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27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014

作者： Ratnapinda, Parot Druzdzel, Marek J. Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Faculty of Computer Science Bialystok University of Technology Wiejska 45A Bialystok15-351 Poland

We report the results of an empirical evaluation of structural simplification of Bayesian networks by removing weak arcs. We conduct a series of experiments on six networks built from real data sets selected from the UC Irvine Machine Learning Repository. We systematically remove arcs from the weakest to the strongest, relying on four measures of arc strength, and measure the classification accuracy of the resulting simplified models. Our results show that removing up to roughly 20 percent of the weakest arcs in a network has minimal effect on its classification accuracy. At the same time, structural simplification of networks leads to significant reduction of both the amount of memory taken by the clique tree and the amount of computation needed to perform inference. Copyright © 2014, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Bayesian networks

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The upregulation of Myb and Peg3 may mediate EGCG inhibition effect on mouse lung adenocarcinoma

The upregulation of Myb and Peg3 may mediate EGCG inhibition...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Hong Zhou Joseph Manthey Ekaterina Lioutikova William Yang Kenji Yoshigoe Mary Qu Yang Hong Wang Department of Mathematical Sciences School of Health and Natural Sciences University of Saint Joseph Department of Computer Science George Washington Donaghey College of Engineering and Information Technology University of Arkansas at Little Rock and University of Arkansas for Medical Sciences Middle South Bioinformatics Center and Joint Bioinformatics Ph.D. Program George Washington Donaghey College of Engineering and Information Technology University of Arkansas at Little Rock and University of Arkansas for Medical Sciences Susan L. Cullman Laboratory for Cancer Research Department of Chemical Biology and Center for Cancer Prevention Research Ernest Mario School of Pharmacy Rutgers The State University of New Jersey

ISBN: (纸本)9781467368001

The antioxidant activity of green tea polyphenol epigallocatechin-3-gallate (EGCG) has been found to be critical in inhibiting carcinogenesis. In our previous study, we identified a set of protein coding genes and microRNAs whose expressions were significantly modulated in response to the EGCG treatment in tobacco carcinogen-induced lung adenocarcinoma in A/J mice. In this study, we further conducted some statistical analysis on our microarray data and employed The Cancer Genome Atlas (TCGA) lung adenocarcinoma datasets as additional control. We postulated that if a gene mediates EGCG's cancer inhibition, its expression level change caused by EGCG should be opposite to what occurred in the carcinogenesis. With this assumption, we identified Myb and Peg3 as the primary genes involved in the cancer inhibitory activities of EGCG.

关键词： EGCG Myb Peg3 Lung adenocarcinoma

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Virtual operating room for collaborative training of surgical nurses

Virtual operating room for collaborative training of surgica...

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20th International Conference on Collaboration and technology, CRIWG 2014

作者： Kleven, Nils Fredrik Prasolova-Førland, Ekaterina Fominykh, Mikhail Hansen, Arne Rasmussen, Guri Sagberg, Lisa Millgård Lindseth, Frank Department of Computer and Information Science Norwegian University of Science and Technology Norway Program for Learning with ICT Norwegian University of Science and Technology Norway Faculty of Nursing Sør-Trøndelag University College Norway Department of Neurosurgery St. Olav's University Hospital Norway Department of Medical Technology SINTEF Norway

ISBN: (纸本)9783319101651

In this paper, we present the first results of a study exploring how to support collaborative learning of surgical nursing students in a 3D virtual world. A Virtual Operating room, resembling the one at St. Olav's University Hospital in Trondheim, Norway was created in Second Life to accommodate an educational role-play. In this role-play, the operating nursing students could practice communication with patients and cooperation in the team while preparing patients for surgery. At the first stage of the evaluation, the virtual simulation has been tested among nine postgraduate nursing students. The participants gave their evaluation and opinions in the form of questionnaires and discussion after the role-plays. Following the analysis of the data, we present a summary of the most important results in this paper. This study provides a number of suggestions for improving the learning process when role-playing in a virtual environment. We demonstrate that an educational simulation can be implemented with limited resources, and yet be practically useful in education of health personnel. Further research with medical and nursing students is highly applicable and feasible, and should include a larger group of participants. In the next stage of our work, the evaluation of the Virtual Operating room has been conducted with nurses, who are on an earlier stage of their study program, as well as anesthesia nurses and non-medics. © 2014 Springer International Publishing.

关键词： Operating rooms

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Design and Development of a Low Cost Ubiquitous Tracking System

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Procedia computer Science 2014年 34卷 220-227页

作者： M. Behzad A. Sana M.A. Khan Z. Walayat U. Qasim Z.A. Khan N. Javaid Department of Electrical Engineering COMSATS Institute of Information Technology Islamabad Pakistan Department of Computer Science COMSATS Institute of Information Technology Islamabad Pakistan CAST COMSATS Institute of Information Technology Islamabad Pakistan University of Alberta Alberta Canada Internetworking Program FE Dalhousie University Halifax Canada

In this paper, design and development of a ubiquitous tracking system is proposed, in which vehicles are tracked and controlled using the prevailing cellular technologies. The system contains a GPS receiver and a GSM modem interfaced with a microcontroller. To track any vehicle, the vehicle's owner has to send an SMS to the tracking system installed inside the vehicle. Upon receiving the SMS, the microcontroller takes the current location's longitude and latitude coordinates from GPS receiver, packs it into an SMS and sends it to the owner and on a web server using GSM modem. When the web server receives the SMS containing vehicles coordinates, it will show location of the vehicle on Google Maps. For android users, the location is also displayed on an android application. In case of vehicle theft, the owner is able to turn off the main ignition switch, check status and speed of the vehicle simply by sending an SMS. The system is also equipped with a special security button for parked vehicles. By turning the button ON, the system will come in ACTIVE mode and will keep a special check on the vehicle's movement meanwhile performing the normal tasks. If the system senses any movement of vehicle during the ACTIVE mode, it will turn the main ignition OFF and will inform the owner immediately be sending 5 SMSs. Record of the vehicle's movement will be continuously managed on the web server where each owner will have vehicle's account. We have used a wide number of technologies including, but not limited to, Global Positioning System (GPS), Global System for Mobile Communication (GSM) and Microcontroller.

关键词： Global Positioning System (GPS) Global System for Mobile Communication (GSM) Google Maps Microcontroller (μC) Mobile Assets Short Message Service (SMS) Tracking

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Robust Handwriting Extraction and Lecture Video Summarization

Robust Handwriting Extraction and Lecture Video Summarizatio...

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International Conference on Intelligent information Hiding and Multimedia Signal Processing (IIHMSP)

作者： Fu Hao Yeh Greg C. Lee Ying Ju Chen Chien Hsing Liao Program of Information Technology Fooyin University Kaohsiung Taiwan Department of Computer Science and Information Engineering Nation Taiwan Normal University Taipei Taiwan

Video tools developed today, teachers can record lecture videos and upload these lecture videos to e-learning system themselves. However, some students may only do not understand some fragments but they have to waste unnecessary time download entire video, and therefore video scene segmentation is relative importance. In addition, in traditional teaching model, students must listen and transcribe content on blackboard, when the lecture's write speed is too fast, students are very difficult to pay attention in class and easy to transcription errors. If we can record the lecture video and automatically extract content on the blackboard, students not only enable index videos easily, but also not easily transcribe the wrong notes. Hence, this paper presents an intelligent assistance system for lecture videos. We use K-mean Segmentation to extract blackboard area and then only update this area to avoid lecturer's body cover the content on blackboard. Then we use adaptive threshold to extract chalk and design a method to reduce noise. After that we detecting the lecture videos split timing with statistics chalk pixel count and analysis the variation. Finally, we further interception the most complete content as lecture note to facilitate the students to search for video clips.

关键词： Image segmentation Image color analysis Indexes Educational institutions Electronic learning Noise Semantics

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Multi-radio over Fiber Architecture for Road Vehicle Communication in VANETs

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Procedia computer Science 2014年 32卷 1022-1029页

作者： H.H.R. Sherazi I. Raza M.H. Chaudary S.A. Hussain M.H. Raza Department of Computer Science COMSATS Institute of Information Technology Lahore 54000 Pakistan Department of Computer Science & IT Lahore Leeds University Lahore Pakistan Internetworking Program Faculty of Engineering Dalhousie University Canada

Vehicular Ad-hoc Networks (VANET) are employing heterogeneous technologies now a days to meet the increasing demands of Intelligent Transportation System (ITS) applications such as enriched multimedia, video conferencing, gaming and online collaboration. Deployment and maintenance cost for infrastructures are also a major concern. This work proposes a framework, capable of catering multiple technologies simultaneously (such as local area network, wide area networks and cellular networks), that deploys wired and wireless integrated technologies to exploit the advantages of both. Therefore, it offers the architecture based on radio over fiber technology to meet the future requirements of high data rate for Road Vehicle Communication (RVC) in VANETs and it comes up with the most important and perhaps desperately needed feature of ‘Future technology Support’ yielding very high data rates support. Several traditionally deployed architectures are striving to come up with the future needs but due to their various limitations they were unable to attain their expected outcomes. The proposed RoF based architecture justifies its need inducing a true value and powerful features to dramatically enhance the overall performance of the entire system. Several evaluation parameters have been chosen that clearly present the strength of proposed RoF framework and prove that RoF framework is the better option for the service providers in the area of ITS applications.

关键词： VANET intelligent transportation systems road vehicle communication radio-over-fiber radio access unit central site

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Fuzziness, uncertainty, vagueness, possibility, and probability in natural language

Fuzziness, uncertainty, vagueness, possibility, and probabil...

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IEEE Conference on Norbert Wiener in the 21st Century (21CW)

作者： V. Raskin J. M. Taylor Department of English & Linguistics Program Purdue University Indiana USA Department of Computer and Information Technology Purdue University Indiana USA

The paper reviews the status of fuzziness, vagueness, uncertainty, and probability in natural language with an emphasis on: aspects of natural language that pertain to each of the four phenomena; methods of their modeling and representation; computational treatment of these phenomena in natural language and information processing. The main thrust of the paper is the procedure for calculating the predominant type of uncertainty for a NL sentence.

关键词： Uncertainty Semantics Fuzzy logic Dictionaries Natural languages Possibility theory

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

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Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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Learning sparse representation for leaf image recognition

Learning sparse representation for leaf image recognition

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Jou-Ken Hsiao Li-Wei Kang Ching-Long Chang Chao-Yung Hsu Chia-Yen Chen Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Engineering Science and Technology-Doctoral Program National Yunlin University of Science and Technology Yunlin Taiwan Institute of Information Science Taipei Taiwan Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, a novel leaf image recognition technique via sparse representation is proposed for automatic plant identification. In order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. For each test leaf image, we calculate the correlation between the image and each learned dictionary of leaf species to achieve the identification of the leaf image. As a result, efficient leaf recognition can be achieved on public leaf dataset based on the proposed framework leading to a more compact and richer representation of leaf images compared to traditional clustering approaches. Moreover, our method is also adapted to newly added leaf species without retraining classifiers and suitable to be highly parallelized as well as integrated with any leaf image descriptors/features.

关键词： Image recognition Dictionaries Training Feature extraction Shape Educational institutions computer vision

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Comparative study of leaf image recognition with a novel learning-based approach

Comparative study of leaf image recognition with a novel lea...

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Science and information Conference (SAI)

作者： Jou-Ken Hsiao Li-Wei Kang Ching-Long Chang Chih-Yang Lin Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Engineering Science and Technology-Doctoral Program National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification. In our learning-based method, in order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Moreover, we also implement a general bag-of-words (BoW) model-based recognition system for leaf images, used for comparison. We experimentally compare the two approaches and show unique characteristics of our sparse coding-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two evaluated methods, where the proposed sparse coding-based framework can perform better.

关键词： Image recognition Dictionaries Training Feature extraction Shape Vectors Image coding

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