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

共 90981 条记录，以下是421-430 订阅

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DialogNTM: Context Reconstruction in Multi-Turn Dialogue Generation using Neural Turing Machines

DialogNTM: Context Reconstruction in Multi-Turn Dialogue Gen...

引用

2024 IEEE international conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Haohao Liu, Meiling Zhou, Jiyun Fu, Kaiquan College of Computer and Control Engineering Northeast Forestry University Harbin China Lieber Institute Johns Hopkins University Baltimore United States South Dakota State University Department of Electrical Engineering and Computer Science BrookingsSD United States

ISBN: (纸本)9781665410205

In actual conversational scenarios, we can often determine which parts of the previous dialogue are more critical based on the current inquiry. However, the existing contextual modeling methods often encode the query sentence and the dialogue history in a unified manner, which fails to effectively highlight the inference effect of the query sentence. Moreover, these methods typically process the dialogue history only at the information extraction level, neglecting the treatment of the context itself. In this paper, we propose a novel conversational context modeling technique called DialogNTM. Based on the guidance of the query sentence, the technology can effectively eliminate redundant information by reconstructing the representation of the context. Specifically, we have tweaked the memory and input flow of the Neural Turing Machine (NTM) to encode contextual information in memory and guide the read, write, and erase operations of memory through query sentence. This design simulates the human brain's dynamic retrieval and renewal mechanism of previous memories when dealing with current problems. We have conducted extensive experiments on three publicly available datasets to verify the effectiveness of the DialogNTM model. Compared to the benchmark model, DialogNTM showed significant performance improvements ranging from 11% to 73% across multiple automated evaluation metrics (3.52% to 8.68% in absolute terms). © 2024 IEEE.

关键词： Turing machines

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modeling and Optimization of Controlled Conveyor systems Using Intelligent Controllers 15th

Modeling and Optimization of Controlled Conveyor Systems U...

引用

15th international conference on Optimization and Applications, OPTIMA 2024

作者： Petrov, Alexey Druzhinina, Olga Masina, Olga Bunin Yelets State University Yelets Russia Federal Research Center "Computer Science and Control" of Russian Academy of Sciences Moscow Russia V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031791185

The construction of generalized mathematical models is proposed that describe the dynamics of a controlled belt conveyor with a variable angle between the horizontal plane and the plane of the belt. The models under consideration are defined using nonlinear nonstationary systems of four differential equations with switching. Switching modes are characterized by a change in the loading and unloading sequence. Statements of optimal control problems are presented under restrictions on the magnitude of control and on the speed of control. Synthesis of neural network controllers for stabilization of the angular position of the conveyor is carried out. Controllers are synthesized using such direct propagation neural networks for which reinforcement learning is implemented based on the optimization algorithm of differential evolution. A comparative analysis of the effectiveness of synthesized controllers is carried out. Algorithmic support for modeling controlled conveyor systems is developed using intelligent analysis and global parametric optimization methods. The results of computational experiments are presented. The obtained results can find application in solving problems of optimal stabilization of dynamic systems with switching. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Belt conveyors

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Performance measurement of Reading Teaching-Learning Business Processes: Case of Whole-Word and Syllabic Reading Methods in Primary Schools 1

引用

23rd international conference on Intelligent systems Design and Applications, ISDA 2023

作者： Sahli, Amel Mejri, Asma Louati, Aymen RIADI Laboratory ENSI Manouba University La Manouba Tunisia ISITCom University of Sousse Sousse Tunisia Higher Institute of Computer Science of Kef University of Jendouba Jendouba Tunisia

ISBN: (数字)9783031647796

ISBN: (纸本)9783031647789

In the educational field, Key Performance Indicators (KPIs) aim to provide an opportunity to evaluate the performance of employees, teachers’ skills and pedagogy, students’ results, learning objects, educational institutions, and teaching-learning methods. In this research work, we focus on studying the performance of reading teaching-learning methods, specifically whole-word and syllabic language learning methods. More precisely, we propose a Business Process Management (BPM) based approach for performance measurement of the corresponding business processes of both methods. We elaborated the reading teaching-learning business processes using the Business Process Model and Notation (BPMN), which is a widely adopted standard for formalizing business process modeling. We prepared a set of KPIs for the different activities and the whole process. We used these KPIs to gauge the performance of each business process and make comparisons between them. To validate our approach, we launched a dedicated questionnaire to primary school teachers in the third grade. By using this questionnaire, we were able to evaluate the overall performance of each reading process. Our findings indicate that the whole-word method is the most effective teaching method compared to the other. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Students

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Enhanced Predictive modeling for Myocardial Infarction Risk Assessment through Multimodal Data Integration 3

Enhanced Predictive Modeling for Myocardial Infarction Risk ...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Abdulkadar, S. Dharun, S. Kirthivashan, V. Janani, B. K.S. Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798331509675

Since myocardial infarction (MI) continues to be a major cause of death worldwide, precise risk prediction is essential. In order to improve MI risk assessment, this work presents an improved predictive model that makes use of multimodal data, including clinical, genetic, imaging, and lifestyle information. By utilizing sophisticated machine learning methods, specifically the XGBClassifier, the model combines various datasets to reveal intricate patterns, attaining exceptional sensitivity and specificity. Robust reliability testing, a comparison with current approaches, and the use of interpretability tools such as SHAP guarantee that the suggested model provides useful information for clinical decision-making. Addressing dataset biases and extending feature integration to improve generalizability are two potential future steps. © 2025 IEEE.

关键词： AI Model and Accuracy Myocardial infarction streamlit metrics Train XGBClassifier

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A Formal modeling Language for Smart Contracts 22nd

A Formal Modeling Language for Smart Contracts

引用

22nd international conference on Software Engineering and Formal Methods, SEFM 2024

作者： Veschetti, Adele Bubel, Richard Hähnle, Reiner Department of Computer Science TU Darmstadt Darmstadt Germany

ISBN: (纸本)9783031773815

Smart contracts codify real-world transactions and automatically execute the terms of the contract when predefined conditions are met. This paper proposes SmartML, a modeling language for smart contracts that is platform independent and easy to comprehend. We detail its formal semantics and type system with a focus on its role in addressing security vulnerabilities. We show along a case study, how SmartML prevents reentrancy attacks, illustrating its efficacy in reinforcing the reliability and security of smart contracts within decentralized systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart contract

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Ontology as a Software Superstructure to the System for Mathematical modeling based on Interval Data 13

Ontology as a Software Superstructure to the System for Math...

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13th international conference on Advanced computer Information Technologies, ACIT 2023

作者： Melnyk, Andriy Pasichnyk, Roman Androshchuk, Ukiolexander Honchar, Lyudmyla Vatslavskyi, Oleh Serhii, Kozibroda West Ukrainian National University Department of Computer Science Ternopil Ukraine Department of Surgery Academy of State Border Service Khmelnytskyi Ukraine West Ukrainian National University Department of Financial Technologies and Banking Business Ternopil Ukraine Ternopil Volodymyr Hnatyuk National Pedagogical University Department of Computer Technologies Ternopil Ukraine

The article explores a significant scientific challenge related to the development of techniques and tools for constructing discrete models of complex objects using interval difference equations. This approach combines ontological principles with the analysis of interval data to broaden the range of applications for these models. It serves as a catalyst for advancing applied research in fields such as national defense, environmental protection, medicine, and other domains where mathematical models of objects with distributed parameters play a crucial role in decision support systems. The fundamental aspect of this mathematical modeling approach, based on interval analysis, involves multiple parameter estimations for input-output models. These models are constructed using experimental data, where output variables are expressed in interval form. The key research findings outlined in the article include: An explanation of how the ontological approach to mathematical modeling, using interval data, can be applied for software development. This application aims to expand the range of scenarios in which models can be used while maintaining their predictive capabilities. A step-by-step framework is proposed for creating an ontology-driven software system for mathematical modeling based on interval analysis. This framework outlines the process of developing, utilizing, and updating the ontological model of the subject area of mathematical modeling with interval data. One distinctive feature of the approaches discussed in this article is their adaptability as software extensions to applied systems of mathematical modeling that utilize interval analysis. By combining interval analysis and ontological representation of the subject area, these approaches enhance the efficiency of computational procedures for identifying models of complex objects. They also allow for the adaptive utilization of different models in various subject areas within decision support systems. © 2023 IEEE

关键词： Decision support systems

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Assessing Unfairness in GNN-Based Recommender systems: A Focus on Metrics for Demographic Sub-Groups 20

Assessing Unfairness in GNN-Based Recommender Systems: A Foc...

引用

20th international conference on Web Information systems and Technologies, WEBIST 2024

作者： Chizari, Nikzad Tajfar, Keywan Moreno-García, María N. Department of Computer Science and Automation University of Salamanca Plaza de los Caídos sn Salamanca37008 Spain College of Science School of Mathematics Statistics and Computer Science Department of Statistics University of Tehran Tehran Iran

ISBN: (纸本)9789897587184

Recommender systems (RS) have become a central tool for providing personalized suggestions, yet the growing complexity of modern methods, such as Graph Neural Networks (GNNs), has introduced new challenges related to bias and fairness. While these methods excel at capturing intricate relationships between users and items, they often amplify biases present in the data, leading to discriminatory outcomes especially against protected demographic groups like gender and age. This study evaluates and measures fairness in GNN-based RS by investigating the extent of unfairness towards various groups and su bgroups within these systems. By employing performance metrics like NDCG, this research highlights disparities in recommendation quality across different demographic groups, emphasizing the importance of accurate, group-level measurement. This analysis not only sheds light on how these biases manifest but also lays the groundwork for developing more equitable recommendation systems that ensure fair treatment across all user groups. Copyright © 2024 by SCITEPRESS.

关键词： Graph neural networks

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Device Image-IV Mapping using Variational Autoencoder for Inverse Design and Forward Prediction

Device Image-IV Mapping using Variational Autoencoder for In...

引用

international conference on Simulation of Semiconductor Processes and Devices (SISPAD)

作者： Lu, Thomas Lu, Albert Wong, Hiu Yung San Jose State Univ M PAC Lab San Jose CA 95192 USA

ISBN: (纸本)9784863488038

This paper demonstrates the learning of the underlying device physics by mapping device structure images to their corresponding Current-Voltage (IV) characteristics using a novel framework based on variational autoencoders (VAE). Since VAE is used, domain expertise is not required and the framework can be quickly deployed on any new device and measurement. This is expected to be useful in the compact modeling of novel devices when only device cross-sectional images and electrical characteristics are available (e.g. novel emerging memory). Technology computer-Aided Design (TCAD) generated and hand-drawn Metal-OxideSemiconductor (MOS) device images and noisy drain-currentgate-voltage curves (IDVG) are used for the demonstration. The framework is formed by stacking two VAEs (one for image manifold learning and one for IDVG manifold learning) which communicate with each other through the latent variables. Five independent variables with different strengths are used. It is shown that it can perform inverse design (generate a design structure for a given IDVG) and forward prediction (predict IDVG for a given structure image, which can be used for compact modeling if the image is treated as device parameters) successfully. Since manifold learning is used, the machine is shown to be robust against noise in the inputs (i.e. using hand-drawn images and noisy IDVG curves) and not confused by weak and irrelevant independent variables.

关键词： Compact modeling Inverse Design Machine Learning Manifold Learning Technology computer-Aided Design (TCAD) Variational Autoencoder

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AI modeling and Water Quality Sensing Technique Proffers Water Security: An Open Review 3

AI Modeling and Water Quality Sensing Technique Proffers Wat...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Wilfred Blessing, N.R. Palarimath, Suresh Gunasekaran, Hemalatha Haidar, Sk Wasim Sutherlin Subitha, G. Sarmila, J. University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Stella Mary's College of Engineering Department of Computer Science and Engineering Kanyakumari District India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Chunkankadai India

ISBN: (纸本)9798331509675

Water Quality Sensors (WQSs) are becoming a promised tool in water quality data assessment and scientific value of aquatic structure. Such sensors are broadly used to produce live results by evaluating major water quality parameters and components. Artificial intelligence (AI) based WQSs and machine learning (ML) algorithms primarily enable long term operations for real time detention of water quality information at different time periods. Messaging services, alarms, alerts and other proper warnings are given to water quality managers or assessing communities while the quality deterioration occurs at the particular water quality checking sites. A good number of impressive challenges are identified in hosting the variety of sensors to manage, warehouse and analyze incessant water quality information that they generate periodically. This paper places interests in highlighting lack of suitable assessment standards and determine logistical deliberations for fruitful implementations together with transportation, storage services and appropriate trustworthy communication. The main objective is to identify the designs targeting water security that support to monitor, reassure and protect the water resources all the way through uninterrupted quality supervision in addition to AI features. This open review also discusses a variety of applications in water quality sensors, AI modeling, electronic tongues and indices, hence results the knowledge of improvement required for upgrading the quality in water resources through computer technologies and models. © 2025 IEEE.

关键词： Electronic tongues

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SimNet: Accurate and High-Performance computer Architecture Simulation using Deep Learning 22

SimNet: Accurate and High-Performance Computer Architecture ...

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2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint international conference on measurement and modeling of computer systems, SIGMETRICS/PERFORMANCE 2022

作者： Li, Lingda Pandey, Santosh Flynn, Thomas Liu, Hang Wheeler, Noel Hoisie, Adolfy Brookhaven National Laboratory UptonNY United States Stevens Institute of Technology HobokenNJ United States Laboratory for Physical Sciences College ParkMD United States

ISBN: (纸本)9781450391412

While cycle-accurate simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for realistic applications under investigation. This work describes a concerted effort, where machine learning (ML) is used to accelerate microarchitecture simulation. First, an ML-based instruction latency prediction framework that accounts for both static instruction properties and dynamic processor states is constructed. Then, a GPU-accelerated parallel simulator is implemented based on the proposed instruction latency predictor, and its simulation accuracy and throughput are validated and evaluated against a state-of-the-art simulator. Leveraging modern GPUs, the ML-based simulator outperforms traditional CPU-based simulators significantly. © 2022 Owner/Author.

关键词： deep learning gpu computer architecture simulation

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