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5th international conference on Science and Technology, ICST 2019

作者： Pinasti, Arinda Widjajanti, Nurrohmat Pratama, Cecep Parseno, Parseno Lestari, Dwi Sunantyo, Aris Heliani, Leni Ulinnuha, Hilmiyati Universitas Gadjah Mada Geomatics Engineering Graduate Student Yogyakarta Indonesia Universitas Gadjah Mada Department of Geodetic Engineering Yogyakarta Indonesia

ISBN: (纸本)9781728123691

Special Region of Yogyakarta is one of the Indonesian regions with high seismic activity. Subduction zones and onland fault structures influence these activities. One of onland fault structures that affect this region is the Opak Fault. therefore, deformation monitoring is required. this study used five years of geodetic data that is acquired periodically. the deformation rate of Global Navigation Satellite System (GNSS) station around the Opak Fault ranging from 5.73 to 23.59 mm/yr and -30.16 to 118.61 mm/yr for horizontal and vertical components with respect to Sundaland Block, respectively. those deformation rates have been reduced by Sundaland Block effect but have not deliberate error and noise from the other effects. the estimated deformation rate is used to compute the preliminary deformation model. the strain rate analysis suggests that the western region of the fault is dominated by compression. Meanwhile, the eastern region of the fault is dominated by extension. © 2019 IEEE.

关键词： Strain rate

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A Generalized Denoising Method with an Optimized Loss Function for Automated Bird Sound recognition

A Generalized Denoising Method with an Optimized Loss Functi...

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international conference on Computers, Communications, and Systems (ICCCS)

作者： Huangqiang Fang Yulin He Wanyang Xu Yanyan Xu Dengfeng Ke Kaile Su School of Information Science and Technology Beijing Forestry University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Institute for Integrated and Intelligent Systems Griffith University Nathan Australia

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

In natural environments, bird sounds are often accompanied by background noise, so denoising becomes crucial to automated bird sound recognition. Recently, thanks to neural network embeddings, the deep clustering method has achieved better performances than traditional denoising methods, like filter-based methods, due to its ability to solve the problem when noise is in the same frequency range as bird sounds. In this paper, we propose a generalized denoising method based on deep clustering, which can process more complex recordings with less distortion. Also, we optimize the original affinity loss function to get a novel loss function to ensure the embedding vectors with the minimum distance belong to the same source, named Joint Center Loss (JCL), which can both increase the inter-class variance and decrease the intra-class variance of embeddings. Experiments are conducted on the gated convolutional neural network architecture and the bidirectional long short term memory architecture respectively with different loss functions. Given the signal-noise ratio being -3dB, the recognition accuracy increases relatively by 9.5% with the proposed denoising method in the best case, and the Relative Root Mean Square Error (RRMSE) increases relatively by 14.2% by using JCL, compared with the original affinity loss function AL.

关键词： Birds Noise reduction Logic gates Training Noise measurement Time-frequency analysis Matrix converters

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Recognisability of Triangular Pictures by Online Tessellation Automata

Recognisability of Triangular Pictures by Online Tessellatio...

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international conference on Advanced Computing and Communication Systems (ICACCS)

作者： S. Jebasingh Department of Mathematics Karunya Institute of Technology and Sciences Coimbatore

Researchers in image processing and pattern recognition proposed several formal models to generate and recognize two dimensional picture languages. In this paper local and recognizable triangular picture languages are considered and a new method to identify such pictures by two dimensional online tessellation automata is presented.

关键词： Automata Communication systems Computer science pattern recognition Image recognition Finite element analysis

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Atmosphere Sharing with TV Chat Agents for Increase of User’s Motivation for Conversation 1

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5th international conference on Human Aspects of IT for the Aged Population, ITAP 2019, held as part of the 21st international conference on Human-Computer Interaction, HCI international 2019

作者： Nishimura, Shogo Kanbara, Masayuki Hagita, Norihiro Nara Institute of Science and Technology 8916-5 Takayama IkomaNara Japan Advanced Telecommunications Research Institute International 2-2 Hikari-dai SeikaKyoto619-0237 Japan

ISBN: (数字)9783030220129

ISBN: (纸本)9783030220112

this paper proposes atmosphere sharing method with chatting agent during TV watching in order to improve user’s motivation for conversation with the agent. Dialogue agents like interactive robots which include physical and virtual robots are expected to be companion to talk with the elderly or social isolated people to monitor or support them. Although the technologies of interactive robots, which are speech recognition and synthesis or auto chatting engine etc., have been developed, the agents are not continuously utilized for daily-life yet. We have developed TV chat agent which have light conversation with user during TV watching. To increase user’s motivation of conversation with the agent, the agent gives interesting and funny talk by using SNS comments that are provided by users who are watching the TV program. this study attempts to share atmosphere, such as scene with exciting or of bursting into laughter, by using the TV chat agent. © 2019, Springer Nature Switzerland AG.

关键词： Human robot interaction

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Computer evidence analysis technology based on weighted frequent pattern growth algorithm 5th

Computer evidence analysis technology based on weighted freq...

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5th international conference on Artificial Intelligence and Security, ICAIS 2019

作者： Xue, Tianxiao Li, Qingbao Zhang, Ping Chen, Zhifeng Feng, Peijun Luo, Nan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783030242701

the current analysis of computer forensics is still dependent on the investigation personnel, leading to the problem of heavy workload and low efficiency. At the same time, computer evidences have the characteristics of complex structure and large amount of data, which is prone to the problem of association rules redundancy. In order to improve the efficiency of computer evidence analysis, this paper uses association analysis technology to analyze the user's behavior habits from the obtained computer system log, browser records, file operation traces and other information;combined with the idea of weighted features, the weight set was generated according to the difference of attribute importance, and the Frequent pattern Growth (FP-Growth) algorithm is improved. the experimental results show that the improved FP-Growth algorithm can effectively remove the redundant rules in evidence analysis, and is more practical for user behavior analysis. © Springer Nature Switzerland AG 2019.

关键词： Efficiency

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5th international conference on Artificial Intelligence and Security, ICAIS 2019

5th International Conference on Artificial Intelligence and ...

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5th international conference on Artificial Intelligence and Security, ICAIS 2019

ISBN: (纸本)9783030242732

the proceedings contain 229 papers. the special focus in this conference is on Artificial Intelligence and Security. the topics include: Detection of Microblog Overlapping Community Based on Multidimensional Information and Edge Distance Matrix;research on Settlement Deformation of Asphalt Pavement Structure in Soft Soil Zone Based on Pavement Settlement theory;legal Case Inspection: An Analogy-Based Approach to Judgment Evaluation;a Novel Noise Filter Based on Multiple Voting;fuzzy Control Method for Path Tracking System of Combine Harvester;research on Opinion Spam Detection by Time Series Anomaly Detection;application of Machine Learning Methods for Material Classification with Multi-energy X-Ray Transmission Images;Intra-class Classification of Architectural Styles Using Visualization of CNN;MSE-Net: Pedestrian Attribute recognition Using MLSC and SE-Blocks;NOAA-LSTM: A New Method of Dialect Identification;CBAM-GAN: Generative Adversarial Networks Based on Convolutional Block Attention Module;a Novel Distributed Knowledge Reasoning Model;Research on Detection Method of Abnormal Traffic in SDN;Research on Constructing Technology of Implicit Hierarchical Topic Network Based on FP-Growth;PPD-DL: Privacy-Preserving Decentralized Deep Learning;towards Edge Computing Based Distributed Data Analytics Framework in Smart Grids;android Malware Identification Based on Traffic Analysis;unsupervised Traditional Chinese Medicine Text Segmentation Combined with Domain Dictionary;research on the Efficiency and Application of Ship-Helicopter Cooperative Search;cellular Neural Network Based Contour Detection for Seismic Image;Evaluation Method of Teachers’ Teaching Ability Based on BP Neural Network;discovering New Sensitive Words Based on Sensitive Information Categorization;a Dynamic Event Region Tracking Approach Based on Node Calibration;a Tutorial of Graph Representation.

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A Review Study: recognition of Co-located pattern in Video Stream

A Review Study: Recognition of Co-located Pattern in Video S...

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international conference on Advanced Computing and Communication Systems (ICACCS)

作者： C.V. Jayaram Nagappa U. Bhajantri Asst prof Dept. of CS&E MYCEM Mysore Karnataka Prof and Head Dept. of CS&E Government Engineering College Chamrajnagar Karnataka

this review article provides a vast study on automated recognition of co-located patterns in video streaming. Traditionally numerous glitches such as face recognition, pattern or object recognition, scene understanding, co-located pattern recognition etc., have emerged through practices in pattern recognition domain. Our review study, purposes delivering an all-inclusive state-of-the-art review in the domain, and also discourses numerous encounters and trials in relation with its applications and its system. In this assessment, several applications are conversed in boundless aspect.

关键词： Data mining Spatial databases Streaming media Clustering algorithms Geographic information systems

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Jamming pattern recognition Using Spectrum Waterfall: A Deep Learning Method

Jamming Pattern Recognition Using Spectrum Waterfall: A Deep...

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IEEE international conference on Computer and Communications

作者： Yuan Cai Kai Shi Fei Song Yifan Xu Ximing Wang Heyu Luan Coege of Counicions Engineering Ary Engineering Universiy of PLAPLA 31121 TroosCoege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLA

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

It is very important for communication equipment to realize the recognition and prediction of jamming signals in a short time. In the communication environment with variable jamming mode and noise, it is very difficult to achieve rapidly jamming recognition. To solve this problem, this paper constructs a spectrum waterfall containing white gaussian noise signals, designs a jamming pattern recognition method based on deep learning, and realizes rapid recognition of jamming patterns and important parameters. Simulation results show that the training process of this method has considerable accuracy and the test process has high recognition rate.

关键词： Jamming Convolution Training Feature extraction Receivers pattern recognition Machine learning

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Multi-object tracking with pre-classified detection 5th

Multi-object tracking with pre-classified detection

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5th international conference on Robot Intelligence Technology and Applications, RiTA 2017

作者： Ren, Siqi Zhou, Yue He, Liming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783319784519

Tracking-by-detection is a popular tracking framework nowadays. the paradigm determines that detections will bring huge impact on the final tracking result. Based on the idea, to improve the tracking precision, we propose a novel algorithm which will first divide the detections into false ones, high uncertainty and low uncertainty ones. After that, we first penalize the false detection boundingboxes and then we construct low and high uncertainty tree for different types of detections. For low uncertainty detections, we construct their tracking trees once, for high uncertainty detections, we adopt an improved MHT to delay the data association decision till the end of the sliding windows and make the decision with all the information in the sliding window. Experiments demonstrate that our algorithm can achieved competitive results with state-of-the-art trackers on some challenging datasets such as MOTChallenge2016 [11]. © Springer international Publishing AG, part of Springer Nature 2019.

关键词： Object detection

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2021年 12964 LNCS卷 ix-x页

作者： Haq, Nandinee Johnson, Patricia Maier, Andreas Würfl, Tobias Yoo, Jaejun Sunnybrook Health Science Centre TorontoON Canada NYU Grossman School of Medicine New York CityNY United States Pattern Recognition Lab at FAU Erlangen Germany Siemens Healthineers Erlangen Germany Ulsan National Institute of Science and Technology Ulsan Korea Republic of

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