Miracle-dz/UniFAR-FTUs huggingface dataset:
Steering of audio input in hearing aids by eye gaze through electrooculography

Proceedings of the International Symposium on Auditory and Audiological Research (Proc. ISAAR), Vol. 6: Adaptive Processes in Hearing, August 2017, Nyborg, Denmark. Edited by S. Santurette, T. Dau, J. C. -Dalsgaard, L. Tranebjærg, T. Andersen, and T. Poulsen. The Danavox Jubilee Foundation, 2017. © The Authors. ISBN: 978-87-990013-6-1. Steering of audio input in hearing aids by eye gaze through electrooculography
Implementation of automated diagnostic systems: ophthalmic arterial disorders detection case

This article intends to give an integrated view of the automated diagnostic systems combined with spectral analysis techniques in the detection of arterial disorders. The article includes illustrative and detailed information about implementation of automated diagnostic systems and feature extraction/selection from signals recorded from ophthalmic arteries. The major objective of the article is to be a guide for readers who want to develop an automated diagnostic systems for detection of arterial disorders. Towards achieving this objective, this article presents the techniques which should be considered in developing automated diagnostic systems. The author suggests that the content of the article will assist people in gaining a better understanding of the techniques in the detection of arterial disorders.
Characteristics of intestinal bacterium Akkermansia muciniphila and the association with host health

Akkermansia muciniphila was isolated from human faeces in anaerobic medium containing gastric mucin as the sole carbon and nitrogen source. It represents approximately 1% to 3% of the total microbiota in the intestine of healthy adult, and it produces propionic acid as main metabolite. This mucin-degrading bacterium has been closely correlated with host health. A. muciniphila administration could enhance glucose tolerance, reduce insulin resistance, modulate pathways involved in establishing homeostasis for basal metabolism and immune tolerance toward commensal microbiota. But on the contrary, the abundance of the mucin-degrading A. muciniphila was significantly increased by heme diet, which is associated with epithelial hyperproliferation, and destroyed mucus layer. The mechanisms of mucin utilization by this bacterium in the gut and the interactive mechanisms between A. muciniphila and host is still unknown and need further explorations.
Realizing Corner States in Artificial Crystals Based on Topological Spin Textures

The recent discovery of higher-order topological insulators (HOTIs) has significantly extended our understanding of topological phases of matter. Here, we predict that second-order corner states can emerge in the dipolar-coupled dynamics of topological spin textures in two-dimensional artificial crystals. Taking a breathing honeycomb lattice of magnetic vortices as an example, we derive the full phase diagram of collective vortex gyrations and identify three types of corner states that have not been discovered before. We show that the topological"zero-energy"corner modes are protected by a generalized chiral symmetry in the sexpartite lattice, leading to particular robustness against disorder and defects, although the conventional chiral symmetry of bipartite lattices is absent. We propose the use of the quantized $\mathbb{Z}_{6}$ Berry phase to characterize the nontrivial topology. Interestingly, we observe corner states at either obtuse-angled or acute-angled corners, depending on whether the lattice boundary has an armchair or zigzag shape. Full micromagnetic simulations confirm the theoretical predictions with good agreement. Experimentally, we suggest using the recently developed ultrafast Lorentz microscopy technique [M\"{o}ller \emph{et al}. ,{arXiv:1907.04608}] to detect the topological corner states by tracking the nanometer-scale vortex orbits in a time-resolved manner. Our findings open up a promising route for realizing higher-order topologically protected corner states in magnetic systems and finally achieving topological spintronic memory and computing.
The Relationship between Emotional Intelligence and Psychological Wellbeing of Undergraduates of the Faculty of Management Studies and Commerce , University of Sri

Area of the Study This study seeks to explore the relationship between emotional intelligence (EI) and psychological wellbeing of management undergraduates of the Faculty of Management Studies and Commerce, University of Sri Jayewardenepura as a contemporary topic among the scholars. Problem of the Study There is an empirical and knowledge gap in the Sri Lankan context on the relationship between emotional intelligence and psychological well being of undergraduates. This study is aimed to reveal the relationship between emotional intelligence and psychological well being of management undergraduates in the University of Sri Jayewardenepura. Method of the Study The data were collected from 120 undergraduates (60 males and 60 females) and stratified sampling method was used to categorize the population, department wise and then the simple random sampling procedure was used for selecting the sample from the departments to collect data. Structured questionnaire which consisted Singh’s (2006) 22 items scenario based EI quotient and Ryff’s (1989) 42 items psychological wellbeing questionnaire were administered. Pearson Product Moment of Coefficient of Correlation and One Way Anova was used in order to find out the relationship of EI with psychological wellbeing of undergraduates. Findings of the Study This study found out that emotional intelligence is positively correlated with psychological well being. Moreover, emotional intelligence is positively correlated with each dimension of psychological wellbeing namely self acceptance, purpose in life, positive relations, personal growth, environmental mastery and autonomy. Conclusion of the Study It is concluded that the greater emotional intelligence is positively correlated with high level of psychological wellbeing of management undergraduates. Further, this research supported the view that emotionally intelligent undergraduates enjoy greatest happiness in their life.
Predictive Model for Assessment of Pathological Response of Colorectal Liver Metastases to Chemotherapy from CT Images

In this work, we propose a predictive deep learning model for assessment of pathological response of colorectal liver metastases to chemotherapy from CT images. We conducted a retrospective analysis of a prospectively maintained database of patients who underwent partial hepatectomy or biopsy of colorectal liver metastases. We introduce a novel variant of the Inception module that includes instance normalization layers to accommodate for various contrast agent timing and baseline examinations. The clinical Rubbia-Brandt tumor regression grade (TRG) obtained from histopathology images of the resected lesions was used as ground truth. For the most common TRG dichotomization, our model achieves an AUC of 0.87 ${\pm}$ 0.03. The results show that the model is able to establish a link between CT images and the pathological assessment.
Low power CMOS preamplifier for neural recording applications

A fully-differential bandpass CMOS (complementary metal oxide semiconductor) preamplifier for extracellular neural recording is presented. The capacitive-coupled and capacitive-feedback topology is adopted. The preamplifier has a midband gain of 20.4 dB and a DC gain of 0. The −3 dB upper cut-off frequency of the preamplifier is 6.7 kHz. The lower cut-off frequency can be adjusted for amplifying the field or action potentials located in different bands. It has an input-referred noise of 8.2 μVrms integrated from 0.15 Hz to 6.7 kHz for recording the local field potentials and the mixed neural spikes with a power dissipation of 23.1 μW from a 3.3 V supply. A bandgap reference circuitry is also designed for providing the biasing voltage and current. The 0.22 mm2 prototype chip, including the preamplifier and its biasing circuitry, is fabricated in the 0.35-μm N-well CMOS 2P4M process.
HeartID:A Modern way of Authentication

There is need to develop a powerful mechanism so Instead of using unreliable and hackable passwords, the software, called HeartID, uses a person's heartbeat to authenticate their identity and protect access to important information. HeartID is more convenient than existing biometric identification techniques; like fingerprints, irises; a cardiac signal requires a simple touch. Cardiac signals are also highly secure and their accuracy is rated at greater than 99 %. "HeartID is currently the only commercially available biometric authentication solution that uses the cardiac signal". An individual holds a mouse-like controller with built-in sensors, and their unique cardiac rhythm is recognized by the connected computer. Authorized users with a recognized cardiac rhythm are immediately allowed access and logged in. As a further security measure, HeartID uses continuous monitoring, immediately logging off any user without an authenticated cardiac rhythm.
Briefing: Atterberg limits and peat

The Atterberg limits are the most common tests specified by practising geotechnical engineers worldwide. These tests, which have physical meaning for fine-grained mineral soils, are regularly specified for peat and other highly organic soils despite fundamental issues regarding their appropriateness for such materials. These issues are explored in this briefing from which it is concluded that Atterberg limit concepts are inappropriate for peat and are unlikely to meaningfully correlate with mechanical (strength) behaviour. In assessing likely engineering behaviour of peat, a more useful suite of index tests is natural water content, organic content, fibre content and degree of humification. For correlations with strength and compressibility parameters, Dutch organic soil practice has found that water content and bulk density are usually sufficient.
Utilização do método FMEA na identificação e análise dos impactos ambientais causados pelos postos de combustíveis: um estudo de caso.

The retail of fuel is one of the activities that generates significant impacts on the environment. In this sense, this study aims to identify and analyze the potential environmental impacts caused by the activities carried out by a fuel station in the light of the method Failure Analysis and its Effects (FMEA). It was used as a theoretical basis the model proposed by Vandenbrande FMEA (1998). Regarding the methodology, the research is classified as exploratory and descriptive, using the case study method. To this end, we collected primary and secondary data, the primary were obtained through semi-structured interviews with the owner, manager, attendants, vendors and cookers of the restaurant, and on-site observation. As for the secondary data were obtained through reports, articles, environmental permits, among others. As a result of impact was identified as the high risk of human exposure, medium risk of contamination of soil and water and fire, and the low risk of waste, it is concluded therefore that the use of the FMEA method contributes for the constant evaluation of environmental risk and the adoption of mitigation actions, which will lead the organization to improve its processes and monitoring of such improvements by measuring the levels of criticality.
Time Reversal Focusing Applied to Lithotripsy

Time reversal of ultrasonic field allows a very efficient approach to focusing pulsed ultrasonic waves through lossless inhomogeneous media. Time reversal mirrors (TRM) are made of large transducer arrays, allowing the incident acoustic field to be sampled, time reversed and re-emitted. Time reversal processing permits a choice of any temporal window to be time reversed, allowing operation in an iterative mode. In multitarget media, this process converges on the most reflective target, i.e., the dominant scatterer. In this paper, the time reversal process is applied to track, in real time, a moving gall bladder or kidney stone embedded in its surrounding medium. We investigate the feasibility of a piezoelectric shock wave generator in which the focal zone is moved electronically to track the stone during a lithotripsy treatment. We show that TRM allows us to obtain sharp focusing on one bright point of the stone. The time of flight profile is then determined and used in a least-mean-square method to calculate the spatial coordinates of the stone.
A Real-time Algorithm for Obstacle Avoidance of Autonomous Mobile Robots

SUMMARY A real-time obstacle avoidance algorithm is proposed for autonomous mobile robots. The algorithm is sensor-based and consists of a H-mode and T-mode. The algorithm can deal with a complicated obstacle environment, such as multiple concave and convex obstacles. It will be shown that the algorithm is more efficient and more robust than other sensor-based algorithms. In addition, the algorithm will guarantee a solution for the obstacle avoidance problem. Since the algorithm only takes up a small computational time, it can be implemented in real time.
A Traffic Prediction Method based on ANN and Adaptive Template Matching

Considering the significant burstiness in self-similar network traffic, this paper presents a one-step traffic prediction method based on ANN (artificial neural network) and adaptive template matching. Firstly control chart is used to decompose the original traffic into non-burst part and burst part. Then the two parts are predicted by ANN and adaptive template matching method, respectively. Finally, the prediction of the original traffic is obtained by fusing the predictions of the two parts. Simulation results show that the accuracy of method proposed in this paper is much higher compared with the traditional traffic prediction methods. Streszczenie. Artykul prezentuje metode przewidywania przeplywu informacji bazującą na sztucznych sieciach neuronowych i adaptacyjnym dopasowaniu szablonu. Początkowo przeplyw dzielony jest na pogrupowany i nie pogrupowany. Nastepnie analizuje sie obie cześci. Wreszcie dokonuje sie fuzji rezultatow. Rezultaty symulacji wskazują ze proponowana metoda ma lepszą dokladnośc niz metody tradycyjne. (Metoda przewidywania przeplywu informacji bazująca na sztucznych sieciach neuronowych i adaptacyjnym dopasowaniu szablonu)
Notating disfluencies and temporal deviations in music and arrhythmia

Expressive music performance and cardiac arrhythmia can be viewed as deformations of, or deviations from, an underlying pulse stream. I propose that the results of these pulse displacements can be treated as actual rhythms and represented accurately via a literal application of common music notation, which encodes proportional relations among duration categories, and figural and metric groupings. I apply the theory to recorded music containing extreme timing deviations and to electrocardiographic (ECG) recordings of cardiac arrhythmias. The rhythm transcriptions are based on rigorous computer-assisted quantitative measurements of onset timings and durations. The root-mean-square error ranges for the rhythm transcriptions were (19.1, 87.4) ms for the music samples and (24.8, 53.0) ms for the arrhythmia examples. For the performed music, the representation makes concrete the gap between the score and performance. For the arrhythmia ECGs, the transcriptions show rhythmic patterns evolving through time, progressions which are obscured by predominant individual beat morphology- and frequency-based representations. To make tangible the similarities between cardiac and music rhythms, I match the heart rhythms to music with similar rhythms to form assemblage pieces. The use of music notation leads to representations that enable formal comparisons and automated as well as human-readable analysis of the time structures of performed music and of arrhythmia ECG sequences beyond what is currently possible.
Impact analysis of wind turbines blockage on doppler weather radar

The split-step solution to the parabolic wave equation describing beam propagation permits examination of the signal degradation in a Doppler weather radar caused by wind turbines blockage under general atmospheric conditions and at arbitrary transmitter and receiver configurations. At radar wavelengths, an understanding of turbine and terrain obscuration effects is essential for deciding the reliability of radar measurements affected by blockage.
BUSINESS FAILURE PREDICTION WITH SUPPORT VECTOR MACHINES AND NEURAL NETWORKS: A COMPARATIVE STUDY

Bankruptcy prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning technique, support vector machines (SVM), to the bankruptcy prediction problem in an attempt to provide a model with better explanatory power. To evaluate the prediction accuracy of SVM, we compare its performance with three-layer fully connected backpropagation neural networks (BNN). The experiment results show that SVM outperforms the BNN.
Rotor side control of grid-connected wound rotor induction machine

This paper deals with modeling, simulation and implementation of rotor side control strategies for a grid connected wound rotor induction machine. In the system under consideration. the stator is directly connected to the constant frequency three-phase grid and the rotor is supplied by two back-to-back three-phase voltage source inventers with a common OC link. Such a configuration is attractive in large power applications with limited speed range of operation. The rotor currents are controlled at any desired phase frequency and magnitude to control the active and reactive powers or the machine independently. A stator flux-oriented model of the doubly fed wound rolor induction machine is presented. The current controllers are designed in the field coordinates. Simulation waveforms exhibit excellent transient response of the current loops; the dynamics or the direct and quadrature axes are also observed to be decoupled. An experimental setup consisting of IGBT inverters and a TMS320F240 DSP-based digital controller is developed in the laboratory to implement the control algorithms. Relevant experimental waveforms are presented they are observed to be in good agreement with the simulation results.
Speaker identification system using MFCC procedure and noise reduction method

The most commonly method use by people to protect their secured data or information is using password or PIN/ID protection. This method require user to authenticate themselves by entering password that they had already created. However, due to lack of security the data is not secured enough. There are cases of fraud and theft when people can easily know the password. But as time goes by, there is a new technology known as Biometric Identification System. It uses biometric characteristics of an individual that is unique and different from everyone else and therefore can be use to authenticate the user authority access. This research is an implementation of speech recognition as medium security access control to restricted services such as phone banking system, voicemail or access to database services. First, speaker signal will go to pre-treatment process, where it will remove the background noise. Then, features from speech signal will be extracted using Me1 Frequency Cepstrum Coefficients (MFCC) method. Then, using Vector Quantization, the features will be matched with the reference speech in database. The real speaker is identified by clustering the speech signal from the tested speaker to codebook of each speaker using K-means algorithm and the speaker with the lowest distortion Euclidean distances is chose as the correct speaker. The main focus of tlns research is speaker identification, which compared speech signal from unknown speaker to a database of known speaker using text-dependent utterances. From the experimental results, this method has showed that it was able to recognize the correct voice perfectly.
Emotion Sound Classification with Support Vector Machine Algorithm

Speech one of the biometric characteristic owned by human being, as well as fingerprint, DNA, retina of the eyes and so not the two human beings who have the same voice. Human emotion is a matter that can only be predicted through the face of a person, or from the change of facial expression but it turns out human emotions can also be detected through the spoken voice. Someone emotion are happy, angry, neutral, sad, and surprise can be detected through speech signal. The development of voice recognition system is still running at this moment. So that ini this research, the analysis of someone emotion through speech signal. Some related research about the sound aims to have process of identity recognition gender recognition, Emotion recognition based on conversation. In this research the writer does research on the emotional classification of speech two classes started from happy, angry, neutral, sad and surprise while the used algorithm in this research is SVM (Support Vector Machine) with alghoritmMFCC (Mel-frequency cepstral coefficient)for extraction where it contains filter process that adapted to human’s listening. The result of the implementation process of both algorithms gives accuracy level ashappy=68.54%, angry=75.24%, neutral=78.50%, sad=74.22% and surprise=68.23%.
Collaborative robot zero moment control for direct teaching based on self-measured gravity and friction

The focus of this study is a moment compensation control algorithm driven by a direct current servo motor. Zero moment robot teaching is achieved with a joint moment compensation algorithm. The moment equilibrium equation is derived based on moment compensation. The current signal detected by a Hall effect sensor is multiplied by a torque constant to estimate the torque value of the robot joint. The compensation current is obtained through parameter identification to overcome gravitational and friction torques. The two variables of speed and position are separately controlled, allowing the compensation current of Coulomb friction and viscous friction force to be separated from the compensation current of friction torque. This study presents the system research, design, and development of a high-precision position control theory of a robot zero moment teaching control method. A collaborative robot is used as the test and verification platform to confirm the feasibility and effectiveness of the proposed theoretical method and implementation technology.
Deprecating TLSv1.0 and TLSv1.1

This document, if approved, formally deprecates Transport Layer Security (TLS) versions 1.0 [RFC2246] and 1.1 [RFC4346] and moves these documents to the historic state. These versions lack support for current and recommended cipher suites, and various government and industry profiles of applications using TLS now mandate avoiding these old TLS versions. TLSv1.2 has been the recommended version for IETF protocols since 2008, providing sufficient time to transition away from older versions. Products having to support older versions increase the attack surface unnecessarily and increase opportunities for misconfigurations. Supporting these older versions also requires additional effort for library and product maintenance. This document also deprecates Datagram TLS (DTLS) version 1.0 [RFC6347] (but not DTLS version 1.2, and there is no DTLS version 1.1). This document updates many RFCs that normatively refer to TLSv1.0 or TLSv1.1 as described herein. This document also updates RFC 7525 and hence is part of BCP195.