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2017 ICCBR Workshops on Computational Analogy and Case-Based Reasoning, CAW 2017, Case-Based Reasoning and Deep Learning, CBRDL 2017 and Process-Oriented Case-Based Reasoning, POCBR 2017, Doctoral Consortium, and Competitions, ICCBR-WS 2017

作者： Nasiri, Sara Department of Electrical Engineering and Computer Science University of Siegen Institute of Knowledge Based Systems and Knowledge Management Hölderlinstr. 3 Siegen57076 Germany

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Developing a prototype of case-based system utilizing fuzzy sets to detect faults of injection molding process 9

Developing a prototype of case-based system utilizing fuzzy ...

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2017 Tagungsband der 9. Konferenz Professionelles Wissensmanagement (Professional knowledge Management), WM 2017 - 9th Conference on Professional knowledge Management, WM 2017

作者： Nasiri, Sara Allayarov, Ildar Fathi, Madjid Department of Electrical Engineering and Computer Science University of Siegen Institute of Knowledge Based Systems and Knowledge Management Hölderlinstr. 3 Siegen57076 Germany

The purpose of this research is to develop a case-based system for detecting faults of injection molding machine by utilizing the information retrieved from troubleshooting guidelines. It is also reduced the system downtime. Case-based fault detection system guides users to detect their failure through the retrieval procedure. It utilizes fuzzy sets based on the relationship between parameters of the process, part, and mold to define the weight of features which is important for occurrence the faults. In this paper, a case-based system is also utilized the occurrence weights of features to capture the problems in injection molding processes and to recommend the way of fixing these faults. © 2017, CEUR-WS. All rights reserved.

关键词： Fuzzy logic

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Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm

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Journal of Physics: Conference Series 2019年第5期1345卷

作者： Gang Lin Yanchun Liang Xiaoyang Fu Guangshun Chen Shaocan Cai Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Department of Computer Science and Technology School of Computer Science Zhuhai 519041 Guangdong China Department of Computer Science and Technology School of Computer Science Zhuhai College of Jilin University Zhuhai 519041 Guangdong China

In order to generate a report for an enterprise where there is neither the API supporting from their existing website systems nor the granted database access rights approval, a daily business report generator system based on web scraping with k nearest neighbor (kNN) classification algorithm is proposed in this paper. It covers the web crawler technology that is to access existing website system and extract business data. The kNN algorithm is applied to identify the verification code on the login page, and the brief daily report generating in a spread-sheet style grid. Compared with some OCR engine for image recognition, the system in Python can automatically generate the brief daily business reports by the kNN algorithm, which is better than some library with default training set on validating the verification code.

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Understanding gender equity in author order assignment

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Proceedings of the ACM on Human-computer Interaction 2018年第CSCW期2卷 1–21页

作者： Early, Kirstin Hammer, Jessica Hofmann, Megan Rode, Jennifer A. Wong, Anna Mankoff, Jennifer Yahoo Research 701 First Avenue SunnyvaleCA94089 United States Carnegie Mellon University Human-Computer Interaction Institute Entertainment Technology Center 5000 Forbes Avenue PittsburghPA15213 United States University College London Knowledge Lab Gower Street London United Kingdom Carnegie Mellon University Department of Statistics 5000 Forbes Avenue PittsburghPA15213 United States University of Washington Paul G. Allen School of Computer Science and Engineering 185 E Stevens Way NE SeattleWA98195 United States

Academic success and promotion are heavily infuenced by publication record. In many felds, including computer science, multi-author papers are the norm. Evidence from other felds shows that norms for ordering author names can infuence the assignment of credit. We interviewed 38 students and faculty in human-computer interaction (HCI) and machine learning (ML) at two institutions to determine factors related to assignment of author order in collaborative publication in the feld of computer science. Interview outcomes informed metrics for our bibliometric analysis of gender and collaboration in papers published between 1996 and 2016 in three top HCI and ML conferences. Based on our fndings, we make recommendations for assignment of credit in multi-author papers and interpretation of author order, particularly with respect to how these factors afect women. © 2018 Association for Computing Machinery.

关键词： Paper

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A Novel Texture Generation Super Resolution Model

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Procedia computer science 2019年 162卷 924-931页

作者： Biao Li Yong Shi Sujuan Li Bo Wang Zhiquan Qi Jiabin Liu School of Economics and Management University of Chinese Academy of Sciences Beijing 101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA Department of Otology the First Affiliated Hospital of Zhengzhou University No. 1 eastern Jianshe road Zhengzhou Henan 450052 China School of Information Technology and Management University of International Business and Economics Beijing 100029 China Department of Computer Science and Engineering Texas A&M University TX 77843 USA

Recently, super-resolution methods pursue visual pleasant details attract more attention in academic circle. Unlike former accurate driving models, they leverage new losses measured the difference of features extracting from a pre-trained deep learning model. Moreover, the popular GANs structure are introduced to approach better outputs. In general, these new visual feeling promoting losses are constrained by the learning ability of model and effected by the weakness of GANs. In order to address these problems, we propose a novel SR method to generating abundant details. Based on former SR methods and texture generated models, we combine new model constructure and integrated loss function to achieve our goal. The new constructure derives from the very thought that more information from the LR input leads to more accurate results. The new integrated loss try to decrease the weakness of each one by corresponding loss.. Experiments show that our model achieves higher quality generations than former methods.

关键词： Super Resolution Multi Scale Smooth Loss Visual Quality

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Development of the 3-Dimensional Map in the Bandung Regency Government Complex

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IOP Conference Series: Materials science and engineering 2019年第2期662卷

作者： H Maulana R Andriana H Kanai Department of Informatics Engineering and Computer Science Universitas Komputer Indonesia Bandung Indonesia School of Knowledge Science Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa Japan

The purpose of this research is the development of 3-D Maps in the Government Complex of Bandung Regency to facilitate the people in Bandung Regency to find the location of official offices related to administrative needs. The research method used was qualitative and quantitative methods and there are several stages carried out in this study consisting of 4 stages. The first stage is data collection and analysis, software development, system testing and the last stage is drawing conclusions. The application built models the agency office and environment in 3D. The application built also has the closest route search feature using the Dijkstra algorithm. The results of this study are that the system also provides information relating to the agency office. Based on the results of user acceptance tests conducted on residents, more than 70% of the population agree that this 3D map makes it easy for residents to find the location of a building and determine the closest distance to that location. With this 3D Map, residents no longer need to worry if they get lost in the complex area of the Bandung Regency Government and can also quickly find the location of an official service office.

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Partitioning vectors into quadruples: Worst-case analysis of a matching-based algorithm

arXiv

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arXiv 2018年

作者： Ficker, Annette M.C. Erlebach, Thomas Mihalák, Matúš Spieksma, Frits C.R. Faculty of Economics and Business KU Leuven Leuven Belgium Department of Informatics University of Leicester Leicester United Kingdom Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands

Consider a problem where 4k given vectors need to be partitioned into k clusters of four vectors each. A cluster of four vectors is called a quad, and the cost of a quad is the sum of the component-wise maxima of the four vectors in the quad. The problem is to partition the given 4k vectors into k quads with minimum total cost. We analyze a straightforward matching-based algorithm, and prove that this algorithm is a 23-approximation algorithm for this problem. We further analyze the performance of this algorithm on a hierarchy of special cases of the problem, and prove that, in one particular case, the algorithm is a 54-approximation algorithm. Our analysis is tight in all cases except *** codes 68Q25 Copyright © 2018, The Authors. All rights reserved.

关键词： Vectors

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Multi-objective optimization for distributed collaborative beamforming in mobile wireless sensor networks

Multi-objective optimization for distributed collaborative b...

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IEEE Symposium on computers and Communications

作者： Geng Sun Yanheng Liu Guojun Shen Aimin Wang Ying Zhang Victor C. M. Leung School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

Mobile wireless sensor networks (MWSN) are resource constrained, and have limited energy and transmission range. Distributed collaborative beamforming (DCB) in MWSN based on a virtual node antenna array (VNAA) can increase the transmission distance and enhance energy efficiency of a single sensor node. To achieve a lower maximum sidelobe level (SLL), sensor nodes can move to optimal locations with optimal excitation currents for DCB. However, this leads to an extra motion energy consumption. In this paper, we construct a multi-objective optimization framework to jointly optimize the maximum SLL, the transmission power and the motion energy consumption of the DCB nodes in MWSN. Moreover, an improved non-dorminated sorting genetic algorithm-II (INSGA-II) is proposed for solving the optimization problem. Simulation results show that the maximum SLL, the transmission power and the motion energy consumption of the VNAA can be effectively optimized by the proposed algorithms.

关键词： Mobile wireless sensor networks Collaborative beamforming Antenna arrays Genetic algorithm

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PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

arXiv

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arXiv 2018年

作者： Ferdaus, Md Meftahul Pratama, Mahardhika Anavatti, Sreenatha G. Garratt, Matthew A. Lughofer, Edwin School of Engineering and Information Technology Unsw CanberraACT2612 Australia School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Knowledge Based Mathematical Systems Johannes Kepler University Linz Austria

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feedforward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller. Copyright © 2018, The Authors. All rights reserved.

关键词： Controllers

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