Влияние результатов ЕГЭ на успеваемость студентов ВУЗ
The article analyzes the impact of the results of the unified state examination on the academic performance of students. We used for calculations the information of exam results and current progress for students enrolled in the HSE Moscow Institute of Electronics and Mathematicsin 2014 and 2015. Data for analysis are taken from documents of the LMS (Learning Management System) HSEMIEM. The influence of the Unified National Exam on the performance on similar items, as well as the impact of average use on academic performance at the totality of objects. Also described trends in student performance for several modules. On the basis of the conducted analysis it is concluded from the results of the exam in three subjects (mathematics, physics and Russian language) for formation of a contingent successfully successful students
The Semantic Evaluation (SemEval) series of workshops focuses on the evaluation and comparison of systems that can analyse diverse semantic phenomena in text with the aim of extending the current state of the art in semantic analysis and creating high quality annotated datasets in a range of increasingly challenging problems in natural language semantics. SemEval provides an exciting forum for researchers to propose challenging research problems in semantics and to build systems/techniques to address such research problems. SemEval-2016 is the tenth workshop in the series of International Workshops on Semantic Evaluation Exercises. The first three workshops, SensEval-1 (1998), SensEval-2 (2001), and SensEval-3 (2004), focused on word sense disambiguation, each time growing in the number of languages offered, in the number of tasks, and also in the number of participating teams. In 2007, the workshop was renamed to SemEval, and the subsequent SemEval workshops evolved to include semantic analysis tasks beyond word sense disambiguation. In 2012, SemEval turned into a yearly event. It currently runs every year, but on a two-year cycle, i.e., the tasks for SemEval-2016 were proposed in 2015. SemEval-2016 was co-located with the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT’2016) in San Diego, California. It included the following 14 shared tasks organized in five tracks: • Text Similarity and Question Answering Track – Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation – Task 2: Interpretable Semantic Textual Similarity – Task 3: Community Question Answering • Sentiment Analysis Track – Task 4: Sentiment Analysis in Twitter – Task 5: Aspect-Based Sentiment Analysis – Task 6: Detecting Stance in Tweets – Task 7: Determining Sentiment Intensity of English and Arabic Phrases • Semantic Parsing Track – Task 8: Meaning Representation Parsing – Task 9: Chinese Semantic Dependency Parsing • Semantic Analysis Track – Task 10: Detecting Minimal Semantic Units and their Meanings – Task 11: Complex Word Identification – Task 12: Clinical TempEval iii • Semantic Taxonomy Track – Task 13: TExEval-2 – Taxonomy Extraction – Task 14: Semantic Taxonomy Enrichment This volume contains both Task Description papers that describe each of the above tasks and System Description papers that describe the systems that participated in the above tasks. A total of 14 task description papers and 198 system description papers are included in this volume. We are grateful to all task organisers as well as the large number of participants whose enthusiastic participation has made SemEval once again a successful event. We are thankful to the task organisers who also served as area chairs, and to task organisers and participants who reviewed paper submissions. These proceedings have greatly benefited from their detailed and thoughtful feedback. We also thank the NAACL 2016 conference organizers for their support. Finally, we most gratefully acknowledge the support of our sponsor, the ACL Special Interest Group on the Lexicon (SIGLEX). The SemEval-2016 organizers, Steven Bethard, Daniel Cer, Marine Carpuat, David Jurgens, Preslav Nakov and Torsten Zesch
Proceedings of the 16th International interdisciplinary scientific multiconference “Information Society”, considering topical technical and social problems of modern information society
In many areas, such as social science, politics or market research, people need to track sentiment and their changes over time. For sentiment analysis in this field it is more important to correctly estimate proportions of each sentiment expressed in the set of documents (quantification task) than to accurately estimate sentiment of a particular document (classification). Basically, our study was aimed to analyze the effectiveness of two iterative quantification techniques and to compare their effectiveness with baseline methods. All the techniques are evaluated using a set of synthesized data and the SemEval-2016 Task4 dataset. We made the quantification methods from this paper available as a Python open source library. The results of comparison and possible limitations of the quantification techniques are discussed.
Apache Spark is one of the most popular Big Data frameworks. Performance evaluation of Big Data frameworks is a topic of interest due to the increasing number and importance of data analytics applications within the context of HPC and Big Data convergence. In the paper we present early performance evaluation of a typical supervised graph anomaly detection problem implemented using GraphX and MLlib libraries in Apache Spark on a cluster.
LoRaWAN is a relatively new protocol designed to provide cheap and reliable wireless connectivity in various Internet of Things scenarios. Being a Low Power Wide Area Network technology operating in the ISM band, it rapidly got popularity in both industry and academic communities. Literature review shows that in spite of numerous studies of its PHY layer –, the MAC layer got little attention, even though it has multiple issues ,  that limit its performance. However, as LoRaWAN is designed to support networks of thousands of devices, it is crucial not only to consider the performance of this technology in point-to-point scenarios, but also to evaluate its applicability in case of highly-populated networks.
In Russia from 2009 College admission is based on results of Unified State Exam. Entrant applies to no more than five universities. Admission mechanism is defined by government for all state universities. In the paper the authors model how entrant chooses university for application and, based on the entrant's choice prediction, the shortages of the current admission mechanism are revealed.
Entrepreneurship education is included into the innovative development programs of numerous countries. To start the large-scale entrepreneurship educational process basing on the existing educational system is comparatively difficult, as the latter is aimed at training of standard well-known skills, while entrepreneurship isn’t one of them. Currently many universities in the world. mostly technical are inventing formats and contents of the entrepreneurship educational programs. In the article the relevant programs from 20 leading technical universities in Russia and USA are compared by using the official open information. According to the results of the analysis as well as Anatoly Korotkov’s experience in the relevant field in Russia two possible ways of raising the effectiveness of Russian entrepreneurship educational programs are defined in the article.