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Semantic Template-based Convolutional Neural Network for Text Classification

ACM Transactions on Asian and Low-Resource Language information Processing 2023年第11期22卷 1-21页

作者： Chang, Yung-Chun Ng, Siu Hin Chen, Jung-Peng Liang, Yu-Chi Hsu, Wen-Lian Graduate Institute of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Snhcc Tigp Academia Sinica National Cheng Chi University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering College of Information and Electrical Engineering Asia University Taichung Taiwan Pervasive Ai Research Labs Ministry of Science and Technology Hsinchu City Taiwan

We propose a semantic template-based distributed representation for the convolutional neural network called Semantic Template-based Convolutional Neural Network (STCNN) for text categorization that imitates the perceptual behavior of human comprehension. STCNN is a highly automatic approach that learns semantic templates that characterize a domain from raw text and recognizes categories of documents using a semantic-infused convolutional neural network that allows a template to be partially matched through a statistical scoring system. Our experiment results show that STCNN effectively classifies documents in about 140,000 Chinese news articles into predefined categories by capturing the most prominent and expressive patterns and achieves the best performance among all compared methods for Chinese topic classification. Finally, the same knowledge can be directly used to perform a semantic analysis task. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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An Attention-based LSTM price prediction model based on a single time series information 6

An Attention-based LSTM price prediction model based on a si...

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6th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2024

作者： Tian, Jiaming Dang, Yuzhuo Huang, Hongbin National University of Defense Technology Laboratory for Big Data and Decision Changsha China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9798331516550

With the continuous improvement of people's living standards, people's income from work has gradually increased, and their remaining savings have also gradually increased. In order to avoid the problem of currency devaluation and shrinking savings caused by inflation, investment is regarded as an indispensable asset allocation method. Among investment products, gold has always been favored for its store of value;in the past decade, the cryptocurrency Bitcoin has been limited in number due to its unique technology, causing a new wave of investment in Bitcoin. At present, most of the existing forecasting models are for forecasting stock prices, and there are multiple forecasting models. We used grayscale prediction, time series analysis method and LSTM prediction model based on Attention mechanism to predict and analyze the results of two investment products. The experiment found that the LSTM prediction model based on Attention mechanism had the best prediction performance of the two investment products. © 2024 IEEE.

关键词： Prediction models

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Paired Robotic Devices with Subtle Expression of Sadness for Enriching Social Connectedness 15th

Paired Robotic Devices with Subtle Expression of Sadness f...

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15th International Conference on Social Robotics, ICSR 2023

作者： Uchida, Misako Nunez, Eleuda Hassan, Modar Hirokawa, Masakazu Suzuki, Kenji Institute of Systems and Information Engineering University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8573 Japan Data Science Laboratories NEC Corporation Kawasaki Japan

ISBN: (纸本)9789819987146

Various factors contribute to feelings of loneliness and compromised emotional well-being. One potential strategy to address this issue involves creating systems that enhance social connectedness between users at distance. In the literature, many of these systems utilize physically embodied devices and foster a sense of presence and intimacy. However, there has been limited exploration of targeting and sharing negative emotions. This study introduces a novel robotic device with a caricatured appearance named BlueBot, capable of subtle expressions of sadness. This study outlines the technical aspects and design principles underpinning touch-based non-verbal communication. Additionally, we present findings from a pilot test and in-the-wild field trial, describing users’ responses and behaviors to investigate the impact on social connectedness. Our observations indicate that the developed system offers a nuanced approach that not only heightens awareness of the other individual’s presence but also enhances emotional sensitivity. Several design insights for future similar studies are derived from our research. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Robotics

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A Non-Parametric Scheme for Identifying data Characteristic Based on Curve Similarity Matching

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1424-1437页

作者： Quanbo Ge Yang Cheng Hong Li Ziyi Ye Yi Zhu Gang Yao School of Automation Nanjing University of Information Science&TechnologyJiangsu Provincial University Key Laboratory of Big Data Analysis and Intelligent SystemsNanjing University of Information Science&Technologyand the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science&TechnologyNanjing 210044China School of Department of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China China Classification Society Nantong Office Nantong 226006China Chinese Flight Test Establishment Xi’an 710089China School of Mathematics Tsinghua UniversityBeijing 100084China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044China Department of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China IEEE

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity *** the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and *** com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

关键词： Curve similarity matching Gaussian-like noise non-parametric scheme parzen window.

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A Hypergraph Framework Based on Neighborhood and Category Association Relationships for Session-Based Recommendation

A Hypergraph Framework Based on Neighborhood and Category As...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big data Computing, 2023 International Conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Liu, Dengfeng Wang, Yitong Cai, Fei National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China Beijing Big Data Center Beijing China

ISBN: (纸本)9798350304602

Session-based recommendation aims to generate personalized recommendations based on user behaviors within a browsing session. The traditional graph neural network (GNN) methods focus on modeling pair-wise relationships but ignore neighborhood and category relationships which is crucial for improving the accuracy of recommendations. This paper proposes a hypergraph framework based on neighborhood and category association relationships for session-based recommendation (HLNCA). In detail, we first construct a hypergraph that can acquire the neighborhood and category association relationships among items;then aggregate hyperedge representation to obtain the neighborhood and category relationships of nodes;and finally obtain the users' preference through the attention mechanism to calculate the predicted score of items in the candidate set according to the preference. The proposed HL-NCA model outperforms baseline models in terms of Recall@20 and MRR@20 evaluation metrics. © 2023 IEEE.

关键词： Behavioral research

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Forecasting Breakthroughs: Identifying Future Leaders in the Semiconductor Industry

Forecasting Breakthroughs: Identifying Future Leaders in the...

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IEEE Symposium on systems and information engineering Design, SIEDS

作者： Adam Rogers Carter Dibsie Ethan Kuzneski David Underwood Robert Brozey Lauren Sullivan Donald Robinson Michael D. Porter Systems and Information Engineering University of Virginia Titus Technology LLC Systems and Information Engineering Data Science University of Virginia

ISBN: (数字)9798350385144

ISBN: (纸本)9798350385151

Breakthrough technologies have the potential to disrupt markets and society. Anticipating such disruptions is crucial for policymakers, investors, and businesses in being proactive with regard to regulatory policies and in allocating resources effectively. This project aims to develop an analytical approach to identify companies that will lead in developing breakthrough technologies. The analysis focuses on the semiconductor industry, which has seen rapid growth in recent decades, surging from $139 billion in revenue in 2001 to $573.5 billion in 2022. Our systematic approach to predicting technological disruption in the semiconductor industry involves leveraging a combination of quantitative company data, human-centric elements, and feature engineering. data was collected on 244 private semiconductor companies between 2012 and 2018, encompassing information about leadership profiles, research endeavors, media exposure, and financial performance. Two models were developed: a penalized regression model, and a boosted tree model, both aimed at forecasting the probability of a company achieving a valuation exceeding $500 million within five years of its first funding deal. Key variables such as the number of employees, year founded, total invested equity, number of active patents, and country of origin emerged as significant predictors of company success. This paper discusses the performance of our models and explores applying our findings to identify disruptive companies across industries.

关键词： Patents Leadership Systematics Electronics industry Companies Predictive models Media

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Trustworthy DNN partition for blockchain-enabled digital twin in wireless IIoT networks

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science China(information sciences) 2024年第11期67卷 373-374页

作者： Xiumei DENG Jun LI Long SHI Kang WEI Ming DING Yumeng SHAO Wen CHEN Shi JIN Pillar of Information Systems Technology and Design Singapore University of Technology and Design National Mobile Communications Research Laboratory Southeast University School of Electronic and Optical Engineering Nanjing University of Science and Technology Department of Computing Hong Kong Polytechnic University Data61 CSIRO Department of Electronics Engineering Shanghai Jiao Tong University

The integration of artificial intelligence(AI) and digital twin(DT) technology has revolutionized the industrial Internet of Things(IIoT), enabling advanced automation and intelligent manufacturing [1]. Through sophisticated interactions between physical entities and their virtual counterparts,AI-driven DTs facilitate performance monitoring, analysis,simulation, and optimization of physical assets, enabling predictive maintenance and informed decision-making [2].

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Feature selection using modified chaotic satin bowerbird algorithm with deep transfer learning for Multispectral Image Classification

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International Journal of information Technology (Singapore) 2024年 1-13页

作者： Rajakani, M. Kavitha, R.J. Rajesh, S. Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Electronics and Communication Engineering University College of Engineering Panruti Panruti 607106 India Department of Information Technology Mepco Schlenk Engineering College Sivakasi India

Multispectral image classification has received significant attention among research communities and academicians. Recently, several artificial intelligence (AI) models can be used for the extraction of prominent features. Besides, deep learning (DL) methods become more familiar and gained interest in the remote sensing community for the classification of multispectral and hyperspectral images. With this motivation, this paper presents an optimized feature learning with deep transfer learning enabled multispectral image classification (OFLDTL-MSIC) technique. The proposed OFLDTL-MSIC technique aims to categorize the different class labels of the multispectral images. Besides, the OFLDTL-MSIC technique applies multi-level discrete wavelet transform (DWT) based image decomposition technique. Moreover, the EfficientNet technique is applied as a feature extractor to generate a collection of features. Furthermore, selecting optimal features extracted by a EfficientNet involves identifying and retaining the most relevant and informative features The improved chaotic satin bowerbird optimization (CSBO) algorithm which uses dynamic population size is proposed for feature selection and kernel extreme learning machine (KELM) model is applied for the classification process. In order to ensure the enhanced performance of the OFLTDL-MSIC technique, a wide range of simulations take place using the Madurai LISS IV multispectral images and the results are examined under several aspects. The extensive comparative study highlighted the better performance of the OFLTDL-MSIC technique over the recent methods. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Deep learning Image classification Multispectral images Parameter tuning Remote sensing

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Grey Wolf Algorithm with Rat Swarm Optimizer for Constrained Optimization and engineering Design Problems

Grey Wolf Algorithm with Rat Swarm Optimizer for Constrained...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： Kathiroli, Panimalar Kanmani, S. Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu District603203 India Department of Information Technology Puducherry Technological University Puducherry India

ISBN: (纸本)9789819746491

This paper proposes a new hybrid optimization algorithm that combines the grey wolf optimization (GWO) algorithm with rat swarm optimizer (RSO) to improve the exploration and exploitation capabilities of the search space. The proposed algorithm, named GRO, is evaluated using benchmark functions and engineering design problems. The GRO algorithm leverages the social hierarchy and hunting strategies of wolf packs and incorporates the swarm intelligence of rats and the diversity-enhancing effects of GWO to guide the search process. Experimental results demonstrate that the GRO algorithm outperforms existing state-of-the-art optimization algorithms in terms of convergence speed, accuracy, and robustness for most benchmark functions. The hybrid algorithm’s ability to balance exploration and exploitation is shown to be superior to other metaheuristic algorithms. The algorithm is further validated by solving two well-known engineering design problems, where it demonstrates its efficacy in solving practical constrained problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Constrained optimization

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Average Performance Analysis of Multi-Class Classification Based on Error-Correcting Output Codes

Average Performance Analysis of Multi-Class Classification B...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Kobayashi, Manabu Kumoi, Gendo Yagi, Hideki Hirasawa, Shigeichi Center for Data Science Waseda University Tokyo Japan Nagaoka University of Technology Department of Information and Management Systems Engineering Niigata Japan University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

ISBN: (纸本)9781665410205

In machine learning, one of the methods to solve multiclass classification problems is a framework called Error-Correcting Output Codes (ECOC), which constructs a multiclass classifier by combining a lot of binary classifiers. ECOC assigns binary codewords to each category, and the multiclass classification performance varies depending on the code. In this study, we treat each element of the codeword as a random variable and evaluate the average performance of ECOC. As a result, for M class classification if the number of binary classifiers is O(M), then the average error probability of various codes approaches that of MAP estimation. We show that the important points are the ratio between the number of binary classifiers and M and the difference between the maximum posterior probability and the second highest posterior probability for the categories. © 2024 IEEE.

关键词： Coding errors

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