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Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

computers, Materials & Continua 2023年第6期75卷 6305-6322页

作者： Muhammad Imran Khan Khalil Syed Adeel Ali Shah Izaz Ahmad Khan Mohammad Hijji Muhammad Shiraz Qaisar Shaheen Department of Computer Science&Information Technology University of Engineering&TechnologyPeshawar25000Pakistan Department of Computer Science Bacha Khan UniversityCharsadda24541Pakistan Computer Science Department University of TabukTabuk47512Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and Technology Islamabad44000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan

Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion *** CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload *** research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains *** this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed *** primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save *** the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming *** results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

关键词： String matching optimization geo-distributed data centers geographical load balancing green energy

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Static video summarization with multi-objective constrained optimization

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2621-2639页

作者： Dhanushree, M. Priya, R. Aruna, P. Bhavani, R. Department of Computer Science and Engineering Annamalai University Annamalainagar Tamil Nadu Chidambaram608001 India

Video summarization is an emerging research field. In particular, static video summarization plays a major role in abstraction and indexing of video repositories. It extracts the vital events in a video such that it covers the entire content of the video. Frames having those important events are called keyframes which are eventually used in video indexing. It also helps in giving an abstract view of the video content such that the internet users are aware of the events present in the video before watching it completely. The proposed research work is focused on efficient static video summarization by extracting various visual features namely color, texture and shape features. These features are aggregated and clustered using a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. In order to produce good video summary by clustering, the parameters of DBSCAN algorithm are optimized by using a meta heuristic population based optimization called Artificial Algae Algorithm (AAA). The experimental results on two public datasets namely VSUMM and OVP dataset show that the proposed Static Video Summarization with Multi-objective Constrained Optimization (SVS_MCO) achieves better results when compared to existing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Algae

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Toward actionable testing of deep learning models

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science China(Information sciences) 2023年第7期66卷 300-302页

作者： Yingfei XIONG Yongqiang TIAN Yepang LIU Shing-Chi CHEUNG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education School of Computer Science Peking University Department of Computer Science and Engineering The Hong Kong University of Science and Technology School of Computer Science University of Waterloo Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology

Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.

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Improving coronary heart disease prediction with real-life dataset: a stacked generalization framework with maximum clinical attributes and SMOTE balancing for imbalanced data

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Multimedia Tools and Applications 2024年第37期83卷 85139-85168页

作者： Dubey, Madhuri Tembhurne, Jitendra Makhijani, Richa Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India

Heart disease increases the strain on the heart by reducing its ability to pump blood throughout the body, which can lead to heart attacks and strokes. Heart disease is becoming a global threat to the world due to people’s unhealthy lifestyles, prevalent stroke history, physical inactivity, and current medical background. In predictive analytics, many studies were proposed to get alerts about forthcoming heart disease based on various attributes. However, the performance metrics were good, but the model was trained with few features. This study aims to train the model with all the essential attributes for heart disease prediction on Framingham Heart Study (FHS) dataset. The dataset is pre-processed with IQR (Inter Quartile Range) outlier detection followed by data oversampling using Synthetic Minority Oversampling Technique (SMOTE). We proposed a stack generalization approach, wherein various machine learning classifiers, namely logistic regression, random forest, K-nearest neighbour, Naïve Bayes, support vector machine, XGBoost, and decision tree with optimized hyperparameter trained the model to offer the best learner for the prediction of Coronary Heart disease with improved performance. The proposed model is tested on the original imbalanced and SMOTE-balanced FHS dataset. It is observed that logistic regression on the original (imbalanced) FHS dataset provides 86.51% accuracy, while the support vector machine on the SMOTE (balanced) FHS dataset outperformed the other models with an accuracy of 93.07%. Also, the proposed approach of stacked generalization with cross-validation provided 97.2% accuracy on SMOTE (balanced) dataset, which is remarkable. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Anomaly detection

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Ladder-Type Resonant Switched-Capacitor Converter Achieving 99.1% Peak Efficiency and 4750 w/in3Power Density for High-Current Application

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IEEE Transactions on Power Electronics 2024年第7期39卷 8040-8053页

作者： You, Dong-Ying Chiu, Huang-Jen Kuo, Shih-Hao National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan

A nonisolated fixed-ratio resonant switched-capacitor converter (RSCC) with high peak efficiency and high power density is encouraged as intermediate bus converter. With higher input voltages, stacked topologies achieve better component stress, but the relative current stress will be multiplied, e.g., ladder and Cockcroft-Walton topologies are not well suited for high-power high step-down ratio applications. Accordingly, in this article, a ladder-type RSCC is proposed to meet high current demands by combining symmetrical auxiliary resonant tanks with two interleaved phases. In addition to having a high-resolution scalability that satisfies any integer conversion ratio, the voltage stress on the flying capacitors does not increase significantly as the conversion ratio increases, making their capacitance minimally susceptible to component tolerances. Meanwhile, all mosfets are always well clamped at 1× output low-voltage level, which means that low-voltage devices are permitted to be used, resulting in a good factor of merit. By combining adaptive constant on-time with dead-time modulation, an effective duty cycle is optimized, and controlled current sharing is achieved, making multistage parallel expansion very flexible and efficient. To demonstrate the feasibility and practicality, a compact 3-to-1 hardware prototype was realized with a load current of up to 75 A, resulting in an excellent power density of 4750 W/in3. A peak efficiency of 99.1% and a full-load efficiency of 98.1% were also achieved. © 1986-2012 IEEE.

关键词： Topology

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A Channel Usage Probability Awareness Anti-Jamming Channel Hopping Protocol for Cognitive Radio Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第4期10卷 1327-1338页

作者： Lei, Sheng-Wei Chao, Chih-Min National Taiwan Ocean University Department of Computer Science and Engineering Keelung City202 Taiwan

To solve the rendezvous problem between any pair of Secondary Users (SUs) and potential jamming attacks in cognitive radio networks, many solutions have employed a channel hopping mechanism. The Primary User (PU) occupancy probability significantly affects the performance of these channel hopping protocols. Certain existing solutions adapt the frequency of channel usage based on predicted PU occupancy probability derived from historical averages. In this paper, we demonstrate the inadequacy of such predictions and propose a rendezvous-guaranteed anti-jamming channel hopping protocol with channel usage adjustment capability, termed OLAA_P. By using deep learning technology to predict future PU occupancy probability for each channel, OLAA_P can properly adjust the usage ratio of each channel. Simulation results validate that OLAA_P outperforms recently-proposed practical anti-jamming hopping protocols in terms of throughput. © 2015 IEEE.

关键词： Cognitive radio

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Build Yourself before Collaboration: Vertical Federated Learning with Limited Aligned Samples

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6503-6516页

作者： Shen, Wei Ye, Mang Yu, Wei Yuen, Pong C. Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan China Hong Kong Baptist University Department of Computer Science Hong Kong Hong Kong

Vertical Federated Learning (VFL) has emerged as a crucial privacy-preserving learning paradigm that involves training models using distributed features from shared samples. However, the performance of VFL can be hindered when the number of shared or aligned samples is limited, a common issue in mobile environments where user data are diverse and unaligned across multiple devices. Existing approaches use feature generation and pseudo-label estimation for unaligned samples to address this issue, unavoidably introducing noise during the generation process. In this work, we propose Local Enhanced Effective Vertical Federated Learning (LEEF-VFL), which fully utilizes unaligned samples in the local learning before collaboration. Unlike previous methods that overlook private labels owned by each client, we leverage these private labels to learn from all local samples, constructing robust local models to serve as solid foundations for collaborative learning. Additionally, we reveal that the limited number of aligned samples introduces distribution bias from global data distribution. In this case, we propose to minimize the distribution discrepancies between the aligned samples and the global data distribution to enhance collaboration. Extensive experiments demonstrate the effectiveness of LEEF-VFL in addressing the challenges of limited aligned samples, making it suitable for VFL in mobile computing environments. Codes are available at https://***/shentt67/LEEF-VFL. © 2025 IEEE.

关键词： Federated learning

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SLOG: smartphone-based semantic lifelogging framework for digital prosthetic memory development

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Personal and Ubiquitous Computing 2023年第6期27卷 2071-2102页

作者： Ali, Shaukat Khusro, Shah Khan, Akif Khan, Inayat Alam, Iftikhar Solehria, Salman Faiz Department of Computer Science University of Peshawar Peshawar25120 Pakistan Department of Computer Science Sarhad University of Information Technology Peshawar25000 Pakistan Department of Computer Science University of Engineering and Technology Mardan Mardan23200 Pakistan

The advancements in sensors, processing, storage and networking technologies have turned smartphone into de facto lifelogging device. Realizing the lifelogging potential of smartphone, researchers have postulated with smartphone-based lifelogging systems with varying capabilities of capturing and storing information about peoples’ daily life activities, behaviors, interactions, locations, environments, etc., into their lifelog archives. However, the systems are lacking with several capturing, storing, retrieving and design issues, importantly not organizing the lifelog information with the same semantics as they exist in the real world and encoded in human episodic memory. The semantic organization of lifelog information will develop a true digital prosthetic memory to solve problems in different real-world domains including memory augmentation. In this paper, we have presented the architecture and implementation of Semantic LifelOGging (SLOG) framework for smartphone-based semantic lifelogging using Semantic Web technologies. The SLOG framework incorporates smartphone sensors and processing capabilities, and Semantic Web technologies to develop a semantically enriched digital prosthetic memory on smartphone. The semantic lifelog data model is developed by extending SmartOntoSensor ontology with relevant concepts and properties to represent, store and relate lifelog and contextual information. The semantic lifelog data model will provide contextual semantics to organize, annotate and relate lifelog information like human episodic memory and provide enough contextual cues to recall lifelog information like associative recall in human memory. The proof-of-concept applications are developed to show practicality of the SLOG framework. The quantitative and empirical evaluations have shown the SLOG framework and system has improved features and is acceptable in usability and effectiveness. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part

关键词： Smartphones

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A differential privacy-preserving federated learning scheme with predictive maintenance of wind turbines based on deep learning for feature compression and anomaly detection with state assessment

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Journal of Mechanical science and Technology 2024年第7期38卷 3413-3429页

作者： Chen, Huan Hsu, Hsin-Yao Hsieh, Jia-You Hung, Hsiang-En Department of Computer Science and Engineering National Chung Hsing University Taichung City40227 Taiwan

The transport and supply of traditional energy have been greatly affected, which has attracted the attention of countries worldwide and has become an urgent issue. Limited by its own traditional energy shortage and relying on many imported energy raw materials, Taiwan has gradually transformed natural resources into abundant renewable energy through energy transformation, of which wind power has increased up to 1.79 %. It mainly relies on the operation of wind turbines to convert wind power energy into electricity, so the operation of wind turbines is an important issue to maintain stable power collection. This study mainly discusses the wind turbine failure prediction model based on the supervisory control and monitoring system (SCADA) data of 31 wind turbines, and used deep learning and federated learning architecture combined with differential privacy method to find the most suitable model combination through experiments, and establishes wind turbines for state assessment of failure architecture with transmission and prediction, so as to improve wind turbine efficiency and reduce maintenance costs. The proposed method uses 1-dimensional convolutional neural network and bidirectional long short-term memory denoising autoencoder with the transformer (Conv1D-BiLSTM-DAE-TF) for feature compression, and then uses 1-dimensional convolutional neural network (Conv1D), recurrent neural network (RNN), long short term memory (LSTM) and gated recurrent unit (GRU) model for abnormal detection with state assessment. Combined with federated learning and differential privacy architecture for wind turbine failure prediction it can effectively predict performance and privacy security. The results show that it has the best for feature compression convergence performance and fault prediction based on differential privacy for protect model, and construction of models between different wind turbine equipment through the platform. © The Korean Society of Mechanical Engineers and Spring

关键词： Anomaly detection

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AnonMAKE: Toward Secure and Anonymous Mutually Authenticated Key Exchange Protocol for Vehicular Communications

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IEEE Transactions on Intelligent Transportation Systems 2024年第10期25卷 14245-14258页

作者： Karati, Arijit Chang, Li-Chun National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung80424 Taiwan

Due to real-time data sharing in the Intelligent Transportation System (ITS), the automobile industry has evolved enormously in recent years. However, as more vehicles become connected, the peril of cyberattacks increases, necessitating safeguarding traffic safety-related data provided by individual vehicles and public transportation to avoid perilous situations and infringement of confidentiality. In this regard, a recent work (10.1109/TITS.2021.3099488) suggested an anonymous batch data authentication and key exchange protocol. This paper shows that the work lacks true anonymity during authentication, employs a session-independent key, and is prone to session key disclosure and replay attacks. Apart from cryptanalysis, we present a novel privacy-enhancing mutual authentication and key agreement protocol supporting batch authentication resisting comprehensive security attacks in the vehicular system, including traffic data replay, vehicle impersonation, and man-in-the-middle attacks. The suggested scheme achieves security correctness under formal BAN logic analysis and is safer against the STRIDE effect. The empirical performance estimation reveals that the novel approach under adequate security assumptions outperforms the state-of-the-art solutions by achieving about 20% higher security traits while lowering 15% authentication delay, consuming 15% less bandwidth, and increasing 44% storage efficiency. Thus, our protocol is more suitable for lightweight vehicular communications. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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