New librarians often feel as though they have something to prove. This desire can lead feelings of inadequacy and failure in the workplace that manifest anxiety fear. The idea someone is a fraud or only succeeding by luck called Imposter Phenomenon Syndrome. As early career instruction librarians, authors set out find tools for dealing with their Phenomenon. paper outlines faith-based like exer...

The paradigm of ubiquitous computing has become a reference for the design of Smart Spaces. Current trends in Ambient Intelligence are increasingly related to the scope of Internet of Things. This paradigm has the potential to support cost-effective solutions in the fields of telecare, e-health and Ambient Assisted Living. Nevertheless, ubiquitous computing does not provide end users with a rol...

In this paper, we propose a multi-agent approach to building more Cooperative Intelligent Distance Learning Environments (CIDLE). We define a Confidence Agent in Intelligent Tutoring System (ITS) in such a way that an ITS would improve the quality and efficiency of its teaching. To achieve this goal, we propose a Confidence Intelligent Tutoring System (CITS) to manage negotiations within a comm...

We systematically explore regularizing neural networks by penalizing low entropy output distributions. We show that penalizing low entropy output distributions, which has been shown to improve exploration in reinforcement learning, acts as a strong regularizer in supervised learning. Furthermore, we connect a maximum entropy based confidence penalty to label smoothing through the direction of t...

The paper presents a software system that embodies a lexico-syntactic approach to the task of Textual Entailment. Although the approach is based on a minimal set of resources it is highly confident. The architecture of the system is open and can be easily expanded with more and deeper processing modules. Results on a standard data set are presented.

Most of the performance bounds for on-line learning algorithms are proven assuming a constant learning rate. To optimize these bounds, the learning rate must be tuned based on quantities that are generally unknown, as they depend on the whole sequence of examples. In this paper we show that essentially the same optimized bounds can be obtained when the algorithms adaptively tune their learning ...

The aim of this project is to improve human decision-making using explainability; specifically, how explain the (un)certainty machine learning models. Prior research has used uncertainty measures promote trust and decision-making. However, direction explaining why AI prediction confident (or not confident) in its needs be addressed. By model uncertainty, we can trust, understanding for users.

when in doubt, ask information theory when you feel confident, ask anyway dedicated to my wife, Ilia