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Wearable Parkinson’s Disease Finger Tapping Quantitative Evaluation Algorithm Combined with Impedance Sensing

Wearable Parkinson’s Disease Finger Tapping Quantitative Ev...

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ACIS international conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Jhih-Syong Fong Ya-Hui Chuang Fu-Sheng Yu I-Chyn Wey San Fu Wang Graduate Institute of Electrical Engineering Chang Gung University Tainan Taiwan Artificial Intelligence Research Center Chang Gung University Taiwan Chang Gung Memorial Hospital Taiwan National Chin-Yi University of Technology Taiping Taichung Taiwan

ISBN: (纸本)9781665447324

This paper proposes an Artificial Intelligence (AI) identification algorithm that combined the human body resistance and capacitance sensing. The measured human body impedance data is analyzed by a simple four-arithmetic algorithm, and then four different AI algorithms are used to determine whether or not according to the characteristics of Parkinson’s Disease (PD) patients. The algorithm of this paper is based on the impedance data of normal people and PD patients through the calculation circuit proposed in this paper to analyze the difference in body resistance, the number of finger fits, finger kneading cycles, and finger kneading amplitude to accurately distinguish the fingers of PD patients Symptoms of tremor and stiffness. Through the feature analysis of four AI algorithms, it is judged that the accuracy rate of PD patients is higher than 90%.

关键词： Machine learning algorithms Wearable computers Fingers Capacitance Classification algorithms Sensors Impedance

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Deep ConvLSTM-Inception Network for Traffic Prediction in Smart Cities 22

Deep ConvLSTM-Inception Network for Traffic Prediction in Sm...

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22nd IEEE international conference on High Performance Computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Huang, Penggui Huang, Bi Zhao, Fan Zhang, Yanfeng Chen, Mingong Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9781728176499

Accurate and real-Time traffic prediction is essential for smart cities. However, due to the diversity of traffic point of interest in the city, the spatial correlation of urban traffic is extremely complicated. The use of ordinary spatial correlation feature extraction methods cannot cope with this complex spatial correlation. In addition, traffic flow changes also have temporal correlation. The traditional time series feature extraction method can't extract the temporal correlation of traffic data well. Therefore, we propose a ConvLSTM-Inception Network(CL-IncNet) model for traffic prediction. The CL-IncNet uses the Inception module to extract the multi-granularity spatial correlation and uses the Inception module's stack to extract the indirectly affected spatial correlation. In terms of extracting time-related features, we chose ConvLSTM structure. Compared with LSTM and GRU, this structure has fewer parameters and can guarantee the spatial relative relation of output information. The experimental results on the NYC-Taxi dataset show that our method outperforms four well-known baseline results. © 2020 IEEE.

关键词： Smart city

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Psychological and Pedagogical Problems of Beginning Lecturers and Postgraduate Students at engineering University 2nd

Psychological and Pedagogical Problems of Beginning Lecturer...

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Gulk, Elena B. Baranova, Tatyana A. Kruglirov, Victor N. Tabolina, Anastasia, V Kozlovskii, Pavel Peter Great St Petersburg Polytech Univ Dept Engn Pedag Psychol & Appl Linguist Inst Humanities St Petersburg Russia Peter Great St Petersburg Polytech Univ Sci Lab Strateg Dev Engn Markets Adv Mfg Technol Ctr Natl Technol Initiat St Petersburg Russia

ISBN: (纸本)9783030402747;9783030402730

The paper discusses the psychological and pedagogical problems faced by beginning teachers and postgraduate students in their pedagogical work at an engineering and technical university. The study shows that the most significant problems concern the variety of the roles of the respondents and their lack of psychological and pedagogical training. Postgraduate students and beginning teachers must be engineers, researchers and educators at the same time. The paper demonstrates the interrelation between the role positions of a teacher at a technical university. However, to implement these roles, a specialist has to have a diverse set of competences. Every specialist is more apt to one or another of these roles. Moreover, the role of an educator is not of top priority for postgraduate students. In the authors' opinion, this calls for correcting the way training is organized for future teachers of an engineering and technical university with due consideration of personal priorities in their professional work and the needs of universities for replenishing their pedagogical cadres with specialists educated in accordance with the necessities of the time.

关键词： Teacher-engineer Postgraduate student Beginning teacher engineering pedagogy Psychological and pedagogical problems

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Deep learning-Based Emotion Recognition from Real-Time Videos

Deep Learning-Based Emotion Recognition from Real-Time Video...

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Human-Computer Interaction Thematic Area conference (HCI) Held as Part of the 22nd international conference on Human-Computer Interaction (HCII)

作者： Zhou, Wenbin Cheng, Justin Lei, Xingyu Benes, Bedrich Adamo, Nicoletta Purdue Univ W Lafayette IN 47906 USA

ISBN: (纸本)9783030490614;9783030490621

We introduce a novel framework for emotional state detection from facial expression targeted to learning environments. Our framework is based on a convolutional deep neural network that classifies people's emotions that are captured through a web-cam. For our classification outcome we adopt Russel's model of core affect in which any particular emotion can be placed in one of four quadrants: pleasant-active, pleasant-inactive, unpleasant-active, and unpleasant-inactive. We gathered data from various datasets that were normalized and used to train the deep learning model. We use the fully-connected layers of the VGG_S network which was trained on human facial expressions that were manually labeled. We have tested our application by splitting the data into 80:20 and re-training the model. The overall test accuracy of all detected emotions was 66%. We have a working application that is capable of reporting the user emotional state at about five frames per second on a standard laptop computer with a web-cam. The emotional state detector will be integrated into an affective pedagogical agent system where it will serve as a feedback to an intelligent animated educational tutor.

关键词： Facial expression recognition Deep learning Education

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Attention-guided Multi-view Stereo Network for Depth Estimation 22

Attention-guided Multi-view Stereo Network for Depth Estimat...

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作者： Sun, Penghui Wu, Suping Lin, Kui Ningxia University School of Information Engineering Yinchuan750021 China

ISBN: (纸本)9781728176499

The purpose of the Multi-View Stereo is to restore the target 3D geometric model from multi-perspective images. There are several problems with the existing approaches based on deep learning, such as missing the detailed information in the predicted depth map, the low surface accuracy, and the incomplete reconstructed 3D point cloud model. In order to overcome these problems, we propose the Attention-guided Multiview Stereo Network For 3D Depth Estimation(AG-MVSNet). We combine the camera geometry with the deep neural network. And we adopt the coarse-To-fine deep learning framework to restore the target 3D geometry model. High-quality detailed feature information has an important influence on multi-view 3D reconstruction, and reference images in the natural environment contain detailed feature information which is needed in the reconstruction process. Therefore, we use the detailed feature information from different scales of reference images to restore the lost details of the high-level features. The quantitative and qualitative experimental results show that the proposed algorithm is more complete than the common multi-view 3D reconstruction algorithms. © 2020 IEEE.

关键词： Deep neural networks

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Towards Aggregated Features: A Novel Proxy Detection Method Using NetFlow data 22

Towards Aggregated Features: A Novel Proxy Detection Method ...

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作者： Fu, Peipei Yang, Qingya Liu, Chang Li, Zhenzhen Gou, Gaopeng Xiong, Gang Guo, Li Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728176499

Proxies can provide privacy and anonymity protection for users, but can also be exploited by attackers to hide their malicious behaviors. In order to strengthen the monitoring and management of network, proxy detection has become an urgent and challenging task. Although a great deal of efforts has been made for proxy detection, existing methods mostly rely on the packet-level features of a single flow, such as packet inter-Arrival time and payload size. In addition, handling the raw traffic throughout the communication process may lead to user privacy leakage to a certain extent. Considering the use of multi-flow statistics and reducing the invasion of user privacy, in this paper, we propose a machine learning based approach for proxy detection with NetFlow data, which only contains session-level statistical information. We extract features through NetFlow data aggregation to build machine learning model and verify the performances on different combinations of features to get the optimal feature sets. Furthermore, the importance of appropriate time windows and minimum flow numbers of NetFlow data aggregation are demonstrated by comprehensive experiments. Based on the real-world datasets, our detection method can distinguish proxy and normal traffic accurately, and achieve about 96% True Positive Rate(TPR) in our experiments with random forest classifier. © 2020 IEEE.

关键词： Decision trees

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intelligent recognition of insulator video stream based on embedded edge computing

Intelligent recognition of insulator video stream based on e...

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22nd international Symposium on High Voltage engineering (ISH 2021)

作者： Y Sun Y. Zhang S. Chen Y. Chen K. Sun State Grid Shandong Electric Power Research Institute Jinan People's Republic of China Department of Electrical Engine ering North China Electric Power University Beijing People's Republic of China

Traditional intelligent recognition algorithm models of insulators are large and mostly completed by back-end servers, with large data transmission volume, high power consumption and poor real-time performance at the front and rear ends. In this paper, a miniature SSD convolutional neural network model is designed to enable it to be deployed in embedded edge computing devices and to recognize video images of insulators. Firstly, the low-latitude Core convolution group subnetwork is used to replace the high-latitude VGG network to extract the shallow features of the insulator, and then the auxiliary feature extraction subnetwork is used to predict the target, so as to reduce the number of model parameters. The model weight files generated by the training were transplanted to the edge computing platform of Jetson nano equipped with vision sensor. OpenCV was used to extract frames from the video images captured by vision sensor every 1 ms and real-time recognition performance was compared under different video resolutions. Experimental results show that the scheme reduces data transmission and realizes low delay recognition. The average recognition frame rate of insulator video images is 16.8 FPS and 19.7 FPS respectively at 1280*720 and 640*480 resolutions. The recognition process is relatively smooth and the recognition accuracy can reach 91.53%. The energy consumption of the whole identification process is less than 5W, which is an order of magnitude lower than that of traditional servers.

关键词： computer vision distributed processing feature extraction image recognition image resolution image sensors insulators power engineering computing power grids video signal processing video streaming convolutional neural nets

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Dynamic Transit Flow Graph Prediction in Spatial-Temporal Network 22nd

Dynamic Transit Flow Graph Prediction in Spatial-Temporal Ne...

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22nd international conference on Web Information Systems engineering (WISE)

作者： Jiang, Liying Lai, Yongxuan Chen, Quan Zeng, Wenhua Yang, Fan Yi, Fan Liao, Qisheng Xiamen Univ Sch Informat Shenzhen Res Inst Shenzhen Peoples R China Xiamen Univ Malaysia Dept Comp Sci Sepang Malaysia Xiamen Univ Dept Automat Xiamen Peoples R China Qiannan Normal Univ Nationalities Sch Math & Stat Key Lab Complex Syst & Intelligent Comp Dunyun Peoples R China New York Univ New York NY USA

ISBN: (纸本)9783030908881;9783030908874

Traffic flow prediction is of great importance for traffic management. However, most existing researches only focus on region flow or road segment flow (vertex value) prediction, and the transit flow (edge weight) prediction is largely untouched. Compared to region flow and road segment flow prediction, transit flow prediction is more challenging in that 1) the transit flow between pairs of regions has complex spatial-temporal dependencies, and 2) it has larger changes over time due to the large number of region pairs. To address these issues, in this paper we define the transit flow as edges in directed graphs and formulate the transit flow prediction problem as a dynamic weighted link prediction problem. We propose a deep learning based method called Spatial-Temporal Network (STN) to make an accurate prediction of the transit flow. The STN model combines graph convolutional network (GCN) and long short-term memory (LSTM) to capture the dynamic spatial-temporal correlations. To capture the static topological structure, the neighborhood relation graph is adopted as an auxiliary graph to improve the prediction accuracy, and a two-stage-skip strategy is adopted to allow edge features reused which makes the STN focus more on the edge values compared to simple GCN modeling. We conduct the proposed STN model and verify its effectiveness in transit flow prediction on two real-world taxi datasets. Experiments demonstrate that our model reduces the prediction RMSE error by approximately 15.88%-52.48% on real-world datasets compared to state-of-the-art methods.

关键词： Transit flow prediction Spatio-temporal network LSTM GCN

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A review of deep learning models for traffic flow prediction in Autonomous Vehicles 2

A review of deep learning models for traffic flow prediction...

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2nd IEEE international conference on Advances in Computing, Communication Control and Networking, ICACCCN 2020

作者： Kumar, Avinash Saha, Priya Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9781728183374

More and more rapid development has been seen in the last decade in self-driving car technologies, mostly developments in the field of machine learning and deep learning. The goal of the paper is to review the latest state of the art in the area of automated driving by using machine learning technologies. For autonomous vehicle usage the estimation of traffic flows is necessary and they agree to make changes about their relevant artefacts (e.g., turn left or correct, travel straight, shift direction, stop or speed). Work on autonomous vehicles has been seen from the current paper on machine learning methods. Moreover, the non-linear dynamic relationship between spatial and temporal data obtained from the surroundings at the previously described adaptive decision-making periods by vehicles does not extend explicitly to current machine learning models in this context. Throughout this paper, we discussed the learning models for autonomous vehicle traffic flux prediction throughout order to equate such models with their applicability in contemporary intelligent transport systems. In comparison, the paper further addresses problems and possible recommendations for science. © 2020 IEEE.

关键词： Autonomous vehicles

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Increasing College Student Engagement with Digital Media and a Dedicated Space: An Exploratory Approach 2nd

Increasing College Student Engagement with Digital Media and...

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作者： Perez-Lozano, Delia Rocha-Lona, Luis Tecnol Monterrey Monterrey Mexico Inst Politecn Nacl ESCA Santo Tomas Mexico City DF Mexico

ISBN: (纸本)9783030402747;9783030402730

Nowadays, college students have spent most-if not all-of their lives seeing the Internet as something natural. They are immersed in a digital environment and can't conceive of the world without it. But there is a huge difference between making use of these digital technologies in a personal manner and using them in the context of a business. The purpose of this study was to analyse if giving students a dedicated space (laboratory) equipped with appropriate digital media has an impact on their engagement, considering engagement in its multidimensional forms that include behavioural, emotional, and cognitive aspects. A research group of 21 students was used, and participants were assigned to one of six teams that each worked with a different local NGO. To create the appropriate environment, an improvised lab was created in a 13 x 16-foot office using different elements to make the students feel comfortable. After assessment using qualitative and quantitative methods, the results indicated greater engagement in all three components that was sustained for the entire semester. Behavioural engagement involved commitment and dedication to activities;emotional engagement involved energy levels and joy felt in activities;and cognitive engagement encompassed concentration and weekday work hours. the results also showed that engagement in every dimension was greater in this course than in a traditional one. the educational environment was measured as a mixture of physical environment, emotional climate, and intellectual climate, with the last element considered the most important. This research shows that giving college students working with a challenging subject a dedicated space to work in has a strong impact on engagement levels.

关键词： Engagement Digital media Social media learning experience learning space Laboratory Educational innovation Higher education

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