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Stabilization of THz waves using chaos supremacy

Stabilization of THz waves using chaos supremacy

引用

2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Kuwashima, Fumiyoshi Jarrahi, Mona Cakmakyapan, Semih Wada, Kenji Haraguchi, Masanobu Kawakami, Yuki Moriyasu, Takeshi Morikawa, Osamu Kurihara, Kazuyoshi Kitahara, Hideaki Furuya, Takashi Nakajima, Makoto Tani, Masahiko Fukui University of Technology Dep. of Electr. and Electron. Eng. Fukui Japan Ucla Electrical and Computer Eng. Los AngelesCA90095 United States Osaka Metropolitan University Dep. of Physics and Electronics Osaka Japan Tokushima Univ. Industrial and Social Science Grad. Sch. of Tech. Tokushima Japan Fukui Col. Natl. Inst. of Tech. Dep. of Elec. and Inf. Eng. Fukui Japan University of Fukui Faculty of Engineering Fukui Japan Japan Coast Guard Academy Liberal Arts Kure Japan University. of Fukui School of Education Fukui Japan FIR-UF University of Fukui Fukui Japan Osaka Univ. Inst. of Laser Engineering Osaka Japan

ISBN: (纸本)9798350382822

We apply the chaos supremacy of laser chaos system to the high stabilization of THz waves. Stability was demonstrated by down converting the optical beats between laser longitudinal modes in the THz domain to the radio frequency domain and comparing a conventional free running CW laser diode to a chaotically oscillating laser diode. Because of their exceptional stability, chaotically oscillating lasers are attractive options for optical pump sources in terahertz time-domain spectroscopy systems. © 2023 IEEE.

关键词： Stabilization

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A binary i-ching divination evolutionary algorithm for feature selection

A binary i-ching divination evolutionary algorithm for featu...

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2019 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2019

作者： Chen, Bianna Zhang, Tong Jia, Xue Jin, Jianxiu Philip Chen, C.L. Xu, Xiangmin South China University of Technology Sch. of Electronics and Inform. Guangzhou China South China University of Technology Sch. of Computer Science Engineering Guangzhou China

ISBN: (纸本)9781728159287

Feature selection is used to extract the most essential features from the data without degrading the performance of an algorithm, especially a classification algorithm. Various evolutionary algorithms (EAs) combined with classification algorithms are commonly used for feature selection. This paper suggests an innovative feature selection algorithm based on I-Ching Divination Evolutionary Algorithm, called binary IDEA (BIDEA). The main idea is to use a series of hexagrams encoded as binary vectors, which is called the hexagram state to represent the solutions of selected features. After three flexible operations, intrication, turnover and mutual, the transformed hexagram state can be obtained as candidate solutions. Then the optimized hexagram state can be searched to form the new state in the next iteration by evaluating candidate solutions. Experiments checked out with standard datasets reveal that the proposed BIDEA performs better in terms of classification accuracy, precision, recall and feature reduction than the competing feature selection methods. © 2019 IEEE.

关键词： Evolutionary algorithms

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Hijacking Tracker: A Powerful Adversarial Attack on Visual Tracking

Hijacking Tracker: A Powerful Adversarial Attack on Visual T...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Xiyu Yan Xuesong Chen Yong Jiang Shu-Tao Xia Yong Zhao Feng Zheng Dept. of Computer Science and Technology Tsinghua University China Shenzhen Graduate School of Peking University Sch. of Electronic and Computer Engineering China Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Visual object tracking has made important breakthroughs with the assistance of deep learning models. Unfortunately, recent research has clearly proved that deep learning models are vulnerable to malicious adversarial attacks, which mislead the models making wrong decisions by perturbing the input image. The threat to the models alerts us to pay attention to the model security of deep learning- based tracking algorithms. Therefore, we study the adversarial attacks against advanced trackers based on deep learning to better identify the vulnerability of tracking algorithms. In this paper, we propose to add slight adversarial perturbations to the input image by an inconspicuous but powerful attack strategy-hijacking algorithm. Specifically, the hijacking strategy misleads trackers in two aspects: one is shape hijacking that changes the shape of the model output;the other is position hijacking that gradually pushes the output to any position in the image frame. Besides, we further propose an adaptive optimization approach to integrate two hijacking mechanisms efficiently. Eventually, the hijacking algorithm results in fooling the tracker to track the wrong target gradually. The experimental results demonstrate the powerful attack ability of our method-quickly hijacking state-of-the-art trackers and reducing the accuracy of these models by more than 90% on OTB2015.

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Fusing Multi-Features for Rumor tweets detection in Wechat 8

Fusing Multi-Features for Rumor tweets detection in Wechat

引用

8th International Conference on Digital Home, ICDH 2020

作者： Chen, Xiangping Ma, Yingxun Zhou, Fan Lin, Ge Zhang, Zhian Sun Yat-sen University Guangdong Key Lab. for Big Data Anal. and Simulat. of Pub. Opinion the Sch. of Commun. and Design Guangzhou China Sun Yat-sen University School of Data Science and Computer National Engineering Research Center of Digital Life Guangzhou China

ISBN: (纸本)9781728192345

Recently, rumor detection has attracted lots of research interest because of the quick development of social networks and the widely spread of rumor. The exiting studies is mainly about the open social network such as Weibo and Facebook. However, there is little research about closed social networks, especially Wechat. As a famous app with more than one billion daily active user, the tweets in Wechat can attract lots of attention, which gives the spread of rumor tweets more chance. In this paper, we empirically study the tweets spread in Wechat. We first analyze the text information and users' behavior of Wechat tweets, then we come up with several important features needed for rumor detection. Furthermore, we propose a new model which can capture the impact of both users' behavior and text information. We conduct experiments using a large tweets dataset collected in Wechat, the result shows that our proposed method can effectively detect the rumor tweets. © 2020 IEEE.

关键词： Large dataset

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Optimizing NARX-RNN Performance to Predict Precious Metal Futures market

Optimizing NARX-RNN Performance to Predict Precious Metal Fu...

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Green Energy, Computing and Sustainable Technology (GECOST), International Conference on

作者： Stephanie Dhanuskodi Rengasamy Filbert H. Juwono Jobrun Nandong Andrew J. Brennan Lenin Gopal Dept. of Acct. Finance and Economics Faculty of Business Curtin University Malaysia Miri Sarawak Malaysia Computer Science Program University of Southampton Malaysia Nusajaya Johor Malaysia Dept. of Chemical and Energy Eng. Faculty of Engineering and Science Curtin University Malaysia Miri Sarawak Malaysia Sch. of Acct. Economics and Finance Faculty of Business and Law Curtin University Bentley Perth Australia Dept. of Electrical and Electronic Eng. University of Southampton Malaysia Nusajaya Johor Malaysia

ISBN: (纸本)9781665486644

Precious metals offer lucrative investments appealing to investors globally, leading to a surge in demand for accurate forecasts. Published literature for prediction applications often employs Artificial Neural Networks (ANNs), possessing desirable generalization over nonlinear data and design flexibility. Recurrent Neural Networks (RNNs) are a class of ANNs designed for time series forecasts providing superior approximations. Nonlinear Autoregressive with Exogenous input (NARX) is an RNN model with high memory retention properties, applied in this study to predict ten assets from the precious metal futures market, for three-month predictions (April 2021-June 2021). Network inputs are evaluated through feature selection to filter uncorrelated factors from the network dataset. Accuracy of prediction is enhanced through multi-objective Response Surface Methodology (RSM) optimization, as several variables characterize RNN performance. Three key variables are selected for analysis through RSM, providing optimum configuration to obtain targeted outcome. Simulation results reveal that five assets produce acceptable result, showing an improved fitness through RSM-suggested configurations. Observations indicate intercorrelation between RSM inputs, highlighting its efficiency over conventional methods. Implementing additional RSM inputs to develop more complex models might achieve further reliability. This research provides performance improvement measures for RNNs utilized in financial data projections.

关键词： Recurrent neural networks Simulation Time series analysis Metals Predictive models Response surface methodology Reliability

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Learning to Search Efficient DenseNet with Layer-wise Pruning

Learning to Search Efficient DenseNet with Layer-wise Prunin...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xuanyang Zhang Hao Liu Zhanxing Zhu Zenglin Xu SMILE Lab Sch. Computer Science & Engineering University of Electronic Science & Technology of China Chengdu Sichuan China Electronic Engineering and Computer Science University of California Berkeley California USA School of Mathematical Science Peking University Beijing China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong China Center for Artificial Intelligence Peng Cheng Lab Shenzhen Guangdong China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Deep neural networks have achieved out-standing performance in many real-world applications with the expense of huge computational resources. The DenseNet, one of the recently proposed neural network architecture, has achieved the state-of-the-art performance in many visual tasks. However, it has great redundancy due to the dense connections of the internal structure, which leads to high computational costs in inference with such dense networks. To address this issue, we design a reinforcement learning framework to search for efficient DenseNet architectures with layer-wise pruning (LWP) for different tasks, while retaining the original advantages of DenseNet, such as feature reuse, short paths, etc. In this framework, an agent evaluates the importance of each connection between any two block layers, and prunes the redundant connections. In addition, a novel reward-shaping trick is introduced to make DenseNet reach a better trade-off between accuracy and float point operations (FLOPs). Our experiments show that DenseNet with LWP is more compact and efficient than existing alternatives.

关键词： Training Neural networks computer architecture Task analysis Image coding Learning (artificial intelligence) Load modeling

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Sentiment Analysis Using Autoregressive Language Modeling and Broad Learning System

Sentiment Analysis Using Autoregressive Language Modeling an...

引用

IEEE International Conference on Bioinformatics and Biomedicine

作者： Xin-rong Gong Jian-xiu Jin Tong Zhang Sch. of Electronics and Information South China University of Technology Sch. of Computer Science and Engineering South China University of Technology

ISBN: (纸本)9781728118680

In the era of big data, mining the emotional tendency of opinions through natural language processing technology is meaningful for the timely understanding of human behavior data. Nowadays, Generalized Autoregressive Pre-training Language Modeling (XLNet) can not only capture bidirectional contextual knowledge but also learn the word dependency. However, its sentence-level representation didn't take broad features into account. In this paper, we design a novel architecture, called Broad Autoregressive Language Model (BroXLNet), to automatically process sentiment analysis task. BroXLNet integrates the advantage of generalized autoregressive language modeling and broad learning system, which has the ability of extracting deep contextual features and randomly searching high-level contextual representation in broad spaces. We evaluated our algorithm on binary Stanford Sentiment Treebank dataset. Compared with the state-of-the-art methods, e.g., BiLSTM, ELMo, OpenAI GPT, BERT and XLNet, BroXLNet achieved the best result of 94.0% in sentiment analysis task of binary Stanford Sentiment Treebank. The result demonstrated the excellent classifying ability of BroXLNet in sentiment analysis.

关键词： Sentiment analysis broad learning system autoregressive language modeling emotion classification transfer learning Sentiment analysis modelling languages Transfer (Psychology) big data Bit error rate test

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Stabilization of THz waves using chaos supremacy

Stabilization of THz waves using chaos supremacy

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International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS)

作者： Fumiyoshi Kuwashima Mona Jarrahi Semih Cakmakyapan Kenji Wada Masanobu Haraguchi Yuki Kawakami Takeshi Moriyasu Osamu Morikawa Kazuyoshi Kurihara Hideaki Kitahara Takashi Furuya Makoto Nakajima Masahiko Tani Dep. of Electr. and Electron. Eng. Fukui University of Technology Fukui Japan Electrical and Computer Eng. UCLA Los Angeles CA USA Dep. of Physics and Electronics Osaka Metropolitan University Osaka Japan Industrial and Social Science Grad. Sch. of Tech. Tokushima Univ. Tokushima Japan Dep. of Elec. and Inf. Eng. Natl. Inst. of Tech. Fukui Col. Fukui Japan Faculty of Engineering University of Fukui Fukui Japan Chair of Liberal Arts Japan Coast Guard Academy Kure Japan School of Education University. of Fukui Fukui Japan FIR-UF University of Fukui Fukui Japan Inst. of Laser Engineering Osaka Univ. Osaka Japan

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Pre-cue EEG Rhythms Associated with MI-BCI Performance Variation 20

Pre-cue EEG Rhythms Associated with MI-BCI Performance Varia...

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5th International Conference on Biomedical Signal and Image Processing, ICBIP 2020

作者： Zhou, Qing Jiang, Yingyan Lin, Jiafan Yao, Lin Xv, Kedi Department of Biomedical Engineering Zhejiang University Hangzhou China Department of Computer Science Zhejiang University Hangzhou China Department of Neurobiology NHC and CAMS Key Laboratory of Medical Neurobiology Sch. of Brain Sci. and Brain Med. MOE Frontier Sci. Ctr. for Brain Res. and Brain-Mach. Integration Zhejiang University School of Medicine Hangzhou China Zhejiang Lab Hangzhou311100 China Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781450387767

In recent years, Motor Imagery (MI) based Brain-computer Interface (BCI) has been proven to be a promising approach for stroke neurorehabilitation. However, the potential therapeutic benefit was limited by BCI performance variation and the 'BCI-illiterate' problem, in which a non-negligible portion of users cannot reach adequate BCI control. In this paper, we investigated the MI-BCI performance variation across subjects (inter-subject) and within-subjects over an extended period (intra-subject). Eight subjects were recruited for a 7-session MI-based BCI intervention. The results showed that pre-cue theta and beta power were significantly correlated with BCI performance both inter and intra-subject aspects. The pre-cue rhythmic dynamic as a neurophysiological index would provide a way for future predicting and neurofeedback training to improve BCI performance. © 2020 ACM.

关键词： Electroencephalography

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A Binary I-Ching Divination Evolutionary Algorithm for Feature Selection

A Binary I-Ching Divination Evolutionary Algorithm for Featu...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Bianna Chen Tong Zhang Xue Jia Jianxiu Jin C. L. Philip Chen Xiangmin Xu Sch. of Electronics and Inform. South China University of Technology Guangzhou China Sch. of Computer Science & Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728159287

ISBN: (纸本)9781728159294

关键词： Feature extraction Evolutionary computation Optimization Genetic algorithms Security Pattern analysis Cybernetics

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