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IEEE International Conference on Management of Innovation and Technology (ICMIT)

作者： Tilouche, Shaima Bassetto, Samuel Nia, Vahid Partovi Ecole Polytech Montreal Dept Math & Ind Engn Montreal PQ Canada Ecole Polytech Montreal Dept Math & Ind Engn GERAD Res Ctr Montreal PQ Canada

ISBN: (纸本)9781479955299

Virtual metrology in quality control deals with drifts in product quality that occur during non-sampling periods. This approach enables a hundred percent control and improves the precision of statistical control, specially while there is no sampling activity in manufacturing process. The main challenge in virtual metrology is inaccurate predictions. As such, the choice of an appropriate algorithm for prediction is crucial. We compare several algorithms that can be used for prediction in virtual metrology. The comparison over different prediction algorithms is made on a simulated data inspired from virtual metrology application.

关键词： Biological system modeling Manufacturing Metrology Neural networks Prediction algorithms Semiconductor device modeling Support vector machines metrology Prediction algorithms Biological system modeling Semiconductor device models Support Vector Network sorting algorithm Neural network Virtual

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FixingTIM: Interactive exploration of sequence and structural data to identify functional mutations in protein families

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BMC Proceedings 2014年第SUPPL 2 PROCEEDINGS OF THE 3RD ANNUAL SYMPOSIUM ON BIOLOGICA期8卷 1-9页

作者： Luciani, Timothy Wenskovitch, John Chen, Koonwah Koes, David Travers, Timothy Elisabeta Marai, G. Department of Computer Science Brown University Box 1910 Providence 02912 RI United States Department of Computer Science University of Pittsburgh 210 South Bouquet Pittsburgh 15260 PA United States School of Information Science University of Pittsburgh 135 North Bellefield Avenue Pittsburgh 15260 PA United States Department of Computational and Systems Biology University of Pittsburgh 3501 Fifth Avenue Pittsburgh 15260 PA United States Department of Computer Science University of Illinois at Chicago 851 S. Morgan St. Chicago 60607 IL United States

Background: Knowledge of the 3D structure and functionality of proteins can lead to insight into the associated cellular processes, speed up the creation of pharmaceutical products, and develop drugs that are more effective in combating disease. Methods: We present the design and implementation of a visual mining and analysis tool to help identify protein mutations across a family of structural models and to help discover the effect of these mutations on protein function. We integrate 3D structure and sequence information in a common visual interface;multiple linked views and a computational backbone allow comparison at the molecular and atomic levels, while a novel trend-image visual abstraction allows for the sorting and mining of large collections of sequences and of their residues. Results: We evaluate our approach on the triosephosphate isomerase (TIM) family structural models and sequence data and show that our tool provides an effective, scalable way to navigate a family of proteins, as well as a means to inspect the structure and sequence of individual proteins. Conclusions: The TIM application shows that our tool can assist in the navigation of families of proteins, as well as in the exploration of individual protein structures. In conjunction with domain expert knowledge, this interactive tool can help provide biophysical insight into why specific mutations affect function and potentially suggest additional modifications to the protein that could be used to rescue functionality. © 2014 Luciani et al.;licensee BioMed Central Ltd.

关键词： sorting algorithm Triosephosphate Isomerase Residue Sequence Common Residue Haemophilus Ducreyi

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The Model of Remote Video Surveillance in Hierarchical Autonomous E-testing WbeTS System for Knowledge Testing

The Model of Remote Video Surveillance in Hierarchical Auton...

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37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Purkovic, Damir Ban, Eugen Fac Humanities & Social Sci Rijeka Croatia Branch Off Rijeka Educ & Teacher Training Agcy Rijeka Croatia

ISBN: (纸本)9789532330816

The web-based system for online knowledge testing (WbeTS) is a hierarchical and modular autonomous system for remote knowledge assessment whose autonomy is based on remote video surveillance as one of the testing validation mechanisms. The main problem facing the system is the physical implementation of the video surveillance unit for remote monitoring of a student, as well as development of a decision making mechanism based on such monitoring. The monitoring would need to take place in conditions of widely available broadband Internet access and the use of a webcam. A possible solution is a model of the remote control described in this paper, which could solve this problem and make the system more reliable. The model is based on built-in elements and algorithms for visual recognition and motion tracking of the student, sound tracking, user (teacher) interface for assessing the validity of testing and of the system's kernel responsible for assessing the validity of the tests and reporting. The model is envisaged to be implemented accordingly to the wishes and needs of users, and can be integrated as a part of an integral e-learning environment. Although the model described is in principle technologically feasible, its practical realization requires professional refinement and experimental validation. Only by in-depth testing and experiences gained from the actual application of the system will be possible to identify all deficiencies and requirements that currently cannot be predicted.

关键词： Internet computer aided instruction decision making object recognition object tracking user interfaces video surveillance broadband Internet access decision making mechanism hierarchical autonomous e-testing WbeTS system integral e-learning environment motion tracking online knowledge testing remote control remote knowledge assessment remote monitoring remote video surveillance sound tracking user interface visual recognition web-based system webcam Classification algorithms Electronic learning Speech Testing Video surveillance video monitoring User Interface Device Component e-learning telemonitoring Web-based systems webcam REMOTE CONTROL computer-assisted instruction sorting algorithm object recognition Speech Internets

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Dependency Network Methods for Hierarchical Multi-label Classification of Gene Functions 5

Dependency Network Methods for Hierarchical Multi-label Clas...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Fabris, Fabio Freitas, Alex A. Univ Kent Sch Comp Canterbury CT2 7NF Kent England

ISBN: (纸本)9781479945184

Hierarchical Multi-label Classification (HMC) is a challenging real-world problem that naturally emerges in several areas. This work proposes two new algorithms using a Probabilistic Graphical Model based on Dependency Networks (DN) to solve the HMC problem of classifying gene functions into pre-established class hierarchies. DNs are especially attractive for their capability of using traditional, "out-of-the-shelf", classification algorithms to model the relationship among classes and for their ability to cope with cyclic dependencies, resulting in greater flexibility with respect to Bayesian Networks. We tested our two algorithms: the first is a stand-alone Hierarchical Dependency Network (HDN) algorithm, and the second is a hybrid between the HDN and the Predictive Clustering Tree (PCT) algorithm, a well-known classifier for HMC. Based on our experiments, the hybrid classifier, using SVMs as base classifiers, obtained higher predictive accuracy than both the standard PCT algorithm and the HDN algorithm, considering 22 bioinformatics datasets and two out of three predictive accuracy measures specific for hierarchical classification (AU((PRC) over bar) and (AUPRC) over bar (w)).

关键词： Bayes methods bioinformatics genetics network theory (graphs) pattern classification support vector machines Bayesian networks DN HMC problem PCT algorithm SVM base classifiers bioinformatics datasets class hierarchies cyclic dependencies gene function classification hierarchical multilabel classification hybrid algorithm hybrid classifier out-of-the-shelf classification algorithms predictive accuracy measures predictive clustering tree algorithm probabilistic graphical model real-world problem stand-alone HDN algorithm stand-alone hierarchical dependency network algorithm standard PCT algorithm Accuracy Classification algorithms Clustering algorithms Inference algorithms Markov processes Prediction algorithms Vectors Support Vector Network sorting algorithm Inference algorithms Bayesian networks Prediction algorithms Clustering algorithms dwarf novae Bayes methods Pattern recognition Bioinformatics Markov chain hardware motion compensation Genetics World problems Hierarchical

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Proposing a high performance face detector based on UKF 7

Proposing a high performance face detector based on UKF

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IEEE/SICE International Symposium on System Integration (SII)

作者： Lamsal, Bikash Matsumoto, Naofumi Ashikaga Inst Technol 268-1 Ohmae Cho Ashikaga Tochigi 3268558 Japan

ISBN: (纸本)9781479969449

In this paper, we propose a high performance algorithm for detecting human faces in a still image. Human faces help to communicate and interact in a better way that may be either in human-human interaction or human-machine interaction. The success points for our algorithm is the use of different individual algorithms along with the Unscented Kalman filter (UKF) process, as a novelty of our process. We have modified the algorithms as well. We used Viola Jones eye detector, skin color detector and the Haar cascade classifier for face detection process. Finally, we have conducted a benchmark test for our proposed algorithm using image databases of CMU-MIT, MIT training sets, INRIA Graz-01, and FDDB database. Then, we clarify its effectiveness using ROC curves.

关键词： Kalman filters eye face recognition human computer interaction image classification image colour analysis nonlinear filters object detection skin visual databases CMU-MIT database FDDB database Haar cascade classifier INRIA Graz-01 database MIT training sets ROC curves UKF Viola Jones eye detector benchmark test high performance face detector human face detection human-human interaction human-machine interaction image databases skin color detector still image unscented Kalman filter Classification algorithms Databases Detectors Face Face detection Image color analysis Skin visual databases Facial Recognition image colour analysis Benchmark testing Still images Kalman filters ROC Curve Face Nonlinear filters sorting algorithm computer human interaction skin Object detection Image classification Face

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MicroEye: Visual Summary of Microblogsphere from the Eye of Celebrities 23

MicroEye: Visual Summary of Microblogsphere from the Eye of ...

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23rd International Conference on Computer Communication and Networks (ICCCN)

作者： Liu, Qingsong Hu, Yifan Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100864 Peoples R China Yahoo Labs New York New York NY USA

ISBN: (纸本)9781479935727

Micro-blogging is a new kind of online broadcast medium rising in latest years which is called weibo in China. Every day, weibo users are fed with a large number of incurious micro-blogs. They may also pay close attention to what celebrities concerned about, but they would rather roughly know the summary of micro-blogs the celebrities, however, they can't add attentions to all the celebrities' friends. In this work, we provide weibo users with an overview of the relevant micro-blogs and offer them a visual summary of the micro-blogs that appeal to famous people. Our goal is to help users select their most interested micro-blogs from a large number of micro-blogs.

关键词： Blogs Classification algorithms Clustering algorithms Force Layout Twitter Visualization Famous Persons sorting algorithm blogs Clustering algorithms Visualization layouts Force Social Media TWITTER Eye Consumers

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Meta-algorithms for Software-based Packet Classification 22

Meta-algorithms for Software-based Packet Classification

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22nd IEEE International Conference on Network Protocols (ICNP)

作者： He, Peng Xie, Gaogang Salamatian, Kave Mathy, Laurent Chinese Acad Sci ICT Beijing Peoples R China Univ CAS Beijing Peoples R China Univ Savoie Chambery France Univ Liege B-4000 Liege Belgium

ISBN: (纸本)9781479962044

We observe that a same ruleset can induce very different memory requirement, as well as varying classification performance, when using various well known decision tree based packet classification algorithms. Worse, two similar rulesets, in terms of types and number of rules, can give rise to widely differing performance behaviour for a same classification algorithms. We identify the intrinsic characteristics of rulesets that yield such performance differences, allowing us to understand and predict the performance behaviour of a ruleset for various modern packet classification algorithms. Indeed, from our observations, we are able to derive a memory consumption model and an offline algorithm capable of quickly identifying which packet classification is suited to a give ruleset. By splitting a large ruleset in several subsets and using different packet classification algorithms for different subsets, our SmartSplit algorithm is shown to be capable of configuring a multi-component packet classification system that exhibits up to 11 times less memory consumption, as well as up to about 4x faster classification speed, than the state-of-art work [20] for large rulesets. Our AutoPC framework obtains further performance gain by avoiding splitting large rulesets if the memory size of the built decision tree is shown by the memory consumption model to be small.

关键词： algorithm design and analysis Decision trees Estimation IP networks Memory management Prediction algorithms Random access memory packet classification Decision Trees algorithm design and analysis random access machine Prediction algorithms Store management Internet Protocol networks Ruleset performance behavior sorting algorithm Rule

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Feature Selection for Twitter Classification

Feature Selection for Twitter Classification

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8th IEEE International Conference on Semantic Computing

作者： Ostrowski, David Alfred Ford Motor Co Res & Innovat Ctr Syst Analyt Dearborn MI 48121 USA

ISBN: (纸本)9781479940035

Twitter-based messages have presented challenges in the identification of features as applied to classification. This paper explores filtering techniques for improved trend detection and information extraction. Starting with a pre-filtered source (Twitter), we will examine the application of both information theory and Natural Language Processing (NLP) based techniques as a means of preprocessing for classification. Results demonstrate that both means allow for improved results in classification among highly idiosyncratic data (Twitter).

关键词： Classification algorithms Filtering theory Market research Measurement Mutual information Twitter Classification Machine learning Natural Language Processing Natural Language Processing filtering theory TWITTER Social Media sorting algorithm market research Feature extraction Taxonomy Artificial Intelligence Machine Learning

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Supervised Facial Recognition Based on Multi-Resolution Analysis and Feature Alignment 57

Supervised Facial Recognition Based on Multi-Resolution Anal...

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57th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Aldhahab, Ahmed Atia, George Mikhael, Wasfy B. Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781479941322

A new supervised algorithm for face recognition based on the integration of Two-Dimensional Discrete Multiwavelet Transform (2-D DMWT), 2-D Radon Transform, and 2-D Discrete Wavelet Transform (2-D DWT) is proposed1. In the feature extraction step, Multiwavelet filter banks are used to extract useful information from the face images. The extracted information is then aligned using the Radon Transform, and localized into a single band using 2-D DWT for efficient sparse data representation. This information is fed into a Neural Network based classifier for training and testing. The proposed method is tested on three different databases, namely, ORL, YALE and subset fc of FERET, which comprise different poses and lighting conditions. It is shown that this approach can significantly improve the classification performance and the storage requirements of the overall recognition system.

关键词： Classification algorithms Databases Discrete wavelet transforms Feature extraction Multiresolution analysis Training Multiresolution analysis discrete wavelet transform sorting algorithm Feature extraction Database Radon transforms Facial Recognition Functional training Storage requirements recognition system Training

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Integrate Classifier Diversity Evaluation to Feature Selection Based Classifier Ensemble Reduction 14

Integrate Classifier Diversity Evaluation to Feature Selecti...

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14th UK Annual Workshop on Computational Intelligence (UKCI)

作者： Yao, Gang Chao, Fei Zeng, Hualin Shi, Minghui Jiang, Min Zhou, Changle Xiamen Univ Sch Informat Sci & Engn Dept Cognit Sci Fujian Prov Key Lab Brain Intelligent Syst Xiamen 361005 Peoples R China

ISBN: (纸本)9781479955381

Classifier ensembles improve the performance of single classifier system. However, a classifier ensemble with too many classifiers may occupy a large number of computational time. This paper proposes a new ensemble subset evaluation method that integrates classifier diversity measures into a classifier ensemble reduction framework. The approach is implemented by using three conventional diversity algorithms and one new developed diversity measure method to calculate the diversity's merits within the classifier ensemble reduction framework. The subset evaluation method is demonstrated by the experimental data: the method not only can meet the requirements of high accuracy rate and fewer size, but also its running time is greatly shortened. When the accuracy requirements are not very strict, but the the running time requirements is more stringent, the proposed method is a good choice.

关键词： Accuracy Classification algorithms Diversity methods Extraterrestrial measurements Machine learning algorithms Q measurement Training Classifier Ensembles Q measurement Extraterrestrial measurements Diversity methods Machine learning algorithms sorting algorithm Functional training required precision Classifiers Runtime accuracy rate

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