Reviewerly/exHarmony huggingface dataset:
Mental health facility codesign: A new research method for integrating the service user voice in design processes using virtual reality

Within healthcare environments, the emerging field of evidence-based design (EBD) explores the links between wellbeing and good design practice of the built environment.By optimising both design processes and design outcomes, knowledge produced within this field seeks to improve staff performance, augment patient healing and enhance service outcomes and experiences.In a prior study by the author, a mental health service building design was developed which integrated feedback from mental health service users relative to what aspects of the built environments of their care would enhance their service outcomes and experiences, encourage them to avail themselves of services and/or engage in therapy, and those that would reduce their willingness to avail themselves of services.The research project protocol detailed here is the final testing stage of this body of work, where service users are invited to evaluate the final building design, experienced through virtual reality. This study addresses a gap in the literature, and aims to advance the field of EBD, and codesign with mental health service users, using virtual reality.This research method details the aims, study design, methods and limitations of the study, with recommendations for future researchers.
Cascaded regression using landmark displacement for 3D face reconstruction

This paper proposes a novel model fitting algorithm for 3D facial expression reconstruction from a single image. Face expression reconstruction from a single image is a challenging task in computer vision. Most state-of-the-art methods fit the input image to a 3D Morphable Model (3DMM). These methods need to solve a stochastic problem and cannot deal with expression and pose variations. To solve this problem, we adopt a 3D face expression model and use a combined feature which is robust to scale, rotation and different lighting conditions. The proposed method applies a cascaded regression framework to estimate parameters for the 3DMM. 2D landmarks are detected and used to initialize the 3D shape and mapping matrices. In each iteration, residues between the current 3DMM parameters and the ground truth are estimated and then used to update the 3D shapes. The mapping matrices are also calculated based on the updated shapes and 2D landmarks. HOG features of the local patches and displacements between 3D landmark projections and 2D landmarks are exploited. Compared with existing methods, the proposed method is robust to expression and pose changes and can reconstruct higher fidelity 3D face shape.
Fast collision detection between high resolution polygonal models

In this paper, we address the problem of collision detection between very high resolution polygonal models. We propose an approach that uses the EBP-Octree data structure in a single-core CPU architecture. The algorithm manages a set of files to support a cache-like out-of-core algorithm that performs the collision and intersection tests. Our method uses a data structure called EBP-Octree (Extended Bounding-Planes Octree), which is a very tight hierarchy of convex bounding volumes that, based on a spatial decomposition of the model using an octree, defines a bounding volume at each node by a subset of the planes of the portion of polygonal model contained at that node. The system adapts the memory consumption to the system’s hardware features so it can provide exact triangle collision detection in almost any environment, since the amount of information in the main memory can be limited just varying the threshold of the EBP-Octree. We have tested the data structure and the algorithm in scenarios where two models of 28 million polygons move through one another, achieving real time frame rates for first collision tests on a commonly available computer.
A Systematic Framework for Analyzing Patient-Generated Narrative Data: Protocol for a Content Analysis

Patient narrative data in online health care forums (communities) are receiving increasing attention from the scientific community for implementing patient-centered care. Natural language processing (NLP) methods are gaining more and more attention because of the enormous data volume. However, state-of-the-art NLP still cannot meet the need of high-resolution analysis of patients' narratives. Manual qualitative analysis still plays a pivotal role in answering complicated research questions from analyzing patient narratives.This study aimed to develop a systematic framework for qualitative analysis of patient-generated narratives in online health care forums.Our systematic framework consists of 4 phases: (1) data collection, (2) data preparation, (3) content analysis, and (4) interpretation of the results. Data collection and data preparation phases are constructed based on text mining methods for identifying appropriate online health forums for data collection, differentiating posts of patients from other stakeholders, protecting patients' privacy, sampling, and choosing the unit of analysis. Content analysis phase is built on the framework method, which facilitates and accelerates the identification of patterns and themes by an interdisciplinary research team. In the end, the focus of interpretation of the results phase is to measure the data quality and interpret the findings regarding the dimensions and aspects of patients' experiences and concerns in their original contexts.We demonstrated the usability of the proposed systematic framework using 2 case studies: one on determining factors affecting patients' attitudes toward antidepressants and another on identifying the disease management strategies in patient with diabetes facing financial difficulties. The framework provides a clear step-by-step process for systematic content analysis of patient narratives and produces high-quality structured results that can be used for describing patterns or regularities in patients' experiences, generating and testing hypotheses, and identifying areas of improvement in the health care systems.The systematic framework is a rigorous and standardized method for qualitative analysis of patient narratives. Findings obtained through such a process indicate authentic dimensions and aspects of patient experiences and shed light on patients' concerns, needs, preferences, and values, which are the core of patient-centered care.RR1-10.2196/13914.
Variable Speed Robot Navigation by an ACO Approach

A variable-speed-based navigation and map building method of an autonomous mobile robot is developed in this paper in cooperation with an ant colony optimization algorithm (ACO). In real-world applications, an autonomous mobile robot is expected to operate at variable speed. It should slow down in vicinity of obstacles, whereas moving at high speed in open areas. A LIDAR-based local navigator algorithm integrated with a variable speed module is implemented for local navigation and obstacle avoidance. A variable speed navigation paradigm is developed in integration with the ACO algorithm to dynamically adapt its speed to the environment scenarios. In addition to the variable speed ACO based navigation, grid-based map representations are imposed for real-time autonomous robot navigation. Simulation and comparison studies demonstrate effectiveness of the proposed real-time variable-speed-based ACO approach of an autonomous mobile robot.
Role of Cloud Computing in School Education

Government and non-government organizations throughout the world are investing a lot in education because it is a primary tool to reduce poverty and crime in society. Still, the drop-out rates in primary as well as K12 education creates big worries for upcoming demands. This requires new strategies to make education motivating and improve student's commitment in learning. Student learning needs to be more flexible in time and place with interesting assignments and engagements. Technology can play a vital role in this and can enrich the student leaning in many ways. However, with the advent of new educational tools, their affordability and access has become a significant concern for the schools. The cost factor for adopting technology in education can be addressed by cloud computing technology. School education on cloud computing platform will facilitate the low-cost technology access with inherent features like flexibility, privacy, scalability, provisioning, reliability, and security. Cloud computing-based school education infrastructure will inculcate the participation and self-evaluation skills in the students. This chapter reviews the educational attainment and educational challenges in indifferent countries including the choice of subjects in K12 education for students as well as the technology options available for the students. It then outlines the school education improvement strategy with integration of cloud computing benefits in the school education system.
Alexa/Google Home Forensics

Amazon Alexa and Google Home are a potential source of digital evidence, partly because of their popularity. In additional, these devices are also always-on and listening to our day-to day conversations. Therefore, in this chapter we study Amazon Alexa and Google Home, the two most popular speakers. We will discuss how the voice service of these two devices work, what all information these devices potentially record, and where this information stored. Then, we will compare the accessibility of data stored locally on each device as well as data stored remotely associated with the specific device. The goal of the research is to see if the information collected by these smart devices could potentially have substantial value in a forensics investigation.
Recurrent Iterative Gating Networks for Semantic Segmentation

In this paper, we present an approach for Recurrent Iterative Gating called RIGNet. The core elements of RIGNet involve recurrent connections that control the flow of information in neural networks in a top-down manner, and different variants on the core structure are considered. The iterative nature of this mechanism allows for gating to spread in both spatial extent and feature space. This is revealed to be a powerful mechanism with broad compatibility with common existing networks. Analysis shows how gating interacts with different network characteristics, and we also show that more shallow networks with gating may be made to perform better than much deeper networks that do not include RIGNet modules.
An ontology-based healthcare monitoring system in the Internet of Things

Continuous health monitoring is a hopeful solution that can efficiently provide health-related services to elderly people suffering from chronic diseases. The emergence of the Internet of Things (IoT) technologies have led to their adoption in the development of new healthcare systems for efficient healthcare monitoring, diagnosis and treatment. This paper presents a healthcare-IoT based system where an ontology is proposed to provide semantic interoperability among heterogeneous devices and users in healthcare domain. Our work consists on integrating existing ontologies related to health, IoT domain and time, instantiating classes, and establishing reasoning rules. The model created has been validated by semantic querying. The results show the feasibility and efficiency of the proposed ontology and its capability to grow into a more understanding and specialized ontology for health monitoring and treatment.
Distance-based camera network topology inference for person re-identification

In this study, we propose a novel distance-based camera network topology inference method for efficient person re-identification. We first calibrate each camera, and estimate the relative scales between cameras. Using the calibration results for multiple cameras, we estimate the speed of each person, and infer the distances between cameras to generate a distance-based camera network topology. The proposed distance-based topology can be adaptively applied to each person according to their speed, and can handle diverse transition times of people between non-overlapping cameras. To validate the proposed method, we test it using open person re-identification datasets, and compare the results with state-of-the-art methods. The experimental results demonstrate that the proposed method is effective for person re-identification in a large-scale camera network with various transition times for people.
Quantitative integration of sedimentological core descriptions and petrophysical data using high-resolution XRF core scans

In light of the huge investments needed to acquire sediment cores and the growing need of energy-providing companies to predict reservoir quality, it is remarkable that the standard workflow in core analysis has not been optimized to extract as much information from cores as possible. The Integrated Core Analysis (ICA) protocol presented in this study provides a statistical framework for multivariate calibration and prediction of a wide range of properties measured in core, based on integration of high-resolution X-ray fluorescence core-scanning (XRF-CS) proxy records with records of sparsely sampled petrophysical data and sedimentological core descriptions. The downscaling of numerical data involves the application of invertible transformations to remove range constraints on data, followed by calibration with Partial Least Squares regression through cross validation. Categorical data (i.e. lithofacies) are downscaled by associating each class with a statistical model based on the XRF-CS data. All data points are reassigned to their most likely class by applying Quadratic Discriminant Analysis. The result of the ICA is a multivariate data set in which all properties are specified at the same high resolution along the core with their prediction uncertainties. Downscaling of all variables to 1-cm vertical resolution permits investigation of the variability among petrophysical properties, geochemical proxies, and lithofacies memberships. Petrographic analysis is fundamental for interpretation of the XRF-CS records (element-mineral affinity) and for understanding the sedimentological controls on predicted petrophysical properties. Application of the ICA protocol to a 32 m thick, heterogeneous, Upper Carboniferous fluvial sandstone interval resulted in a near 30-fold increase of the petrophysical data base, which allowed identification of the main depositional and diagenetic controls on the spatial distribution of reservoir quality. Successful implementation of the novel ICA protocol will greatly increase the economic value of legacy core data in studies that aim to re-use depleted hydrocarbon reservoirs.
Robust entanglement transfer through a disordered qubit ladder

We study an entanglement transfer protocol in a two-leg ladder spin-1/2 chain in the presence of disorder. In the scenario where the on-site energies and intrachain couplings are correlated, following approximately constant proportions along the chain, we set up a scheme for high-fidelity state transfer via a particular subspace wherein effective fluctuations in the parameters ultimately depend on the degree of such correlations, rather on the disorder featured by each leg individually, accounted by a box distribution of strength W. Moreover, we find that the leakage of information out of that subspace is suppressed upon increasing W and thus the transfer fidelity, evaluated through the entanglement concurrence at the other end of the ladder, also builds up with W.
Vehicle Detection in Aerial Images Using a Fast Oriented Region Search and the Vector of Locally Aggregated Descriptors

Vehicle detection in aerial images plays a significant role in civil and military applications and it faces many challenges including the overhead-view perspective, the highly complex background, and the variants of vehicles. This paper presents a robust vehicle detection scheme to overcome these issues. In the detection stage, we propose a novel algorithm to generate oriented proposals that could enclose the vehicle objects properly as rotated rectangles with orientations. To discriminate the object and background in the proposals, we propose a modified vector of locally aggregated descriptors (VLAD) image representation model with a recently proposed image feature, i.e., local steering kernel (LSK) feature. By applying non-maximum suppression (NMS) after classification, we show that each vehicle object is detected with a single-oriented bounding box. Experiments are conducted on aerial images to compare the proposed method with state-of-art methods and evaluate the impact of the components in the model. The results have proven the robustness of the proposed method under various circumstances and the superior performance over other existing vehicle detection approaches.
Quantum Dynamical Mutual Entropy Based on AOW Entropy

The classical dynamical mutual entropy measures the average of information content going through a channel. Classical Markovian sources are important in communication theory since they constitute reasonable models for languages. In this paper, we define the quantum dynamical mutual entropy through quantum Markov chains, and we calculate it for several simple models.
Taking visual motion prediction to new heightfields

While the basic laws of Newtonian mechanics are well understood, explaining a physical scenario still requires manually modeling the problem with suitable equations and estimating the associated parameters. In order to be able to leverage the approximation capabilities of artificial intelligence techniques in such physics related contexts, researchers have handcrafted relevant states, and then used neural networks to learn the state transitions using simulation runs as training data. Unfortunately, such approaches are unsuited for modeling complex real-world scenarios, where manually authoring relevant state spaces tend to be tedious and challenging. In this work, we investigate if neural networks can implicitly learn physical states of real-world mechanical processes only based on visual data while internally modeling non-homogeneous environment and in the process enable long-term physical extrapolation. We develop a recurrent neural network architecture for this task and also characterize resultant uncertainties in the form of evolving variance estimates. We evaluate our setup, to extrapolate motion of rolling ball(s) on bowls of varying shape and orientation, and on arbitrary heightfields using only images as input. We report significant improvements over existing image-based methods both in terms of accuracy of predictions and complexity of scenarios; and report competitive performance with approaches that, unlike us, assume access to internal physical states.
Visual Graphs from Motion (VGfM): Scene Understanding with Object Geometry Reasoning

Recent approaches on visual scene understanding attempt to build a scene graph – a computational representation of objects and their pairwise relationships. Such rich semantic representation is very appealing, yet difficult to obtain from a single image, especially when considering complex spatial arrangements in the scene. Differently, an image sequence conveys useful information using the multi-view geometric relations arising from camera motions. Indeed, object relationships are naturally related to the 3D scene structure. To this end, this paper proposes a system that first computes the geometrical location of objects in a generic scene and then efficiently constructs scene graphs from video by embedding such geometrical reasoning. Such compelling representation is obtained using a new model where geometric and visual features are merged using an RNN framework. We report results on a dataset we created for the task of 3D scene graph generation in multiple views.
Space walk

Redirected walking (RDW) denotes a collection of techniques for immersive virtual environments (IVEs), in which users are unknowingly guided on paths in the real world that vary from the paths they perceive in the IVE. For this Emerging Technologies exhibit we present a playful virtual reality (VR) experience that introduces a combination of those RDW techniques such as bending gains, rotation gains, and impossible spaces, which are all subtly integrated with the gameplay and narration to perfectly fit the given environment. Those perceptual tricks allow users to explore a virtual space station of 45 m2 in a room-scale setup by natural walking only.
Multiple-Surface-Approximation-Based FCM With Interval Memberships for Bias Correction and Segmentation of Brain MRI

Fuzzy c-means (FCM) is a popular clustering method for image segmentation. However, FCM has difficulties in handling artifacts in brain magnetic resonance imaging (MRI), especially when it comes to bias field and noise. We propose a novel multiple-surface-approximation-based FCM with interval membership method for simultaneous bias correction and segmentation of Brain MRI. First, multiple surface representation of bias field is embedded into FCM to estimate and correct bias field. Then memberships of the improved FCM are extended to intervals. After the extension, clustering centers of different MR brain tissues could be solved more properly by the proposed method. Moreover, the proposed method is less sensitive to noise by introducing effects of neighboring pixels. Experiments conducted on artificial images and synthetic and real clinical Brain MRI show that the proposed method is effective and obtains better results of both bias field correction and segmentation than comparing methods.
Self-conjugate-reciprocal irreducible monic factors of x − 1 over finite fields and their applications

Self-reciprocal and self-conjugate-reciprocal polynomials over finite fields have been of interest due to their rich algebraic structures and wide applications. Self-reciprocal irreducible monic factors of x n − 1 over finite fields and their applications have been quite well studied. In this paper, self-conjugate-reciprocal irreducible monic (SCRIM) factors of x n − 1 over finite fields of square order are focused on. The characterization of such factors is given together with the enumeration formula. In many cases, recursive formulas for the number of SCRIM factors of x n − 1 are given as well. As applications, Hermitian complementary dual cyclic codes over finite fields and Hermitian self-dual cyclic codes over finite chain rings of prime characteristic are discussed.
Modelling Accrual of Arguments in ASPIC+

In this paper a new formal model of argument accrual is proposed as an adaptation of the ASPIC+ framework for structured argumentation. The new model aims to overcome several weaknesses of existing proposals. It is shown to have desirable formal properties that are in line with standard work on formal argumentation, and to be applicable to a range of situations in legal reasoning.
A modified camel travelling behaviour algorithm for engineering applications

This paper suggests a novel modified algorithm that has a high computational speed and simplified structure optimisation based on the camel travelling behaviour. In terms of execution speed and memory size, the results show that the modified camel algorithm (modified CA) is superior to the particle swarm optimisation (PSO) and the crow searching algorithm (CSA) in addition to the original camel algorithm (CA). Similar to CSA, the accuracy of modified CA in low-dimensional optimisation (D ≤ 10) is as precise as that of PSO. The performance of modified CA is also verified by optimising two constrained engineering applications, the anti-jamming smart antenna optimisation and the optimisation of the proportional-integral-derivative (PID) controller parameters. As a result, it is found that the modified CA is a very good candidate for the online optimisation systems which require low memory size and fast response to the abrupt variation in their parameters in addition to its convenience for the low-dimensional offline applications.