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NH-CIL: A Nested Hierarchy Algorithm for Class Incremental Learning

NH-CIL: A Nested Hierarchy Algorithm for Class Incremental L...

引用

international conference on Computer Supported Cooperative Work in Design

作者： Mingyu Liu Yijie Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R.China

ISBN: (纸本)9781728165981

Class incremental learning is widely applied in the classification scenarios as the number of classes is usually dynamically changing. However, the existing algorithms increase computational cost to implement class incremental learning in order to increase classification quality. In this paper, we propose a nested hierarchy algorithm based on OCSVM for class incremental learning, called NH-CIL. We reuse support vectors to eliminate redundant instances and catch the key ones to replace the whole model because of the generalization ability of OCSVM. When a new class arrives, NH-CIL adopts OCSVM on the new class and the old classes respectively to get the corresponding sketching supports vectors. then NH-CIL reuses these two kinds of sketching support vectors to build a binary sub-classifier. these two steps are repeatedly nested to form a hierarchy classification model in a bottom-up manner while the number of classes increases. On the contrary, the testing phase is in a top-down manner. NH-CIL can be used as a flexible approach in the classification scenarios during the collaborative information processing. We conduct the experiments on 8 real-world benchmark datasets to compare NH-CIL with some other class incremental learning algorithms, e.g. SD-CIL, HS-CIL and OP-CIL. the experiment results show that NH-CIL averagely achieves more than 5.1%, 8.6% and 11.6% accuracy improvement and 39.8%, 24.7% and 12.6% efficiency improvement over SD-CIL, HS-CIL and OP-CIL, respectively.

关键词： Machine learning algorithms Computational modeling Heuristic algorithms conferences Support vector machine classification Information processing Benchmark testing

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Automatic code parallelization for data-intensive computing in multicore systems 8

Automatic code parallelization for data-intensive computing ...

引用

8th international conference on Engineering, mathematics and Physics, ICEMP 2019

作者： Subramanian, Ranjini Zhang, Hui University of Louisville Computer Science Department LouisvilleKY United States

A major driving force behind the increasing popularity of data science is the increasing need for data-driven analytics fuelled by massive amounts of complex data. Increasingly, parallel processing has become a cost-effective method for computationally large and data-intensive problems. Many existing applications are sequential in nature and if such applications are ported to multi-processor systems for execution, they would make use of only one core and the optimal usage of all cores is not guaranteed. Knowledge of parallel programming is necessary to ensure the use of processing power offered by multi-processor systems in order to achieve better performance. However, many users do not possess the skills and knowledge required to convert existing sequential code to parallel code to achieve speedups and scalability. In this paper, we introduce a framework that automatically transforms existing sequential code to parallel code while ensuring functional correctness using divide-and-conquer paradigm, so that the benefits offered by multi-core systems can be maximized. the paper will outline the implementation of the framework and demonstrate its usage with practical use cases. © Published under licence by IOP Publishing Ltd.

关键词： parallel programming

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Towards explainable ai in text features engineering for concept recognition 8th

Towards explainable ai in text features engineering for conc...

引用

8th international conference on Statistical Language and Speech processing, SLSP 2020

作者： Waldis, Andreas Mazzola, Luca Denzler, Alexander School of Information Technology Lucerne University of Applied Sciences and Arts Rotkreuz Switzerland

ISBN: (纸本)9783030594299

the rapid and pervasive development of methods from Artificial Intelligence (AI) affects our everyday life. Its application improves the users’ experience of many daily tasks. Despite the enhancements provided, such approaches have a substantial limitation in the shortfall of people’s trust connected with their lack of explainability. In natural language understanding (NLU) and processing (NLP), a fundamental objective is to support human interactions using sense-making of the language for communication. Such methods try to comprehend and reproduce the self-evident processes of human communication. this applies either in receiving speech signals or in extracting relevant information from a text. Furthermore, the pervasiveness of AI methods in the workplace and on the free time demands a sustainable and verified support of users’ trust, as a natural condition for their acceptance. the objective of this work is to introduce a framework for the calculation and selection of understandable text features. Such features can increase the confidence placed into adopted NLP solutions. the following work outlines the Text Feature Framework and its text features, based on statistical information coming from a general text corpus. the showcase experiment uses those features to verify them on the concept recognition task. the results shows their capability to explain a model and its predictions. the resulting concept recognition models are competitive with other methods existing in the literature. It has the definitive advantage of being able to externalize the supporting evidence for a choice of concept identification. © Springer Nature Switzerland AG 2020.

关键词： Natural language processing systems

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Performance optimizations for parallel modeling of solidification with dynamic intensity of computation 13th

Performance optimizations for parallel modeling of solidific...

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13th international conference on parallel processing and applied mathematics, PPAM 2019

作者： Halbiniak, Kamil Szustak, Lukasz Kulawik, Adam Gepner, Pawel Czestochowa University of Technology Dabrowskiego 69 Czestochowa42-201 Poland Intel Corporation Santa Clara United States

ISBN: (纸本)9783030432287

In our previous works, a parallel application dedicated to the numerical modeling of alloy solidification was developed and tested using various programming environments on hybrid shared-memory platforms with multicore CPUs and manycore Intel Xeon Phi accelerators. While this solution allows obtaining a reasonable good performance in the case of the static intensity of computations, the performance results achieved for the dynamic intensity of computations indicates pretty large room for further optimizations. In this work, we focus on improving the overall performance of the application with the dynamic computational intensity. For this aim, we propose to modify the application code significantly using the loop fusion technique. the proposed method permits us to execute all kernels in a single nested loop, as well as reduce the number of conditional operators performed within a single time step. As a result, the proposed optimizations allows increasing the application performance for all tested configurations of computing resources. the highest performance gain is achieved for a single Intel Xeon SP CPU, where the new code yields the speedup of up to 1.78 times against the original version. the developed method is vital for further optimizations of the application performance. It allows introducing an algorithm for the dynamic workload prediction and load balancing in successive time steps of simulation. In this work, we propose the workload prediction algorithm with 1D computational map. © Springer Nature Switzerland AG 2020.

关键词： Numerical models

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parallel Method of Neural Network Synthesis Based on a Modified Genetic Algorithm Application 8

Parallel Method of Neural Network Synthesis Based on a Modif...

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8th international conference on mathematics, Information Technologies, Education (MoMLeT and DS)

作者： Leoshchenko, Serhii Oliinyk, Andrii Skrupsky, Stepan Subbotin, Sergey Zaiko, Tetiana Natl Univ Zaporizhzhia Polytech Dept Software Tools UA-69063 Zaporizhzhia Ukraine Natl Univ Zaporizhzhia Polytech Dept Software Tools Dept Comp Syst & Networks UA-69063 Zaporizhzhia Ukraine

In our days, the using of neural network technologies is relevant in solving applied problems in various fields of science and industry. Such tasks successfully solved by artificial neural networks include: forecasting, classification, as well as diagnostics and detection of pre-emergency situations at hazardous industrial facilities. However, one of the main problems of neural networks implementation is their synthesis: the choosing of topology and setting of the weights (training process). this paper describes a parallel method of neural network synthesis based on a modified genetic algorithm.

关键词： neural networks synthesis sequential parallel genetic method

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A Novel Method of Dynamic Cloud Workflow processing Based on 3D_DWFN 19

A Novel Method of Dynamic Cloud Workflow Processing Based on...

引用

8th international conference on Software and Information Engineering (ICSIE)

作者： Heng, Nannan Yang, Zhengyi Zhu, Shengnan Zeng, Jie Chongqing Univ Chongqing 401331 Peoples R China

ISBN: (纸本)9781450361057

Cloud workflow is the combination of workflow management system and cloud platform. Compared with general workflow system, it can provide optimization of resource management and automatic scheduling of task. However, cloud workflow technology still lacks flexibility and cannot response to the changing business requirements in real time. In order to cope with the changing of business requirements, a dynamic workflow change model 3D-DWFN is proposed. the model is verified to be correct and the migration strategy is set up, thus the dynamic change algorithm is realized. It is applied to a business management system;the experiment shows that it is more effective than general workflow model.

关键词： Dynamic change Cloud Workflow Migration Strategy 3D_DWFN Change Algorithm

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A parallel Algorithm for Constructing Two Edge-Disjoint Hamiltonian Cycles in Crossed Cubes 14th

A Parallel Algorithm for Constructing Two Edge-Disjoint Hami...

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14th international conference on Algorithmic Aspects in Information and Management, AAIM 2020

作者： Pai, Kung-jui Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City Taiwan

ISBN: (纸本)9783030576011

the n-dimensional crossed cube CQn, a variation of the hypercube Qn, has the same number of vertices and the same number of edges as Qn, but it has only about half of the diameter of Qn. In the interconnection network, some efficient communication algorithms can be designed based on edge-disjoint Hamiltonian cycles. In addition, two edge-disjoint Hamiltonian cycles also provide the edge-fault tolerant Hamiltonicity for the interconnection network. Hung [Discrete applied mathematics 181, 109–122, 2015] designed a recursive algorithm to construct two edge-disjoint Hamiltonian cycles on CQn in O(n2n) time. In this paper, we provide an O(n) time algorithm for each vertex in CQn to determine which two edges were used in Hamiltonian cycles 1 and 2, respectively. With the information of each vertex, we can construct two edge-disjoint Hamiltonian cycles in CQn with n ≥ 4. © 2020, Springer Nature Switzerland AG.

关键词： Hamiltonians

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parallelized Sequential Indexing Tables for Fast High-Volume Data processing

Parallelized Sequential Indexing Tables for Fast High-Volume...

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IEEE Instrumentation and Measurement Technology conference

作者： Balázs Tusor János T. Tóth Annamári R. Várkonyi-Kóczy Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Department of Mathematics and Informatics J. Selye University Komarno Slovakia

ISBN: (数字)9781728144603

ISBN: (纸本)9781728144610

In the age of Big Data, processing high volumes of data as fast as possible is an important task. parallel computing is a useful tool that has become more and more available and widespread in the past decade, making it possible to accelerate traditional data processing methods or create new ones that are built upon parallelization techniques. Sequential Fuzzy Indexing Tables are classifiers that expand the capabilities of Lookup Tables in order to achieve a fast classification. However, due to the size of their structure, they cannot be used for problems with larger complexity. In this paper, a new classifier architecture is proposed that uses parallelization techniques to be used in quick processing of large volumes of data.

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Newsvendor Model for Multi-Inputs and -Outputs with Random Yield: Applications to Agricultural processing Industries 8th

Newsvendor Model for Multi-Inputs and -Outputs with Random Y...

引用

8th international conference on Operations Research and Enterprise Systems (ICORES)

作者： Amaruchkul, Kannapha Natl Inst Dev Adm NIDA Grad Sch Appl Stat Bangkok Thailand

ISBN: (纸本)9789897583520

Consider a newsvendor model, which we extend to include both multiple inputs and outputs. Different input types possess different levels of quality, and are purchased at different prices by a processing firm. Each type of input is processed into multiple outputs, which are sold at different prices. the yield for each output type is random and depends on the input type. We need to determine the purchase quantities of different types of input, before demands of different types of output are known. In our analytical results, we show that the expected total profit is jointly concave in the purchasing quantities and derive the optimality condition. Our multi-input and -output newsvendor model is suitable for processing industries in agriculture supply chain. In our numerical example, we apply our model to the rice milling industry, whose primary output is head rice and byproducts are broken rice, bran and husk. Our model can help the rice mill to decide which paddy types to procure and how much, in order to maximize the total expected profit from all outputs. We also show that the expected profit can be significantly better than using the standard newsvendor model.

关键词： Agriculture Supply Chain applied Operations Research Stochastic Model Applications Newsvendor Models

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Research on parallel incremental association rule algorithm based on data stream 8

Research on parallel incremental association rule algorithm ...

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8th international conference on Data Science, Technology and Applications, DATA 2019

作者： Hua, Zheng Du, Tao Qu, Shouning Mi, Tao School of Information Science and Engineering University of Jinan No. 336 West Road of Nan Xinzhuang Jinan Shandong250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9789897583773

Data stream association rule is one of the most interesting problems in the data mining community. However, the bottleneck that the computational power of a single computer is limited and the number of candidate itemsets far surpasses memory space cannot be solved by the traditional algorithm. this paper focuses on this problem and introduces a novel algorithm named a parallel Incremental Association Rule Mining based on a Hierarchical method (PIMH), which utilizes the Spark parallel computing platform as a framework solve the problem of parallel processing the partition data. In the PIMH algorithm, the existing mining results is used to solve the repeated mining inefficiency, consequently, association rules can be quickly updated. Additionally, the local pruning method is performed which avoids the problem that the memory is difficult to stored. According to the experimental results, the proposed algorithm showed to be the characteristics of high accuracy and spend less computational time in comparison with existing data stream algorithms. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Association rules

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