From the Publisher: 
A Question of Identity 
Life on the Screen is a fascinating and wide-ranging investigation of the impact of computers and networking on society, peoples' perceptions of themselves, and the individual's relationship to machines. Sherry Turkle, a Professor of the Sociology of Science at MIT and a licensed psychologist, uses Internet MUDs (multi-user domains, or in older gaming parlance multi-user dungeons) as a launching pad for explorations of software design, user interfaces, simulation, artificial intelligence, artificial life, agents, "bots," virtual reality, and "the on-line way of life." 
Turkle's discussion of postmodernism is particularly enlightening. She shows how postmodern concepts in art, architecture, and ethics are related to concrete topics much closer to home, for example AI research (Minsky's "Society of Mind") and even MUDs (exemplified by students with X-window terminals who are doing homework in one window and simultaneously playing out several different roles in the same MUD in other windows). Those of you who have (like me) been turned off by the shallow, pretentious, meaningless paintings and sculptures that litter our museums of modern art may have a different perspective after hearing what Turkle has to say. 
This is a psychoanalytical book, not a technical one. However, software developers and engineers will find it highly accessible because of the depth of the author's technical understanding and credibility. Unlike most other authors in this genre, Turkle does not constantly jar the technically-literate reader with blatant errors or bogus assertions about how things work. Although I personally don't have time or patience for MUDs,view most of AI as snake-oil, and abhor postmodern architecture, I thought the time spent reading this book was an extremely good investment.

스크린 위의 삶 = Life on the screen : identity in the age of the internet

The Statistics of Canada

Morpholino oligonucleotides are the most common anti-sense “knockdown” technique used in zebrafish (Danio rerio). This review discusses common practices for the design, preparation, and deployment of morpholinos in this vertebrate model system. Off-targeting effects of morpholinos are discussed as well as method to minimize this potentially confounding variable via co-injection of a tP53-targeting morpholino. Finally, new uses of morpholinos are summarized and contextualized with respect to the complementary, DNA-based knockout technologies recently developed for zebrafish.

A primer for morpholino use in zebrafish.

Enjoyment has been identified as a central component of the player experience (PX), but various, overlapping concepts within PX make it difficult to develop valid measures and a common understanding of game enjoyment. We conducted a systematic review of 87 quantitative studies, analyzing different operationalizations and measures of game enjoyment, its determinants, and how these were related to other components of PX, such as flow, presence and immersion. Results suggest that game enjoyment describes the positive cognitive and affective appraisal of the game experience, and may in part be associated with the support of player needs and values. Further, we outline that enjoyment is distinct from flow in that it may occur independently of challenge and cognitive involvement, and argue that enjoyment may be understood as the valence of the player experience. We conclude with a discussion of methodological challenges and point out opportunities for future research on game enjoyment.

A systematic review of quantitative studies on the enjoyment of digital entertainment games

Fluorescence in situ hybridization (FISH) is a well-established technique that is used for a variety of purposes, ranging from pathogen detection in clinical diagnostics to the determination of chromosomal stability in stem cell research. The key step of FISH involves the detection of a nucleic acid region and as such, DNA molecules have typically been used to probe for the sequences of interest. However, since the turn of the century, an increasing number of laboratories have started to move on to the more robust DNA mimics methods, most notably peptide and locked nucleic acids (PNA and LNA). In this review, we will cover the state-of-the-art of the different DNA mimics in regard to their application as efficient markers for the presence of individual microbial cells, and consider their potential advantages and pitfalls. Available PNA probes are then reassessed in terms of sensitivity and specificity using rRNA databases. In addition, we also attempt to predict the applicability of DNA mimics in well-known techniques attempting to detect in situ low number of copies of specific nucleic acid sequences such as catalyzed reporter deposition (CARD) and recognition of individual genes (RING) FISH.

https://www.mdpi.com/1422-0067/9/10/1944/pdf

DNA Mimics for the Rapid Identification of Microorganisms by Fluorescence in situ Hybridization (FISH)

In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of game players. This study uses performance data of game players in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models for game players. The prediction models provide a projection of player’s future performance based on his past performance, which is expected to be a useful addition to existing player performance monitoring tools. First, we show that variations of PECOTA [2] and MARCEL [3], two most popular baseball home run prediction methods, can be used for game player performance prediction. Second, we evaluate the effects of varying lengths of past performance and show that past performance can be a good predictor of future performance up to a certain degree. Third, we show that game players do not regress towards the mean and that prediction models built on buckets using discretization based on binning and histograms lead to higher prediction coverage.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=2509&context=sis_research

Team Performance Prediction in Massively Multiplayer Online Role-Playing Games (MMORPGs)

In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of game players This study uses performance data of game players in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models for game players The prediction models provide a projection of player's future performance based on his past performance, which is expected to be a useful addition to existing player performance monitoring tools First, we show that variations of PECOTA [2] and MARCEL [3], two most popular baseball home run prediction methods, can be used for game player performance prediction Second, we evaluate the effects of varying lengths of past performance and show that past performance can be a good predictor of future performance up to a certain degree Third, we show that game players do not regress towards the mean and that prediction models built on buckets using discretization based on binning and histograms lead to higher prediction coverage.

Player performance prediction in massively multiplayer online role-playing games (MMORPGs)

This paper examines online player performance in EverQuest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. The study uses the game's player performance data to devise performance metrics for online players. We report three major findings. First, we show that the game's point-scaling system overestimates performances of lower level players and underestimates performances of higher level players. We present a novel point-scaling system based on the game's player performance data that addresses the underestimation and overestimation problems. Second, we present a highly accurate predictive model for player performance as a function of past behavior. Third, we show that playing in groups impacts individual performance and that player-level characteristics alone are insufficient in explaining an individual's performance, which calls for a different set of performance metrics methods.

Inferring Player Rating from Performance Data in Massively Multiplayer Online Role-Playing Games (MMORPGs)

In this paper, we introduce a framework for modeling elements of "team chemistry" in the skill assessment process using the performances of subsets of teams and four approaches which make use of this framework to estimate the collective skill of a team. A new dataset based on the Xbox 360 video game, Halo 3, is used for evaluation. The dataset is comprised of online scrimmage and tournament games played between professional Halo 3 teams competing in the Major League Gaming (MLG) Pro Circuit during the 2008 and 2009 seasons. Using the Elo, Glicko, and TrueSkill rating systems as "base learners" for our approaches, we predict the outcomes of games based on subsets of the overall dataset in order to investigate their performance given differing game histories and playing environments. We find that Glicko and TrueSkill benefit greatly from our approaches (TeamSkill-AllK-EV in particular), significantly boosting prediction accuracy in close games and improving performance overall, while Elo performs better without them. We also find that the ways in which each rating system handles skill variance largely determines whether or not it will benefit from our techniques.

TeamSkill: modeling team chemistry in online multi-player games

This study examines behavioral profiles of different character types in EverQuest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment The study uses the game's player activity data to construct behavioral profiles of online game players for the applications of normal behavior recognition and anomaly detection The behavioral profiles give insight into not only what players do in the game to level up but also how they perform on different types of task and how they work with other players The behavioral profiles this study reports provide insight into online game player behavior, and it provides information useful for choosing a character type suitable for one's objectives in playing the game In this study, we develop a framework for automatic behavior profiling The proposed framework consists of the following key components; 1) segmentation analysis of historical player behaviors, 2) behavior profiling of input users (new players whose behaviors we want to label, and 3) recommendation of tasks based on input users' objectives

Kyong Jin Shim

Papers

Team Performance Prediction in Massively Multiplayer Online Role-Playing Games (MMORPGs)

Player performance prediction in massively multiplayer online role-playing games (MMORPGs)

Inferring Player Rating from Performance Data in Massively Multiplayer Online Role-Playing Games (MMORPGs)

TeamSkill: modeling team chemistry in online multi-player games

Behavioral profiles of character types in EverQuest II