检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Dugré, Mathieu Hayot-Sasson, Valérie Glatard, Tristan Department of Computer Science and Software Engineering Concordia University Quebec Canada

The general increase in data size and data sharing motivates the adoption of Big Data strategies in several scientific disciplines. However, while several options are available, no particular guidelines exist for selecting a Big Data engine. In this paper, we compare the runtime performance of two popular Big Data engines with Python APIs, Apache Spark, and Dask, in processing neuroimaging pipelines. Our experiments use three synthetic neuroimaging applications to process the 606 GiB BigBrain image and an actual pipeline to process data from thousands of anatomical images. We benchmark these applications on a dedicated HPC cluster running the Lustre file system while using varying combinations of the number of nodes, file size, and task duration. Our results show that although there are slight differences between Dask and Spark, the performance of the engines is comparable for data-intensive applications. However, Spark requires more memory than Dask, which can lead to slower runtime depending on configuration and infrastructure. In general, the limiting factor was the data transfer time. While both engines are suitable for neuroimaging, more efforts need to be put to reduce the data transfer time and the memory footprint of applications. © 2024, CC BY.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Predicting the Lifetime of Flaky Tests on Chrome

Predicting the Lifetime of Flaky Tests on Chrome

引用

Flaky Tests Workshop (FTW), IEEE/ACM International

作者： Samaneh Malmir Peter C. Rigby Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9798400705588

ISBN: (纸本)9798350364521

Flaky tests not only disrupt the testing process but can also introduce significant delays in software release cycles, leading to increased costs and potential missed market opportunities. The ability to predict the lifetime class to which the test belongs to can empower development teams to allocate resources efficiently, prioritize bug fixes, and streamline the testing pipeline. Despite the critical role that this estimation plays in software development, there is a noticeable gap in the existing research literature. Bridging this gap is essential for enhancing testing efficiency and optimizing resource allocation and project planning in software *** our investigation of the historical patterns of flaky tests in Chrome, we identified that 40% of flaky tests remain unresolved, while 38% are typically addressed within the initial 15 days of introduction. Subsequently, we developed a predictive model focused on identifying tests with quicker resolutions. Our model demonstrated a precision of 73% and a Matthews Correlation Coefficient (MCC) approaching 0.39 in forecasting the lifespan class of flaky tests.

关键词： software testing Pipelines Predictive models Market research software Planning Resource management

来源：评论

学校读者我要写书评

暂无评论

Spark Spectrum Allocation for D2D Communication in Cellular Networks

引用

computers, Materials & Continua 2022年第3期70卷 6381-6394页

作者： Tanveer Ahmad Imran Khan Azeem Irshad Shafiq Ahmad Ahmed TSoliman Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Department of Electrical and Electronic Engineering Auckland University of TechnologyAuckland1010New Zealand Department of Electrical Engineering University of Engineering and TechnologyPeshawar25120Pakistan Department of Computer Science and Software Engineering International Islamic University IslamabadIslamabad44000Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

The device-to-device(D2D)technology performs explicit communication between the terminal and the base station(BS)terminal,so there is no need to transmit data through the BS *** establishment of a short-distance D2D communication link can greatly reduce the burden on the BS *** present,D2D is one of the key technologies in 5G technology and has been studied in depth.D2D communication reuses the resources of cellular users to improve system key parameters like utilization and ***,repeated use of the spectrum and coexistence of cellular users can cause co-channel *** at the interference problem under the constraint of fair resource allocation and improving the system throughput,this paper proposes an effective resource optimization scheme based on the firework *** main idea is to expand the weighted sum rate and convert the allocated resource expression into fireworks to determine the correlation *** simulation results show that,compared with the existing scheme,this scheme improves system performance by reducing interference.

关键词： D2D communication 5G technology spectrum allocation

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Approach for Expression Detection in Healthcare Monitoring Systems

引用

computers, Materials & Continua 2021年第5期67卷 2123-2139页

作者： Muhammad Kashif Ayyaz Hussain Asim Munir Abdul Basit Siddiqui AaqifAfzaal Abbasi Muhammad Aakif Arif Jamal Malik Fayez Eid Alazemi Oh-Young Song Department of Computer Science&Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science Quaid-i-Azam UniversityIslamabad44000Pakistan Department of Computer Science Capital University of Science&TechnologyIslamabad44000Pakistan Department of Software Engineering Foundation UniversityIslamabad44000Pakistan Department of Computer Science Abdul Wali Khan UniversityMardan23200Pakistan Department of Computer Science and Information Systems College of Business StudiesPAAET12062Kuwait Department of Software Sejong UniversitySeoul05006Korea

Expression detection plays a vital role to determine the patient’s condition in healthcare *** helps the monitoring teams to respond swiftly in case of *** to the lack of suitable methods,results are often compromised in an unconstrained environment because of pose,scale,occlusion and illumination variations in the image of the face of the patient.A novel patch-based multiple local binary patterns(LBP)feature extraction technique is proposed for analyzing human behavior using facial expression *** consists of three-patch[TPLBP]and four-patch LBPs[FPLBP]based feature engineering *** representation is encoded from local patch statistics using these *** and FPLBP capture information that is encoded to find likenesses between adjacent patches of pixels by using short bit strings contrary to pixel-based *** images are transformed into the frequency domain using a discrete cosine transform(DCT).Most discriminant features extracted from coded DCT images are combined to generate a feature *** vector machine(SVM),k-nearest neighbor(KNN),and Naïve Bayes(NB)are used for the classification of facial expressions using selected *** experimentation is performed to analyze human behavior by considering standard extended Cohn Kanade(CK+)and Oulu–CASIA *** demonstrate that the proposed methodology outperforms the other techniques used for comparison.

关键词： Detection expressions gestures analytics pain patch-based local binary descriptor discrete cosine transform healthcare

来源：评论

学校读者我要写书评

暂无评论

An Applied Artificial Intelligence Technique for Early-Stage Alzheimer's Disease Prediction 4

An Applied Artificial Intelligence Technique for Early-Stage...

引用

4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Ali, Hassan Imtiaz, Hussain Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Alshammeri, Menwa Mir, Azka Kumar, Himanshu University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait College of CIS Al Jouf University Department of Computer Science Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Alzheimer's disease is a neurodegenerative disorder in which central nervous cells gradually die that poses a significant health challenge globally, especially in older people. As life expectancy increases which suggests a large amount of society will be affected. This increasing number of Alzheimer's cases demands the urgency to develop predictive models for early intervention as the real effect of Alzheimer's shows very late. Although there are no treatments capable of reversing the natural pathological changes, we can delay the development of Alzheimer's. Our goal is to, detect this neurodegenerative disease before it becomes more rooted, we can help the patient identify the disease at an early age so they can adapt to their new condition and perform treatments to help manage symptoms. We used the OASIS dataset which consists of multiple features collection of subjects whose age varies from 18 to 96. This research focuses on employing supervised machine learning, we have used various classifiers such as Decision trees, Naïve Bayes, k-NN, random forest, and logistic regression, and evaluated each classification's effectiveness using several well-known performance indicators to forecast Alzheimer's disease status. We used Rapid Miner to test different models and found that the Naïve Bayes classifier achieves the highest accuracy at 97.5% this model has a superior predictive capability and outperforms alternative classifiers. The insights gained from this research contribute to advancing early diagnosis and intervention for Alzheimer's disease. © 2024 IEEE.

关键词： Alzheimer's disease Classification Health care Machine Learning OASIS Prediction

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Cluster Optimization Algorithm for Smart Body Area Networks

引用

computers, Materials & Continua 2021年第6期67卷 3795-3814页

作者： Adil Mushtaq Muhammad Nadeem Majeed Farhan Aadil Muhammad Fahad Khan Sangsoon Lim Department of Software Engineering University of Engineering and TechnologyTaxilaPakistan Department of Computer Science COMSATS University IslamabadAttock CampusPakistan Department of Computer Engineering Sungkyul UniversityAnyang430010Korea

Body Area Networks(BODYNETs)or Wireless Body Area Networks(WBAN),being an important type of ad-hoc network,plays a vital role in multimedia,safety,and traffic management *** BODYNETs,rapid topology changes occur due to high node mobility,which affects the scalability of the *** clustering is one mechanism among many others,which is used to overcome this issue in *** are many clustering algorithms used in this domain to overcome this ***,these algorithms generate a large number of Cluster Heads(CHs),which results in scarce resource utilization and degraded *** this research,an efficient clustering technique is proposed to handle these *** transmission range of BODYNET nodes is dynamically tuned accordingly as per their operational *** optimizing the transmission range,the packet loss ratio is minimized,and link quality is improved,which leads to reduced energy *** select optimal CHs the Whale Optimization Algorithm(WOA)is used based on their fitness,which enhances the network performance by reducing routing *** proposed scheme outclasses the existing state-of-the-art techniques,e.g.,Ant Colony Optimization(ACO),Gray Wolf Optimization(GWO),and Dragonfly Optimization Algorithm(DFA)in terms of energy consumption and cluster building time.

关键词： Bodynets WBAN clustering ad-hoc networks whale optimizer artificial neural networks intelligent transportation system

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Machine Learning Models for PDF Malware Detection:Evaluating Different Training and Testing Criteria

引用

Journal of Cyber Security 2023年第1期5卷 1-11页

作者： Bilal Khan Muhammad Arshad Sarwar Shah Khan Department of Computer Science City University of Science and Information TechnologyPeshawarPakistan Department of Computer Software Engineering University of Engineering and TechnologyMardanPakistan Department of Computer and Software Technology University of SwatSwatPakistan Department of Computer Science IQRA National UniversitySwatPakistan

The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated *** Document Format(PDF)files have emerged as a major attack vector for malware due to their adaptability and wide *** malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts,exploits,and malicious *** paper presents a comparative analysis of machine learning(ML)techniques,including Naive Bayes(NB),K-Nearest Neighbor(KNN),Average One Dependency Estimator(A1DE),RandomForest(RF),and SupportVectorMachine(SVM)forPDFmalware *** study utilizes a dataset obtained from the Canadian Institute for Cyber-security and employs different testing criteria,namely percentage splitting and 10-fold *** performance of the techniques is evaluated using F1-score,precision,recall,and accuracy *** results indicate that KNNoutperforms other models,achieving an accuracy of 99.8599%using 10-fold *** findings highlight the effectiveness of ML models in accurately detecting PDF malware and provide insights for developing robust systems to protect against malicious activities.

关键词： Cyber-security PDF malware model training testing

来源：评论

学校读者我要写书评

暂无评论

Strategy Masking: A Method for Guardrails in Value-based Reinforcement Learning Agents

arXiv

引用

arXiv 2025年

作者： Keane, Jonathan Keyser, Sam Kedziora, Jeremy Department of Computer Science and Software Engineering Milwaukee School of Engineering MilwaukeeWI United States

The use of reward functions to structure AI learning and decision making is core to the current reinforcement learning paradigm;however, without careful design of reward functions, agents can learn to solve problems in ways that may be considered "undesirable" or "unethical. Without thorough understanding of the incentives a reward function creates, it can be difficult to impose principled yet general control mechanisms over its behavior. In this paper, we study methods for constructing guardrails for AI agents that use reward functions to learn decision making. We introduce a novel approach, which we call strategy masking, to explicitly learn and then suppress undesirable AI agent behavior. We apply our method to study lying in AI agents and show that it can be used to effectively modify agent behavior by suppressing lying post-training without compromising agent ability to perform effectively. © 2025, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing road safety with DL vision: Do driver distraction alerts hold the key?

引用

Neural Computing and Applications 2025年 1-21页

作者： Ali, Luqman Swavaf, Muhammad Alnajjar, Fady Zou, Zhao Mohan Ambali Parambil, Medha Mubin, Omar AlJassmi, Hamad Emirates Center for Mobility Research UAE University Abu Dhabi Al Ain United Arab Emirates Department of Civil and Environmental Engineering College of Engineering UAE University Abu Dhabi Al Ain United Arab Emirates Department of Computer Science and Software Engineering College of IT UAE University Abu Dhabi Al Ain United Arab Emirates School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW Australia

Driver distraction is a significant contributor to road accidents and traffic safety challenges. This study evaluates the performance of various YOLO models trained on a combination of custom and publicly available datasets, with their performance assessed using metrics such as mAP@50, precision, recall, F1-score, inference time, FPS, and model parameters. The aim is to determine the best trade-off between accuracy, speed, and computational efficiency, providing insights into the practical applicability of these models for distracted driving detection. Among the models, YOLOv8l achieves the highest accuracy with a mAP@50 of 0.921, precision of 0.911, recall of 0.895, and an F1-score of 0.899, making it suitable for scenarios prioritizing detection precision. YOLOv5m balances accuracy and efficiency, achieving a mAP@50 of 0.917 and an F1-score of 0.896, while maintaining moderate inference times. For real-time applications, YOLOv5n and YOLOv8n offer the fastest speeds, with inference times of 0.9 ms and FPS of 1111, at the cost of reduced accuracy. YOLOv8m provides a balance between performance and speed, achieving a mAP@50 of 0.915 and F1-score of 0.898, with an inference time of 3.4 ms and FPS of 294. A post-simulation survey reveals that participants find the visual warning system effective in mitigating driver distraction and enhancing perceived road safety. By integrating simulation data and user feedback, this study highlights the potential of advanced deep learning models like YOLO in improving driver assistance systems and promoting safer driving practices. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Highway accidents

来源：评论

学校读者我要写书评

暂无评论

Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

引用

5th International Conference on Advancements in Computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：