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共 355 条记录，以下是1-10 订阅

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Designing an Interpretable Question Answering System for Vertical Domains Based on Large Language Model and Knowledge Graph 5

Designing an Interpretable Question Answering System for Ver...

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5th asia-pacific conference on Image Processing, Electronics and computers, IPEC 2024

作者： Huang, Xiaobin Ku, Chin Soon Xiamen University of Technology Xiamen China Department of Computer Science Universiti Tunku Abdul Rahman Kampar 31900 Malaysia

ISBN: (纸本)9781643685243

Given the low interpretability of large language models (LLMs) due to their extensive parameters and intricate features, this study aims to enhance the understandability and interpretability of automatic QA systems powered by LLMs, thereby addressing a critical gap in the field. To achieve this, we introduce an interpretable architecture for a domain-specific LLM-based question-answering (QA) system. the research decomposes the QA system into six modules: operation recognition, intent recognition, normalization, triplet structured data conversion, knowledge graph querying, and query result processing. through this approach, the input and output of each module in the QA system are human-readable text data, enhancing the interpretability of the QA system's processing. the use of knowledge graph data increases the credibility of the answers provided by the QA system. the QA system architecture proposed in this study attempts to integrate the powerful natural language understanding capabilities of large language models with the data querying capacity of knowledge graphs, offering a reference for addressing the issue of low interpretability in automatic QA systems based on large language models (LLMs). © 2024 the Authors.

关键词： Knowledge graph

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Understanding Human Balance Strategies through Clustering of Motion Analysis Data 10

Understanding Human Balance Strategies Through Clustering of...

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10th International conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Lapawong, Werapat Uttama, Surapong Rueangsirarak, Worasak Temdee, Punnarumol Hu, Shanfeng Aslam, Nauman Mae Fah Luang University Digital Transformation Technology School of Applied Digital Technology Chiang Rai Thailand Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center Chiang Rai Thailand Northumbria University Department of Computer and Information Sciences Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798331543273

Balance is the result of complex interactions among individuals, tasks, and the environment. It plays a crucial role in daily activities, relying on sensory systems and joint movement strategies to maintain postural stability. Effective balance not only improves movement and physical activities but also enhances overall quality of life. Balance strategies, such as ankle, hip, knee, and stepping strategies, are essential for maintaining stability in various situations and environments. this study focuses on utilizing public data from the Clinical Test of Sensory Interaction on Balance (CTSIB) to assess balance strategies. Traditional methods rely on subjective observations, limiting the identification of nuanced strategy combinations. To overcome this, motion capture (mocap) data and unsupervised machine learning techniques, clustering and dimensionality. reduction using Agglomerative, Gaussian Mixture, and K-Means and classify balance strategies. the results demonstrate the potential of this approach to identify distinct clusters of balance strategies, offering a more detailed, objective, and comprehensive understanding of postural stability. Among the clustering algorithms applied, K-Means proved to be particularly effective, achieving reliable groupings with consistent results. the Silhouette Scores indicated that k=3, identified as the optimal number of clusters from the elbow test, provided the best balance between cohesion and separation for K-Means. this suggests that K-Means delivers interpretable clusters of balance strategies. the three possible outcomes are good balance, A slight unbalance and a poor balance. this innovative methodology advances the field of balance assessment by providing a clear and systematic approach to classifying balance strategies, allowing for better understanding and targeted interventions. © 2025 IEEE.

关键词： K-means clustering

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H4H: Hybrid Convolution-Transformer architecture Search for NPU-CIM Heterogeneous systems for AR/VR Applications 25

H4H: Hybrid Convolution-Transformer Architecture Search for ...

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30th asia and South pacific Design Automation conference, ASP-DAC 2025

作者： Zhao, Yiwei Chen, Jinhui Zhang, Sai Qian Sarwar, Syed Shakib Stangherlin, Kleber Hugo Gomez, Jorge Tomas Seo, Jae-Sun De Salvo, Barbara Liu, Chiao Gibbons, Phillip B. Li, Ziyun Carnegie Mellon Univ. PittsburghPA United States Reality Labs Research Meta RedmondWA United States New York Univ. New YorkNY United States

ISBN: (纸本)9798400706356

Low-latency and low-power edge AI is crucial for Augmented/Virtual Reality applications. Recent advances demonstrate that hybrid models, combining convolution layers (CNN) and transformers (ViT), often achieve a superior accuracy/performance tradeoff on various computer vision and machine learning (ML) tasks. However, hybrid ML models can present system challenges for latency and energy efficiency due to their diverse nature in dataflow and memory access patterns. In this work, we leverage architecture heterogeneity from Neural Processing Units (NPU) and Compute-In-Memory (CIM) and explore diverse execution schemas for efficient hybrid model executions. We introduce H4H-NAS, a two-stage Neural architecture Search (NAS) framework to automate the design of hybrid CNN/ViT models for heterogeneous edge systems featuring both NPU and CIM. We propose a two-phase incremental supernet training in our NAS to resolve gradient conflicts between sampled subnets caused by different block types in a hybrid model search space. Our H4H-NAS approach is also powered by a performance estimator built with NPU performance results measured on real silicon, and CIM performance based on industry IPs. H4H-NAS searches hybrid CNN-ViT models with fine granularity and achieves significant (up to 1.34%) top-1 accuracy improvement on ImageNet-1k. Moreover, results from our algorithm/hardware co-design reveal up to 56.08% overall latency and 41.72% energy improvements by introducing heterogeneous computing over baseline solutions. Overall, our framework guides the design of hybrid network architectures and system architectures for NPU+CIM heterogeneous systems. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Inference engines

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Investigation of Intelligent Reflecting Surfaces Using Lumped Elements for 5G Wireless Communication Networks 10

Investigation of Intelligent Reflecting Surfaces Using Lumpe...

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10th IEEE asia-pacific conference on Applied Electromagnetics, APACE 2024

作者： Alias, Nur Asyiqin Binti Malek, Norun Farihah Abdul Bashri, Mohd Saiful Riza Rahman, Farah Diyana Abdul Ramli, Huda Adibah Mohd Azemi, Saidatul Norlyana Islam, Md. Rafiqul Research Group Kuala Lumpur Malaysia Faculty of Electronic Engineering Perlis Malaysia

ISBN: (纸本)9798350364163

In this study, an Intelligent Reflecting Surface (IRS) has been designed to operate at 3.5 GHz, featuring a compact unit cell measuring 42 mm x 42 mm with a Circular Patch and Ring configuration. the metasurface utilizes an FR-4 substrate with a dielectric constant of 4.3, a loss tangent of 0.008, and copper as the ground plane. A lumped element is incorporated into the unit cell to function as a switch in both ON (1) and OFF (0) states. Four positions have been identified for the ON OFF resulting to 16 bits configurations. the S-Parameter is the primary focus of the analysis. At 3.5 GHz, for a unit cell using Floquet port simulation indicated that the 0000-bit configuration exhibited the highest reflection efficiency with an S11 magnitude of -0.7534 dB and a phase shift of -16.7°. this result suggests that the unit cell design with lumped elements perform well as a reflector, with the S11 magnitude approaching 0 dB indicating effective gain. © 2024 IEEE.

关键词： 5G mobile communication systems

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Question Classification of University Admission using Named-Entity Recognition (NER) 10

Question Classification of University Admission using Named-...

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10th International conference on Information Technology, computer, and Electrical Engineering, ICITACEE 2023

作者： Yossy, Emny Harna Suhartono, Derwin Trisetyarso, Agung Budiharto, Widodo Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta11480 Indonesia Bina Nusantara University Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Mathematics Department School of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9798350322729

the university admission process can be overwhelming for applicants and admission officers, with many questions being asked and answered every year. the manual classification of these questions can be time-consuming and may lead to errors, making exploring automated solutions to this problem essential. In recent years, advances in natural language processing (NLP), including question classification. the research proposes an architecture for a question classification system using the Indonesian Language in closed-domain question answering. the purpose of this research is to find out the classification of questions. We created a system with the Named-Entity Recognition method to recognize the next word that will be used as a candidate type of question and candidate answer. the type of question used is a factoid question. Research makes patterns and rules for extracting important words as features to determine the taxonomy of question classification. Datasets are collected from one private university in Indonesia. Data sources from Binus Online Learning websites and data on questions frequently asked by users, especially prospective students. the results of the question classification are presented using descriptive statistics. the results show that the question classification with users' most frequent question type is 'What' by 63.3%, and the answer category is 'tuition fees' by 22.4%. It can be concluded that the most frequent question type asked by users is 'What', and the most frequent topic of interest is 'tuition fees'. Organizations can use this information to improve customer service by providing more relevant information and resources about tuition fees. © 2023 IEEE.

关键词： Natural language processing systems

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Robust and Secure Watermarking of Medical Images Using Möbius Transforms

Robust and Secure Watermarking of Medical Images Using Möbi...

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conference on Artificial Intelligence and Robotics and asia-pacific International Symposium

作者： Atheer Al-Rammahi Hedieh Sajedi Department of Mathematics Statistics and Computer Science College of Science University of Tehran Tehran Iran

Watermarking medical images is essential to ensure data integrity, authentication, and secure information transfer in healthcare systems. this paper explores the application of Möbius transforms, a non-linear transformation technique, for robust and secure watermarking of medical images. Our approach optimizes Möbius transform parameters through an evolutionary algorithm. Comprehensive experiments subject medical images to common attacks, evaluating the robustness of the Möbius transform-based watermarking (MTW) method using performance measures. the findings demonstrate significant resilience, maintaining image quality and watermark integrity. this paper enhances medical data security, contributing to secure medical imaging systems, advancing reliability, and confidentiality in healthcarepractices.

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Mitigation of the cold start problem in movie recommender systems using fuzzification of users’ social network information

Mitigation of the cold start problem in movie recommender sy...

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conference on Artificial Intelligence and Robotics and asia-pacific International Symposium

作者： Zahra Zokaei Behrooz Shahrokhzadeh Mohammad Farahani Sasan Hosseinalizdeh Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Digital Transformation Skills Training ICT Research Institute (Iran Telecommunication Research Center) Tehran Iran

Recommender systems can significantly assist users in making decisions by helping them find potentially relevant items from a vast range of choices. One significant challenge in the field of recommender systems is the "cold start problem," which arises when there is no prior information available for new users or items, and consequently, the system cannot make proper inferences. To address this issue, this study utilizes a multi-source dataset called the CSP Dataset, which includes user profiles, movie features, ratings, and contextual data. using this behavioral user dataset resolves the lack of prior information problem. additionally, a fuzzy membership function is employed to introduce fuzziness to the selected features of the dataset based on numerical variables, creating a fuzzy ratings measure for each combination of user, item, and context. these fuzzy ratings were then used to develop a fuzzy-based recommender system capable of handling user cold start scenarios. the findings indicate that the proposed approach outperforms the state-of-the-art methods as well as the existing methods in terms of accuracy.

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Bioactivity classification of SARS-CoV-2 Proteinase using Machine Learning Approaches 10

Bioactivity classification of SARS-CoV-2 Proteinase using Ma...

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10th International conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Begum, Fatema Mridha, Krishna Rabbani, Md. Golam Ashfaq, Shamin Iftekhar Islam, S Md. Sinha, Sapna University of Asia Pacific Pharmaceutical Technology Dhaka Bangladesh Marwadi University Computer Engineering Gujarati Rajkot India Pabna University Science and Technology Pharmacy Pabna Bangladesh Southeast University Department of Computer Science and Engineering Bangladesh American International University Department of Electrical and Electronic Engineering Bangladesh Aiit Amity University Uttar Pradesh Noida India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

Machine learning has seen a considerable increase in performance and interest in scientific research and industrial applications over the previous decade. the success of most current state-of-the-art methods can be linked to recent deep learning advancements. Deep learning has been demonstrated to outperform not only standard machine learning but also highly specialized tools designed by domain specialists when applied to many scientific fields involving the processing of non-tabular data, such as pictures or text. this article will cover ML-based research on SARS-Co V-2 Proteinase Biological Activity classification, with an emphasis on the most recent successes and research trends. SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) produced a global pandemic of coronavirus illness (COVID-19), which prompted a rush to find treatment options. Despite the attempts, no vaccine or medicine for therapy has been approved. In this paper, we mentioned some previous articles that have resulted in successful bioactivity prediction. the discussion of the machine having to learn technology that has been used for bioactivity prediction in general and has the potential to lead the way for successful working with complex molecules in the future is also a focus of this review. the study finishes with a brief viewpoint on contemporary machine learning research advances, including student engagement and semi-supervised learning, which offer considerable potential for increasing bioactive discovery. © 2022 IEEE.

关键词： Coronavirus

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Acceleration Object Detection using FPGA platform based on Improved CNN

Acceleration Object Detection using FPGA platform based on I...

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conference on Artificial Intelligence and Robotics and asia-pacific International Symposium

作者： Mohammad Hossein Salehzadeh Vahid Rostami Mahdi Salamati Mohammad Hamidi Monfared Department of Computer and Information Technology Engineering Science and Research Tehran Branch Islamic Azad University Tehran Iran Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran

Deep neural networks have emerged as a leading approach for object detection in recent years. However, their intensive computational requirements often make them unsuitable for embedded systems. the TinyYolo network is a widely-used deep neural network is designed to be simpler and smaller compared to its counterparts. Yet, it still presents challenges in FPGA implementations due to its large number of nodes. In this research, we focus on adapting the TinyYolo network for FPGA implementation. Our modified network comprises four deep neural sub-networks. One serves as the input, two as intermediate networks that extract both local and global features and the final one aggregates the extracted features. Based on these aggregated features, the target objects are detected. Our adaptation offers several advantages including Hardware-based implementation leading to increased computing power, Significant power savings compared to software implementations and Optimal parallel processing, power efficiency and memory usage making it suitable for embedded applications across various domains Our network implemented on the ZU3EG A484 chip achieved a processing speed of 58 FPS and consumed 4.105 W making it competitive with similar systems.

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Towards Area-Efficient Path-Based In-Memory Computing using Graph Isomorphisms

Towards Area-Efficient Path-Based In-Memory Computing using ...

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asia and South pacific Design Automation conference

作者： Sven thijssen Muhammad Rashedul Haq Rashed Hao Zheng Sumit Kumar Jha Rickard Ewetz Department of Computer Science University of Central Florida Orlando USA Department of Electrical and Computer Engineering University of Central Florida Orlando USA Computer Science Department Florida International University Miami USA

In-memory computing has attracted significant attention due to its potential to alleviate the issues caused by the von Neumann bottleneck. Path-based computing is a recently proposed in-memory computing paradigm for evaluating Boolean functions using nanoscale crossbars. Unlike state-of-the-art paradigms that use expensive WRITE operations to execute functions, path-based computing only relies on READ operations, which translates into benefits of low power consumption and low computational delay. Unfortunately, path-based computing comes with the penalty of substantial area overhead. In this paper, we introduce the ISO framework, a hardware-software solution for minimizing the area overhead of path-based computing systems. the framework is based on mapping computation to in-memory kernels using an intermediate k-LUT representation. the k-LUTs facilitate reusing hardware resources that realize the same computational structures. the reuse is performed by detecting identical subfunctions using isomorphic graphs. We also present a set of program instruction and scheduling algorithms to facilitate the hardware reuse. We have evaluated our proposed ISO framework on the 10 ISCAS85 benchmarks. Our experimental evaluation indicates that our proposed architecture improves energy consumption, latency, and area by $1.30\times, 76.59\times$, and $2.79\times$ on the average compared with previous state-of-the-art methods for path-based computing.

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