Miracle-dz/UniFAR-FTUs huggingface dataset:
"My smartwatch told me to see a sleep doctor": a study of activity tracker use

Purpose The purpose of this paper is to investigate the use of wearable health and fitness trackers in everyday life, and users’ motivations and their understanding and use of the data derived from devices, and understand the results using the lens of information behaviours. Design/methodology/approach The study used a qualitative, constructionist approach, based on 21 interviews with users of a range of wearable activity trackers used for health and fitness. Findings Findings show that the lifelogging devices have become companion tools that enable users to take information from their bodily indicators and make some decisions about their health and fitness, and also track the results when they act on it, thus giving them a sense of gratification and a sense of control over their own health. Practical implications The findings have implications on how health professionals can talk to their lifelogging patients about how to deal with and understand the information provided by their activity-tracking devices. Some participants in the study already discuss these data regularly with their health professionals. Originality/value As the self-tracking practices attract wide range research interests from human–computer interaction, information systems, digital sociology, health informatics and marketing among others. This study provides important everyday information-seeking perspective that contributes to the understanding of the practices of how people make sense of the data, how the data improves their wellbeing, i.e. physical health improvement or fitness, and implications to users health behaviour. Additionally the study adds to the lifelogging literature through a constructionist, qualitative approach rather than a technological deterministic approach.
Evolutionary algorithms and computational methods for derivatives pricing

This work aims to provide novel computational solutions to the problem of derivative pricing. To achieve this, a novel hybrid evolutionary algorithm (EA) based on particle swarm optimisation (PSO) and differential evolution (DE) is introduced and applied, along with various other state-of-the-art variants of PSO and DE, to the problem of calibrating the Heston stochastic volatility model. It is found that state-of-the-art DEs provide excellent calibration performance, and that previous use of rudimentary DEs in the literature undervalued the use of these methods. The use of neural networks with EAs for approximating the solution to derivatives pricing models is next investigated. A set of neural networks are trained from Monte Carlo (MC) simulation data to approximate the closed form solution for European, Asian and American style options. The results are comparable to MC pricing, but with offline evaluation of the price using the neural networks being orders of magnitudes faster and computationally more efficient. Finally, the use of custom hardware for numerical pricing of derivatives is introduced. The solver presented here provides an energy efficient data-flow implementation for pricing derivatives, which has the potential to be incorporated into larger high-speed/low energy trading systems.
Data Analytics and Performance Enhancement in Edge-Cloud Collaborative Internet of Things Systems

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets self-organized by IoT devices. First of all, the issues on outlier detection and data aggregation are addressed through the development of recursive principal component analysis (R-PCA) based data analysis framework. The framework is developed in a cluster-based structure to fully exploit the spatial correlation of IoT data. Specifically, the sensing devices are gathered into clusters based on spatial data correlation. Edge devices are assigned to the clusters for the R-PCA based outlier detection and data aggregation. The outlier-free and aggregated data are forwarded to the remote cloud server for data reconstruction and storage. Moreover, a data reduction scheme is further proposed to relieve the burden on the trunk link for data uploading by utilizing the temporal data correlation. Kalman filters (KFs) with identical parameters are maintained at the edge and cloud for data prediction. The amount of data uploading is reduced by using the data predicted by the KF in the cloud instead of uploading all the practically measured data. Furthermore, an unmanned aerial vehicle (UAV) assisted IoT system is particularly designed for large-scale monitoring. Wireless sensor nodes are flexibly deployed for environmental sensing and self-organized into wireless sensor networks (WSNs). A physical topology discovery scheme is proposed to construct the physical topology of WSNs in the cloud server to facilitate performance optimization, where the physical topology indicates both the logical connectivity statuses of WSNs and the physical locations of WSN nodes. The physical topology discovery scheme is implemented through the newly developed parallel Metropolis-Hastings random walk based information sampling and network-wide 3D localization algorithms, where UAVs are served as the mobile edge devices and anchor nodes. Based on the physical topology constructed in the cloud, a UAV-enabled spatial data sampling scheme is further proposed to
Original article Supra-aortic extracranial artery atherosclerotic lesions in patients diagnosed for coronary artery disease: prevalence and predictors

Background: In patients with coronary artery disease (CAD), the presence of atherosclerotic lesions in other vascular beds is associated with a markedly worse prognosis. Aim: To determine the prevalence and predictors of extracranial supra-aortic artery atherosclerotic disease (SAD) in patients with suspected CAD. Methods: Supra-aortic artery angiography was performed in 379 consecutive patients aged 64.2 ± 8.8 years (231 male) referred for coronary angiography. Clinical and laboratory data (total cholesterol, LDL, HDL cholesterol, hs-CRP, creatinine level) and left ventricular ejection fraction were analysed. Results: Significant stenosis (l 50% by quantitative angiography) within at least one main branch of the coronary arteries was found in 314 (82.8%) patients, including 87 (27.7%), 96 (30.6%) and 131 (41.7%) with 1-vessel, 2-vessel, and 3-vessel CAD, respectively. Among all 379 patients, stenosis l 50% of the carotid artery was documented in 9.5%, vertebral in 13.7%, and subclavian in 7.4% of patients. We found 130 stenoses l 50% within the supra-aortic arteries in 90 patients (23.7% of the whole study group, and 28.7% of CAD patients), including 42 internal carotid artery stenoses in 36 patients, 58 vertebral artery stenoses in 52, and 30 subclavian stenoses in 28 patients. In 24 (6.3%) patients more than one SAD was present. The SAD l 50% was found in 8 (12.3%) patients without significant CAD, in 22 (25.3%), 17 (17.7%) and 43 (32.8%) with 1-, 2- and 3-vessel CAD, respectively (p = 0.001). Independent predictors of SAD l 50% identified by multivariate analysis were: previous neurological ischaemic event (p = 0.001), CAD (p = 0.015), creatinine level (p = 0.031), male gender (p = 0.001), claudication (p Conclusions: In a large patient sample, we have found that significant supra-aortic atherosclerosis is present in a quater of patients with suspected CAD. Importantly, SAD prevalence increases with CAD severity. Previous neurological ischaemic event, CAD, creatinine level, male gender, claudication and hyperlipidaemia were identified as independent predictors of SAD l 50%.
NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS

Shan Yang: Non-rigid Body Mechanical Property Recovery from Images and Videos (Under the direction of Ming C. Lin) Material property has great importance in surgical simulation and virtual reality. The mechanical properties of the human soft tissue are critical to characterize the tissue deformation of each patient. Studies have shown that the tissue stiffness described by the tissue properties may indicate abnormal pathological process. The (recovered) elasticity parameters can assist surgeons to perform better pre-op surgical planning and enable medical robots to carry out personalized surgical procedures. Traditional elasticity parameters estimation methods rely largely on known external forces measured by special devices and strain field estimated by landmarks on the deformable bodies. Or they are limited to mechanical property estimation for quasi-static deformation. For virtual reality applications such as virtual try-on, garment material capturing is of equal significance as the geometry reconstruction. In this thesis, I present novel approaches for automatically estimating the material properties of soft bodies from images or from a video capturing the motion of the deformable body. I use a coupled simulation-optimization-identification framework to deform one soft body at its original, non-deformed state to match the deformed geometry of the same object in its deformed state. The optimal set of material parameters is thereby determined by minimizing the error metric function. This method can simultaneously recover the elasticity parameters of multiple regions of soft bodies using Finite Element Method-based simulation (of either linear or nonlinear materials undergoing large deformation) and particle-swarm optimization methods. I demonstrate the effectiveness of this approach on real-time interaction with virtual organs in patient-specific surgical simulation, using parameters acquired from low-resolution medical images. With the recovered elasticity parameters and the age of the prostate cancer patients as features, I build a cancer grading and staging classifier.
Plasma progestagens in pregnant mares.

: Peripheral plasma progestagens were quantified by a competitive protein-binding assay throughout pregnancy. The level of progesterone increased significantly between Days 0 and 8 (P less than 0-05) and again between Days 28 and 44 and reached a maximum on Day 64. Subsequently, it fell slowly until about Day 300 and then rose again during the last 30 days before reaching a minimum on the day after foaling. Very low concentrations of 17alpha-hydroxyprogesterone were found except between Days 40 and 120 and during the last 30 days before birth. Two unidentified compounds, one eluting slightly before and one after progesterone on Sephadex LH-20 columns, first appeared in plasma between Days 30 and 60 of gestation, and increased gradually to Day 300. Both compounds increased significantly (P less than 0-05) at about 5 days before birth and were not detectable on the day after foaling.
THP: A Novel Authentication Scheme to Prevent Multiple Attacks in SDN-Based IoT Network

SDN has provided significant convenience for network providers and operators in cloud computing. Such a great advantage is extending to the Internet of Things network. However, it also increases the risk if the security of an SDN network is compromised. For example, if the network operator’s permission is illegally obtained by a hacker, he/she can control the entry of the SDN network. Therefore, an effective authentication scheme is needed to fit various application scenarios with high-security requirements. In this article, we design, implement, and evaluate a new authentication scheme called the hidden pattern (THP), which combines graphics password and digital challenge value to prevent multiple types of authentication attacks at the same time. We examined THP in the perspectives of both security and usability, with a total number of 694 participants in 63 days. Our evaluation shows that THP can provide better performance than the existing schemes in terms of security and usability.
Emotion Recognition Based on Fusion of Local Cortical Activations and Dynamic Functional Networks Connectivity: An EEG Study

In this paper, we present a method to improve emotion recognition based on the fusion of local cortical activations and dynamic functional network patterns. We estimate the cortical activations using power spectral density (PSD) with the Burg autoregressive model. On the other hand, we estimate the functional connectivity networks by utilizing the phase locking value (PLV). The results of cortical activations and connectivity networks show different patterns across three emotions at all frequency bands. Similarly, the results of fusion significantly improve the classification rate in terms of accuracy, sensitivity, specificity and the area under the receiver operator characteristics curve (AROC), p < 0.05. The average improvement with fusion in all evaluation metrics are 6.84% and 4.1% when compared to PSD and PLV alone, respectively. The results clearly demonstrate the advantage of fusion of cortical activations with dynamic functional networks for developing human-computer interaction system in real-world applications.
Pathogenesis of rheumatoid arthritis: its relevance to therapy in the '90s.

Rheumatoid arthritis (RA) may be a relatively new disease (1), but its force as a cause of morbidity and mortality in modern days has been significant (2). In one recently-published study of 100 patients studied prospectively over 25 years (3), it was found that 63 patients had died, and that RA had either directly caused or contributed to death in 19. This is an excess mortality of 40% over the 25 years in both men and women. Cardiovascular (including cerebrovascular) diseases caused death in 46 percent of these patients, infection in 17 percent and malignancy in 13. Rheumatoid disease (including vasculitis) was the case in an additional 19 percent, and in a further 14% RA was a contributory factor. These data conform with numerous other surveys published in the past 40 years (4-13). It is not generally appreciated by most of us who treat the disease on a regular basis that no matter what therapy we use, the evidence shows that loss of joint function has inexorable progression (14, 15). These discouraging data could lead us to therapeutic nihilism in treatment, or to the consideration that we need to use effective therapies in the disease earlier than generally prescribed. The thesis of this paper is that we understand enough about the pathophysiology of RA to be introducing currently accepted "aggressive" therapy at earlier stages of RA, and to plan using new therapies targeted at specific determinants in pathogenic schemes. A longer review has recently been published that provides additional detail and references (16). Rheumatoid arthritis can be considered as a disease that has progressive amplification as new groups of cells and mediators become involved and the nonspecific synovitis present in early stages of RA becomes highly proliferative and invasive of joint structures, resembling at its worst a localized malignancy.
Superparamagnetic Particles as an Mri Contrast Agent for the Gastrointestinal Tract

Non-biodegradable superparamagnetic particles containing magnetite were evaluated as a potential contrast agent for the gastrointestinal tract. The particles were administered to pigs perorally either suspended in water or mixed with food. The gastrointestinal tract structures were excellently depicted through a lowered signal intensity of their content in both T1 and T2 weighted images. Relatively large volumes with low particle concentration seemed to give a good contrast agent distribution, especially when the administration was extended over a period of time. Problems with artefacts and blurring in the images caused by a too high local concentration of the contrast agent should also be minimized by using low particle concentrations.
An Empirical Evaluation of Deep Learning for Network Anomaly Detection

Deep learning has been widely studied in many technical domains such as image analysis and speech recognition, with its benefits that effectively deal with complex and high-dimensional data. Our preliminary experiments show a high degree of non-linearity from the network connection data, which explains why it is hard to improve the performance of identifying network anomalies by using conventional learning methods (e.g., Adaboosting, SVM, and Random Forest). In this study, we design and examine deep learning models constructed based on Fully Connected Networks (FCNs), Variational AutoEncoder (VAE), and Sequence-to-Sequence (Seq2Seq) structures. For the extensive evaluation, we employ a broad range of the public datasets with unique characteristics. Our experimental results confirm the feasibility of deep learning-based network anomaly detection, with the improved performance compared to the conventional learning techniques. In particular, the detection model based on Seq2Seq with LSTM is highly promising, consistently yielding over 99% of accuracy to identify network anomalies from the entire datasets employed in the evaluation.
Prospects sportization system of physical education students

Purpose: to assess the status and prospects of development of sports -oriented direction in the system of physical education students. Material and Methods: the analysis of literature , which are developed the theoretical and practical aspects of the development of physical education students. Results: it is determined that the system of physical education students in groups of physical and sports education is the most common sports-oriented form of organization studies. It is noted that the traditional system of physical education is not effective enough to improve the physical health of students . Conclusions: the solution to provide the necessary level of physical health of students is formation of their personality a certain level of physical culture.
Geometric frequency reuse for irregular cellular networks

This thesis uniquely addresses challenges of bandwidth management in cellular networks. The need for enhanced frequency assignment strategies in Long term evolution (LTE) systems arises due to the limiting effects of intercell interference (ICI). In this study, the realistic scenario of irregular network coverage patterns is considered, and in addition, Heterogeneous cellular networks (HetNets). Firstly, extensive analysis using simulations is presented for static frequency reuse (FR) techniques in irregular Homogeneous (single-tier) cellular networks. Investigation was carried out over several network positional and deployment layouts. Second, a model is developed for irregular networks by defining frameworks for their location parameters and relationships, FR bandwidth and power assignment, and the probability of interference in partitioned FR schemes. A novel Geometric FR (GeoFRe) algorithm is then proposed for single-tier networks with random BS placements. Third, an optimization framework based on user fairness is proposed and implemented for single-tier networks based on the concept of virtual UEs in different BS regions. Finally, a framework for HetNets is presented where macro and small BS deployments have imperfect coverage grid patterns. Performance analysis is then carried out for two implementations of the Soft FR (SFR) algorithm. Results from this research provide detailed analysis on impact of BS irregularity on UE performance under FR schemes, a simplified framework for modelling irregular macro BS, an improved FR model, accurate computations for the area of irregular network coverage patterns for intelligent bandwidth assignment, an optimization framework to improve user fairness (and edge UE performance) in single-tier networks and an FR model with performance analysis for irregular HetNets.
Spectral Efficiency Optimization of Spatially-Correlated Multi-Pair Full-Duplex Massive MIMO Relaying

We consider a multi-pair two-way full-duplex (FD) amplify-and-forward relaying, where multiple FD multi-input multi-output (MIMO) users exchange information via a shared FD massive MIMO relay. We derive a closed-form spectral efficiency (SE) lower-bound for maximal-ratio combining/maximal-ratio transmission relay processing considering spatially correlated relay and user antennas, which most of the existing works have ignored. The derived SE lower-bound is applicable for arbitrary number of relay antennas, and simplifies to the following results available in the literature i) the asymptotic SE for number of relay antennas tending to infinity; and ii) the SE lower-bound with independent relay antennas and single-antenna users. We use this SE lower-bound to maximize the non-convex SE, which is of matrix fractional form. We optimize SE by approximating it as concave-convex function, and then by applying matrix quadratic transformation. We numerically validate the SE lower-bound for various system configurations and also characterize their performance for different spatial correlation and interference values. We investigate the gains achieved by the optimal SE over equal power allocation.
“Okay Okay Okay, Now the Video Is On”: An Analysis of Young Children’s Orientations to the Video Camera in Recordings of Family Interactions

This paper explores 3- to 6-year-old children’s orientations to the video camera in video recordings of everyday family interactions. Children’s orientations to the video camera in these recordings were identified and analyzed using the constant comparative method. Types of orientations to the video camera included talking about the camera, engaging in camera-directed talk and/or action, and interacting with the camera. In some cases, these orientations occurred after a parent or sibling first oriented to the video camera; however, in other cases no prior orientation was evident. Theoretical and practical implications of the results are discussed.
Register Variation Online

While other books focus on special online registers, such as tweets or texting, no previous study describes the full range of everyday registers found on the searchable web. These registers are the documents that readers encounter every time they do a Google search; registers like news reports, product reviews, travel blogs, discussion forums, FAQs, etc. Based on analysis of a large, near-random corpus of web documents, this monograph provides comprehensive situational, lexical, and grammatical descriptions of those registers. Beginning with a coding of each document in the corpus, the description identiies the registers that are especially common on the searchable web versus those that are less commonly found. Multidimensional analysis is used to describe the overall patterns of linguistic variation among web registers, while the second half of the book provides an in-depth description of each individual register, including analyses of situational contexts and communicative purposes, together with the typical lexical and grammatical characteristics associated with those contexts.