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25th International Conference on Internet computing and IoT, ICOMP 2024, and 22nd International Conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied computing, CSCE 2024

作者： Alamgir, Shariar M. Bohra, Dhruv L. Nguyen, Elisabeth A. Embedded and Specialized Computing Department The Aerospace Corporation El SegundoCA United States Software Architecture and Engineering Department The Aerospace Corporation El SegundoCA United States

ISBN: (纸本)9783031859229

Critical embedded software, such as spacecraft flight software, requires high reliability, efficiency, and real-time performance while being capable enough to meet application needs. The increasing prevalence of Linux, and in particular Linux containers, presents opportunities for space systems but also poses potential risks since Linux was not designed to be a real-time operating system. In this study, we conducted a variety of experiments to look at both the overhead imposed by containers and the latency experienced in Linux using various combinations of patched OS and containers running on a Raspberry Pi 4. We found that containers have little impact on CPU utilization but do incur penalties in RAM, disk storage, and startup time. We also found the unpatched Linux kernel performs well for real time;and that, while the real-time kernel patch reduces both overall and maximum latency, it can negatively impact performance, particularly on single-core systems running non-real-time tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Linux

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Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

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Computers, Materials & Continua 2023年第1期74卷 2261-2278页

作者： Hanadi AlZaabi Khaled Shaalan Taher M.Ghazal Muhammad A.Khan Sagheer Abbas Beenu Mago Mohsen A.A.Tomh Munir Ahmad Faculty of Engineering and IT The British University in DubaiUnited Arab Emirates Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)Bangi43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Faculty of Computer Science NCBA&ELahore54660Pakistan

Energy is essential to practically all exercises and is imperative for the development of personal ***,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current ***,there is a shortage of energy,as the energy required is higher than that *** new plans are being designed to meet the consumer’s energy *** many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future *** overcome the challenges of energy consumption optimization,in this study,we apply an energy management *** fusion has recently attracted much energy efficiency in buildings,where numerous types of information are *** proposed research developed a data fusion model to predict energy consumption for accuracy and miss *** results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

关键词： Energy consumption intelligent machine learning technique smart homes prediction

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Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets

Automatically Classifying Emotions based on Text: A Comparat...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Anna Koufakou Jairo Garciga Adam Paul Joseph Morelli Christopher Frank Department of Computing and Software Engineering Florida Gulf Coast University Fort Myers Florida

Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This paper presents a preliminary study with the goal to help researchers and practitioners gain insight into relatively new datasets as well as emotion classification in general. We focus on three datasets that were recently presented in the related literature, and we explore the performance of traditional as well as state-of-the-art deep learning models in the presence of different characteristics in the data. We also explore the use of data augmentation in order to improve performance. Our experi-mental work shows that state-of-the-art models such as RoBERTa perform the best for all cases. We also provide observations and discussion that highlight the complexity of emotion classification in these datasets and test out the applicability of the models to actual social media posts we collected and labeled.

关键词： Deep learning Emotion recognition Social networking (online) Taxonomy Predictive models Data models Complexity theory

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ATT&CKViz: Enhancing Cybersecurity Insights by Visual Analytics of Attack Patterns

ATT&CKViz: Enhancing Cybersecurity Insights by Visual Analyt...

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engineering & computing Technologies (ICECT), International Conference on

作者： Inbasat Fiza Fizza Khurshid Manahil Fatima Umara Noor Zahid Rashid Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul South Korea

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

The ATT&CK MITRE framework serves as an expansive repository of adversary tactics, techniques, and procedures. Given the sheer volume of these intricate attack patterns, the conventional manual navigation method proves to be time-intensive and less efficient for security analysts. As a remedy, there arises a compelling necessity to present the rich content encapsulated within the ATT&CK MITRE repository through interactive visualizations. In this paper ATT&CKViz an interactive web-based cyber-attack patterns visualization tool is presented that aims to empower security analysts with the ability to effortlessly explore the repository from diverse angles, which facilitates deep insights into cyber threats. The aim is to understand cyber threat efficiently with a timely response. The empirical evaluations depict that effectiveness and user-friendliness of ATT&CKViz.

关键词： Computers Navigation Visual analytics Manuals Cyberattack

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Automatic Detection of Peripheral Facial Landmarks Using 3D Facial Data

Automatic Detection of Peripheral Facial Landmarks Using 3D ...

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International Conference on Image and Vision computing New Zealand, IVCNZ

作者： Mehraan Chowdhury Mohammed Bennamoun Farid Boussaid Syed Mohammed Shamsul Islam Department of Computer Science and Software Engineering University of Western Australia Perth Australia Department of Electrical Electronic and Computer Engineering University of Western Australia Perth Australia Discipline of Computing and Security Edith Cowan University Perth Australia

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Facial landmarking research has traditionally fo-cused on midface landmarks, largely overlooking peripheral facial features that are crucial in specialized medical applications. This study addresses this gap by developing a novel model to detect 12 peripheral landmarks, which are valuable in clinical scenarios such as obstructive sleep apnea detection and facial palsy assessment. Using the FRGC v2 dataset of 3D facial scans, we manually annotated a subset of 900 scans with peripheral landmarks following a rigorous pre-processing protocol. Building upon a high-performance deep learning model initially designed for midface landmark detection, we restructured its architecture to predict peripheral landmarks by modifying its output layers and training it from scratch using 2D projections of 3D scans. We conducted a comprehensive set of experiments, systematically testing different input configurations by varying both the number of images per scan (16, 32, and 64) and the image types (mesh-only, RGB, and depth maps). The model demonstrated its potential by achieving a mean error of 10.72 mm, highlighting the promise of deep learning techniques in addressing the complex challenge of peripheral landmark detection. These findings underscore the model's capability to support niche medical applications and open pathways for future advancements that could refine its accuracy, ultimately contributing to improved diagnostic tools in healthcare.

关键词： Training Deep learning Solid modeling Biomedical equipment Three-dimensional displays Protocols Medical services Transforms Sleep apnea Testing

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Expressive Completeness of Two-Variable First-Order Logic with Counting for First-Order Logic Queries on Rooted Unranked Trees

Expressive Completeness of Two-Variable First-Order Logic wi...

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Annual Symposium on Logic in Computer Science

作者： Jelle Hellings Marc Gyssens Jan Van Den Bussche Dirk Van Gucht Department of Computing and Software McMaster University Hamilton Canada Data Science Institute Hasselt University Diepenbeek Belgium Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

We consider the class of finite, rooted, unranked, unordered, node-labeled trees. Such trees are represented as structures with only the parent-child relation, in addition to any number of unary predicates for node labels. We prove that every unary first-order query over the considered class of trees is already expressible in two-variable first-order logic with counting. Somewhat to our surprise, we have not seen this result being conjectured in the extensive literature on logics for trees. Our proof is based on a global variant of local equivalence notions on nodes of trees. This variant applies to entire trees, and involves counting ancestors of locally equivalent nodes.

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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Computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Network Coverage Improvement during Natural Disaster using Self-Organizing Maps

Network Coverage Improvement during Natural Disaster using S...

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Electrical, Computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Nam Hoang Prathyusha Devabhakthini Raj Mani Shukla Suman Bhunia Department of Computer Science and Software Engineering Miami University Oxford Ohio Computing and Information Science Anglia Ruskin University Cambridge UK

This paper presents an emergency response management system to tackle the problem of the absence of network connectivity during the time of a natural disaster. Network connectivity is often enabled by the base stations on the ground. However, during the time of the disaster, the connectivity is disrupted due to the base station being damaged. During such scenarios, the Unmanned Aerial Vehicles (UAV) based stations could help in partially providing the network connectivity and help in the rescue operations. But, the UAVs need to be quickly deployed and placed at a suitable location based on the population coverage and base stations being impacted due to the disruptions. In this paper, we propose the Self Organizing Map (SOM) based optimal UAV deployment to enhance the network coverage, and increase the percentage of people having network access. In contrast to other Artificial Intelligence-based approaches, like Deep Neural Networks, our method does not require to be heavily trained using train and the test dataset.

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Examining the Effects of Layout and Working Memory on UML Class Diagram Defect Identification

Examining the Effects of Layout and Working Memory on UML Cl...

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IEEE International Workshop on Visualizing software for Understanding and Analysis (VISSOFT)

作者： Bonita Sharif Kang-il Park Michael P. DeJournett Isaac Baysinger Mohammed Aly Jonathan I. Maletic School of Computing University of Nebraska - Lincoln Lincoln Nebraska USA Software Engineering Department Assiut University Assiut Egypt Department of Computer Science Kent State University Kent Ohio USA

ISBN: (数字)9798331528485

ISBN: (纸本)9798331528492

A controlled experiment investigating the effect layout has on how students find defects in UML class diagrams with respect to requirements is presented. Two layout schemes from prior literature namely, multi-cluster and orthogonal layouts, are compared with respect to two open source systems, Doxygen and Qt. The experiment is conducted with 89 students from two universities in a classroom lab setting. Each participant is placed in one of two groups where each group are given 2 defect detection tasks (with five sub-parts) with each task using one of the two layouts in each subject system. The only difference between groups is that the layouts were flipped between the two tasks. Feedback is collected after each task. A mental rotation and object memory task is conducted at the end of the two tasks to correlate their spatial and working memory skills to the task performance. Results indicate that the multi-cluster layout performed better in terms of accuracy of finding defects, but not significantly. There is also not much difference in time to find them. Furthermore, it is found that the object memory skills are sometimes correlated with the performance of the defect detection tasks. These results can be used to help improve the teaching of UML class diagram defect detection skills by incorporating clustered layouts and object memory tasks. In addition, they can help identify people who are best suited for finding critical defects in design.

关键词： Visualization Accuracy Layout Unified modeling language Education software Defect detection

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A Framework for Blockchain-Based Secure Management of Mobile Healthcare (mHealth) Systems

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Journal of Web engineering 2025年第3期24卷 317-354页

作者： Adel Alkhalil Abdul Razzaq Aakash Ahmad Magdy Abdelrhman Yaser Altameemi Mohammed Altamimi Zhang Tao Department of Software Engineering College of Computer Science and Engineering University of Ha'il Ha'il Saudi Arabia School of Software Northwestern Polytechnical University Xi'an Shaanxi P.R. China School of Computing and Communications Lancaster University Leipzig Germany Department of Foundation of Education College of Education New Valley University Egypt Applied College University of Ha'il Ha'il Saudi Arabia

In recent years, several research and development initiatives have focused on developing secure and trustworthy systems for the healthcare industry via pervasive and mobile healthcare (mHealth) solutions. State-of-the-art mHealth solutions primarily rely on centralized storage, such as cloud computing servers, which may escalate the maintenance costs, require ever-increasing storage infrastructure, and pose privacy and security risks to the health-critical data produced, consumed, and transmitted over ad hoc networks. To overcome these limitations, we conducted this study intending to synergize mobile computing (devices to process health-critical data) and blockchain technology (infrastructure to secure storage and retrieval of health-critical data), specifically addressing data security and privacy using a blockchain mHealth system. The research employs an incremental method by (i) developing a framework that acts as a blueprint to architect blockchain-enabled mHealth systems, (ii) implementing a suite of algorithms as a proof-of-concept to automate the framework, and (iii) experimental evaluations to validate the scalability, computation, and energy efficiency of the proposed solution. The proposed framework has been implemented as a frontend using a mobile application interface that exploits the backend via the InterPlanetary File System (IPFS) system and Ethereum blockchain for secure management of mHealth data. We use a case-study-based approach demonstrating how health units, medics, and patients can securely access and distribute health-critical data. For evaluation, we deployed a smart contract prototype on the Ethereum TESTNET network in a Windows environment to test the proposed framework. Results of the evaluation indicate (a) scalability with query response time (range: 10–41 ms), (b) computational performance (CPU utilization: 1.5% – 2.5%), and (c) energy efficiency (gas consumption: 40000 units for 1000 bytes). The proposed solution – framework, algori

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