Humanoid Motion Description Language

In this paper we propose a description language for specifying motions for humanoid robots and for allowing humanoid robots to acquire motor skills. Locomotion greatly increases our ability to interact with our environments, which in turn increases our mental abilities. This principle also applies to humanoid robots. However, there are great difficulties to specify humanoid motions and to represent motor skills, which in most cases require four-dimensional space representations. We propose a representation framework that includes the following attributes: motion description layers, egocentric reference system, progressive quantized refinement, and automatic constraint satisfaction. We also outline strategies for acquiring new motor skills by learning from trial and error, macro approach, and programming. Then, we outline the development of a new humanoid motion description language called Cybele

Complete Subdivision Algorithms, II: Isotopic Meshing of Singular Algebraic Curves

Given a real valued function f(X,Y), a box region B_0 in R^2 and a positive epsilon, we want to compute an epsilon-isotopic polygonal approximation to the restriction of the curve S=f^{-1}(0)={p in R^2: f(p)=0} to B_0. We focus on subdivision algorithms because of their adaptive complexity and ease of implementation. Plantinga and Vegter gave a numerical subdivision algorithm that is exact when the curve S is bounded and non-singular. They used a computational model that relied only on function evaluation and interval arithmetic. We generalize their algorithm to any bounded (but possibly non-simply connected) region that does not contain singularities of S. With this generalization as a subroutine, we provide a method to detect isolated algebraic singularities and their branching degree. This appears to be the first complete purely numerical method to compute isotopic approximations of algebraic curves with isolated singularities

Unlocking Sustainability with Visualizations: Driving the Driven through the Whys and Hows

Visualizations have been broadly employed to help individuals understand complex environmental issues and encourage sustainable behaviors. However, sustainability knowledge only sometimes transpires to actual green practices. In this study, we explain the effects of post-trip visualized storytelling on eco-driving behaviors. We conducted a laboratory experiment involving eye-tracking and driving simulation. This study contributes to the literature by unraveling the impact of visualized narratives on behaviors and demonstrating eco-driving behaviors in multiple manifestations

Promoting Eco-driving through Persuasive Visualization

The goal of sustainability is to preserve resources for future generations. Climate change is a major environmental problem that violates the goal of sustainability. A key strategy to combat climate change is to reduce carbon emission that is largely generated by road transport. Traditional interventions on promoting eco-driving behaviors often fail to convince people to alter their driving behaviors. The growing use of persuasive visualization allows individuals to become aware of the relationship between their driving behaviors and the associated environmental impact. Drawing on the expectancy theory of motivation, this study plans to explore ways to design effective visualization to promote eco-driving behaviors. Additionally, this study proposes a unique lab experiment that enables the manipulation of visualizations and presents an opportunity to observe individuals’ driving behavioral changes

Promoting Driving Safety with Self-Evaluation Maintenance: Human-Human and Human-Artificial Intelligence Performance Comparisons

In this study, we develop and test a model that explains individuals’ behavioral changes in driving safety after viewing the visualizations, which depict their driving performance against that of artificial intelligence (AI). This study draws on the self-evaluation literature to understand performance comparisons and extends the self-evaluation perspective to the context of human-AI comparisons. Furthermore, this study illustrates that individuals can be incited emotionally by performance comparisons, and these emotional responses influence their driving behaviors subsequently. The results of this study generally support our model. Overall, this study sheds light on how competition between humans and computers can be utilized to promote desirable behaviors

Debunking Sustainability Excuses with Instrumentality and Expectancy Visualizations: A Physiological Perspective

This study advances the IS literature by investigating the effects of visualization on promoting sustainability knowledge and pro-environmental behaviors. Specifically, drawing on the visualization literature, we explain how the key visualization features, expectancy illustration, and interactivity affect individuals’ understanding of the impact of their behaviors on the environment, encouraging pro-environmental behaviors. Additionally, we draw on the pedagogy literature to explicate that the effects of visualization on learning outcomes and pro-environmental practices can be explained through individuals’ psychological responses in their course of interpreting the visualization. Collectively, this study presents our endeavor in understanding the roles of visualization in ecological discourse by integrating the visualization literature and sustainability research. Moreover, by unboxing individuals’ psychological processes in interpreting visualization, we offer a fresh perspective to understanding the promises and challenges of using visualization for knowledge acquisition

Promoting Eco driving with Post Trip Visualized Storytelling

Visualized storytelling is often used to explain complicated environmental issues, raise ecological consciousness, and promote sustainable behavior. In this study, we develop and test a model demonstrating how post-trip visualized storytelling encourages eco driving behaviors. We explore the effect of post trip visualizations on eco driving behaviors by examining the literature on human-computer interaction. We test our hypothesis in an experiment using eye tracking and driving simulation. Results indicate that animated illustrations and narrative sequence improved eco driving practices. Overall, this study contributes to information systems literature by unraveling the effects of post-trip visualized storytelling on eco driving behaviors

Expecting the Unexpected in Security Violations in Mobile Apps

personal data. This increased access and control may raise users’ perception of heightened privacy leakage and security issues. This is especially the case if users’ awareness and expectations of this external access and control is not accurately recognized through proper security declarations. This proposal thus attempts to put forth an investigation on the effect of mobile users’ privacy expectation disconfirmation on their continued usage intention of mobile apps sourced from app distribution stores. Drawing upon the APCO framework, security awareness literature and the expectation-disconfirmation perspective, two key types of security awareness information are identified; namely access annotation and modification annotation. It is noted that these types of information can be emphasized in app distribution stores to reduce subsequent privacy expectation disconfirmation. Hence, this study plans to examine the downstream effect of privacy expectation disconfirmation on users’ continued usage intention. To operationalize this research, a laboratory experiment will be conducted