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检索条件"任意字段=8th International Conference on Computing and Pattern Recognition, ICCPR 2019"

共 394 条记录，以下是131-140 订阅

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Using Topic Specific Features for Argument Stance recognition 8

Using Topic Specific Features for Argument Stance Recognitio...

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8th international conference on Data Science, Technology and Applications (DATA)

作者： Eljasik-Swoboda, Tobias Engel, Felix Hemmje, Matthias Univ Hagen Fac Math & Comp Sci Hagen Germany FTK Ev Forschungsinst Telekommunikat & Kooperat Dortmund Germany

ISBN: (纸本)9789897583773

Argument detection and its representation through ontologies are important parts of today's attempt in automated recognition and processing of useful information in the vast amount of constantly produced data. However, due to the highly complex nature of an argument and its characteristics, its automated recognition is hard to implement. Given this overall challenge, as part of the objectives of the RecomRatio project, we are interested in the traceable, automated stance detection of arguments, to enable the construction of explainable pro/con argument ontologies. In our research, we design and evaluate an explainable machine learning based classifier, trained on two publicly available data sets. the evaluation results proved that explainable argument stance recognition is possible with up to .96 F1 when working within the same set of topics and .6 F1 when working with entirely different topics. this informed our hypothesis, that there are two sets of features in argument stance recognition: General features and topic specific features.

关键词： Argument Stance Detection Explainability Machine Learning Trainer-athlete pattern Ontology Creation Support Vector Machines Text Analytics Architectural Concepts

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Introducing the 'Unified Side Channel Attack - Model' (USCA-M) 8

Introducing the 'Unified Side Channel Attack - Model' (USCA-...

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8th international Symposium on Digital Forensics and Security, ISDFS 2020

作者： Johnson, Andrew Ward, Richard University of South Wales Faculty of Computing Engineering and Mathematics Pontypridd United Kingdom

ISBN: (纸本)9781728169392

this paper presents a 'Unified Side Channel Attack Model' (USCA-M). the USCA-M model is compiled by the research undertaken of side-channel attacks (SCAs) from published journal articles and conference papers between 2015-2019. the research found that SCAs can be categorised into three main areas: SCA types, SCA methods and SCA techniques. By using this categorisation as a foundation, the USCA-M was compiled. USCA-M has three main contributions to the research field:1.A unified model that can categorize present and future SCA vulnerabilities and exploit techniques found.2.A reference point for organisations to be able to identify and place a found SCA within a common or unified *** decomposition of SCA methods and techniques into identifiable components to assist in the defence of SCAs, such as code pattern recognition. © 2020 IEEE.

关键词： Side channel attack

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Modulation-mode assignment for SVD-assisted downlink multiuser MIMO systems

Modulation-mode assignment for SVD-assisted downlink multius...

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8th IASTED international conference on Signal Processing, pattern recognition, and Applications, SPPRA 2011

作者： Ahrens, Andreas Ali Abu-Rgheff, Mosa Hochschule Wismar University of Technology Business and Design Philipp-Müller-Straße 14 23966 Wismar Germany University of Plymouth School of Computing and Mathematics Plymouth Drake Circus Devon PL4 8AA United Kingdom

ISBN: (纸本)9780889868656

Multiuser multiple-input multiple-output (MIMO) down-link (DL) transmission schemes experience both multiuser interference as well as inter-antenna interference. Instead of treating all the users jointly as in zero-forcing (ZF) multiuser transmission techniques, the investigated singular value decomposition (SVD) assisted DL multiuser MIMO system takes the individual user's channel characteristics into account. this translates to a choice of modulation constellation and transmitter power and, in our proposed system, to a choice of number of activated user-specific MIMO layers. the performed joint optimization of the number of activated MIMO layers and the number of bits per symbol along with the appropriate allocation of the transmit power shows that not necessarily all user-specific MIMO layers has to be activated in both frequency selective and frequency non-selective MIMO channels in order to minimize the overall BER under the constraint of a given fixed data throughput.

关键词： Singular value decomposition

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Proceedings - 2012 8th international conference on computing Technology and Information Management, ICCM 2012

Proceedings - 2012 8th International Conference on Computing...

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2012 8th international conference on computing Technology and Information Management, ICCM 2012

ISBN: (纸本)9788988678671

the proceedings contain 164 papers. the topics discussed include: performance comparison of IEEE 802.1Q and IEEE 802.1 AVB in an Ethernet-based in-vehicle network;a fast seat assignment algorithm based-on bucket data structure;a two-phase iterative pre-copy strategy for live migration of virtual machines;an empirical evaluation and improvement of the item balancing algorithm in P2P systems;towards high performance and usability programming model for heterogeneous HPC platforms;saving streaming bandwidth via wireless sharing for a tree-based live streaming system on public-shared network;a novel pattern of distributed low-rate denial of service attack disrupts Internet routing;achieving maximum performance for matrix multiplication using set associative cache;a user context recognition method for ubiquitous computing systems;the influence factors to the enterprise microblogs - a research of the restaurant enterprise microblogs on ***;and common-sense reasoning in constructive discursive logic.

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A Potent Model to Recognize Bangla Sign Language Digits Using Convolutional Neural Network 8

A Potent Model to Recognize Bangla Sign Language Digits Usin...

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8th international conference on Advances in computing & Communications (ICACC)

作者： Islam, Md. Sanzidul Mousumi, Sadia Sultana Sharmin Rabby, A. K. M. Shahariar Azad Hossain, Sayed Akhter Abujar, Sheikh Daffodil Int Univ Dept Comp Sci & Engn Dhaka 1205 Bangladesh

Hearing impaired people have own language called Sign Language but it is difficult for understanding to general people. Sign language is the basic method of communication for deaf people during their everyday of life. Sign digits are also a major part of sign language. So machine translator is necessary to allow them to communicate with general people. For making their language understandable to general people, computer vision based solutions are well known nowadays. In this research work we aims at constructing a model in deep learning approach to recognize Bangla Sign Language (BdSL) digits. In this approach there used Convolutional Neural Network (CNN) to train particular signs with a respective training dataset (Eshara-Lipi) for acquiring our aim. the model trained and tested with respectively 860 training images and 215 (20%) test images of tent classes of digits. Finally, the training model gained about 95% accuracy at recognition of Bangla sign language digits. this model will contribute for moving one step forward to make BdSL machine translator. (C) 2018 the Authors. Published by Elsevier B.V.

关键词： BdSL Bangla Sign Language CNN Machine Learning Deep Learning NLP Computer Vision pattern recognition Sign Digits Sign Language

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Bayesian approach to the concept drift in the pattern recognition problems

Bayesian approach to the concept drift in the pattern recogn...

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8th international conference on Machine Learning and Data Mining in pattern recognition, MLDM 2012

作者： Turkov, Pavel Krasotkina, Olga Mottl, Vadim Tula State University 92 Lenina Ave. Tula 300600 Russia Computing Center Russian Academy of Science 40 Vavilov St. Moscow 119333 Russia

ISBN: (纸本)9783642315367

We can face with the pattern recognition problems where the influence of hidden context leads to more or less radical changes in the target concept. this paper proposes the mathematical and algorithmic framework for the concept drift in the pattern recognition problems. the probabilistic basis described in this paper is based on the Bayesian approach to the estimation of decision rule parameters. the pattern recognition procedure derived from this approach uses the general principle of the dynamic programming and has linear computational complexity in contrast to polynomial computational complexity in general kind of pattern recognition procedure. © 2012 Springer-Verlag.

关键词： Computational complexity

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Multi-Scale Channel Attention for Chinese Scene Text recognition 11

Multi-Scale Channel Attention for Chinese Scene Text Recogni...

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11th international conference on computing and pattern recognition, iccpr 2022

作者： Liao, Hai-Qing Du, Xia Wu, Yun Wang, Da-Han Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Scene text recognition have proven to be highly effective in solving various computer vision tasks. Recently, numerous recognition algorithms based on the encoder-decoder framework have been proposed for handling scene texts with perspective distortion and curve shape. Nevertheless, most of these methods only consider single-scale features while not taking multi-scale features into account. Meanwhile, the existing text recognition methods are mainly used for English texts, whereas ignoring Chinese texts' pivotal role. In this paper, we proposed an end-to-end method to integrate multi-scale features for Chinese scene text recognition (CSTR). Specifically, we adopted and customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture multi-scale features of the input image while simultaneously extending the receptive fields. Moreover, we added Squeeze-and-Excitation Networks (SE) to capture attentional features with global information to improve the performance of CSTR further. the experimental results of the Chinese scene text datasets demonstrate that the proposed method can efficiently mitigate the impacts of the loss of contextual information caused by the text scale varying and outperforms the state-of-the-art approaches. © 2022 ACM.

关键词： Character recognition

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Fuzzy min-max classifier based on new membership function for pattern classification: A conceptual solution 8

Fuzzy min-max classifier based on new membership function fo...

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8th IEEE international conference on Control System, computing and Engineering, ICCSCE 2018

作者： Alhroob, Essam Ghani, Ngahzaifa Ab Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Malaysia

ISBN: (纸本)9781538663240

the fuzzy min-max (FMM) neural network is one of the most powerful neural networks that combines neural network and fuzzy set theory into a common framework for tackling pattern classification problems. FMM neural network carries out learning processes that consist of hyperbox expansion, hyperbox overlap test and hyperbox contraction to execute pattern classification. Although these processes provide FMM with several outstanding features and make it a unique pattern classifier, the contraction process is considered a major limitation that affects the FMM learning process and hinders it from handling hyperbox overlapped boundaries appropriately. this drawback could affect membership decision making and cause the classifier to provide random decisions when test samples have the same fitness values with different hyperboxes from different classes (ambiguity issue). the performance of the classifier consequently declines. thus, this study aims to provide a conceptual solution called 'fuzzy min-max classifier based on new membership function' through a new method, 'Euclidean distance', in the test phase to handle the hyperbox overlapping boundaries of different classes. the conceptual solution has not been implemented and tested in a real scenario. Hence, the application of the conceptual solution to real scenarios is recommended in future studies to assess its performance. © 2018 IEEE.

关键词： Membership functions

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the Method of Probabilistic Nodes Combination in 2D Information Retrieval, pattern recognition and Biometric Modeling

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8th international conference on Image Processing and Communications (IP and C)

作者： Jakobczak, Dariusz Jacek Tech Univ Koszalin Dept Elect & Comp Sci Sniadeckich 2 PL-75453 Koszalin Poland

ISBN: (纸本)9783319238142;9783319238135

Proposed method, called Probabilistic Nodes Combination (PNC), is the method of 2D curve modeling and interpolation using the set of key points. Nodes are treated as characteristic points of unknown object for modeling and recognition. Identification of shapes or symbols need modeling and each model of the pattern is built by a choice of probability distribution function and nodes combination. PNC modeling via nodes combination and parameter. as probability distribution function enables curve parameterization and interpolation for each specific object or symbol. Two-dimensional curve is modeled and interpolated via nodes combination and different functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.

关键词： pattern recognition Shape modeling Curve interpolation PNC method Nodes combination Probabilistic modeling

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FOCA: A System for Classification, Digitalization and Information Retrieval of Trial Balance Documents 8

FOCA: A System for Classification, Digitalization and Inform...

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8th international conference on Data Science, Technology and Applications (DATA)

作者： Baydar, Gokce Aydugan Arslan, Secil Yapi Kredi Teknol R&D & Special Projects Dept Istanbul Turkey

ISBN: (纸本)9789897583773

Credit risk evaluation and sales target optimization are core businesses for financial institutions. Financial documents like t-balances, balance sheets, income statements are the most important inputs for both of these core businesses. T-balance is a semi-structured financial document which is constructed periodically by accountants and contains detailed accounting transactions. FOCA is an end to end system which first classifies financial documents in order to recognize t-balances, then digitalizes them into a tree-structured form and finally extracts valuable information such as bank names, human-company distinction, deposit type and liability term from free format text fields of t-balances. the information extracted is also enriched by matching human and company names who are in a relationship with existing customers of the bank from the customer database. pattern recognition, natural language processing, and information retrieval techniques are utilized for these capabilities. FOCA supports both decision/operational processes of corporate/commercial/SME sales and financial analysis departments in order to empower new customer engagement, cross-sell and up-sell to the existing customers and ease financial analysis operations by digitalizing t-balances.

关键词： pattern recognition Document Digitalization Information Retrieval Classification ElasticSearch

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