What May Visualization Processes Optimize?

In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.Comment: 10 page

Orienting Graphs to Optimize Reachability

The paper focuses on two problems: (i) how to orient the edges of an undirected graph in order to maximize the number of ordered vertex pairs (x,y) such that there is a directed path from x to y, and (ii) how to orient the edges so as to minimize the number of such pairs. The paper describes a quadratic-time algorithm for the first problem, and a proof that the second problem is NP-hard to approximate within some constant 1+epsilon > 1. The latter proof also shows that the second problem is equivalent to ``comparability graph completion''; neither problem was previously known to be NP-hard

Powtoon: a Digital Medium to Optimize Students' Cultural Presentation in ELT Classroom

Facing industrial revolution 4.0 requires university students to provide themselves with a skill that they can use to compete with machines or computers. One of the skills is negotiation which involves mastering language, especially English as a means of International communication. However, learning English as a foreign language is not as easy as it seems. The students need to use a proper learning media which match their characteristics as digital native and motivate them in learning English such as multimedia. By using multimedia, the students will not only learn about language but also the skill about how to use the media or a computer to come up with industrial revolution 4.0. Thus, the researchers who are English lecturers aimed to encourage students in Universitas Teknokrat Indonesia (UTI) to use more web-based medium as a medium in learning English in a class, exclusively Powtoon. This research used a qualitative method since it disclosed how UTI students use Powtoon in a class and what their opinion toward Powtoon for learning English. During the research, the students used Powtoon in a class as a presentation medium for a half-semester because after mid-test they were divided into 13 groups to present the topics given by the lecturer. For each meeting, there were 2-3 groups presentation. At the end of the semester, the students were given questionnaire related to multimedia usage and responded that they felt motivated in learning English by using technology especially Powtoon. As a result, using a web-based medium in learning English can increase not only student's ability to language but also technology

Learning to Optimize under Non-Stationarity

We introduce algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary linear stochastic bandit setting. It captures natural applications such as dynamic pricing and ads allocation in a changing environment. We show how the difficulty posed by the non-stationarity can be overcome by a novel marriage between stochastic and adversarial bandits learning algorithms. Defining $d,B_T,$ and $T$ as the problem dimension, the \emph{variation budget}, and the total time horizon, respectively, our main contributions are the tuned Sliding Window UCB (\texttt{SW-UCB}) algorithm with optimal $\widetilde{O}(d^{2/3}(B_T+1)^{1/3}T^{2/3})$ dynamic regret, and the tuning free bandit-over-bandit (\texttt{BOB}) framework built on top of the \texttt{SW-UCB} algorithm with best $\widetilde{O}(d^{2/3}(B_T+1)^{1/4}T^{3/4})$ dynamic regret