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19th Chinese National Conference on Computational Linguistic, CCL 2020

作者： Sha, Jiu Zhou, Luqin Feng, Chong Li, Hongzheng Zhang, Tianfu Hui, Hui Beijing Engineering Research Center of High Volume Lange Information Processing and Cloud Compution Applications School of Computer Science and Technology Beijing Institute of Technology China School of Information and Electronics Beijing Institute of Technology China Diebu County Junior High School Gansu Province China No. 5 Zhongguancun South Street Haidian District Beijing China No. 38 Xingdie East Street Diebu County Gansu Province China

To date, the Tibetan-Chinese (Ti-Zh) Machine Translation in the judicial domain confronts a data-sparse severe problem. In this work, we tackle the problem from two aspects: 1) judicial Tibetan needs more rigorous logical expression and professional terminology vocabulary than the public domain. However, there hardly exists the high-quality Ti-Zh corpus in the judicial domain, which contains professional terminology and syntactic structure. 2) It is challenging to construct a Ti-Zh parallel corpus due to the unique lexical expression and specific syntactic structure. To this end, we propose a lightweight Ti-Zh parallel corpus construction method for the judicial domain. First, we construct a medium-scale Tibetan-Chinese terminology glossary of the judicial domain to be our prior knowledge, which can avoid the logical expression and domain terminology missing problems caused by the out-of-domain phenomenon. Secondly, we collect the instance data, such as judgment documents, from the official websites of Chinese courts in various places. To avoid losing the Tibetan lexical expressions and syntactic structures, we firstly search for Tibetan case data, followed by Chinese. Based on the above principles, we build a high-quality Tibetan-Chinese parallel corpus, which consists of the following methods: crawling corpus, document segmentation alignment detection, sentence boundary recognition, automatic corpus cleaning. Lastly, we construct a f 60,000 Ti-Zh parallel corpus of the judicial domain, and we evaluate the quality and robustness of the constructed corpus by performing a variety of translation models and cross-validation experiments. Besides, this corpus will be an open-source to provide to other researchers for related research. © 2020 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Syntactics

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Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure

arXiv

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arXiv 2020年

作者： Huerta, E.A. Khan, Asad Davis, Edward Bushell, Colleen Gropp, William D. Katz, Daniel S. Kindratenko, Volodymyr Koric, Seid Kramer, William T.C. McGinty, Brendan McHenry, Kenton Saxton, Aaron National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States University of Queensland St LuciaQLD4072 Australia Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

—Significant investments to upgrade or construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. The remarkable success of Artificial Intelligence (AI) algorithms to turn big-data challenges in industry and technology into transformational digital solutions that drive a multi-billion dollar industry, which play an ever increasing role shaping human social patterns, has promoted AI as the most sought after signal processing tool in big-data research. As AI continues to evolve into a computing tool endowed with statistical and mathematical rigor, and which encodes domain expertise to inform and inspire AI architectures and optimization algorithms, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight and to produce robust, reliable, trustworthy, and computationally efficient AI solutions. In this white paper, we present a summary of recent developments in this field, and discuss avenues to accelerate and streamline the use of HPC platforms to design accelerated AI *** Codes 68T35, 68M14, 68N15, 68N30 Copyright © 2020, The Authors. All rights reserved.

关键词： Artificial intelligence

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Unified quality assessment of in-the-wild videos with mixed datasets training

arXiv

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arXiv 2020年

作者： Li, Dingquan Jiang, Tingting Jiang, Ming National Engineering Laboratory for Video Technology Peking University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China Department of Computer Science Peking University Beijing China Laboratory of Mathematics and Its Applications School of Mathematical Sciences Peking University Beijing China Beijing International Center for Mathematical Research Peking University Beijing China Advanced Innovation Center for Future Visual Entertainment Beijing Film Academy Beijing China

Video quality assessment (VQA) is an important problem in computer vision. The videos in computer vision applications are usually captured in the wild. We focus on automatically assessing the quality of in-the-wild videos, which is a challenging problem due to the absence of reference videos, the complexity of distortions, and the diversity of video contents. Moreover, the video contents and distortions among existing datasets are quite different, which leads to poor performance of data-driven methods in the cross-dataset evaluation setting. To improve the performance of quality assessment models, we borrow intuitions from human perception, specifically, content dependency and temporal-memory effects of human visual system. To face the cross-dataset evaluation challenge, we explore a mixed datasets training strategy for training a single VQA model with multiple datasets. The proposed unified framework explicitly includes three stages: relative quality assessor, nonlinear mapping, and dataset-specific perceptual scale alignment, to jointly predict relative quality, perceptual quality, and subjective quality. Experiments are conducted on four publicly available datasets for VQA in the wild, i.e., LIVE-VQC, LIVE-Qualcomm, KoNViD-1k, and CVD2014. The experimental results verify the effectiveness of the mixed datasets training strategy and prove the superior performance of the unified model in comparison with the state-of-the-art models. For reproducible research, we make the PyTorch implementation of our method available at https://***/lidq92/MDTVSFA. Copyright © 2020, The Authors. All rights reserved.

关键词： Video recording

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Toeplitz Determinant whose Its Entries are the Coefficients for Class of Non-Bazilevi´c Functions

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Journal of Physics: Conference Series 2020年第1期1660卷

作者： Saba N. Al-Khafaji Ali Al-Fayadh Ahmed Hadi Hussain Sameer Annon Abbas Department of Mathematics Faculty of Computer Science and Mathematics University of Kufa Department of Mathematics and Computer Applications Al-Nahrain University-Iraq University of Babylon College of Engineering Al-musayab Department of Energy Engineering Iraqi Ministry of Education The Directorate of Education of the Governorate Babylon

The famous Toeplitz matrix is a matrix in which each descending diagonals form left to right is constant, this mean . Mathematician, engineers, and physicists are interested into this matrix for their computational properties and appearances in various areas: C*-dynamical systems [1], dynamical systems [6], operator algebra [2], Pseudospectrum and signal processing [10]. The object of this research is to define a new class Non-Bazilevi´c functions Nδ in unit disk ↁ = {z ∈ : |z| < 1} related to exponential function. As well as, we obtained coefficient estimates and an upper bound for the second and third determinant of the Toeplitz matrix such that the entries these matrix are belong to this class.

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Personal Digital Life Coach for Physical Therapy

Personal Digital Life Coach for Physical Therapy

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IEEE International Conference on Big Data

作者： Goce Popovski Vasco Ponciano Gonçalo Marques Ivan Miguel Pires Eftim Zdravevski Nuno M. Garcia Faculty of Electrical Engineering and Information Technologies Ss Cyril and Methodius University Skopje North Macedonia R&D Unit in Digital Services Applications and Content Polytechnic Institute of Castelo Branco Castelo Branco Portugal Altranportugal Lisbon Portugal Instituto de Telecomunicações Universidade da Beira Interior Covilhã Portugal UICISA:E Research Centre School of Health Polytechnic Institute of Viseu Viseu Portugal Polytechnic Institute of Viseu Viseu Portugal Faculty of Computer Science and Engineering Ss Cyril and Methodius University Skopje North Macedonia

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

The functional tests are essential to test the functionality of different types of people, and specialty for older adults. The primary purpose of this paper is to create a method for the automatic measurement of the results of the different functional tests. These are the Heel-rise Test, Functional Reach Test, Timed Up and Go Test, Ten Meter Walk Test, Eight hop test, Up-down hop test, Side hop test, Single hop test, Chair Stand Test, Arm Curl Test, and Chair Sit and Reach test. The use of sensors may increase the accuracy of the measurements of these tests. These tests may identify several diseases, and it will be subject to further research in the future.

关键词： Meters Conferences Medical treatment Big Data Sensors Diseases

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Context-Based Knowledge Discovery and Querying for Social Media Data

Context-Based Knowledge Discovery and Querying for Social Me...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Jedsada Phengsuwan Nipun Balan Thekkummal Teja Shah Philip James Dhavalkumar Thakker Rui Sun Divya Pullarkatt T. Hemalatha Maneesha Vinodini Ramesh Rajiv Ranjan School of Computing Newcastle University Newcastle Upon Tyne UK School of Engineering Newcastle University Newcastle Upon Tyne UK School of Electrical Engineering and Computer Science University of Bradford Bradford UK Amrita Center for Wireless Networks Applications (AmritaWNA) Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

Modern Early Warning Systems (EWS) rely on scientific methods to analyse a variety of Earth Observation (EO) and ancillary data provided by multiple and heterogeneous data sources for the prediction and monitoring of hazard events. Furthermore, through social media, the general public can also contribute to the monitoring by reporting warning signs related to hazardous events. However, the warning signs reported by people require additional processing to verify the possibility of the occurrence of hazards. Such processing requires potential data sources to be discovered and accessed. However, the complexity and high variety of these data sources makes this particularly challenging. Moreover, sophisticated domain knowledge of natural hazards and risk management are also required to enable dynamic and timely decision making about serious hazards. In this paper we propose a data integration and analytics system which allows social media users to contribute to hazard monitoring and supports decision making for its prediction. We prototype the system using landslides as an example hazard. Essentially, the system consists of background knowledge about landslides as well as information about data sources to facilitate the process of data integration and analysis. The system also consists of an interactive agent that allows social media users to report their observations. Using the knowledge modelled within the system, the agent can raise an alert about a potential occurrence of landslides and perform new processes using the data sources suggested by the knowledge base to verify the event.

关键词： Ontologies Hazards Terrain factors Social networking (online) Sensors Monitoring Knowledge based systems

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Task-oriented word embedding for text classification 27

Task-oriented word embedding for text classification

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27th International Conference on Computational Linguistics, COLING 2018

作者： Liu, Qian Huang, Heyan Gao, Yang Wei, Xiaochi Tian, Yuxin Liu, Luyang Department of Computer Science Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Centre for Artificial Intelligence University of Technology Sydney Australia

ISBN: (纸本)9781948087506

Distributed word representation plays a pivotal role in various natural language processing tasks. In spite of its success, most existing methods only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features. The rational word embeddings should have the ability to capture both the semantic features and task-specific features of words. In this paper, we propose a task-oriented word embedding method and apply it to the text classification task. With the function-aware component, our method regularizes the distribution of words to enable the embedding space to have a clear classification boundary. We evaluate our method using five text classification datasets. The experiment results show that our method significantly outperforms the state-of-the-art methods. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Embeddings

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Steganography in Grey Scale Animated GIF using Hash based Pixel Value Differencing

Steganography in Grey Scale Animated GIF using Hash based Pi...

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IEEE International Conference on Research in Computational Intelligence and Communication Networks

作者： Ratan Kumar Basak Kousik Dasgupta Paramartha Dutta Department of Computer Applications University of Engineering & Management Department of Computer Science & Engineering Kalyani Government Engineering College Department of Computer & System Sciences Visva-Bharati University

ISBN: (纸本)9781538676394;9781538676387

In company with immense popularity of social network, modern business houses are using it extensively for branding and advertising of their products. One of the preferred media for the advertisement is the animated Graphics Interchanged Format (GIF). The advantage of using GIF is adding emotions in the advertisement which makes a direct impact on the consumers. A novel steganography scheme has been put forwarded here on GIF format for secure message passing. Pixel-Value-Differencing (PVD) has been one of the popular method for steganography implemented mostly on images and videos. In this work, a modified PVD based steganographic blueprint has been brought in where an animated Graphics Interchanged Format (GIF) file is considered as the cover media. Here, an extra hash function has been employed which estimates the number of bits, in contrast to the original PVD, to be concealed in a pair of pixel. The proposed improvement affords to embed an extensive volume of concealed information by maintaining the imperceptibility of human eye. Moreover, the proposed method guarantees more security compared to the original PVD method.

关键词： Steganography GIF image PVD Hash function

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Deep learning enabled strain mapping of single-atom defects in 2D transition metal dichalcogenides with sub-picometer precision

arXiv

引用

arXiv 2020年

作者： Lee, Chia-Hao Khan, Abid Luo, Di Santos, Tatiane P. Shi, Chuqiao Janicek, Blanka E. Kang, Sangmin Zhu, Wenjuan Sobh, Nahil A. Schleife, André Clark, Bryan K. Huang, Pinshane Y. Department of Materials Science and Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign UrbanaIL61801 United States Materials Research Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States

2D materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so-far limited electron microscopy methods to probe these atomic-scale strain fields. Here, we demonstrate an approach to probe single-atom defects with sub-picometer precision in a monolayer 2D transition metal dichalcogenide, WSe2-2xTe2x. We utilize deep learning to mine large datasets of aberration-corrected scanning transmission electron microscopy images to locate and classify point defects. By combining hundreds of images of nominally identical defects, we generate high signal-to-noise class-averages which allow us to measure 2D atomic coordinates with up to 0.3 pm precision. Our methods reveal that Se vacancies introduce complex, oscillating strain fields in the WSe2-2xTe2xlattice which cannot be explained by continuum elastic theory. These results indicate the potential impact of computer vision for the development of high-precision electron microscopy methods for beam-sensitive materials. Copyright © 2020, The Authors. All rights reserved.

关键词： Transition metals

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Negative correlation incremental integration classification method for underwater target recognition

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International Journal of Performability engineering 2018年第5期14卷 1040-1049页

作者： He, Ming Wang, Nianbin Wang, Hongbin Chu, Ci Zhong, Songyan College of Computer Science and Technology Harbin Engineering University Harbin150001 China College of Computer and Information Engineering Heilongjiang University of Science and Technology Harbin150022 China Beijing Institute of Computer Technology and Applications Beijing100854 China

In this paper, an incremental learning algorithm based on negative correlation learning (NCL) is used as an identification classifier for underwater targets. Based on Selective negative incremental learning SNCL (Selective NCL) algorithm in the process of training, there are numbers of hidden layer nodes that are difficult to determine training time. Problems such as over fitting analysis arise. The algorithm combined with Bagging makes the difference between individual network further increase, and ensures the generalization performance of the whole. On the basis of this method, the use of the selective integration method based on clustering and a new proposed algorithm called SANCLBag, combined with the convolution of underwater target recognition neural network shows that the proposed integration approach can make the difference between individual network in the classification process further increase, and ensure the whole generalization performance. The model has higher identification accuracy, and can effectively solve the problem of incremental learning. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Learning algorithms

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