48 results on '"Selection strategy"'
Search Results
2. Average Cuboid Volume as a Convergence Indicator and Selection Criterion for Multi-objective Biochemical Optimization
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Rosenthal, Susanne, Borschbach, Markus, Kacprzyk, Janusz, Series editor, Emmerich, Michael, editor, Deutz, André, editor, Schütze, Oliver, editor, Legrand, Pierrick, editor, Tantar, Emilia, editor, and Tantar, Alexandru-Adrian, editor
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- 2017
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3. Adaptive Fireworks Algorithm Based on Two-Master Sub-population and New Selection Strategy
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Li, Xiguang, Han, Shoufei, Zhao, Liang, Gong, Changqing, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor
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- 2017
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4. Selecting the Selection
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Hoder, Kryštof, Reger, Giles, Suda, Martin, Voronkov, Andrei, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Olivetti, Nicola, editor, and Tiwari, Ashish, editor
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- 2016
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5. Distributed Adaptive Metaheuristic Selection: Comparisons of Selection Strategies
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Jankee, Christopher, Verel, Sébastien, Derbel, Bilel, Fonlupt, Cyril, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bonnevay, Stéphane, editor, Legrand, Pierrick, editor, Monmarché, Nicolas, editor, Lutton, Evelyne, editor, and Schoenauer, Marc, editor
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- 2016
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6. Optimization of Selection Strategies for P2P Streaming Network Based on Daily Users’ Behavior and Users’ Distribution over Time Zones
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Gaidamaka, Yuliya, Vasiliev, Ivan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Galinina, Olga, editor, Balandin, Sergey, editor, and Koucheryavy, Yevgeni, editor
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- 2016
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7. The Selection Strategy of Form-Finding Methods for Transmission Lines Based on ANSYS
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Jin, Ying, Liu, Changsheng, Xie, Yunyun, Zhang, Linghao, and Wang, Wego, editor
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- 2015
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8. Tournament Selection Based Artificial Bee Colony Algorithm with Elitist Strategy
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Zhang, Meng-Dan, Zhan, Zhi-Hui, Li, Jing-Jing, Zhang, Jun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Cheng, Shin-Ming, editor, and Day, Min-Yuh, editor
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- 2014
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9. A Robust Skip-Till-Next-Match Selection Strategy for Event Pattern Matching
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Cadonna, Bruno, Gamper, Johann, Böhlen, Michael H., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Manolopoulos, Yannis, editor, Trajcevski, Goce, editor, and Kon-Popovska, Margita, editor
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- 2014
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10. Immunomodulation of Resistant Depression
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Emmanuel Haffen and Djamila Bennabi
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Treatment response ,business.industry ,Selection strategy ,Central nervous system ,Inflammation ,Context (language use) ,Bioinformatics ,Pathogenesis ,medicine.anatomical_structure ,Medicine ,medicine.symptom ,Treatment resistance ,business ,Depression (differential diagnoses) - Abstract
Growing evidence indicates that inflammatory processes, both within that inflammatory processes, both within the central nervous system and the periphery of the body, play a role in the pathogenesis, course and treatment of depression. In treatment-resistant depression (TRD) recent studies indicate that inflammatory activity may be particularly elevated. Moreover, several inflammatory markers (IL-6 and CRP/hsCRP) hold some promise as markers for the prediction of treatment response in TRD. In this context, immunomodulatory therapies represent one of the most interesting therapeutic alternatives in depression, especially for patients with an immunological dysregulation profile and/or treatment resistance to conventional therapy. However, there is currently insufficient evidence to support a personalised treatment selection strategy based on inflammatory markers and to recommend the clinical use of any particular agent in these patients.
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- 2021
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11. Bacterial Foraging Optimization with Leader Selection Strategy for Bi-objective Optimization
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Yixin Wang, Yikun Ou, Hong Wang, and Ben Niu
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DBSCAN ,Mathematical optimization ,Speedup ,Computer science ,Foraging ,Selection strategy ,Convergence (routing) ,Noise (video) ,Multi-objective optimization ,Swarm intelligence - Abstract
Multi-objective problem (MOP) has long been a challenging issue. Many novel Swarm Intelligence (SI) method like Bacterial Foraging Optimization (BFO) has been extended to tackle MOPs recent years. To further improve the efficiency of BFO in multi-objective optimization, this paper proposes a novel BFO for Bi-objective optimization (abbreviated as BIBFO) with enhanced leader selection strategy. The leader selection strategy incorporating with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method in comprehensive learning mechanism can direct evolution and enhances the search efficiency. Besides, the strategies of reproduction and elimination are improved using elitism strategy to enhance the collaboration between search group and the external archive, which can speed up the convergence and improve the search efficiency. In addition, the external archive control strategy is further applied to balance the convergence and the solution diversity. The effectiveness of BIBFO is demonstrated on six frequently used benchmarks, and comparative studies have been conducted among bacterial-based multi-objective optimization algorithms. Experimental results indicate that the proposed BIBFO performs well in generation distance (GD) and diversity \((\Delta )\) metrics of obtained Pareto front.
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- 2021
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12. SSRGAN: A Generative Adversarial Network for Streaming Sequential Recommendation
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Jiajie Xu, Junhua Fang, Chengfei Liu, Rui Zhou, and Yao Lv
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Online model ,050101 languages & linguistics ,Exploit ,Computer science ,business.industry ,05 social sciences ,Selection strategy ,Sampling (statistics) ,02 engineering and technology ,Machine learning ,computer.software_genre ,Subsequence ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,0501 psychology and cognitive sciences ,Artificial intelligence ,business ,Generative adversarial network ,computer ,Block (data storage) - Abstract
Studying the sequential recommendation in streaming settings becomes meaningful because large volumes of user-item interactions are generated in a chronological order. Although a few streaming update strategies have been developed, they cannot be applied in sequential recommendation, because they can hardly capture the long-term user preference only by updating the model with random sampled new instances. Besides, some latent information is ignored because the existing streaming update strategies are designed for individual interactions, without considering the interaction subsequence. In this paper, we propose a Streaming Sequential Recommendation with Generative Adversarial Network (SSRGAN) to solve the streaming sequential recommendation problem. To maintain the long-term memory and keep sequential information, we use the reservoir-based streaming storage mechanism and exploit an active subsequence selection strategy to update model. Moreover, to improve the effectiveness and efficiency of online model training, we propose a novel negative sampling strategy based on GAN to generate the most informative negative samples and use Gumble-Softmax to overcome the gradient block problem. We conduct extensive experiments on two real-world datasets and the results shows the superiority of our approaches in streaming sequential recommendation.
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- 2021
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13. Variances in Party Selection Behavior
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Sara Ceyhan
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Politics ,media_common.quotation_subject ,Selection strategy ,Immigration ,Candidacy ,Context (language use) ,Affect (psychology) ,Psychology ,Social psychology ,Selection (genetic algorithm) ,Representation (politics) ,media_common - Abstract
Beyond the general selection behavior of political parties towards immigrant origin candidates, this book is also interested in a better understanding of which selection strategy is applied under which circumstances. While previous studies have assessed the effect of contextual factors on the overall level of minority representation, this chapter explores whether these factors also affect party selection behavior towards immigrant origin candidates at the micro-level. In line with previous theoretical considerations, the book expects variances across immigrant subgroups, political parties, the mode of candidacy, and SMD context.
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- 2020
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14. Investigating the Measurement of Resilience Engineering for Improving Organisational Safety
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Udara C. R. Ranawalage, Ishanka Weerasekara, Emmanuel B. Boateng, and Manikam Pillay
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Range (mathematics) ,Process management ,Operationalization ,Computer science ,Selection strategy ,Resilience engineering ,Benchmarking ,Limit (mathematics) ,Systematic search - Abstract
This project investigates the measurement of resilience engineering. A growing body of peer-reviewed studies continues to be published on resilience engineering, demonstrates its recognition and importance to safety across a range of industrial contexts. However, little attention has focused on developing an understanding of how it has been conceptualized and measured. This is a significant gap which can limit its operationalization, benchmarking and evaluation n for research and practice. This paper presents an integrative review project currently underway which seeks to address this gap. After completing a systematic search and selection strategy seventeen articles were selected for analysis. Initial findings suggest fifteen survey instruments have been used in these studies.
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- 2020
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15. Investigating the Measurement of High Reliability Organisations for Health Care Safety
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Andrew Enya, Emmanuel B. Boateng, and Manikam Pillay
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Data collection ,Operationalization ,Risk analysis (engineering) ,Computer science ,business.industry ,Health care ,Selection strategy ,Limit (mathematics) ,Health care safety ,business ,Reliability (statistics) ,Systematic search - Abstract
This project investigates the measurement of high reliability organisations for improving health care safety. A growing body of peer-reviewed studies continues to be published on high reliability organisations, demonstrates its recognition and importance for improving safety across high risk contexts such as health care. However, little attention has focused on developing an understanding of how it has conceptualized, and factors that have been used in its measurement. This is a significant gap which can limit its operationalization for research and practice. This paper presents a systematic review project currently underway which seeks to address this gap. After completing a systematic search and selection strategy twenty-one articles were selected for analysis. Results indicate fourteen survey instruments have been used in these studies. Seventeen different definitions of HRO were identified. Nine studies used surveys for data collection. Both independent and outcomes variables were reported, these can be used to inform an initial theoretical framework and a survey instrument.
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- 2020
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16. Diversity Promoting Strategies in a Multi- and Many-Objective Evolutionary Algorithm for Molecular Optimization
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Susanne Rosenthal
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Genetic diversity ,Optimization problem ,Process (engineering) ,Computer science ,business.industry ,Selection strategy ,Evolutionary algorithm ,Machine learning ,computer.software_genre ,Ranking ,Artificial intelligence ,business ,human activities ,computer ,Diversity (business) ,Premature convergence - Abstract
Computer-aided drug design is an approach to effectively identify and analyse molecules for therapeutic and diagnostic interventions. Generally, libraries with a broad range of compounds revealing a high genetic diversity with an at most similar behavior in bioactivity have to be created. For this purpose, an evolutionary process for multi- and many-objective Molecular Optimization (MO) has been designed and improved during the past decade. Diversity plays a central role in Evolutionary Algorithms (EAs) to prevent premature convergence to suboptimal solutions and several methods to promote diversity on different levels of an EA have been proposed. The aspect of genetic diversity in MO is a further challenge that has to be controlled and promoted by different strategies on various stages of a problem-specific EA. This work presents an application-specific re-interpretation of different diversity aspects on various stages of an EA for MO. A sophisticated survival selection strategy combining a specific ranking method with application-specific diversity promoting technologies is introduced and benchmarked to the recently proposed many-objective evolutionary algorithm AnD on four molecular optimization problems with 3 up to 6 objectives.
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- 2020
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17. How to Construct a Stock Selection Strategy: Multi-Factor Analysis
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Lingjie Ma
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Computer science ,Selection strategy ,Ordinary least squares ,Econometrics ,Capital asset pricing model ,Inference ,Portfolio ,Security level ,Inefficiency ,Stock (geology) - Abstract
In this chapter, we introduce stock selection strategies and demonstrate how to employ a multi-factor model to build alphas for such a strategy. How can we forecast stock returns? To answer this critical question, we first discuss market inefficiency and identify sources of return anomalies. We then show how to transform these fundamental sources into a multi-factor alpha model. Regarding related finance theory, we introduce the capital asset pricing model (CAPM). On the quantitative side, we present the ordinary least squares (OLS) method. We explore estimation, inference, and properties and conditions of OLS estimates. Regarding industry insights, we show, using the Russell 1000 security level data, how to construct a multi-factor alpha model for a large-cap core stock selection portfolio. For R programming, we introduce commonly used utility functions in quantitative investing.
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- 2020
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18. Good Pivots for Small Sparse Matrices
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Jakob Moosbauer and Manuel Kauers
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050101 languages & linguistics ,05 social sciences ,Selection strategy ,02 engineering and technology ,symbols.namesake ,Gaussian elimination ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,020201 artificial intelligence & image processing ,0501 psychology and cognitive sciences ,Algorithm ,Selection (genetic algorithm) ,Mathematics ,Sparse matrix - Abstract
For sparse matrices up to size \(8\times 8\), we determine optimal choices for pivot selection in Gaussian elimination. It turns out that they are slightly better than the pivots chosen by a popular pivot selection strategy, so there is some room for improvement. We then create a pivot selection strategy using machine learning and find that it indeed leads to a small improvement compared to the classical strategy.
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- 2020
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19. Intention-Aware Model to Support Agent Deliberation in a Large-Scale Dynamic Multi-Agent Application
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Vince Antal, Miklós Miskolczi, László Zsolt Varga, Alex Kiss, and Tamás Farkas
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Route assignment ,Trustworthiness ,Scale (ratio) ,Computer science ,Distributed computing ,media_common.quotation_subject ,Selection strategy ,Autonomous agent ,Routing (electronic design automation) ,Deliberation ,Selection (genetic algorithm) ,media_common - Abstract
It is hoped that the traffic in the cities will be almost optimal when autonomous vehicles will dominate the traffic. We investigate the route selection of autonomous vehicles. We extend, implement and apply a formal model to support the trustworthy route selection of real-world autonomous agents. We trust a model, if the route selection strategy of the model selects routes which are close to the possible fastest all the time. The formal model extends the intention-aware online routing game model with parallel lanes, traffic lights and give way intersections. These extensions are needed for real-world applications. The actual parameters of the formal model are derived from real-world OpenStreetMap data. The large-scale real-world testing of the model uses the SUMO (Simulation of Urban MObility) open source simulator. The implemented intention-aware online routing game model can execute the route selection for each vehicle faster than real-time. Our hypothesis is that the extended intention-aware online routing game model produces at least as good traffic as the dynamic equilibrium route assignment. This hypothesis is confirmed in a real-world scenario.
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- 2020
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20. More on Stock Selection Strategy: Alpha Hunting, Risk Adjustment, and Nonparametric Diagnostics
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Lingjie Ma
- Subjects
Parametric analysis ,Computer science ,Selection strategy ,Econometrics ,Nonparametric statistics ,Risk adjustment ,Stock (geology) - Abstract
In the previous chapter, we introduced a general procedure and multi-factor framework for alpha construction of stock selection strategies. In this chapter, we continue to explore stock selection strategy with more advanced topics. In particular, we focus on alpha (new factor) hunting, risk adjustment, and nonparametric diagnostics. Regarding new alpha discovery, we present the guidance of IPARE. From a methodological perspective, we introduce the weighted least squares (WLS) method, which provides a tool to integrate risk into a multi-factor alpha model. We then introduce nonparametric approaches as a complement to parametric analysis. In the industry insights section, we provide a nonparametric diagnostics package used in the industry to investigate a new factor. The last section on R programming shows how to refine plots with parameters.
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- 2020
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21. Integrating Research into Care
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Julia Downing and Jan Aldridge
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Critical appraisal ,Framing (social sciences) ,Systematic review ,Management science ,Computer science ,Research methodology ,Selection strategy ,Research question ,Cohort study - Abstract
1. There are different types of evidence to consider including systematic reviews, randomised controlled trials (RCTs), case-controlled studies, cohort studies, case series. 2. A thorough, critical review of the existing literature informs thinking about research and practice and should have a clear search and selection strategy, critically evaluate the existing literature, be well-structured and well-referenced. 3. Undertaking a literature search can be divided into four steps: framing the question; choosing a search method; identifying criteria and keywords; narrowing the search results. 4. In developing your own study it is important to get the research question right as this will aid the identification of the appropriate research methodology and the feasibility of the study. It is also important that any tools used to address the research question are reliable and well validated and data analysis is appropriate and transparent. 5. Dissemination of findings of sound research is important. Through dissemination we keep learning and improve the care that is delivered.
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- 2020
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22. Cascaded Region Proposal Networks for Proposal-Based Tracking
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Xuewu Fan, Ximing Zhang, and Shujuan Luo
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Computer science ,BitTorrent tracker ,Feature extraction ,Selection strategy ,Region proposal ,02 engineering and technology ,010501 environmental sciences ,computer.software_genre ,01 natural sciences ,Active appearance model ,Scale variation ,0202 electrical engineering, electronic engineering, information engineering ,Eye tracking ,020201 artificial intelligence & image processing ,Data mining ,Spatial analysis ,computer ,0105 earth and related environmental sciences - Abstract
There still remains some problems which have not been solved in RPN-based trackers, including data imbalance, inappropriate proposals and poor robustness to spatial rotation even scale variation. We propose a cascaded region proposal network framework for visual tracking based on region proposal networks, spatial transformer networks and proposal selection strategy. We first to extract the features from deep and shallow layers via cascaded region proposal network to ensure the spatial information and semantic cue of the appearance model. Then, the feature extraction model based on spatial transformer networks is performed to calculate the parameters of spatial transformer and obtain the fused features. During the tracking and testing of proposed networks, the proposals are generated and re-ranked by formulating the proposals selection strategy to ensure the localization and scale of the estimated target. We extensively prove the effectiveness of the proposed method though the ablation studies of the tracking benchmark which include OTB2015, VOT2016 and UAV123. The experimental results perform that the accuracy and robustness of the proposed method as the real-time tracker and the long-term tracker as well. In the meantime, the test on the benchmark UAV123 shows that the tracker can be employed to some engineering area.
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- 2020
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23. Exploration Enhanced Expected Improvement for Bayesian Optimization
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Vu Nguyen, Julian Berk, Santu Rana, Svetha Venkatesh, and Sunil Gupta
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Mathematical optimization ,021103 operations research ,Computer science ,Selection strategy ,Bayesian optimization ,0211 other engineering and technologies ,02 engineering and technology ,Function (mathematics) ,010501 environmental sciences ,01 natural sciences ,Range (mathematics) ,Probabilistic method ,Rate of convergence ,Code (cryptography) ,Global optimization ,0105 earth and related environmental sciences - Abstract
Bayesian optimization (BO) is a sample-efficient method for global optimization of expensive, noisy, black-box functions using probabilistic methods. The performance of a BO method depends on its selection strategy through an acquisition function. This must balance improving our understanding of the function in unknown regions (exploration) with locally improving on known promising samples (exploitation). Expected improvement (EI) is one of the most widely used acquisition functions for BO. Unfortunately, it has a tendency to over-exploit, meaning that it can be slow in finding new peaks. We propose a modification to EI that will allow for increased early exploration while providing similar exploitation once the system has been suitably explored. We also prove that our method has a sub-linear convergence rate and test it on a range of functions to compare its performance against the standard EI and other competing methods. Code related to this paper is available at: https://github.com/jmaberk/BO_with_E3I.
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- 2019
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24. Budget and Cost-Aware Resources Selection Strategy in Cloud Computing Environments
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Victor V. Toporkov, Dmitry Yemelyanov, and Andrei Tchernykh
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Cloud resources ,business.industry ,Computer science ,Distributed computing ,Selection strategy ,Novelty ,Cloud computing ,computer.software_genre ,Economic scheduling ,Virtual machine ,CloudSim ,Bandwidth (computing) ,business ,computer - Abstract
In this work, we introduce algorithms for resource selection in heterogeneous cloud computing environments. Cloud resources are represented as virtual machine instances ready to start with characteristics including performance, RAM, storage, bandwidth, and usage price. User request contains requirements that can be satisfied by different bundles of the virtual machines. We propose and analyze algorithms and scenarios for efficient resources selection and compare them with known approaches. The novelty of the proposed approach is in multiobjective selection of cloud resource bundles according to the specified limited budget.
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- 2019
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25. An Adaptive Mesh Selection Strategy for Solving Singularly Perturbed Parabolic Partial Differential Equations with a Small Delay
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P. Pramod Chakravarthy, R. Nageshwar Rao, Trun Gupta, and Kamalesh Kumar
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Binary entropy function ,Partial differential equation ,Selection strategy ,Perturbation (astronomy) ,Applied mathematics ,Polygon mesh ,Prior information ,Mathematics - Abstract
In this paper, an adaptive mesh has been generated using the concept of entropy function for solving convection-diffusion singularly perturbed parabolic partial differential equations with a small delay. Similar problems are associated with a furnace used to process a metal sheet in control theory. The beauty of the method is, unlike the popular adaptive meshes (Bakhvalov and Shishkin), prior information of the width and position of the layers are not required. The method is independent of perturbation parameter e and gives us an oscillation-free solution, without any user-introduced parameters. The applicability of the proposed method is illustrated by means of two examples.
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- 2019
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26. Exploiting Global Impact Ordering for Higher Throughput in Selective Search
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Juan Rodriguez, Michał Siedlaczek, and Torsten Suel
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Computer science ,Distributed computing ,05 social sciences ,Selection strategy ,02 engineering and technology ,Search-oriented architecture ,Shard ,Peak load ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Query throughput ,State (computer science) ,0509 other social sciences ,050904 information & library sciences ,Throughput (business) - Abstract
We investigate potential benefits of exploiting a global impact ordering in a selective search architecture. We propose a generalized, ordering-aware version of the learning-to-rank-resources framework [9] along with a modified selection strategy. By allowing partial shard processing we are able to achieve a better initial trade-off between query cost and precision than the current state of the art. Thus, our solution is suitable for increasing query throughput during periods of peak load or in low-resource systems.
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- 2019
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27. Towards understanding the traits contributing to performance of pearl millet open-pollinated varieties in phosphorus-limited environments of West Africa
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Gemenet, Dorcus C., Beggi, Francesca, Hash, C. Tom, Sy, Ousmane, Sanogo, Mousa D., Zangre, Roger G., Falalou, Hamidou, Buerkert, Andreas, and Haussmann, Bettina I. G.
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- 2016
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28. EREL Selection Using Morphological Relation
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Yuying Li and Mehdi Faraji
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Jaccard index ,Correlation coefficient ,Selection strategy ,02 engineering and technology ,Ellipse ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Compact space ,Hausdorff distance ,Test set ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Segmentation ,Algorithm ,Mathematics - Abstract
This work concentrates on Extremal Regions of Extremum Level (EREL) selection. EREL is a recently proposed feature detector aiming at detecting regions from a set of extremal regions. This is a branching problem derived from segmentation of arterial wall boundaries from Intravascular Ultrasound (IVUS) images. For each IVUS frame, a set of EREL regions is generated to describe the luminal area of human coronary. Each EREL is then fitted by an ellipse to represent the luminal border. The goal is to assign the most appropriate EREL as the lumen. In this work, EREL selection carries out in two rounds. In the first round, the pattern in a set of EREL regions is analyzed and used to generate an approximate luminal region. Then, the two-dimensional (2D) correlation coefficients are computed between this approximate region and each EREL to keep the ones with tightest relevance. In the second round, a compactness measure is calculated for each EREL and its fitted ellipse to guarantee that the resulting EREL has not affected by the common artifacts such as bifurcations, shadows, and side branches. We evaluated the selected ERELs in terms of Hausdorff Distance (HD) and Jaccard Measure (JM) on the train and test set of a publicly available dataset. The results show that our selection strategy outperforms the current state-of-the-art.
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- 2018
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29. Indicator-Based Versus Aspect-Based Selection in Multi- and Many-Objective Biochemical Optimization
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Markus Borschbach and Susanne Rosenthal
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0301 basic medicine ,education.field_of_study ,Optimization problem ,business.industry ,Computer science ,media_common.quotation_subject ,Population ,Selection strategy ,02 engineering and technology ,Machine learning ,computer.software_genre ,03 medical and health sciences ,Identification (information) ,030104 developmental biology ,Dimension (vector space) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Quality (business) ,Artificial intelligence ,business ,education ,computer ,Selection (genetic algorithm) ,media_common - Abstract
The identification of qualified peptides as ligands for diagnostic and therapeutic interventions requires the solution of multi- and many-objective biochemical optimization problems. A MOEA has been designed for molecular optimization with a combined indicator- and Pareto-based selection strategy that encounters common classification problems of the solutions’ quality with the rise of the problem dimension. Therefore, a sophisticated selection strategy is presented in this work that selects the individuals for the succeeding generation related to two general aspects in biochemical optimization: the first aspect reflects the peptide quality and the second one the genetic dissimilarity among the peptides in a population. The search behavior of this aspect-based selection is compared to the traditional selection on generic 3- to 6-dimensional physiochemical optimization problems and the impact of the reference point in the aspect-based selection is investigated.
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- 2018
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30. A Sparse and Low-Rank Matrix Recovery Model for Saliency Detection
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Jing Li, Yi Zhuang, KeXin Li, and Chao Wang
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Pixel ,business.industry ,Computer science ,Selection strategy ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Low-rank approximation ,Pattern recognition ,02 engineering and technology ,Object (computer science) ,01 natural sciences ,Image (mathematics) ,Matrix (mathematics) ,Transformation matrix ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Enhanced Data Rates for GSM Evolution ,010306 general physics ,business - Abstract
The previous low-rank matrix recovery model for saliency detection have a large of problem that the transform matrix obtained on the open datasets may not be suitable for the detecting image and the transform matrix fails to combine the low-level features of the image. In this paper, we propose a novel salient object detection model that combines sparse and low-rank matrix recovery (SLRR) with the adaptive background template. Our SLRR model using a selection strategy is presented to establish the adaptive background template by removing the potential saliency super pixels from the image border regions, and the background template is obtained. And the sparse and low rank matrix recovery model solved by Inexact Augmented Lagrange Multiplier (ALM). Both quantitative and qualitative experimental results on two challenging datasets show competitive results as compared with other state-of-the-art methods. In addition, a new datasets which saliency object on the edge (SOE), containing 500 images is constructed for evaluating saliency detection.
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- 2018
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31. An Improved Genetic Algorithm on Task Scheduling
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Fangyuan Zheng and Jingmei Li
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education.field_of_study ,Mathematical optimization ,Computer science ,Mutation probability ,Population ,Crossover ,Selection strategy ,education ,Scheduling (computing) - Abstract
Efficient task scheduling algorithm is critical for achieving high performance in heterogeneous multi-core processors. Because the existing genetic algorithm converges to local optimal solution, so an improved genetic algorithm is proposed to solve the above problems in this thesis. Firstly, the initial population is generated randomly according to the task height value, and then adopting the selection strategy based on competition scale. Finally, the crossover and mutation probability is improved to avoid premature phenomenon. The experiment based on randomly generated graphs shows that the proposed algorithm can improve the efficiency of convergence.
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- 2018
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32. Extremized PICEA-g for Nadir Point Estimation in Many-Objective Optimization
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Ling Wang, Lining Xing, Rui Wang, Wen-ying Gong, and Meng-jun Ming
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Ideal point ,0209 industrial biotechnology ,Mathematical optimization ,Pareto optimal ,020901 industrial engineering & automation ,Closeness ,Selection strategy ,0202 electrical engineering, electronic engineering, information engineering ,Evolutionary algorithm ,020201 artificial intelligence & image processing ,02 engineering and technology ,Nadir point ,Mathematics - Abstract
Nadir point, constructed by the worst Pareto optimal objective values, plays an important role in multi-objective optimization and decision making. For example, the nadir point is often a pre-requisite in many multi-criterion decision making approaches. Along with the ideal point, the nadir point can be applied to normalize solutions so as to facilitate a comparison and aggregation of objectives. Moreover, nadir point is useful in visualization software catered for multi-objective optimization. However, the estimation of nadir point is still a challenging problem, particularly, for optimization and/or decision-making problems with many objectives. In this paper, a modified preference-inspired coevolutionary algorithm using goal vectors (PICEA-g) called extremized PICEA-g is proposed to estimate the nadir point. The extremized PICEA-g, denoted as e-PICEA-g, is an \( \left( {N + N} \right) \) elitist algorithm and employs a two-phase selection strategy. In the first-phase \( \left( {N + K} \right) \) solutions are selected out from the overall \( 2N \) solutions based on the dominance-level and an angle based closeness indicator. In the second-phase the selected \( \left( {N + K} \right) \) solutions are further filtered by removing \( K \) poor ones in terms of their fitness calculated by a slightly modified PICEA-g fitness scheme. By the two-phase selection strategy, the e-PICEA-g skillfully harnesses the advantages of edge-point-to-nadir and extreme-to-nadir principles. Experimental results demonstrate the efficiency and effectiveness of the e-PICEA-g on many-objective optimization benchmarks with up to 13 objectives.
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- 2018
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33. A New Selection Without Replacement for Non-dominated Sorting Genetic Algorithm II
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Zhuanghua Zhu, Zhihua Cui, Yechuang Wang, Maoqing Zhang, and Xingjuan Cai
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Mathematical optimization ,Optimization problem ,Computer science ,020208 electrical & electronic engineering ,Crossover ,Selection strategy ,Sorting ,02 engineering and technology ,Tournament selection ,Mutation (genetic algorithm) ,Genetic algorithm ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Selection (genetic algorithm) - Abstract
NSGA-II has shown good performance in solving multi-objective optimization problems, However, the tournament selection strategy in NSGA-II always generates many duplicate individuals. This phenomenon not only affects the crossover, mutation and updating operations and finally deteriorates the performance significantly. To overcome this problem, this paper introduces a new strategy, namely selection strategy without replacement, which can produces different individuals to increase the diversity. Simulation results show the proposed tournament selection without replacement achieve better performance.
- Published
- 2018
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34. Adaptive Temporal Verification and Violation Handling for Time-Constrained Business Cloud Workflows
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Haoyu Luo, Xiao Liu, Bo Han, Yun Yang, and Jin Liu
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Adaptive strategies ,Matching (statistics) ,business.industry ,Cost effectiveness ,Computer science ,Time constrained ,Distributed computing ,Selection strategy ,020207 software engineering ,Cloud computing ,Cloud workflow ,02 engineering and technology ,Workflow ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,business - Abstract
To achieve on-time completion of time-constrained business cloud workflows, a large number of parallel cloud workflow instances need to be constantly monitored so that temporal violations (namely intermediate runtime delays) can be detected and handled timely. Over the last few years, many strategies have been proposed but they are not adaptive enough to capture the dynamic behaviors of business cloud workflows. In this paper, we introduce the idea of “adaptiveness” into our strategy design. Specifically, we first present an adaptive temporal checkpoint selection strategy where the time intervals between checkpoints are adaptively determined at runtime, and then propose a matching temporal violation handling strategy which can determine the required lifecycle of cloud services. The evaluation results demonstrate that our adaptive strategy can achieve both higher efficiency and better cost effectiveness compared with conventional strategies.
- Published
- 2018
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35. Flight Arrival Scheduling Optimization on Two Runways Based on IGEP Algorithm
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Minglei Qu, Rui Wang, and Fuzheng Wang
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020301 aerospace & aeronautics ,Fitness function ,010504 meteorology & atmospheric sciences ,Operations research ,Aviation ,business.industry ,Computer science ,Selection strategy ,Scheduling (production processes) ,ComputerApplications_COMPUTERSINOTHERSYSTEMS ,02 engineering and technology ,01 natural sciences ,International airport ,0203 mechanical engineering ,Runway ,Travel mode ,business ,Pace of life ,0105 earth and related environmental sciences - Abstract
With the accelerated pace of life, more and more tourists’ travel mode change from the traditional land travel into air travel. It has contributed to the rapid development of the aviation industry. But also produced some problems that troubled the airline. Among them, the flight delays problem has not been effectively addressed. The cost of flight delays is still high. This paper launches the research, analyze the cause of the cost of flight delays. For the factor of terminal area flight scheduling unreasonable to improve. Combined with the two-runway actual situation of Chengdu Shuangliu International Airport, minimize the cost of flight arrival delays, construct the model of flight arrival on two runways. At the same time, the coding method, selection strategy and fitness function of GEP are improved combined with the specific problem. Finally, IGEP and simulation are utilized to solve the practical problem. Compared with the traditional FCFS rules, the cost of flight arrival delays is significantly reduced, the efficiency of flight arrival and runway utilization is improved, and the interests of airlines are guaranteed. It also shows the superiority of IGEP in addressing the issue of two-runway flight arrival.
- Published
- 2017
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36. A Weight-Selection Strategy on Training Deep Neural Networks for Imbalanced Classification
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Antonio Sze-To and Andrew K. C. Wong
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Source code ,business.industry ,Computer science ,media_common.quotation_subject ,Selection strategy ,Training (meteorology) ,Pattern recognition ,02 engineering and technology ,Machine learning ,computer.software_genre ,Class (biology) ,Set (abstract data type) ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Deep neural networks ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer ,Selection (genetic algorithm) ,MNIST database ,media_common - Abstract
Deep Neural Networks (DNN) have recently received great attention due to their superior performance in many machining-learning problems. However, the use of DNN is still impeded, if the input data is imbalanced. Imbalanced classification refers to the problem that one class contains a much smaller number of samples than the others in classification. It poses a great challenge to existing classifiers including DNN, due to the difficulty in recognizing the minority class. So far, there are still limited studies on how to train DNN for imbalanced classification. In this study, we propose a new strategy to reduce over-fitting in training DNN for imbalanced classification based on weight selection. In training DNN, by splitting the original training set into two subsets, one used for training to update weights, and the other for validation to select weights, the weights that render the best performance in the validation set would be selected. To our knowledge, it is the first systematic study to examine a weight-selection strategy on training DNN for imbalanced classification. Demonstrated by experiments on 10 imbalanced datasets obtained from MNIST, the DNN trained by our new strategy outperformed the DNN trained by a standard strategy and the DNN trained by cost-sensitive learning with statistical significance (p = 0.00512). Surprisingly, the DNN trained by our new strategy was trained on 20% less training images, corresponding to 12,000 less training images, but still achieved an outperforming performance in all 10 imbalanced datasets. The source code is available in https://github.com/antoniosehk/WSDeepNN.
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- 2017
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37. A Multi-task Oriented Selection Strategy for Efficient Cooperation of Collocated Mobile Devices
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Jun Feng, Hui Gao, Wendong Wang, and Ruidong Wang
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020203 distributed computing ,business.industry ,Computer science ,Distributed computing ,Selection strategy ,020206 networking & telecommunications ,Cloud computing ,02 engineering and technology ,Task (project management) ,User experience design ,0202 electrical engineering, electronic engineering, information engineering ,Task oriented ,Set (psychology) ,business ,Heuristics ,Mobile device - Abstract
As the resource consumption of mobile applications continues to outweigh the hardware capacities of mobile devices, the user experience can be improved by leveraging the resources of the nearby mobile devices. In other words, a mobile device should be able to request surrounding devices to execute tasks on its behalf. By treating the resources of the devices in the vicinity collectively, one can orchestrate a distributed interaction that uses these resources efficiently, thereby improving individual user experiences. In this paper, we study the problem of allocating multiple tasks across multiple mobile devices with the goal of reducing the collective execution time, while limiting the resources consumed by each participating device. Our selection strategy uses a set of heuristics to allocate the task execution in the aforementioned type of distributed interaction scenarios. The results of our experimental evaluation indicate that this strategy can be successfully applied in Mobile Device Cloud applications.
- Published
- 2017
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38. Task Selection Strategies
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Steven J. Ross
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Interview ,Scale (social sciences) ,Selection strategy ,Selection (linguistics) ,Psychology ,Cognitive psychology ,Focus (linguistics) ,Task (project management) - Abstract
This chapter features a full-length interview with interpretive commentary between each of the tasks selected by the interviewer. The focus here is to interpret the evidence of proficiency in the candidate’s discourse in the light of the real-time rating criteria the interviewer must attend to. The interviewer’s task selection strategy is considered in detail. As the task selection sequence is contingent on task fulfillment, we examine the timing of probes in the interview and how they can be interpreted by raters as evidence of the ongoing formation of a hypothesis about the candidate’s maximum proficiency level on the ILR scale. The distinction between task-constructive talk and transitional talk is made as well as features of descriptive preludes that introduce probes to the candidate’s upper limit of proficiency.
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- 2017
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39. NCFR: Network Coding Fair Routing for Multi-success-rate Wireless Sensor Networks
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Binbin Xie, Anwen Wang, Wei Wang, Dingyi Fang, Xiaoyan Yin, Xiaojiang Chen, Jun Guo, Lvju Wang, and Chun Ma
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Routing protocol ,business.industry ,Wireless network ,Computer science ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,Selection strategy ,020206 networking & telecommunications ,Wireless WAN ,02 engineering and technology ,021001 nanoscience & nanotechnology ,Linear network coding ,0202 electrical engineering, electronic engineering, information engineering ,Maximum throughput scheduling ,0210 nano-technology ,business ,Wireless sensor network ,Computer network ,Coding (social sciences) - Abstract
Recently network coding has become a promising approach to improve the network throughput in wireless networks. However, the existing network coding-based routing protocols can't well work at improving the network throughput in wireless network with multi-success-rate and link asymmetry, which use low quality links for coding. In this paper, we use transmission count as link quality and provide a theoretical formulation for computing the transmission count of network coding in wireless network with multi-success-rate and link asymmetry. Then we advocate a simple route selection strategy called: NCF-network coding fair routing to maximize the network throughput and reduce delay by minimizing transmission count in this wireless network. NCF computes source-destination routes by optimizing initial routing paths resulting in more coding opportunities. The simulation results demonstrate that NCF improves coding opportunity leading to higher end-to-end throughout when compared to SPATH.
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- 2016
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40. A Goodness Based Vertical Handoff Algorithm for Heterogeneous Networks
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Sasthi C. Ghosh and Shankar K. Ghosh
- Subjects
021103 operations research ,Access network ,Computer science ,Quality of service ,RSS ,Distributed computing ,Selection strategy ,0211 other engineering and technologies ,020206 networking & telecommunications ,02 engineering and technology ,computer.file_format ,Scheduling (computing) ,Handoff algorithms ,Handover ,0202 electrical engineering, electronic engineering, information engineering ,computer ,Heterogeneous network - Abstract
While moving across heterogeneous networks with strict rate requirement, the possibility of getting the required rate from the target network, depends on the QoS-awareness of the network selection strategy of the concerned vertical handoff (VHO) algorithm. Inclusion of MAC layer scheduling information in the design of different VHO algorithms has previously been very limited though it is important as both user and system performance depend on it. In this paper, we introduce the notion of goodness of an access network and based on it propose a goodness based VHO (GVHO) algorithm. The notion of goodness explicitly considers the MAC layer scheduling along with current load and interference of the candidate networks. The GVHO algorithm accounts the goodness values of the candidate networks to select the target network. Simulation results confirm that GVHO algorithm improves both user and system performance compared to RSS and SINR based VHO algorithms.
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- 2016
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41. A Generic Feature Selection Method for Background Subtraction Using Global Foreground Models
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Marc Van Droogenbroeck and Marc Braham
- Subjects
Background subtraction ,Computer science ,business.industry ,Selection strategy ,Process (computing) ,Segmentation ,Computer vision ,Feature selection ,Artificial intelligence ,business ,Selection (genetic algorithm) ,Change detection - Abstract
Over the last few years, a wide variety of background subtraction algorithms have been proposed for the detection of moving objects in videos acquired with a static camera. While much effort have been devoted to the development of robust background models, the automatic spatial selection of useful features for representing the background has been neglected. In this paper, we propose a generic and tractable feature selection method. Interesting contributions of this work are the proposal of a selection process coherent with the segmentation process and the exploitation of global foreground models in the selection strategy. Experiments conducted on the ViBe algorithm show that our feature selection technique improves the segmentation results.
- Published
- 2015
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42. The Selection Strategy of Form-Finding Methods for Transmission Lines Based on ANSYS
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Linghao Zhang, Xie Yunyun, Changsheng Liu, and Jin Ying
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Electric power transmission ,Basis (linear algebra) ,Iterative method ,Computer science ,Selection strategy ,State (computer science) ,Algorithm - Abstract
The aim of this chapter is to obtain the selection strategy of form-finding methods for the initial state of transmission lines based on ANSYS. First, this chapter introduces the theory and the main characteristic of four kinds of form-finding methods for transmission lines, which are the “V”-shape method, the little elastic modulus method, the direct iteration method, and the across-the-lowest-point method. Second, in order to get the selection strategy of these four methods, design a group of calculation models. Then, this chapter organizes simulation results on the basis of different methods, and makes further analyses of these data. The selection strategy proposed in this chapter can provide a convenient way to choose the proper form-finding method for the initial state of transmission lines based on ANSYS.
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- 2015
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43. Elaboration of General Lower Bounds for the Total Completion Time in Flowshop Scheduling Problems through MaxPlus Approach
- Author
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Pauline Fouillet, Nhat Vinh Vo, and Christophe Lenté
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Mathematical optimization ,021103 operations research ,Job shop scheduling ,Computer science ,Selection strategy ,0211 other engineering and technologies ,Scheduling (production processes) ,010103 numerical & computational mathematics ,02 engineering and technology ,01 natural sciences ,Travelling salesman problem ,Upper and lower bounds ,Blocking (computing) ,Permutation ,0101 mathematics ,Completion time - Abstract
As a type of scheduling problem, the flowshop problem has been largely studied for 60 years. The total completion time is a very interesting criterion because it reflects “the total manufacturing waiting time experienced by all customers”(Emmons and Vairaktarakis). There have been many studies in the past but they focused on a limited number of machines and/or on specific constraints. Therefore, this study presents a new approach to tackle a general permutation flowshop problem, with various additional constraints, to elaborate on lower bounds for the total completion time. These lower bounds can take into account several constraints, like delays, blocking or setup times, but they imply solving a Traveling Salesman Problem. The theory is developed first, based on a MaxPlus modeling of flowshop problems and experimental results of a branch-and-bound procedure with a lower bound selection strategy are then presented.
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- 2015
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44. Considering Reputation in the Selection Strategy of Genetic Programming
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Rung-Tzuo Liaw, Chuan-Kang Ting, Chiao-Jou Lin, and Chien-Chih Liao
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Recall ,Basis (linear algebra) ,business.industry ,Computer science ,Genetic programming algorithm ,media_common.quotation_subject ,Selection strategy ,Evolutionary algorithm ,Genetic programming ,Biological evolution ,Machine learning ,computer.software_genre ,Artificial intelligence ,business ,computer ,Reputation ,media_common - Abstract
Genetic programming (GP) is an evolutionary algorithm inspired by biological evolution. GP has shown to be effective to build prediction and classification model with high accuracy. Individuals in GP are evaluated by fitness, which serves as the basis of selection strategy: GP selects individuals for reproducing their offspring based on fitness. In addition to fitness, this study considers the reputation of individuals in the selection strategy of GP. Reputation is commonly used in social networks, where users earn reputation from others through recognized performance or effort. In this study, we define the reputation of an individual according to its potential to produce good offspring. Therefore, selecting parents with high reputation is expected to increase the opportunity for generating good candidate solutions. This study applies the proposed algorithm, called the RepGP, to solve the classification problems. Experimental results on four data sets show that RepGP with certain degrees of consanguinity can outperform two GP algorithms in terms of classification accuracy, precision, and recall.
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- 2015
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45. The Effect of Reactant and Product Selection Strategies on Cycle Evolution in an Artificial Chemistry
- Author
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Kourosh Neshatian and Thomas J. Young
- Subjects
Minimal effect ,Selection strategy ,Artificial chemistry ,Operations management ,Product selection ,Biochemical engineering ,Biology ,Selection (genetic algorithm) - Abstract
The molecules within an Artificial Chemistry form an evolutionary system, capable under certain conditions of displaying interesting emergent behaviours. We investigate experimentally the effect on emergence of the combinations of selected strategies for choosing reactants (Uniform and Kinetic selection) and products (Uniform and Least Energy selection) as measured by three measures of reaction cycle formation. Emergence is maximised by a Kinetic reactant selection strategy; the choice of product selection strategy has minimal effect.
- Published
- 2015
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46. Compatibility Analysis: A Country Selection Strategy for Investment in LDCS
- Author
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Melodie R. Phillips and Janeen E. Olsen
- Subjects
Host country ,business.industry ,Selection strategy ,Economics ,International trade ,Foreign direct investment ,Investment opportunities ,Emerging markets ,business ,Developed country - Abstract
The global economy is continually being reshaped as companies expand their operations by means of foreign investment. Increasingly, companies are looking for investment opportunities in Lesser Developed Countries (LDCs). Some companies invest in LDCs for more favorable labor or material costs to help them compete in world markets, while other companies seek access to the fast growing, emerging markets found within LDCs. With either objective, the decision of which LDC to enter is a difficult one. LDCs do share many economic similarities, yet individual countries may differ greatly with regards to the governmental policies used to promote economic growth. This paper provides a managerially oriented framework for identifying governmental policies which complement a company’s investment objectives.
- Published
- 2014
- Full Text
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47. Alamouti Coding Based Relay Selection Strategies for Wireless Multi-hop Network
- Author
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Mei Song, Linglong Cui, Hong Yuan, Junxiang Wang, Yihai Xing, and Yinglei Teng
- Subjects
business.industry ,Computer science ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,Selection strategy ,MIMO ,Data_CODINGANDINFORMATIONTHEORY ,Performance results ,law.invention ,Hop (networking) ,Relay ,law ,Computer Science::Networking and Internet Architecture ,Bit error rate ,Wireless ,Condensed Matter::Strongly Correlated Electrons ,business ,Computer Science::Information Theory ,Computer network ,Coding (social sciences) - Abstract
Researches on wireless multi-hop networks have always raised doubt about the performance of the same relay selection strategy in all hops which leads to the neglect of different transmission scenarios between hops in wireless multi-hop networks. In this paper, we have determined to provide a relay selection strategy for wireless multi-hop network, which divides the hops into three parts according to different receive-transmission mode: the first hop (2×1), the middle hops (2×2) and the last hop (1×2), and Alamouti coding based transmission strategy has been investigated. Since the specificity of the last hop, we make consideration on jointing the last two hops, aiming to ensure the performance of the optimal relay selection in the whole path. Performance results are given from the aspects of network system capacity and bit error ratio (BER). Numerical results show that the proposed multi-hop relay selection strategy is definitely able to gain more throughputs and reduce the BER compared with random relay selection strategy in SISO system and virtual MIMO (2×2) system.
- Published
- 2014
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48. How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning
- Author
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Sebastian Gross, Niels Pinkwart, Barbara Hammer, and Bassam Mokbel
- Subjects
Java ,Computer science ,business.industry ,Selection strategy ,Sample (statistics) ,Machine learning ,computer.software_genre ,Intelligent tutoring system ,Example based learning ,ComputingMilieux_COMPUTERSANDEDUCATION ,Selection (linguistics) ,Domain knowledge ,Artificial intelligence ,business ,computer ,computer.programming_language - Abstract
In this paper, we investigate an Intelligent Tutoring System (ITS) for Java programming that implements an example-based learning approach. The approach does not require an explicit formalization of the domain knowledge but automatically identifies appropriate examples from a data set consisting of learners’ solution attempts and sample solution steps created by experts. In a field experiment conducted in an introductory course for Java programming, we examined four example selection strategies for selecting appropriate examples for feedback provision and analyzed how learners’ solution attempts changed depending on the selection strategy. The results indicate that solutions created by experts are more beneficial to support learning than solution attempts of other learners, and that examples modeling steps of problem solving are more appropriate for very beginners than complete sample solutions.
- Published
- 2014
- Full Text
- View/download PDF
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