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Pattern Reorder for Test Cost Reduction Through Improved SVMRANK Algorithm

IEEE ACCESS 2020年 8卷 147965-147972页

作者： Song, Tai Liang, Huaguo Ni, Tianming Huang, Zhengfeng Lu, Yingchun Wan, Jinlei Yan, Aibin Hefei Univ Technol Sch Elect Sci & Appl Phys Hefei 230009 Peoples R China Anhui Polytech Univ Coll Elect Engn Wuhu 241000 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

With the growing complexity of integrated circuits (IC), more and more test patterns are added to test set to test more defects, making the number of test pattern and individual test pattern length continues to increase as the size of IC gets larger, boosting test time and consequently test cost. To solve this problem, this paper proposes a kind of valid pattern identification method. The method uses machine learning to reorder the test pattern which can select the most effective patterns, to determine the optimal training set and test set first. Then, by means of the weighted SVMRANK algorithm to find the optimal pattern sequence. Experiment results demonstrate that the method only sacrifices 2% prediction accuracy in exchange for 3.89 times the time saving. The method aims at maximizing the accuracy of test, and minimizing the number of patterns. The proposed idea significantly improves the test time and test efficiency compared to conventional test flows. This is an innovative test cost reduction method with the growing complexity of IC.

关键词： Testing Integrated circuits Pressing Redundancy Complexity theory machine learning machine learning algorithms Adaptive test machine learning test reorder

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Incremental Cost-Sensitive Support Vector machine With Linear-Exponential Loss

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IEEE ACCESS 2020年 8卷 149899-149914页

作者： Ma, Yue Zhao, Kun Wang, Qi Tian, Yingjie Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Beijing Wuzi Univ Sch Logist Beijing 101149 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

Incremental learning or online learning as a branch of machine learning has attracted more attention recently. For large-scale problems and dynamic data problem, incremental learning overwhelms batch learning, because of its efficient treatment for new data. However, class imbalance problem, which always appears in online classification brings a considerable challenge for incremental learning. The serious class imbalance problem may directly lead to a useless learning system. Cost-sensitive learning is an important learning paradigm for class imbalance problems and widely used in many applications. In this article, we propose an incremental cost-sensitive learning method to tackle the class imbalance problems in the online situation. This proposed algorithm is based on a novel cost-sensitive support vector machine, which uses the Linear-exponential (LINEX) loss to implement high cost for minority class and low cost for majority class. Using the half-quadratic optimization, we first put forward the algorithm for the cost-sensitive support vector machine, called CSLINEX-SVM*. Then we propose the incremental cost-sensitive algorithm, ICSL-SVM. The results of numeric experiments demonstrate that the proposed incremental algorithm outperforms some conventional batch algorithms except the proposed CSLINEX-SVM*.

关键词： Support vector machines Optimization machine learning algorithms machine learning Computational modeling Fasteners learning systems Online learning incremental learning class imbalance cost-sensitive LINEX loss support vector machine

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Investigating Educational and Noneducational Answer Selection for Educational Question Generation

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IEEE ACCESS 2022年 10卷 63522-63531页

作者： Steuer, Tim Filighera, Anna Tregel, Thomas Tech Univ Darmstadt Multimedia Commun Lab KOM D-64283 Darmstadt Germany

Educational automatic question generation (AQG) is often unable to realize its full potential in educational applications due to insufficient training data. For this reason, current research relies on noneducational question answering datasets for system training and evaluation. However, noneducational training data may comprise different language patterns than educational data. Consequently, the research question of whether models trained on noneducational datasets transfer well to the educational AQG task arises. In this work, we investigate the AQG subtask of answer selection, which aims to extract meaningful answers for the questions to be generated. We train and evaluate six modern and well-established BERT-based machine learning model architectures on two widely used noneducational datasets. Furthermore, we introduce a novel, midsized educational dataset for answer selection called TQA-A. TQA-A is used to investigate the transfer capabilities of the noneducational models to the educational domain. In terms of phrase-level evaluation metrics, noneducational models perform similar to models trained directly on the novel educational TQA-A dataset, although trained with considerably more training data. Moreover, models trained directly on TQA-A select fewer named entity-based and more verb-based answers than noneducational models. This provides evidence for differences in noneducational and educational answer selection tasks.

关键词： Gold Training data Training Task analysis Insects Data models machine learning algorithms Automatic question generation content selection education natural language processing

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Ten simple rules for engaging with artificial intelligence in biomedicine

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PLOS COMPUTATIONAL BIOLOGY 2021年第2期17卷 e1008531-e1008531页

作者： Malik, Avni Patel, Paranjay Ehsan, Lubaina Guleria, Shan Hartka, Thomas Adewole, Sodiq Syed, Sana Univ Virginia Coll Arts & Sci Charlottesville VA USA Univ Virginia Sch Med Charlottesville VA 22908 USA Univ Virginia Sch Med Dept Pediat Div Gastroenterol Hepatol & Nutr Charlottesville VA 22908 USA Univ Virginia Sch Med Dept Emergency Med Charlottesville VA 22908 USA Univ Virginia Dept Syst & Informat Engn Charlottesville VA USA

Only recently, the exponential growth of technology has facilitated the widespread integration of computers into the biomedical domain from electronization of medical data analysis to automated detection of biomedical images [2–3]. The lack of understanding around AI and the tantalizing benefits of this new wave of technology suggest the need for professionals in biomedical fields to acquire a basic understanding of AI and its medical applications in order to understand its clinical utility and engage with cutting-edge research. The importance of biomedical involvement in emerging technology is highlighted in the flaws of contemporary electronic medical records (EMR) that are widely used across the healthcare system. The ideal AI adaptation of EMR would be able to facilitate patient care through a variety of features like tracking changes in medical history of a patient and alerting caretakers of concerning health patterns; however, with the current state of EMR, tasks as simple as sharing medical records between healthcare facilities are burdensome.

关键词： Artificial intelligence Medicine and health sciences machine learning Electronic medical records machine learning algorithms Deep learning Health care facilities Radiologists

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The role of data frequency and method selection in electricity price estimation: Comparative evidence from Turkey in pre-pandemic and pandemic periods

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RENEWABLE ENERGY 2022年第0期186卷 217-225页

作者： Depren, Serpil Kilic Kartal, Mustafa Tevfik Ertugrul, Hasan Murat Depren, Ozer Yildiz Tech Univ Dept Stat Istanbul Turkey Borsa Istanbul Financial Reporting & Subsidiari Istanbul Turkey Minist Treasury & Finance Ankara Turkey Yapi Kredi Bank Customer Experience Res Ctr Istanbul Turkey

The study examines the role of data frequency and estimation methods in electricity price estimation by applying selected machine learning algorithms and time series econometric models. In this context, Turkey is selected as an emerging country example, seven explanatory variables including COVID-19 pandemic is considered, and daily and weekly data between February 20, 2019 and March 26, 2021 that includes pre-pandemic and pandemic periods are used. The empirical results show that (i) machine learning algorithms perform better than time series econometric models for both pre-pandemic and pandemic periods;(ii) high-frequency data increases the performance of estimation models;(iii) machine learning algorithms perform better with high-frequency (daily) data with regard to low-frequency (weekly) data;(iv) the pandemic causes an adverse effect on the performance of estimation models;(v) energy-related variables are more important than other variables although all are significant;(vi) the share of renewable sources in electricity production is the most important variable on the electricity prices in both periods and data types. Hence, the findings highlight the role of data frequency and method selection in electricity prices estimation. Moreover, policy implications are discussed.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Electricity prices Data frequency machine learning algorithms Time series econometric models Turkey

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Can Software Engineering Harness the Benefits of Advanced AI?

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IEEE SOFTWARE 2022年第6期39卷 99-104页

作者： Shaw, Mary Zhu, Liming Carnegie Mellon Univ Comp Sci Pittsburgh PA 15217 USA Univ New South Wales Sydney NSW 2015 Australia

Artificial intelligence (AI) has allowed us to build systems beyond anything deemed possible earlier. Can we evolve existing techniques in software engineering to meet the needs of AI enabled systems or do we need to ... 详细信息

关键词： Artificial Intelligence Software Engineering Software Systems Codes Software Reliability machine learning algorithms Deep learning machine learning

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Prediction of Player Churn and Disengagement Based on User Activity Data of a Freemium Online Strategy Game

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IEEE TRANSACTIONS ON GAMES 2021年第1期13卷 78-88页

作者： Rothmeier, Karsten Pflanzl, Nicolas Huellmann, Joschka A. Preuss, Mike Deloitte Consulting GmbH D-40476 Dusseldorf Germany Westfalische Wilhelms Univ Munster Dept Informat Syst D-48149 Munster Germany Leiden Univ Leiden Inst Adv Comp Sci NL-2300 RA Leiden Netherlands

Churn describes customer defection from a service provider. This can be observed in online freemium games, where users can leave without further notice. Game companies are looking for methods to detect and predict churn to enable management reaction. The recorded data of games can be analyzed for this purpose. In this article, we conducted a case study based on data from the freemium game The Settlers Online. Churn detection was achieved by application of four different labeling approaches, based on common churn and disengagement definitions within the game analytics literature. In order to model predictive classifiers, features were computed from the raw game data. Eight different machine learning algorithms returning binary classifications were applied. The results were compared for all algorithms regarding all labeling approaches. Random forests with sliding windows were the best solution in our case, returning area under curve values higher than 0.99, thereby enabling prediction accuracies of 97% in our dataset. The results were confirmed by tests on an independent dataset and in our discussion, we offer guidance on the interplay of feature engineering, labeling approaches-in particular, disengagement-and machine learning algorithms for churn prediction. Our recommendations are valuable for game companies and academics, who pursue similar studies.

关键词： Games Labeling Training Companies Buildings Data models machine learning algorithms Classification disengagement machine learning player churn

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Stratiform/convective rain delineation for TRMM microwave imager

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JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS 2015年 133卷 25-35页

作者： Islam, Tanvir Srivastava, Prashant K. Dai, Qiang Gupta, Manika Jaafar, Wan Zurina Wan CALTECH Jet Prop Lab Pasadena CA USA NOAA NESDIS Ctr Satellite Applicat & Res College Pk MD USA Colorado State Univ Cooperat Inst Res Atmosphere Ft Collins CO 80523 USA NASA Goddard Space Flight Ctr Greenbelt MD USA Univ Maryland Earth Syst Sci Interdisciplinary Ctr College Pk MD 20742 USA Univ Bristol Dept Civil Engn Bristol Avon England Univ Space Res Assoc Columbia MD USA Univ Malaya Dept Civil Engn Kuala Lumpur Malaysia

This article investigates the potential for using machine learning algorithms to delineate stratiform/convective (S/C) rain regimes for passive microwave imager taking calibrated brightness temperatures as only spectral parameters. The algorithms have been implemented for the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI), and calibrated as well as validated taking the Precipitation Radar (PR) S/C information as the target class variables. Two different algorithms are particularly explored for the delineation. The first one is metaheuristic adaptive boosting algorithm that includes the real, gentle, and modest versions of the AdaBoost. The second one is the classical linear discriminant analysis that includes the Fisher's and penalized versions of the linear discriminant analysis. Furthermore, prior to the development of the delineation algorithms, a feature selection analysis has been conducted for a total of 85 features, which contains the combinations of brightness temperatures from 10 GHz to 85 GHz and some derived indexes, such as scattering index, polarization corrected temperature, and polarization difference with the help of mutual information aided minimal redundancy maximal relevance criterion (mRMR). It has been found that the polarization corrected temperature at 85 GHz and the features derived from the "addition" operator associated with the 85 GHz channels have good statistical dependency to the S/C target class variables. Further, it has been shown how the mRMR feature selection technique helps to reduce the number of features without deteriorating the results when applying through the machine learning algorithms. The proposed scheme is able to delineate the S/C rain regimes with reasonable accuracy. Based on the statistical validation experience from the validation period, the Matthews correlation coefficients are in the range of similar to 0.60-0.70. Since, the proposed method does not rely on any a priori information, this makes

关键词： Stratiform/convective rain Precipitation type classification Passive microwave (PMW) sensor Satellite measurements Precipitation radar (PR) Brightness temperature machine learning algorithms

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Development and Assessment of SPM: A Sigmoid-Based Model for Probability Estimation in Non-Repetitive Unit Selection With Replacement

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IEEE ACCESS 2024年 12卷 16421-16430页

作者： Godara, Samarth Avinash, G. Parsad, Rajender Marwaha, Sudeep Faiz, Mukhtar Ahmad Bana, Ram Swaroop ICAR Indian Agr Stat Res Inst New Delhi 110012 India Afghanistan Natl Agr Sci & Technol Univ Dept Agron Kandahar 3801 Afghanistan ICAR Indian Agr Res Inst New Delhi 110012 India

Probability estimation plays a pivotal role across diverse domains, particularly in scenarios where the objective is to select non-repetitive units one at a time, with the option of replacement, from a predefined set of units. Traditional probability calculations in this scenario pose three challenges: the number of floating-point operations to be executed is directly proportional to the chosen set size, susceptibility to floating-point precision errors, and exponential growth in storage needs with increasing number of chosen units. In this scenario, the presented work aims to develop SPM: a sigmoid function-based model that estimates probabilities for such problems with a fixed number of calculations (independent of the input parameter), achieving a constant time complexity algorithm. The research methodology involves generating probability data points, selecting the optimal sigmoid function, augmenting additional data to enhance parameter estimation, identifying parameter estimation equations, and evaluating the model. Moreover, the study's second objective includes training and comparing six established machine learning-based models (including Decision Tree, Random Forest, Support Vector, Linear Regression, Nearest Neighbour, and Artificial Neural Network) against the proposed SPM. The rigorous assessment of the model's performance, utilising metrics including RMSE, MAE and r(2) across a wide range of scenarios involving varying values of the total units, affirms the model's accuracy and resilience. The study findings can improve decision-making processes in various domains, including statistics, cryptography, machine learning and optimisation, by offering a faster, more adaptable solution for probability estimation in units' selection with replacement.

关键词： Mathematical models Probability Estimation Training Optimization machine learning algorithms Logistics Probability estimation sigmoid function modeling non-repetitive units selection optimization

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Efficient FPGA Parallelization of Lipschitz Interpolation for Real-Time Decision-Making

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2022年第5期30卷 2163-2175页

作者： Nadales, J. M. Manzano, J. M. Barriga, A. Limon, D. Univ Seville Dept Automat Control & Syst Engn Seville 41004 Spain Univ Loyola Andalucia Dept Engn Dos Hermanas 41704 Spain Univ Seville Dept Elect & Electromagnetism Seville 41004 Spain

One of the main open challenges in the field of learning-based control is the design of computing architectures able to process data in an efficient way. This is of particular importance when time constraints must be met, as, for instance, in real-time decision-making systems operating at high frequencies or when a vast amount of data must be processed. In this respect, field-programmable gate array (FPGA)-based parallel processing architectures have been hailed as a potential solution to this problem. In this article, a low-level design methodology for the implementation on FPGA platforms of Lipschitz interpolation (LI) algorithms is presented. The proposed design procedure exploits the potential parallelism of the LI algorithm and allows the user to optimize the area and energy resources of the resulting implementation. Besides, the proposed design allows to know in advance a tight bound of the error committed by the FPGA due to the representation format. Therefore, the resulting implementation is a highly parallelized and a fast architecture with an optimal use of the resources and consumption and with a fixed numerical error bound. These facts flawlessly suit the desirable specifications of learning-based control devices. As an illustrative case study, the proposed algorithm and architecture have been used to learn a nonlinear model predictive control law applied to self-balance a two-wheel robot. The results show how computational times are several orders of magnitude reduced by employing the proposed parallel architecture, rather than sequentially running the algorithm on an embedded ARM-CPU-based platform.

关键词： Prediction algorithms Field programmable gate arrays Real-time systems Parallel architectures machine learning algorithms Interpolation Decision making Data-driven control field-programmable gate array (FPGA) implementation machine learning algorithms parallel embedded systems real-time control real-time decision-making

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