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Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System

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Computational Intelligence and Neuroscience 2022年第0期2022卷 5906797页

作者： Sumathy, B. Kumar, Anand Sungeetha, D. Hashmi, Arshad Saxena, Ankur Kumar Shukla, Piyush Nuagah, Stephen Jeswinde Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India School of Computer Science and Applications REVA University Bangalore India Department Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India King Abdulaziz University Jeddah Saudi Arabia Indus Institute of Information and Communication Technology Indus University Gujarat Ahmedabad India Computer Science and Engineering Department University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Technological University of Madhya Pradesh Bhopal462033 India Department of Electrical Engineering Tamale Technical University Tamale Ghana

The emergence of social media has allowed people to express their feelings on products, services, films, and so on. The feeling is the user's view or attitude towards any topic, object, event, or service. Overall, feelings have always influenced people's decision-making. In recent years, emotions have been analyzed intensively in natural language, but many problems still have to be watched. One of the most important problems is the lack of precise classification resources. Most of the research into feeling gradation is concerned with the issue of polarity grading, although, in many practical applications, this relatively grounded feeling measure is insufficient. Design methods are therefore essential, which can accurately classify feelings into a natural language. The principal goal of the research is to develop an overflow of grammatical rules-based classification of Indian language tweets. In this work, three main challenges are identified to classify feelings in Indian language tweets and possible methods for tackling such issues. Firstly, it has been found that the informal nature of tweets is crucial for the classification of feelings. Based on the tweets, the mental illness of the person has been classified. Therefore, to categorize Indian language tweets, a combination of grammar rules based on adjectives and negations is proposed. Secondly, people often express their feelings with slang words, abbreviations, and mixed words. A technique called field tags is used to include nongrammatical arguments such as slang words and diverse words. Thirdly, if a tweet is more complex, the morphological richness of the Indian language results in a loss of performance. The grammar rules are embedded in N-gram techniques and machine learning methods. These methods are grouped into three approaches, which functionally predict Indian language tweets with syntactic words. © 2022 B. Sumathy et al.

关键词： Decision making

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Optimization of Directional Landmark Deployment for Visual Observer on SE(3)

arXiv

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arXiv 2022年

作者： Lei, Zike Chen, Xi Tan, Ying Chen, Xiang Chai, Li The School of Information Science and Engineering Wuhan University of Science and Technology Hubei 430081 China The Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and Technology Hubei 430081 China The School of Electrical Mechanical and Infrastructure Engineering The University of Melbourne VIC3010 Australia The Department of Electrical and Computer Engineering University of Windsor ONN9B 3P4 Canada The College of Control Science and Engineering Zhejiang University Hangzhou310027 China

An optimization method is proposed in this paper for novel deployment of given number of directional landmarks (location and pose) within a given region in the 3-D task space. This new deployment technique is built on the geometric models of both landmarks and the monocular camera. In particular, a new concept of Multiple Coverage Probability (MCP) is defined to characterize the probability of at least n landmarks being covered simultaneously by a camera at a fixed position. The optimization is conducted with respect to the position and pose of the given number of landmarks to maximize MCP through globally exploration of the given 3-D space. By adopting the elimination genetic algorithm, the global optimal solutions can be obtained, which are then applied to improve the convergent performance of the visual observer on SE(3) as a demonstration example. Both simulation and experimental results are presented to validate the effectiveness of the proposed landmark deployment optimization method. © 2022, CC BY-NC-SA.

关键词： Probability

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A Hardware Simulation Platform of Artificial Noise-Assisted MIMO communication System Based on LabVIEW-USRP

A Hardware Simulation Platform of Artificial Noise-Assisted ...

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IEEE International Symposium on Personal, Indoor and Mobile Radio communications (PIMRC)

作者： Ziyang Yu Huanhuan Song Hong Wen Yonghuang Liu Wen Li School of Aeronautics and Astronautics University of Elec. Science and Tech. of China Chengdu China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Lab. of Sichuan Province UESTC China Intelligent IoT Communication Technology Engineering Research Center of Sichuan Province UESTC China

Due to the broadcast nature of wireless communication, it is susceptible to eavesdropping by unauthorized users. Artificial noise, as an important physical layer security technique, is different from traditional cryptography-based communication security mechanisms with high computational complexity, and cannot be cracked by the rapidly advancing computing power. The theoretical research on the security of artificial noise-assisted multiple input multiple output (MIMO) communication systems is quite comprehensive. However, since artificial noise is deeply dependent on the accurate instantaneous channel feedback, its practical application effect still lacks further experiment verification in actual MIMO systems. Based on LabVIEW and universal software radio peripheral (USRP) software defined radio technology, this paper presents a hardware simulation platform of artificial noise-assisted MIMO communication systems. The experimental data obtained from this platform demonstrates that artificial noise can effectively enhance system security with a slight influence on the system reliability.

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ACP: Automatic channel pruning via clustering and swarm intelligence optimization for CNN

arXiv

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arXiv 2021年

作者： Chang, Jingfei Lu, Yang Xue, Ping Xu, Yiqun Wei, Zhen School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei University of Technology China

As the convolutional neural network (CNN) gets deeper and wider in recent years, the requirements for the amount of data and hardware resources have gradually increased. Meanwhile, CNN also reveals salient redundancy in several tasks. The existing magnitude-based pruning methods are efficient, but the performance of the compressed network is unpredictable. While the accuracy loss after pruning based on the structure sensitivity is relatively slight, the process is time-consuming and the algorithm complexity is notable. In this article, we propose a novel automatic channel pruning method (ACP). Specifically, we firstly perform layer-wise channel clustering via the similarity of the feature maps to perform preliminary pruning on the network. Then a population initialization method is introduced to transform the pruned structure into a candidate population. Finally, we conduct searching and optimizing iteratively based on the particle swarm optimization (PSO) to find the optimal compressed structure. The compact network is then retrained to mitigate the accuracy loss from pruning. Our method is evaluated against several state-of-the-art CNNs on three different classification datasets CIFAR-10/100 and ILSVRC-2012. On the ILSVRC-2012, when removing 64.36% parameters and 63.34% floating-point operations (FLOPs) of ResNet-50, the Top-1 and Top-5 accuracy drop are less than 0.9%. Moreover, we demonstrate that without harming overall performance it is possible to compress SSD by more than 50% on the target detection dataset PASCAL VOC. It further verifies that the proposed method can also be applied to other CNNs and application scenarios. © 2021, CC BY-NC-ND.

关键词： Convolution

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Modulation Signal Recognition Based on Feature Fusion and Residual CNN in Fractal Noise Background 6

Modulation Signal Recognition Based on Feature Fusion and Re...

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6th International Conference on Signal and Image Processing, ICSIP 2021

作者： Chen, Di Xiong, Gang Huang, Baigang Wang, Lijun No.36 Research Institute of Cetc Science and Technology On Communication Information Security Control Laboratory Jiaxing China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Navigation and Sensing Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9780738133737

In this paper, a modulated signal recognition method based on feature fusion and ResCNN (FF-ResCNN) under generalized fractal noise background is proposed. Firstly, in the feature extraction and fusion part, considering the robustness, anti-noise and interpretability of the features, the fusion feature vectors of generalized fractal spectrum, instantaneous feature and high-order cumulant feature are constructed. Secondly, in order to suppress the possible gradient and degradation problems, the ResCNN model is used to train and classify the fusion features when the CNN model is used to further collect the deep-level abstract features. Finally, feature extraction, feature fusion and classification experiments are carried out for nine modulated signals under two noise types (GWN and fractal noise). Experimental results show that in the case of generalized fractal noise, when SNR=0dB, the recognition accuracy of signal modulation type reaches 95.42%, which verifies the superiority of the proposed method. At the same time, the simulation analysis is carried out for different feature fusion schemes, which provides a valuable reference for feature combination optimization under different noise environment. © 2021 IEEE.

关键词： Feature extraction

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Privacy Protection of the Smart Grid System Based on Blockchain

Privacy Protection of the Smart Grid System Based on Blockch...

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2020 International Conference on Mechanical Automation and Computer engineering, MACE 2020

作者： Liu, Siyuan Zhang, Qiong Liu, Honggang School of Control and Computer Engineering North China Electric Power University Baoding China Guodian Nari Technology Co. Ltd. NanjingJiangsu Province China Information and Communication Branch of Chongqing Electric Power Company of State Power Grid Chongqing China

With the development of new technologies for smart grid construction, the smart grid is gradually developing. Smart grid construction is an important field of next generation information technology, and its value and significance have been widely recognized. Smart grid construction not only provides development opportunities, but also brings great challenges. Security and privacy have become serious problems in the development of smart grid. Based on this, this paper discusses the system structure of the block chain, and puts forward the security diagnosis scheme of the smart grid according to the existing problems in the smart grid. Based on the theoretical basis of smart grid big data technology, this paper combines the grid data encryption process with the privacy protection process under the premise of protecting privacy and security, and develops the latest network security protocol. Then use block chain distributed storage to reduce the connection protection process in privacy, and use multi-level encryption. The experimental results show that the technology can improve the rate of sensitive data hiding and has good anti-cracking ability, which provides a theoretical reference for the construction of enterprise power grid. © Published under licence by IOP Publishing Ltd.

关键词： Smart power grids

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Experimental Study on Ag Modified CeO2 as Catalyst for Soot Oxidation

SSRN

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SSRN 2022年

作者： Pu, Ping Fang, Jia Yang, Yi Chen, Chen Yi, Chengcheng Yan, Yan Meng, Zhongwei Pan, Suozhu Lin, Bin Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province School of Automobile and Transportation Xihua University Chengdu610039 China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan Xihua University Chengdu610039 China Sichuan Yilan Zhichuang Technology Co. Ltd Chengdu610213 China Xihua University China

Ag/CeO2 catalyst is synthesized by hydrothermal method, which has been study under the effects of different catalyst content, Ag load and ash through activity and characterization tests. Ag loading increases the catalytic activity of CeO2, and the peak combustion temperature of soot decreases by 74°C. The catalytic effect of Ag/CeO2 is affected by catalyst content, Ag load and ash content. The optimum weight ratio of soot/catalyst is 1/10, and 10wt% is the saturation value of Ag load at CeO2. Ash inhibits the promoting effect of Ag/CeO2 in the oxidation process of soot. This study provides a theoretical basis for the industrial application of Ag/CeO2 catalyst in gasoline engine, and also provides a research idea for the low temperature oxidation of soot. © 2022, The Authors. All rights reserved.

关键词： Cerium oxide

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Bridging the Domain Gap in Satellite Pose Estimation: a Self-Training Approach based on Geometrical Constraints

arXiv

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arXiv 2022年

作者： Wang, Zi Chen, Minglin Guo, Yulan Li, Zhang Yu, Qifeng The College of Aerospace Science and Engineering National University of Defense Technology Changsha410073 China Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation Changsha410073 China The School of Electronics and Communication Engineering Sun Yat-Sen University Shenzhen518000 China The College of Electronic Science and Technology National University of Defense Technology Changsha410073 China

Recently, unsupervised domain adaptation in satellite pose estimation has gained increasing attention, aiming at alleviating the annotation cost for training deep models. To this end, we propose a self-training framework based on the domain-agnostic geometrical constraints. Specifically, we train a neural network to predict the 2D keypoints of a satellite and then use PnP to estimate the pose. The poses of target samples are regarded as latent variables to formulate the task as a minimization problem. Furthermore, we leverage fine-grained segmentation to tackle the information loss issue caused by abstracting the satellite as sparse keypoints. Finally, we iteratively solve the minimization problem in two steps: pseudo-label generation and network training. Experimental results show that our method adapts well to the target domain. Moreover, our method won the 1st place on the sunlamp task of the second international Satellite Pose Estimation Competition. Copyright © 2022, The Authors. All rights reserved.

关键词： Satellites

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Optimal control of linear Gaussian quantum systems via quantum learning control

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Physical Review A 2024年第6期109卷 063508-063508页

作者： Yu-Hong Liu Yexiong Zeng Qing-Shou Tan Daoyi Dong Franco Nori Jie-Qiao Liao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Theoretical Quantum Physics Laboratory Cluster for Pioneering Research Quantum Computing Center Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics School of Engineering and Information Technology Department of Physics Institute of Interdisciplinary Studies

Efficiently controlling linear Gaussian quantum (LGQ) systems is a significant task in both the study of fundamental quantum theory and the development of modern quantum technology. Here, we propose a general quantum-learning-control method for optimally controlling LGQ systems based on the gradient-descent algorithm. Our approach flexibly designs the loss function for diverse tasks by utilizing first- and second-order moments that completely describe the quantum state of LGQ systems. We demonstrate both deep optomechanical cooling and large optomechanical entanglement using this approach. Our approach enables the fast and deep ground-state cooling of a mechanical resonator within a short time, surpassing the limitations of sideband cooling in the continuous-wave driven strong-coupling regime. Furthermore, optomechanical entanglement could be generated remarkably fast and surpass several times the corresponding steady-state entanglement, even when the thermal phonon occupation reaches one hundred. This work will not only broaden the application of quantum learning control, but also open an avenue for optimal control of LGQ systems.

关键词： Optomechanics Quantum optics

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