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5th International Conference on Learning Analytics and Knowledge, LAK 2015

作者： Joksimović, Srećko Zouaq, Amal Kovanović, Vitomir Gašević, Dragan Jovanović, Jelena Hatala, Marek School of Interactive Arts and Technology Simon Fraser University Burnaby Canada Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada School of Informatics University of Edinburgh Edinburgh United Kingdom Schools of Education and Informatics University of Edinburgh Edinburgh United Kingdom Department of Software Engineering Faculty of Organizational Sciences Belgrade Serbia

ISBN: (纸本)9781450334174

Creating meaning from a wide variety of available information and being able to choose what to learn are highly relevant skills for learning in a connectivist setting. In this work, various approaches have been utilized to gain insights into learning processes occurring within a network of learners and understand the factors that shape learners' interests and the topics to which learners devote a significant attention. This study combines different methods to develop a scalable analytic approach for a comprehensive analysis of learners' discourse in a connectivist massive open online course (cMOOC). By linking techniques for semantic annotation and graph analysis with a qualitative analysis of learner-generated discourse, we examined how social media platforms (blogs, Twitter, and Facebook) and course recommendations influence content creation and topics discussed within a cMOOC. Our findings indicate that learners tend to focus on several prominent topics that emerge very quickly in the course. They maintain that focus, with some exceptions, throughout the course, regardless of readings suggested by the instructor. Moreover, the topics discussed across different social media differ, which can likely be attributed to the affordances of different media. Finally, our results indicate a relatively low level of cohesion in the topics discussed which might be an indicator of a diversity of the conceptual coverage discussed by the course participants.

关键词： Social networking (online)

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How engineering students study: Alone, together, or start alone, end together 122

How engineering students study: Alone, together, or start al...

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2015 122nd ASEE Annual Conference and Exposition

作者： Wilson, Denise Allendoerfer, Cheryl Bates, Rebecca A. Smith, Tamara Floyd Plett, Melani I. Veilleux, Nanette M. Department of Electrical Engineering University of Washington Seattle United States College of Engineering University of Washington United States Department of Integrated Engineering Minnesota State University Mankato United States Department of Chemical Engineering Tuskegee University United States Department of Electrical Engineering Seattle Pacific University United States Computer Science and Informatics Program Simmons College BostonMA United States

This study interviewed 33 students from four different institutions in a variety of engineering and computer science majors in order to understand how and why students study the way they do. The institutions involved in this research include two types of Carnegie 2010 Classifications (Master's L and RU-VH), geographical locations in the northwest, northeast, and midwest, both public and private institutions, and enrollments varying from 3,500 to 29,000 students (total undergraduates among all majors). Although this interview pool does not provide a large sample size, and thus results must be interpreted qualitatively and with caution, we found that a majority of students begin their studying alone and seek help only later when they run into trouble with their coursework. Despite the fact that this Start alone, End together model for studying was the predominant approach among interviewees, the reasons for choosing this mode of studying vary. For example, students at the small Women's college in this study reported easy accessibility to faculty as a reason to refrain from studying in groups, while students at the large research university reported a need to focus properly, learn fully, and make the most efficient use of limited time as the prevalent reason for beginning their study alone. Furthermore, when asked how they prefer to study, many students shift from a Start alone, End together model to a Start alone, End alone model, suggesting that students may have unrealistic expectations of being able to complete assignments and study on their own. Still other students, while making the best of Start alone, End together, expressed that they would much rather work in a group from start to finish. The results of our student interviews suggest the need for a larger study that investigates how prevalent the Start alone, End together model of studying is among a large, representative sample of engineering and computer science students and if it is an arrangement that stud

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Efforts to Overcome Cyber Crime Actions in Indonesia

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Muhammad Hatta Ramalinggam Rajamanickam Dahlan Abdullah H Hartono Marten Bunga Hari Sapto Adji Amiruddin Hardianto Djanggih Nasrun Hipan S Salle S Salmawati Abdul Azis Mirnawanti Wahab Kisman Karinda A A Saleh I Ketut Sudarsana Agung Purnomo Fatkul Anam Dian Rianita Department of Criminal Law Universitas Malikusssaleh Aceh Indonesia Faculty of Law National University of Malaysia Selangor Malaysia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Law Universitas Gorontalo Indonesia Department of Law Universitas Tompotika Luwuk Banggai Indonesia Department of Law Universitas Muhammadiyah Luwuk Banggai Indonesia Department of Law Universitas Muslim Indonesia Indonesia Department of State Adminstration Sekolah Tinggi Ilmu Administrasi YAPPI Makassar Indonesia Department of Management Sekolah Tinggi Ilmu Ekonomi AMKOP Makassar Indonesia Department of Goverment Studies Universitas Muhammadiyah Luwuk Banggai Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Denpasar Indonesia Entrepreneurship Program Bina Nusantara Institute of Creative Technology Malang Indonesia Department of Mathematics Education Universitas Wijaya Kusuma Surabaya Indonesia Department of Administration University of Lancang Kuning Riau Indonesia

The presence of the internet can penetrate the boundaries between countries and accelerate the spread and exchange of information throughout the world. Although there are many positive impacts of internet use, there are also negative impacts that disturb the community. One can easily become a criminal (cyber crime) only by creating blogs, accounts, applications, programs, sites in various mass media and social media to commit fraud, data theft, illegal use of credit cards, the spread of pornographic content, online gambling, online prostitution, hate speech, radicalism, terrorism and so on. To overcome crime in the virtual space, the government has issued Law No. 19 of 2016 concerning Amendments to Law No. 11 of 2008 concerning Information and Electronic Transactions. But not all internet users know and understand the substance of the law. Therefore, the government, employers and institutions that are engaged in information and communication technology have the responsibility to socialize the law to the public to prevent crime in the virtual space.

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Retraction: Efforts to Overcome Cyber Crime Actions in Indonesia (J. Phys.: Conf. Ser. 1114 012081)

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Journal of Physics: Conference Series 2018年第1期1114卷

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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Modification of key scheduling for security improvement in XTEA

Modification of key scheduling for security improvement in X...

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International Conference on Information, Communication Technology and System (ICTS)

作者： Osvari Arsalan Achmad Imam Kistijantoro Faculty of Computer Science Sriwijaya University Palembang Indonesia School of Electrical Engineering and Informatics Institute of Technology Bandung Bandung Indonesia

XTEA algorithm has been used as one of the solutions for securing digital data based on cryptosystem. The algorithm was developed by researchers from the University of Cambridge UK known as a lightweight, low cost and fast algorithm. However, such an attack impossible differential and related-key differential proved able to break through to the XTEA algorithm. XTEA algorithm weaknesses in the key scheduling used to establish the characteristics of the attack so as to find the pieces of the key are used. In this research, security improvement strategies developed by modifying the scheduling algorithm XTEA key into the key dynamic scheduling. This strategy proved to be able to produce different patterns of diffusion of derivative input bits for each use different keys. In addition, the key scheduling modification strategy is capable of producing good-value diffusion bits (over 50% of the total input 64-bit) 10 times more than the original algorithm XTEA. With the level of the round sub key distribution difference reaches 100%, it is certain that the process of guessing the right key value for each round will be very difficult.

关键词： Information and communication technology

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Preface

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9972 LNCS卷 vi页

作者： Chmielewski, Leszek J. Datta, Amitava Kozera, Ryszard Wojciechowski, Konrad Faculty of Applied Informatics and Mathematics Warsaw University of Life Sciences - SGGW Warsaw Poland School of Computer Science and Software Engineering The University of Western Australia Perth Australia Institute of Computer Science Silesian University of Technology Gliwice Poland

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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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RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

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Theoretical and Applied Climatology 2015年第3期125卷 679-689页

作者： Gani, Abdullah Mohammadi, Kasra Shamshirband, Shahaboddin Khorasanizadeh, Hossein Seyed Danesh, Amir Piri, Jamshid Ismail, Zuraini Zamani, Mazdak Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Mechanical Engineering University of Kashan Kashan Iran Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Water Engineering Soil and Water College University of Zabol Zabol Iran Advanced Informatics School Universiti Teknologi Malaysia Johor Bahru Malaysia

The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neuro-fuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44–0.61 kWh/m2, 0.50–0.71 kWh/m2, and 0.78–0.91, respectively.

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Automatic apex frame spotting in micro-expression database

Automatic apex frame spotting in micro-expression database

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Asian Conference on Pattern Recognition (ACPR)

作者： Sze-Teng Liong John See KokSheik Wong Anh Cat Le Ngo Yee-Hui Oh Raphael Phan Faculty of Computer Science Information Technology University of Malaya (UM) Malaysia Faculty of Computing Informatics Multimedia University (MMU) Malaysia Faculty of Engineering Multimedia University (MMU) Malaysia

ISBN: (纸本)9781479961016

Micro-expression usually occurs at high-stakes situations and may provide useful information in the field of behavioral psychology for better interpretion and analysis. Unfortunately, it is technically challenging to detect and recognize micro-expressions due to its brief duration and the subtle facial distortions. Apex frame, which is the instant indicating the most expressive emotional state in a video, is effective to classify the emotion in that particular frame. In this work, we present a novel method to spot the apex frame of a spontaneous micro-expression video sequence. A binary search approach is employed to locate the index of the frame in which the peak facial changes occur. Features from specific facial regions are extracted to better represent and describe the expression details. The defined facial regions are selected based on the action unit and landmark coordinates of the subject, in which case these processes are automated. We consider three distinct feature descriptors to evaluate the reliability of the proposed approach. Improvements of at least 20% are achieved when compared to the baselines.

关键词： Feature extraction Face Strain Mouth Databases Face recognition Eyebrows

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROscience 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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