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Q&A sites for developers (like Stack Overflow) provide various types of game-specific social contents that can support the development of effective serious games – games that can really train, educate, and motivate players. These social contents include valuable information for effective serious games development, such as design principles and best practices, game-specific algorithms, game engines, API documentation and game libraries. However, existing techniques and tools for analysing social contents are mainly focusing on recognizing person, location, and organization, and thus are not designed to support the recent advance of entity-centric search systems, such as direct answers and knowledge-graph for serious games domain. In this research, we study the problem of NER to support the development of serious games. We address the challenges of recognizing game-specific named entities in social contents and develop a machine-learning based model that can recognize a broad category of named entities that game developers really care about. We conduct systematic experiments to evaluate our machine learning-based NER against a well-designed rule-based baseline system and to study the effectiveness of various NER techniques and features against the unique challenges of game-specific social contents. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyse game statistics at different phases of the game by the table tennis players at Olympic Games in Rio 2016 and London 2012, by determining the differences in game statistics indicators in these two competitions. We analysed 236 games matches from Rio and 214 games matches from London, played during team event. From each game were collected 27 parameters. In this analysis, the average match and rally durations with different rally analysis among 16 teams played at Olympics has been provided. The official website of 2016 Rio Olympics and 2012 London Olympics were used as a means of collecting data and during the matches, the results and analysis of a total 450 matches published on this site were recorded and evaluated. The study was carried out on total of 346 players (174 women and 172 men) who participated from 16 countries. Descriptive statistical analysis of data (mean, standard deviation, minimum and maximum values, percentage distribution) was done by using SPSS 17.0 for Windows. Statistically significant differences in performance indicators between the table tennis matches played on the Olympic Games in Rio and London are found for the variables: Whole match duration / without breaks between sets, Game duration of 1st set, Game duration of 2nd set, Game duration of 3rd set, Game duration of 4th set, Total points won in match and Total points lost on own serve in whole match (all in the direction of higher means for the matches played in Rio Olympic Games). The only statistically significant higher mean for the matches played in London Olympic Games is found for the indicator Total points won on own serve in 5th set. When looked at the data obtained in this study, it is observed that the highest mean match durations are influenced by the style of play and the longest mean rally durations comes with defensive players. As a conclusion, according to the 2016 Rio Olympics and 2012 London Olympics analysis, table tennis coaches must revise technical and tactical elements when playing on best of five matches and they have to prepare special schedules for category properties. The results presented are important to monitor the athlete's performance during the game and to readjust strategies based on point difference. [ABSTRACT FROM AUTHOR]

The European (EU)-based industry for non-leisure games (so called Applied Games, AGs) is an emerging business. As such it is still fragmented and needs to achieve critical mass to compete globally. Nevertheless, its growth potential is widely recognized and even suggested to exceed the growth potential of the leisure games market. The European project Realizing an Applied Gaming Ecosystem (RAGE) is aiming at supporting this challenge. RAGE will help to seize these opportunities by making available an interoperable set of advanced Applied Game (AG) technology assets, as well as proven practices of using such AG assets in various real-world contexts. As described in [1], RAGE will finally provide a centralized access to a wide range of applied gaming software modules, relevant information, knowledge and community services, and related scientific documents, taxonomies, media, and educational resources within an online community portal called the RAGE Ecosystem. Besides this, an integration between the RAGE Ecosystem and relevant social network interaction spaces that arranges and facilitates collaboration that underlie Research and Development (R&D), as well as marketoriented innovation and exploitation will be created in order to support community building, as well as collaborative asset exploitation of User Generated Contents (UGCs) of the RAGE Ecosystem. In this paper, we will describe the integration of the Scientific Publication Platform (SPP) Mendeley [2] into the RAGE Ecosystem. This will allow for automating repetitive tasks, reducing errors, and speeding up time consuming tasks. On the other hand it will support information, UGC, and knowledge sharing, as well as persistency of social interaction threads within Social Networking Sites (SNSs) and Groupware Systems (GWSs) that are connected to the RAGE Ecosystem. The paper reviews relevant use cases and scenarios, as well as related authentication, access, and information integration challenges. In this way, on the one hand a qualitative evaluation regarding an optimal technical integration is facilitated while on the other hand design approaches for supporting features of resulting user interfaces are initiated. [ABSTRACT FROM AUTHOR]

The European (EU)-based industry for non-leisure games (Applied Games, AGs or formally called serious games) is an emerging business. As such it is still fragmented and needs to achieve critical mass to compete globally. Nevertheless, its growth potential is widely recognized and even suggested to exceed the growth potential of the leisure games market. To become competitive the relevant applied gaming communities require support by fostering the generation of innovation potential. The European project Realizing an Applied Gaming Ecosystem (RAGE) (Netvision 2015) is aiming at supporting this challenge. RAGE will help to seize these opportunities by making available an interoperable set of advanced Applied Game (AG) technology assets, as well as proven practices of using asset-based AGs in various real-world contexts. As described in (Salman, Heutelbeck, et al. 2015a), RAGE will finally provide a centralized access to a wide range of applied gaming software modules, relevant information, knowledge and community services, and related scientific documents, taxonomies, media, and educational resources within an online community portal called the RAGE Ecosystem. This Knowledge Management Ecosystem (KM-ES) is based on an integrational, user-centered approach of Knowledge Management (KM) and Innovation Processes in the shape of a service-based implementation (Becker et al. 2015). In this paper, we will describe the integration of the Scientific Publication and Presentation Platforms (SPPs) Mendeley (Netvision 2016e) and SlideShare (Netvision 2016l) into the RAGE Ecosystem. This will support information, User Generated Content (UGC), and knowledge sharing, as well as persistency of social interaction threads and Know-how transfer within Social Networking Sites (SNSs) and Groupware Systems (GWSs) that are connected to the RAGE Ecosystem. This paper reviews relevant use cases and scenarios, as well as related authentication, access, and information integration challenges, besides this, it shows the basic considerations influencing the Ecosystem concept and development as well as the economic potential of Ecosystems and the importance of KM and Know-how transfer in the execution of innovation and value added processes. [ABSTRACT FROM AUTHOR]