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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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AutoQ 2.0: From Verification of Quantum Circuits to Verification of Quantum Programs 31th

AutoQ 2.0: From Verification of Quantum Circuits to Verific...

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31st international conference on Tools and Algorithms for the Construction and Analysis of systems, TACAS 2025, which was held as part of the international Joint conferences on Theory and Practice of Software, ETAPS 2025

作者： Chen, Yu-Fang Chung, Kai-Min Hsieh, Min-Hsiu Huang, Wei-Jia Lengál, Ondřej Lin, Jyun-Ao Tsai, Wei-Lun Institute of Information Science Academia Sinica Taipei Taiwan Hon Hai Quantum Computing Research Center Taipei Taiwan Faculty of Information Technology Brno University of Technology Brno Czech Republic Innovation Frontier Institute of Research for Science and Technology & Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783031906596

We present a verifier of quantum programs called AutoQ 2.0. Quantum programs extend quantum circuits (the domain of AutoQ 1.0) by classical control flow constructs, which enable users to describe advanced quantum algorithms in a formal and precise manner. The extension is highly non-trivial, as we needed to tackle both theoretical challenges (such as the treatment of measurement, the normalization problem, and lifting techniques for verification of classical programs with loops to the quantum world), and engineering issues (such as extending the input format with a support for specifying loop invariants). We have successfully used AutoQ 2.0 to verify two types of advanced quantum programs that cannot be expressed using only quantum circuits: the repeat-until-success (RUS) algorithm and the weak-measurement-based version of Grover’s search algorithm. AutoQ 2.0 can efficiently verify all our benchmarks: all RUS algorithms were verified instantly and, for the weak-measurement-based version of Grover’s search, we were able to handle the case of 100 qubits in ∼20 minutes. © The Author(s) 2025.

关键词： Qubits

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Optimized Temporal Convolutional Networks with Multi-Stage Attention for Efficient Human Action Recognition

Optimized Temporal Convolutional Networks with Multi-Stage A...

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international conference on New Trends in Computing Sciences (ICTCS)

作者： Deepthi Reddy Sama Pranay Goud Pulimamidi Nikhileshwar Reddy Japa Bhanu Prasad Andraju Computer Science and Engineering Vardhaman College Of Engineering Hyderabad India Vardhaman College Of Engineering Hyderabad India

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

The advancement of computer vision applications, especially in the areas of surveillance, healthcare, and human-computer interaction, depends heavily on human action recognition. Despite its effectiveness, traditional temporal convolutional networks (TCNs) frequently fall short in capturing intricate temporal dynamics and prioritising crucial action segments. In order to overcome these constraints, this study introduces a unique Optimized Temporal Convolutional Network (OTCN) architecture that incorporates a multi-stage attention mechanism. The hierarchical attention module ensures improved representation at a lower computing cost by dynamically identifying and amplifying important features across a range of temporal resolutions. Comprehensive tests on several benchmark datasets show that OTCN is perfect for real-time applications since it not only achieves high accuracy but also provides computing efficiency. Our findings demonstrate how multi-stage attention can revolutionise the development of reliable, scalable, and effective human action recognition systems.

关键词： Accuracy Attention mechanisms Computational modeling Surveillance Medical services computer architecture Real-time systems Human activity recognition Convolutional neural networks Context modeling

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13th CCF international conference on Natural Language Processing and Chinese Computing, NLPCC 2024

13th CCF International Conference on Natural Language Proces...

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13th CCF international conference on Natural Language Processing and Chinese Computing, NLPCC 2024

ISBN: (纸本)9789819794300

The proceedings contain 194 papers. The special focus in this conference is on Natural Language Processing and Chinese Computing. The topics include: Hierarchical Knowledge Aggregation for Personalized Response Generation in Dialogue systems;multi-hop Reading Comprehension Model Based on Abstract Meaning Representation and Multi-task Joint Learning;Leveraging Large Language Models for QA Dialogue Dataset Construction and Analysis in Public Services;MCFC: A Momentum-Driven Clicked Feature Compressed Pre-trained Language Model for Information Retrieval;integrating Syntax Tree and Graph Neural Network for Conversational Question Answering over Heterogeneous Sources;pqE: Zero-Shot Document Expansion for Dense Retrieval with Large Language Models;CKF: Conditional Knowledge Fusion Method for CommonSense Question Answering;MPPQA: Structure-Aware Extractive Multi-span Question Answering for Procedural Documents;GraphLLM: A General Framework for Multi-hop Question Answering over Knowledge Graphs Using Large Language Models;local or Global Optimization for Dialogue Discourse Parsing;structure and Behavior Dual-Graph Reasoning with Integrated Key-Clue Parsing for Multi-party Dialogue Reading Comprehension;enhancing Emotional Support Conversation with Cognitive Chain-of-Thought Reasoning;a Simple and Effective Span Interaction modeling Method for Enhancing Multiple Span Question Answering;FacGPT: An Effective and Efficient Method for Evaluating Knowledge-Based Visual Question Answering;PAPER: A Persona-Aware Chain-of-Thought Learning Framework for Personalized Dialogue Response Generation;towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models;model-Agnostic Knowledge Distillation Between Heterogeneous Models;exploring Multimodal Information Fusion in Spoken Off-Topic Degree Assessment;integrating Hierarchical Key Information and Semantic Difference Features for Long Text Matching;CausalAPM: Generalizable Literal Disentanglement for NLU

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28th international Symposium on Database Engineered Applications, IDEAS 2024

28th International Symposium on Database Engineered Applicat...

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28th international Symposium on Database Engineered Applications, IDEAS 2024

ISBN: (纸本)9783031834714

The proceedings contain 26 papers. The special focus in this conference is on Database Engineered Applications. The topics include: Enhanced Classification of Embedded System Vulnerabilities Using Ensemble Embedding and BiLSTM Networks;predicting Dyskinetic Events Through Verified Multivariate Time Series Classification;R2-LGBM: Sales Informative Prediction System in E-commerce Application Using Ensemble Classifier;comparing Related Languages with a Fuzzy Morphism Matching Algorithm;Enhancing LLM Code Generation Using Natural Language Processing in the Context of Machine Learning;LaSSI: Logical, Structural, and Semantic Text Interpretation;clustEm4Ano: Clustering Text Embeddings of Nominal Textual Attributes for Microdata Anonymization;about Relationships in Data Lakes;from Theory to Practice of Multidimensional Big Data Analytics over Big Healthcare Data: A Real-Life Case Study;a Database Engineered System for Big Data Analytics on Tornado Climatology;siren Federate: Bridging the Gap Between Document and Relational Data systems for Efficient Exploratory Graph Analysis;outlier-Weighted Traffic Flow Prediction Using Online Autoencoders;Speculative Query Support for RDBMS with Flexible Query Order and Benefit Verification;made to Measure: Towards Approximability of Query Evaluation Engines;DGGS-Based Continuous Trajectory Similarity Comparison;predictive modeling of Key Performance Indicators for Greenhouse Gas Emission Reduction Using Machine Learning;validation Without Rules: A Data Integration Case Study;fairness in Group Recommender systems Using Variational Autoencoders;a Supervised Contrastive Learning Framework for Aspect-Based Recommendations;multi-attribute Bias Mitigation in Recommender systems;UpLIF: An Updatable Self-tuning Learned Index Framework;scheduling of Intermittent Query Processing;monitoring Environmental Factors for Optimal Health and Welfare of Broiler Chickens Using Sensor Technology in Southwest Nigeria;river Levels Affecting Firefighter Inte

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Advancing Bangla NLP: Transformer-Based Question Generation using Fine-Tuned LLM

Advancing Bangla NLP: Transformer-Based Question Generation ...

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international conference on Electrical, computer and Communication Engineering (ECCE)

作者： Washik Wali Faieaz Sayma Jannat Pronoy Kumar Mondal Sadman Sadik Khan Shuvo Karmaker Md Sadekur Rahman Dept. of CSE Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

This research explores the development of a question generation model for Bangla text using the Bangla T5 base model, a transformer-based architecture tailored for the language. The study addresses the underrepresentation of Bangla in natural language processing (NLP) by leveraging a carefully curated dataset of Bangla paragraphs and corresponding questions. The Bangla T5 model, pre-trained on a large Bangla corpus, is fine-tuned to generate contextually relevant and coherent questions from input paragraphs. The model’s performance is evaluated using standard metrics such as Exact Match (EM) and ROUGE, achieving a 28.65% Exact Match score. The ROUGE scores demonstrate strong alignment with the reference questions, with ROUGE-1 F1 at 73.64%, ROUGE-2 F1 at 57.86%, and ROUGE-L F1 at 70.77%. These results highlight the model’s ability to capture the linguistic nuances of Bangla and generate questions that are semantically and syntactically accurate. This work contributes to the advancement of Bangla NLP and underscores the potential for extending similar methodologies to other low-resource languages, promoting inclusivity and linguistic diversity in AI systems.

关键词： Training measurement Pipelines Linguistics Transformers Question generation Decoding Artificial intelligence Standards Context modeling

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13th CCF international conference on Natural Language Processing and Chinese Computing, NLPCC 2024

13th CCF International Conference on Natural Language Proces...

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13th CCF international conference on Natural Language Processing and Chinese Computing, NLPCC 2024

ISBN: (纸本)9789819794362

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Conv-TasNet Adaptive Noise Cancellation Model Enhanced by WaveNet

Conv-TasNet Adaptive Noise Cancellation Model Enhanced by Wa...

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Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Kun Zhao Yaojie Hou College of Computer Science and Technology (College of Big Data) Taiyuan University of Technology Jinzhong China College of Electrical and Automation Henan Polytechnic University Jiaozuo China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Traditional speech denoising methods often struggle with low signal-to-noise ratios (SNR) and poor clarity in complex noise environments. To address these limitations, this study introduces an adaptive denoising model that integrates Conv-TasNet with WaveNet enhancements. The proposed model incorporates causal convolution within the Conv-TasNet separator to better capture long-term dependencies and employs WaveNet's gating activation mechanism to effectively handle nonlinear speech features. Additionally, a mask weighting strategy is implemented to emphasize target speech signals while suppressing background noise. Experimental results demonstrate that the enhanced model significantly outperforms the original Conv-TasNet, achieving an SNR improvement from –5 dB to 33.74 dB and reducing mean squared error (MSE) to nearly zero. These findings confirm the model's superior performance and robustness in complex noisy environments, highlighting its potential for advanced speech enhancement applications.

关键词： Adaptation models Convolution Computational modeling Particle separators Noise reduction Speech enhancement Feature extraction Computational efficiency Noise measurement Signal to noise ratio

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A Survey on computer Aided systems for Diabetic Retinopathy Detection and Classification using Deep Learning

A Survey on Computer Aided Systems for Diabetic Retinopathy ...

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Electronics and Renewable systems (ICEARS), international conference on

作者： Abini M. A S Sridevi Sathya Priya Department of Electronics and Communication Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Diabetic Retinopathy (DR) is a prominent contributor to visual disablement on a global scale, especially in those diagnosed with diabetes mellitus. Timely identification and cate-gorization of DR are essential for prompt management and the avoidance of permanent visual ***, computer aided diagnostic (CAD) systems that utilize deep convolutional neural networks (CNNs) have become a promising approach for automatically recognizing and categorizing retinal fundus pictures for Diabetic Retinopathy (DR).This paper presents a thorough examination of the latest progress, difficulties, and future prospects in the creation and use of computer aided diagnostic (CAD) systems for diabetic retinopathy (DR) diagnosing with deep convolutional neural networks (CNNs).This study evaluates fundamental techniques, Retinal Fundus image datasets, performance measures, and clinical implications of current CAD systems, emphasizing their advantages and *** addition, we investigate current trends like various preprocessing techniques and deep learning models, which have improved the effectiveness and practicality of CAD systems for diagnosing diabetic retinopathy (DR).

关键词： Deep learning Surveys Diabetic retinopathy Visualization Solid modeling Renewable energy sources Design automation Retina Market research Convolutional neural networks

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Advanced Deep Learning Framework for Early Breast Cancer Detection with Graph Convolutional Networks

Advanced Deep Learning Framework for Early Breast Cancer Det...

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Integrated Circuits and Communication systems (ICICACS), IEEE international conference on

作者： A. Sadeeshkumar Seema Sharma Amshavalli M Asma Anjum Ashwani Gupta N Aparna Department of Electrical and Electronics Engineering St. Joseph's College of Engineering OMR Chennai India Department of Computer Science and Engineering JECRC University Jaipur India Department of Computer Science and Engineering Erode Sengunthar Engineering College Thudupathi India Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India Department of Computer Science and Information Technology MJP Rohilkhand University Bareilly India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Valley Campus Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Women with breast cancer are very common. Early signs are rare; hence the condition is often identified late. Later-stage treatment outcomes are less predictable and harder. Complementary screening programs and preventative checkups have considerably improved early breast cancer identification. Reducing the number of unscreened women and increasing early-stage breast cancer detection demands better screening methods. Iterative model training included preprocessing, segmentation, feature extraction, and detection. Preprocessing was necessary for well-read results. Corners were identified for segmentation using Shi-Tomasi. Feature extraction uses SIFT. Without modifying photo size, this approach discovers similar photos. The research models were all GCN-trained. CNNs and SVMs were employed to evaluate the GCN-based model. GCN's mean accuracy of 92.34 percent beat the competition. This shows that the GCN-based technique improves breast cancer diagnosis. Due to early discovery and treatment, this strategy may improve patient outcomes.

关键词： Training Temperature measurement Accuracy Graph convolutional networks Infrared imaging Transforms Feature extraction Breast cancer Integrated circuit modeling Tumors

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Analyzing of the Alignment between Course Learning Outcomes (CLOs) and Program Learning Outcomes (PLOs) Using Textual Similarity-Based Methods

Analyzing of the Alignment between Course Learning Outcomes ...

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Advancement in Data Science, E-learning and Information systems (ICADEIS), international conference on

作者： Afra Mahendra Prasetyawati Werihisfenli Saragih Mariana Frima Ade Romadhony School of Computing Telkom University Bandung Indonesia Secretariat & Strategic Planning Directorate Telkom University Bandung Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Universities are the pinnacle of education in Indonesia to prepare the younger generation before entering the workforce. For example, in Indonesia, there is a government regulation (Permendikbudristek Number 53 of 2023) that concerning the Assurance of Higher Education Quality, each study program must define Program Learning Outcomes (PLOs) to achieve the university's objectives, which will then be translated into Course Learning Outcomes (CLOs) at the course level. Defining CLOs that align with specific PLOs is quite challenging. Therefore, in our study, we conducted an analysis of the alignment of PLOs and CLOs based on a text similarity approach. We modeled the PLOs and CLOs texts using TF-IDF and fastText representations, with cosine similarity as the similarity measurement. We evaluated the PLOs and CLOs from the computer science program at a university in Indonesia. The experimental results show that the fastText representation performs better in identifying the alignment between PLO and CLO, demonstrating its effectiveness in capturing semantic meaning beyond simple lexical similarities. This study highlights the importance of leveraging advanced text representation techniques to improve curriculum mapping and ensure a more structured and coherent learning outcome framework.

关键词： computer science Translation Electronic learning Computational modeling Semantics Education Government Data science Regulation Information systems

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