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6th Information systems International Conference, ISICO 2021

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021 The Authors. Published by Elsevier B.V.

关键词： Quality of service

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers

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Procedia computer science 2022年 197卷 238-246页

作者： Huned Materwala Leila Ismail Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users’ tasks. The pervasive adoption of cloud computing in smart cities’ services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.

关键词： Autonomous agents cloud computing energy-efficiency evolutionary algorithm genetic algorithm quality of service multi-objective optimization performance

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IoT-Edge-Cloud Computing Framework for QoS-Aware Computation Offloading in Autonomous Mobile Agents: Modeling and Simulation 1

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6th International Conference on Mobile, Secure and Programmable Networking, MSPN 2020

作者： Ismail, Leila Materwala, Huned Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates

ISBN: (数字)9783030675509

ISBN: (纸本)9783030675493

Edge-cloud computing is an emerging computational model that allows offloading of service requests by the autonomous mobile agents from the edge-server to the cloud-server. This is to reduce the network latency prevalent in the cloud-IoT model. However, Quality-of-Service (QoS)-Aware computation offloading in a heterogeneous and dynamic edge-cloud environment remains an open problem. In this paper, we propose a queuing theory-based edge-cloud computing framework for QoS-aware offloading in mobile autonomous agents. This framework model decides whether to execute an incoming request to the edge-server on the edge itself or to offload to one of the heterogeneous cloud servers such that the request’s execution time is the minimum. To model a request’s execution time, we consider the processing capabilities and the queues overheads of the edge and cloud servers, and the edge-cloud communications’ time. The details of the evaluation results, using dataset generated from real-life scenarios, are presented in the paper. © 2021, Springer Nature Switzerland AG.

关键词： Quality of service

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Domain adaptive person re-identification via homogeneous hierarchical progressive adaptation and multi-view consistency

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Advanced engineering Informatics 2025年 67卷

作者： Zhengyang Wang Xiufen Ye Xue Shang Shuxiang Guo College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin 150001 China Department of Electrical Computer and Software Engineering University of Auckland Auckland 1142 New Zealand Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen 518055 China

Domain adaptive person re-identification (Re-ID) encounters significant challenges when models trained on one dataset are deployed in new environments. It is particularly critical in engineering applications where surveillance infrastructure must be rapidly adapted to new environments without compromising security system reliability. The problem represents a knowledge-intensive engineering task that requires sophisticated knowledge representation and transfer mechanisms to support automated system reconfiguration decisions. These challenges include preserving fine-grained visual details, handling camera-specific variations, generating reliable pseudo-labels, maintaining consistency across multiple camera views, and managing varying difficulty levels in domain transfer. To address these issues, we propose a novel framework that separates adaptation into intra-domain and cross-domain learning phases. Our approach introduces several key innovations: an adaptive fine-grained network that preserves spatial details while ensuring domain invariance, a memory-guided learning method for stable feature representation, and a hierarchical adaptation strategy for reliable intra-domain learning. Additionally, we incorporate a progressive learning strategy for gradual domain adaptation and a consistency reinforcement mechanism to resolve cross-camera identity conflicts. Experiments on the Market-1501, DukeMTMC-reID, and MSMT17 datasets across four domain adaptation tasks show that our method outperforms state-of-the-art approaches, significantly improving both mAP and CMC accuracy. This work contributes to engineering informatics by providing a reliable solution for automating knowledge-intensive tasks across diverse engineering domains, while also laying the foundation for advancements in intelligent surveillance systems and automated decision-making in engineering infrastructures.

关键词： Automatic identification

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Automated identification of security discussions in microservices systems: Industrial surveys and experiments

arXiv

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arXiv 2021年

作者： Rezaei-Nasab, Ali Shahin, Mojtaba Liang, Peng Basiri, Mohammad Ehsan Raviz, Seyed Ali Hoseyni Khalajzadeh, Hourieh Waseem, Muhammad Naseri, Amineh School of Computer Science Wuhan University Wuhan430072 China Department of Software Systems and Cybersecurity Faculty of IT Monash University Melbourne3800 Australia Department of Computer Engineering Shahrekord University Shahrekord64165478 Iran Department of Computer Engineering Sirjan University of Technology Sirjan*** Iran

Lack of awareness and knowledge of microservices-specific security challenges and solutions often leads to ill-informed security decisions in microservices system development. We claim that identifying and leveraging security discussions scattered in existing microservices systems can partially close this gap. We define security discussion as "a paragraph from developer discussions that includes design decisions, challenges, or solutions relating to security". We first surveyed 67 practitioners and found that securing microservices systems is a unique challenge and that having access to security discussions is useful for making security decisions. The survey also confirms the usefulness of potential tools that can automatically identify such security discussions. We developed fifteen machine/deep learning models to automatically identify security discussions. We applied these models on a manually constructed dataset consisting of 4,813 security discussions and 12,464 non-security discussions. We found that all the models can effectively identify security discussions: an average precision of 84.86%, recall of 72.80%, F1-score of 77.89%, AUC of 83.75% and G-mean 82.77%. DeepM1, a deep learning model, performs the best, achieving above 84% in all metrics and significantly outperforms three baselines. Finally, the practitioners' feedback collected from a validation survey reveals that security discussions identified by DeepM1 have promising applications in practice. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment

arXiv

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arXiv 2024年

作者： Wu, Jiaqi Zhang, Shihao Chen, Simin Wang, Lixu Wang, Zehua Chen, Wei He, Fangyuan Tian, Zijian Yu, F. Richard Leung, Victor C.M. Beijing100083 China Department of Electrical and Computer Engineering University of British Columbia Vancouver 2332 Main Mall VancouverBCV6T 1Z4 Canada University of Texas at dallas 800 W Campbell Rd RichardsonTX75080 United States Northwestern University 633 Clark St EvanstonIL60208 United States School of Computer Science and Technology China University of Mining and Technology Jiangsu province Xuzhou221116 China Department of Electrical and Computer Engineering The University of British Columbia Vancouver 2332 Main Mall VancouverBCV6T 1Z4 Canada Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China College of Computer Science and Software Engineering Shenzhen University Shenzhen528060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada College of Applied Science and Technology Beijing Union University Beijing100012 China

Recently, edge computing has emerged as a prevailing paradigm in applying deep learning-based object detection models, offering a promising solution for time-sensitive tasks. However, existing edge object detection faces several challenges: 1) These methods struggle to balance detection precision and model lightweightness. 2) Existing generalized edge-deployment designs offer limited adaptability for object detection. 3) Current works lack real-world evaluation and validation. To address these challenges, we propose the Edge Detection Toolbox (ED-TOOLBOX), which leverages generalizable plug-and-play components to enable edge-site adaptation of object detection models. Specifically, we propose a lightweight Reparameterized Dynamic Convolutional Network (Rep-DConvNet) that employs a weighted multi-shape convolutional branch structure to enhance detection performance. Furthermore, ED-TOOLBOX includes a Sparse Cross-Attention (SC-A) network that adopts a localized-mapping-assisted self-attention mechanism to facilitate a well-crafted Joint Module in adaptively transferring features for further performance improvement. In real-world implementation and evaluation, we incorporate an Efficient Head into the popular You-Only-Look-Once (YOLO) approach to achieve faster edge model optimization. Moreover, we identify that helmet detection—one of the most representative edge object detection tasks—overlooks band fastening, which introduces potential safety hazards. To address this, we build a Helmet Band Detection Dataset (HBDD) and apply an edge object detection model optimized by the ED-TOOLBOX to tackle this real-world task. Extensive experiments validate the effectiveness of components in ED-TOOLBOX. In visual surveillance simulations, ED-TOOLBOX-assisted edge detection models outperform six state-of-the-art methods, enabling real-time and accurate detection. These results demonstrate that our approach offers a superior solution for edge object detection. Copyright © 2024, T

关键词： Deep learning

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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A Guide to Image and Video based Small Object Detection using Deep Learning: Case Study of Maritime Surveillance

arXiv

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arXiv 2022年

作者： Rekavandi, Aref Miri Xu, Lian Boussaid, Farid Seghouane, Abd-Krim Hoefs, Stephen Bennamoun, Mohammed The Department of Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia The Department of Electrical Electronics and Computer Engineering The University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia The School of Mathematics and Statistics The University of Melbourne Melbourne Australia submarine optronics undersea combat systems and undersea command and control maritime division Defence Science and Technology Group Australia

Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided. © 2022, CC BY-NC-SA.

关键词： Object recognition

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Pl-Densenet: Enhancing Disease Classification in Pear Leaves Using Deep Learning

SSRN

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SSRN 2023年

作者： Gulzar, Yonis Ünal, Zeynep Ayoub, Shahnawaz Wani, Sharyar Mir, Mohammad Shuaib Soomro, Arjumand Bano Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Department of Biosystem Engineering Niğde Ömer Halisdemir University Central Campus Türkiye Niğde51240 Turkey Mirzapur Pole U.P. Saharanpur247121 India Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Software Engineering Faculty of Engineering and Technology University of Sindh Sindh Pakistan

Over the past two decades, climate changes have negatively impacted agriculture. It hasn’t left any stage of production unaffected. These noticeable changes have left formers no choice but to adopt new technological measures to tackle such changes. Deep learning has played a great role in improving decision-making in agriculture. This research introduces a novel deep learning model, PL-DenseNet, designed to accurately identify various disease types present in pear leaves. The PL-DenseNet model is an adaptation of the original DenseNet architecture, incorporating significant modifications to enhance its disease classification capabilities. The modifications involve replacing the classification layer of the DenseNet with three additional layers: Global Average Pooling 2D, Batch Normalization, and Dropout layers. Moreover, the final layer of the PL-DenseNet model comprises four nodes dedicated to classifying pear leaf diseases. The integration of these modifications aims to improve disease identification accuracy and enhance overall classification performance within the context of pear leaf diseases. The PL-DenseNet model also incorporated transfer learning and data augmentation to increase the variety and quantity of training data. The model's performance was evaluated on the DiaMOS Plant dataset, which comprises four different pear leaf diseases under real field conditions with varying brightness, disease similarity, complex background, and multiple leaves. Experimental evaluation reveals that the proposed PL-DenseNet model achieves notable advancements compared to the baseline model. It demonstrates higher accuracy (99.18%), precision (98.83), recall (99.06), and F1-score (93.58). Additionally, the proposed model surpasses other state-of-the-art models, including EfficientNetB0, InceptionV2, MobileNetV2, ResNet50, and VGG16, underscoring its superior performance in disease classification. Moreover, this research demonstrates how transfer learning and data augmentati

关键词： Image classification

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Learning assistant to acquire the fundamental language skills for non-native learners using AI 2

Learning assistant to acquire the fundamental language skill...

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2nd International Conference on Advancements in Computing, ICAC 2020

作者： Srikanthan, Praveenkumar Nizar, Roshan Ravikumar, Abishaan Lalitharan, Keerthigan Harshanath, S.M.B. Alosius, Jesuthasan Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781728184128

The ability to speak and learn a language properly requires good practice, experience and good learning strategies but the existing solutions do not provide proper guidance to learn a language with instant feedback. This research is an approach to devise an improved language learning assistant with practices that will help to improve the fundamental language skills for non-native learners and children who are in the early stage of their education. The four main skills focused on this application will be conversation, pronunciation, listening and grammatical skills. The implementation of this research is done by using technologies like natural language processing, machine learning, and deep learning approaches to come up with components to train the learner. The solution of this research is delivered by using a cross-platform application called GLIB which facilities to improve all the English language skills mentioned above along with guides, tips, practices, and feedback based on an evaluation to improve the English language. © 2020 IEEE.

关键词： Deep learning

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