In recent years, ABET accreditation has placed a heavy emphasis not only on the assessment of objectives and outcomes, but also on the evaluation of them and subsequent efforts for continuous improvement based on such an evaluation. Currently, a plethora of assessment tools and conceptual frameworks notwithstanding, there exists a relative paucity of documented efforts on the actual evaluation ...

Abstract The concept behind IoT is as powerful it complex, and for the entities modules in solution to mesh together perfectly, they all must be part of a well-thought-out structure. That where accomplishing deep understanding, architecture becomes paramount given complexity domains platforms. In this paper, we present comparative analysis IoT-architecture models based on reference proposed by ...

Abstract Deep Learning (DL) has achieved remarkable progress over the last decade on various tasks such as image recognition, speech and natural language processing. In general, three main crucial aspects fueled this progress: increasing availability of large amount digitized data, affordable parallel powerful computing resources (e.g., GPU) growing number open source deep learning frameworks t...

Approximation/model management optimization (AMMO) is a rigorous methodology for attaining solutions of highdelity optimization problems with minimal expense in highdelity function and derivative evaluation. First-order AMMO frameworks allow for a wide variety of models and underlying optimization algorithms. Recent demonstrations with aerodynamic optimization achieved three-fold savings in ter...

Information visualization is characterized by a collection of design principles and evaluation frameworks, rooted in cognitive science research, human-computer interaction studies, and experiences from the fine arts community. Information visualization has been coupled with computational linguistics algorithms to present visualizations of many types of linguistic data. Evaluation is often not c...

A serious bottleneck of comparative parser evaluation is the fact that different parsers subscribe to different formal frameworks and theoretical assumptions. Converting outputs from one framework to another is less than optimal as it easily introduces noise into the process. Here we present a principled protocol for evaluating parsing results across frameworks based on function trees, tree gen...