Miracle-dz/UniFAR-FTUs huggingface dataset:
FPGA-Based Multi Heart Diseases Classification System with the Aid of LabVIEW

Electrocardiography (ECG) is one of the most important recording processes used in medicine; it provides a clear description of situation of the heart. The development of technological and computer science, which led to the emergence of high-resolution screens placed on the wrist and able to record the heart signal, increased the importance of developing a real time and portable multi heart diseases diagnosis system. In this paper, we propose an FPGA-based multi heart diseases classification system that identify eight different heart malfunctions depending on the standard ECG features. Our proposed ECG system, achieved with the aid of LabView, consists of three parts: Acquisition System, Feature Extraction and Making Decision using two different classifiers; Threshold Decision (TD) and Numeral Virtual Generalizing Random-Access Memory (NVG-RAM) weightless neural network. The proposed classifiers were implemented using Verilog HDL and Xilinx Spartan 3AN FPGA. The FPGA mapping showed that TD classifier occupy 1% of the hardware platform slices with execution time of 16 ns, while the NVG-RAM classifier utilize 21% of the FPGA slices with an increase of the execution time equal to 12.81 μs. On the other hand, the NVG-RAM outperforms the TD algorithm and the other proposed classifiers in the literature. In case of the experimental data, the probability of correct classification (PCC) of heart conditions was 100% for NVG-RAM and 98.84% for TD classifier. Whereas, the success rates in case of generated data for the executed TD and NVG-RAM classifiers were 98% and 100 %, respectively.
HEVC Decoder Analysis on ARM Processor

In this paper, we perform an implementation of the High Efficiency Video Coding (HEVC) decoder on ARM processor. The complexity of HM10.0 reference software decoder has been profiled, using GNU gprof under the embedded Linux. In order to give indication of the most complex modules, numerous sequences were tested, for different resolutions and with two different Quantization Parameter (QP) values. This paper reveals the bottlenecks of HM decoder and provides implementation guidelines for future real-time HEVC decoder.
Ensemble-of-Concept Models for Unsupervised Formation of Multiple Categories

Recent studies have shown that robots can form concepts and understand the meanings of words through inference. The key idea underlying these studies is the “multimodal categorization” of a robot’s experiences. Despite the success in the formation of concepts by robots, a major drawback of previous studies stems from the fact that they have been mainly focused on object concepts. Obviously, human concepts are limited not only to object concepts but also to other kinds such as those connected to the tactile sense and color. In this paper, we propose a novel model called the ensemble-of-concept models (EoCMs) to form various kinds of concepts. In EoCMs, we introduce weights that represent the strength connecting modalities and concepts. By changing these weights, many concepts that are connected to particular modalities can be formed; however, meaningless concepts for humans are included in these concepts. To communicate with humans, robots are required to form meaningful concepts for us. Therefore, we utilize utterances taught by human users as the robot observes objects. The robot connects words included in the teaching utterances with formed concepts and selects meaningful concepts to communicate with users. The experimental results show that the robot can form not only object concepts but also others such as color-related concepts and haptic concepts. Furthermore, using word2vec, we compare the meanings of the words acquired by the robot in connecting them to the concepts formed.
The Connection between Music and Science Education, and the Practice of Innovative Instructional Design

Music is composed of many notes; machine is also composed of many parts, according to a certain combination regulations. Nobel Prize laureate Tsung-Dao Lee said: Science and art are inseparable, just like both sides of a coin. This illustrates the commonality of music and science. On the other hand, music, in fact, is the sound of material.This study uses creative thinking teaching technique, and John Dewey' theory “Learning by Doing”, focus on instructional design, students experience and combined with art, science and musical instruments, creating an innovative teaching model.The research results developed a series of instrumental and mechanical design, students will enable to consciously notice the interaction between mechanical components and music. And then learning music theory naturally. Because of the innovative teaching model, students and teacher are getting new inspiration and benefit.
Real-Time Object Detection and Classification for Autonomous Driving

In this paper, a single deep convolutional neural network for real-time detection and classification of on-road objects has been proposed. The resulted network could to be used for implementing a cost-effective and useful system in the domain of self-driving vehicles. Our network has been trained on KITTI Road dataset and could be used to recognize various on-road objects including vehicles, bicyclist, and pedestrians. The final network processes 448×448 input images at 47 frame per second (fps) on a NVIDIA GeForce GTX960 GPU. Our model achieves 78.4% mAP on the KITTI dataset, which is 11.9% higher than traditional YOLO and 5.2% more than SSD300, two of the top real-time object detection systems. Although our system is about 12 fps slower than SSD300, it is still well above the real-time performance.
Microwave vs. Millimeter-Wave Propagation Channels: Key Differences and Impact on 5G Cellular Systems

Fifth generation cellular systems will be deployed in the microwave and millimeterwave (mmWave) frequency bands (i.e., between 0.5100 GHz). Propagation characteristics at these bands have a fundamental impact on each aspect of the cellular architecture, ranging from equipment design to real-time performance in the field. While we have a reasonable understanding of the propagation characteristics at microwave (< 6 GHz) frequencies, the same cannot be said for mmWave. This article explains key differences in the propagation characteristics between the microwave and mmWave bands, and further gives examples of how these differences impact 5G system design.
Learning Effective Distributed Representation of Complex Biomedical Concepts

Word embedding is the state-of-the-art representation to capture semantic information of terms. It benefits a wide range of natural language processing and related applications, not only in general fields of artificial intelligence but also in bioinformatics. Although recent efforts of using word embedding to represent medical concepts have provided remarkable analyses, many essential problems remain unsolved. Examples include representation of complex concepts (i.e., formed by multiple tokens), leveraging of a large corpus to maximize the trainable concepts, and downstream analyses on a biomedical-related dataset. Our study focused on training effective representations for biomedical concepts including complex ones. We used an efficient technique to index all possible concepts of UMLS thesaurus (Unified Medical Language System) in a huge corpus of 15,4 billion tokens. By this way, we can obtain the vector representations for more than 650,000 concepts, the largest ever reported resource to date. Furthermore, evaluations of trained vectors on retrieval task show superior performance compared to recent studies.
A Deep Look into Logarithmic Quantization of Model Parameters in Neural Networks

Based on the fact that parameters of pre-trained neural networks naturally have non-uniform distributions, logarithmic quantization of network parameters achieves better classification results than linear quantization of the same resolution. In our practice, we found that the logarithmic quantization suffers huge accuracy decrease on small size neural networks. This is because the parameters of trained small neural networks are not highly concentrated around 0. In this paper, we analyse in depth the attributes of logarithmic quantization. In addition, existing compression algorithms highly rely on retraining which requires heavy computational power. In such a situation, we propose a new logarithmic quantization algorithm to mitigate the deterioration on neural networks which contain layers of small size. As the result, our method achieves the minimum accuracy loss on GoogLeNet after direct quantization compared to quantized counterparts.
Android Apps Management System to Ensure Mobile Security

It is certain that the future of the network will be the mobile utter. Google‘s Android platform is a widely forecast open source operating system for mobile phones. This article is about Android‘s security model and seeks to reveal the complexity of secure application development, identifying lessons and opportunities for future enhancements. This article provides a secure way to download an application and managing access permission for using the Android mobile phone. Following article shows how to download an application without virus or secure user android in a convenient way. This application provides user to manage the access permissions both automatically and manually. The user can access permissions when the user installs an application or user can manually go to settings and update the permissions. Thus, we provide a permission system through which uses android devices can propose abstract authorization rules, provide high- level rules and learn user privacy preferences. Therefore, concepts and approaches towards effective privacy management for mobile platforms are reviewed.
Survey of Cross Device Matching Approaches with a Case Study on a Novel Database

Due to the convergence of digital marketing and advertising efforts with variety of digital devices like tablets, smart-phones, PCs, smart TVs and even wearable devices, tracking the relations among these devices has become a key component for personalised targeting of their users. In other words, as online tracking is evolving from device tracking to people tracking, providing user-centric experience on the online domains is getting prevalent for product recommendations and online advertising. Although some cross device tracking services have been provided in recent years and some global competitions have been held by using dataset comprised of anonymous users’ device statistics and online behavioural patterns, very few attempts have been put forward in order to compare these efforts comprehensively. This study is putting out a survey for investigating latest researches related with cross-device targeting, and proposes a new probabilistic cross device matching approach along with a new dataset which can be used as a benchmark for future studies.
Esomeprazole Increases Airway Surface Liquid pH in Primary Cystic Fibrosis Epithelial Cells

Respiratory failure, driven by airways mucus obstruction, chronic inflammation and bacterial infections, is the main cause of mortality and morbidity in people with cystic fibrosis (CF) due to defects in the Cl- and HCO3− transport activity of the CF Transmembrane conductance Regulator (CFTR). Most recent pre-clinical and clinical studies have focused on restoring CFTR function by enhancing its trafficking or transport activity and show promising results. However, there are a significant number of patients that will not benefit from these CFTR-targeted therapies and it is therefore important to identify new non-CFTR targets that will restore lung function, by-passing CFTR dysfunction. The H+/K+-ATPase, ATP12A, has recently been identified as a potential novel target for CF therapies, since its acute inhibition by ouabain was shown to help restore mucus viscosity, mucociliary transport, and antimicrobial activity using in vitro CF airway models, and this effect was linked to an increase in the pH of the airway surface liquid (ASL). Here, we have evaluated the potential therapeutic use of ouabain by investigating the effect of chronically treating fully differentiated CF primary human airway epithelial cells (hAECs) with ouabain, under thin film conditions, resembling the in vivo situation. Our results show that although chronic treatment increased ASL pH, this correlated with a deleterious effect on epithelial integrity as assessed by LDH release, transepithelial electrical resistance, fluorescein flux, and ion transport. Since ATP12A shares approximately 65% identity with the gastric H+/K+-ATPase (ATP4A), we investigated the potential of using clinically approved ATP4A proton pump inhibitors (PPIs) for their ability to restore ASL pH in CF hAECs. We show that, despite not expressing ATP4A transcripts, acute exposure to the PPI esomeprezole, produced changes in intracellular pH that were consistent with the inhibition of H+ secretion, but this response was independent of ATP12A. More importantly, chronic exposure of CF hAECs to esomeprazole alkalinized the ASL without disrupting the epithelial barrier integrity, but this increase in ASL pH was consistent with a decrease in mRNA expression of ATP12A. We conclude that PPIs may offer a new approach to restore ASL pH in CF airways, which is independent of CFTR.
Magnitude of Anemia and Associated Factors among Pregnant Women Attending Antenatal Care in Public Hospitals of Ilu Abba Bora Zone, South West Ethiopia: A Cross-Sectional Study

Background Anemia is a global public health problem affecting all population particularly pregnant women. Hence, this study assessed the magnitude of anemia and associated factors among pregnant. Methods Institution based cross-sectional study was conducted among 416 pregnant women attending antenatal clinic in three public hospitals of Ilu Aba Bora zone. The study participants were selected by proportional allocation based on the number of pregnant women that the respective health facilities contain. Semistructured questionnaire was used for data collection. Midupper arm circumference was employed to assess the nutritional status and standard mood depression assessment tool was used to assess depression. Data were centered and analyzed using SPSS version 20.0. Logistic regression analyses were used to see the association of different variables. Results In this study, 31.5% of pregnant women were anemic. In addition, having family size five and above [AOR = 2.97, 95% CI (1.69, 5.27)], being rural resident [AOR=2.74, (95%CI) (2.11, 5.06)], had a higher odds of anemia. Similarly, having soil transmitted helminthes infection [AOR= 3.19, 95% CI (1.5, 6.65)] and history of malaria infection in the last one year [AOR= 3.10, 95% CI (2.10, 5.06)] had also a higher odds anemia during pregnancy. Moreover, being undernourished [AOR= 2.74 95% CI (1.34, 5.57)] was negatively associated with magnitude of anemia. Conclusions The magnitude of anemia among pregnant women was found to be significant. Residence, family sizes, history of malaria infection during the last one year, and undernourishment were significantly associated with anemia during pregnancy.
Feature Learning With a Divergence-Encouraging Autoencoder for Imbalanced Data Classification

Imbalanced data exists commonly in machine learning classification applications. Popular classification algorithms are based on the assumption that data in different classes are roughly equally distributed; however, extremely skewed data, with instances from one class taking up most of the dataset, is not exceptional in practice. Thus, performance of algorithm often degrades significantly when encountering skewed data. Mitigating the problem caused by imbalanced data has been an open challenge for years, and previous researches mostly have proposed solutions from the perspectives of data re-sampling and algorithm improvement. In this paper, focusing on two-class imbalanced data, we have proposed a novel divergence-encouraging autoencoder (DEA) to explicitly learn features from both of the two classes and have designed an imbalanced data classification algorithm based on the proposed autoencoder. By encouraging maximization of divergence loss between different classes in the bottleneck layer, the proposed DEA can learn features for both majority and minority classes simultaneously. The training procedure of the proposed autoencoder is to alternately optimize reconstruction and divergence losses. After obtaining the features, we directly compute the cosine distances between the training and testing features and compare the median of distances between classes to perform classification. Experimental results illustrate that our algorithm outperform ordinary and loss-sensitive CNN models both in terms of performance evaluation metrics and convergence properties. To the best of our knowledge, this is the first paper proposed to solve the imbalanced data classification problem from the perspective of explicitly learning representations of different classes simultaneously. In addition, designing of the proposed DEA is also an innovative work, which could improve the performance of imbalanced data classification without data re-sampling and benefit future researches in the field.
The Amiloride Derivatives Regulate the Alternative Splicing of Apoptotic Gene Transcripts

Alternative splicing of precursor mRNA is an important mechanism for increasing the complexity of gene expression, and it plays a key role in cell differentiation and organism development. Accurate alternative splicing profiles and regulation will affect the cellular functions and destiny. Our previous studies showed that amiloride have a potential effect on alternative splicing, but the high effective concentration of amiloride limits its clinical application. In this study, the molecular docking calculation was performed to estimate the binding affinity between a series synthesized amiloride derivatives and SRPK1 protein in silico and then detect its activity in alternative splicing in vitro. The results showed that amiloride derivatives DS010, DS150, and ES013 have better binding affinity and could regulate the alternative splicing of BCL-X transcripts to increase the proportion of BCL-XL in Huh-7 cells. In addition, the effective concentration of DS010 and ES013 are lower than others. These findings suggested that the amiloride derivative DS010 and ES013 may provide therapeutic potential for future cancer treatment.
Hardware Trojan Detection Based on Signal Correlation

Hardware Trojan has attracted more and more attention from academia and industry because of its significant potential threat. Long activation time is a major concern during Trojan detection process. Traditional pre-silicon verification and post-silicon testing cannot be extended to detect hardware Trojans efficiently because Trojan is usually activated under specific rare conditions. In this paper, we propose a novel approach to expose Trojans efficiently by increasing the transition activities of ASIC logic regions hard to reach. Specifically, the proposed approach detects the "local" regions with low reachability by calculating signals statistical correlation, and detect the "local" regions with low reachability. In addition, by analyzing the global correlations between primary inputs and these rare regions, a retrospective test stimulus generation algorithm is developed to better control the internal logic alteration. Besides, we propose an output sequence model based on CRC check. The experiment results show that the Trojan activation time can be significantly decreased and the Trojans being exposed can be increased dramatically with the proposed method.
Severe refractory asthma: current treatment options and ongoing research

Patients with severe asthma have a greater risk of asthma-related symptoms, morbidities, and exacerbations. Moreover, healthcare costs of patients with severe refractory asthma are at least 80% higher than those with stable asthma, mainly because of a higher use of healthcare resources and chronic side effects of oral corticosteroids (OCS). The advent of new promising biologicals provides a unique therapeutic option that could achieve asthma control without OCS. However, the increasing number of available molecules poses a new challenge: the identification and selection of the most appropriate treatment. Thanks to a better understanding of the basic mechanisms of the disease and the use of predictive biomarkers, especially regarding the Th2-high endotype, it is now easier than before to tailor therapy and guide clinicians toward the most suitable therapeutic choice, thus reducing the number of uncontrolled patients and therapeutic failures. In this review, we will discuss the different biological options available for the treatment of severe refractory asthma, their mechanism of action, and the overlapping aspects of their usage in clinical practice. The availability of new molecules, specific for different molecular targets, is a key topic, especially when considering that the same targets are sometimes part of the same phenotype. The aim of this review is to help clarify these doubts, which may facilitate the clinical decision-making process and the achievement of the best possible outcomes.
Power Control of DFIG-Based Wind Farm for System Frequency Support

As installed capacity of wind energy is significantly increasing around the world, the wind power starts to replace conventional power generations and wind farm active power control has become a major concern to ensure the security and stability of the entire power system. it is thus imperative to develop advanced dispatching strategies so that the wind farm power satisfies the quality requirements of the power grid. Among the various active power control requirements, this paper focuses on power deviation control for a double fed induction generator (DFIG) - based wind farm. This type of control is crucial for wind farm frequency support especially in the case of frequency drops. Hence, this work proposes an optimization algorithm for power deviation control considering system frequency regulation demand. This algorithm aims to bring improved functioning constraints to the considered power control in order to enhance the whole system performance. Finally, the simulation results obtained from the studied power control technique implementation are carried out in order to validate the performance of the proposed control.
The Effect of Different Optimization Techniques on End-to-End Turkish Speech Recognition Systems that use Connectionist Temporal Classification

In the production of acoustic models for speech recognition applications, the use of Long Short Term Memory(LSTM) based Recurrent Neural Network(RNN) has begun to get better results than the use of Gaussian Mixture Model(GMM). The creation of GMM-based acoustic models is prolonging the deep learning process due to the need for aligned Hidden Markov Model(HMM). As a solution to this problem, another method to generate acoustic models is proposed that is based on Connectionist Temporal Classification(CTC). In this study, a CTC based model is created and the effect of different optimization techniques on the classification performance is compared. These tests were applied on Turkish speech datasets to determine the best optimization techniques to be used in speech recognition applications. Our evaluation results showed that GradientDescent, ProximalGradientDescent and RMSPROP produce better results than other algorithms.