Navigating Workload Compatibility Between a Recommender System and a NoSQL Database: An Interactive Tutorial

In this tutorial, the issue of compatibility between a big data storage technology and an analytic workload is explored using a fictitious streaming company as an example. The tutorial offers an interactive approach to help students understand the importance of considering workload compatibility when adopting new technologies. We provide instructors with two Jupyter Notebooks that analyze the compatibility, a detailed instructor guide on how to execute these notebooks, lessons learned, and appendices containing solutions and explanations. This tutorial provides a valuable resource for instructors teaching courses in database systems, big data, and analytic concepts, helping students develop practical skills to navigate the complexities of big data technologies effectively

Collaborative development of the Arrowsmith two node search interface designed for laboratory investigators.

Arrowsmith is a unique computer-assisted strategy designed to assist investigators in detecting biologically-relevant connections between two disparate sets of articles in Medline. This paper describes how an inter-institutional consortium of neuroscientists used the UIC Arrowsmith web interface http://arrowsmith.psych.uic.edu in their daily work and guided the development, refinement and expansion of the system into a suite of tools intended for use by the wider scientific community

Space for Two to Think: Large, High-Resolution Displays for Co-located Collaborative Sensemaking

Large, high-resolution displays carry the potential to enhance single display groupware collaborative sensemaking for intelligence analysis tasks by providing space for common ground to develop, but it is up to the visual analytics tools to utilize this space effectively. In an exploratory study, we compared two tools (Jigsaw and a document viewer), which were adapted to support multiple input devices, to observe how the large display space was used in establishing and maintaining common ground during an intelligence analysis scenario using 50 textual documents. We discuss the spatial strategies employed by the pairs of participants, which were largely dependent on tool type (data-centric or function-centric), as well as how different visual analytics tools used collaboratively on large, high-resolution displays impact common ground in both process and solution. Using these findings, we suggest design considerations to enable future co-located collaborative sensemaking tools to take advantage of the benefits of collaborating on large, high-resolution displays

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary

The complex physical properties of highly deformable materials such as clothes pose significant challenges fanipulation systems. We present a novel visual feedback dictionary-based method for manipulating defoor autonomous robotic mrmable objects towards a desired configuration. Our approach is based on visual servoing and we use an efficient technique to extract key features from the RGB sensor stream in the form of a histogram of deformable model features. These histogram features serve as high-level representations of the state of the deformable material. Next, we collect manipulation data and use a visual feedback dictionary that maps the velocity in the high-dimensional feature space to the velocity of the robotic end-effectors for manipulation. We have evaluated our approach on a set of complex manipulation tasks and human-robot manipulation tasks on different cloth pieces with varying material characteristics.Comment: The video is available at goo.gl/mDSC4

Undergraduate curriculum to teach and provide research skills on hardware design for SDR applications in FPGA technology

Software Defined Radio (SDR) technologies play today an important role in modern wireless networks due to their flexibility to implement re-configurable hardware designs. In light of the importance to operate and develop such technologies, academic programs in the communications engineering field demand an introduction to Digital Signal Processing (DSP) and SDR communication schemes accordingly. Typically, the teaching of this subject is afforded through projects and hands-on activities in classrooms. However, provided their relevance in the current state-of-the-art, this topic also provides a framework to teach soft skills concerning research abilities in students. This paper introduces an academic program to the development of SDR functionality as well as research skills based on exposure to state-of-the-art research. Through projects, hands-on activities are conducted to teach digital signal processing designs using Field Programmable Gate Array (FPGA) technology. The course aims to develop technical skills to implement communication system blocks. Besides, workshops and seminars are prepared to support the development of research and communication skills. The proposed course is flexible to incorporate on a given academic program as an elective subject to further support topics related to communication theory and discrete-time signals. Learning outcomes are designed to develop enhanced technical skills in SDR design and simultaneously a critical discussion of the devised solutions in light of the state-of-the-art. Also, skills related to identifying, formulating, and discussing engineering problems are further reinforced. Results from supported projects developed in the classroom exhibit completed assignments superior to 90% of participant students. Learning objectives concerning the technical skills were successfully covered (90%) in comparison to research and communicating skills (80%). Additionally, research skills and the ability to disseminate knowledge gradually improved in seminars. Finally, results of the current course exhibit improvements of 25% regarding the acquired skills in digital signal processing in comparison to previous courses.This work was supported in part by the Spanish National Project Hybrid Terrestrial/Satellite Air Interface for 5G and Beyond-Areas of Diffcult Access (TERESA-ADA) through the [Ministerio de Economía, Industria y Competitividad (MINECO)/Agencia Estatal de Investigación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea (UE)] under Grant TEC2017-90093-C3-2-R.Publicad

Tell me more?: the effects of mental model soundness on personalizing an intelligent agent

What does a user need to know to productively work with an intelligent agent? Intelligent agents and recommender systems are gaining widespread use, potentially creating a need for end users to understand how these systems operate in order to fix their agent's personalized behavior. This paper explores the effects of mental model soundness on such personalization by providing structural knowledge of a music recommender system in an empirical study. Our findings show that participants were able to quickly build sound mental models of the recommender system's reasoning, and that participants who most improved their mental models during the study were significantly more likely to make the recommender operate to their satisfaction. These results suggest that by helping end users understand a system's reasoning, intelligent agents may elicit more and better feedback, thus more closely aligning their output with each user's intentions