15 research outputs found

    Embedded non-interactive CAPTCHA for Fischer Random Chess.

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    Cheating in chess can take many forms and has existed almost as long as the game itself. The advent of computers has introduced a new form of cheating into the game. Thanks to the computational power of modern-day computers, a player can use a program to calculate thousands of moves for him or her, and determine the best possible scenario for each move and counter-move. These programs are often referred to as “bots,” and can even play the game without any user interaction. In this paper, we describe a methodology aimed at preventing bots from participating in online chess games. The proposed approach is based on the integration of a CAPTCHA protocol into a game scenario, and the subsequent inability of bots to accurately track the game states. Preliminary experimental results provide favorable feedback for further development of the proposed algorithm

    Development of Embedded CAPTCHA Elements for Bot Prevention in Fischer Random Chess

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    Cheating in chess can take many forms and has existed almost as long as the game itself. The advent of computers has introduced a new form of cheating into the game. Thanks to the computational power of modern-day computers, a player can use a program to calculate thousands of moves for him or her, and determine the best possible scenario for each move and countermove. These programs are often referred to as “bots,” and can even play the game without any user interaction. In this paper, we describe a methodology aimed at preventing bots from participating in online chess games. The proposed approach is based on the integration of a CAPTCHA protocol into a game scenario, and the subsequent inability of bots to accurately track the game states. This is achieved by rotating the images of the individual chess pieces and adjusting their resolution in an attempt to render them unreadable by a bot. Feedback from users during testing shows that there is minimal impact on their ability to play the game. Players rated the difficulty of reading the pieces on a scale of one to ten, with an average rank of 6.5. However, the average number of moves to adjust to the distorted pieces was only 3.75. This tells us that, although it is difficult to read the pieces at first, it is easy to adjust quickly to the new image

    Future of the Internet--and how to stop it

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    vi, 342 p. : ill. ; 25 cmLibro ElectrónicoOn January 9, 2007, Steve Jobs introduced the iPhone to an eager audience crammed into San Francisco’s Moscone Center.1 A beautiful and brilliantly engineered device, the iPhone blended three products into one: an iPod, with the highest-quality screen Apple had ever produced; a phone, with cleverly integrated functionality, such as voicemail that came wrapped as separately accessible messages; and a device to access the Internet, with a smart and elegant browser, and with built-in map, weather, stock, and e-mail capabilities. It was a technical and design triumph for Jobs, bringing the company into a market with an extraordinary potential for growth, and pushing the industry to a new level of competition in ways to connect us to each other and to the Web.Includes bibliographical references (p. 249-328) and index Acceso restringido a miembros del Consorcio de Bibliotecas Universitarias de Andalucía Electronic reproduction. Palo Alto, Calif. : ebrary, 2009 Modo de acceso : World Wide Webpt. 1. The rise and stall of the generative Net -- Battle of the boxes -- Battle of the networks -- Cybersecurity and the generative dilemma -- pt. 2. After the stall -- The generative pattern -- Tethered appliances, software as service, and perfect enforcement -- The lessons of Wikipedia -- pt. 3. Solutions -- Stopping the future of the Internet : stability on a generative Net -- Strategies for a generative future -- Meeting the risks of generativity : Privacy 2.0. Index32

    Learning outcomes of classroom research

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    Learning Outcomes of Classroom Research

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    Personal pronouns are a linguistic device that is used to engage students at various educational levels. Personal pronouns are multifunctional, and their functions range from inclusion to exclusion, and include establishing of rapport with students. In this chapter, we compare the use of personal pronouns at university and secondary school levels. Our previous study (Yeo & Ting, 2014) showed the frequent use of you in lecture introductions (2,170 instances in the 37,373-word corpus) to acknowledge the presence of students. The arts lecturers were more inclusive than the science lecturers, reflected in the less frequent use of exclusive-we and we for one, as well as the frequent use of you-generalised. We have also compiled and analysed a 43,511-word corpus from 15 English lessons in three Malaysian secondary schools. This corpus yielded 2,019 instances of personal pronoun use. The results showed that you was the most frequently used personal pronoun, followed by we and I. You-audience was used more than you-generalised, and the main function was to give instructions to students. The teachers appeared to be more directive than the lecturers in the previous study, who sometimes used the inclusive-we for you and I and we for I to lessen the social distance with students, indicating that the discourse functions of personal pronouns vary with the educational context. The findings suggest that educators can be alerted to the versatility of personal pronouns, for example, for engaging students in the lesson and for asserting authority in the subject matter. Keywords: student engagement; personal pronouns; lecture; classroom; teache
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