Miracle-dz/UniFAR-FTUs huggingface dataset:
An Intelligent Real-time Heart Diseases Diagnosis Algorithm

Among all diseases, the heart diseases are responsible for highest number of deaths in the world especially in undeveloped countries. One of the reasons for this high mortality rate is due to delayed diagnosis based on ECG records. On the other hand, the heart diseases are computationally complex and intensive, and are difficult to manually analyze. Therefore, with the increase in the growth of heart disease databases and information, a high performance computing and easy to use programming environment is required to solve medical application. In this work we have proposed development an Intelligent Heart Diseases Diagnosis Algorithm (IHDDA) which reads heart signals (e.g. ECG graphs etc.) and reaches on a flawless diagnosis in fraction of seconds. The proposed algorithm executed on a supercomputing cluster and tested with 300 patients of different heart problems, the results shows that the IHDDA identify heart problem in 1.5 seconds with an accuracy of 97%.
Gender Classification From NIR Images by Using Quadrature Encoding Filters of the Most Relevant Features

In the past few years, accuracy in determining gender from iris images has increased significantly, approaching levels that make novel applications of this biometric technology feasible. In this paper, we report the gender classification rate by using a 2-D Quadrature Quaternionic filter, and a selection of the most relevant features from the normalized iris images. We encoded the phase information of the normalized images using 4 bits per pixel with a 2-D-Gabor filter and selected the best bits from the four resulting images (1 real and 3 imaginary) instead of the 1-D log-Gabor traditional encoding method. We used traditional hand-crafted and automatic methods to select and extract the most relevant features from the whole iris images, blocks from images, and pixel features and compared how effective these methods were in separating features from female and male iris images. Selecting iris blocks and features reduce the computational time and, at a basic science level, is of great value in understanding what information features, as well as pixels from the iris, can be extracted to classify gender. The Quaternionic-Code with the complementary feature selection method achieved the best results on the GFI-UND database with 93.45% for the left iris and 95.45% for the right iris, both with 2400 selected features. We compared our results and found them to be advantageous to the best results previously published, and also to those obtained using convolutional neural network feature extraction.
Occupational therapists’ perceptions of implementing a client-centered intervention in close collaboration with researchers: A mixed methods study

Abstract Background: Integration of research-based knowledge in health care is challenging. Occupational therapists (OTs) need to implement new research-based interventions in clinical practice. Therefore it is crucial to recognize and understand the factors of specific barriers and facilitators affecting the implementation process. Aim: To identify the key factors important for OTs during the implementation process of a complex intervention. Materials and methods: A cross-sectional study with a combination of qualitative and quantitative data in a mixed method design. Forty-one OTs and 23 managers from three county councils in Sweden, responded to a questionnaire one year after the OTs participation in a workshop to prepare for implementation of a client-centered activity of daily living intervention for persons with stroke. Results: Over 70% of the OTs benefitted from reading and discussing articles in the workshop; 60% had faith in the intervention; 69% reported usability of the intervention. High level of support from managers was reported, but less from team members. The therapists’ interaction, perceptions of own efforts and contextual influence affected the implementation process. Conclusion: The workshop context with facilitation and access to evidence, supportive organizations and teams, sufficient interaction with researchers and satisfying self-image were successful key factors when involved in research.
Speeding-Up Robustness Assessment of HDL Models through Profiling and Multi-Level Fault Injection

Simulation-based fault injection is an indispensable technique to assess the robustness of hardware components defined by means of hardware description languages (HDL). However, the high complexity of modern hardware and its strict verification accuracy requirements lead to an unfeasible number of fault injection experiments, even when following statistical (instead of exhaustive) approaches, as accurate implementation-level models are up to three orders of magnitude slower than (inaccurate) behavioural ones. This paper proposes the combined use of multi-level fault injection in sequential logic and the profiling of the use of combinational logic to guarantee results' accuracy while keeping experimentation duration within reasonable time-bounds. First, the sequential logic generated at the implementation-level model is matched with associated structures at its related behavioural-level model. In such a way, most fault injection experiments targeting sequential logic could be executed at the much faster behavioural level, while maintaining the accuracy of results. Second, by profiling the implementation-level model, run-time statistics (inactive macrocells, switching activity, etc.) can be exploited to keep result precision while reducing the number of experiments targeting combinational logic. The case study of three embedded processor models illustrates both approaches and quantifies the experimental speed-up derived from their combined use.
Efficient PatchMatch-Based Synthesis for Cartoon Animation

Automating the production of 2D hand-drawn animations is a significant and interesting component in computer graphics and vision. However, traditional methods in animation production pipeline always use physically or geometrically based models which are consuming due to complicated and massive computations, reducing their practicability. In this paper, we propose an efficient data-driven approach to create hand-drawn animations in an automatic manner. The key idea is to employ a correspondence match-based random search process to extract the geometry samples and the global motion pattern in an input animation sequence and then to generate a new output sequence through a coarse-to-fine sample-based synthesis algorithm. Our experiments demonstrate that our method achieves good results with high quality and performance, producing a range of artistic effects that previously required disparate and professional techniques.
A Hybrid Vector Wave Equation- and Maxwell’s Equation-Based Discontinuous Galerkin Time Domain Method

A hybrid scheme composed of vector wave equation-based discontinuous Galerkin time domain method (DGTD-WE) and Maxwell’s equation-based discontinuous Galerkin time domain method (DGTD-ME) has been proposed to solve the electromagnetic problems. An upwind flux scheme is developed to hybridize the DGTD-WE and DGTD-ME. Numerical results demonstrate good performance of the proposed hybrid algorithm.
Incorporating Safety Into Parametric Dynamic Movement Primitives

Parametric dynamic movement primitives (PDMPs) are powerful motion representation algorithms, which encode multiple demonstrations and generalize them. As an online trajectory from PDMPs emulates the provided demonstrations, managing the safety guarantee of the demonstrations for a given scenario is an important issue. This letter presents a process to manage the demonstration set in PDMPs when some demonstrations are poor in terms of safety. Our proposed process distinguishes safe motion primitives from unsafe ones. In order to establish a criterion for determining whether a motion is safe or not, we calculate the safe region of the PDMPs parameters called style parameters using an optimization technique. In the optimization formulation, we calculate the unsafe style parameters that produce the closest motion to the unsafe region of the state space. By eliminating unsafe demonstrations with the parameters based on the safety criterion, and replacing them with new safe ones, we incorporate safety in the PDMPs framework. Simulation and experimental results validate that the proposed process can expand the motion primitives in the PDMPs framework to the new environmental settings by efficiently utilizing the previous demonstrations.
Emotion Recognition using Facial Expressions in Children using the NAO Robot

The detection of human emotions from facial expressions is crucial for social interaction. Therefore, several systems of behavioral computing in robotics try to recognize human emotion from images and video, but most of them are trained to classify emotions in adults only. Using the standard of 6 basic emotions: sadness, happiness, surprise, anger, disgust, and fear, we try to classify the facial expressions using the NAO robot in children. In this study, we make the comparison between the AFFDEX SDK, and a Convolution Neural Network (CNN) with Viola-Jones trained with the AffectNet dataset, and tuned with the NIMH-ChEF dataset using transfer learning to classify facial expressions in children. Then, we test our system comparing the CNN and the AFFDEX SDK for classification in the Child Affective Facial Expression (CAFE) dataset. Finally, we compare both systems using the NAO robot in a subset of the AM-FED and EmoReact datasets.
Functional Itô calculus†

We extend some results of the Itô calculus to functionals of the current path of a process to reflect the fact that often the impact of randomness is cumulative and depends on the history of the process, not merely on its current value. We express the differential of the functional in terms of adequately defined partial derivatives to obtain an Itô formula. We develop an extension of the Feynman-Kac formula to the functional case and an explicit expression of the integrand in the Martingale Representation Theorem. We establish that under certain conditions, even path dependent options prices satisfy a partial differential equation in a local sense. We exploit this fact to find an expression of the price difference between two models and compute variational derivatives with respect to the volatility surface.
BLAS-3 Optimized by OmpSs Regions (LASs Library)

In this paper we propose a set of optimizations for the BLAS-3 routines of LASs library (Linear Algebra routines on OmpSs) and perform a detailed analysis of the impact of the proposed changes in terms of performance and execution time. OmpSs allows to use regions in the dependences of the tasks. This helps not only in the programming of the algorithmic optimizations, but also in the reduction of the execution time achieved by such optimizations. Different strategies are implemented in order to reduce the amount of tasks created (when there is enough parallelism) during the execution of BLAS-3 operations in the original LASs. Also a better IPC is obtained thanks to a better memory hierarchy exploitation. More specifically, we increase the performance, in particular on big matrices, about 12% for TRSM, and 17% for GEMM with respect to the original version of LASs, even using less cores in the case of GEMM/SYMM. Moreover, when LASs is compared to the OpenMP reference dense linear algebra library PLASMA, performance is increased up to 12.5% for GEMM/SYMM, while for TRSM/TRMM this value raises to 15%.
On Real-Time Monitoring on Data Stream for Traffic Flow Anomalies

Activities held in cities often lead to crowd gathering, and activities involving large numbers of people may have hidden public safety risks. Therefore, detecting the aggregation of the population and discovering the anomalies of the human flows in a timely manner are of significance in urban public safety, traffic management, emergency control, and terrorism prevention. To detect abnormal crowds in urban areas, the paper presents an approach Pherkad/A and implements Pherkad/A in the corresponding tool (named Pherkad/T). Pherkad/A adapts a three-state MMPP (Markov-Modulated Poisson Process) model to take continuous flow data as input. It thus can detect two kinds of anomaly, i.e., flow increase and flow decrease in real time from incoming traffic flows. On the other hand, Pherkad/T permits users to specify their interested monitoring areas and time intervals and then report the anomaly results in a visualized way. Extensive experiments are conducted on the real data. The experimental results show that Pherkad/A has high accuracy in detecting traffic flow anomalies and Pherkad/T can respond various monitoring requirements in real time.
Efficient Coflow Scheduling of Multi-Stage Jobs with Isolation Guarantee

Coflow scheduling is critical for data-parallel computing performance in datacenters. Performance and isolation guarantee have become two major objectives for coflow scheduling. However, in the context of multi-stage jobs, existing coflow scheduling frameworks only focus on minimizing the average job completion time (JCT) while overlooking the isolation guarantee. To address this problem, in this paper we propose the first coflow scheduling scheme that aims to achieve both objectives. We show that our scheduler outperforms existing alternatives significantly in minimizing average JCT while guaranteeing that no job will be delayed beyond a constant time than its JCT in a fair scheme. Our evaluation results show that our scheduler reduces the average JCT by at least 86\% compared with state-of-the-art schedulers.
The triple shift: student-mothers, identity work and engagement with low-status vocationally related higher education

Abstract This paper discusses a piece of qualitative research that examined the narratives that a group of learners articulated when they discussed their experiences of studying on a relatively low-status, vocationally related higher education programme. These students were school-based teaching assistants who were undertaking foundation degree study at a post-1992 English university. Data collection was primarily undertaken via semi-structured interviewing with first- and second-year foundation degree students. Eight group interviews were carried out with 44 participants. These were followed up with 12 individual interviews. Participant observations and documentary analysis of course-related documents were also drawn upon as contextualising sources of information. The accounts that were provided by students who undertook study alongside mothering are focused upon within this paper. These learners were routinely found to be involved in a triple shift of identity work. This stemmed from their positioning in the home, higher education and their workplaces. It is suggested that the concepts of identity management, disidentifiers and stigma can be usefully employed alongside habitus and field to make sense of the accounts that are documented.
Functional characterizations of trace spaces in Lipschitz domains

Using a factorization theorem of Douglas, we prove functional characterizations of trace spaces $H^s(\partial \Omega)$ involving a family of positive self-adjoint operators. Our method is based on the use of a suitable operator by taking the trace on the boundary $\partial \Omega$ of a bounded Lipschitz domain $\Omega \subset \mathbb R^d$ and applying Moore--Penrose pseudo-inverse properties together with a special inner product on $H^1(\Omega)$. Moreover, generalized results of the Moore--Penrose pseudo-inverse are also established.
Hip moment and knee power eccentric utilisation ratios determine lower-extremity stretch-shortening cycle performance

ABSTRACT The eccentric utilisation ratio (EUR) is calculated as the ratio between countermovement jump (CMJ) and squat jump (SJ) heights, and is an indicator of lower-extremity stretch-shortening cycle (SSC) performance in athletes. Joint-based EUR can also be calculated but have never been reported. The purpose of this study was to investigate whether jump height-based (JH-based) EUR can be predicted by joint-specific EUR. Nine NCAA Division I college athletes (age: 21 ± 1 year, height: 1.75 ± 0.15 m, mass: 71 ± 20 kg) performed three SJ and CMJ. During all jumps, kinematic and kinetic data were obtained and used to calculate hip, knee and ankle net joint moments (NJM) and net joint powers (NJP). JH was calculated from pelvis marker data. EUR (CMJ/SJ [unitless]) were calculated for JH, NJM, and NJP. JH-EUR was 1.11 ± 0.70. NJM-EUR were 1.07 ± 0.17, 1.17 ± 0.25, and 1.07 ± 0.18 for the hip, knee and ankle joint, respectively. NJP-EUR were 1.41 ± 0.12, 1.26 ± 0.28 and 1.06 ± 0.11 for the hip, knee and ankle joint, respectively. Regularised regression showed that Hip-NJM-EUR, Knee-NJP-EUR and Ankle-NJM-EUR were able to predict 83% of the variance in JH-EUR, which suggests that the enhancement of lower-extremity SSC performance during CMJ arises from a combination of these parameters.
Use of an Anthropomorphic Chest Model to Evaluate Multiple Scanning Protocols for High-Definition and Standard-Definition Computed Tomography to Detect Small Pulmonary Nodules

Background This study aimed to use the LUNGMAN N1 anthropomorphic chest model to evaluate protocols for high-definition computed tomography (HDCT) and standard-definition CT (SDCT) to detect and compare small pulmonary nodules and determine the most appropriate low-dose scanning protocols. Material/Methods HDCT imaging used the Discovery HD750 scanner (80, 100, 120 and 140 kVp; 360, 320, 280, 240, 200, 160, 120, 80, 40, and 20 mA), and SDCT imaging used the Lightspeed VCT scanner (80, 120, and 140 kVp; 360, 320, 280, 240, 200, 160, 120, 80, 40, and 20 mA). The LUNGMAN N1 anthropomorphic chest model contained artificial pulmonary nodules (diameter: 5, 8, 10, and 12 mm). Low-dose scanning protocols were used in image acquisition. Two experienced radiologists evaluated the image quality. The combinations of voltage, tube current, image noise, and radiation dose were recorded. Consistency of the image quality between raters was assessed by kappa statistical analysis. Results Seventy CT scans of pulmonary nodules (diameter, 5–12 mm) were performed. There was a high degree of consistency for image quality between the two observers (K=0.929 for 5 mm nodules; K=0.819 for overall image quality). For 8 mm nodules, 100% were detected on both SDCT and HDCT. HDCT outperformed SDCT by 5%, in terms of effective dose. There was no significant difference in image quality between the SDCT and HDCT scanners. Conclusions Using an anthropomorphic chest model, the identification and image quality using SDCT was similar to that of HDCT for small pulmonary nodules between 5–12 mm.
Improving Micro-Expression Recognition with Shift Matrices and Improving Micro-Expression Recognition with Shift Matrices and Database Combination Database Combination

Micro-expressions are brief, subtle changes in facial expressions associated with emotional responses, and researchers have worked for decades on automatic recognition of them. As convolutional neural networks have been widely used in many areas of computer vision, such as image recognition and motion detection, it has also drawn the attention of scientists to use it for micro-expression recognition. However, none of them have been able to achieve an accuracy high enough for practical use. One of the biggest problems is the limited number of available datasets. The most popular datasets are SMIC, CASME, CASMEII, and SAMM. Most groups have worked on the datasets separately, but few have tried to combine them. In our approach, we combined the datasets and extracted the shared features. If new datasets under the same classifying rules (FACS) are created in the future, they can easily be combined using our approach. In addition to this novel approach for combining datasets, we use a new way of extracting the features instead of the Local Binary Pattern from Three Orthogonal Planes (LBP-TOP). To be more specific, we create shift matrices, the changing pattern of pixels, to keep the spatial information of the videos. Our highest recorded accuracy from 100 experiments was 88 percent, but we chose to report 72.5 percent. This is the median accuracy and a more convincing result though it’s a little bit lower than the best result to date. However, our f1 score is 72.3 percent and higher than the best result to date. Our paper presents an extendable approach to micro-expression recognition that should increase in accuracy as more datasets become available.
Gut Microbiota Composition and Blood Pressure: The CARDIA Study

Animal models support a role for the gut microbiota in the development of hypertension. There has been a lack of epidemiological cohort studies to confirm these findings in human populations. We examined cross-sectional associations between measures of gut microbial diversity and taxonomic composition and blood pressure (BP) in 529 participants of the biracial (black and white) CARDIA study (Coronary Artery Risk Development in Young Adults). We sequenced V3-V4 regions of the 16S ribosomal RNA marker gene using DNA extracted from stool samples collected at CARDIA’s Year 30 follow-up examination (2015–2016; aged 48–60 years). We quantified associations between BP (hypertension [defined as systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg or antihypertension medication use] and systolic BP) and within and between-person diversity measures. We conducted genera-specific multivariable-adjusted regression analysis, accounting for multiple comparisons using the false discovery rate. Hypertension and systolic BP were inversely associated with measures of &agr;-diversity, including richness and the Shannon Diversity Index, and were distinguished with respect to principal coordinates based on a similarity matrix of genera abundance. Several specific genera were significantly associated with hypertension and systolic BP, though results were attenuated with adjustment for body mass index. Our findings support associations between within-person and between-person gut microbial community diversity and taxonomic composition and BP in a diverse population-based cohort of middle-aged adults. Future study is needed to define functional pathways that underlie observed associations and identify specific microbial targets for intervention.
Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion

Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R2 = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products.