2369938
Human-Currency Interaction: learning from virtual currency use in China
What happens when the domains of HCI design and money intersect? This paper presents analyses from an ethnographic study of virtual currency use in China to discuss implications for game design, and HCI design more broadly. We found that how virtual currency is perceived, obtained, and spent can critically shape gamers' behavior and experience. Virtual and real currencies can interact in complex ways that promote, extend, and/or interfere with the value and character of game worlds. Bringing money into HCI design heightens existing issues of realness, trust, and fairness, and thus presents new challenges and opportunities for user experience innovation.
269891
Respecting Users' Individual Privacy Constraints in Web Personalization
Web personalization has demonstrated to be advantageous for both online customers and vendors. However, its benefits may be severely counter acted by privacy constraints. Personalized systems need to take users' privacy concerns into account, as well as privacy laws and industry self-regulation that may be in effect. In this paper, we first discuss how these constraints may affect web-based personalized systems. We then explain in what way current approaches to this problem fall short of their aims, specifically regarding the need to tailor privacy to the constraints of each individual user. We present a dynamic privacy-enhancing user modeling framework as a superior alternative, which is based on a software product line architecture. Our system dynamically selects personalization methods during runtime that respect users' current privacy concerns as well as the privacy laws and regulations that apply to them.
1173197
Situating Productive Play: Online Gaming Practices and Guanxi in China
Economic activities in and around online gaming in China are often correlated in the West with practices of gold farming, or selling in-game currency to players for real money in online games. What can we learn about online gaming in China and about online gaming and online sociality more broadly when we look at economic and other "pragmatic" practices through which online gaming becomes meaningful to players? In this paper, we present findings from an ethnographic study of online gaming in China's urban Internet cafes to discuss implications for game design, and HCI design more broadly. Considering the ties between socio-economic practices, development of trust and culturally situated imaginings of self-hood and otherness, brings to the fore how online gaming in and of itself constitutes the means for practical achievements in day-to-day management of guanxi (social connection).
12678817
Developing a multifunctional network laboratory for teaching and research
Adequate hands-on experience on networking and computing is becoming vital and necessary for students majored in programs such as Information Technology and Computer Science. However, building a hands-on experimental lab environment turns out to be challenging for many institutions, particularly for a new-born program or an urban university, due to space constraints, budget limitations, maintenance difficulty and so on. This paper presents and explores the design of a multifunctional lab environment for both teaching and research while taking the cost and space challenges into consideration. On one hand, the lab utilizes a number of industrial-strength Cisco routers, switches and wireless access points to setup a practical platform that allows students to configure and administrate their network design by applying the knowledge learned in the classroom. On the other hand, the lab is equipped with useful (and mostly free) software packages such as OPNET, Network Simulator (NS-2), Virtual PC, and CPLEX, which enable students to conduct various network modeling, simulation, optimization, and emulation together with their research projects. Accordingly, each iteration for the lab design and implementation as well as experiences learned and future integration with additional curriculum, are presented in this paper.
46437694
Estimation of multiple directional illuminants from a single image
We present a new method for the detection and estimation of multiple directional illuminants, using only one single image of an object with known geometry. It obviates the need to modify the imaged scene by inserting calibration objects of any particular geometry, relying instead on partial knowledge of the geometry of the scene. We first develop our method for the case of a Lambertian sphere with known size, illuminated by a set of directional light sources. A novel and robust way is proposed to segment the surface into regions, with each region illuminated by a different set of sources. Our region-based least-squares method is impervious to noise and missing data, which is crucial to extending the method to arbitrary smooth geometry and to surfaces having both Lambertian and specular properties. We propose a novel methodology that integrates information from shadows and shading in the presence of strong directional sources of illumination, even when significant non-directional sources exist in the scene and the object surface is not Lambertian. We demonstrate experimentally the accuracy of our method, both in detecting the number of light sources and in estimating their directions, by testing on images of a variety of synthetic and real objects.
59364798
The modeling of piezoceramic patch interactions with shells, plates, and beams
Abstract : General models describing the interactions between a pair of piezoceramic patches and elastic substructures consisting of a cylindrical shell, plate and beam are presented. In each case, the manner in which the patch loads enter both the strong and weak forms of the time-dependent structural equations of motion is described. Through force and moment balancing, these loads are then determined in terms of material properties of the patch and substructure (thickness, elastic properties, Poisson ratios), the geometry of the patch placement, and the voltages into the patches. In the case of the shell, the coupling between bending and inplane deformations, which is due to the curvature, is retained. These models are sufficiently general to allow for potentially different patch voltages which implies that they can be suitably employed when using piezoceramic patches for controlling system dynamics when both extensional and bending vibrations are present.
6225988
A global optimal algorithm for class-dependent discretization of continuous data
This paper presents a new method to convert continuous variables into discrete variables for inductive machine learning. The method can be applied to pattern classification problems in machine learning and data mining. The discretization process is formulated as an optimization problem. We first use the normalized mutual information that measures the interdependence between the class labels and the variable to be discretized as the objective function, and then use fractional programming (iterative dynamic programming) to find its optimum. Unlike the majority of class-dependent discretization methods in the literature which only find the local optimum of the objective functions, the proposed method, OCDD, or Optimal Class-Dependent Discretization, finds the global optimum. The experimental results demonstrate that this algorithm is very effective in classification when coupled with popular learning systems such as C4.5 decision trees and Naive-Bayes classifier. It can be used to discretize continuous variables for many existing inductive learning systems.
16798311
Parameter Inference of Cost-Sensitive Boosting Algorithms
Several cost-sensitive boosting algorithms have been reported as effective methods in dealing with class imbalance problem. Misclassification costs, which reflect the different level of class identification importance, are integrated into the weight update formula of AdaBoost algorithm. Yet, it has been shown that the weight update parameter of AdaBoost is induced so as the training error can be reduced most rapidly. This is the most crucial step of AdaBoost in converting a weak learning algorithm into a strong one. However, most reported cost-sensitive boosting algorithms ignore such a property. In this paper, we come up with three versions of cost-sensitive AdaBoost algorithms where the parameters for sample weight updating are induced. Then, their identification abilities on the small classes are tested on four “real world” medical data sets taken from UCI Machine Learning Database based on F-measure. Our experimental results show that one of our proposed cost-sensitive AdaBoost algorithms is superior in achieving the best identification ability on the small class among all reported cost-sensitive boosting algorithms.