collection curated by zhiping https://academictorrents.com/collection/cogsci @article{, title= {Democratic Development by Larry Diamond}, journal= {}, author= {Larry Diamond}, year= {}, url= {}, abstract= {About the Course Democratic Development is intended as a broad, introductory survey of the political, social, cultural, economic, institutional, and international factors that foster or obstruct the development, and consolidation, of democracy. Topics will be examined in historical and comparative perspective, and reference a variety of different national experiences. It is hoped that students in developing or prospective democracies will use the theories, ideas, and lessons in the class to help build or improve democracy in their own countries. This course is primarily intended for individuals in college or beyond, with some academic background or preparation in political science or the social sciences. However, it seeks to be accessible and useful to a diverse international audience, including educators at the secondary and college levels, government officials, development professionals, civil society leaders, journalists, bloggers, activists, and individuals involved in a wide range of activities and professions related to the development and deepening of democracy. Course Syllabus Week 1 Introduction to the Course, Why Democracy? What Is Democracy? Regime Types The Third Wave of Democratization and its Ebb Week 2 Legitimacy, Authority and Effectiveness Democratic Consolidation Week 3 Political Culture and Democracy Are Democratic Values Universal? Week 4 Economic Development Class Structure and Inequality Civil Society Week 5 Democratic Transition: Paths and Drivers Democratic Transition: Types and Means Week 6 Constitutional Design Presidential vs. Parliamentary Government Parties and Party Systems Week 7 Electoral Systems Choosing between Different Systems Week 8 Ethnicity and Ethnic Conflict Managing Ethnic Conflict Federalism Week 9 Horizontal Accountability and the Rule of Law Controlling Corruption Democratic Breakdowns Week 10 International Factors Promoting Democracy Week 11 The Future of Democracy}, keywords= {Coursera, Democracy, Politics}, terms= {} } https://academictorrents.com/download/1452d6a906d5d164c1adfc4b81a74a6690a8ec4b @inproceedings{Stowell:2014f, title = {freefield1010 - an open dataset for research on audio field recording archives}, booktitle={Proceedings of the Audio Engineering Society 53rd Conference on Semantic Audio (AES53)}, author={Stowell, D. and Plumbley, M. D.}, year={2014}, publisher={Audio Engineering Society}, abstract = {A free and open dataset of 7690 10-second audio clips sampled from the field-recording tag in the Freesound audio archive. The dataset is designed for use in research related to data mining in audio archives of field recordings / soundscapes. Audio is standardised, and audio and metadata are Creative Commons licensed. For more information see http://arxiv.org/abs/1309.5275}, } https://academictorrents.com/download/d247b92fa7b606e0914367c0839365499dd20121 @article{, title= {Viking Merged Color Mosaic}, journal= {}, author= {ASU }, year= {}, url= {}, abstract= { |Attribute|Value| |---------|-----| |Resolution:|64ppd| |Scale:|920mpp| |Projection:|Simple cylindrical, -180E to 180E, 90N to -90N, 'ocentric| |Layout:|Single file| |Total Size:|23040x11520 pixels| |Details: |Viking color mosaic sharpened with MDIM 1.0. 64ppd/920m. NASA Viking Orbiter.| }, keywords= {Mars, ASU}, terms= {} } https://academictorrents.com/download/059ed25558b4587143db637ac3ca94bebb57d88d @article{, title = {Arizona State University Flixster Data Set}, journal = {}, author = {Flixter }, year = {}, url = {http://socialcomputing.asu.edu/datasets/Flixster}, abstract = {Flixster is a social movie site allowing users to share movie ratings, discover new movies and meet others with similar movie taste. Number of Nodes: 2523386 Number of Edges: 9197338 Missing Values? no Source: N/A Data Set Information: 2 files are included: 1. nodes.csv -- it's the file of all the users. This file works as a dictionary of all the users in this data set. It's useful for fast reference. It contains all the node ids used in the dataset 2. edges.csv -- this is the friendship network among the users. The friends are represented using edges. Here is an example. 1,2 This means user with id "1" is friend with user id "2". Attribute Information: Flixster is a social movie site allowing users to share movie ratings, discover new movies and meet others with similar movie taste. This contains the friendship network crawled in December 2010 by Javier Parra (Javier.Parra@asu.edu). For easier understanding, all the contents are organized in CSV file format. -. Basic statistics Number of Nodes: 2,523,386 Number of Edges: 9,197,338} } https://academictorrents.com/download/4960373ea6dec89153639b0975ea92f9e3d3c914 @article{, title= {Arizona State University Twitter Data Set }, journal= {}, author= {R. Zafarani and H. Liu}, year= {2009}, institution= {Arizona State University, School of Computing, Informatics and Decision Systems Engineering}, url= {http://socialcomputing.asu.edu/datasets/Twitter}, abstract= {Twitter is a social news website. It can be viewed as a hybrid of email, instant messaging and sms messaging all rolled into one neat and simple package. It's a new and easy way to discover the latest news related to subjects you care about. |Attribute|Value| |-|-| |Number of Nodes: |11316811| |Number of Edges: |85331846| |Missing Values? |no| |Source:| N/A| ##Data Set Information: 1. nodes.csv -- it's the file of all the users. This file works as a dictionary of all the users in this data set. It's useful for fast reference. It contains all the node ids used in the dataset 2. edges.csv -- this is the friendship/followership network among the users. The friends/followers are represented using edges. Edges are directed. Here is an example. 1,2 This means user with id "1" is followering user with id "2". ##Attribute Information: Twitter is a social news website. It can be viewed as a hybrid of email, instant messaging and sms messaging all rolled into one neat and simple package. It's a new and easy way to discover the latest news related to subjects you care about.}, keywords= {ASU, Twitter, Social, Graph}, terms= {} } https://academictorrents.com/download/2399616d26eeb4ae9ac3d05c7fdd98958299efa9 @article{, title= {UK Road Safety Data 1979 to 2004}, journal= {}, author= {UK Department for Transport }, year= {}, url= {http://data.gov.uk/dataset/road-accidents-safety-data/resource/80b76aec-a0a1-4e14-8235-09cc6b92574a}, abstract= {These files provide detailed road safety data about the circumstances of personal injury road accidents in GB from 1979, the types (including Make and Model) of vehicles involved and the consequential casualties. The statistics relate only to personal injury accidents on public roads that are reported to the police, and subsequently recorded, using the STATS19 accident reporting form. Also includes: Results of breath-test screening data from recently introduced digital breath testing devices, as provided by Police Authorities in England and Wales Results of blood alcohol levels (milligrams / 100 millilitres of blood) provided by matching coroners? data (provided by Coroners in England and Wales and by Procurators Fiscal in Scotland) with fatality data from the STATS19 police data of road accidents in Great Britain. For cases when the Blood Alcohol Levels for a fatality are "unknown" are a consequence of an unsuccessful match between the two data sets. Open Government Licence}, keywords= {}, terms= {} } https://academictorrents.com/download/c7d2d7a91ae3fd0256dd2ba2d7344960cb3c4dbb @article{, title= {UCI Machine Learning Datasets 12/2013}, journal= {}, author= {UCI }, year= {2013}, url= {}, abstract= {The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited "papers" in all of computer science. The current version of the web site was designed in 2007 by Arthur Asuncion and David Newman, and this project is in collaboration with Rexa.info at the University of Massachusetts Amherst. Funding support from the National Science Foundation is gratefully acknowledged. Many people deserve thanks for making the repository a success. Foremost among them are the donors and creators of the databases and data generators. Special thanks should also go to the past librarians of the repository: David Aha, Patrick Murphy, Christopher Merz, Eamonn Keogh, Cathy Blake, Seth Hettich, and David Newman.}, keywords= {UCI}, terms= {}, license= {}, superseded= {} } https://academictorrents.com/download/7fafb101f9c7961f9b840daeb4af43039107ddef @article{, title= {Visual Object Classes Challenge 2012 Dataset (VOC2012) VOCtrainval_11-May-2012.tar}, journal= {}, author= {Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.}, year= {2012}, url= {http://host.robots.ox.ac.uk/pascal/VOC/voc2012/}, abstract= {##Introduction The main goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are: * Person: person * Animal: bird, cat, cow, dog, horse, sheep * Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train * Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor There are three main object recognition competitions: classification, detection, and segmentation, a competition on action classification, and a competition on large scale recognition run by ImageNet. In addition there is a "taster" competition on person layout. ##Classification/Detection Competitions Classification: For each of the twenty classes, predicting presence/absence of an example of that class in the test image. Detection: Predicting the bounding box and label of each object from the twenty target classes in the test image. 20 classes  * aeroplane * bicycle * bird * boat * bottle * bus * car * cat * chair * cow * dining table * dog * horse * motorbike * person * potted plant * sheep * sofa * train * tv/monitor Participants may enter either (or both) of these competitions, and can choose to tackle any (or all) of the twenty object classes. The challenge allows for two approaches to each of the competitions: 1. Participants may use systems built or trained using any methods or data excluding the provided test sets. 2. Systems are to be built or trained using only the provided training/validation data. The intention in the first case is to establish just what level of success can currently be achieved on these problems and by what method; in the second case the intention is to establish which method is most successful given a specified training set. Segmentation Competition Segmentation: Generating pixel-wise segmentations giving the class of the object visible at each pixel, or "background" otherwise.  ##Action Classification Competition Action Classification: Predicting the action(s) being performed by a person in a still image.  * jumping * phoning * playinginstrument * reading * ridingbike * ridinghorse * running * takingphoto * usingcomputer * walking In 2012 there are two variations of this competition, depending on how the person whose actions are to be classified is identified in a test image: (i) by a tight bounding box around the person; (ii) by only a single point located somewhere on the body. The latter competition aims to investigate the performance of methods given only approximate localization of a person, as might be the output from a generic person detector. ##ImageNet Large Scale Visual Recognition Competition The goal of this competition is to estimate the content of photographs for the purpose of retrieval and automatic annotation using a subset of the large hand-labeled ImageNet dataset (10,000,000 labeled images depicting 10,000+ object categories) as training. Test images will be presented with no initial annotation - no segmentation or labels - and algorithms will have to produce labelings specifying what objects are present in the images. In this initial version of the challenge, the goal is only to identify the main objects present in images, not to specify the location of objects. Further details can be found at the ImageNet website. ##Person Layout Taster Competition Person Layout: Predicting the bounding box and label of each part of a person (head, hands, feet).  ##Data To download the training/validation data, see the development kit. The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image. Note that multiple objects from multiple classes may be present in the same image. Annotation was performed according to a set of guidelines distributed to all annotators. A subset of images are also annotated with pixel-wise segmentation of each object present, to support the segmentation competition. Images for the action classification task are disjoint from those of the classification/detection/segmentation tasks. They have been partially annotated with people, bounding boxes, reference points and their actions. Annotation was performed according to a set of guidelines distributed to all annotators. Images for the person layout taster, where the test set is disjoint from the main tasks, have been additionally annotated with parts of the people (head/hands/feet). The data will be made available in two stages; in the first stage, a development kit will be released consisting of training and validation data, plus evaluation software (written in MATLAB). One purpose of the validation set is to demonstrate how the evaluation software works ahead of the competition submission. In the second stage, the test set will be made available for the actual competition. As in the VOC2008-2011 challenges, no ground truth for the test data will be released. The data has been split into 50% for training/validation and 50% for testing. The distributions of images and objects by class are approximately equal across the training/validation and test sets. Statistics of the database are online. }, keywords= {VOC}, terms= {} } https://academictorrents.com/download/df0aad374e63b3214ef9e92e178580ce27570e59 @article{, title = {Wikipedia English Official Offline Edition (version 20130805) [Xprt]}, journal = {}, author = {Wikipedia}, date = {2013-08-06}, year = {2013}, url = {http://meta.wikimedia.org/wiki/Data_dumps}, abstract = {Wikipedia offers free copies of all available content to interested users. These databases can be used for mirroring, personal use, informal backups, offline use or database queries (such as for Wikipedia:Maintenance). All text content is multi-licensed under the Creative Commons Attribution-ShareAlike 3.0 License (CC-BY-SA) and the GNU Free Documentation License (GFDL). Images and other files are available under different terms, as detailed on their description pages. For our advice about complying with these licenses, see Wikipedia:Copyrights. }, license ={Creative Commons Attribution-ShareAlike 3.0 License (CC-BY-SA) and the GNU Free Documentation License (GFDL)}, superseded = {e18b8cce7d9cb2726f5f40dcb857111ec573cad4} } https://academictorrents.com/download/30ac2ef27829b1b5a7d0644097f55f335ca5241b