Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

An IoT System for Converting Handwritten Text to Editable Format via Gesture Recognition

Evaluation of traditional classroom has led to electronic classroom i.e. e-learning. Growth of traditional classroom doesn’t stop at e-learning or distance learning. Next step to electronic classroom is a smart classroom. Most popular features of electronic classroom is capturing video/photos of lecture content and extracting handwriting for note-taking. Numerous techniques have been implemented in order to extract handwriting from video/photo of the lecture but still the deficiency of few techniques can be resolved, and which can turn electronic classroom into smart classroom. In this thesis, we present a real-time IoT system to convert handwritten text into editable format by implementing hand gesture recognition (HGR) with Raspberry Pi and camera. Hand Gesture Recognition (HGR) is built using edge detection algorithm and HGR is used in this system to reduce computational complexity of previous systems i.e. removal of redundant images and lecture’s body from image, recollecting text from previous images to fill area from where lecture’s body has been removed. Raspberry Pi is used to retrieve, perceive HGR and to build a smart classroom based on IoT. Handwritten images are converted into editable format by using OpenCV and machine learning algorithms. In text conversion, recognition of uppercase and lowercase alphabets, numbers, special characters, mathematical symbols, equations, graphs and figures are included with recognition of word, lines, blocks, and paragraphs. With the help of Raspberry Pi and IoT, the editable format of lecture notes is given to students via desktop application which helps students to edit notes and images according to their necessity

Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with 4th International Symposium on Mobile Internet Security (MobiSec 2019)

The International Symposium on Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) was held on 17–19 October 2019 in Taichung, Taiwan. The symposium provided academic and industry professionals an opportunity to discuss the latest issues and progress in advancing smart applications based on future ICT and its relative security. The symposium aimed to publish high-quality papers strictly related to the various theories and practical applications concerning advanced smart applications, future ICT, and related communications and networks. It was expected that the symposium and its publications would be a trigger for further related research and technology improvements in this field

ACMS 18th Biennial Conference Proceedings

Association of Christians in the Mathematical Sciences 18th Biennial Conference Proceedings, June 1-4, 2011, Westmont College, Santa Barbara, CA

The Echo: October 26, 2018

Irish journalist visits Taylor – TU Professor takes on Hollywood – Upland Print and Stitch enters association – Trevor Osswald’s photos of the week – Bree’s Beat International News – The Grains and Grill fall festival of the year – Students take on Dreamforce conference – Fall Fashion Week 2018 – The forgotten choir chimes from the shadows – A biblical and romantic view on engagement – Spooktacular short stories – A&E Stay Up To Date – Weekly Crossword: State Capitals – Weekly Sudoku – Our View – Trick or treat? – The Res Publica resumed – The library is a sacred space for students – Trojans gaining ground before tournament – Football fights back after tough stretch – Weekly Preview – Scoreboard – Athletes of the Weekhttps://pillars.taylor.edu/echo-2018-2019/1007/thumbnail.jp

Incorporating prior knowledge into deep neural networks without handcrafted features

Deep learning (DL) is currently the largest area of research within artificial intelligence (AI). This success can largely be attributed to the data-driven nature of the DL algorithms themselves: unlike previous approaches in AI which required handcrafting and significant human intervention, DL models can be implemented and trained with little to no human involvement. The lack of handcrafting, however, can be a two-edged sword. DL algorithms are notorious for producing uninterpretable features, generalising badly to new tasks and relying on extraordinarily large datasets for training. In this thesis, on the assumption that these shortcomings are symptoms of the under-constrained training setup of deep networks, we address the question of how to incorporate knowledge into DL algorithms without reverting to complete handcrafting in order to train more data efficient algorithms. % In this thesis we consider different alternatives to this problem. We start by motivating this line of work with an example of a DL architecture which, inspired by symbolic AI, aims at extracting symbols from a simple environment and using those for quickly learning downstream tasks. Our proof-of-concept model shows that it is possible to address some of the data efficiency issues as well as obtaining more interpretable representations by reasoning at this higher level of abstraction. Our second approach for data-efficiency is based on pre-training: the idea is to pre-train some parts of the DL network on a different, but related, task to first learn useful feature extractors. For our experiments we pre-train the encoder of a reinforcement learning agent on a 3D scene prediction task and then use the features produced by the encoder to train a simulated robot arm on a reaching task. Crucially, unlike previous approaches that could only learn from fixed view-points, we are able to train an agent using observations captured from randomly changing positions around the robot arm, without having to train a separate policy for each observation position. Lastly, we focus on how to build in prior knowledge through the choice of dataset. To this end, instead of training DL models on a single dataset, we train them on a distribution over datasets that captures the space of tasks we are interested in. This training regime produces models that can flexibly adapt to any dataset within the distribution at test time. Crucially they only need a small number of observations in order to adapt their predictions, thus addressing the data-efficiency challenge at test time. We call this family of meta-learning models for few-shot prediction Neural Processes (NPs). In addition to successfully learning how to carry out few-shot regression and classification, NPs produce uncertainty estimates and can generate coherent samples at arbitrary resolutions.Open Acces

The Free Press Vol 51, Issue 4, 09-30-2019

Woodbury Campus Center closed due to flooding--New vaccination laws create a grassroots movement--Solving Maine’s lead poisoning problem--USM Joins the University of the Arctic--Dr. Robert Sanford appointed to Board of Environmental Protection--UMaine School of Law will separate from USM in 2022--Art professor Michael Shaughnessy running for Mayor of Westbrook--Following the impeachment investigation on President Trumphttps://digitalcommons.usm.maine.edu/free_press/1242/thumbnail.jp

Spartan Daily, March 3, 2008

Volume 130, Issue 22https://scholarworks.sjsu.edu/spartandaily/10448/thumbnail.jp

The Ithacan, 2007-11-29

https://digitalcommons.ithaca.edu/ithacan_2007-08/1002/thumbnail.jp