Miracle-dz/UniFAR-FTUs huggingface dataset:
Being More Focused and Engaged in Firefighting Training: Applying User-Centered Design to VR System Development

Although virtual reality (VR) programs to provide firefighting training continue to be developed and adopted, our investigation with 15 firefighters indicates that a current VR training system tends to convey behavioral tips and does not sufficiently reflect actual firefighters’ needs and realities in the field. It often provides somewhat simplified fire simulations and actually lowers the effectiveness of the training. In this paper, we employ Human-Computer Interaction (HCI) methods to examine and identify core elements in firefighting scenarios and develop a VR system that incorporates such elements. We evaluate our system with respect to presence and three design components of the VR simulation (i.e., reality, meaning, play) through a user study with 22 participants. Our study results demonstrate greater user experience and perception toward the four elements in firefighting training with our VR system compared to the existing one. We discuss design implications (e.g., move control, degree of freedom, sight hindrance by smoke, unexpected events) of our study that are expected to help implement and provide an effective VR training system for firefighters.
Effectiveness of tuberculosis smear microscopy in laboratory diagnosis of Mycobacterium tuberculosis in resource constrained settings.

Aim: To determine the correlation of accuracy of direct smear microscopy compared with BACTEC MGIT 960. Design: The study prospectively compare direct smear microscopy with BACTEC MGIT 960 using the reference standard, Lowenstein Jensen culture. Place and Duration : The study was conducted in Zankli Medical Centre, Abuja, between November 2004 and July 2005. Methodology: 340 suspected patients for Mycobacterium tuberculosis referred from direct observation therapy clinics located in six different government owned health facilities were referred to our facility. These patients; male (192) and female (148) were between the age of 10 and 64 years old. Three sputa samples were collected over two consecutive days and direct smear microscopy and culture were performed on these samples. Results: When compared with the reference standard, BACTEC MGIT 960 has a sensitivity and specificity of 100.0% and 56.4% respectively, and a negative predictive value of 100.0%; indicating the proportion of AFB negative participants were actually not infected with M. tuberculosis when tested with BACTEC MGIT 960. The sensitivity of direct microscopy was significantly lower than BACTEC MGIT 960 (84.9% versus 100%, p<0.001) and the specificity was significantly higher (96.6% versus 56.4%, p<0.001). Conclusions: For the purpose of effectiveness of tuberculosis program in developing countries, direct smear microscopy may still be relevant in the diagnosis of Mycobacterium tuberculosis.
"Place" and the Mealtime Experience for those Living with Dementia: Transitions to Long-term Care

“PLACE” AND THE MEALTIME EXPERIENCE FOR THOSE LIVING WITH DEMENTIA: TRANSITIONS TO LONG-TERM CARE Caitlin S. Way Advisor: University of Guelph, 2011 Dr. Heather Keller This study is a secondary analysis of data collected from a six-year longitudinal qualitative study called Eating Together (ET), which sought to better understand the mealtime experience of community dwelling persons with dementia and their primary partners in care. Findings from the primary study highlighted the importance of mealtimes for these families, and as persons with dementia moved into long-term care homes it was noted that the meaning of the mealtime experience was challenged. Data from select dyads in the primary study was thematically analyzed, to understand the mealtime experience of persons with dementia and their primary partners in care, when persons with dementia relocated to a ―new place‖; a long-term care home. Findings revealed five themes related to the new mealtime experience in the long-term care home, including: systemizing the meal, adjusting to dining with others, holding on to ―home‖, evolving roles, and becoming ―at home‖.
Depth Perception in Projective Augmented Reality: An Evaluation of Advanced Visualization Techniques

Augmented reality (AR) is a promising tool to convey useful information at the place where it is needed. However, perceptual issues with augmented reality visualizations affect the estimation of distances and depth and thus can lead to critically wrong assumptions. These issues have been successfully investigated for video see-through modalities. Moreover, advanced visualization methods encoding depth information by displaying additional depth cues were developed. In this work, state-of-the-art visualization concepts were adopted for a projective AR setup. We conducted a user study to assess the concepts’ suitability to convey depth information. Participants were asked to sort virtual cubes by using the provided depth cues. The investigated visualization concepts consisted of conventional Phong shading, a virtual mirror, depth-encoding silhouettes, pseudo-chromadepth rendering and an illustrative visualization using supporting line depth cues. Besides different concepts, we altered between a monoscopic and a stereoscopic display mode to examine the effects of stereopsis. Consistent results across variables show a clear ranking of examined concepts. The supporting lines approach and the pseudo-chromadepth rendering performed best. Stereopsis was shown to provide significant advantages for depth perception, while the current visualization technique had only little effect on investigated measures in this condition. However, similar results were achieved using the supporting lines and the pseudo-chromadepth concepts in a monoscopic setup. Our study showed the suitability of advanced visualization concepts for the rendering of virtual content in projective AR. Specific depth estimation results contribute to the future design and development of applications for these systems.
The Stroop Room: A Virtual Reality-Enhanced Stroop Test

The Stroop Test is a well known and regularly employed stressor in laboratory research. In contrast to other methods, it is not based on fear of physical harm or social shame. Consequently, it is more likely accepted by a wide population. In our always-on, technology-driven, social-media centered world, large-scale in-field stress research will need adequate experimental tools to explore the increasing prevalence of stress-related diseases without bringing subjects into laboratories. This is why we designed the Stroop Room: A virtual reality-based adaptation of the Stroop Test using elements of the virtual world to extend the demands of the original test and at the same time make it easily accessible. It is open source and can be used and improved by anyone as an in-the-wild, repeatable, laboratory-quality stressor. In this work, the method is presented and an evaluation study described, to demonstrate its effectiveness in provoking cognitive stress. 16 male and 16 female subjects were tested in the Stroop Room while recording the electrocardiogram, electrodermal activity, saliva based cortisol and alpha-amylase, performance metrics and an array of questionnaire-based assessments regarding psychological confounders, stress state and likability of the simulation. Our results show that the Stroop Room increases heart rate on average by 19%, other heart rate variability time-domain parameters (RMSSD, pNN50) decrease by 24%-47%, and its most stress-correlated frequency-parameter (LF/HF) increases by 107%. Skin conductance (SC) level increases by 63% and non-specific SC responses by 135% on average. Salivary cortisol and alpha-amylase concentrations increase significantly in some specific conditions. Compared to related work using the Stroop Test, this is an improvement for some metrics by around 30%-40%. Questionnaire evaluation show a strong engagement of users with the simulation and some aspects of a flow-induction. These findings support the effectiveness of a Stroop Test involving 3-dimensional interactivity and thus the Stroop Room demonstrates how this can be applied in a playful interaction that could be used pervasively.
An assessment of gene polymorphisms in young South African Indians with coronary artery disease and the effect of atorvastan in vitro.

xiii List of figures xvii List of tables xxii List of appendices xxiv List of abbreviations xxv Introduction 1 Study Rationale and Aims 5 CHAPTER 1 1. Literature Review 7 1.1 Overview of Coronary Artery Disease 7 1.2 Epidemiology of CAD 8 1.3 Classification of CAD risk factors 9 1.3. 1 Non-Modifiable risk factors 10 1.3. 1.1 Age 10 1.3. 1.2 Genetic/Hereditary 10
News Topic Recommendation Using an Extended Bayesian Personalized Ranking

Bayesian Personalized Ranking (BPR) is a recommendation approach which learns to rank candidate items based on user’s implicit feedback. In this study I use an extended version of BPR using consumption behavior of user on news articles to recommend them news topics. The extended version of BPR performs better compare to the original version based on two evaluation measures.
Nonlinear Causality Inference in Microarray Time Series

Detecting causal relationships between time series data has been widely studied in many areas. One of the most popular causality inference methods is Granger causality that is a linear regression-based model for determining whether a single time series is useful in forecasting another; however, this approach cannot detect nonlinear relations in the data. In this work, we use Elastic-net regularization to infer linear Granger causalities in gene expression data. Moreover, we have proposed a method which uses the properties of kernel algorithms to infer nonlinear causalities.
Impact of Comprehensive Nurse-Managed Transitional Care

The purpose of this study was to test the effectiveness of nurse-managed transitional care on the quality of care and functional ability of individuals following discharge from subacute units. Registered nurses employed on subacute units in a skilled nursing facility provided the nurse-managed transitional care. Using a quasi-experimental design, data were collected on admission to the subacute unit, at the time of discharge, 1 week following discharge, and 3 months following discharge on 242 treatment and comparison participants. The treatment group participants' overall function and quality of the care environment were significantly higher than the comparison group at 1 week and 3 months following discharge. Participants did not differ significantly on basic activities of daily living or number of readmissions.
Maximum Entropy Bayesian Actor Critic

In recent years Deep Reinforcement Learning [12] has achieved human-like performance or better on a variety of benchmarks such as the Atari Arcade [2]; however, Deep RL often has problems with sample efficiency and convergence brittleness. That is, to learn even the simplest tasks, Deep RL requires a huge amount of meaningful samples, and will only converge if the parameters are tuned just right [4]. This paper seeks to ameliorate these problems of sample inefficiency and convergence brittleness with the combination of two different reinforcement learning paradigms: Bayesian RL and Maximum Entropy RL. Bayesian reinforcement learning [8] utilizes Bayesian statistics to model the confidence in a given model, which has been shown to greatly increase sample efficiency [3]. Maximum entropy RL is a technique that modifies the standard reward to promote more exploration in the agent [20]. Hopefully, combining the two will retain the best of both of these properties and avoid the problems faced in deep RL altogether. This paper first derives a soft policy gradient that introduces a entropyweighted term to the standard policy gradient function, and then applies this to the the Bayesian actor critic paradigm to augment the parameter update rule to account for the entropy-weighted value function. After determining a closed-form solution of the gradient with the softmax policy, the method was implemented and evaluated on the Cartpole environment, signalling that there are avenues ripe for future research in this area.
Deep Learning Applied to Sign Language

The French Belgian Sign Language laboratory (LSFB-lab) recorded 150 hours of conversations to create a corpus useful to analyze the Belgian sign language and its usage [3]. The videos are captured in RGB without depth information. 15 hours of these video have been annotated. They picture 52 different speakers and contains 670 signs with, at least, 10 occurrences. The LSFB-lab is interested in the development of an algorithm able to translate sign language to french. It could help the deaf community during medical or administrative appointments as finding the right word is difficult in those specific contexts. This thesis aims to make a first step in that direction by exploring existing methods developed in video recognition and analyzing the LSFB dataset.
Explainable Robotics Applied to Bipedal Walking Gait Development

Explainability is becoming an important topic in artiﬁcial intelligence (AI). A well explainable system can increase the trust in the application of that system. The same holds for robotics where the walking gait controller can be some AI system. We will show that a simple and explainable controller that enables an energy eﬃcient walking gait and can handle uneven terrains, can be developed by a well structured design method. The main part of the controller consist of three simple neural networks with 4, 6 and 8 neurons. So, although creating a stable and energy eﬃcient walking gait is a complex problem, it can be generated without some deep neural network or some complex mathematical model.
Inductive Learning of Concept Representations from Library-Scale Bibliographic Corpora

: Automated research analyses are becoming more and more important as the volume of research items grows at an increasing pace. We pursue a new direction for the analysis of research dynamics with graph neural networks. So far, graph neural networks have only been applied to small-scale datasets and primarily supervised tasks such as node classiĄcation. We propose to use an unsupervised training objective for concept representation learning that is tailored towards bibliographic data with millions of research papers and thousands of concepts from a controlled vocabulary. We have evaluated the learned representations in clustering and classiĄcation downstream tasks. Furthermore, we have conducted nearest concept queries in the representation space. Our results show that the representations learned by graph convolution with our training objective are comparable to the ones learned by the DeepWalk algorithm. Our Ąndings suggest that concept embeddings can be solely derived from the text of associated documents without using a lookup-table embedding. Thus, graph neural networks can operate on arbitrary document collections without re-training. This property makes graph neural networks useful for the analysis of research dynamics, which is often conducted on time-based snapshots of bibliographic data.
Generic tools and individual research needs in the Digital Humanities - Can agile development help?

: Many Digital Humanities research projects from many different target disciplines regularly encounter the same recurring key problems and key procedures such as preprocessing, standard text analytics, and visualization, which would be very time consuming if conducted without DH tools. This calls for the use of generic platforms. However, there is a trade-off, since different researchers from many different disciplines look at their objects from different theoretical perspectives. This raises the general question how we can deal with this very typical conĆict, and whether agile development might be a suitable development method to cope with it. By addressing this question, we report on an experience during a DH summer school as a condensed experiment in dealing with this trade-off using the iLCM as a generic platform. In summary, although we have not arrived at a procedural solutions for balancing individual user needs and generic problems which call for generic tools, our summer academy experience well illustrates the high potential of a software eco-system supporting the approach of agile development in Digital Humanities, and may help to better understand the role of generic software tools and their role in DH.
Micro-MRI at 11.7 T of a Murine Brain Tumor Model Using Delayed Contrast Enhancement

In vivo imaging methodologies allow for serial measurement of tumor size, circumventing the need for sacrificing mice at given time points. In orthotopically transplanted murine models of brain tumors, cross-section micro-MRI allows for visualization and measurement of the physically inaccessible tumors. To allow for long resident times of a contrast agent in the tumor, intraperitoneal administration was used as a route of injection for contrast-enhanced micro-MRI, and a simple method for relative tumor volume measurements was examined. A strategy for visualizing the variability of the delayed tumor enhancement was developed. These strategies were applied to monitor the growth of brain tumors xenotransplanted into nude mice and either treated with the antiangiogenic peptide EMD 121974 or an inactive control peptide. Each mouse was used as its own control. Serial imaging was done weekly, beginning at Day 7 after tumor cell implantation and continued for 7 weeks. Images obtained were reconstructed on the MRI instrument. The image files were transferred off line to be postprocessed to assess tumor growth (volume) and variability in enhancement (three-dimensional [3-D] intensity models). In a small study, tumor growth and response to treatment were followed using this methodology and the high-resolution images displayed in 3-D allowed for straightforward qualitative assessment of variable enhancement related to vascular factors and tumor age.
DeepMM: Deep Learning Based Map Matching With Data Augmentation

As a fundamental component in map service, map matching is of great importance for many trajectory-based applications, e.g., route optimization, traffic scheduling, and fleet management. In practice, Hidden Markov Model and its variants are widely used to provide accurate and efficient map matching service. However, HMM-based methods fail to utilize the knowledge (e.g., the mobility pattern) of enormous trajectory big data, which are useful for intelligent map matching. Furthermore, with many following-up works, they are still easily influenced by the common noisy and sparse records in the reality. In this paper, we revisit the map matching task from the data perspective and propose to utilize the great power of massive data and deep learning to solve these problems. Based on the seq2seq learning framework, we build a trajectory2road model with attention mechanism to map the sparse and noisy trajectory into the accurate road network. Different from previous algorithms, our deep learning based model complete the map matching in the latent space, which provides the high tolerance to the noisy trajectory and also enhances the matching with the knowledge of mobility pattern. Extensive experiments demonstrate that the proposed model outperforms the widely used HMM-based methods by more than 10 percent (absolute accuracy) in various situations especially the noisy and sparse settings.
Interactive Learning of Grounded Concepts

Autonomous agents perceive the world through streams of continuous sensorimotor data. Yet, in order to reason and communicate about their environment, agents need to be able to distil meaningful symbolic concepts from their raw observations. Without such a repertoire of concepts, communication would need to happen by directly sending sensori-motor values. Such a system easily leads to miscommunication when perfect calibration is not possible. Existing approaches to bridge between the continuous and symbolic domain include deep learning techniques (e.g. [1]) and version space learning [2]. Deep learning techniques generally achieve high levels of accuracy. However, they rely on very large amounts of training data, they often fail to adapt to unseen scenarios, and the resulting concepts lack transparency. Version space learning, on the other hand, can yield human-interpretable concept representations, but are notoriously brittle when faced with noisy training data. In this interactive demo, we introduce a novel approach to grounded concept learning. Using the language game methodology [3], we set up a tutor-learner scenario where the learner is an autonomous agent, grounded in the world using a Nao humanoid robot, and the participant is its tutor. Using blocks of various shapes, sizes and colours, the tutor first creates a scene. In this scene, the tutor chooses a topic and describes it to the learner using an informative concept, such as ‘red’ or ‘cube’. The learner robot observes the world through human-interpretable streams of numeric data, such as ‘area’, ‘colour’ and ‘XY-coordinates’. These are obtained through standard computer vision techniques. The robot tries to find the object described by the tutor. After each interaction, whether is was successful or not, the tutor provides feedback to the learner by showing the intended topic object. The robot maximally benefits from this feedback to newly create or extend the representation of the concept. For each concept, the robot has to find out which data streams are important and what the typical values for each data stream within a concept are. To make these decisions, the learner makes use of the notion of discrimination, i.e. separating one particular object from the other objects in the scene. Over the course of many such interactions, the learner incrementally and in real-time builds a complete repertoire of concepts that is functional in the world. An overview of the experimental set-up is shown in Figure 1. A video of the demonstration can be found at https://ehai.ai. vub.ac. be/demos/interactive-concept-learning.
Energy Efficient Full Duplex Massive MIMO Multi-carrier Bidirectional Communication with Hardware Impairments

In this paper, we address the power allocation problem for a bi-directional communication system, where a full duplex (FD) massive multiple-input-multiple-output (mMIMO) multi-carrier (MC) node communicates with multiple FD MC single antenna nodes. We consider orthogonal frequency division multiplexing (OFDM) as our MC strategy. The impact of hardware distortions resulting in residual self-interference and inter-carrier leakage (ICL), and also imperfect channel state information (CSI) are jointly taken into account. We formulate a joint sub-carrier and power allocation problem to maximize the spectral efficiency (sum rate maximization) and an iterative optimization method is proposed, which follows successive inner approximation (SIA) framework to reach the convergence point that satisfies the Karush-Kuhn-Tucker (KKT) conditions. Then, we extend it to an energy efficiency (EE) maximization problem which is solved using a two stage iterative algorithm which follows the SIA and Dinkelbach algorithm. Numerical results show the significance of distortion aware design for such systems and also the significant gain in terms of sum rate and energy efficiency compared to its half duplex (HD) counterpart.