Supporting primary students' learning of fraction conceptual knowledge through digital games

With the advent of mobile technologies, well-designed fraction apps can be used to help children gain fraction knowledge, a challenging topic for both teachers and students. The present pilot study adopted a quasi-experimental design to investigate whether children can learn fraction concepts equally well if half of the lesson time (20 min) is replaced with game-based learning. Keeping the total lesson time (40 min) identical, the control group (N = 33) received traditional instruction, and the experimental group (N = 32) was presented with a blended learning approach spending half of the class time (20 min) playing tablet-based fraction games, where each of the learners had their own tablet. The results suggested that in the posttest, the experimental group achieved similar learning gains to the control group and appear to have achieved better performance in the transfer test than the control group. This paper also discusses the efficiency of game-based learning, the mechanism of how fraction games might enhance learning, and the potential of integrating game-based learning in educational settings.     

Evolutionary collective behavior decomposition model for time series data mining

In this research, we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals. The strategy of an agent is modeled by certain simple game theory models with limited information. Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data. The trained model can be used for collective behavior prediction. For modeling individual behavior, two simple games, the minority game and mixed game are investigated in experiments on the real-world stock prices and foreign-exchange rate. Experimental results are presented to show the effectiveness of the new proposed model.     

运动会新闻发布系统的设计与实现

针对运动会比赛期间随时需要方便、快捷、高效地更新大量新闻的需求 ,研制和开发一套运动会新闻发布系统。利用微软最新推出的asp .net动态网页开发工具 ,采用web分布式三层设计模型设计实现了一套操作简单、功能完善、具有通用性的运动会新闻发布系统     

Measuring College Students' Motives for Playing Drinking Games.

Students may choose to play drinking games not only for reasons related to alcohol consumption but also because of incentives related to other aspects of play (competition, fun, interpersonal dominance, etc.). College students (120 men and 167 women) completed measures of motives for playing (based on T. J. Johnson, S. Hamilton, & V. L. Sheets, 1999) and consequences of playing drinking games. Exploratory principal-components analysis identified 8 reasons for playing. Men and women differed in their endorsement of the factors. Motives for play directly predicted consequences of play independently of alcohol consumption. Specific motives predicted specific types of consequences. In multiple regression analyses, Conformity motives were negatively related to consequences and may represent a form of protective motive. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

一个用户接口管理系统

本文介绍了一个用户接口管理系统NUUIMS的设计和实现.重点叙述了该系统基于规范化目标用户接口模型的设计思想,系统组成及描述方法.文章也讨论了使用NUUIMS设计而成的用户接口的特点.     

MOBSY: Integration of vision and dialogue in service robots

Abstract. This contribution introduces MOBSY, a fully integrated, autonomous mobile service robot system. It acts as an automatic dialogue-based receptionist for visitors to our institute. MOBSY incorporates many techniques from different research areas into one working stand-alone system. The techniques involved range from computer vision over speech understanding to classical robotics. Along with the two main aspects of vision and speech, we also focus on the integration aspect, both on the methodological and on the technical level. We describe the task and the techniques involved. Finally, we discuss the experiences that we gained with MOBSY during a live performance at our institute.     

不完全信息下的最终报价仲裁

基于争议双地仲裁人的裁决值有不同的概率估计，而这些估计是双方共同知识这一前提分析最终报价中仲裁，把它构造成一个不完全信息的非零和对策模型。然后给出了局部、全局Ｎａｓｈ平衡报价策略存在唯一的充分条件，最后探讨了双方认识存在的差异对争议人报价行为的影响。     

Neural networks for contract bridge bidding

The objective of this study is to explore the possibility of capturing the reasoning process used in bidding a hand in a bridge game by an artificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new hand. The game of bridge, like many other games used in artificial intelligence, can easily be represented in a machine. But, unlike most games used in artificial intelligence, bridge uses subtle reasoning over and above the agreed conventional system, to make a bid from the pattern of a given hand. Although it is difficult for a player to spell out the precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-level opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.     

Controlling the losing probability in a monotone game

We deal with a complex game between Alice and Bob where each contender’s probability of victory grows monotonically by unknown amounts with the resources employed. For a fixed effort on Alice’s part, Bob increases his resources on the basis of the results for each round (victory, tie or defeat) with the aim of reducing the probability of defeat to below a given threshold. We read this goal in terms of computing a confidence interval for the probability of losing and realize that the moves in some contests may bring in an indeterminacy trap: in certain games Bob cannot simultaneously have both a low probability-of-defeat measure and a narrow confidence interval. We use the inferential mechanism called twisting argument to compute the above interval on the basis of two joint statistics. Careful use of such statistics allows us to avoid indeterminacy.