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Personalization of learning paths in online communities of creators 9

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Personalization of learning paths in online communities of c...

9th International Conference on Educational Data Mining, EDM 2016

作者： Sun, Mingxuan Yang, Seungwon Division of Computer Science and Engineering Louisiana State University United States School of Library and Information Science Center for Computation and Technology Louisiana State University United States

In massive online communities of creators (OCOCs), one of the core challenges is to encourage users to learn to create original contents using basic components. Recommending the right learning components at the right time is critical for improving user engagement and has not been fully studied due to the unstructured nature of online communities. To address the problem, we propose in this paper a novel recommendation model which integrates Cox’s survival analysis and collaborative filtering. Our model can incorporate factors such as user learning history and social engagements, which provides us insights in improving the personalized service. We apply our method to the user data from Scratch online platform and demonstrate the performance of the model. © 2016 International Educational Data Mining Society. All rights reserved.

关键词： Collaborative filtering

Author Correction: National identity predicts public health support during a global pandemic

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Nature communications 2022年第1期13卷 1949页

作者： Jay J Van Bavel Aleksandra Cichocka Valerio Capraro Hallgeir Sjåstad John B Nezlek Tomislav Pavlović Mark Alfano Michele J Gelfand Flavio Azevedo Michèle D Birtel Aleksandra Cislak Patricia L Lockwood Robert Malcolm Ross Koen Abts Elena Agadullina John Jamir Benzon Aruta Sahba Nomvula Besharati Alexander Bor Becky L Choma Charles David Crabtree William A Cunningham Koustav De Waqas Ejaz Christian T Elbaek Andrej Findor Daniel Flichtentrei Renata Franc Biljana Gjoneska June Gruber Estrella Gualda Yusaku Horiuchi Toan Luu Duc Huynh Agustin Ibanez Mostak Ahamed Imran Jacob Israelashvili Katarzyna Jasko Jaroslaw Kantorowicz Elena Kantorowicz-Reznichenko André Krouwel Michael Laakasuo Claus Lamm Caroline Leygue Ming-Jen Lin Mohammad Sabbir Mansoor Antoine Marie Lewend Mayiwar Honorata Mazepus Cillian McHugh John Paul Minda Panagiotis Mitkidis Andreas Olsson Tobias Otterbring Dominic J Packer Anat Perry Michael Bang Petersen Arathy Puthillam Julián C Riaño-Moreno Tobias Rothmund Hernando Santamaría-García Petra C Schmid Drozdstoy Stoyanov Shruti Tewari Bojan Todosijević Manos Tsakiris Hans H Tung Radu G Umbreș Edmunds Vanags Madalina Vlasceanu Andrew Vonasch Meltem Yucel Yucheng Zhang Mohcine Abad Eli Adler Narin Akrawi Hamza Alaoui Mdarhri Hanane Amara David M Amodio Benedict G Antazo Matthew Apps F Ceren Ay Mouhamadou Hady Ba Sergio Barbosa Brock Bastian Anton Berg Maria P Bernal-Zárate Michael Bernstein Michał Białek Ennio Bilancini Natalia Bogatyreva Leonardo Boncinelli Jonathan E Booth Sylvie Borau Ondrej Buchel C Daryl Cameron Chrissie F Carvalho Tatiana Celadin Chiara Cerami Hom Nath Chalise Xiaojun Cheng Luca Cian Kate Cockcroft Jane Conway Mateo Andres Córdoba-Delgado Chiara Crespi Marie Crouzevialle Jo Cutler Marzena Cypryańska Justyna Dabrowska Michael A Daniels Victoria H Davis Pamala N Dayley Sylvain Delouvee Ognjan Denkovski Guillaume Dezecache Nathan A Dhaliwal Alelie B Diato Roberto Di Paolo Marianna Drosinou Uwe Dulleck Jānis Ekmanis Arhan S Ertan Tom W Etienne Hapsa Hossain Farhana Fahima Farkhari Har Department of Psychology and Neural Science New York University New York NY USA. jay.vanbavel@nyu.edu. School of Psychology University of Kent Canterbury England. Department of Economics Middlesex University London London England. Department of Strategy and Management Norwegian School of Economics Bergen Norway. SWPS University of Social Sciences and Humanities Poznań Poland. Department of Psychological Sciences College of William and Mary Williamsburg VA USA. Institute of Social Sciences Ivo Pilar Zagreb Croatia. Department of Philosophy Macquarie University Sydney NSW Australia. Stanford Graduate School of Business Stanford University Stanford CA USA. Institute of Communication Science Friedrich-Schiller University Jena Jena Germany. flavio.azevedo@uni-jena.de. School of Human Sciences Institute for Lifecourse Development University of Greenwich London England. Department of Experimental Psychology University of Oxford Oxford England. Center for Human Brain Health School of Psychology University of Birmingham Birmingham England. Department of Psychology Macquarie University Sydney NSW Australia. KU Leuven Leuven Belgium. National Research University Higher School of Economics (HSE) Moscow Russia. De La Salle University Manila Philippines. Department of Psychology University of the Witwatersrand Johannesburg South Africa. Department of Political Science Aarhus University Aarhus Denmark. X University Toronto ON Canada. Department of Government Dartmouth College Hanover NH USA. Department of Psychology University of Toronto Toronto ON Canada. Gatton College of Business and Economics University of Kentucky Lexington KY USA. Department of Mass Communication National University of Science and Technology (NUST) Islamabad Pakistan. Department of Management Aarhus University Aarhus Denmark. Faculty of Social and Economic Sciences Comenius University Bratislava Slovakia. IntraMed Buenos Aires Argentina. Macedonian Academy of Sciences and

Correction: Improving Nutrition and Activity Behaviors Using Digital Technology and Tailored Feedback: Protocol for the Tailored Diet and Activity (ToDAy) Randomized Controlled Trial

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JMIR research protocols 2020年第12期9卷 e25940页

作者： Rhiannon E Halse Charlene L Shoneye Christina M Pollard Jonine Jancey Jane A Scott Iain S Pratt Satvinder S Dhaliwal Richard Norman Leon M Straker Carol J Boushey Edward J Delp Fengqing Zhu Amelia J Harray Maria A Szybiak Anne Finch Joanne A McVeigh Barbara Mullan Clare E Collins Syed Aqif Mukhtar Kieran N Edwards Janelle D Healy Deborah A Kerr School of Public Health Curtin University Perth Western Australia Australia. East Metropolitan Health Service Perth Western Australia Australia. Cancer Council WA Perth Western Australia Australia. Health Psychology & Behavioural Medicine Research Group School of Psychology Curtin University Perth Western Australia Australia. School of Physiotherapy and Exercise Science Curtin University Perth Western Australia Australia. Cancer Epidemiology Program University of Hawaii Cancer Center Honolulu HI United States. Department of Nutrition Purdue University West Lafayette IN United States. School of Electrical and Computer Engineering Purdue University West Lafayette IN United States. School of Occupational Therapy Speech Therapy & Social Work Curtin University Perth Western Australia Australia. Movement Physiology Laboratory University of Witwatersrand Johannesburg South Africa. School of Health Sciences Faculty of Health and Medicine University of Newcastle Callaghan New South Wales Australia. Priority Research Centre in Physical Activity and Nutrition University of Newcastle Callaghan New South Wales Australia. Curtin Institute of Computation Curtin University Perth Western Australia Australia.

[This corrects the article DOI: 10.2196/12782.].

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Enhanced photothermal cooling of nanowires

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arXiv 2017年

作者： Guccione, G. Hosseini, M. Mirzaei, A. Slatyer, H.J. Buchler, B.C. Lam, P.K. Centre for Quantum Computation and Communication Technology Department of Quantum Science Research School of Physics and Engineering Australian National University CanberraACT2601 Australia Birck Nanotechnology Center School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Nonlinear Physics Centre Research School of Physics and Engineering Australian National University CanberraACT2601 Australia

We investigate the optomechanical interaction between light and metallic nanowires through the action of bolometric forces. We show that the response time of the photothermal forces induced on the nanowire is fast and the strength of the interaction can overcome the radiation pressure force. Furthermore, we suggest the photothermal forces can be enhanced by surface plasmon excitation to cool the sub-megahertz vibrational modes of the nanowires close to its quantum limit. Copyright © 2017, The Authors. All rights reserved.

关键词： Nanowires

An efficient npn boolean matching algorithm based on structural signature and shannon expansion

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arXiv 2017年

作者： Zhang, Juling Yang, Guowu Hung, William N.N. Zhang, Yan Wu, Jinzhao Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Synopsys Inc. Mountain ViewCA United States Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning530006 China

An efficient pairwise Boolean matching algorithm for solving the problem of matching single-output specified Boolean functions under input negation and/or input permutation and/or output negation (NPN) is proposed in this paper. We present the structural signature (SS) vector, which comprises a first-order signature value, two symmetry marks, and a group mark. As a necessary condition for NPN Boolean matching, the SS is more effective than the traditional signature. Two Boolean functions, f and g, may be equivalent when they have the same SS vector. A symmetry mark can distinguish symmetric variables and asymmetric variables and be used to search for multiple variable mappings in a single variable-mapping search operation, which reduces the search space significantly. Updating the SS vector via Shannon decomposition provides benefits in distinguishing unidentified variables, and the group mark and phase collision check can be used to discover incorrect variable mappings quickly, which also speeds up the NPN Boolean matching process. Using the algorithm proposed in this paper, we test both equivalent and non-equivalent matching speeds on the MCNC benchmark circuit sets and random circuit sets. In the experiment, our algorithm is shown to be 4.2 times faster than competitors when testing equivalent circuits and 172 times faster, on average, when testing non-equivalent circuits. The experimental results show that our approach is highly effective at solving the NPN Boolean matching problem. Copyright © 2017, The Authors. All rights reserved.

关键词： Mapping

A canonical-based NPN boolean matching algorithm utilizing boolean difference and cofactor signature

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arXiv 2017年

作者： Zhang, Juling Yang, Guowu Hung, W.N.N. Wu, Jinzhao Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Synopsys Inc. Mountain ViewCA United States Guangxi Key Laboratory of Hybrid Computation IC Design Analysis Guangxi University for Nationalities Nanning530006 China

This paper presents a new compact canonical-based algorithm to solve the problem of single-output completely specified NPN Boolean matching. We propose a new signature vector Boolean difference and cofactor (DC) signature vector. Our algorithm utilizes the Boolean difference, cofactor signature and symmetry properties to search for canonical transformations. The use of symmetry and Boolean difference notably reduces the search space and speeds up the Boolean matching process compared to the algorithm proposed in [1]. We tested our algorithm on a large number of circuits. The experimental results showed that the average runtime of our algorithm 37% higher and its average search space 67% smaller compared to [1] when tested on general circuits. Copyright © 2017, The Authors. All rights reserved.

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Truthful mechanisms for agents that value privacy

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ACM Transactions on Economics and computation 2016年第3期4卷 1–30页

作者： Chen, Yiling Chong, Stephen Kash, Ian A. Moran, Tal Vadhan, Salil Center for Research on Computation and Society John A. Paulson School of Engineering and Applied Sciences Harvard University 33 Oxford Street CambridgeMA United States Microsoft Research Cambridge 21 Station Road CambridgeCB1 2FB United Kingdom Efi Arazi School of Computer Science IDC Herzliya Israel

Recent work has constructed economic mechanisms that are both truthful and differentially private. In these mechanisms, privacy is treated separately from truthfulness;it is not incorporated in players' utility functions (and doing so has been shown to lead to nontruthfulness in some cases). In this work, we propose a new, general way of modeling privacy in players' utility functions. Specifically, we only assume that if an outcome o has the property that any report of player i would have led to o with approximately the same probability, then o has a small privacy cost to player i. We give three mechanisms that are truthful with respect to our modeling of privacy: for an election between two candidates, for a discrete version of the facility location problem, and for a general social choice problem with discrete utilities (via a VCG-like mechanism). As the number n of players increases, the social welfare achieved by our mechanisms approaches optimal (as a fraction of n). © 2016 ACM.

关键词： Machine design

Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer

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arXiv 2017年

作者： Hao, Hongxia Zhou, Zhiguo Li, Shulong Maquilan, Genevieve Folkert, Michael R. Iyengar, Puneeth Westover, Kenneth D. Albuquerque, Kevin Liu, Fang Choy, Hak Timmerman, Robert Yang, Lin Wang, Jing School of Computer Science and Technology Xidian University Xi'an710071 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation Xidian University Xi'an710071 China Department of Radiation Oncology University of Texas Southwestern Medical Center DallasTX75235 United States School of Biomedical Engineering Southern medical university Guangzhou510515 China Department of Pathology National Cancer Center/Cancer Hospital Chinese Academy of Medical Sciences Beijing100021 China

Purpose To develop and demonstrate a novel tumor shell feature for predicting distant failure in non-small cell lung cancer (NSCLC) and cervical cancer (CC) patients. Patients and Methods The shell predictive model was constructed using pre-treatment positron emission tomography (PET) images from 48 NSCLC patients received stereotactic body radiation therapy (SBRT) and 52 CC patients underwent external beam radiation therapy and concurrent chemotherapy followed with high-dose-rate intracavitary brachytherapy. A shell feature, consisting of outer voxels around the tumor boundary, was extracted from a series of axial PET slices. The hypothesis behind this feature is that non-invasive and invasive tumors may have different morphologic patterns in the tumor periphery, in turn reflecting the differences in radiological presentations in the PET images. The shell’s utility was evaluated by the support vector machine (SVM) classifier in comparison with intensity, geometry, gray level co-occurrence matrix (GLCM)-based texture, neighborhood gray tone difference matrix (NGTDM)-based texture, and a combination of these four features. The results were assessed in terms of accuracy, sensitivity, specificity, and the area under the receiver operating curve (AUC). Results For NSCLC, the AUC achieved by the shell feature was 0.82 while the highest AUC achieved by the other features was 0.76. Similarly, for CC, the AUC achieved by the shell feature was 0.83 while the highest AUC achieved by the other features was 0.76. Also, the difference in performance between shell and the other features was significant (P Copyright © 2017, The Authors. All rights reserved.

关键词： Radiotherapy

Global contrast based salient region boundary sampling for action recognition 22nd

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Global contrast based salient region boundary sampling for a...

22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Xu, Zengmin Hu, Ruimin Chen, Jun Chen, Huafeng Li, Hongyang National Engineering Research Center for Multimedia Software School of Computer Wuhan University Wuhan China Collaborative Innovation Center of Geospatial Technology Wuhan China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783319276700

Although the excellent representation ability of improved Dense Trajectory (iDT) based features for action video had been proved on several action datasets, the performance of action recognition still suffers from large camera motion of videos. In this paper, we improve the iDT method by advancing a novel salient region boundary based dense sampling strategy, which reduces the number of trajectories while preserves the discriminative power. We first implement the iDT sampling based on motion boundary image, then introduce a global contrast based salient object segmentation method in interest points sampling step of action recognition. To overcome the flaws of global color contrast-based salient region sampling, we apply morphological gradient to generate a more robust mask for sampling dense points, as motion boundaries are much clearer. To evaluate the proposed method, we conduct extensive experiments on two benchmarks including HMDB51 and UCF 50. The results show that our sampling strategy can improve the performance of action recognition with minor computational cost of mask production. In particular, on the HMDB51 dataset, the improvement over the original iDT result is 3%. Meanwhile, any other dense features of action recognition can achieve more competitive performance by utilizing our sampling strategy and Fisher vector encoding method simply. © Springer International Publishing Switzerland 2016.

关键词： Trajectories