Что в профиле тебе моем: Данные «ВКонтакте» как инструмент изучения интересов современных подростков
Children’s interests play a key role in their psychological development. However, research in this field is associated with serious methodological problems, as it has traditionally used questionnaire surveys that cannot adequately describe the diverse and dynamic world of interests of a developing person. The article suggests using the information on VKontakte communities followed by teenagers, in order to explore their interests. Apart from being comprehensive, Vkontakte data is, unlike questionnaire answers, also uncensored. The method’s potential demonstrated through the example of a Moscow school with 674 students following 20,203 various VKontakte communities. It reveals that teenagers’ interests vary depending on their gender, age, and academic performance. The degree of such variance is demonstrated on an extended set of data on the interests of 290,182 VKontakte users. It transpires that communities followed by teenagers predict with high accuracy not only their gender (97%) and age (98%) but also the performance of the schools they attend (83%). The findings point to the heterogeneity of age-related behavior patterns, in particular to their correlation with gender and academic achievements. Acknowledgement of the heterogeneity of interests and the diversity of age-related behavior patterns creates conditions for the further development of student-centered education, in the absence of which education is becoming more and more alienated from real life, ignoring the interests of real people.
Graph-based approaches are empirically shown to be very successful for the nearest neighbor search (NNS). However, there has been very little research on their theoretical guarantees. We fill this gap and rigorously analyze the performance of graph-based NNS algorithms, specifically focusing on the low-dimensional (d << log n) regime. In addition to the basic greedy algorithm on nearest neighbor graphs, we also analyze the most successful heuristics commonly used in practice: speeding up via adding shortcut edges and improving accuracy via maintaining a dynamic list of candidates. We believe that our theoretical insights supported by experimental analysis are an important step towards understanding the limits and benefits of graph-based NNS algorithms.
The paper makes a brief introduction into multiple classifier systems and describes a particular algorithm which improves classification accuracy by making a recommendation of an algorithm to an object. This recommendation is done under a hypothesis that a classifier is likely to predict the label of the object correctly if it has correctly classified its neighbors. The process of assigning a classifier to each object involves here the apparatus of Formal Concept Analysis. We explain the principle of the algorithm on a toy example and describe experiments with real-world datasets.
The volume contains the abstracts of the 12th International Conference "Intelligent Data Processing: Theory and Applications". The conference is organized by the Russian Academy of Sciences, the Federal Research Center "Informatics and Control" of the Russian Academy of Sciences and the Scientific and Coordination Center "Digital Methods of Data Mining". The conference has being held biennially since 1989. It is one of the most recognizable scientific forums on data mining, machine learning, pattern recognition, image analysis, signal processing, and discrete analysis. The Organizing Committee of IDP-2018 is grateful to Forecsys Co. and CFRS Co. for providing assistance in the conference preparation and execution. The conference is funded by RFBR, grant 18-07-20075. The conference website http://mmro.ru/en/.
There are many different methods for computing relevant patterns in sequential data and interpreting the results. In this paper, we compute emerging patterns (EP) in demographic sequences using sequence-based pattern structures, along with different algorithmic solutions. The purpose of this method is to meet the following domain requirement: the obtained patterns must be (closed) frequent contiguous prefixes of the input sequences. This is required in order for demographers to fully understand and interpret the results.
Data management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas.
In an effort to make reading more accessible, an automated readability formula can help students to retrieve appropriate material for their language level. This study attempts to discover and analyze a set of possible features that can be used for single-sentence readability prediction in Russian. We test the influence of syntactic features on predictability of structural complexity. The readability of sentences from SynTagRus corpus was marked up manually and used for evaluation.
This paper is an overview of the current issues and tendencies in Computational linguistics. The overview is based on the materials of the conference on computational linguistics COLING’2012. The modern approaches to the traditional NLP domains such as pos-tagging, syntactic parsing, machine translation are discussed. The highlights of automated information extraction, such as fact extraction, opinion mining are also in focus. The main tendency of modern technologies in Computational linguistics is to accumulate the higher level of linguistic analysis (discourse analysis, cognitive modeling) in the models and to combine machine learning technologies with the algorithmic methods on the basis of deep expert linguistic knowledge.
Institutions affect investment decisions, including investments in human capital. Hence institutions are relevant for the allocation of talent. Good market-supporting institutions attract talent to productive value-creating activities, whereas poor ones raise the appeal of rent-seeking. We propose a theoretical model that predicts that more talented individuals are particularly sensitive in their career choices to the quality of institutions, and test these predictions on a sample of around 95 countries of the world. We find a strong positive association between the quality of institutions and graduation of college and university students in science, and an even stronger negative correlation with graduation in law. Our findings are robust to various specifications of empirical models, including smaller samples of former colonies and transition countries. The quality of human capital makes the distinction between educational choices under strong and weak institutions particularly sharp. We show that the allocation of talent is an important link between institutions and growth.
This book contains the proceedings of the 4th International Conference on Computer Supported Education (CSEDU 2012) which was organized and sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC) and technically co-sponsored by SPEE (Portuguese Society for Engineering Education), IGIP (International Society for Engineering Education), ROLE (Responsive Open Learning Environments) and IFIP TC3 (International Federation for Information Processing - Technical Committee 3 - ICT and Education).
CSEDU has become an annual meeting place for presenting and discussing learning paradigms, best practices and case studies that concern innovative computer-supported learning strategies, institutional policies on technology-enhanced learning including learning from distance, supported by technology. The Web is currently a preferred medium for distance learning and the learning practice in this context is usually referred to as e-learning or technology-enhanced learning. CSEDU 2012 is expected to give an overview of the state of the art in technology-enhanced learning and to also outline upcoming trends and promote discussions about the education potential of new learning technologies in the academic and corporate world.
This conference brings together researchers and practitioners interested in methodologies and applications related to the education field. It has five main topic areas, covering different aspects of Computer Supported Education, including "Information Technologies Supporting Learning", "Learning/Teaching Methodologies and Assessment", "Social Context and Learning Environments", "Domain Applications and Case Studies" and "Ubiquitous Learning". We believe the proceedings, demonstrate new and innovative solutions, and highlight technical problems in each field that are challenging and worthwhile.
CSEDU 2012 received 243 paper submissions from 58 countries in all continents. A double-blind review process was enforced, with the help of the 297 experts who are members of the conference program committee, all of them internationally recognized in one of the main conference topic areas. Only 29 papers were selected to be published and presented as full papers, i.e. completed work (10 pages in proceedings / 30' oral presentations). 73 papers, describing work-in-progress, were selected as short papers for 20' oral presentation. Furthermore 37 papers were presented as posters. The full-paper acceptance ratio was thus 12%, and the total oral paper acceptance ratio was less than 42%. These ratios denote a high level of quality, which we intend to maintain and reinforce in the next edition of this conference.
The high quality of the CSEDU 2012 programme is enhanced by three keynote lectures, delivered by distinguished guests who are renowned experts in their fields, including (alphabetically): Joseph Trimmer (Ball State University, United States), David Kaufman (Simon Fraser University, Canada) and Hugh Davis (University of Southampton, United Kingdom).
For the fourth edition of the conference we extended and ensured appropriate indexing of the proceedings of CSEDU including DBLP, INSPEC, EI and Thomson Reuters Conference Proceedings Citation Index. Besides the proceedings edited by SciTePress, a short list of papers presented at the conference will be selected for publication of extended and revised versions in the Journal of Education and Information Technologies. Furthermore, all presented papers will soon be available at the SciTePress digital library.
The conference is complemented with two special sessions, focusing on specialized aspects of computer supported education; namely, a Special Session on Enhancing Student Engagement in e-Learning (ESEeL 2012) and a Special Session on Serious Games on Computer Science Learning (SGoCSL 2012).
Building an interesting and successful program for the conference required the dedicated effort of many people. Firstly, we must thank the authors, whose research and development efforts are recorded here. Secondly, we thank the members of the program committee and additional reviewers for their diligence and expert reviewing. We also wish to include here a word of appreciation for the excellent organization provided by the conference secretariat, from INSTICC, who have smoothly and efficiently prepared the most appropriate environment for a productive meeting and scientific networking. Last but not least, we thank the invited speakers for their invaluable contribution and for taking the time to synthesize and deliver their talks.