Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Changes in Physical Activity Behaviours That Lead to Weight Gain in First Year University Students

ii ACKNOWLEDGEMENTS . iii TABLE OF CONTENTS . iv LIST OF TABLES . viii LIST OF FIGURES . ix ABBREVIATIONS . x CHAPTER 1: Introduction . 1 1.1 Rationale . 1 1.2 Objectives and Hypothesis . 2 CHAPTER 2: Review of the Literature . 3 2.1 Health Risks Associated with Excess Body Weight . 3 2.1.1 Overweight and Obesity on a Global Scale . 3 2.1.2 Assessing the Health Risk Associated with Excess Body Weight . 4 2.1. 3 Health Concerns Related to Excess Adiposity . 6 2.1. 4 Young Adulthood as a Critical Period for Weight Gain . 7 2.2 Weight Gain in First Year University Students . 8 2.2.1 Defining the ‘Freshman Fifteen’ . 8 2.2.2 Overview of Weight Change in First Year University Students . 9
Participatory change : an integrative approach toward occupational therapy practice development

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Pseudo-Depth/Intercept-Time Monotonicity Requirements in the Inverse Scattering Algorithm for Predicting Internal Multiple Reflections

In this paper we discuss the inverse scattering algorithm for predicting internal multiple reflections (reverberation artefacts), focusing our attention on the construction mechanisms. Roughly speaking, the algorithm combines amplitude and phase information of three different arrivals (sub-events) in the data set to predict one multiple reflection. The three events are conditioned by a certain relation which requires that their pseudo-depths, defined as the depths of their turning points relative to the constant background velocity, satisfy a lower-higher-lower relationship. This implicitly assumes a pseudo-depth monotonicity condition, i.e., the relation between the actual depths and the pseudo-depths of any two sub-events is the same. We study this relation in pseudo-depth and show that it is directly connected with a similar relation between the vertical or intercept times of the sub-events. The paper also provides the first multidimensional analysis of the algorithm (for a vertically varying acoustic model) with analytical data. We show that the construction of internal multiples is performed in the plane waves domain and, as a consequence, the internal multiples with headwaves sub-events are also predicted by the algorithm. Furthermore we analyze the differences between the time monotonicity condition in vertical or intercept time and total travel time and show a 2D example which satisfies the former but not the latter. Finally we discuss one case in which the monotonicity condition is not satisfied by the sub-events of an internal multiple and discuss ways of lowering these restrictions and of expanding the algorithm to address these types of multiples. AMS subject classifications: 86A22, 35P25, 86-08
Eﬀects of Robot’s Facial Expressions on Children’s Perception of Trustworthiness in First Encounters

Facial expressions of emotion have an impact on interpersonal trait inference. Facial cues of emotions serve as indicators to know when to approach or avoid a person. In child-robot interaction, the ﬁrst impression of a robot matters to motivate children to engage in long-term interaction. We constructed a set of facial expressions to be projected onto a Furhat robot to evaluate the perception of trustworthiness, likability, and competence. We investigated how the emotion type and intensity of the facial expression, together with the gender-likeness of the robot, aﬀected the perception of the robot in ﬁrst encounters. Re-sults from a 2x2x3 mixed design study show that happiness and anger at a diﬀerent level of intensity inﬂuence children’s judgment of likability and competence, but do not inﬂuence the judgment of trustworthiness. Our results demonstrate the importance of considering dynamic facial expression when introducing robots to children.
Prediction of Infectious Disease outbreaks based on limited information

Information about infectious disease outbreaks is often gathered indirectly, from doctor’s reports and health board records. It also typically underestimates the actual number of cases, but the relationship between the observed proxies and the numbers that drive the diseases is complicated, nonlinear and potentially timeand state-dependent. We use a combination of data collection from the 2009-2010 H1N1 outbreak in Malta, compartmental modelling and Bayesian inference to explore the effect of using various sources of information (consultations, doctor’s diagnose, swabbing and molecular testing) on estimation of the effective basic reproduction ratio, Rt. Different proxies and different sampling rates (daily and weekly) lead to similar behaviour of Rt as the epidemic unfolds, although individual parameters (force of infection, length of latent and infectious period) vary. We also demonstrate that the relationship between different proxies varies as epidemic progresses, with the first period characterised by high ratio of consultations and influenza diagnoses to actual confirmed cases of H1N1. This has important consequences for modelling that is based on reconstructing influenza cases from doctor’s reports.
Optimal Generalized Sequential Monte Carlo Test

Conventional Monte Carlo tests require the simulation of m independent copies from the test statistic U under the null hypothesis H0. The execution time of these procedures can be substantially reduced by a sequential monitoring of the simulations. The sequential Monte Carlo test power and its expected time are analytically intractable. The literature has evaluated the properties of sequential Monte Carlo tests implementations by using some restrictions on the probability distribution of the p-value statistic. Such restrictions are used to bound the resampling risk, the probability that the accept/reject decision is different from the decision from the exact test. This paper develops a generalized sequential Monte Carlo test that includes the main previous proposals and that allows an analytical treatment of the power and the expected execution time. This results are valid for any test statistic. We also bound the resampling risk and obtain optimal schemes minimizing the expected execution time within a large class of sequential design.
Recognition of Dynamic Facial Expression Using Atlas Construction and Sparsity Orthogonal Matching

: Human face can exhibits complex and strong changes that are both unpredictable and varying in time Now a days, facial expression recognition has become a emerging and research topic due to advancement in this field. To overcome the disadvantages of conventional group-wise registration methods Spatio-temporal information and SOM algorithm used in recognition stage.This paper presents sparse representation with sparsity estimation and variable step size to improve recognition efficiency and accuracy. We have adopted SOM algorithm for better performance with reference to sparse representation classification algorithm. We also explored Sparsity Orthogonal matching algorithm is used in atlas construction to reach to true sparsity and lead to improved sparse representation as well as to resolve sparse coefficient and larger step size to shorten the time of iterations. The recognition rate is higher than other method shows by experimental result.
A Comparison between Chatbots for Handling Administrative Healthcare Tasks

Chatbots are used in many fields to reduce the amount of repetitive work. While chatbots are proved to be great help in retailer business, tourism related services, it is little known about usability of chatbots in more sensitive field such as healthcare. A study was conducted to examine the possibilities whether a chatbot could be used to automate administrative tasks within the Swedish healthcare. Three chatbots were developed, each one using different framework. Each chatbot was tested with participants acting as a patient, freely using each chatbot, and filling out a quantitative evaluation after the test. Results from participants' responses to the quantitative evaluation and SWOT analysis showed that chatbots are helpful, user-friendly and have the opportunity to integrate with various databases and many services. Participants preferred the chatbot that could handle voice interaction. The results also showed that the chatbots were helpful in registering cases and answering frequently asked questions. Additionally, it was observed that chatbots are efficient when their work is complemented by a healthcare specialist. This allows concluding that a chatbot is a promising potential tool to automate administrative tasks within the Swedish healthcare.
AugPOD: Augmentation-oriented Probabilistic Object Detection

The Probability Object Detection (POD) aims to measure spatial and label uncertainty of an object detector. The uncertainty measurement is important in robotic applications where actions triggered by erroneous but high-conﬁdence perception can lead to catastrophic results. In this work, we propose the AugPOD, which augments the state-of-the-art models using several approaches, including i) MC Dropout ii) Gamma Correction iii) Virtual Dataset Collection. The experimental studies demonstrate that our method outperforms all the models involved in the competition with the score of 22 . 563 , which is 2 . 72 times improvement on the original Mask R-CNN.
Multi-Level Wavelet-CNN for Denoising Natural Noise Benchmarks

Denoising images is a common application of optimization algorithms. In this course we primarily covered methods for solving the deconvolution problem by modeling the inverse of the transfer function that produced the current noisy output. Generally, these methods rely on strong priors (TV, NLM, etc) in order to produce useful results. Neural networks on the other hand, require the use of a dataset and architecture, but are known to be more generalizable than purely convex methods. Neural networks of sufficient depth are known to be universal function approximators, which gives them a strong representational power for denoising tasks [1]. In this work we use one such network on a variety of different noise types to explore the effects of natural noise models and the efficacy of the Multi-Level Wavelet-CNN to denoise them.
Early Diagnosis of Prostate Cancer from the Perspective of Chinese Physicians

Prostate cancer (PCa) is the seventh most diagnosed cancer and the tenth leading cause of cancer mortality in China. Unlike the USA, both incidence and mortality continue to increase. In China, PCa is often diagnosed at a locally advanced or metastatic stage, resulting in a high mortality-to-incidence ratio. Implementing regular screening using a well-validated biomarker may result in the earlier diagnosis of localized disease. Furthermore, it is important to be able to distinguish between low-grade and high-grade disease, to avoid subjecting patients to unnecessary biopsies, undertreatment of significant disease, or overtreatment of indolent disease. While prostate-specific antigen (PSA) is commonly used in PCa screening around the world, its relationship to PCa is still unclear and results vary widely across different studies. New biomarkers, imaging techniques and risk predictive models have been developed in recent years to improve upon the accurate detection of high-grade PCa. Blood- and urine-based biomarkers, such as PSA isoforms, prostate cancer antigen 3, or mRNA transcripts, have been used to improve the detection of high-grade PCa. These markers have also been used to create risk predictive models, which can further improve PCa detection. Furthermore, multiparametric magnetic resonance imaging is becoming increasingly accessible for the detection of PCa. Because of ethnic variations, biomarkers and risk predictive models validated in Western populations cannot be directly applied to Chinese men. Validation of new biomarkers and risk predictive models in the Chinese population may improve PCa screening and reduce mortality of this disease in China.
Line-Adaptive Color Transforms for Lossless Frame Memory Compression

This paper proposes line-adaptive color transforms for lossless frame memory compression. Line-based compression methods are necessary for reducing memory requirement for display devices. A color transform is not included in the existing lossless frame memory compression based on the modified Hadamard Transform (MHT) and adaptive Golomb-Rice (AGR) coding. To achieve the better coding efficient for a line-based lossless compression, a method of line-adaptive color transforms is essential. By applying a lossless color transform with the best compression ratio in each line, it achieves higher compression efficiency. This paper describes the analysis of eight color transforms in terms of compression performance for the proposed method. Experimental results show that the proposed method provides reduced bitrate compared with the existing method without a color transform.
Iterative training for unsupervised word embedding mapping

The problem of word embedding mapping is to map word embeddings trained independently for two languages one onto another, i.e., to map each word in one language onto a word in the other language. We propose an iterative training of the state-of-the-art method but we modify the loss function of the generator of the GAN used. In addition, we introduce an iterative adjustment method to produce a pseudo-dictionary which expands at each iteration while minimising the average Euclidean distance between source and target words in the pseudo-dictionary. We present a range of experiments with several variants. In our experiments, we obtained slight but consistent improvements over the state-of-the art method. We provide measures of correlation with machine translation scores for all the variants.
Influence of Reflected Arc Light on Vision Sensors for Automatic GTAW Systems

Because of the difficulty in applying arc sensors to the welding of thin corrugated plates, vision sensors that provide three-dimensional geometry information by optical triangulation have been widely used for robot guidance in automatic welding of plates. The reliability of vision sensors, however, is influenced considerably by arc light reflected from the base metal surface in spite of blocking the direct arc light with a light isolator. A reflectance model of a welding arc was developed to estimate arc noise for various configurations of sensors, base metals, and welding arcs in three-dimensional space by assuming the welding arc as a point and extended light source. Various experiments were conducted to determine the bidirectional reflectance distribution function (BRDF) parameters of the model, and to verify the validity of the proposed model. Two proposed models, the point source model and the extended source model, were compared with the gray level of the reflected arc caught by a CCD camera. The experimental data of the gray level of the reflected arc generally agreed well with calculated results obtained by the two models, while the model with a point light source resulted in a greater discrepancy for short distances between the welding arc and reflecting surface of the base metal. The proposed models and experimental results can be effectively used for optimal design of the configuration and the moving path of vision sensors according to base metal shape, which improves the measuring efficiency of vision sensors (Refs. 1, 2) and reduces the effect of arc J.-Y YU and J.-L KIM are with Mechanical Engineering Division, Advanced Reactor Development, Korea Atomic Energy Research Institute, Korea. S. -J. NA is with Department of Mechanical Engineering, Korea Advanced Institute o f Science and Technology, Korea. noise on them (Ref. 3), thus enhancing the performance of the vision sensor in automatic joint tracking of the arc welding process.
Section 8 Development of and advances in ocean modelling and data assimilation, sea-ice modelling, wave modeling Inclusion of Significant Wave Height Analysis to NCEP’s UnRestricted Mesoscale Analysis (URMA)

The National Centers for Environmental Prediction (NCEP) of NOAA provides weather guidance to the United States National Weather Service (NWS), according to the demands of our customers and the public. In this effort, two significant components of NWS operations are combined: windwave predictions and NCEP’s UnRestricted Mesoscale Analysis (URMA). The approach is currently being developed at NCEP’s Environmental Modeling Center (EMC).
Evaluation of Selected Finite-Difference and Finite-Volume Approaches to Rotational Shallow-Water Flow

The shallow-water equations in a rotating frame of reference are important for capturing geophysical flows in the ocean. In this paper, we examine and compare two traditional finite-difference schemes and two modern finite-volume schemes for simulating these equations. We evaluate how well they capture the relevant physics for problems such as storm surge and drift trajectory modelling, and the schemes are put through a set of six test cases. The results are presented in a systematic manner through several tables, and we compare the qualitative and quantitative performance from a cost-benefit perspective. Of the four schemes, one of the traditional finitedifference schemes performs best in cases dominated by geostrophic balance, and one of the modern finite-volume schemes is superior for capturing gravity-driven motion. The traditional finite-difference schemes are significantly faster computationally than the modern finite-volume schemes. AMS subject classifications: 76U05, 86A05, 35L65, 65M08
Use of Small Unmanned Aerial Systems in Construction Management Curriculum in Compliance with FAA Regulations: A Case Study

This paper describes the use of Small Unmanned Aerial Systems (sUAS) in a graduate course in a construction education program. It is meant to share the experience of the author in implementing this technology in compliance with U.S. Federal Aviation Administration (FAA) regulations. The process for inclusion of the technology, its applications in the course, and challenges and opportunities are discussed. The author’s goal is that by sharing this experience, faculty in construction education programs will be able to incorporate sUAS technology into their courses in compliance with FAA regulations governing its use in education environments.
An Approach Towards Handling Packet Loss Ratio Using Eaack (Iddsa) Algorithm

: Mobile ad hoc Network (MANET) is a collection of mobile nodes equipped with every wireless-transmitter and receiver that communicate with one another via bi-directional wireless links either directly or indirectly. A replacement intrusion detection system named Enhanced Adaptive Acknowledgement (EAACK) specially designed for MANETs. Throughout this thesis, a replacement intrusion-detection system named Enhanced Adaptive ACKnowledgment (EAACK) is specially designed for MANETs and ID based mostly digital Signature scheme is projected. The results will demonstrate positive performances against Watchdog, TWOACK and AACK within the cases of receiver collision, restricted transmission power and false misbehavior report, packet delivery Ratio. This EAACK IDentity-based Digital Signature Algorithm (IDDSA) scheme and Packet Loss Ratio is calculated in the proposed work and compared with existing methods for better performance.
Bruce: A system-of-systems solution to the 2018 Maritime RobotX Challenge

This paper provides an overview of the hardware and software systems developed for Bruce, the Queensland University of Technology’s Autonomous Surface Vehicle (ASV) for entry in the 2018 Maritime RobotX Challenge. Bruce is considered a system-of-systems platform consisting of the ASV itself, a self-contained vision-based Autonomous Underwater Vehicle (AUV), and an automated vision-based racquet ball launcher. Bruce’s software and hardware architecture builds on 4 years of development from the 2014 and 2016 RobotX Challenges. Key hardware upgrades include integrated bow thrusters and control system, a refined safety system, and an automated AUV deployment and retrieval mechanism. Key software upgrades include a fused LiDAR and vision object detection and classification system, a vision system for underwater ring detection, improved vision system for the ball launcher, and a new mission architecture with task optimizer. To facilitate software development and offline testing, a high-fidelity simulation model developed in 2016 was extensively used. The ASV’s control, mapping, and task-specific algorithms were evaluated both in simulation and through field experiments. Results demonstrating capabilities as well as discussions on lessons learnt are also presented.
“Bouncing-Back” and Relaxation were the Most Valued Skills Acquired by 369 Students During a School-Based Resiliency Program

Background: Drug and alcohol use during adolescence continues to be concerning, and evidence suggests this can be reduced with resiliency training. Here we provide results from a qualitative inquiry into the possible effectiveness of a resiliency program given to students in Grades 6-8 (aged 11-14) who were taking part in a larger program. Methods: As part of a larger series of interventions in schools (EMPATHY program), a translated resiliency program for youth, called Op Volle Kracht (OVK) in its original Dutch this in turn is a modified version of the Penn Resiliency Program (PRP). It was delivered during classroom time to 2,063 students in Grades 6-8 (aged 11-14). Qualitative data on student perspective on OVK was collected on an anonymous basis via electronic tablets. Of this population of 2,063 a total of 369 (18%) students participated in this qualitative study. Findings: Through content analysis, four main themes were identified. Students across all Grades reported learning new skills as the most appreciated feature of the OVK, specifically learning how to “bounce-back” from difficult situations. Learning relaxation techniques and tools to relieve stress were also reported as useful. Students in Grade 8 favoured incentives more than their younger peers, while the need for an interactive program was reported to be less useful with the
A DAYLIGHT SIMULATION TOOL INCLUDING TRANSMITTED DIRECT AND DIFFUSE LIGHT

The paper describes new improvements in SOLENE, a set of numerical models for the simulation of natural light in both the urban morphologies and the indoor architectural spaces which is developed in our laboratory. This tool takes into account the direct solar and the diffuse sky luminous energies for the evaluation of luminance, that can be computed on the real surfaces of the environment (ground, walls), or on a false working plane. The tool allows the computation of some classical parameters (daylight factor), but makes it possible to get other informative descriptors. After a brief presentation of the tool and its general features, we will specially focus on the model of the physical behaviour of light and our original way to manage with multiple transparencies. This last makes it possible to take into account some complex cases : intermediates spaces (atriums, covered galleries, glazed corridors), multi-layered fenestration systems, or semi-transparent screens (vegetation).