection and classification. In contrast to the deep features, complex interpretation needs and large training sample requirements, the handcrafted features used in this work are mainly produced intuitively. A new feature fusion approach called multi-layer visual feature fusion (MLVSF) is created for better classification of apple images. The MLVSF model, which incorporates deep and handcrafted components obtained from bag-of-visual words, local binary pattern variations, and CNN, can improve the discriminatory power of features used for apple quality identification. With its ability to improve CNN and achieve more accurate classification—resulting in a classification accuracy of 98.93%—MLVSF beats other modern approaches when evaluated on apple image datasets.

关键词： Training Visualization Correlation Databases Computational modeling Gray-scale Feature extraction

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RGB Color Model Aware Computational Color Naming and Its Application to Data Augmentation

RGB Color Model Aware Computational Color Naming and Its App...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Yan, Zipei Xu, Linchuan Suzuki, Atsushi Wang, Jing Cao, Jiannong Huang, Jun The Hong Kong Polytechnic University Department of Computing Kowloon Hung Hom Hong Kong King's College London Department of Informatics London United Kingdom University of Greenwich Department of Computing and Mathematical Sciences London United Kingdom Anhui University of Technology School of Computer Science and Technology Ma'anshan China

ISBN: (纸本)9781665480451

Computational color naming (CCN) aims to learn a mapping from pixels into semantic color names, e.g., red, green and blue. CCN has wide applications including color vision deficiency assistance and color image retrieval. Existing research on CCN mainly studies pixels collected under laboratory settings or studies images collected from the web. However, laboratory pixels are very limited such that the learned mapping may not generalize well on unseen pixels, and the mapping discovered from images is usually data-specific. In this paper, we aim to learn a universal mapping by studying pixels collected from the web. To this end, we formulate a novel classification problem that incorporates both the pixels and the RGB color model. The RGB color model is beneficial for learning the mapping because it characterizes the production of colors, e.g., the addition of red and green produces yellow. However, the characterization is rather qualitative. To solve this problem, we propose ColorMLP, which is a multilayer perceptron (MLP) embedded with graph attention networks (GATs). Here, the GATs are designed to capture color relations that we construct by referring to the RGB color model. In this way, the parameters of the MLP can be regularized to comply with the RGB model. We conduct comprehensive experiments to demonstrate the superiority of ColorMLP to alternative *** expand the application of CCN, we design a novel data augmentation method named partial color jitter (PCJ), which performs color jitter (CJ) on a subset of pixels belonging to the same color of an image. In this way, PCJ partially changes the color properties of images, thereby significantly increasing images' diversity. We conduct extensive experiments on CIFAR10/100 and ImageNet datasets, showing that PCJ can consistently improve the classification performance. Our data and software can be found at https://https://***/yanzipei/CCN_and_ItsApp. © 2022 IEEE.

关键词： Mapping

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