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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Dayong Sun, Yu Li, Weisheng Xie, Lele Lu, Xin Dufaux, Frederic Zhu, Ce Chongqing Key Laboratory on Big Data for Bio Intelligence Department of Computer Science China University of Central Arkansas Computing Department of Computer Science United States Chongqing Key Laboratory of Image Cognition School of Information and Communication Engineering China Chongqing University of Posts and Telecommunications School of Information and Communication Engineering China United Kingdom Université Paris-Saclay Cnrs CentraleSupélec Laboratoire des Signaux et Systèmes France University of Electronic Science and Technology of China School of Information and Communication Engineering China

ISBN: (纸本)9781728163277

Due to multi-layer encoding and Inter-layer prediction, Spatial Scalable High-Efficiency Video Coding (SSHVC) has extremely high coding complexity. It is very crucial to improve its coding speed so as to promote widespread and cost-effective SSHVC applications. In this paper, we have proposed a novel Mode Selection-Based Fast Intra Prediction algorithm for SSHVC. We reveal the RD costs of Inter-layer Reference (ILR) mode and Intra mode have a significant difference, and the RD costs of these two modes follow Gaussian distribution. Based on this observation, we propose to apply the classic Gaussian Mixture Model and Expectation Maximization in machine learning to determine whether ILR is the best mode so as to skip the Intra mode. Experimental results demonstrate that the proposed algorithm can significantly improve the coding speed with negligible coding efficiency loss. © 2023 IEEE.

关键词： Gaussian distribution

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Automated Health Monitoring: Integrating AI and IoT for Continuous Patient Observation

Automated Health Monitoring: Integrating AI and IoT for Cont...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： E V N Jyothi Sighakolli Sailaja M. SriRama Lakshmi Reddy T. Sunitha Department of CSE (AI&ML) CMR College of Engineering & Technology Hyderabad Telangana India Department of Computer Science and Engineering Rise krishna sai prakasam group of institutions Ongole A.P India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad Telangana India Department of Computer Science and Engineering (Data Science) Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Integrating Artificial Intelligence (AI) with the Internet of Things (IoT) has recently become more advantageous for various complex fields like innovative health, intrusion detection in various devices, and continuous monitoring of crops yielding in various farming lands. From the health point of view, it is essential to monitor multiple patients' health according to their abnormal health conditions. The proposed Integrated Health Monitoring System (IHMS) is developed in this work by combining AI-based models such as Recurrent Neural Networks (RNNs) and advanced IoT sensors. The IHMS is mainly used in health care applications such as heart care analysis and other abnormal health conditions like blood pressure, levels of oxygen, and glucose readings, and it transmits this information to AI-powered systems. The IHMS mainly analyzes abnormal patterns, detects anomalies, and provides timely analytical analysis of patient data. Finally, the proposed approach IHMS analyzes several datasets and shows the performance in terms of classification.

关键词： Recurrent neural networks Medical services Sepsis Real-time systems Blood pressure Sensor systems Internet of Things Biomedical monitoring Monitoring Wearable sensors

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Prompt Transfer for Dual-Aspect Cross Domain Cognitive Diagnosis

arXiv

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arXiv 2024年

作者： Liu, Fei Zhang, Yizhong Liu, Shuochen Ji, Shengwei Yu, Kui Wu, Le School of Computer Science and Information Engineering Hefei University of Technology Hefei China Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China School of Big Data and Artificial Intelligence Hefei University Hefei China

Cognitive Diagnosis (CD) aims to evaluate students' cognitive states based on their interaction data, enabling downstream applications such as exercise recommendation and personalized learning guidance. However, existing methods often struggle with accuracy drops in cross-domain cognitive diagnosis (CDCD), a practical yet challenging task. While some efforts have explored exercise-aspect CDCD, such as cross-subject scenarios, they fail to address the broader dual-aspect nature of CDCD, encompassing both student- and exercise-aspect variations. This diversity creates significant challenges in developing a scenario-agnostic framework. To address these gaps, we propose PromptCD, a simple yet effective framework that leverages soft prompt transfer for cognitive diagnosis. PromptCD is designed to adapt seamlessly across diverse CDCD scenarios, introducing PromptCD-S for student-aspect CDCD and PromptCD-E for exercise-aspect CDCD. Extensive experiments on real-world datasets demonstrate the robustness and effectiveness of PromptCD, consistently achieving superior performance across various CDCD scenarios. Our work offers a unified and generalizable approach to CDCD, advancing both theoretical and practical understanding in this critical domain. The implementation of our framework is publicly available at https://***/Publisher-PromptCD/PromptCD. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Integrating Branching and Pruning for Efficient Hyperdimensional Computing

Integrating Branching and Pruning for Efficient Hyperdimensi...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Jing Liu Zhiqian Guan Di Liu Shengfa Miao Fei Dai School of Software Yunnan University China Department of Computer Science Norwegian University of Science and Technology Norway School of Big Data and Intelligent Engineering Southwest Forestry University China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

As an emerging brain-inspired computing method, hyperdimensional computing (HDC) has attracted increasing attention. HDC encodes data into a high-dimensional hypervector and makes predictions based on the similarity comparison of the hypervector. However, the computational overhead of an HDC model increases, and the accuracy decreases as the number of predicted classes increases. Branching has been proposed to address this issue in HDC models, but it also introduces a new challenge: increased memory overhead for storing new branching hypervectors. In this paper, we aim to address this issue by integrating branching with the pruning method in HDC models. To this end, we propose a simple yet effective two-level branching structure that can reduce the memory overhead required by branching hypervectors. In addition, we propose a novel variable-position pruning method capable of removing redundant dimensions from hypervectors at various positions. This can best identify the important dimensions of a hypervector and further mitigate the memory issue caused by the branching structure. Experimental results demonstrate that our two-level branching structure can slightly increase the accuracy across various datasets. With only a 0.5 % accuracy loss, our pruning method can remove more than half of the dimensions, thereby improving the HDC model's inference latency and energy consumption.

关键词： Energy consumption Accuracy Computational modeling Memory management Predictive models Brain modeling Encoding

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Building a portable parallel asynchronous PDE solver using Kokkos

Building a portable parallel asynchronous PDE solver using K...

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IEEE International Conference on High Performance Computing Workshops (HiPCW)

作者： Ranjan Bhat Konduri Aditya Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal Institute of Technology Manipal India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

In this study, we outline the design and implementation of a portable massively parallel asynchronous solver for time-dependent partial differential equations (PDEs). The solver is implemented using Kokkos library for portability across different node architectures and is coupled with the communication-avoiding algorithm that relaxes the communication across nodes, thus improving the scalability. Asynchrony-tolerant finite difference schemes for spatial derivatives along with the low storage Runge-Kutta scheme for time integration are used to achieve high-order accuracy. The efficacy of the implementation is demonstrated using a mini-app that solves the three-dimensional diffusion problem.

关键词： Scalability Partial differential equations High performance computing Conferences Buildings computer architecture Benchmark testing Libraries Performance analysis Finite difference methods

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Prediction of global mean temperature based on SARIMA combined model 4

Prediction of global mean temperature based on SARIMA combin...

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4rth International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2024

作者： Li, Dongliang Chu, Chengxu Gao, Huihui Yang, Chenming Liu, Xin Huang, Zhenwu School of Humanities and Law Southwest Forestry University Kunming650224 China School of Mechanical Engineering and Automation Dalian Polytechnic University Dalian116034 China Data Science and Intelligent Media School Communication University of China Beijing100024 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9781510681637

With the continuous development of human society, the global warming caused by greenhouse gas emissions is becoming more and more serious. In order to better cope with global warming, this paper uses the SARIMA model to predict global temperature. Then, SARIMA (1,1,1) (1,1,1) model is established to forecast the temperature. It is found that the temperature in 2050 and 2100 will not reach 20℃, and the global average temperature is expected to reach 20℃ in about 2180. Through model evaluation, it is found that the R-square of SARIMA model is 0.87. © 2024 SPIE.

关键词： Global warming

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A Multimodal Deep Learning Approach for Predicting Dementia’s Disease Progression Using MRI Clinical data

A Multimodal Deep Learning Approach for Predicting Dementia...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： M. Shamini S. K Muthusundar K. Selvamani S. Kanimozhi Chandrasekaran N Antony Doss A Department of Computer Science and Engineering Chennai Institute Of Technology Chennai India Department of Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Anna University Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of CSBS MNM Jain Engineering College Chennai India Department of CSBS S.A Engineering College Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Dementia’s disease (DD) is a progressive neurological disease that primarily affects the elderly. Its primary symptom is cognitive impairment. There is currently no known cure; the only available treatments are meant to improve the patient’s quality of life. Given the challenges in treating DD, predicting its trajectory can be crucial in guiding management and treatment strategies. In this work, we propose a multimodal approach that uses MRI scans and clinical data to predict the phases of DD. The VGG19 deep learning model is utilized to interpret MRI images for image classification, and LightGBM (LGBMClassifier) is used to evaluate patient clinical data in CSV format. A multimodal deep learning technique is then used to combine the predictions from both modalities in order to increase the overall prediction accuracy. The integrated model aims to provide a comprehensive and accurate assessment of DD’s progression. This multimodal method offers a promising step toward a better understanding and management of Dementia’s disease by providing doctors with improved prediction insights into the development of the disease.

关键词： Deep learning Neurological diseases Accuracy Magnetic resonance imaging Medical services Predictive models Trajectory Older adults Image classification Dementia

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Evaluation of China's Digital Economy: A Case Study using Entropy and TOPSIS 11

Evaluation of China's Digital Economy: A Case Study using En...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Liu, Junjie Wang, Boya Xue, Jiayu Qu, Yi Shi, Yong Sino-Danish College University of Chinese Academy of Sciences Beijing100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315100 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Recently, digital economy in social science has become increasingly significant. A well-constructed evaluation structure could generally and promptly describe and reveal its overall social digitalization development. Building on existent researches, this paper improves the evaluation system, which comprises four first-level indicators and thirty-one second-level indicators. Notably, this paper innovatively incorporates higher education into digital innovation of the evaluation. The Entropy method is utilized to assign weights to various indicators in an objective way, with employing the TOPSIS approach to prioritize the development of digital economy in places based on the closeness to the ideal solution. By analysing related data related from 31 provinces in China between 2017 to 2021, this paper greatly finds: Firstly, the development of digital economy vary significantly across provinces, with most provinces at a relatively poor level, indicating considerable potential for overall growth;Secondly, Guangdong, Jiangsu, Zhejiang, and Shandong consistently ranked in the top four from 2017 to 2021, reflecting their specific development attributes and their emphasis on digital economy;Thirdly, significant disparities exist both between regions and among provinces. Consequently, this paper suggests establishing a comprehensive national digital economy development plan, formulating region-specific digital economy policies tailored to local conditions, and encouraging leading provinces to actively share experiences with other provinces. © 2024 The Authors.

关键词： Digital economy Evaluation Multi-source information TOPSIS

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Evaluation and Design of Robotic Hand Picking Operations using Intelligent Motor Unit

Evaluation and Design of Robotic Hand Picking Operations usi...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Gnanavel, R. Vinod, D. Nalini, M.K. Dhinakaran, K. Elantamilan, D. R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of CSE Chennai India BMS College of Engineering Department of ISE Bangalore India Dhanalakshmi College of Eneineering Department of Artificial Intelligence and Data Science Chennai India VallalP. T. Lee Chengalvaraya Naicker Arts and Science College Department of Computer Science Chennai India

ISBN: (纸本)9781665495295

Picking actions performed by industrial robots may be evaluated using a new architecture in this article. It has always been a goal of humankind to imbue their products with lifelike characteristics in an effort to create a substitute for themselves to carry out their commands and to work in difficult environments like the present. The most common image of a robot is that of a machine that resembles a human person in both appearance and operation. Automation is being replaced by robotics in an effort to boost output and ensure consistency in product quality. Despite the fact that every effort is being made to provide industrial robots increasingly anthropomorphic and humanlike traits and superhuman skills, today's industrial robots may not resemble humans in the slightest. A robotic manipulator, more generally known as a robotic arm, is a typical form of industrial robot. Moving joints connect stiff chains in either an open or closed kinematic structure. It is possible to think of a link's structure as a human torso with the waist, upper arm and forearm as joints [1]. The wrist joint at the end of the arm connects end effectors such as a tool and its mounting fixture, or a gripper or any other device for functioning. This workstation features a 6-axis Force/Torque pick and place robot for loading and packaging products. A six-axis Force/Torque design was used to properly evaluate and account for all of the possible loading process concerns when building the pick and place robot. © 2022 IEEE.

关键词： Robotic arms

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MMFNet: A Multi-modal and Multi-attention Fusion Network for Multi-task Skin Disease Classification

MMFNet: A Multi-modal and Multi-attention Fusion Network for...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Xinlei Qian, Pengjiang Wang, Chuang Liu, Li Gao, Ming Cai, Weiwei Ng, Eddie Yin-Kwee Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China Jiangnan University Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education Wuxi China Affiliated Hospital Jiangnan University Big Data Center Wuxi China Nanyang Technological University School of Mechanical and Aerospace Engineering Nanyang Singapore

ISBN: (纸本)9798350386226

Skin diseases rank among the most prevalent ailments in humans, which underscores the critical importance of early detection and diagnosis. Considering clinical practice, the integration of information from various modalities holds significant potential to enhance diagnostic accuracy and precision. In this study, we propose a multi-modal and multi-attention fusion network for multi-task skin disease classification. We initially devise the Shifted Window Cross-attention Fusion (SWCF) module based on the shifted window self-attention mechanism, leveraging the advantages of the attention mechanism to learn the correlations between different modalities and fully integrate multi-modal features. Subsequently, taking into account the heterogeneity of the data, we employ the Heterogeneous data Cross-attention Fusion (HDCF) module using the idea of information decoupling and separation processing to integrate image and text data. Additionally, we suggest the Dynamic Loss Weight Allocation (DLWA) method for multi-task learning to refine the training procedure. We confirm the superiority of the proposed method on the publicly available multi-modal skin lesion dataset, Derm7pt. The average accuracy of multi-modal skin disease classification is 79.14%, surpassing the current state-of-the-art methods. © 2024 IEEE.

关键词： Multi-task learning

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