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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Wen, Yingying Cheng, Guanjie Huawei Cloud Department of Architecture and Technique Innovation Hangzhou China Zhejiang University Department of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350331820

Hundreds of thousands of Java applications have been deployed in data centers at our production Cloud to support burst peak traffic. However, detecting performance bugs in Java can be difficult as they may not prevent the applications from running correctly, and may not even manifest at low loads. Profiling data collected from production provides insight into the actual running states of applications. In this paper, we aim to identify hot spots for further performance debugging by analyzing profiling data from tens of thousands of machines in the data center using a module-based approach. We present our practical experience with module classification, which allows for filtering of out-of-range modules and long-duration modules of high utilization. Our study proposes a heuristic solution to detect performance bugs in Java at scale. © 2023 IEEE.

关键词： Program debugging

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Design of an Efficient and Provable Secure Key Exchange Protocol for HTTP Cookies

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computers, Materials & Continua 2024年第7期80卷 263-280页

作者： Waseem Akram Khalid Mahmood Hafiz Burhan ul Haq Muhammad Asif Shehzad Ashraf Chaudhry Taeshik Shon Graduate School of Engineering Science and Technology National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Department of Computer Science Lahore Garrison UniversityLahore54920Pakistan Department of Computer Science and Information Technology College of EngineeringAbu Dhabi UniversityAbu Dhabi69911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbul34398Turkey Department of Cybersecurity Ajou UniversitySuwon16499Republic of Korea

Cookies are considered a fundamental means of web application services for authenticating various Hypertext Transfer Protocol(HTTP)requests andmaintains the states of clients’information over the *** cookies are exploited to carry client patterns observed by a *** client patterns facilitate the particular client’s future visit to the corresponding ***,security and privacy are the primary concerns owing to the value of information over public channels and the storage of client information on the *** protocols have been introduced that maintain HTTP cookies,but many of those fail to achieve the required security,or require a lot of resource *** this article,we have introduced a lightweight Elliptic Curve Cryptographic(ECC)based protocol for authenticating client and server transactions to maintain the privacy and security of HTTP *** proposed protocol uses a secret key embedded within a *** proposed protocol ismore efficient and lightweight than related protocols because of its reduced computation,storage,and communication ***,the analysis presented in this paper confirms that proposed protocol resists various known attacks.

关键词： Cookies authentication protocol impersonation attack ECC

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Enhancing the Smart Readiness Indicator Scheme: Methodology Assessment through 20 Use Cases 3

Enhancing the Smart Readiness Indicator Scheme: Methodology ...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Zamanidou, Afroditi Kothona, Despoina Fokaides, Paris Christoforidis, Georgios C. University of Western Macedonia Faculty of Engineering Electrical & Computer Engineering Department Greece Kaunas University of Technology Faculty of Civil Engineering and Architecture Kaunas Lithuania

ISBN: (纸本)9798350375923

The European Union developed the Smart Readiness Indicator (SRI) to enhance energy efficiency and encourage the adoption of smart technologies in buildings, tackling their high energy usage and carbon emissions. This study evaluates the SRI methodology through the assessment of 20 diverse use cases, including residential and non-residential buildings, using Methods A and B of the SRI assessment scheme. Key variables such as climate zone, building type, year of construction, and the integration of smart-ready services were considered to ensure a comprehensive *** engineers assessed the buildings using the SRI EU Excel Tool and rated the methodology based on criteria including accuracy, usability, comprehensiveness, flexibility, and impact on decision-making. The results revealed high ratings for accuracy (45% rated as "Very Good"and 30% as "Excellent") and usability (50% rated as "Very Good"), while comprehensiveness was rated more moderately (40% "Good"). However, flexibility and adaptability emerged as areas requiring improvement, with 45% of assessments rated as "Fair."The SRI methodology demonstrated strong potential but requires further refinement to address the needs of diverse building types and more complex smart technologies. The study concludes that the SRI framework is a valuable tool for promoting energy efficiency and smart technology adoption across the EU building sector. However, additional research is recommended to integrate real-time data and automation technologies, and to enhance the framework's scalability and adaptability for future building systems. © 2024 IEEE.

关键词： Smart homes

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Leveraging Multimodal Shapley Values to Address Multimodal Collapse and Improve Fine-Grained E-Commerce Product Classification 23

Leveraging Multimodal Shapley Values to Address Multimodal C...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Obayemi, Ajibola Nguyen, Khuong School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom University of London Department of Computer Science Royal Holloway London United Kingdom

ISBN: (纸本)9798350374889

Multimodal models can experience multimodal collapse, leading to sub-optimal performance on tasks like fine-grained e-commerce product classification. To address this, we introduce an approach that leverages multimodal Shapley values (MM-SHAP) to quantify the individual contributions of each modality to the model's predictions. By employing weighted stacked ensembles of unimodal and multimodal models, with weights derived from these Shapley values (MM-SHAP), we enhance the overall performance and mitigate the effects of multimodal collapse. Using this approach we improve previous results(f1-score) from 0.67 to 0.79. © 2024 IEEE.

关键词： Marketplaces

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Human-computer Testing of Students’ Knowledge 4

Human-Computer Testing of Students’ Knowledge

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4th IEEE International Conference on Smart Information Systems and Technologies, SIST 2024

作者： Aznaurian, Iryna Pohrebniak, Yurii Riabchun, Yuliia Delembovskyi, Maksym Poltorachenko, Nataliia Panko, Oleksiy Department of physics Kyiv National University of Construction and Architecture Kyiv Ukraine Department of Cybersecurity and Computer Engineering Kyiv National University of Construction and Architecture Kyiv Ukraine Department of Information Technology Kyiv National University of Construction and Architecture Kyiv Ukraine Department of Information Systems and Technologies Taras Shevchenko National University of Kyiv Kyiv Ukraine Department of Information Technology Design and Applied Mathematics Kyiv National University of Construction and Architecture Kyiv Ukraine Department of Architectural Structures Kyiv National University of Construction and Architecture Kyiv Ukraine

ISBN: (纸本)9798350374865

The modern rapid development of means and technologies for education is aimed at reducing classroom classes and remote control of knowledge and skills of education seekers. The adaptation of the education system to the remote regime has become especially relevant throughout the world during the period of quarantine restrictions caused by COVID-19. In Ukraine, the urgency of developing and implementing info-communication systems in the education process has been intensified by the martial law since February 24, 2022. The urgent need to rebuild a large number of buildings and structures destroyed and damaged by hostilities makes special demands on graduates of construction educational institutions. However, the fundamental studying of specialists in this field in remote mode requires specialized info-communication systems, creating of which need taking part of specialists in the field. On-line testing is the main means of guaranteeing the quality of the modern educational process. Therefore, the article presents the specialized on-line testing system "Fast Knowledge Test", which was developed at the Kyiv National University of Construction and architecture with the participation of stakeholders. The system operates during 2021-2023 and while the monitoring of students’ knowledge and skills remains with the teacher, but the service provides for its automation. © 2024 IEEE.

关键词： Students

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Image inpainting via Smooth Tucker decomposition and Low-rank Hankel constraint

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International Journal of computers and Applications 2023年第6期45卷 421-432页

作者： Cai, Jing Jiang, Jiawei Wang, Yibing Zheng, Jianwei Xu, Honghui Department of Forensic Science Zhejiang Police College Hangzhou China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Image inpainting, aiming at exactly recovering missing pixels from partially observed entries, is typically an ill-posed problem. As a powerful constraint, low-rank priors have been widely applied in image inpainting to transform such problems into well-posed ones. However, the low-rank assumption of original visual data is only in an approximate mode, which in turn results in suboptimal recovery of fine-grained details, particularly when the missing rate is extremely high. Moreover, a single prior cannot faithfully capture the complex texture structure of an image. In this paper, we propose a joint usage of Smooth Tucker decomposition and Low-rank Hankel constraint (STLH) for image inpainting, which enables simultaneous capturing of the global low-rankness and local piecewise smoothness. Specifically, based on the Hankelization operation, the original image is mapped to a high-order structure for capturing more spatial and spectral information. By employing Tucker decomposition for optimizing the Hankel tensor and simultaneously applying Discrete Total Variation (DTV) to the Tucker factors, sharper edges are generated and better isotropic properties are enhanced. Moreover, to approximate the essential rank of the Tucker decomposition and avoid facing the uncertainty problem of the upper-rank limit, a reverse strategy is adopted to approximate the true rank of the Tucker decomposition. Finally, the overall image inpainting model is optimized by the well-known alternate least squares (ALS) algorithm. Extensive experiments show that the proposed method achieves state-of-the-art performance both quantitatively and qualitatively. Particularly, in the extreme case with 99% pixels missed, the results from STLH are averagely ahead of others at least 0.9dB in terms of PSNR values. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pixels

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A Survey on Mapping of Urban Green Spaces within Remote Sensing Data Using Machine Learning & Deep Learning Techniques 15

A Survey on Mapping of Urban Green Spaces within Remote Sens...

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15th International Conference on computer and Automation Engineering, ICCAE 2023

作者： Burrewar, Smita Sunil Haque, Mazharul Haider, Tanwir Uddin National Institute of Technology Patna Architecture & Planning Department Bihar Patna India National Institute of Technology Patna Computer Science & Engineering Department Bihar Patna India

ISBN: (纸本)9798350396225

For environmental protection, urban planning, monitoring, and management of the urban ecosystem, mapping urban green spaces is a crucial undertaking. A vital source of information for United Nations Sustainable Development Goal 11.7 could come from urban green space mapping. The standard method of mapping urban green spaces requires field measurements and takes a lot of time. It is also important to update urban green space maps periodically because urban green spaces can change quickly over time due to development. With the advent of high-resolution satellite sensors like Sentinel-1 and Sentinel-2, a large number of remote sensing images may be gathered, providing quick and precise information over urban areas. This work intends to offer a new perspective on how crowd sourced geospatial big data and remote sensing may be combined to enhance the mapping of urban green spaces, including time optimization and accurate information through machine learning and deep learning. For the revitalization of cities, this data will be valuable. Remote sensing imagery data can be classified using machine learning techniques like Support vector machines (SVM), Random forests (RF), and Naive Bayes (NB). In deep learning techniques such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), K Nearest Neighbor (KNN), Generative Adversarial Networks (GAN), and Recurrent Neural Networks (RNN) can be used to classify remote sensing images. © 2023 IEEE.

关键词： Remote sensing

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RedTops: real-time energy-aware dynamic task offloading via federated mountain gazelle optimisation in SDN-enhanced edge computing

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Neural Computing and Applications 2025年 1-39页

作者： Al Aghbari, Zaher Khedr, Ahmed M. Ahmed, Naveed Girija, Shini Baker, Thar Department of Computer Science University of Sharjah Sharjah United Arab Emirates School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Offloading computational tasks is vital for real-time applications on mobile devices with limited resources. Mobile edge computing (MEC) is deemed a solution that puts computational resources closer to users. Nevertheless, there are many associated concerns during the offloading procedure (i.e., privacy, delay, and high energy consumption). Federated learning (FL) has been considered a solution to address MEC’s data privacy issues;however, it comes with its own resource consumption issues. To address these issues, this paper proposes a distributed learning paradigm inspired by FL. We propose an optimisation technique for offloading computational tasks that aims to reduce both total delay and energy consumption by using the mountain gazelle optimisation algorithm, which shows it can reduce both delay and energy consumption in dynamic situations. Additionally, an improved variant known as the improved mountain gazelle optimiser is integrated into a distributed SDN controller architecture to create an offloading policy model for optimal edge node selection. We also present a new SDN-enabled edge computing architecture that achieves the best task distribution through task offloading using federated mountain gazelle optimisation (RedTops). Energy usage, delay, and bandwidth are considered by RedTops, which successfully addresses high training costs, dependability issues, and privacy concerns in MEC. Based on the outcomes of five extensive simulations, RedTops is more energy-efficient and faster at completing tasks than four state-of-the-art offloading methods (DDLO, DROO, DRL without TL and SDN, and DTRL). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Federated learning

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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

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International Journal of Crowd Science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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Machine learning model for predicting long-term energy consumption in buildings

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Discover Internet of Things 2025年第1期5卷 1-24页

作者： Hussien, Aseel Maksoud, Aref Al-Dahhan, Aisha Abdeen, Ahmed Baker, Thar Department of Architectural Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Civil Mining Environmental and Architectural Engineering University of Wollongong Wollongong Australia School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom

The rapid growth in the construction sector has led to increased energy consumption and carbon emissions. Calculating energy usage and emissions is essential to energy security and promoting sustainable sector development. Therefore, the study objective is to investigate the utilazation of machine learning algorithm to predict long-term energy consumption in buildings sector, aiming to improve sustainable design and energy optimization, via the implementation of three machine learning models, XGBoost, Support Vector Regression, and Long-Short-Term Memory networks, to predict energy consumption. These models are adept at capturing complex interactions between building characteristics, environmental factors, and energy patterns. Although previous studies have explored various machine learning techniques for energy efficiency, limited research links these models to practical applications in building performance simulation. Furthermore, there is a lack of comparative evaluation of advanced machine learning models such as XGBoost, Support Vector Regression, and Long-Short-Term Memory to predict the energy consumption of building envelopes, particularly in hot climates such as the UAE. This research aims to fill this gap by providing a detailed comparison of these models against alternative approaches mentioned in the literature. The findings position Long-Short-Term Memory as a transformative force in predictive modeling, demonstrating exceptional precision with an R-squared value of 0.993 and a Mean Squared Error of 0.004. In contrast, Support Vector Regression and XGBoost showed limited predictive capabilities, with R-squared values of 0.462 and 0.94, respectively. This study establishes a solid data-driven foundation for architects and engineers to inform decisions on energy-efficient building designs, advocating Long-Short-Term Memory as the superior model for predicting energy performance. © The Author(s) 2025.

关键词： Green buildings

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