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A Cache-Based Data Movement Infrastructure for On-demand Scientific Cloud computing 1

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5th asian conference on Supercomputing Frontiers (SCFA)

作者： Abramson, David Carroll, Jake Jin, Chao Mallon, Michael van Iperen, Zane Hoang Nguyen Mcrae, Allan Ming, Liang Univ Queensland St Lucia Qld 4072 Australia Huawei Technol Co Ltd Shenzhen Guangdong Peoples R China

ISBN: (数字)9783030186456

ISBN: (纸本)9783030186456;9783030186449

As cloud computing has become the de facto standard for big data processing, there is interest in using a multi-cloud environment that combines public cloud resources with private on-premise infrastructure. However, by decentralizing the infrastructure, a uniform storage solution is required to provide data movement between different clouds to assist on-demand computing. this paper presents a solution based on our earlier work, the MeDiCI (Metropolitan Data Caching Infrastructure) architecture. Specially, we extend MeDiCI to simplify the movement of data between different clouds and a centralized storage site. It uses a hierarchical caching system and supports most popular infrastructure-as-a-service (IaaS) interfaces, including Amazon AWS and OpenStack. As a result, our system allows the existing parallel data intensive application to be offloaded into IaaS clouds directly. the solution is illustrated using a large bioinformatics application, a Genome Wide Association Study (GWAS), with Amazons AWS, HUAWEI Cloud, and a private centralized storage system. the system is evaluated on Amazon AWS and the Australian national cloud.

关键词： Cloud Big data Caching Data migration

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Enhance Top-down method with Meta-Classification for Very Large-scale Hierarchical Classification 5

Enhance Top-down method with Meta-Classification for Very La...

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5th International Joint conference on Natural Language Processing, IJCNLP 2011

作者： Wang, Xiao-Lin Zhao, Hai Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China

ISBN: (纸本)9789744665645

Recent large-scale hierarchical classification tasks typically have tens of thousands of classes as well as a large number of samples, for which the dominant solution is the top-down method due to computational complexity. However, the top-down method suffers from accuracy deficiency, that is, its accuracy is generally lower than that of the flat approach of 1-vs-Rest. In this paper, we employ meta-classification technique to enhance the classifying procedure of the top-down method. We analyze the proposed method on the aspect of accuracy, and then test it with two real-world large-scale data sets. Our method both maintains the efficiency of the conventional top-down method and provides competitive classification accuracies. © 2011 AFNLP

关键词： Natural language processing systems

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Plant Disease Detection: A Comparative Study of Deep Learning Approaches 5

Plant Disease Detection: A Comparative Study of Deep Learnin...

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5th International conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Princy, K.T.M. Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350321425

Demand for food is increasing every year. It causes insecurity in food items. Farmers are using large amounts of fertilizers and pesticides to meet their needs. Fertilizers are substances applied to plants to increase their productivity. According to the survey, India ranks second in fertilizer use globally and first in south asian countries. Pesticides are used to kill and repel certain kinds of pests. the overuse of pesticides and fertilizer causes a lot of problems. Most pesticides are harmful and have a terrible effect on the environment and human health. Sulphur, Malathion, Mutchler, Copper Oxychloride, Prorate, and Mancozebs have commonly used pesticides. Overuse of pesticides causes severe health issues like Cancer and that leads to death. Managing and controlling pesticides is very important to ensure safe public health. there are numerous reasons for the over usage of pesticides. the Main reason is crop diseases and pest attacks. Inefficient ways to determine plant disease and late diagnosis cause more use of pesticides. Determining the correct plant disease and types is essential to overcome this threat. this work deals with an effective way to discover plant disease detection using deep learning models. Numerous learning methods are present for the early detection and classification of diseases. In this work, we apply different deep learning algorithms such as CNN, ResNet, Inception model, Vgg16 and Vgg19. We get an accuracy in the range of 72% to 92%. © 2023 IEEE.

关键词： Diagnosis

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Multifactorial Bare Bones Sine Cosine algorithm with Chaotic-based elite learning for Multi-tasking Optimization 5

Multifactorial Bare Bones Sine Cosine algorithm with Chaotic...

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5th asian conference on Artificial Intelligence Technology, ACAIT 2021

作者： Li, Ning Wang, Lei Jiang, Qiaoyong Li, Xiaoyu Wang, Bin Zhang, Guangnan Xi'an University of Technology Computer Science and Engineering Xi'an China School of Computer Science Baoji University of Arts and Sciences Baoji China Key Laboratory of Industrial Automation of Shaanxi Provice Shaanxi University of Technolog Hanzhong China

ISBN: (纸本)9781665426305

the problem of multi-task optimization is a new research topic in the field of evolutionary computing in recent years, and it has attracted more and more attention from the academic community. Compared with single-objective optimization and multi-objective optimization, multi-task optimization can use the similarity and complementarity between tasks to solve different optimization tasks at the same time. However, as the population evolves to the later stage, the ability of the two tasks to learn from each other gradually declines. In order to solve this problem and enhance the effectiveness of knowledge transfer between different tasks, this paper combines the bare bone sine cosine algorithm (BBSCA) and the elite learning strategy based on chaos mapping (ELM) into MFEA, and proposes the MFBBSCA-ELM algorithm. Since BBSCA and ELM have different search neighborhoods and are highly complementary to the analog binary crossover used in the classic MFEA algorithm, this article combines BBSCA and ELM, which is also the motivation of this article. In addition, the integration of BBSCA and ELM can reduce the probability of MFEA falling into a local optimum. Finally, this article verifies the effectiveness of integrating BBSCA and ELM into MFEA on the classic MTO benchmark problem. the experimental results show that compared with the classic MFEA, the performance of the MFBBSCA-ELM proposed in this paper has been significantly improved. © 2021 IEEE.

关键词： Evolutionary algorithms

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Improving Chinese POS Tagging with Dependency Parsing 5

Improving Chinese POS Tagging with Dependency Parsing

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5th International Joint conference on Natural Language Processing, IJCNLP 2011

作者： Li, Zhenghua Che, Wanxiang Liu, Ting Research Center for Social Computing and Information Retrieval MOE-Microsoft Key Laboratory of Natural Language Processing and Speech School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)9789744665645

Recent research usually models POS tagging as a sequential labeling problem, in which only local context features can be used. Due to the lack of morphological inflections, many tagging ambiguities in Chinese are difficult to handle unless consulting larger contexts. In this paper, we try to improve Chinese POS tagging by using long-distance dependencies produced by a statistical dependency parser. Experimental results show that, despite error propagation, the syntactic features can significantly improve the tagging accuracy from 93.88% to 94.41% (p -5). Detailed analysis shows that these features are helpful for ambiguous pairs like {NN,VV} and {DEC,DEG}. © 2011 AFNLP

关键词： Syntactics

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5th International conference on Natural Language Processing and Chinese computing (NLPCC) / 24th International conference on Computer Processing of Oriental Languages (ICCPOL)

作者： Guo, Shaoru Guan, Yong Li, Ru Zhang, Qi Shanxi Univ Sch Comp & Informat Technol Taiyuan Peoples R China Shanxi Univ Minist Educ Computat Intelligence & Chinese Infor Key Lab Taiyuan Peoples R China Fuzhou Univ Coll Math & Comp Sci Fujian Peoples R China

ISBN: (纸本)9783319504964;9783319504957

Chinese word similarity computing is a fundamental task for natural language processing. this paper presents a method to calculate the similarity between Chinese words based on combination strategy. We apply Baidubaike to train Word2Vector model, and then integrate different methods, semantic Dictionary-based method, Word2Vector-based method and Chinese FrameNet (CFN)-based method, to calculate the semantic similarity between Chinese words. the semantic Dictionary-based method includes dictionaries such as HowNet, DaCilin, Tongyici Cilin (Extended) and Antonym. the experiments are performed on 500 pairs of words and the Spearman correlation coefficient of test data is 0.524, which shows that the proposed method is feasible and effective.

关键词： Chinese word similarity computing Combination strategy Semantic dictionary Word2Vector Chinese FrameNet

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A Graph-based Method for Entity Linking 5

A Graph-based Method for Entity Linking

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5th International Joint conference on Natural Language Processing, IJCNLP 2011

作者： Guo, Yuhang Che, Wanxiang Liu, Ting Li, Sheng Research Center for Social Computing and Information Retrieval MOE-Microsoft Key Laboratory of Natural Language Processing and Speech School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)9789744665645

In this paper, we formalize the task of finding a knowledge base entry that a given named entity mention refers to, namely entity linking, by identifying the most "important" node among the graph nodes representing the candidate entries. With the aim of ranking these entities by their "importance", we introduce three degree-based measures of graph connectivity. Experimental results on the TAC-KBP benchmark data sets show that our graph-based method performs comparably with the state-of-the-art methods. We also show that using the name phrase feature outperforms the commonly used bag-of-word feature for entity linking. © 2011 AFNLP

关键词： Knowledge based systems

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Progress on educational digital libraries: Current developments in the national science foundation’s (NSF) national science, technology, engineering, and mathematics education digital library (NSDL) program 5th

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5th International conference on asian Digital Libraries, ICADL 2002

作者： Prey, Jane C. Zia, Lee L. Division of Undergraduate Education National Science Foundation ArlingtonVA22230 United States

ISBN: (纸本)3540002618

Technological advances in computing and communication continue to challenge the “why, where, and when” of the educational enterprise. Policymakers and advocates from organizations such as EDUCAUSE2, the Coalition for Networked Information (CNI)3, the European Schoolnet partnership4, and UNESCO5 have long argued for the potential that information technology has to transform education by improving access — both spatially (“anyplace”) and temporally (“anytime”), by redefining the nature of the classroom and learning, and in some cases by changing cost structures. In recent years various consortia of higher education institutions have formed to explore international collaborations in distance learning, for example: Universitas 21 (http://***/) and the Global University Alliance (***) both of which involve institutions from Asia, Europe, and the United States. Many individual institutions have also embarked on distance education ventures and efforts in the K-12 sector are growing as well (see http://***/indepth/distance/ ***#virtual). Development of online course management systems continues to progress on the commercial front, e.g. WebCT (***) and Blackboard (***), and more recently an open-source effort called the Open Knowledge Initiative (http://***/oki/) has begun.

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Distributed Minimum Error Rate Training of SMT using Particle Swarm Optimization 5

Distributed Minimum Error Rate Training of SMT using Particl...

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5th International Joint conference on Natural Language Processing, IJCNLP 2011

作者： Suzuki, Jun Duh, Kevin Nagata, Masaaki NTT Communication Science Laboratories NTT Corp. 2-4 Hikaridai Seika-cho Soraku-gun Kyoto619-0237 Japan

ISBN: (纸本)9789744665645

the direct optimization of a translation metric is an integral part of building state-of-the-art SMT systems. Unfortunately, widely used translation metrics such as BLEU-score are non-smooth, non-convex, and non-trivial to optimize. thus, standard optimizers such as minimum error rate training (MERT) can be extremely time-consuming, leading to a slow turnaround rate for SMT research and experimentation. We propose an alternative approach based on particle swarm optimization (PSO), which can easily exploit the fast growth of distributed computing to obtain solutions quickly. For example in our experiments on NIST 2008 Chinese-to-English data with 512 cores, we demonstrate a speed increase of up to 15x and reduce the parameter tuning time from 10 hours to 40 minutes with no degradation in BLEU-score. © 2011 AFNLP

关键词： Particle swarm optimization (PSO)

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Hindi-English Code Mixed Hate Speech Detection using Character Level Embeddings 5

Hindi-English Code Mixed Hate Speech Detection using Charact...

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5th International conference on computing Methodologies and Communication, ICCMC 2021

作者： Rahul Gupta, Vasu Sehra, Vibhu Vardhan, Yashaswi Raj Delhi Technological University Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665403603

Hinglish is a portmanteau word for 'Hindi' and 'English', and refers to the informal "language"predominantly used in the South-asian (Indian) Sub-Continent, a blend of the two languages it derives its name from. It considerably differs from the English language in grammar, syntax, punctuations, phonetics and accent, as well as in *** it is more convenient to use English for certain technical words, sports events, scientific phenomena, and other things, mixed usage of English and regional languages has gained considerable prominence in day-to-day conversations and Social Media. this research aims to create an independent and self-sufficing model that classifies Hingish texts as Hate Speech, Abusive or *** prevalent use of code-mixed language in the subcontinent, the sensitive nature of hate speeches, and the need of a self-sufficient model for Hinglish, together serve as the motivation for this *** have used character level embeddings for Hinglish Language which has the potential to most efficiently extract the context from Hinglish sentences given the level of variation in syntax and semantics of the code-mixed (a language that is a combination of two or more languages) language. Later we trained various deep learning classifier models. Hybridisation of GRU with Attention Model performed best among more than 12 models experimented with. the use of Character Level Embeddings, GRU, and attention layer are novel to Hate Speech Detection in Hinglish Code-Mixed Language. © 2021 IEEE.

关键词： Embeddings

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