Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and preliminary evaluation of a novel adaptive staircase procedure for automated speech-in-noise testing

This article introduces a novel automated staircase procedure for a speech-in-noise test based on Vowel-Consonant-Vowel (VCV) stimuli. Conventional staircase procedures are based on pre-determined changes in stimulus presentation levels under the assumption of homogeneous set. The proposed staircase takes into account differences in intelligibility across the set by using changes in stimulus presentation levels that vary depending on the stimulus and the presentation level. Preliminary evaluation of the proposed staircase, as compared to a conventional staircase, demonstrated test-retest reliability, agreement with the conventional method in terms of speech-in-noise threshold estimation, and lower test duration. As such, the proposed approach shows promise for the implementation of rapid and reliable speech-in-noise tests in adults. Further research is needed to assess test performance in a larger sample of participants, also including subjects of various mother tongue and subjects with hearing loss.
Two-Step Control using Invariant Manifold on Connected Vehicle Model

The purpose of this study is to construct control using characteristics of non-holonomic system for connected vehicle models such as trailers. By extension to this system in the future, it can be expected to comprehensively represent the motion characteristics of the coupled vehicle such as jackknife. Various methods have been tried for non-linear control approaches in articulated vehicles. However, continuous control is difficult due to the non-linear nature. Therefore, in this paper, we derived invariant manifold and constructed control aiming at relatively continuous control. Therefore, in this paper, we derived invariant manifold and constructed control aiming at relatively continuous control. The proposed method is extended to two-stage control by partially using quasi-continuous exponential stabilization control. The controller was verified using simulation to show the effectiveness against convergence.
Review on the Applications of Deep Learning in the Analysis of Gastrointestinal Endoscopy Images

Gastrointestinal (GI) disease is one of the most common diseases and primarily examined by GI endoscopy. Recently, deep learning (DL), in particular convolutional neural networks (CNNs) have made achievements in GI endoscopy image analysis. This review focuses on the applications of DL methods in the analysis of GI images. We summarized and compared the latest published literature related to the common clinical GI diseases and covers the key applications of DL in GI image detection, classification, segmentation, recognition, location, and other tasks. At the end, we give a discussion on the challenges and the research directions of GI image analysis based on DL in the future.
PCB Fabricated Passive RF Balun for 3 T MRI Applications

In this article, a passive balun for 3 T magnetic resonance imaging (MRI) scanners operating at 128 MHz was designed and analyzed to successfully convert a balanced input signal from a dipole/loop antenna into an unbalanced output signal connected to preamplifiers or transmission line. The lumped circuit element, mathematical modeling and simulations and measurement results for the balun performance are shown. The proposed balun is intended to be integrated in electric and magnetic field probes for time domain monitoring of the time-varying RF signals during medical device testing. The implemented balun is fabricated on a low-cost, copper cladded, four-layer FR4 printed circuit board (PCB) with overall footprint of 13.4 mm $\times 14$ mm. A comparison among the ADS momentum simulations and the measured results indicates good agreement with the measured insertion and return losses of better than -1.5 dB and -15 dB, respectively.
Class Imbalance Impact on the Prediction of Complications during Home Hospitalization: A Comparative Study

Home hospitalization (HH) is presented as a healthcare alternative capable of providing high standards of care when patients no longer need hospital facilities. Although HH seems to lower healthcare costs by shortening hospital stays and improving patient’s quality of life, the lack of continuous observation at home may lead to complications in some patients. Since blood tests have been proven to provide relevant prognosis information in many diseases, this paper analyzes the impact of different sampling methods on the prediction of HH outcomes. After a first exploratory analysis, some variables extracted from routine blood tests performed at the moment of HH admission, such as hemoglobin, lymphocytes or creatinine, were found to unmask statistically significant differences between patients undergoing successful and unsucessful HH stays. Then, predictive models were built with these data, in order to identify unsuccessful cases eventually needing hospital facilities. However, since these hospital admissions during HH programs are rare, their identification through conventional machine-learning approaches is challenging. Thus, several sampling strategies designed to face class imbalance were herein overviewed and compared. Among the analyzed approaches, over-sampling strategies, such as ROSE (Random Over-Sampling Examples) and conventional random over-sampling, showed the best performances. Nevertheless, further improvements should be proposed in the future so as to better identify those patients not benefiting from HH.
Suicidal Tendency on Social Media: A Case Study

Suicide is one of the major grounds of death worldwide. According to WHO analysis, one death transpires in every 20 sec or 3000 per day. It predicts in year 2020 approx 1.53 million people will die from suicide. Not only mental illness induces suicide but various other reasons, such as, depression, bipolar disorder, anxiety, or schizophenmonia may also stimuli the same. Social media out-turns its user’s life especially teenagers of (10-24 years) age group. There are various reason of suicide attemptation and these are cyberbullying, Discrimination, Peer Pressure. Primarily data is to be collected, by various means of social media platforms such as Facebook, Twitter, Instagram and Tumblr and then preprocessed and finally analysed to predict various suicidal attempts. Data analysis extracts the sentiments of the person who want to die through their tweets. As sentiments are the main reason to attempt suicide and this is discussed by various researchers. There are various data Analysis techniques i.e. text mining, sentiment analysis, network analysis, quantitative data analysis. This paper predicts and presents a review of these techniques and challenges of the above mentioned problem. The motivation of this paper is to provide general review about the prognostics of suicide attempts on social media.
Reckoning respiratory signals to affectively decipher mental state

Recognizing mental states from physiological signal is a concern not only for medical diagnostics, but also for cognitive science, behavioral studies as well as brain machine interfaces. This study employs an unique approach of solely utilizing the respiration signals in order to decipher mental states. A public dataset, Affective Pacman, is considered for this study, where the various physiological signals are acquired during normal and frustrated mental states. An efficient way to remove the non-linear baseline drifts in the signal is implemented to extract the respiratory features in most effective way. Another major adversity is the presence of class imbalance, which is effectively rectified using Synthetic Minority Oversampling TEchnique (SMOTE). Application of SMOTE algorithm to resolve class imbalance problem, not only increased the classification accuracy, but also reduced the classifier bias towards the majority class, which in turn exceedingly enhanced the classifier sensitivity. The multilayer perceptron classifier performed best with SMOTE generated feature set, with classification accuracy (CA), true positive rate (TPR) and true negative rate (TNR) of 97.9%, 92.6% and 99.3% respectively. The current approach is found to perform better compared to relevant literature.
Vision-Based Mouth Motion Analysis in Epilepsy: A 3D Perspective

Epilepsy monitoring involves the study of videos to assess clinical signs (semiology) to assist with the diagnosis of seizures. Recent advances in the application of vision-based approaches to epilepsy analysis have demonstrated significant potential to automate this assessment. Nevertheless, current proposed computer vision based techniques are unable to accurately quantify specific facial modifications, e.g. mouth motions, which are examined by neurologists to distinguish between seizure types. 2D approaches that analyse facial landmarks have been proposed to quantify mouth motions, however, they are unable to fully represent motions in the mouth and cheeks (ictal pouting) due to a lack of landmarks in the the cheek regions. Additionally, 2D region-based techniques based on the detection of the mouth have limitations when dealing with large pose variations, and thus make a fair comparison between samples difficult due to the variety of poses present. 3D approaches, on the other hand, retain rich information about the shape and appearance of faces, simplifying alignment for comparison between sequences. In this paper, we propose a novel network method based on a 3D reconstruction of the face and deep learning to detect and quantify mouth semiology in our video dataset of 20 seizures, recorded from patients with mesial temporal and extra-temporal lobe epilepsy. The proposed network is capable of distinguishing between seizures of both types of epilepsy. An average classification accuracy of 89% demonstrates the benefits of computer vision and deep learning for clinical applications of non-contact systems to identify semiology commonly encountered in a natural clinical setting.
A 3D Deep Residual Convolutional Neural Network for Differential Diagnosis of Parkinsonian Syndromes on 18F-FDG PET Images

Idiopathic Parkinsons disease and atypical parkinsonian syndromes have similar symptoms at early disease stages, which makes the early differential diagnosis difficult. Positron emission tomography with 18F-FDG shows the ability to assess early neuronal dysfunction of neurodegenerative diseases and is well established for clinical use. In the past decades, machine learning methods have been widely used for the differential diagnosis of parkinsonism based on metabolic patterns. Unlike these conventional machine learning methods relying on hand-crafted features, the deep convolutional neural networks, which have achieved significant success in medical applications recently, have the advantage of learning salient feature representations automatically and effectively. This advantage may offer more appropriate invisible features extracted from data for the enhancement of the diagnosis accuracy. Therefore, this paper develops a 3D deep convolutional neural network on 18F-FDG PET images for the automated early diagnosis. Furthermore, we depicted in saliency maps the decision mechanism of the deep learning method to assist the physiological interpretation of deep learning performance. The proposed method was evaluated on a dataset with 920 patients. In addition to improving the accuracy in the differential diagnosis of parkinsonism compared to state-of-the-art approaches, the deep learning methods also discovered saliency features in a number of critical regions (e.g., midbrain), which are widely accepted as characteristic pathological regions for movement disorders but were ignored in the conventional analysis of FDG PET images.
Detecting Android Security Vulnerabilities Using Machine Learning and System Calls Analysis

Android operating systems have become a prime target for cyber attackers due to security vulnerabilities in the underlying operating system and application design. Recently, anomaly detection techniques are widely studied for security vulnerabilities detection and classification. However, the ability of the attackers to create new variants of existing malware using various masking techniques makes it harder to deploy these techniques effectively. In this research, we present a robust and effective vulnerabilities detection approach based on anomaly detection in a system calls of benign and malicious Android application. The anomaly in our study is type, frequency, and sequence of system calls that represent a vulnerability. Our system monitors the processes of benign and malicious application and detects security vulnerabilities based on the combination of parameters and metrics, i.e., type, frequency and sequence of system calls to classify the process behavior as benign or malign. The detection algorithm detects the anomaly based on the defined scoring function f and threshold ρ. The system refines the detection process by applying machine learning techniques to find a combination of system call metrics and explore the relationship between security bugs and the pattern of system calls detected. The experiment results show the detection rate of the proposed algorithm based on precision, recall, and f-score for different machine learning algorithms.
Human Wrist Impedance Estimation Based on Impulse Response Induced by Snap-Through Buckling of Closed-Elastica

A human joint impedance estimation method employing a wearable device using the snap-through buckling of closed-elastica has been proposed in our previous study and tested for the ankle joint. In the present study, the method is applied to the wrist impedance estimation scheme. Our method aims at the task-dependent scenarios. The wrist joint is involved in a variety of tasks, where the impedance plays a key role in physical interaction with environments. Because of the unique design and actuation concept, the proposed device does not disrupt the voluntary movements of the wrist and can be used in the estimation during the user’s own motion planning. In this paper, to verify our method, the wrist impedance under the quasi-stationary condition is estimated, and the results are compared with the related works. Also, we discuss the profiles of the induced impulse responses and suggest an alternative estimation manner which does not require any force (torque) information.
Pattern detection from seating pressure distribution during wheelchair motion using deep embedded clustering

To minimize the occurrence of ulcers in high-risk mobile individuals such as wheelchair users, it is necessary to detect all typical distribution patterns and to indentify the patterns that may be associated with pressure ulcers. However, pattern detection is difficult because the pressure distribution during motion includes a variety of patterns compared to those of static postures. Thus, the establishment of a method to detect typical patterns based on distribution patterns is important. We utilized deep embedded clustering for identification purposes. This clustering technique extracts features using auto-encoder and simultaneously optimizes data points into the clusters, which might realize good clustering performance due to the detected optimal features. We used a pressure distribution dataset that was pre-labeled by nursing experts. The dataset consisted of a total of 26944 distribution images with ten class annotations. The clustering method including traditional approaches (k-means and principal component analysis plus k-means) were compared with deep embedded clustering while the threshold to noise reduction was changing. The deep embedded clustering with 80 mmHg threshold achieved the best performance. This approach also tended to be less dependent on the threshold values.
Assessment of Laboratory Mouse Activity in Video Recordings Using Deep Learning Methods

Analysis of laboratory animal behavior allows assessment of animal wellbeing. We present a method for the classification of different activities of laboratory mice by analyzing video clips using three deep learning methods. Animals placed in observation cages are filmed and short video clips are labelled as belonging to one of five defined behaviors. Subsequently, three different methods based on convolutional neural networks (CNNS) are applied to classify the clips. The best performing method - a two-stream network that analyzes individual frames as well as the video’s optical flow - achieves an accuracy of 86.4%, including detection of important behavioral patterns such as self-grooming. These results show that the presented analysis protocol allows automated assessment of animal behavior by algorithmic analysis of videos of mice on observation boxes.
Real-Time Detection of Ureteral Orifice in Urinary Endoscopy Videos Based on Deep Learning

In urology endoscopic procedures, the Ureteral Orifice (UO) finding is crucial but may be challenging for inexperienced doctors. Generally, it is difficult to identify UOs intraoperatively due to the presence of a large median lobe, obstructing tumor, previous surgery, etc. To automatically identify various types of UOs in the video, we propose a real-time deep learning system in UO identification and localization in urinary endoscopy videos, and it can be applied to different types of urinary endoscopes. Our UO detection system is mainly based on Single Shot MultiBox Detector (SSD), which is one of the state-of-the-art deep-learning based detection networks in natural image domain. For the preprocessing, we apply both general and specific data augmentation strategies which have significantly improved all evaluation metrics. For the training steps, we only utilize rescetoscopy images which have more complex background information, and then, we use ureteroscopy images for testing. Simultaneously, we demonstrate that the model trained with rescetoscopy images can be successfully applied in the other type of urinary endoscopy images with four evaluation metrics (precision, recall, F1 and F2 scores) greater than 0.8. We further evaluate our model based on four independent video datasets which comprise both rescectoscopy videos and ureteroscopy videos. Extensive experiments on the four video datasets demonstrate that our deep-learning based UO detection system can identify and locate UOs of two different urinary endoscopes in real time with average processing time equal to 25 ms per frame and simultaneously achieve satisfactory recall and specificity.
Sleep Apnea Severity Estimation from Respiratory Related Movements Using Deep Learning

Sleep apnea is a common chronic respiratory disorder which occurs due to the repetitive complete or partial cessations of breathing during sleep. The gold standard assessment of sleep apnea requires full night polysomnography in a sleep laboratory which is expensive, time consuming, and inconvenient. Hence, there is an urgent need for a convenient, robust and wearable monitoring device for screening of sleep apnea. A simple and convenient accelerometer-based portable system is presented to estimate the severity of sleep apnea by analyzing tracheal movements. Respiratory related movements were recorded over the suprasternal notch using a 3D accelerometer. Twenty-one physiological features (7 features, 3 accelerometer channels) were extracted. Performance of three different deep learning models – convolutional neural network, recurrent neural network, and their combination – were evaluated for estimating the apnea hypopnea index (AHI). The estimated AHI is compared to the gold standard polysomnography. In 3-fold cross-validation experiments with 20 participants (9 female, age=47.8±18.0 years, BMI=30.8±4.8, AHI=22.2±21.8 events/hr), we achieved a correlation coefficient between gold standard and estimated values (r-value = 0.84). The proposed system is an accurate, convenient, and portable device suitable for home sleep apnea screening.
International Conference on Intelligent Computing and its Emerging Applications ( ICEA ) Static PE Malware Type Classification Using Machine Learning Techniques

Abstract— In recent years, machine learning techniques have become more and more popular. It is also introduced to the research about malware detection. However, most of research are still focused on binary classification issue, which predicts a file as benign or malicious. Only a small fraction of them work on malware type detection or classification of malware family. This work mainly uses several machine learning models to build static malware type classifiers on PE-format files. A recently released dataset for windows malware detection are used and relabeled into multi-class via VirusTotal, and several efficient and scalable machine learning models are considered. The evaluation results show that our best model, random forest, can achieve high performance with micro avg f1 score 0.96 and macro avg f1 score 0.89, which is better than the model used in referred work.
Link Prediction in Evolving Networks Base on Information Propagation

Link prediction is an important issue in graph data mining. In social networks, link prediction is used to predict missing links in current networks and new links in future networks. This process has a wide range of applications including recommender systems, spam mail classification, and the identification of domain experts in various research areas. In order to predict future node similarity, we propose a new model, Common Influence Set, to calculate node similarities. The proposed link prediction algorithm uses the common influence set of two unconnected nodes to calculate a similarity score between the two nodes. We used the area under the ROC curve (AUC) to evaluate the performance of our algorithm and that of previous link prediction algorithms based on similarity over a range of problems. Our experimental results show that our algorithm outperforms previous algorithms.
Comparison of Prediction Methods for Air Pollution Data in Malaysia and Singapore

The process for analyzing and extracting useful information from a large database that employs one or more machine learning techniques is Data Mining. There are many data mining methods that can be used in a variety of data patterns. One of them is prediction modeling. This study compares several data mining performance methods for prediction such as Naïve Bayes, Random Tree, J48, and Rough Set to get the most powerful classifier to extract the knowledge of air pollution data. The parameters being used for observation in the performance of the prediction methods are correctly and incorrectly classified instances, the time taken, and kappa statistic. The experimental result reveals that Rough Set is extremely good for classifying the Air Pollutant Index (API) data from Malaysia and Singapore. Rough Set has the lowest error and the highest performance compared to other methods with the accuracy more than 97%.
The effect of the number of fibers in tractography reconstruction of white matter bundles

The study of white matter (WM) through diffusion Magnetic Resonance Imaging (dMRI) is crucial to obtain a better understanding of human brain connections and functions, at a macroscopic level. A large number of works have focused on long range brain connections, while recently, several studies have also analyzed superficial WM connectivity. In recent years, with the massive use of HCP database, and its processing with known softwares like DSI Studio and MRtrix, it is necessary to evaluate the influence of tractography parameters on the reconstruction of fiber bundles and further analyses. We study the effect of the number of fibers, for whole brain tractography, on the reconstruction of deep and superficial WM bundles based on their segmentation using multi-subject bundle atlases. For DSI Studio (deterministic algorithm), a value of 1M fibers could reconstruct most of deep white matter (DWM) bundles, while a value of 1.5M was required for superficial white matter (SWM) bundles. In the case of MRtrix (probabilistic algorithm), a value of 3M fibers was found to be suitable for the study of both kinds of fibers. Furthermore, we found the tracking of SWM bundles to be more sensitive to several parameters than DWM, for DSI Studio.