Spartan Daily, October 4, 2017

Volume 149, Issue 18https://scholarworks.sjsu.edu/spartan_daily_2017/1059/thumbnail.jp

“You Must Construct Additional Pylons”: Building a Better Framework for Esports Governance

The popularity of “esports,” also known as “electronic sports” or competitive video gaming, has exploded in recent years and captured the attention of cord-cutting millennials—often to the detriment of sports such as basketball, football, baseball, and hockey. In the United States, the commercial dominance of such traditional sports stems from decades of regulatory support. Consequently, while esports regulation is likely to emulate many aspects of traditional sports governance, the esports industry is fraught with challenges that inhibit sophisticated ownership and capital investment. Domestic regulation is complicated by underlying intellectual property ownership and ancillary considerations such as fluctuations in a video game’s popularity. Since analogous reform is nigh impossible, nascent governance organizations have been created to support the professionalization of esports as a new entertainment form. As esports consumption continues to grow, enterprising stakeholders are presented with the unique opportunity to create regulatory bodies that will shape the esports industry. This Note analyzes how the professional sports industry and foreign esports markets have addressed governance challenges that arise from differences between traditional sports and competitive video gaming. It concludes by exploring two potential pathways for domestic esports governance. View PD

Do logarithmic proximity measures outperform plain ones in graph clustering?

We consider a number of graph kernels and proximity measures including commute time kernel, regularized Laplacian kernel, heat kernel, exponential diffusion kernel (also called "communicability"), etc., and the corresponding distances as applied to clustering nodes in random graphs and several well-known datasets. The model of generating random graphs involves edge probabilities for the pairs of nodes that belong to the same class or different predefined classes of nodes. It turns out that in most cases, logarithmic measures (i.e., measures resulting after taking logarithm of the proximities) perform better while distinguishing underlying classes than the "plain" measures. A comparison in terms of reject curves of inter-class and intra-class distances confirms this conclusion. A similar conclusion can be made for several well-known datasets. A possible origin of this effect is that most kernels have a multiplicative nature, while the nature of distances used in cluster algorithms is an additive one (cf. the triangle inequality). The logarithmic transformation is a tool to transform the first nature to the second one. Moreover, some distances corresponding to the logarithmic measures possess a meaningful cutpoint additivity property. In our experiments, the leader is usually the logarithmic Communicability measure. However, we indicate some more complicated cases in which other measures, typically, Communicability and plain Walk, can be the winners.Comment: 11 pages, 5 tables, 9 figures. Accepted for publication in the Proceedings of 6th International Conference on Network Analysis, May 26-28, 2016, Nizhny Novgorod, Russi

Play On: The Use of Games in Libraries

The use of games in the library is a currently trending topic of discussion and writing in the Library and Information Science profession. Upon first consideration, gaming may seem to be irrelevant at best and a waste of time and resources at worst. However, gaming does have several significant implications for all types of libraries, including greater exposure to new information technologies and the sense of community that a gaming program can foster. Thus, libraries should seriously consider the benefits of gaming programs and be prepared to carefully develop collection policies and to properly plan gaming opportunities for their patrons. The following literature review highlights how other libraries have accomplished these goals, provides examples of the different types of gaming programs that can be implemented in libraries, and explains the advantages for the library that come with a gaming program

The global event? The media, football and the FIFA World Cup

An examination of the FIFA World Cup as media mega event and the role played by television in this process

Generating and visualizing a soccer knowledge base

This demo abstract describes the SmartWeb Ontology-based Information Extraction System (SOBIE). A key feature of SOBIE is that all information is extracted and stored with respect to the SmartWeb ontology. In this way, other components of the systems, which use the same ontology, can access this information in a straightforward way. We will show how information extracted by SOBIE is visualized within its original context, thus enhancing the browsing experience of the end user

Knowing the gap - intermediate information in tournaments

Intermediate information is often available to competitors in dynamic tournaments. We develop two simple tournament models with two stages: one with intermediate information on subjects’ relative positions after the first stage, one without. In our models, equilibrium behavior in both stages is not changed by intermediate information. We test our formal analysis using data from laboratory experiments. We find no difference between average first and second stage efforts. With intermediate information, however, subjects adjust their effort to a higher extent. Subjects who lead tend to lower their second stage effort, subjects who lag still try to win the tournament. Overall, intermediate information does not endanger the effectiveness of rank-order tournaments: incentives do neither break down nor does a rat race arise. We also briefly investigate costly intermediate information

The decline of natural sciences : confronting diminishing interest, fewer scientists and poorer working conditions in western countries. A comparative analysis between Spain and the United Kingdom

This study sets out to determine if the interest in and study of natural sciences is declining in western countries as scientists currently contend. Part one demonstrates how survey results reveal a decline of interest in scientific news in the EU. Part two explores the decline of interest further through examining data such as the number of students interested in scientific subjects and scientific careers. We compare data from two different countries: the UK and Spain. Within the study the UK represents the Anglo Saxon culture (traditionally more interested in science) and Spain represents the Latin culture (traditionally less interested). We conclude that in both regions there is a lack of interest in scientific subjects.Este estudio intenta esclarecer si está decayendo el interés por la ciencia y el estudio de materias científicas en los países occidentales tal y como sostienen los científicos. La primera parte demuestra como, efectivamente, el resultado de las encuestas revela que en la Unión Europea existe un declive del interés por las noticias científicas. La segunda parte explora este declive desde el punto de vista del descenso del número de alumnos matriculados en asignaturas de ciencias en secundaria y en carreras universitarias de ciencias naturales. Se comparan, sobre todo, datos de dos países muy diferentes: Reino Unido y España. De esta manera, el estudio del Reino Unido representa la cultura anglosajona (tradicionalmente, muy interesada en la ciencia) y España, la cultura latina (históricamente, menos interesada en la ciencia). Se concluye que en ambos países existe una falta de interés en los estudios de las materias científicas

On predictability of rare events leveraging social media: a machine learning perspective

Information extracted from social media streams has been leveraged to forecast the outcome of a large number of real-world events, from political elections to stock market fluctuations. An increasing amount of studies demonstrates how the analysis of social media conversations provides cheap access to the wisdom of the crowd. However, extents and contexts in which such forecasting power can be effectively leveraged are still unverified at least in a systematic way. It is also unclear how social-media-based predictions compare to those based on alternative information sources. To address these issues, here we develop a machine learning framework that leverages social media streams to automatically identify and predict the outcomes of soccer matches. We focus in particular on matches in which at least one of the possible outcomes is deemed as highly unlikely by professional bookmakers. We argue that sport events offer a systematic approach for testing the predictive power of social media, and allow to compare such power against the rigorous baselines set by external sources. Despite such strict baselines, our framework yields above 8% marginal profit when used to inform simple betting strategies. The system is based on real-time sentiment analysis and exploits data collected immediately before the games, allowing for informed bets. We discuss the rationale behind our approach, describe the learning framework, its prediction performance and the return it provides as compared to a set of betting strategies. To test our framework we use both historical Twitter data from the 2014 FIFA World Cup games, and real-time Twitter data collected by monitoring the conversations about all soccer matches of four major European tournaments (FA Premier League, Serie A, La Liga, and Bundesliga), and the 2014 UEFA Champions League, during the period between Oct. 25th 2014 and Nov. 26th 2014.Comment: 10 pages, 10 tables, 8 figure