The LumberJack, December 01, 2010

The student newspaper of Humboldt State University.https://digitalcommons.humboldt.edu/studentnewspaper/1219/thumbnail.jp

Interaction in motion: designing truly mobile interaction

The use of technology while being mobile now takes place in many areas of people’s lives in a wide range of scenarios, for example users cycle, climb, run and even swim while interacting with devices. Conflict between locomotion and system use can reduce interaction performance and also the ability to safely move. We discuss the risks of such “interaction in motion”, which we argue make it desirable to design with locomotion in mind. To aid such design we present a taxonomy and framework based on two key dimensions: relation of interaction task to locomotion task, and the amount that a locomotion activity inhibits use of input and output interfaces. We accompany this with four strategies for interaction in motion. With this work, we ultimately aim to enhance our understanding of what being “mobile” actually means for interaction, and help practitioners design truly mobile interactions

Interaction in motion: designing truly mobile interaction

The use of technology while being mobile now takes place in many areas of people’s lives in a wide range of scenarios, for example users cycle, climb, run and even swim while interacting with devices. Conflict between locomotion and system use can reduce interaction performance and also the ability to safely move. We discuss the risks of such “interaction in motion”, which we argue make it desirable to design with locomotion in mind. To aid such design we present a taxonomy and framework based on two key dimensions: relation of interaction task to locomotion task, and the amount that a locomotion activity inhibits use of input and output interfaces. We accompany this with four strategies for interaction in motion. With this work, we ultimately aim to enhance our understanding of what being “mobile” actually means for interaction, and help practitioners design truly mobile interactions

The role of app development and mobile computing in motivating the secondary mathematics classroom

An increasing amount of high school students are interested in developing their own mobile application. Incorporating mobile development into the classroom can increase student engagement in the fields of science, technology, engineering, and mathematics. In this paper I present a study done with a group of sophomore level students who created their own mathematics apps with no programming experience. The aim of this study is to assess the knowledge gained and motivational appeal of secondary mathematics students taught basic state of Texas exam concepts with the use of the proposed mobile development labs. Students in this study used algebraic and geometric models to describe situations, geometric transformations, proportions, and used probability models. Students practiced the concepts and then created a mobile application related to each concept taught by their teachers. Using MIT’s Appinventor, students easily developed games by putting puzzle pieces together. An increase in confidence was observed and 43% of the students increased their benchmark score. The results of this study demonstrate that students are motivated to learn their math concepts by developing mobile apps

We are the Reckless, We are the Wild Youth: Decadence and Debauchery in the Art of the Utrecht Caravaggisti

Scenes of prostitution, gambling, drinking and vice personified in the art of the Utrecht Caravaggisti who were the Dutch followers of Caravaggio, featured the street subjects of the Italian Baroque master however these artists infused their work with moralizing content that appealed to a Dutch audience. The social and religious climate of the Netherlands in the 17th century allowed for a self indulgent and hedonistic art to be produced despite the fervent, god-fearing culture surrounding it. The art of the Utrecht Caravaggisti borrows the style and subject matter from Caravaggio however it draws upon the indigenous proverbs and moralizing literature of the Netherlands. The art of the Utrecht Caravaggisti appeals to and delights the viewer however it provides a cautionary message

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author