Designing for Seamless Task Migration in MPUIs: Bridging Task-Disconnects

Today, the proliferation of mobile computing has changed the work environment forever. As a consequence, users are forced to orchestrate a complex interaction between multiple devices, moving data and information back and forth, to accomplish their tasks. Users trudge out USB key drives, remote desktop software, e-mail and network file storage in an attempt to mitigate this orchestration. We refer to this break from the task at hand as task-disconnect. Task-disconnect represents the break in continuity that occurs when a user attempts to accomplish his or her tasks using more than one device. Our objective is to study how software can bridge this task-disconnect, enabling users to seamlessly transition their tasks among their devices. We present the theory, definition, and discussion of task-disconnect; our approach towards bridging this disconnect; and our prototype application that was built to be used across the desktop computer and the Tablet PC platforms. We then describe our subjective evaluation to measure the effectiveness of the prototype in bridging the task-disconnect. We then conclude with the results and insights gained from our evaluation

Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping

Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set. The need for a multi-featured dataset and analytics for cosegmentation becomes critical to better understand and predict plants’ responses to the environment. High-throughput phenotyping produces an abundance of data that can be leveraged to improve segmentation accuracy and plant phenotyping. This paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions. Each dataset has three modalities (Fluorescence, Infrared, and Visible) with 7 to 14 temporal images that are collected in a high-throughput facility at the University of Nebraska-Lincoln. The four datasets (which will be collected under the CosegPP data repository in this paper) are evaluated using three cosegmentation algorithms: Markov random fields-based, Clustering-based, and Deep learning-based cosegmentation, and one commonly used segmentation approach in plant phenotyping. The integration of CosegPP with advanced cosegmentation methods will be the latest benchmark in comparing segmentation accuracy and finding areas of improvement for cosegmentation methodology

EXPLORING THE LEVEL OF STUDENTS’ SELF-EFFICACY IN SPEAKING CLASS

Exploring the level of the students’ self-efficacy toward their speaking ability is the grand design of this study. The participants of this study were 28 non-native students from the suburban area in West Borneo. Those students belong to the third semester of the speaking class. In collecting the data, they were given a questionnaire. An in-depth interview was also conducted with 3 prominent students to validate and triangulate the represented data in the questionnaire result. Adopting Bandura’s theory, the results of this study show that the students manifested slightly high self-efficacy in the magnitude dimension, slightly high self-efficacy in the generality dimension, and very high self-efficacy in the strength dimension. In addition, the in-depth interview affirms that the students’ level in magnitude is influenced by their educational background; the students’ level in generality is affected by their interests in their particular field, and the student's level of strength is determined by their strong belief

Синтез и исследование кристаллической структуры кислого гидратированного комплекса додекавольфрамосиликата с натрий-капролактамовыми фрагментами состава (H3O)4[Na6(C6H10NO)6][SiW12O40]

Синтезирован и исследован методом РСА кислый гидратированный комплекс додекавольфрамосиликата с шестью натрийкапролактамовыми фрагментами (H3O)4[Na6(C6H10NO)6][SiW12O40]. Кристаллы моноклинные, пр. гр. Р21/n; параметры элементарной ячейки: a = 13.744(2), b =11.0726(19), с = 23.464(4) Å, α =90, β =90,202(3), γ = 90°, V = 3570,7(11) Ǻ3, ρвыч = 4.100 мг/м3, Z =