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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Yunqiu Xu Ling Chen Meng Fang Yang Wang Chengqi Zhang Centre for Artificial Intelligence University of Technology Sydney Sydney Australia Tencent Robotics X Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology China

ISBN: (数字)9781728145334

ISBN: (纸本)9781728145341

In this paper, we study transformers for text-based games. As a promising replacement of recurrent modules in Natural Language Processing (NLP) tasks, the transformer architecture could be treated as a powerful state representation generator for reinforcement learning. However, the vanilla transformer is neither effective nor efficient to learn with a huge amount of weight parameters. Unlike existing research that encodes states using LSTMs or GRUs, we develop a novel lightweight transformer-based representation generator featured with reordered layer normalization, weight sharing and block-wise aggregation. The experimental results show that our proposed model not only solves single games with much fewer interactions, but also achieves better generalization on a set of unseen games. Furthermore, our model outperforms state-of-the-art agents in a variety of man-made games.

关键词： Games Generators Task analysis Reinforcement learning Logic gates Buildings Computer architecture

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An efficient aggregate signature scheme for healthcare wireless sensor networks

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

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Refined nonuniform embedding for coupling detection in multivariate time series

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Physical Review E 2020年第6期101卷 062113-062113页

作者： Ziyu Jia Youfang Lin Yunxiao Liu Zehui Jiao Jing Wang School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing 100044 China Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC Beijing 101318 China Beijing Laboratory of National Economic Security Early-warning Engineering Beijing Jiaotong University Beijing 100044 China

State-space reconstruction is essential to analyze the dynamics and internal interactions of complex systems. However, it is difficult to estimate high-dimensional conditional mutual information and select the optimal time delays in most existing nonuniform state-space reconstruction methods. Therefore, we propose a nonuniform embedding method framed in information theory for state-space reconstruction. We use a low-dimensional approximation of conditional mutual information criterion for time delay selection, which is effectively solved by the particle swarm optimization algorithm. The obtained embedded vector has relatively strong independence and low redundancy, which better characterizes multivariable complex systems and detects coupling within complex systems. In addition, the proposed nonuniform embedding method exhibits good performance in coupling detection of linear stochastic, nonlinear stochastic, chaotic systems. In the actual application, the importance of small airports that cause delay propagation has been demonstrated by constructing the delay propagation network.

关键词： Coupled oscillators Dynamical systems Information theory Time series analysis

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Genomic divergence in cotton germplasm related to maturity and heterosis

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Journal of Integrative Plant Biology 2019年第8期61卷 929-942页

作者： Shoupu He Gaofei Sun Longyu Huang Daigang Yang Panhong Dai Dayun Zhou Yuzhen Wu Xiongfeng Ma Xiongming Du Shoujun Wei Jun Peng Meng Kuang Research Base State Key Laboratory of Cotton BiologyAnyang Institute of TechnologyAnyang 455000China Institute of Cotton Research of the Chinese Academy of Agricultural Sciences Anyang 455000China Department of Computer Science and Information Engineering/Data Mining Institute Anyang Institute of TechnologyAnyang 455000China

Commercial varieties of upland cotton(Gossypium hirsutum)have undergone extensive breeding for agronomic traits,such as fiber quality,disease resistance,and *** breeding programs have widely used Chinese upland cotton source germplasm(CUCSG)with excellent agronomic traits.A better understanding of the genetic diversity and genomic characteristics of these accessions could accelerate the identification of desirable ***,we analyzed 10,522 high-quality singlenucleotide polymorphisms(SNP)with the CottonSNP63 K microarray in 137 cotton accessions(including 12 hybrids of upland cotton).These data were used to investigate the genetic diversity,population structure,and genomic characteristics of each population and the contribution of these loci to *** subgroups were identified,in agreement with their knownpedigrees,geographical distributions,and times since *** each group,we identified lineagespecific genomic divergence regions,which potentially harbor key alleles that determine the characteristics of each group,such as early maturity-related *** of the distribution of heterozygous loci,among 12 commercial cotton hybrids,revealed a potential role for these regions in *** study provides insight into the population structure of upland cotton ***,the overlap between lineagespecific regions and heterozygous loci,in the high-yield hybrids,suggests a role for these regions in cotton heterosis.

关键词： upland cotton fiber quality genetic diversity

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Corrigendum to “Exploring the relationship between socioeconomic deprivation index and Alzheimer's disease using summary-level data: from genetic correlation to causality” [Progress in Neuropsychopharmacology & Biological Psychiatry 123 (2023) 110700]

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Progress in Neuro-Psychopharmacology and Biological Psychiatry 2025年

作者： Jing Dai Yue Xu Ting Wang Ping Zeng Department of Biostatistics School of Public Health Xuzhou Medical University Xuzhou Jiangsu 221004 China Center for Medical Statistics and Data Analysis Xuzhou Medical University Xuzhou Jiangsu 221004 China Key Laboratory of Human Genetics and Environmental Medicine Xuzhou Medical University Xuzhou Jiangsu 221004 China Key Laboratory of Environment and Health Xuzhou Medical University Xuzhou Jiangsu 221004 China Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation Xuzhou Medical University Xuzhou Jiangsu 221004 China

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Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview

arXiv

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arXiv 2021年

作者： Fan, Zhaoxin Zhu, Yazhi He, Yulin Sun, Qi Liu, Hongyan He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing China No. 59 Zhongguancun Street Haidian Dist Beijing100872 China Institute of Information Science Beijing Jiaotong University No.3 Shangyuancun Haidian Dist. Beijing China School of Economics and Management Tsinghua University Haidian Dist Beijing100084 China

Object pose detection and tracking has recently attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others. However, survey study about the latest development of deep learning-based methods is lacking. Therefore, this study presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route. To achieve a more thorough introduction, the scope of this study is limited to methods taking monocular RGB/RGBD data as input and covering three kinds of major tasks: instance-level monocular object pose detection, category-level monocular object pose detection, and monocular object pose tracking. In our work, metrics, datasets, and methods of both detection and tracking are presented in detail. Comparative results of current state-of-the-art methods on several publicly available datasets are also presented, together with insightful observations and inspiring future research directions. Copyright © 2021, The Authors. All rights reserved.

关键词： Augmented reality

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

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Dictionary Pair-based data-Free Fast Deep Neural Network Compression

Dictionary Pair-based Data-Free Fast Deep Neural Network Com...

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IEEE International Conference on data mining (ICDM)

作者： Yangcheng Gao Zhao Zhang Haijun Zhang Mingbo Zhao Yi Yang Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China City University of Hong Kong Hong Kong SAR Centre for Artificial Intelligence University of Technology Sydney Sydney NSW Australia

ISBN: (纸本)9781665423991

Deep neural network (DNN) compression can reduce the memory footprint of deep networks effectively, so that the deep model can be deployed on the portable devices. However, most of the existing model compression methods cost lots of time, e.g., vector quantization or pruning, which makes them inept to the real-world applications that need fast online computation. In this paper, we therefore explore how to accelerate the model compression process by reducing the computation cost. Then, we propose a new deep model compression method, termed Dictionary Pair-based data-Free Fast DNN Compression, which aims at reducing the memory consumption of DNNs without extra training and can greatly improve the compression efficiency. Specifically, our proposed method performs tensor decomposition on the DNN model with a fast dictionary pair learning-based reconstruction approach, which can be deployed on different layers (e.g., convolution and fully-connection layers). Given a pre-trained DNN model, we first divide the parameters (i.e., weights) of each layer into a series of partitions for dictionary pair-based fast reconstruction, which can potentially discover more fine-grained information and provide the possibility for parallel model compression. Then, dictionaries of less memory occupation are learned to reconstruct the weights. Extensive experiments on popular DNNs (i.e., VGG-16, ResNet-18 and ResNet-50) showed that our proposed weight compression method can significantly reduce the memory footprint and speed up the compression process, with less performance loss.

关键词： Deep learning Training Performance evaluation Dictionaries Tensors Costs Computational modeling

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Difference in Differences in Marketing Performance Measurement

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Procedia Computer Science 2019年 162卷 275-280页

作者： Wei Deng Nan Xu Haotian Li Fangyao Liu Yong Shi University of Nebraska at Omaha Omaha NE 68182 Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Marketing performance measurement is important in retail companies, it is critical for the budget planning and adjustment of marketing strategies and tactics over time. Compared to digital online marketing, such as social media advertisers where tagging techniques [1] can be applied to track the lifecycle of the consumption and the customer’s behaviours, traditional TV and radio advertising will be harder to evaluate at the individual level. Instead, common approach in this scenario is to study the relationship between ads spend and sales. This paper presents a casual inference methodology – “Difference in Differences”(DID) on the application of TV marketing measurement. Instead of building direct relationship between ads and sales, we attempt to create an experimental research design and study the differential effect on a ‘treatment group’ versus a ‘control group’. In order to study the performance of advertising in a city, the treatment group is sales performance during and after TV advertisings and the control group is the statistical model that mimics the sales of the same city without ads influence. It is proved that the model can significantly predict the sales of city in nature by considering its past sales, the neighbourhood districts and weather conditions. And a case study in industrial data shows that DID methodology can effectively evaluate the TV advertising results on regions.

关键词： Advertising Measurement Difference in Differences Random Forest Lasso Regression

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A New Ontology Meta-Matching Technique with a Hybrid Semantic Similarity Measure 15th

A New Ontology Meta-Matching Technique with a Hybrid Semanti...

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15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019

作者： Lu, Jiawei Xue, Xingsi Lin, Guoxiang Huang, Yikun College of Information Science and Engineering Fujian University of Technology Fuzhou China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou China Concord University College Fujian Normal University Fuzhou China

ISBN: (纸本)9789811397097

Ontology is the kernel technique of semantic web, which can be used to describe the concepts and their relationships in a particular domain. However, different domain experts would construct the ontologies according to different requirements, and there exists a heterogeneity problem among the ontologies, which hinders the interaction between ontology-based intelligent systems. Ontology matching technique can determine the links between heterogeneous concepts, which is an effective method for solving this problem. Semantic similarity measure is a function to calculate to what extent two concepts are similar to each other, which is the key component of ontology matching technique. Generally, multiple semantic similarity measures are used together to improve the accuracy of the concept recognition. How to combine these semantic similarity measures, i.e., the ontology meta-matching problem, is a challenge in the ontology matching domain. To address this challenge, this paper proposes a new ontology meta-matching technique, which applies a novel combination framework to aggregate two broad categories of similarity measures. The experiment uses the famous benchmark provided by the Ontology Alignment Evaluation Initiative (OAEI). Comparing results with the participants of OAEI shows the effectiveness of the proposal. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Ontology

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