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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haijian Liang Weicheng Xie Xilin He Siyang Song Linlin Shen School of Computer Science & Software Engineering Computer Vision Institue Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Computing and Mathematical Sciences University of Leicester National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

Multimodal Sentiment Analysis is a burgeoning research area, leveraging various modalities to predict the sentiment score. Nevertheless, previous studies have disregarded the impact of noise interference on specific modal sentiments during video recording, thereby compromising the accuracy of sentiment prediction. In this paper, we propose the Guided Circular Decomposition and Cross-Modal Recombination (GCD-CMR) model, which aims to eliminate contaminated sentiment features in a fine-grained way. To achieve this, we utilize tailored global information specific to each modality to guide the circular decomposing process in the GCD module, to produce a set of sentiment prototypes. Subsequently, in the CMR module, we align cross-modal sentiment prototypes and remove the contaminated prototypes for recombination. Experimental results on two publicly available datasets demonstrate that our model surpasses state-of-the-art models, confirming the effectiveness of our proposed method. We release the code at: https://***/nianhua20/GCD-CMR.

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FD-LLM: LARGE LANGUAGE MODEL FOR FAULT DIAGNOSIS OF MACHINES

arXiv

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arXiv 2024年

作者： Qaid, Hamzah A.A.M. Li, Dan Zhang, Bo Ng, See-Kiong Li, Wei School of Software Engineering Sun Yat-Sen University Zhuhai China Institute of Data Science National University of Singapore Singapore Singapore School of Mechatronic Engineering China University of Mining and Technology Xuzhou China School of Computer Science and Technology China University of Mining and Technology Xuzhou China

Large language models (LLMs) are effective at capturing complex, valuable conceptual representations from textual data for a wide range of real-world applications. However, in fields like Intelligent Fault Diagnosis (IFD), incorporating additional sensor data—such as vibration signals, temperature readings, and operational metrics—is essential but it is challenging to capture such sensor data information within traditional text corpora. This study introduces a novel IFD approach by effectively adapting LLMs to numerical data inputs for identifying various machine faults from time-series sensor data. We propose FD-LLM, an LLM framework specifically designed for fault diagnosis by formulating the training of the LLM as a multi-class classification problem. We explore two methods for encoding vibration signals: the first method uses a string-based tokenization technique to encode vibration signals into text representations, while the second extracts statistical features from both the time and frequency domains as statistical summaries of each signal. We assess the fault diagnosis capabilities of four open-sourced LLMs based on the FD-LLM framework, and evaluate the models’ adaptability and generalizability under various operational conditions and machine components, namely for traditional fault diagnosis, cross-operational conditions, and cross-machine component settings. Our results show that LLMs such as Llama3 and Llama3-instruct demonstrate strong fault detection capabilities and significant adaptability across different operational conditions, outperforming state-of-the-art deep learning (DL) approaches in many cases. © 2024, CC BY-NC-SA.

关键词： Shafts (machine components)

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Causality fields in nonlinear causal effect analysis

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Frontiers of Information technology & Electronic engineering 2022年第8期23卷 1277-1286页

作者： Aiguo WANG Li LIU Jiaoyun YANG Lian LI School of Electronic Information Engineering Foshan UniversityFoshan 528225China School of Big Data&Software Engineering Chongqing UniversityChongqing 400044China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China

Compared with linear causality,nonlinear causality has more complex characteristics and *** this paper,we discuss certain issues related to nonlinear causality with an emphasis on the concept of causality *** on widely used computation models and methods,we present some viewpoints and opinions on the analysis and computation of nonlinear causality and the identification problem of causality *** also reveal the importance and practical significance of nonlinear causality in handling complex causal inference problems via several specific examples.

关键词： field nonlinear viewpoint

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High-Fidelity and High-Efficiency Talking Portrait Synthesis With Detail-Aware Neural Radiance Fields

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IEEE Transactions on Visualization and computer Graphics 2024年 PP卷 PP-PP页

作者： Wang, Muyu Zhao, Sanyuan Dong, Xingping Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China Beijing Institute of Technology School of Computer Science Beijing China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China

In this paper, we propose a novel rendering framework based on neural radiance fields (NeRF) named HH-NeRF that can generate high-resolution audio-driven talking portrait videos with high fidelity and fast rendering. Specifically, our framework includes a detail-aware NeRF module and an efficient conditional super-resolution module. Firstly, a detail-aware NeRF is proposed to efficiently generate a high-fidelity low-resolution talking head, by using the encoded volume density estimation and audio-eye-aware color calculation. This module can capture natural eye blinks and high-frequency details, and maintain a similar rendering time as previous fast methods. Secondly, we present an efficient conditional super-resolution module on the dynamic scene to directly generate the high-resolution portrait with our low-resolution head. Incorporated with the prior information, such as depth map and audio features, our new proposed efficient conditional super resolution module can adopt a lightweight network to efficiently generate realistic and distinct high-resolution videos. Extensive experiments demonstrate that our method can generate more distinct and fidelity talking portraits on high resolution (900 × 900) videos compared to state-of-the-art methods. Our code is available at https://***/muyuWang/HHNeRF. © 1995-2012 IEEE.

关键词： Rendering (computer graphics)

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A Multimodal Interaction System for Speech-Based Autism Intervention in Sinhala-Speaking Sri Lankan Children using the NAO Robot

A Multimodal Interaction System for Speech-Based Autism Inte...

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Advancements in Computing (ICAC), International Conference on

作者： Aparna Jayawardena Chan Sri Manukalpa Heshani Bopage Poorna Panduwawala Koliya Pulasinghe Samantha Rajapakshe Department of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This paper presents a multimodal interaction system designed for speech-based autism intervention in Sinhala-speaking Sri Lankan children, utilizing the NAO robot. The system integrates four components, language content detection, dialog management, voice synthesis, and gesture synthesis, to create an engaging and adaptive therapeutic environment. By leveraging these technologies, the system provides real-time, personalized responses that align with the cultural and linguistic needs of Sinhala-speaking children with autism spectrum disorder (ASD). The NAO robot facilitates natural and supportive interaction through synchronized speech and gestures, aiming to improve communication and social skills in children with ASD. Preliminary evaluations demonstrate the potential of the system to enhance engagement and responsiveness in therapy sessions, marking a significant step forward in developing specialized interventions for autism in non-Western, non-English-speaking contexts.

关键词： Autism Visualization Human-robot interaction Medical treatment Speech recognition Real-time systems Natural language processing Synchronization Speech synthesis Robots

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IoHT System Authentication Through the Blockchain technology: A Review

IoHT System Authentication Through the Blockchain Technology...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Muwafaq Jawad Ali A. Yassin Hamid Ali Abed Al-Asadi Zaid Ameen Abduljabbar Vincent Omollo Nyangaresi Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and Technology Bando Kenya Department of Applied Electronics Saveetha School of Engineering SIMATS Chennai Tamilnadu India

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The growing adoption of the Internet of Things (IoT) terminology in recent years in several domains, including healthcare, has yielded multiple benefits. One of the roles is to support professionals in delivering outstanding and suitable healthcare for patients, while also empowering patients to independently track their health state. However, this wide growth is accompanied by many significant security concerns and cyber risks, including unauthorized access, data leakage, information modification, and data erasure. This article outlines the advantages of employing groundbreaking blockchain technology to provide robust and verified authentication methods for different systems. Additionally, it imparts a foundational comprehension of the technologies employed in the procedure of constructing and advancing blockchain-based platforms. In addition, we explore tools like Scythe and Avispa that are employed to assess the system’s resilience against different types of attacks.

关键词： Terminology Reviews Instruments Authentication Medical services Throughput Blockchains Internet of Things Information technology Resilience

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Research on Improved Quick Sort Algorithm with Duplicate Value Handling

Research on Improved Quick Sort Algorithm with Duplicate Val...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Hemin Amit Yadav Asif Khan Abhishek Pratap Sah College of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

An improved algorithm based on Quick Sort algorithm research method is proposed to deal with prevailing duplicate values in the sorting of data. The duplicate values are specially processed, which effectively reduces the number of division and greatly reduces the number of recursions. The experimental results show that the time efficiency is improved by more than 50% when dealing with large data than the traditional Quick Sort algorithm without reducing the space efficiency.

关键词： Distributed processing Multicore processing Big Data Stability analysis Hardware Distributed computing Optimization Sorting

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Incomplete Multi-View Representation Learning Through Anchor Graph-Based GCN and Information Bottleneck

Incomplete Multi-View Representation Learning Through Anchor...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhenjiao Liu Xiao Wang Xiaodi Huang Guanlin Li Ke Sun Zhikui Chen School of Software Technology Dalian University of Technology Dalian China Samovar Telecom SudParis Institut Polytechnique de Paris Palaiseau France School of Computing Mathematics and Engineering Charles Sturt University Albury Australia School of Computer Science and Technology Dalian University of Technology

Real-world data often contain incomplete views with varying degrees of missing information. While there are existing methods for learning representations from such data, effectively utilizing all incomplete view data and ensuring robustness to different levels of completeness remains a challenging task. To address this problem, we propose a novel framework named IMRL-AGI. IMRL-AGI combines the anchor graph-based Graph Convolutional Network (GCN) and information bottleneck. Specifically, the framework starts by constructing an anchor graph to effectively captures the nonlinear information between instances. Next, an anchor graph-based GCN is designed to extract feature information from various views. IMRL-AGI maximizes the mutual information between the views obtained by the common representation and the anchor-graph-based GCN, ensuring the accurate extraction of view information. Furthermore, the minimization of mutual information is applied to promote diversity and reduce redundancy in the multi-view representation. Extensive experiments are conducted on several real-world datasets, and the results demonstrate the superiority of IMRL-AGI.

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Exploring Multi-Perspective Document Structures for News Discourse Profiling

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2349-2359页

作者： Hang Yang Dianbo Sui Yubo Chen Kang Liu Jun Zhao School of Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China School of Computer Science and Software Engineering Harbin Institute of Technology Weihai China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Learning the discourse structures of news articles is vital for obtaining valuable structural information around main events, which has been shown useful for many natural language processing applications. This paper proposes the extraction of graphs explicitly representing the discourse structure, coreference structure, and semantic similarities within a document. Concept embeddings are then updated using a weighted combination of feature embeddings, modeled after capsule networks, and resulting in improved context-dependent representation of the content of each news article. Experimental tests on the NewsDiscourse dataset demonstrate the effectiveness of our proposed method.

关键词： Speech processing Routing Computational modeling Weapons Encoding Transformers Representation learning Natural language processing Context modeling Artificial intelligence

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CPF: A Cross-Layer Prefetching Framework for High-Density Flash-Based Storage

CPF: A Cross-Layer Prefetching Framework for High-Density Fl...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Longfei Luo Han Wang Dingcui Yu Yina Lv Liang Shi Software/Hardware Co-design Engineering Research Center Ministry of Education School of Computer Science and Technology East China Normal University Shanghai China Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The pseudo-single-level-cell (pSLC) technique is widely adopted in high-density flash-based storage to mitigate the performance and endurance problem of high-density flash memory. Furthermore, prefetching schemes can compensate for performance differences among storage tiers. Existing prefetchers are implemented in the operating system (OS) or storage layers. However, OS layer prefetchers are conservative since it is a challenge to achieve both high accuracy and large coverage simultaneously. Storage layer prefetchers are sub-optimal due to the performance differences between pSLC and DRAM. In this paper, a cross-layer prefetching framework (CPF) is proposed to prefetch data selectively. The basic idea is that high-accuracy data will be prefetched to DRAM and large-coverage data will be prefetched to the pSLC flash in storage. To make it practical, an adaptive regulator is further designed to dynamically adjust the cross-layer prefetching to ensure accuracy and coverage. Evaluations show that CPF can improve read performance and reduce data transfer costs significantly.

关键词： Cross layer design Costs Regulators Adaptive systems Prefetching Operating systems Random access memory

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