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检索条件"任意字段=Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition"

共 788 条记录，以下是351-360 订阅

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Joint Optimization of Server Placement and Content Caching in Mobile Edge computing Networks 2019

Joint Optimization of Server Placement and Content Caching i...

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proceedings of the 2019 8th international conference on Networks, Communication and computing

作者： Zhen Liu Jiawei Zhang Jingyan Wu Beijing Univ. of Posts and Telecommunications (BUPT) Beijing China Henan University of Science and Technology Luoyang China

ISBN: (纸本)9781450377027

To address the severe challenge from the rapid growth of number of downloads of contents via mobile network, it is imperative to develop efficient content caching scheme in mobile edge computing (MEC) networks, which can alleviate the heavy burden on backhaul links and reduce the delay for content delivery. However, the caching performance is highly related to the MEC server placement. In this paper, we jointly optimize the server placement problem and content caching problem in MEC networks. We propose a heuristic algorithm based on Kuhn-Munkres (KM) algorithm and greedy algorithm to minimize the average delay and average bandwidth resource usage. the simulation shows that our proposed algorithm can reduce delay and bandwidth, compared with other schemes.

关键词： Content caching Delay Bandwidth Mobile edge computing Server placement

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"She offered no argument": Constrained probabilistic modeling for mnemonic device generation 10

"She offered no argument": Constrained probabilistic modelin...

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10th international conference on Computational Creativity, ICCC 2019

作者： Bodily, Paul M. Glines, Porter Biggs, Brandon Department of Computer Science Idaho State University 921 S. 8th Ave PocatelloID83209 United States

ISBN: (纸本)9789895416011

A common aspect to creativity as described by creative theorists is the juxtaposition and balance of two opposing qualities, namely novelty and typicality. Practical models of computational creativity are needed that effectively leverage the contributions of each of these qualities in a synchronous manner. We discuss the effectiveness of constrained probabilistic models in representing this duality in generative models of creativity. We illustrate constrained Markov models as an example of a constrained probabilistic model and demonstrate its application to computational creativity in the elaboration of a system called NhMMonic for generating mnemonic devices. We demonstrate the effectiveness of the system1 using a qualitative survey. Our findings suggest that the constrained Markov model is particularly effective at generating mnemonics that exhibit novelty and typicality in grammatical and semantic flow with the overall result of more effective mnemonics for the purpose of memorization. Source code as well as our mnemonic device generator are both freely accessible online. © ICCC 2019.

关键词： Semantics

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Design and Evaluation of Reinforcement Learning Based AI Agent: A Case Study in Gaming 8th

Design and Evaluation of Reinforcement Learning Based AI Age...

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8th international conference on Soft computing and pattern recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Jayashree, P. Ramakrishnan, K. Anna Univ Dept Comp Technol Madras Inst Technol Madras 600044 Tamil Nadu India

ISBN: (纸本)9783319606187;9783319606170

Soft computing is extensively used in the field of computer games to create AI agents for computers. A case study of reinforcement learning is presented, by designing an AI agent for chopsticks game, with a probabilistic algorithm devised to make use of past game experience as its only tool to guide itself to victory. It has been experimentally verified that the AI agent's performance increases with learning and nears saturation beyond a point of learning. Constant order space and time complexity is achieved with proper design of knowledge base.

关键词： AI agent Chopsticks Reinforcement learning Probabilistic model Soft computing

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A Cloud Based Environment for Big Data Analytics in Healthcare 8th

A Cloud Based Environment for Big Data Analytics in Healthca...

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8th international conference on Soft computing and pattern recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Chauhan, Ritu Jangade, Rajesh Mudunuru, Vimal K. Amity Univ Noida Uttar Pradesh India

ISBN: (纸本)9783319606187;9783319606170

A large amount of modern healthcare data is generated through imaging, Electronic Health Report (EHR), sensor based technology and other various healthcare processes. An elaborative perspective in technological advancement has enabled practitioners to answer questions for governance and future decision making. However, very few tools exist to critically analyze such big data for future knowledge discovery. We can further say that cloud computing technology can be a benchmark to substantiate big data which may lead to discover of hidden patterns and trends to enhance knowledge for progression of disease. this paper approached various aspects of cloud based services to enable big data analytic in healthcare data management system.

关键词： Cloud computing Big data Analytics as service Healthcare

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5th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2018

5th International Conference on Emerging Research in Computi...

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5th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2018

ISBN: (纸本)9789811359521

the proceedings contain 56 papers. the special focus in this conference is on Emerging Research in computing, Information, Communication and Applications. the topics include: IoT-Enabled Medicine Bottle;revamp Perception of Bitcoin Using Cognizant Merkle;A Novel Algorithm for DNA Sequence Compression;digiPen: An Intelligent Pen Using Accelerometer for Character recognition;Design of FPGA-Based Radar and Beam Controller;effect of Lattice Topologies and Distance Measurements in Self-Organizing Map for Better Classification;evaluation and Classification of Road Accidents Using Machine Learning Techniques;Multi-language Handwritten recognition in DWT Accuracy Analysis;a Novel H-∞ Filter Based Indicator for Health Monitoring of Components in a Smart Grid;developing Ontology for Smart Irrigation of Vineyards;a Survey on Intelligent Transportation System Using Internet of things;CRUST: A C/C++ to Rust Transpiler Using a “Nano-parser Methodology” to Avoid C/C++ Safety Issues in Legacy Code;species Environmental Niche Distribution Modeling for Panthera Tigris Tigris ‘Royal Bengal Tiger’ Using Machine Learning;organizational Digital Footprint for Traceability, Provenance Approach;bidirectional Long Short-Term Memory for Automatic English to Kannada Back-Transliteration;a Dominant Point-Based Algorithm for Finding Multiple Longest Common Subsequences in Comparative Genomics;fast and Accurate Fingerprint recognition in Principal Component Subspace;smart Meter Analysis Using Big Data Techniques;movie Recommendation System;a Semiautomated Question Paper Builder Using Long Short-Term Memory Neural Networks;an Intensive Review of Data Replication Algorithms for Cloud Systems;time-Critical Transmission Protocols in Wireless Sensor Networks: A Survey;impact of Shuffler Design pattern on Software Quality;privacy-Preserving Lightweight Image Encryption in Mobile Cloud;cyclic Scheduling Algorithm.

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Modified FP-Growth: An Efficient Frequent pattern Mining Approach from FP-Tree 8th

Modified FP-Growth: An Efficient Frequent Pattern Mining App...

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8th international conference on pattern recognition and Machine Intelligence (PReMI)

作者： Ahmed, Shafiul Alom Nath, Bhabesh Tezpur Univ Tezpur 784028 Assam India

ISBN: (纸本)9783030348694;9783030348687

Prefix-tree based FP-growth algorithm is a two step process: construction of frequent pattern tree (FP-tree) and then generates the frequent patterns from the tree. After constructing the FP-tree, if we merely use the conditional FP-trees (CFP-tree) to generate the patterns of frequent items, we may encounter the problem of recursive CFP-tree construction and a huge number of redundant itemset generation. Which also leads to huge search space and massive memory requirement. In this paper, we have proposed a new data structure layout called Modified Conditional FP-tree (MCFP-tree). Moreover, we have proposed a new pattern growth algorithm called Modified FP-Growth (MFP-Growth), which uses both top-down and bottom-up approaches to efficiently generate the frequent patterns without recursively constructing the MCFP-tree. During mining phase only one MCFP-tree is maintained in main memory at any instance and immediately deleted or discarded from the memory after performing the mining. From the experimental analysis, it is noticed that the proposed MFP-Growth algorithm requires less memory to construct the MCFP-tree as compared to conditional FP-tree. Moreover, the execution of the MFP-Growth method is found significantly faster than the traditional FP-Growth as it does not generate redundant patterns.

关键词： Association Rule (AR) FP-growth Frequent pattern (FP) FP-tree pattern Mining (PM) Data Mining (DM) Frequent Itemset (FI)

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Advanced Deep Neural Networks for pattern recognition: An Experimental Study 8th

Advanced Deep Neural Networks for Pattern Recognition: An Ex...

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8th international conference on Soft computing and pattern recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Padmanabhan, Jayashree Premkumar, J. Melvin Jose Anna Univ Madras Inst Technol Madras 600044 Tamil Nadu India Google Inc Software Engn Intern Mountain View CA 94043 USA

ISBN: (纸本)9783319606187;9783319606170

Difficulty in pattern recognition is perceptible and neural networks approach the problem by way of learning from similar known patterns. Interest in Neural Networks started in the early 1980s when they were deemed to effectively model the human thought process. Speech recognition which first used Artificial Neural Networks (ANNs) to model the states of a Hidden Markov Models (HMMs) later started using Gaussian Mixture Models (GMMs). GMM-HMM systems have been the standard until recently when a new concept of Deep Neural Networks (DNNs) pre-trained using Restricted Boltzmann Machines (RBMs) came into existence. the discriminative capability of the resulting DNN is found to improve the performance of the recognition systems. the experimental work with DNN for recognizing patterns in handwriting and speech corpus has been carried out. In this work we implemented Deep Neural Networks for the above tasks and the pre trained DNN has been used for extracting bottleneck features and hereby improving the performance of the baseline systems with respect to recognition errors.

关键词： Deep Neural Networks Boltzmann machine Speech recognition Artificial Neural Networks Machine learning

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Mobile Application: Mobile Assistance for Visually Impaired People - Speech Interface System (SIS)

Mobile Application: Mobile Assistance for Visually Impaired ...

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international conference on Information Technology and Multimedia (ICIM)

作者： Abdul Majid Norkhalid Masyura Ahmad Faudzi Azimah Abdul Ghapar Fiza Abdul Rahim College of Computing and Informatics Universiti Tenaga Nasional Malaysia Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia

ISBN: (数字)9781728173108

ISBN: (纸本)9781728173115

the evolution of technology has been made to bring the most benefits to human-being on communication and to facilitate their day-to-day activities. Technologies contribution, such as mobile application, has been proved to have a high capability to cater to the needs of a person with disabilities. the usage of smartphone is limited for visually impaired(VI) community as most of the currently available mobile application are not user-friendly for people with vision disability. Although smartphones nowadays are offering accessibility services such as TalkBack for Android and VoiceOver for iOS, these accessibilities provide fewer functions on navigating the VI community. A speech interface system(SIS) in mobile application combined with object and distance detection could help the VI community to navigate to their destination only with the use of a smartphone. this Systematic Literature Review (SLR) provide our findings on the present study of speech recognition projects and the requirement issues of speech recognition in mobile application platform throughout supporting VI people. three journal databases (Google Scholar, IEEE Explore and Science Direct) has been searching throughout this SLR with articles ranging from the year of 2013-2019. A result of 136 article titles, abstracts of 73 articles were examined, 35 full-text articles were selected for final review. A total of 19 articles are analyzed. the purpose of the SLR is to collect the technique, evaluation methods and the validation of the projects.

关键词： Systematics Navigation Databases Bibliographies Speech recognition Distance measurement Mobile applications

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Efficient Modeling of Long Temporal Contexts for Continuous Emotion recognition

Efficient Modeling of Long Temporal Contexts for Continuous ...

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international conference on Affective computing and Intelligent Interaction and Workshops, ACII

作者： Jian Huang Jianhua Tao Bin Liu Zhen Lian Mingyue Niu National Laboratory of Pattern Recognition (NLPR)Institute of Automation CAS School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China National Laboratory of Pattern Recognition (NLPR)Institute of Automation CAS School of Artificial Intelligence University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China

Continuous emotion recognition is a challenging task due to its difficulty in modeling long-term contexts dependencies. Prior researches have exploited emotional temporal contexts from two perspectives, which are based on feature representations and emotional models. In this paper, we explore the model based approaches for continuous emotion recognition. Specifically, three temporal models including LSTM, TDNN and multi-head attention models are utilized to learn long-term contexts dependencies based on short-term feature representations. the temporal information learned by the temporal models allows the network to more easily exploit the slow changing dynamics between emotional states. Our experimental results demonstrate that the temporal models can model emotional long-term dynamic information effectively. Multi-head attention model achieves best performance among three models and multi-model combination models further improve the performance of continuous emotion recognition significantly.

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Modeling of bubble behavior in a slab continuous casting strand using multiple size group approach 8

Modeling of bubble behavior in a slab continuous casting str...

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8th international conference on Modeling and Simulation of Metallurgical Processes in Steelmaking, STEELSIM 2019

作者： Zhou, Haichen Zhang, Lifeng School of Metallurgical and Ecological Engineering University of Science and Technology Beijing Beijing100083 China

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