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This article examines recent anti-cheating practices in the sport of chess with a focus on situational crime prevention. On the one hand, anti-cheating protection measures in chess could be considered relatively belated compared with other sports. On the other hand, however, this ‘lag’ might be appropriate since chess governing bodies have not yet introduced overly intrusive rules. These two interacting perspectives shape the aim and objectives of this research designed to protect the chess community from cheating by identifying adequate protection measures in the light of environmental criminology and sports law.

\"This is a book of the detailed rules for tournament chess\"-- Provided by publisher.

A series of recent works studying a database of chronologically sorted chess games-containing 1.4 million games played by humans between 1998 and 2007- have shown that the popularity distribution of chess game-lines follows a Zipf's law, and that time series inferred from the sequences of those game-lines exhibit long-range memory effects. The presence of Zipf's law together with long-range memory effects was observed in several systems, however, the simultaneous emergence of these two phenomena were always studied separately up to now. In this work, by making use of a variant of the Yule-Simon preferential growth model, introduced by Cattuto et al., we provide an explanation for the simultaneous emergence of Zipf's law and long-range correlations memory effects in a chess database. We find that Cattuto's Model (CM) is able to reproduce both, Zipf's law and the long-range correlations, including size-dependent scaling of the Hurst exponent for the corresponding time series. CM allows an explanation for the simultaneous emergence of these two phenomena via a preferential growth dynamics, including a memory kernel, in the popularity distribution of chess game-lines. This mechanism results in an aging process in the chess game-line choice as the database grows. Moreover, we find burstiness in the activity of subsets of the most active players, although the aggregated activity of the pool of players displays inter-event times without burstiness. We show that CM is not able to produce time series with bursty behavior providing evidence that burstiness is not required for the explanation of the long-range correlation effects in the chess database. Our results provide further evidence favoring the hypothesis that long-range correlations effects are a consequence of the aging of game-lines and not burstiness, and shed light on the mechanism that operates in the simultaneous emergence of Zipf's law and long-range correlations in a community of chess players.

Discover why the world's first Black Grandmaster Maurice Ashley thinks that chess is one of the best skills in life. Follow him on a journey from the Bronx to a world stage where he has taught thousands of young people the life philosophies of the game.

Fencing, often referred to as a physical game of chess, is an organized sport involving the use of a sword, epee, foil, or saber for attack and defense according to set movements and rules. Fencing, one of the first nine sports included in the first Olympic Games in 1896, has a long history. This paper has systematically reviewed literature and evaluated the role of fencing in the empowerment of women through a combination of qualitative and quantitative research methods including first-hand observation, interviews, archival analysis, and secondary statistical data collection. It has attempted to narrow the empirical gap by exploring the gender perspective of fencing as a sport. It reveals that due to historical, social and cultural bias, financial constraints, as well as a lack of leadership, women's involvement in fencing had been limited throughout fencing's history. Since the 2nd half of the 19th century, fencing has witnessed tremendous strides in breaking the barriers influenced by the changing society propelled by a long history of feminist and civil rights activists who took a blend of the consciousness-raising and organizing approach and the pragmatic approach. Women's fencing shows a history of breaking barriers, and with the enacting of Title IX to eliminate gender discrimination in sports and education it is the most significant turning point. Title IX has not only broadened and deepened the scope and participation of women in fencing but also made fencing an enabler and driver to enhance women's grit and leadership, leading to the empowerment of women in society. The paper develops a framework to highlight the linkage between Title IX, women's fencing, and empowerment. The impact of Title IX on women with fencing as an enabler and drivers is farreaching. It has widened the scope of women in fencing by reaching the general public and deepened the scope of women's fencing by raising the visibility of this women's sport in national and international arenas.

PurposeAssociation rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.Design/methodology/approachThe primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.FindingsThe experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.Originality/valueData mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

The aim of the present study was to see how well the power law of practice, studied mainly with simple skills acquired over short periods with task time as the performance measure, generalizes to chess skill. Chess playing is a complex cognitive skill acquired over years, and expertise can be measured by a performance rating based on game results and relative strengths of opponents. Participants were 75 highly skilled players who entered the domain very young and improved skill greatly. With number of games as the practice measure, the traditional 2-parameter power law fit the mean curve and most individual curves quite well. However, a new formulation of the power law found by artificial intelligence program Eureqa often worked even better. Power models with an asymptote parameter did not work well with chess skill unless that parameter was set to a constant. Another study aim was to see how well various models predict future performance from varying practice levels. For three models, the less practice the greater was the underprediction of future performance, but the new power law formulation predicted well from early in practice. Another study aim was to test model fits with time as the practice measure. With time, power models fit well, but an exponential model and a quadratic model fitted most individual curves better. The power law as traditionally formulated does generalize well to chess skill development but is not always the best model, and no single model always fit best for all participants.

The process of clearing land for agriculture results in widespread deforestation and contributes to 40 percent of global methane production. [...]to confront climate change, it is necessary to ensure reforestation-but how? Data storage and processing centers that deliver digital services like entertainment and cloud computing are already responsible for two percent of global greenhouse gas emissions, a number comparable to the overall percentage of pollution contributed by the aviation industry. Countries will both need experts in the field who can successfully use the technology and internet connection, neither of which are always readily available. [...]in order for developing countries to take advantage of the benefits of AI and improve their food security, there will need to be a focus on developing the infrastructure necessary for internet access and teaching professionals how to use the technology.

The classic trade-off between exploration and exploitation reflects the tension between gaining new information about alternatives to improve future returns and using the information currently available to improve present returns. By considering these issues in the context of a multistage, as opposed to a repeated, problem environment, we show that exploratory behavior has value quite apart from its role in revising beliefs. We show that even if current beliefs provide an unbiased characterization of the problem environment, maximizing with respect to these beliefs may lead to an inferior expected payoff relative to other mechanisms that make less aggressive use of the organization's beliefs. Search can lead to more robust actions in multistage decision problems than maximization, a benefit quite apart from its role in the updating of beliefs.