Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnosis of Heart Disease Using a Mixed Classifier

Nowadays, a large amount of medical information can be used to help for medical diagnosis. Especially in computer science, many researchers attempt to use data mining techniques for analyzing large amount of data to discover pattern or knowledge from those data. In this study, a classification technique with supervised learning is considered. From the preliminary study, each classification technique yields different performance. With this reason, a new classification model that combines the advantage of individual classifiers is proposed to achieve higher performance. The proposed model consists of two classification stages. Three techniques composed of Support Vector Machine (SVM), K-Nearest Neighbor (KNN) method and Naïve Bayes approach are applied to an instance separately for the first stage. Then, the results from the techniques will be fed into multi-layer perceptron with back-propagation learning for the second classification stage. Moreover, heart disease, one of the leading causes of death in Thailand, has been selected for diagnosis in this study. A dataset of the disease consists of two classes: risk and non-risk. From the experimental results, the proposed model mixed from three techniques outperforms the existing classifiers including the individual techniques.
Comprehensive In Vitro Proarrhythmia Assay (CiPA) Update from a Cardiac Safety Research Consortium / Health and Environmental Sciences Institute / FDA Meeting

A Cardiac Safety Research Consortium / Health and Environmental Sciences Institute / FDA-sponsored Think Tank Meeting was convened in Washington, DC, on May 21, 2018, to bring together scientists, clinicians, and regulators from multiple geographic regions to evaluate progress to date in the Comprehensive In Vitro Proarrhythmia Assay (CiPA) Initiative, a new paradigm to evaluate proarrhythmic risk. Study reports from the 4 different components of the CiPA paradigm (ionic current studies, in silico modeling to generate a Torsade Metric Score, human induced pluripotent stem cell—derived ventricular cardiomyocytes, and clinical ECG assessments including J-Tpeakc) were presented and discussed. This paper provides a high-level summary of the CiPA data presented at the meeting.
Comparison of the Control Designs of an Human Co-Working Endoscope Holder

The research addresses the design of an endoscope holding robot. The minimally invasive surgery (MIS) usually takes many doctors to work together; only one surgeon is actually performing the operation. One of the doctors is required to hold the endoscope and to point the camera at the surgical area. This research aims to design a robot to fulfill the function of this doctor to hold the endoscope in place. This paper demonstrates how to achieve the two main functions of the endoscope holders namely: to maintain the entry point into the body and to actively move away from blocking the doctor in their operation. This paper describes how to achieve these goals based on a regular six degree-of-freedom industrial robots. The control could be implemented at the different levels of control. The experimental results show that the implementation at different level of controls each has its advantages and disadvantages.
Distributed Average Tracking in Multi-Agent Coordination: Extensions and Experiments

This paper addresses the distributed average tracking (DAT) problem for a group of agents to track the average of multiple time-varying reference signals, each of which is available to only one agent, under local interaction with neighbors and an undirected graph. We consider three cases: 1) DAT for single-integrator dynamics with a chain of integrators in algorithm design; 2) DAT with swarm behavior for single-integrator dynamics; and 3) DAT with swarm behavior for double-integrator dynamics. First, a continuous distributed algorithm with a chain of two integrators is proposed for single-integrator dynamics, where each agent needs its own and its neighbors’ filter outputs obtained through communication besides its absolute position, local relative positions, reference signal, and reference velocity. The introduced algorithm can deal with a wide class of reference signals with steady deviations among reference velocities. Then, two swarm-based DAT algorithms for, respectively, single- and double-integrator dynamics are presented. In the first swarm-based algorithm for single-integrator dynamics, each agent needs to measure the relative positions between itself and its neighbors, while in the second swarm-based algorithm for double-integrator dynamics, relative velocity information is required too. In both cases, the information can be obtained through local sensing. If correct initialization constraints can be achieved, the center of the agents will track the average of the reference signals and the agents will maintain connectivity and avoid interagent collision. Numerical simulations are also presented to illustrate the theoretical results. Finally, the algorithms presented in this paper are experimentally implemented and validated on a multirobot platform.
Anatomy, Physiology and Pathology

The anterior surface of the liver, except for the insertion of the falciform ligament, is covered with peritoneum. In the adult in the supine position a liver of ordinary size usually lies in contact with the abdominal wall over a triangular area delimited by the whole, or the greater part of, the right costal arch, 5 em or more of the left costal arch and the lower border of the liver (Fig. 1). In, for instance, obstruction of extrahepatic bile ducts and acute hepatitis, the liver, and hence this region, may be greatly enlarged. Exceptionally, as in the case of men with a large sagittal dimension of the lower thoracic aperture, the border of the liver may extend only very little below the costal arch. The part of the anterior wall of the thorax covering the liver without interposition of the lungs consists largely of the plate formed by the costal cartilages. In rare cases part of the intestine, usually the colon, lies between the liver on one side and the diaphragm and abdominal and thoracic walls on the other. The right surface of the liver is covered with peritoneum and by the thoracic wall. In the mid-axillary line the pleura reaches to about the tenth rib, and the relaxed lung to about the eighth rib. The dorsal surface of the right lobe of the liver has no covering peritoneum over an area 8-10 by 4-7 em (Prioton 1960). In most, if not all, of this area the thoracic wall and the liver are separated by the pleura (Fig. 1). The lung in relaxation covers only a minor part of the extraperitoneal surface of the liver. The craniolateral part of the kidney and the suprarenal gland lie between the liver and the abdominal and thoracic walls. Except for the hilus region the lower surface of the liver is covered with peritoneum. Next to the hilus are the duodenal bulb and the superior duodenal flexure. The part of the wall of the gall bladder that extends from the surface of the liver varies widely in size. The site of the hilus in relation to the skeleton and the surface of the body when the patient is in the supine position has been described as follows on the basis of radiographic examinations: "The hilus is nearly always to be found exactly above the 11th intercostal space and some three finger-breadths to the right of the lateral margin of the vertebral column, in the frontal plane immedi-
A mechanistic hydro-epidemiological model of liver fluke risk

The majority of existing models for predicting disease risk in response to climate change are empirical. These models exploit correlations between historical data, rather than explicitly describing relationships between cause and response variables. Therefore, they are unsuitable for capturing impacts beyond historically observed variability and cannot be employed to assess interventions. In this study, we integrate environmental and epidemiological processes into a new mechanistic model, taking the widespread parasitic disease of fasciolosis as an example. The model simulates environmental suitability for disease transmission, explicitly linking the parasite life-cycle to key weather-water-environment conditions. First, using epidemiological data, we show that the model can reproduce observed infection levels in time and space over two case studies in the UK. Second, to overcome data limitations, we propose a calibration approach based on Monte Carlo sampling and expert opinion, which allows constraint of the model in a process-based way, including a quantification of uncertainty. Finally, comparison with information from the literature and a widely-used empirical risk index shows that the simulated disease dynamics agree with what has been traditionally observed, and that the new model gives better insight into the time-space patterns of infection, which will be valuable for decision support.
Analysis of GPGPU Programs for Data-race and Barrier Divergence

Todays business and scientific applications have a high computing demand due to the increasing data size and the demand for responsiveness. Many such applications have a high degree of parallelism and GPGPUs emerge as a fit candidate for the demand. GPGPUs can offer an extremely high degree of data parallelism owing to its architecture that has many computing cores. However, unless the programs written to exploit the architecture are correct, the potential gain in performance cannot be achieved. In this paper, we focus on the two important properties of the programs written for GPGPUs, namely i) the data-race conditions and ii) the barrier divergence. We present a technique to identify the existence of these properties in a CUDA program using a static property verification method. The proposed approach can be utilized in tandem with normal application development process to help the programmer to remove the bugs that can have an impact on the performance and improve the safety of a CUDA program.
Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification

This paper presents a tensor alignment (TA) based domain adaptation (DA) method for hyperspectral image (HSI) classification. To be specific, HSIs in both domains are first segmented into superpixels, and tensors of both domains are constructed to include neighboring samples from a single superpixel. Then the subspace alignment (SA) between the two domains is achieved through alignment matrices, and the original tensors are projected as core tensors with lower dimensions into the invariant tensor subspace by applying projection matrices. To preserve the geometric information of original tensors, we employ a manifold regularization term for core tensors into the optimization process. The alignment matrices, projection matrices, and core tensors are solved in the framework of Tucker decomposition with an alternating optimization strategy. In addition, a postprocessing strategy is defined via pure samples extraction for each superpixel to further improve classification performance. Experimental results on four real HSIs demonstrate that the proposed method can achieve better performance compared with the state-of-the-art subspace learning methods when a limited amount of source labeled samples are available.
Factors that Complicate the Selection of Software Requirements - Validating Factors from Literature in an Empirical Study

In market-driven software product development, new features may be added to the software, based on a collection of candidate requirements. Selecting requirements however is difficult. Despite all work done on this problem, known as the next release problem, what is missing, is a comprehensive overview of the factors that complicate selecting software requirements. This paper aims at getting such overview. The authors performed a systematic literature review, searching for occurrences in the literature where a causal relation was suggested between certain conditions and the difficulty of selecting software requirements. Analyzing 544 papers led to 156 findings. Clustering them resulted in 33 complicating factors that were classified in eight groups. The complicating factors were validated in semi-structured interviews with twelve experts from three different industrial organizations. These interviews consisted of questions about participant’s experiences with the complicating factors, and of questions how these factors complicated selecting requirements. The results aid in getting a better understanding of the complexity of selecting requirements.
Epidemiology, Biology, and Impact of Clonal Pseudomonas aeruginosa Infections in Cystic Fibrosis

Chronic lower airway infection with Pseudomonas aeruginosa is a major contributor to morbidity and mortality in individuals suffering from the genetic disease cystic fibrosis (CF). Whereas it was long presumed that each patient independently acquired unique strains of P. aeruginosa present in their living environment, multiple studies have since demonstrated that shared strains of P. aeruginosa exist among individuals with CF. SUMMARY Chronic lower airway infection with Pseudomonas aeruginosa is a major contributor to morbidity and mortality in individuals suffering from the genetic disease cystic fibrosis (CF). Whereas it was long presumed that each patient independently acquired unique strains of P. aeruginosa present in their living environment, multiple studies have since demonstrated that shared strains of P. aeruginosa exist among individuals with CF. Many of these shared strains, often referred to as clonal or epidemic strains, can be transmitted from one CF individual to another, potentially reaching epidemic status. Numerous epidemic P. aeruginosa strains have been described from different parts of the world and are often associated with an antibiotic-resistant phenotype. Importantly, infection with these strains often portends a worse prognosis than for infection with nonclonal strains, including an increased pulmonary exacerbation rate, exaggerated lung function decline, and progression to end-stage lung disease. This review describes the global epidemiology of clonal P. aeruginosa strains in CF and summarizes the current literature regarding the underlying biology and clinical impact of globally important CF clones. Mechanisms associated with patient-to-patient transmission are discussed, and best-evidence practices to prevent infections are highlighted. Preventing new infections with epidemic P. aeruginosa strains is of paramount importance in mitigating CF disease progression.
Software Tool for Audio Signal Analysis and Automatic Music Transcription

Music transcription could be defined as an act of listening to a piece of music and writing down music notation for the piece. In this paper we describe our work on automatic music transcription of single instrument polyphonic music. We developed a software tool that can be used to address all the phases of automatic music transcription process.
An Interval Power Flow Analysis Through Optimizing-Scenarios Method

Interval power flow (IPF) is a special kind of uncertain power flow problem whose demand load and active power generation are regarded as interval uncertainties. A novel optimizing-scenarios method (OSM) is envisaged in this paper to solve this problem to obtain more accurate results. The OSM includes two kinds of approaches, namely the minimum and maximum programming models, in which the interval uncertainties are regarded as variables with varying bounds, and the objective function under study is set to determine these unknown variables. By solving these nonlinear programming models through the interior point method, the changing variables of the IPF problems are determined. Performance of the proposed approach is compared with that of previously established methods, including the affine arithmetic-based method as well as Monte Carlo simulation method, and the overall simulation results demonstrate the effectiveness and robustness of the proposed OSM.
Automatic fascia extraction and classification for measurement of muscle layer thickness

In this report, we proposed a method of discriminating of fascia using Histograms of Oriented Gradients (HOG) and Support Vector Machine (SVM) in ultrasound images. In modern society, aging is progressing due to medical development. Along with that, the decline of muscle due to aging is regarded as a serious problem. To cope with this problem, we proposed a method of automatic fascia classification to visualize muscle thickness. Our method use SVM based on the texture of ultrasound images. In addition to this method, our method achieves about 90% Accuracy and Recall by considering that the fascia is a continuous tissue. Experimental results show the effectiveness of our proposed automatic fascia extraction method.
Automated Authentication Credential Derivation for the Secured Configuration of IoT Devices

The number of embedded systems and resource constrained devices is steadily increasing due to trends such as the Internet of Things (IoT) and the Industrial IoT. With that, also the frequency and extent of cyber-attacks that target these systems are rapidly increasing. Not only are most devices not adequately secured against such attacks, but also use default settings and authentication credentials. These problems are especially critical if the devices are deployed in industrial contexts where managing configurations and authentication credentials is a complex and inconvenient process. Therefore, in this paper, we present a device configuration approach that automatically derives authentication credentials from device configurations. We demonstrate that the additional performance required by our approach is acceptable while the provided security is reasonable when compared to traditional authentication approaches such as passwords. The security benefits of our approach are highlighted by an extensive security and threat analysis that demonstrates that 9 out of 10 identified threats are mitigated by our approach.
Temporal Saliency Adaptation in Egocentric Videos

This work adapts a deep neural model for image saliency prediction to the temporal domain of egocentric video. We compute the saliency map for each video frame, firstly with an off-the-shelf model trained from static images, secondly by adding a a convolutional or conv-LSTM layers trained with a dataset for video saliency prediction. We study each configuration on EgoMon, a new dataset made of seven egocentric videos recorded by three subjects in both free-viewing and task-driven set ups. Our results indicate that the temporal adaptation is beneficial when the viewer is not moving and observing the scene from a narrow field of view. Encouraged by this observation, we compute and publish the saliency maps for the EPIC Kitchens dataset, in which view- ers are cooking.
Cross-Domain Collaborative Learning via Cluster Canonical Correlation Analysis and Random Walker for Hyperspectral Image Classification

This paper introduces a novel heterogeneous domain adaptation (HDA) method for hyperspectral image (HSI) classification with a limited amount of labeled samples in both domains. The method is achieved in the way of cross-domain collaborative learning (CDCL), which is addressed via cluster canonical correlation analysis (C-CCA) and random walker (RW) algorithms. To be specific, the proposed CDCL method is an iterative process of three main components, i.e., RW-based pseudolabeling, cross-domain learning via C-CCA, and final classification based on extended RW (ERW) algorithm. First, given the initially labeled target samples as the training set (TS), the RW-based pseudolabeling is employed to update TS and extract target clusters (TCs) by fusing the segmentation results obtained by RW and ERW classifiers. Second, cross-domain learning via C-CCA is applied using labeled source samples and TCs. The unlabeled target samples are then classified with the estimated probability maps using the model trained in the projected correlation subspace. The newly estimated probability map and TS are used for updating TS again via RW-based pseudolabeling. Finally, when the iterative process converges, the result obtained by the ERW classifier using the final TS and estimated probability maps is regarded as the final classification map. Experimental results on four real HSIs demonstrate that the proposed method can achieve better performance compared with the state-of-the-art HDA and ERW methods.
Recovery of respiratory function in mdx mice co‐treated with neutralizing interleukin‐6 receptor antibodies and urocortin‐2

Impaired ventilatory capacity and diaphragm muscle weakness are prominent features of Duchenne muscular dystrophy, with strong evidence of attendant systemic and muscle inflammation. We performed a 2‐week intervention in young wild‐type and mdx mice, consisting of either injection of saline or co‐administration of a neutralizing interleukin‐6 receptor antibody (xIL‐6R) and urocortin‐2 (Ucn2), a corticotrophin releasing factor receptor 2 agonist. We examined breathing and diaphragm muscle form and function. Breathing and diaphragm muscle functional deficits are improved following xIL‐6R and Ucn2 co‐treatment in mdx mice. The functional improvements were associated with a preservation of mdx diaphragm muscle myosin heavy chain IIx fibre complement. The concentration of the pro‐inflammatory cytokine interleukin‐1β was reduced and the concentration of the anti‐inflammatory cytokine interleukin‐10 was increased in mdx diaphragm following drug co‐treatment. Our novel findings may have implications for the development of pharmacotherapies for the dystrophinopathies with relevance for respiratory muscle performance and breathing.
A Proposed Framework for Recommendations Aggregation in Context Aware Recommender Systems

The contextual information has been proved to be very useful and influential aspect by the researchers and domain experts in the recommender systems to improve accuracy while making recommendations. Generally, there are not enough preferences in multidimensional contexts or the preference matrices are sparse. To handle data sparsity problem and bring contextual effects, it is important to include valid and influential context features. To do so, this proposal first acquire most important and promising contextual information belonging to three different categories to drive data selection or data construction. Then, ratings are predicted on the selected data using two different approaches i.e. collaborative filtering on user based and item based model. Afterwards, a strategy is used to combine recommendations obtained from each category of contextual information. We performed experiments on movie recommendation and music recommendation domain. The results of our experiments show the advantages of this proposal over state-of-the-art methodologies.
Magnetic Transceiver Beamforming for a 2 × 2 Magnetic Resonance Charging System

In this paper, we consider a magnetic resonance charging system consisting of two transmit coils and two receive coils that is designed to wirelessly charge biomedical wearable diagnostic and therapeutic devices for ergonomics, rehabilitation, and neurology. Since it is proved that the power transfer efficiency will be maximized when the phase difference between the transmit currents equals zero, an optimization problem is formulated to maximize the received power at both receive coils by adjusting the amplitude of transmit coil currents and the receive coil resistances equipped on wearable devices. The formulated problem is nonconvex, and we propose an efficient iterative solution by solving a series of geometric programs that approximate the original problem. Both numerical results and simulation results in the COMSOL model are provided to demonstrate the effectiveness of the proposed biomedical wearable system and optimization algorithm.