The SlideBar is a physical linear input device for absolute position control of one degree of freedom, consisting of a physical slider with a graspable knob positioned near or attached to the keyboard. Its range of motion is directly mapped to a one dimensional input widget such as a scrollbar. The SlideBar provides absolute position control in one dimension, is usable in the non-dominant hand ...

In this work, we present an adaptation of the sequence-tosequence model for structured vision tasks. In this model, the output variables for a given input are predicted sequentially using neural networks. The prediction for each output variable depends not only on the input but also on the previously predicted output variables. The model is applied to spatial localization tasks and uses convolu...

While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability. In this paper, we propose a general methodology to analyze and interpret decisions from a neural model by observing the effects on the model of erasing various parts of the representation, such as input word-vector dimensions, intermediate hidden units, or i...

Calculating the worst-case execution time (WCET) of real-time tasks is still a tedious job. Programmers are required to provide additional information on the program flow, analyzing subtle, context dependent loop bounds manually. In this paper, we propose to restrict written and generated code to the class of programs with input-data independent loop counters. The proposed policy builds on the ...

This paper investigates the robustness of NLP against perturbed word forms. While neural approaches can achieve (almost) human-like accuracy for certain tasks and conditions, they often are sensitive to small changes in the input such as non-canonical input (e.g., typos). Yet both stability and robustness are desired properties in applications involving user-generated content, and the more as h...

TRACA (Temporal Reinforcement learning and Classiica-tion Architecture) is a learning system intended for robot-navigation problems (Mitchell 2000). One problem in this area is the input-generalisation problem. Input generalisation requires learning a small set of internal states which represent useful abstractions of the much larger set of actual states. As such, the input-generalisation probl...

As far as incidental vocabulary learning is concerned, the basic contention of the Involvement Load Hypothesis (ILH) is that retention of unfamiliar words is, generally, conditional upon the degree of involvement in processing them. This study examined input modality and incidental vocabulary uptake in a task-induced setting whereby three variously loaded task types (marginal glosses, fill-inta...

System-based methods have been applied to assess trunk motor control in people with and without back pain, although the reliability of these methods has yet to be established. Therefore, the goal of this study was to quantify within- and between-day reliability using systems-based methods involving position and force tracking and stabilization tasks. Ten healthy subjects performed six tasks, in...

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...

this study was designed to find which one of the three different presentations, i.e. input, input-output, and output-input, will be more effective in iranian efl learners' vocabulary acquisitions. to this end, first 54 out of 64 female students, aged from 19 to 23 years, with an average of 21, were selected out of starter-level efl learners at the university of tarbiat moalem in bandar abb...