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检索条件"任意字段=International Conference on Measurement and Modeling of Computer Systems"

共 90452 条记录，以下是161-170 订阅

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Title-Based Topic modeling on E-learning Web Content Titles Using BERTopic Model 5th

Title-Based Topic Modeling on E-learning Web Content Titles ...

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5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Lalitha, T.B. Sreeja, P.S. Department of Computer Applications Hindustan Institute of Technology and Science Chennai India Department of Computer Applications Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819747108

In today’s information-rich digital age, the volume of web content available to users has become overwhelming, making it challenging for individuals to find relevant and personalized content. Recommendation systems have emerged as a transformative solution, catering to individual users by offering customized suggestions aligned with their unique interests. This research explores a novel approach that utilizes topic modeling techniques on web content titles for recommendation purposes. Topic modeling, a subfield of natural language processing (NLP) is utilized to automatically identify latent topics within a large corpus of text. The proposed work begins by collecting a diverse dataset of web content titles across the domains. It employs a combination of other state-of-the-art topic modeling algorithms like BERTopic modeling and statistical model to uncover the underlying topics in the titles. By leveraging this approach on web content titles, aim to extract meaningful themes and categorize the content efficiently. Then preprocess the data to remove irrelevant information, ensuring that the subsequent topic modeling process yields accurate and meaningful results. This approach not only expedites the recommendation process but also conserves computational resource. Once the topics are identified, associate them with appropriate metadata, such as user preferences, and content types. This step forms the foundation of our content-based recommendation approach. Then maps the user’s interests to the most relevant topics, enabling us to present a tailored list of web content titles. By recommending content based on underlying themes rather than just keywords, this approach surpasses traditional methods, ensuring more accurate and diverse suggestions for users. The results demonstrate the system’s ability to provide highly personalized recommendations, enhancing user satisfaction and engagement. By delving into the semantic structure of content rather than relying solely on

关键词： modeling languages

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Predictive Maintenance in Autonomous systems using Improved Support Vector Machine 1

Predictive Maintenance in Autonomous Systems using Improved ...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Mamatha Bai, B.G. Divya, N. Sree Hamedshnain, Ammar Kumar, S. Senthil Vasumathi, T. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Mahatma Gandhi Institute of Technology Department of Information Technology Hyderabad India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, rapid development in the domain of Predictive Maintenance have resulted in improved equipment lifetime span, decrease maintenance costs and reduced the unplanned downtime. Predictive maintenance is an approach that addresses any equipment or component failures in the systems proactively. However, integration of this approach in automated systems has several challenges such as ensuring the accuracy of fault predictions, management of large-scale data and maintenance of system reliability under various operational conditions. To overcome these challenges Improved Support Vector Machine (ISVM) is proposed. Initially, the data is collected from the Predictive Maintenance Dataset (PMD) and the preprocessing is performed through Inter Quartile Range (IQR) which enhances the quality of data and modeling efforts. Further relevant features are extracted using Recursive Feature Elimination (RFE). Finally, the prediction is performed using ISVM which achieved better outcomes compared to existing a One-Class Support Vector Machine (SVM) in terms of accuracy as well as recall by achieving 90.58%, 80.79% respectively. © 2025 IEEE.

关键词： Predictive maintenance

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Comparative Analysis of RL-Based Resource Allocation Methods for Optimization in 5G MMWave Network 5th

Comparative Analysis of RL-Based Resource Allocation Methods...

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5th EAI international conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： Shilpa, V. Ranjan, Rajeev School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Applications REVA University Bangalore India

ISBN: (纸本)9783031770746

In this study, resource allocation techniques based on reinforcement learning (RL) for 5G millimeter wave (mmWave) networks are compared and analyzed. The high bandwidth and large available spectrum in mmWave networks offer a great opportunity for high data rate communication but also pose significant challenges in terms of resource allocation. RL, being a powerful tool for decision making in dynamic and uncertain environments, has been widely studied for resource allocation in mmWave networks. In this paper, we survey the state-of-the-art RL-based resource allocation methods for mmWave networks and we also discuss the challenges and open research directions in this field. This study presents an effective method for balancing stable, low-capacity mmWave links with reliable, high-capacity backhaul links designed using stochastic geometry and analytical channel modeling. We also propose a backhaul resource allocation strategy. To increase system utility with a mmWave network’s limited backhaul capacity, a neural network-based backhaul resource allocation has been proposed. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： 5G mobile communication systems

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Benchmarking Context-Aware Services for IoT-driven Transportation systems 24

Benchmarking Context-Aware Services for IoT-driven Transport...

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14th international conference on the Internet of Things, IoT 2024

作者： Ntallaris, Efstratios Bouloukakis, Georgios Magoutis, Kostas Computer Science Department University of Crete Heraklion Greece Crete Heraklion Greece Télécom SudParis IP Paris Évry-Courcouronnes France

ISBN: (纸本)9798400712852

In the fast-growing realm of smart cities, integrating Internet of Things (IoT) devices into transportation systems is essential for improving efficiency and safety. Deploying these systems in real-world settings demands access to contextual data, and middleware systems to facilitate the exchange of both contextual and IoT data. Existing IoT-based data exchange systems such as Orion-LD, Stellio and Scorpio in the FIWARE space, support the modeling and representation of both context and IoT systems. This paper introduces a comprehensive testbed and a benchmarking platform designed to evaluate the performance of FIWARE context-aware brokers. The testbed incorporates real data from a real Bus Transportation Service in the city of Ioannina, Greece, as well as synthetic data enabling a realistic assessment of query and ingestion performance. The results show that microservices-based architectures like Stellio and Scorpio scale better than traditional designs like Orion-LD under high loads, but all brokers perform similarly at low loads. Furthermore, temporal queries present challenges for IoT applications due to their high cost across all evaluated brokers. However, write-optimized data stores offer an advantage by improving ingestion speed. The paper emphasizes the importance of understanding and addressing the operational inefficiencies of context-aware brokers to improve IoT system performance. Overall, this work introduces a novel benchmarking platform for smart transportation systems, featuring a realistic testbed with both real and synthetic IoT datasets, as well as detailed experimental results that identify key performance bottlenecks and offer potential optimization strategies. © 2024 Copyright held by the owner/author(s).

关键词： Middleware

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Construction Contract Performance measurement: Investigating the Aspects of Responsibility and Digitalization 33rd

Construction Contract Performance Measurement: Investigating...

引用

33rd international conference on Organization and Technology of Maintenance, OTO 2024

作者： Trtílek, Petr Hanák, Tomáš Brno University of Technology Veveří 95 Brno602 00 Czech Republic

ISBN: (纸本)9783031805967

The success of construction projects should be based on management support for performance measurement. It is therefore essential that the performance measurement system provides the managers with the necessary data in a timely manner. This study explores the issue of responsibility for project-level performance in construction companies and reveals how contemporary digital technologies are being used to support performance measurement. An online questionnaire survey was used to collect 53 data samples representing the views and experiences of construction professionals across the Czech Republic. The results of this study show that the role of the person responsible for performance measurement is not uniformly perceived and practiced in the Czech construction industry. Furthermore, the willingness to perform performance measurement audits increases with the size of the company. From the perspective of digitalization, the awareness of modern approaches based on BIM (Building Information modeling) is satisfactory, but the analyzed data indicates that contract management and measurement is still mainly based on traditional approaches based on software tools such as Microsoft Excel and Project. This study has revealed the current approaches of Czech construction companies to contract performance measurement. The results presented suggest that inconsistency in the approach to contract measurement, together with low use of modern tools, may lead to problems in achieving the desired project-level performance. Future research should therefore focus in this direction and explore possible causal connections. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Construction industry

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SBL Algorithms for the Multiple measurement Vector Problem: New modeling and Inference Methods

SBL Algorithms for the Multiple Measurement Vector Problem: ...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Kanakeri, Vinay Meyer, Florian Rao, Bhaskar D. Department of Electrical and Computer Engineering University of California San Diego United States

ISBN: (纸本)9798350368741

This paper introduces new and practically relevant non-Gaussian priors for the Sparse Bayesian Learning (SBL) framework applied to the Multiple measurement Vector (MMV) problem. We extend the Gaussian Scale Mixture (GSM) framework to model prior distributions for row vectors, exploring the use of shared and different hyperparameters across different measurements. We propose Expectation Maximization (EM) based algorithms to estimate the parameters of the prior density along with the hyperparameters. To promote sparsity more effectively in a non-Gaussian setting, we show the importance of incorporating learning of the parameters of the mixing density. Such an approach effectively utilizes the common support notion in the MMV problem and promotes sparsity without explicitly imposing a sparsity-promoting prior, indicating the methods' robustness to model mismatches. Numerical simulations are provided to compare the proposed approaches with the existing SBL algorithm for the MMV problem. © 2025 IEEE.

关键词： Gaussian Scale Mixture Laplace distribution simultaneous sparse approximation sparse Bayesian learning

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Research on the development and application of an interactive AI-based power supply system 2

Research on the development and application of an interactiv...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Gao, Jinming Zhu, Pingfei Xiong, Genxin Wang, Yukun Liu, Lei Shu, Yanfei Beijing China-power Information Technology Co. Ltd. Changping District Beijing102206 China

ISBN: (纸本)9781510689053

With the rapid evolution of digital technologies, the application of artificial intelligence (AI) in power systems has become increasingly critical to addressing the growing complexity and dynamic demands of modern energy markets. Traditional power marketing models are no longer sufficient to meet the need for real-time interaction, intelligent service delivery, and seamless collaboration among stakeholders. This study aims to develop an AI-powered, interactive power supply system to transform marketing processes and enhance service capabilities. Leveraging a co-innovation strategy-"AI + power marketing"-the proposed system integrates multimodal interaction, real-time voice communication, and intelligent solution generation, enabling dynamic engagement between users and service providers. The research employs advanced AI algorithms to optimize interaction design, improve solution accuracy, and streamline digital marketing workflows. Results from simulations and real-world applications demonstrate significant improvements in user-centric service quality, operational efficiency, and the digitalization of power marketing. These findings highlight the effectiveness of AI-based approaches in fostering intelligent transformation within the energy sector, offering a scalable framework for future advancements in power system marketing and services. © 2025 SPIE.

关键词： Artificial intelligence Power supplies Intelligence systems Industry Human computer interaction Industrial applications Inspection Design Power consumption systems modeling

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15th international computer measurement Group conference, CMG 1989

15th International Computer Measurement Group Conference, CM...

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15th international computer measurement Group conference, CMG 1989

The proceedings contain 154 papers. The topics discussed include: corporate cash analysis and department budget analysis methodologies in investment decision-making;expert systems in data processing applications using IBM knowledge tool;using chargeback systems to alleviate resource constraints and improve system performance;a performance measurement facility on a large data processing application;useful computer system metrics, part two. useful metrics for storage subsystems;using data characterization as the basis of a performance methodology for DASD configuration;comparison of hallmark's workload on different central electronic complexes;benchmark workload data normalization in a multiple-vendor environment;back of the envelope performance analysis of a two-level hierarchy of memory in presence of tree data structures;and statistical constructs in the establishment of a capture ratio solution.

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Oracle-guided Dynamic User Preference modeling for Sequential Recommendation 25

Oracle-guided Dynamic User Preference Modeling for Sequentia...

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18th ACM international conference on Web Search and Data Mining, WSDM 2025

作者： Xia, Jiafeng Li, Dongsheng Gu, Hansu Lu, Tun Zhang, Peng Shang, Li Gu, Ning Fudan University Shanghai China Microsoft Research Asia Shanghai China Amazon Seattle United States School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Fudan Institute on Aging MOE Laboratory for National Development and Intelligent Governance Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai China

ISBN: (纸本)9798400713293

Sequential recommendation methods can capture dynamic user preferences from user historical interactions to achieve better performance. However, most existing methods only use past information extracted from user historical interactions to train the models, leading to the deviations of user preference modeling. Besides past information, future information is also available during training, which contains the "oracle" user preferences in the future and will be beneficial to model dynamic user preferences. Therefore, we propose an oracle-guided dynamic user preference modeling method for sequential recommendation (Oracle4Rec), which leverages future information to guide model training on past information, aiming to learn "forward-looking" models. Specifically, Oracle4Rec first extracts past and future information through two separate encoders, then learns a forward-looking model through an oracle-guiding module which minimizes the discrepancy between past and future information. We also tailor a two-phase model training strategy to make the guiding more effective. Extensive experiments demonstrate that Oracle4Rec is superior to state-of-the-art sequential methods. Further experiments show that Oracle4Rec can be leveraged as a generic module in other sequential recommendation methods to improve their performance with a considerable margin. © 2025 Copyright held by the owner/author(s).

关键词： dynamic user preference modeling

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Adaptive Load Balancing for Distributed Multi-Controller SDNs through Switch Migration Strategy 17

Adaptive Load Balancing for Distributed Multi-Controller SDN...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Rao, Allu Sudharshan Khanal, Bipal Alam Ansari, Md. Sarfaraj National Institute of Technology Sikkim Department of Computer Science and Engineering Sikkim Ravangla India

ISBN: (纸本)9798331531195

Software-defined networking (SDN) is a transformative technology that systematically centralises and manages network resources. This paradigm shift allows for greater flexibility, agility, and efficiency in network management. In the ever-evolving landscape of distributed software-defined networks, the escalating demand for cloud services and data traffic underscores the critical need for a sophisticated load-balancing scheme. This paper addresses the pivotal challenge of optimising resource utilisation in SDN controllers to mitigate bottlenecks, enhance responsiveness, and ensure efficient network operations. This paper proposes a switch migration algorithm for efficient load balancing in distributed multi-controller topology. The proposed framework uses resource utilisation, load balancing rate, and re-association time for performance measurement. The proposed algorithm considers the switch to controller distance and remaining processing capacity to select an immigration controller. It also minimizes re-association time in the process of switch migration. The proposed algorithms are compared with benchmark algorithms like 'Closest Switch Migration' and 'Maximum Utilization Switch Migration' using various performance metrics. It has shown promising results and outperforms the benchmark algorithms. © 2025 IEEE.

关键词： Benchmarking

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