159,117 results
Search Results
2. Effects of Data-Collection Designs in the Comparison of Computer-Based and Paper-Based Tests
- Author
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Alvaro J. Arce-Ferrer and Okan Bulut
- Subjects
Data collection ,Computer science ,Item analysis ,05 social sciences ,Computer based ,050301 education ,Test validity ,Paper based ,computer.software_genre ,Education ,Item response theory ,Developmental and Educational Psychology ,Data mining ,0503 education ,computer - Abstract
This study investigated the performance of four widely used data-collection designs in detecting test-mode effects (i.e., computer-based versus paper-based testing). The experimental conditions inc...
- Published
- 2018
3. Auto-generated Test Paper Based on Knowledge Embedding
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Guo-Sheng Hao, Fang Luo, Yi-Yang He, Xiao-Dan He, Zeng-Hui Duan, and Xing-Liu Hu
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Computer science ,Embedding ,Data mining ,Paper based ,computer.software_genre ,computer ,Computer Science Applications ,Education ,Test (assessment) - Published
- 2019
4. Identification of data mining research frontier based on conference papers
- Author
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Jing Pan, Yue Huang, and Hu Liu
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Computer science ,020206 networking & telecommunications ,Sample (statistics) ,02 engineering and technology ,Bibliometrics ,computer.software_genre ,Field (computer science) ,Ranking (information retrieval) ,Identification (information) ,Betweenness centrality ,0202 electrical engineering, electronic engineering, information engineering ,Computer Science (miscellaneous) ,Business, Management and Accounting (miscellaneous) ,020201 artificial intelligence & image processing ,Decision Sciences (miscellaneous) ,Data mining ,Cluster analysis ,Social network analysis ,computer - Abstract
Purpose Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers. Design/methodology/approach This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords. Findings Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich. Originality/value This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.
- Published
- 2021
5. Experimental Comparison in Sensing Breast Cancer Mutations by Signal ON and Signal OFF Paper-Based Electroanalytical Strips
- Author
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Emily P. Nguyen, Fabiana Arduini, Claudio Parolo, Giulia Cinotti, Danila Moscone, Stefano Cinti, Arben Merkoçi, Veronica Caratelli, Cinti, S., Cinotti, G., Parolo, C., Nguyen, E. P., Caratelli, V., Moscone, D., Arduini, F., and Merkoci, A.
- Subjects
Paper ,DNA, Single-Stranded ,Breast Neoplasms ,STRIPS ,Biosensing Techniques ,010402 general chemistry ,computer.software_genre ,01 natural sciences ,Signal ,Field (computer science) ,Analytical Chemistry ,law.invention ,Biosensing Technique ,DNA-based biosensors ,Breast cancer ,Settore CHIM/01 ,Design and Development ,law ,Experimental comparison ,Detection methods ,medicine ,Humans ,Liquid biopsy ,Protocol (science) ,Electrochemical Technique ,Chemistry ,010401 analytical chemistry ,Analytical performance ,Electrochemical Techniques ,medicine.disease ,Signal on ,0104 chemical sciences ,Emerging technologies ,Mutation ,Single strand DNA ,Female ,Data mining ,Detection protocols ,Biosensor ,computer ,Breast Neoplasm ,Human - Abstract
Altres ajuts: the ICN2 is funded by the CERCA Programme/Generalitat de Catalunya. The development of paper-based electroanalytical strips as powerful diagnostic tools has gained a lot of attention within the sensor community. In particular, the detection of nucleic acids in complex matrices represents a trending topic, especially when focused toward the development of emerging technologies, such as liquid biopsy. DNA-based biosensors have been largely applied in this direction, and currently, there are two main approaches based on target/probe hybridization reported in the literature, namely Signal ON and Signal OFF. In this technical note, the two approaches are evaluated in combination with paper-based electrodes, using a single strand DNA relative to H1047R (A3140G) missense mutation in exon 20 in breast cancer as the model target. A detailed comparison among the analytical performances, detection protocol, and cost associated with the two systems is provided, highlighting the advantages and drawbacks depending on the application. The present work is aimed to a wide audience, particularly for those in the field of point-of-care, and it is intended to provide the know-how to manage with the design and development stages, and to optimize the platform for the sensing of nucleic acids using a paper-based detection method.
- Published
- 2019
6. A Repository of Network-Constrained Trajectory Data (Position Paper)
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Stefan Funke and Sabine Storandt
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Work (electrical) ,Scale (ratio) ,Computer science ,Trajectory ,Position paper ,Data mining ,computer.software_genre ,Computer Science::Digital Libraries ,computer - Abstract
We propose the creation of a repository which collects and makes available network-constrained trajectory data. The repository should become a central instance for researchers who want to work with network-constrained trajectory data on a large scale, allowing for efficient filtering and export of selected trajectories based on spatial, temporal and semantic attributes.
- Published
- 2019
7. Challenge Paper: The Vision for Time Profiled Temporal Association Mining
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Vinjamuri Janaki, P. V. Kumar, Gali Suresh Reddy, and Vangipuram Radhakrishna
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Information Systems and Management ,Association mining ,Computer science ,Data mining ,computer.software_genre ,Temporal data mining ,computer ,Similitude ,Information Systems - Published
- 2021
8. Application of COReS to Compute Research Papers Similarity
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Muhammad Abdul Qadir, Muhammad Afzal, and Qamar Mahmood
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General Computer Science ,Process (engineering) ,Computer science ,content based similarity ,02 engineering and technology ,Ontology (information science) ,computer.software_genre ,Semantics ,ranking ,Similarity (network science) ,Comprehensive similarity computation ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,research paper similarity ,General Materials Science ,ontology ,Cluster analysis ,Measure (data warehouse) ,Information retrieval ,General Engineering ,Encyclopedia ,Ontology ,020201 artificial intelligence & image processing ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Data mining ,lcsh:TK1-9971 ,computer - Abstract
Over the decades, the immense growth has been reported in research publications due to continuous developments in science. To date, various approaches have been proposed that find similarity between research papers by applying different similarity measures collectively or individually based on the content of research papers. However, the contemporary schemes are not conceptualized enough to find related research papers in a coherent manner. This paper is aimed at finding related research papers by proposing a comprehensive and conceptualized model via building ontology named COReS: Content-based Ontology for Research Paper Similarity. The ontology is built by finding the explicit relationships (i.e., super-type sub-type, disjointedness, and overlapping) between state-of-the-art similarity techniques. This paper presents the applications of the COReS model in the form of a case study followed by an experiment. The case study uses InText citation-based and vector space-based similarity measures and relationships between these measures as defined in COReS. The experiment focuses on the computation of comprehensive similarity and other content-based similarity measures and rankings of research papers according to these measures. The obtained Spearman correlation coefficient results between ranks of research papers for different similarity measures and user study-based measure, justify the application of COReS for the computation of document similarity. The COReS is in the process of evaluation for ontological errors. In the future, COReS will be enriched to provide more knowledge to improve the process of comprehensive research paper similarity computation.
- Published
- 2017
9. [Paper] Multimodal Stress Estimation Using Multibiological Information: Towards More Accurate and Detailed Stress Estimation
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Takumi Nagasawa, Norimichi Tsumura, Ryo Takahashi, and Keiko Ogawa-Ochiai
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Computer science ,Signal Processing ,Stress estimation ,Media Technology ,Data mining ,computer.software_genre ,Computer Graphics and Computer-Aided Design ,computer - Published
- 2021
10. Reproducibility Companion Paper
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Zhenzhong Kuang, Xinke Li, Zekun Tong, Cise Midoglu, Yabang Zhao, Yuqing Liao, and Andrew Lim
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Source code ,Computer science ,business.industry ,media_common.quotation_subject ,Deep learning ,Point cloud ,computer.software_genre ,File format ,Replication (computing) ,Photogrammetry ,Benchmark (surveying) ,Segmentation ,Artificial intelligence ,Data mining ,business ,computer ,media_common - Abstract
This companion paper is to support the replication of paper "Campus3D: A Photogrammetry Point Cloud Benchmark for Outdoor Scene Hierarchical Understanding", which was presented at ACM Multimedia 2020. The supported paper's main purpose was to provide a photogrammetry point cloud-based dataset with hierarchical multilabels to facilitate the area of 3D deep learning. Based on this provided dataset and source code, in this work, we build a complete package to reimplement the proposed methods and experiments (i.e., the hierarchical learning framework and the benchmarks of the hierarchical semantic segmentation task). Specifically, this paper contains the technical details of the package, including file structure, dataset preparation, installation package, and the conduction of the experiment. We also present the replicated experiment results and indicate our contributions to the original implementation.
- Published
- 2021
11. Findings Seminal Papers Using Data Mining Techniques
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Debrayan Bravo Hidalgo and Alexander Báez Hernández
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Entrepreneurship ,business.industry ,Computer science ,Scopus ,Space (commercial competition) ,computer.software_genre ,Publish or perish ,Software ,Index (publishing) ,Similarity (psychology) ,Anomaly detection ,Data mining ,business ,computer - Abstract
The aim of this contribution is to show the detection of seminal papers using data mining techniques. To achieve the objective of this research, Rapidminer Studio software and its data mining tools are used, based on data created with information extracted from Google Scholar and Scopus, in three different areas of knowledge. In this process, other softwares such as Microsoft Excel and Publish or Perish are used. Comparing the results obtained for the searches in Knowledge Management, Entrepreneurship and Marketing, it was obtained that there is no marked similarity between the sets of articles that were obtained in Google Scholar and Scopus. The values for the Similarity Index remained below 0.52%, similar between Knowledge Management and Entrepreneurship but decreasing for Marketing. The detection of outliers using Data Mining techniques and in particular using Rapidminer, allowed to determine the seminals papers for the three search terms analyzed and allowed to characterize these in the space, in Google Scholar and Scopus. It was shown that the seminal articles can be different if Google Scholar or Scopus is used. The results suggest determining for other search terms whether the trend found is maintained or not.
- Published
- 2020
12. (Short Paper) Effectiveness of Entropy-Based Features in High- and Low-Intensity DDoS Attacks Detection
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Abigail Koay, Winston K. G. Seah, and Ian Welch
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Rényi entropy ,Computer science ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,Short paper ,0202 electrical engineering, electronic engineering, information engineering ,020206 networking & telecommunications ,020201 artificial intelligence & image processing ,Denial-of-service attack ,02 engineering and technology ,Data mining ,computer.software_genre ,computer - Abstract
DDoS attack detection using entropy-based features in network traffic has become a popular approach among researchers in the last five years. The use of traffic distribution features constructed using entropy measures has been proposed as a better approach to detect Distributed Denial of Service (DDoS) attacks compared to conventional volumetric methods, but it still lacks in the generality of detecting various intensity DDoS attacks accurately. In this paper, we focus on identifying effective entropy-based features to detect both high- and low-intensity DDoS attacks by exploring the effectiveness of entropy-based features in distinguishing the attack from normal traffic patterns. We hypothesise that using different entropy measures, window sizes, and entropy-based features may affect the accuracy of detecting DDoS attacks. This means that certain entropy measures, window sizes, and entropy-based features may reveal attack traffic amongst normal traffic better than the others. Our experimental results show that using Shannon, Tsallis and Zhou entropy measures can achieve a clearer distinction between DDoS attack traffic and normal traffic than Renyi entropy. In addition, the window size setting used in entropy construction has minimal influence in differentiating between DDoS attack traffic and normal traffic. The result of the effectiveness ranking shows that the commonly used features are less effective than other features extracted from traffic headers.
- Published
- 2019
13. The impact of a paper’s new combinations and new components on its citation
- Author
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Yan Yan, Jingjing Zhang, and Shanwu Tian
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Computer science ,05 social sciences ,Novelty ,Negative binomial distribution ,General Social Sciences ,Sample (statistics) ,Library and Information Sciences ,050905 science studies ,computer.software_genre ,Field (computer science) ,Computer Science Applications ,Robustness (computer science) ,Related research ,Data mining ,0509 other social sciences ,050904 information & library sciences ,Citation ,Practical implications ,computer - Abstract
A paper’s novelty enhances its impact and citation. In this paper, we examine two dimensions of a paper’s novelty: new combinations and new components. We define new combinations as new pairs of knowledge elements in a related research area, and new components as new knowledge elements that have never appeared in a related research area previously. The importance of both dimensions is stressed, and we analyze the mechanisms that affect the frequency of a paper’s citation; we believe that a paper’s new combinations and new components both have an inverted U-shaped effect on its citation count. Utilizing a text-mining approach, we develop a novel method for constructing new combinations and new components using a paper’s keywords. Using keywords from papers published in the wind energy field between 2002 and 2015 as our sample, we conduct an empirical analysis on the above-mentioned relationships. To do so, we use the negative binomial regression method and several robustness tests. The results provide support for our hypotheses that propose a paper’s new combinations and new components significantly affect its impact. Specifically, new combinations and new components lead to more citation counts up to a specific threshold. When the number of new combinations and new components exceed the threshold, the paper is likely to be cited less frequently. Finally, we discuss the theoretical contributions, methodological contributions, and practical implications of these findings.
- Published
- 2019
14. Keywords-Driven and Weight-aware Paper Recommendation via Paper Correlation Pattern Mining
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Jun Hou, Qianmu Li, Jian Jiang, and Hanwen Liu
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Correlation ,Computer science ,Data mining ,computer.software_genre ,computer - Abstract
Currently, readers often prefer to search for their interested papers based on a set of typed query keywords. As the keywords of a paper is often limited, paper recommender systems often need to recommend a set of papers which collectively satisfy the readers’ keyword query. However, the topics of recommended papers are probably not correlated with each other, which fail to meet the readers’ requirements on in-depth and continuous academic research. Furthermore, although existing paper citation graphs can model the papers’ correlations, they often face the data sparse problem which blocks accurate paper recommendations. To address these issues, we propose a keywords-driven and weight-aware paper recommendation approach, named LP-PRk+w (link prediction-paper recommendation), based on a weighted paper correlation graph. Concretely, we firstly optimize the existing paper citation graph modes by introducing a weighted similarity, after which we obtain a weighted paper correlation graph. Then we recommend a set of correlated papers based on the weighted paper correlation graph and the query keywords from readers. At last, we conduct large-scale experiments on a real-world Hep-Th dataset. Experimental results demonstrate that our proposal can improve the paper recommendation performances considerably, compared to other related solutions.
- Published
- 2021
15. Disaster Damage Estimation from Real-time Population Dynamics using Graph Convolutional Network (Industrial Paper)
- Author
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Keiichi Ochiai, Yamada Wataru, Masayuki Terada, and Hiroto Akatsuka
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education.field_of_study ,Emergency management ,Exploit ,Flood myth ,Computer science ,business.industry ,Population ,computer.software_genre ,Cellular network ,Graph (abstract data type) ,Data mining ,business ,Baseline (configuration management) ,education ,Natural disaster ,computer - Abstract
Storm and flood disasters such as typhoons and torrential rains are becoming more intense and frequent. The national government and municipalities must respond to such natural disasters as soon as possible. When the scale of damage is large; however, it takes much time to investigate the severity of damage, and the initial response can be delayed. If we could precisely and rapidly estimate the severity of damage for each city at an early stage, the national government would be able to better support the municipalities, and consequently respond quickly to help citizens. In this paper, we propose a novel approach to estimate the severity of disaster damage within a short time period after a disaster occurs by exploiting real-time population data generated from cellular networks. First, we investigate the relationship between real-time population data and the severity of damage. Then, we design a Graph Convolutional Networks for Disaster Damage Estimation, called D2E-GCN, which fully exploits the directed and weighted characteristics of human mobility graph. We conduct an offline evaluation on real-world datasets including two typhoons that hit Japan. The evaluation results show that the proposed method outperforms baseline methods which do not consider the graph structure of cities, and the proposed method can estimate the severity of damage approximately 48 hours after typhoons passed. Moreover, we find the experimental insight that the estimation performance can be significantly affected by the graph construction method for GCN models.
- Published
- 2021
16. Image Matching Across Wide Baselines: From Paper to Practice
- Author
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Yuhe Jin, Kwang Moo Yi, Pascal Fua, Eduard Trulls, Jiri Matas, Dmytro Mishkin, and Anastasiia Mishchuk
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FOS: Computer and information sciences ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,02 engineering and technology ,computer.software_genre ,benchmark ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,dataset ,Structure from motion ,local features ,3d reconstruction ,structure from motion ,stereo ,Benchmarking ,Pipeline (software) ,Pattern recognition (psychology) ,Metric (mathematics) ,Benchmark (computing) ,Embedding ,020201 artificial intelligence & image processing ,Computer Vision and Pattern Recognition ,Data mining ,Heuristics ,computer ,performance ,Software - Abstract
We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as our primary metric. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods and heuristics. This is demonstrated by embedding dozens of popular algorithms and evaluating them, from seminal works to the cutting edge of machine learning research. We show that with proper settings, classical solutions may still outperform the perceived state of the art. Besides establishing the actual state of the art, the conducted experiments reveal unexpected properties of Structure from Motion (SfM) pipelines that can help improve their performance, for both algorithmic and learned methods. Data and code are online https://github.com/vcg-uvic/image-matching-benchmark, providing an easy-to-use and flexible framework for the benchmarking of local features and robust estimation methods, both alongside and against top-performing methods. This work provides a basis for the Image Matching Challenge https://vision.uvic.ca/image-matching-challenge., Comment: Added: KeyNet-SOSNet, AffNet-HardNet, TFeat, MKD from kornia
- Published
- 2020
17. FIT position paper on Crowdsourcing of translation services
- Author
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Reiner Heard
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060201 languages & linguistics ,Linguistics and Language ,Information retrieval ,Computer science ,business.industry ,Communication ,06 humanities and the arts ,computer.software_genre ,Translation (geometry) ,Crowdsourcing ,Language and Linguistics ,0602 languages and literature ,Position paper ,Data mining ,business ,computer - Published
- 2017
18. Reproducibility Report for the Paper: 'Differentiable Agent-Based Simulation for Gradient-Guided Simulation-Based Optimization'
- Author
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Emilio Incerto and Matteo Principe
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Upload ,Reproducibility ,Simulation-based optimization ,Software ,Computer science ,business.industry ,Review process ,Data mining ,Artifact (software development) ,Differentiable function ,computer.software_genre ,business ,computer - Abstract
The author claimed for the artifact associated with his paper the following ACM Reproducibility badges:(1) Artifact Available,(2) Artifact Evaluated-Functional,(3) Results Reproduced. After an in-depth review process, we agree to assign all the requested badges as we found it to meet the following requirements:i) it is uploaded on a persistent repository, accessible via a DOI; ii) it is well documented, consistent with the presented data, complete of all the necessary software sources and packages, and exercisable; iii) it is exhaustive in the reproduction of all the relevant data of the paper. Some curves in some reproduced plots are truncated, due to the computational limits imposed by the short-term deadline of the review process. Nevertheless, the overall trends are respected, and the curves are supporting the paper's claims.
- Published
- 2021
19. Position paper: Sensitivity analysis of spatially distributed environmental models- a pragmatic framework for the exploration of uncertainty sources
- Author
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Takuya Iwanaga, Xifu Sun, Anthony Jakeman, Tianxiang Yue, Barry Croke, Xin Li, Hyeongmo Koo, Xintao Liu, Wenping Yuan, Guonian Lü, Min Chen, Jing Yang, and Hsiao-Hsuan Wang
- Subjects
Environmental Engineering ,010504 meteorology & atmospheric sciences ,Soil and Water Assessment Tool ,Spatial structure ,Computer science ,Ecological Modeling ,media_common.quotation_subject ,0208 environmental biotechnology ,02 engineering and technology ,computer.file_format ,computer.software_genre ,01 natural sciences ,020801 environmental engineering ,Environmental modeling ,Information system ,Position paper ,Quality (business) ,Data mining ,Sensitivity (control systems) ,Raster graphics ,computer ,Software ,0105 earth and related environmental sciences ,media_common - Abstract
Sensitivity analysis (SA) has been used to evaluate the behavior and quality of environmental models by estimating the contributions of potential uncertainty sources to quantities of interest (QoI) in the model output. Although there is an increasing literature on applying SA in environmental modeling, a pragmatic and specific framework for spatially distributed environmental models (SD-EMs) is lacking and remains a challenge. This article reviews the SA literature for the purposes of providing a step-by-step pragmatic framework to guide SA, with an emphasis on addressing potential uncertainty sources related to spatial datasets and the consequent impact on model predictive uncertainty in SD-EMs. The framework includes: identifying potential uncertainty sources; selecting appropriate SA methods and QoI in prediction according to SA purposes and SD-EM properties; propagating perturbations of the selected potential uncertainty sources by considering the spatial structure; and verifying the SA measures based on post-processing. The proposed framework was applied to a SWAT (Soil and Water Assessment Tool) application to demonstrate the sensitivities of the selected QoI to spatial inputs, including both raster and vector datasets - for example, DEM and meteorological information - and SWAT (sub)model parameters. The framework should benefit SA users not only in environmental modeling areas but in other modeling domains such as those embraced by geographical information system communities.
- Published
- 2020
20. Position paper Statin intolerance – an attempt at a unified definition. Position paper from an International Lipid Expert Panel
- Author
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Dimitri P. Mikhailidis, Assen Goudev, Manfredi Rizzo, Robert S. Greenfield, Daniel Lighezan, Karam Kostner, Richard Ceska, Dragan M. Djuric, Maciej Banach, Wilbert S. Aronow, Daniel Pella, Michael H. Davidson, Nathan D. Wong, Corina Serban, Marlena Broncel, Marat V. Ezhov, Jacek Rysz, Stephen J. Nicholls, Steven R. Jones, Peter P. Toth, Kausik K. Ray, Raman Puri, Vasilis G. Athyros, Paul Muntner, Zlatko Fras, Michel Farnier, Laszlo Bajnok, Dragana Nikolic, Khalid Al-Rasadi, Patrick M. Moriarty, and G. Kees Hovingh
- Subjects
medicine.medical_specialty ,Statin ,business.industry ,medicine.drug_class ,Alternative medicine ,Placebo-controlled study ,nutritional and metabolic diseases ,General Medicine ,Disease ,computer.software_genre ,3. Good health ,law.invention ,Randomized controlled trial ,law ,Post-hoc analysis ,medicine ,Position paper ,lipids (amino acids, peptides, and proteins) ,cardiovascular diseases ,Data mining ,business ,Intensive care medicine ,Adverse effect ,computer - Abstract
Statins are one of the most commonly prescribed drugs in clinical practice. They are usually well tolerated and effectively prevent cardiovascular events. Most adverse effects associated with statin therapy are muscle-related. The recent statement of the European Atherosclerosis Society (EAS) has focused on statin associated muscle symptoms (SAMS), and avoided the use of the term 'statin intolerance'. Although muscle syndromes are the most common adverse effects observed after statin therapy, excluding other side effects might underestimate the number of patients with statin intolerance, which might be observed in 10-15% of patients. In clinical practice, statin intolerance limits effective treatment of patients at risk of, or with, cardiovascular disease. Knowledge of the most common adverse effects of statin therapy that might cause statin intolerance and the clear definition of this phenomenon is crucial to effectively treat patients with lipid disorders. Therefore, the aim of this position paper was to suggest a unified definition of statin intolerance, and to complement the recent EAS statement on SAMS, where the pathophysiology, diagnosis and the management were comprehensively presented.
- Published
- 2015
21. Brief Paper: Simplified Tag Identification Algorithm by Modifying Tag Collection Command in Active RFID System
- Author
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Intaek Lim
- Subjects
Identification (information) ,Computer science ,Data mining ,computer.software_genre ,computer - Published
- 2020
22. Survey on Research Paper Classification based on TF-IDF and Stemming Technique using Classification Algorithm
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S. A. and Kshitija G.
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Computer science ,Data mining ,tf–idf ,computer.software_genre ,computer - Published
- 2020
23. Automatic Identification of Compare Paper Relations
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Yuliant Sibaroni, Masayu Leylia Khodra, and Dwi H. Widyantoro
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Computer science ,General Engineering ,Identification (biology) ,Data mining ,computer.software_genre ,computer - Published
- 2020
24. Adaptive weights clustering of research papers
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Kirill Efimov, Larisa Adamyan, Wolfgang Karl Härdle, and Cathy Yi-Hsuan Chen
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JEL system ,Adaptive algorithm ,Point (typography) ,Computer science ,330 Wirtschaft ,05 social sciences ,Nonparametric statistics ,020206 networking & telecommunications ,02 engineering and technology ,computer.software_genre ,Clustering ,Weighting ,0502 economics and business ,ddc:330 ,0202 electrical engineering, electronic engineering, information engineering ,Key (cryptography) ,Economic articles ,Nonparametric ,Data mining ,050207 economics ,Cluster analysis ,computer ,Research center - Abstract
The JEL classification system is a standard way of assigning key topics to economic articles to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal assignment. Using the database of the Collaborative Research Center from Humboldt-Universität zu Berlin we employ a new adaptive clustering technique to identify interpretable JEL (sub)clusters. The proposed Adaptive Weights Clustering (AWC) is available on http://www.quantlet.de/ and is based on the idea of locally weighting each point (document, abstract) in terms of cluster membership. Comparison with $$k$$ k -means or CLUTO reveals excellent performance of AWC.
- Published
- 2020
25. A new baseline for retinal vessel segmentation: Numerical identification and correction of methodological inconsistencies affecting 100+ papers
- Author
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Attila Fazekas and György Kovács
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Health Informatics ,computer.software_genre ,Machine Learning (cs.LG) ,Medical imaging ,Image Processing, Computer-Assisted ,FOS: Electrical engineering, electronic engineering, information engineering ,Humans ,Radiology, Nuclear Medicine and imaging ,Segmentation ,Radiological and Ultrasound Technology ,Image and Video Processing (eess.IV) ,Contrast (statistics) ,Retinal Vessels ,Benchmarking ,Electrical Engineering and Systems Science - Image and Video Processing ,Computer Graphics and Computer-Aided Design ,Data set ,Identification (information) ,Ranking ,Test set ,Computer Vision and Pattern Recognition ,Data mining ,computer ,Algorithms - Abstract
In the last 15 years, the segmentation of vessels in retinal images has become an intensively researched problem in medical imaging, with hundreds of algorithms published. One of the de facto benchmarking data sets of vessel segmentation techniques is the DRIVE data set. Since DRIVE contains a predefined split of training and test images, the published performance results of the various segmentation techniques should provide a reliable ranking of the algorithms. Including more than 100 papers in the study, we performed a detailed numerical analysis of the coherence of the published performance scores. We found inconsistencies in the reported scores related to the use of the field of view (FoV), which has a significant impact on the performance scores. We attempted to eliminate the biases using numerical techniques to provide a more realistic picture of the state of the art. Based on the results, we have formulated several findings, most notably: despite the well-defined test set of DRIVE, most rankings in published papers are based on non-comparable figures; in contrast to the near-perfect accuracy scores reported in the literature, the highest accuracy score achieved to date is 0.9582 in the FoV region, which is 1% higher than that of human annotators. The methods we have developed for identifying and eliminating the evaluation biases can be easily applied to other domains where similar problems may arise.
- Published
- 2021
- Full Text
- View/download PDF
26. Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers
- Author
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Hidetaka Kamigaito, Manabu Okumura, Lya Hulliyyatus Suadaa, and Hiroya Takamura
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FOS: Computer and information sciences ,Computer Science - Computation and Language ,Computer science ,Type (model theory) ,Table (information) ,computer.software_genre ,Task (computing) ,Information extraction ,Identification (information) ,Header ,Metric (mathematics) ,Data mining ,Joint (audio engineering) ,Computation and Language (cs.CL) ,computer - Abstract
Numerical tables are widely used to present experimental results in scientific papers. For table understanding, a metric-type is essential to discriminate numbers in the tables. We introduce a new information extraction task, metric-type identification from multi-level header numerical tables, and provide a dataset extracted from scientific papers consisting of header tables, captions, and metric-types. We then propose two joint-learning neural classification and generation schemes featuring pointer-generator-based and BERT-based models. Our results show that the joint models can handle both in-header and out-of-header metric-type identification problems., To appear at EACL 2021
- Published
- 2021
27. Review Paper on Anomaly Detection in Data Streams
- Author
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G. Sandhya Madhuri, M. Usha Rani, and Yamuna
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Data stream ,Computer science ,Data stream mining ,Anomaly (natural sciences) ,Credit card fraud ,Outlier ,Anomaly detection ,Data mining ,computer.software_genre ,computer ,Word (computer architecture) ,Task (project management) - Abstract
Anomaly is in general defined as deviation or diversion from the normal. The word anomaly came from the Greek word anomalia which means “uneven” or “irregular”. In our day-to-day lives, we have seen many such irregularities or deviations from normalcy. For example, a condition monitoring system beeps an alarm when it detects any value or parameter of the machine away from the minimum value limit to the maximum value limit, or a credit card fraud alerts the bank and the customer immediately. Now, the crucial task here is how we detect anomalies in data streams. When there is streaming data that is continuously generated from any source, it is called a data stream. The task of finding anomalies from such a stream of data will be a challenging job. In this paper, we will discuss elaborately about data streams and anomaly detection in data streams by reviewing several papers and articles written on this topic.
- Published
- 2021
28. S2CFT: A New Approach for Paper Submission Recommendation
- Author
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Dac Nguyen, Binh T. Nguyen, Son T. Huynh, Phong Thu Nguyen Huynh, and Cuong V. Dinh
- Subjects
Measure (data warehouse) ,Computer science ,02 engineering and technology ,Recommender system ,computer.software_genre ,Convolutional neural network ,GeneralLiterature_MISCELLANEOUS ,Term (time) ,Recommendation model ,03 medical and health sciences ,0302 clinical medicine ,030221 ophthalmology & optometry ,0202 electrical engineering, electronic engineering, information engineering ,Embedding ,020201 artificial intelligence & image processing ,Data mining ,computer - Abstract
There have been a massive number of conferences and journals in computer science that create a lot of difficulties for scientists, especially for early-stage researchers, to find the most suitable venue for their scientific submission. In this paper, we present a novel approach for building a paper submission recommendation system by using two different types of embedding methods, GloVe and FastText, as well as Convolutional Neural Network (CNN) and LSTM to extract useful features for a paper submission recommendation model. We consider seven combinations of initial attributes from a given submission: title, abstract, keywords, title + keyword, title + abstract, keyword + abstract, and title + keyword + abstract. We measure these approaches’ performance on one dataset, presented by Wang et al., in terms of top K accuracy and compare our methods with the S2RSCS model, the state-of-the-art algorithm on this dataset. The experimental results show that CNN + FastText can outperform other approaches (CNN + GloVe, LSTM + GloVe, LSTM + FastText, S2RSCS) in term of top 1 accuracy for seven types of input data. Without using a list of keywords in the input data, CNN + GloVe/FastText can surpass other techniques. It has a bit lower performance than S2RSCS in terms of the top 3 and top 5 accuracies when using the keyword information. Finally, the combination of S2RSCS and CNN + FastText, namely S2CFT, can create a better model that bypasses all other methods by top K accuracy (K = 1,3,5,10).
- Published
- 2021
29. A novel characteristic optimization method based on combined statistical indicators and random forest for oil-paper insulation state diagnosis
- Author
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Renwu Yan, Qingzhen Liu, Lei Wu, and Chao Cai
- Subjects
Computer science ,media_common.quotation_subject ,Feature vector ,State (functional analysis) ,Space (mathematics) ,computer.software_genre ,Adaptability ,Electronic, Optical and Magnetic Materials ,Random forest ,law.invention ,Set (abstract data type) ,Correlation ,General Energy ,law ,Data mining ,Electrical and Electronic Engineering ,Transformer ,computer ,media_common - Abstract
In order to effectively utilize the dielectric response characteristics of transformers to diagnose the insulation state, this paper proposes a two-level hybrid optimization method for analyzing time-domain dielectric response characteristics. The optimization algorithm is based on the combined statistical indicators (CSI) and random forest (RF) theory. The initial feature space set is formed with 23 time-domain characteristics. In the first-level stage, statistical indices correlation, distance, and information indicator are integrated to assess the synthesis score of the characteristics, while highly redundant and low-class discrimination characteristics are eliminated from the initial space set. In the second-level stage, Random Forest based outside bagging data theory is introduced to evaluate the least important characteristics, and the characteristics with low importance indices are excluded to obtain the final optimal feature space set. The proposed method is carried out on 82 sets of data from actual dielectric response tests on oil-paper insulation transformers. Finally, the final optimal feature space set, along with several other data sets, is tested via different diagnosis methods. The results show that the optimal feature space set obtained via the proposed method outperforms other feature space sets in terms of better adaptability and diagnosis accuracy.
- Published
- 2021
30. Brief Industry Paper: HDAD: Hyperdimensional Computing-based Anomaly Detection for Automotive Sensor Attacks
- Author
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Xun Jiao, Hasshi Sudler, Ruixuan Wang, and Fanxin Kong
- Subjects
Set (abstract data type) ,Spoofing attack ,Mean squared error ,Computer science ,Feature extraction ,Cosine similarity ,Key (cryptography) ,Anomaly detection ,Data mining ,computer.software_genre ,computer ,Encoder - Abstract
As the connectivity of autonomous vehicles keeps growing, it is an accepted fact that they are even more vulnerable to malicious cyber-attacks. Recently, sensor spoofing has become an emerging attack that can compromise vehicle safety as vehicles are equipped with more sensors. Thus, it is critical to validate the sensor readings before utilizing them for future actions. In this paper, we develop HDAD, a hyperdimensional computing-based anomaly detection method. Hyperdimensional computing (HDC) is an emerging brain-inspired computing paradigm that mimics the brain cognition and leverages hyperdimensional vectors with fully distributed holographic representation and (pseudo)randomness. The key idea of HDAD is to use HDC to build encoder and decoder to reconstruct the sensor readings. The anomalous data typically have comparatively higher reconstruction errors than normal sensor readings. We explore three different metrics to measure the reconstruction error including mean squared error, mean absolute error, and cosine similarity. Using a real-world vehicle sensor reading dataset, we demonstrate the feasibility and efficacy of HDAD, opening the door for a new set of anomaly detection algorithm design.
- Published
- 2021
31. AutoKG - An Automotive Domain Knowledge Graph for Software Testing: A position paper
- Author
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Vaibhav Kesri, Anmol Nayak, and Karthikeyan Ponnalagu
- Subjects
business.industry ,Computer science ,Ontology (information science) ,computer.software_genre ,Pipeline (software) ,Domain (software engineering) ,Software ,Systems development life cycle ,Graph (abstract data type) ,Domain knowledge ,Data mining ,Software system ,business ,computer - Abstract
—Industries have a significant amount of data in semi-structured and unstructured formats which are typically captured in text documents, spreadsheets, images, etc. This is especially the case with the software description documents used by domain experts in the automotive domain to perform tasks at various phases of the Software Development Life Cycle (SDLC). In this paper, we propose an end-to-end pipeline to extract an Automotive Knowledge Graph (AutoKG) from textual data using Natural Language Processing (NLP) techniques with the application of automatic test case generation. The proposed pipeline primarily consists of the following components: 1) AutoOntology, an ontology that has been derived by analyzing several industry scale automotive domain software systems, 2) AutoRE, a Relation Extraction (RE) model to extract triplets from various sentence types typically found in the automotive domain, and 3) AutoVec, a neural embedding based algorithm for triplet matching and context-based search. We demonstrate the pipeline with an application of automatic test case generation from requirements using AutoKG.
- Published
- 2021
- Full Text
- View/download PDF
32. An Intelligent Paper Currency Recognition System
- Author
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Muhammad Sarfraz
- Subjects
Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Paper currency ,classification ,computer.software_genre ,image processing ,radial basis function network ,Currency ,Pattern recognition (psychology) ,Recognition system ,General Earth and Planetary Sciences ,Data mining ,intelligent system ,computer ,General Environmental Science - Abstract
Paper currency recognition (PCR) is an important area of pattern recognition. A system for the recognition of paper currency is one kind of intelligent system which is a very important need of the current automation systems in the modern world of today. It has various potential applications including electronic banking, currency monitoring systems, money exchange machines, etc. This paper proposes an automatic paper currency recognition system for paper currency. A method of recognizing paper currencies has been introduced. This is based on interesting features and correlation between images. It uses Radial Basis Function Network for classification. The method uses the case of Saudi Arabian paper currency as a model. The method is quite reasonable in terms of accuracy. The system deals with 110 images, 10 of which are tilted with an angle less than 15o. The rest of the currency images consist of mixed including noisy and normal images 50 each. It uses fourth series (1984–2007) of currency issued by Saudi Arabian Monetary Agency (SAMA) as a model currency under consideration. The system produces accuracy of recognition as 95.37%, 91.65%, and 87.5%, for the Normal Non-Tilted Images, Noisy Non-Tilted Images, and Tilted Images respectively. The overall Average Recognition Rate for the data of 110 images is computed as 91.51%. The proposed algorithm is fully automatic and requires no human intervention. The proposed technique produces quite satisfactory results in terms of recognition and efficiency.
- Published
- 2015
- Full Text
- View/download PDF
33. Parametric definition of the influence of a paper in a citation network using communicability functions
- Author
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J Guillermo Contreras, Juan Antonio Pichardo-Corpus, and José Antonio de la Peña
- Subjects
Citation network ,Control and Optimization ,Computer Networks and Communications ,Computer science ,Applied Mathematics ,0211 other engineering and technologies ,021107 urban & regional planning ,010103 numerical & computational mathematics ,02 engineering and technology ,Management Science and Operations Research ,computer.software_genre ,01 natural sciences ,Computational Mathematics ,Data mining ,0101 mathematics ,computer ,Parametric statistics - Abstract
Communicability functions quantify the flow of information between two nodes of a network. In this work, we use them to explore the concept of the influence of a paper in a citation network. These functions depend on a parameter. By varying the parameter in a continuous way we explore different definitions of influence. We study six citation networks, three from physics and three from computer science. As a benchmark, we compare our results against two frequently used measures: the number of citations of a paper and the PageRank algorithm. We show that the ranking of the articles in a network can be varied from being equivalent to the ranking obtained from the number of citations to a behaviour tending to the eigenvector centrality, these limits correspond to small and large values of the communicability-function parameter, respectively. At an intermediate value of the parameter a PageRank-like behaviour is recovered. As a test case, we apply communicability functions to two sets of articles, where at least one author of each paper was awarded a Nobel Prize for the research presented in the corresponding article.
- Published
- 2019
34. Data Mining Methods for Analysis and Forecast of an Emerging Technology Trend: A Systematic Mapping Study from SCOPUS Papers
- Author
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Nguyen Thanh Viet, Tu Duong Quoc Hoang, and Alla G. Kravets
- Subjects
Trend analysis ,Emerging technologies ,Computer science ,Scopus ,Economic shortage ,Data mining ,Systematic mapping ,computer.software_genre ,Data type ,Competitive advantage ,computer ,Limited resources - Abstract
To stay competitive in an environment of rapidly changing science, it is important to monitor the development of existing technology and to discover new and promising technologies. Similarly, it is necessary for a firm to establish a technology development strategy through emerging technology forecast to gain a competitive edge while utilizing limited resources. Numerous methods of emerging technology trend analysis and forecast (TTAF) have been proposed; however, no study described data mining methods’ review of this research area in a systematic and structured procedure. Hence, this paper intends to give a review of TTAF data mining methods and shortages by surveying and constructing challenging problems, research and resolving approaches. Moreover, the study highlights adopted data mining methods and types of data sources. Specifically, 50 documents from SCOPUS over a ten-year timespan between 2010 and 2019 were systematically reviewed, and each performing step was followed properly in accordance with systematic mapping study.
- Published
- 2021
35. A Multi-population Adaptive Genetic Algorithm for Test Paper Generation
- Author
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Tangjie Wu, Qiang Ling, Lei Wang, Haitao Huang, and Zefeng Lai
- Subjects
Computer science ,Multi population ,Genetic algorithm ,Data mining ,computer.software_genre ,computer ,Test (assessment) - Published
- 2021
36. Brief Industry Paper: An Edge-Based High-Definition Map Crowdsourcing Task Distribution Framework for Autonomous Driving
- Author
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Shaoshan Liu, Jie Tang, and Donghua Li
- Subjects
Source data ,Data collection ,business.industry ,Computer science ,Crowdsourcing ,computer.software_genre ,Task (project management) ,Task analysis ,Enhanced Data Rates for GSM Evolution ,Data mining ,business ,Marginal utility ,computer ,Premature convergence - Abstract
Facing the difficulty and inefficiency of creating and maintaining High-Definition (HD) maps in our commercial deployments, we have developed an edge-based crowdsourcing task distribution framework for HD Map in autonomous driving. Our key observation is that: HD map data crowdsourcing exhibits the diminishing marginal utility thus there exists an inflection point for maximum utility, meanwhile its premature convergence of utility will leave some map updates not notified in time. Based on this observation, we develop a periodic crowdsourcing task distribution framework. It discretizes the demands for collecting source data into different periods and uses an optimal stopping rule to terminate the data collection for the maximum crowdsourcing utility. The experimental results verify that our crowdsourcing framework can achieve high time coverage and high efficiency with lower cost.
- Published
- 2021
37. Measuring the Masses: A Proposed Template for Post-Event Medical Reporting (Paper 4) - CORRIGENDUM
- Author
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Elizabeth Chasmar, Adam Lund, Haddon Rabb, Alison Hutton, Christopher W Callaghan, Matthew Brendan Munn, Jamie Ranse, and Sheila A. Turris
- Subjects
Mass gathering medicine ,Emergency Medical Services ,Computer science ,Event (relativity) ,Emergency Nursing ,computer.software_genre ,Medical Records ,Data modeling ,Crowding ,Mass gathering ,Emergency Medicine ,Humans ,Data mining ,computer ,Mass Behavior - Published
- 2021
38. Paper-Based Methods
- Author
-
Emily R. Christensen, Andres W. Martinez, and Nathaniel W. Martinez
- Subjects
Computer science ,Data mining ,Paper based ,computer.software_genre ,computer - Published
- 2018
39. Clustering Research Papers Using Genetic Algorithm Optimized Self-Organizing Maps
- Author
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Cherif Salama, Reham Fathy M. Ahmed, and Hani Mahdi
- Subjects
Self-organizing map ,Artificial neural network ,Computer science ,05 social sciences ,Dunn index ,02 engineering and technology ,050905 science studies ,Trial and error ,computer.software_genre ,ComputingMethodologies_PATTERNRECOGNITION ,Genetic algorithm ,0202 electrical engineering, electronic engineering, information engineering ,Unsupervised learning ,020201 artificial intelligence & image processing ,Data mining ,0509 other social sciences ,Cluster analysis ,computer ,Data compression - Abstract
With the huge amount of published research papers, retrieving relevant information is a difficult task for any researcher. Effective clustering algorithms can help improve and simplify the retrieval process. Here, we propose an approach for automatic clustering for text document using a Self-Organizing Map (SOM). It is one of unsupervised artificial neural network that widely used for data analysis, data compression, clustering, and data mining. The quality and accuracy of a SOM algorithm depends on the selection of values for some of its parameters which are its initial learning rate, SOM matrix dimensions, and the number of iterations. Best values are typically selected using trial and error; however, in the current paper we suggest a more systematic approach to parameters optimization using the genetic algorithm. The proposed method is applied to cluster 3 scientific papers datasets using their keywords. Similar research papers were mapped closer to each other. Clustering results were validated using the Dunn index.
- Published
- 2020
40. Comparative Study of The Difficulty Index of Items Displayed with The Paper-Based Test and The Computer-Based Testing Paper
- Author
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Syahrul, Lu’mu, and Iwan Suhardi
- Subjects
History ,Index (economics) ,Computer based ,Paper based ,Data mining ,Psychology ,computer.software_genre ,computer ,Computer Science Applications ,Education ,Test (assessment) - Abstract
The focus of this study is to analyze whether there are differences in the difficulty index of items if the same items are displayed with the Paper Based Test (PBT) model and the Computer-Based Testing (CBT) model. The PBT and CBT models have the same paradigm of measuring estimated abilities but have differences in the context and feel aspects. These differences include the number of items in the range of eyesight, use of tools, how to work on items, basic knowledge needs about computer operations, and habit factors. These differences can affect the results of estimating the ability of test participants. This study uses development methods and quantitative methods. Development methods are used to develop package items and CBT software. Two groups of respondents were used with equal ability to work on the same question package. One group uses the PBT model, and the other group uses CBT so that the response choices are obtained in each test model. The results of the respondent’s answer choices were then analyzed by the ITEMAN software to get the item difficulty index. The item difficulty index of each model was tested statistically using SPSS software, whether there was a significant average difference between the two test models. From the results of the study and analysis in classical theory, it can be concluded that statistically there are differences in the average difficulty index of the items if the same item is displayed with the PBT and CBT models. It was found that the items displayed with the PBT model had a more difficult tendency than when displayed with the CBT model.
- Published
- 2019
41. Validation and Quality Control of an ICP‐MS Method for the Quantification and Discrimination of Trace Metals and Application in Paper Analysis: An Overview
- Author
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Hassan Y. Aboul-Enein, Andrei A. Bunaciu, Gabriela Elena Udristioiu, Ion Tanase, and Dana Elena Popa
- Subjects
Paper ,Quality Control ,Detection limit ,Chemistry ,media_common.quotation_subject ,Fitness for purpose ,Analytical chemistry ,Validation Studies as Topic ,computer.software_genre ,Mass Spectrometry ,Trace Elements ,Analytical Chemistry ,Characterization (materials science) ,Working range ,Metals ,Animals ,Humans ,Quality (business) ,Data mining ,Sensitivity (control systems) ,computer ,Inductively coupled plasma mass spectrometry ,TRACE (psycholinguistics) ,media_common - Abstract
Questioned documents analysis includes: handwriting comparison, analysis of the ink and the printer used in the production of the documents, and the physical and chemical characterization of the cellulosic substrate (paper) of the documents. In many situations in life, for financial, social, and personal concerns, we depend on different documents. Therefore, over time, various analytical methods have been developed in order to determine their authenticity, source, and age or to differentiate various papers. In this study a quantitative analytical method for the determination of eight trace level chemical elements (Al, Ba, Fe, Mg, Mn, Pb, Sr, Zn) from document paper samples using inductively coupled plasma-mass spectrometry (ICP-MS) was validated and applied. The evaluation of the performance parameters of the method (applicability, fitness for purpose, linearity, working range, limit of detection and limit of quantification, sensitivity, accuracy, and precision) was accomplished. An overview of the validation parameters are presented and discussed in detail.
- Published
- 2014
42. Information system for calculating paper-forming indicators of fibrous semi-finished products based on regression models
- Author
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V. I. Shurkina, R. A. Marchenko, and S. V. Yarovoy
- Subjects
History ,Computer science ,Information system ,Regression analysis ,Data mining ,computer.software_genre ,computer ,Computer Science Applications ,Education - Abstract
The paper presents an information system for calculating the main paper-forming parameters of the pulp for given technological and design parameters of the grinding plant. The operation of the system is based on mathematical regression models of the processes that were obtained in the course of experimental studies of knife sets of various types. As input parameters in these models, the rotor speed, knife gap, concentration and degree of grinding of the mass are used. The output parameters are the grinding time, water retention capacity and average fiber length. The developed information system is a network web application with a modular structure. The modules are united by the main interface for the user. Each module implements a regression model for a specific type of headset. At the moment, the system has two modules for calculating the parameters of a percussion type headset and a headset with a curvilinear shape of knives. In the future, it is planned to add the ability to calculate indicators for headsets of other designs. It will also add the ability to solve optimization problems by finding the minimum or maximum value of the output parameters.
- Published
- 2021
43. Implementation of Paper Cutting Defect Detection System Based on Local Binary Pattern Analysis
- Author
-
Jin-Soo Kim
- Subjects
General Computer Science ,Local binary patterns ,Computer science ,Order (business) ,Data mining ,computer.software_genre ,computer ,Paper manufacturing - Abstract
Paper manufacturing industries have huge facilities with automatic equipments. Especially, in order to improve the efficiency of the paper manufacturing processes, it is necessary to detect the paper cutting defect effectively and to classify the causes correctly. In this paper, we review the problems of web monitoring system and web inspection system that have been traditionally used in industries for defect detection. Then we propose a novel paper cutting defect detection method based on the local binary pattern analysis and its implementation to mitigate the practical problems in industry environment. The proposed algorithm classifies the defects into edge-type and region-type and then it is shown that the proposed system works stably on the real paper cutting defect detection system.
- Published
- 2013
44. Fuzzy Clustering of Paper Mill Data
- Author
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Abhijit Singh Bhakuni, Pradeep Juneja, Sandeep Kumar Sunori, and Govind Singh Jethi
- Subjects
Fuzzy clustering ,Computer science ,Process (computing) ,Subtractive clustering ,computer.software_genre ,Fuzzy logic ,ComputingMethodologies_PATTERNRECOGNITION ,Data point ,Fuzzy inference system ,Data mining ,Cluster analysis ,MATLAB ,computer ,computer.programming_language - Abstract
In the present work, the 48 input-output data points of the paper mill process have been considered with the desired machine speed as the input and required total head as the output. The data has been taken from the literature. Two clustering techniques are applied on this data using MATLAB. One is the FCM (fuzzy C-means clustering), another is the subtractive clustering. In present work, initial FIS (fuzzy inference system) developed by subtractive clustering is further optimized to improve its performance, and finally their responses are compared.
- Published
- 2020
45. Towards Inverse Uncertainty Quantification in Software Development (Short Paper)
- Author
-
Carlo Bellettini, Patrizia Scandurra, Angelo Gargantini, and Matteo Camilli
- Subjects
Online model ,021103 operations research ,Computer science ,business.industry ,Calibration (statistics) ,Short paper ,0211 other engineering and technologies ,Software development ,Inverse ,020207 software engineering ,02 engineering and technology ,computer.software_genre ,Bayesian inference ,Formal specification ,0202 electrical engineering, electronic engineering, information engineering ,Data mining ,Uncertainty quantification ,business ,computer - Abstract
With the purpose of delivering more robust systems, this paper revisits the problem of Inverse Uncertainty Quantification that is related to the discrepancy between the measured data at runtime (while the system executes) and the formal specification (i.e., a mathematical model) of the system under consideration, and the value calibration of unknown parameters in the model. We foster an approach to quantify and mitigate system uncertainty during the development cycle by combining Bayesian reasoning and online Model-based testing.
- Published
- 2017
46. Review Paper on Identification of Errors and their Computation
- Author
-
Vishal Vaman Mehtre
- Subjects
Identification (information) ,Computer science ,Computation ,Data mining ,computer.software_genre ,computer - Published
- 2019
47. Review Paper on Security of Cloud Computing using Genetic Algorithm
- Author
-
Rajnish Kansal
- Subjects
business.industry ,Computer science ,Genetic algorithm ,Cloud computing ,Data mining ,computer.software_genre ,business ,computer - Published
- 2019
48. A Visualization Approach of Air Quality Index using R Review Paper
- Author
-
Martand Pratap Singh
- Subjects
Computer science ,Data mining ,computer.software_genre ,Air quality index ,computer ,Visualization - Published
- 2019
49. Survey Paper on Data Mining Algorithms Applied for Recommendation System
- Author
-
Mrs.S. Panimalar
- Subjects
Computer science ,Data mining ,Recommender system ,computer.software_genre ,computer ,Data mining algorithm - Published
- 2019
50. Review Paper on Data Mining TechniquesandApplications
- Author
-
Anshu Anshu
- Subjects
Computer science ,Data mining ,computer.software_genre ,computer - Published
- 2019
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