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2024 Systems and Information Engineering Design Symposium, SIEDS 2024

作者： Ghazal, Taher M. Iqbal Janjua, Jamshaid Abbas, Sagheer Fatima, Areej Saleem, Muhammad Khan, Muhammad Adnan Alqarafi, Abdulrahman Khalifa University Centre for Cyber Physical Systems Computer Science Department Abu Dhabi United Arab Emirates Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Malaysia National College of Business Administration and Economics School of Computer Sciences Lahore Pakistan Al-Khawarizmi Institute of Computer Science Lahore Pakistan Bahria University Department of Computer Science Lahore Pakistan LGU Department of Computer Science Lahore Pakistan Minhaj University School of Computer Science Lahore Pakistan Skyline University College School of Computing Sharjah United Arab Emirates Riphah International University Riphah School of Computing and Innovation Lahore Pakistan Taibah University College of Computer Science and Engineering Medina42353 Saudi Arabia

ISBN: (纸本)9798350385144

The considerable surge in energy consumption due to population growth and the use of new technology has posed significant challenges to energy security and the environment. Managing energy consumption on the consumer side is crucial as the demand for energy continues to increase. Consumers need to monitor their daily usage and understand consumption standards to prepare themselves for energy efficiency and cost savings. To address these challenges, a Smart Energy Management System (SEMS) based on the Internet of Energy (IoE) has been developed. This system uses day-Ahead planning and accurate energy availability forecasting to manage energy demand in a smart environment with high sustainable energy penetration. The Fuzzy based Weighted Federated Learning (FWFML) approach is integrated into the system to optimize energy management. The SEMS system monitors energy usage at the user level, and a review of management plans has been introduced to improve energy efficiency in terms of 93.5% accuracy, and 6.5% miss-rate. This research work also highlights future challenges in developing SEMS to increase energy efficiency in a more robust manner. © 2024 IEEE.

关键词： Energy management systems

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LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup 42

LOPO: An Out-of-order Layer Pulling Orchestration Strategy f...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Gu, Lin Huang, Junhao Huang, Shaoxing Zeng, Deze Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China China University of Geosciences School of Computer Science Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions. © 2023 IEEE.

关键词： Containers

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Differential and TriPhase Adaptive Learning-Based Privacy-Preserving Model for Medical Data in Cloud Environment

IEEE Networking Letters

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IEEE Networking Letters 2022年第4期4卷 217-221页

作者： Gupta, Rishabh Saxena, Deepika Gupta, Ishu Singh, Ashutosh Kumar National Institute of Technology Kurukshetra Department of Computer Applications Kurukshetra136119 India National Sun Yat-sen University Cloud Computing Research Center Department of Computer Science and Engineering Kaohsiung804 Taiwan

This letter proposes a novel secure data protection model for privacy preservation in the cloud environment by partitioning, sanitizing, and analyzing the data effectively to improve the model's privacy. Several experiments and comparisons of the proposed model with existing works indicate that it protects data with high accuracy, precision, recall, and F1-score up to 87.03%, 84.87%, 87.03%, and 85.00%, for diverse datasets with a relative improvement up to 15.89%, 27.73%, 15.89%, and 21.44%, respectively. © 2019 IEEE.

关键词： Deep neural networks

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction 23

Self-training through Classifier Disagreement for Cross-Doma...

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2023 World Wide Web Conference, WWW 2023

作者： Sun, Kai Zhang, Richong Samuel, Mensah Nikolaos, Aletras Mao, Yongyi Liu, Xudong Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Sheffield Sheffield United Kingdom School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9781450394161

Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adversarial neural networks that aim to reduce the domain gap between the labelled source and unlabelled target domains to improve target domain performance. However, this approach only aligns feature distributions and does not account for class-wise feature alignment, leading to suboptimal results. Semi-supervised learning (SSL) has been explored as a solution, but is limited by the quality of pseudo-labels generated by the model. Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. Extensive experiments on benchmark cross-domain OTE datasets show that this approach is effective and performs consistently well in settings with large domain shifts. © 2023 ACM.

关键词： Sentiment analysis

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A Perspective of Mango Pest Classification Using Wrapper-Based Feature Selection Algorithm

A Perspective of Mango Pest Classification Using Wrapper-Ba...

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International Conference on Advanced Communications and Machine Intelligence, MICA 2023

作者： Muthaiah U Sonai, Veeramani Roy, Ram Vinod Banerjee, Sayan Ramanathan, C. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Vengal Tamil Nadu Chennai601103 India Department of Computer Science and Engineering School of Engineering Shiv Nadar University Kalavakkam Tamil Nadu Chennai603110 India Peggy and Charles Stephenson Cancer Center University of Oklahoma Health Science Center OKlahoma CityOK73104 United States

ISBN: (纸本)9789819762217

The presence of pests on mango fruits reduces productivity and increases the need for pesticide usage. Therefore, early pest identification can significantly impact productivity. However, identifying various types of pests from images is a challenging and time-consuming *** article introduces a feature selection technique using a wrapper approach to improve classification accuracy. The method involves adapting the basic random forest classifier by choosing a subset of high-accuracy features to input into the classifier. Our proposed modified random forest (MRF) classifier achieves a classification accuracy of 99.8%, representing a substantial improvement of 7.19%, 13.53%, and 7.65% compared to the random forest, k-nearest neighbor, and Ada Boost classifiers, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ada Boost classifiers Feature Selection K-nearest neighbor Mango Pests Random Forest Wrapper-based feature selection

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors 8

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Qian, Hong Wu, Wei Zhu, Qi Wang, Fei Zhao, Jinwei Zheng, Yan School of Computer Science and Technology University of Science and Technology of China Hefei China Jiangnan Institute of Computing Technology System Software Research Department Wuxi China Tsinghua University Department of Computer Science and Technology Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665456128

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs. © 2023 IEEE.

关键词： Supercomputers

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile computing 2025年第07期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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Implementation of a Mixed Triple Logistic Map and the BB84 Quantum Key Distribution for Secure Image Communication

Implementation of a Mixed Triple Logistic Map and the BB84 Q...

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2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Morissa, Virnika Shefira Gina Setiadi, De Rosal Ignatius Moses Universitas Dian Nuswantoro Informatics Engineering Department Semarang50131 Indonesia Universitas Dian Nuswantoro Research Center for Quantum Computing and Materials Informatics Faculty of Computer Science Semarang50131 Indonesia

ISBN: (纸本)9798350366648

In the digital era, data communication security is one of the main challenges. This research proposes a new method for secure image communication, which is a mixed triple logistic map and BB84 quantum key distribution. This approach combines the strengths of classical and quantum cryptographic systems to improve security. BB84 quantum key distribution protocol for securely distributing encryption keys. The resulting key is then processed into a hash value using the SHA-256 algorithm. These hash values are converted into ASCII values, which are used to generate three key sequences using the modified logistic map. The three key sequences are combined to form a keystream used in image encryption. Several security metrics are used to evaluate encryption security, including entropy, the Chi-Square test, and pixel correlation. The analysis results show that the encrypted image has an entropy close to eight. In addition, all chi-square values smaller than 293 indicate a uniform distribution. The correlation of nearby pixels in the encrypted image is also very low, where all values are less than 0.01, which indicates the loss of local patterns in the encrypted image. These results demonstrate that the proposed method effectively secures image communications, providing high security and resistance to conventional and quantum cryptographic attacks. © 2024 IEEE.

关键词： Quantum cryptography

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Multi-Modality and Feature Fusion-Based COVID-19 Detection Through Long Short-Term Memory

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computers, Materials & Continua 2022年第9期72卷 4357-4374页

作者： Noureen Fatima Rashid Jahangir Ghulam Mujtaba Adnan Akhunzada Zahid Hussain Shaikh Faiza Qureshi Center of Excellence for Robotics Artificial Intelligenceand BlockchainDepartment of Computer ScienceSukkur IBA UniversitySukkurPakistan Department of Computer Science COMSATS University Islamabad–Vehari CampusPakistan Faculty of Computing and Informatics University Malaysia SabahKota Kinabalu88400Malaysia Department of Mathematics Sukkur IBA UniversitySukkurPakistan

The Coronavirus Disease 2019(COVID-19)pandemic poses the worldwide challenges surpassing the boundaries of country,religion,race,and *** current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction(RT-PCR)***,this testing method is accurate enough for the diagnosis of ***,it is time-consuming,expensive,expert-dependent,and violates social *** this paper,this research proposed an effective multimodality-based and feature fusion-based(MMFF)COVID-19 detection technique through deep neural *** multi-modality,we have utilized the cough samples,breathe samples and sound samples of healthy as well as COVID-19 patients from publicly available COSWARA *** set of experimental analyses were performed to evaluate the performance of our proposed *** useful features were extracted from the aforementioned modalities that were then fed as an input to long short-term memory recurrent neural network algorithms for the classification *** set of experimental analyses were performed to evaluate the performance of our proposed *** experimental results showed that our proposed approach outperformed compared to four baseline approaches published *** believe that our proposed technique will assists potential users to diagnose the COVID-19 without the intervention of any expert in minimum amount of time.

关键词： Covid-19 detection long short-term memory feature fusion deep learning audio classification

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