Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Embedding Using Semi-Supervised Kernel Nonnegative Matrix Factorization

Network embedding, aiming to learn low-dimensional representations of nodes in networks, is very useful for many vector-based machine learning algorithms and has become a hot research topic in network analysis. Although many methods for network embedding have been proposed before, most of them are unsupervised, which ignores the role of prior information available in the network. In this paper, we propose a novel method for network embedding using semi-supervised kernel nonnegative matrix factorization (SSKNMF), which can incorporate prior information and thus to learn more useful features from the network through introducing kernel methodology. Besides, it can improve robustness against noises by using the objective function based on $L_{2,1}$ norm. Efficient iterative update rules are derived to resolve the network embedding model using the SSKNMF, and the convergence of these rules are strictly proved from the perspective of mathematics. The results from extensive experiments on several real-world networks show that our proposed algorithm is effective and has better performance than the existing representative methods.
mLung: Privacy-Preserving Naturally Windowed Lung Activity Detection for Pulmonary Patients

mLung is a privacy preserving, naturally windowed, mobile-cloud hybrid pulmonary care service for detecting unusual lung sounds like coughing and wheezing from streaming audio and inertial sensor data from a smartphone for pulmonary patients. mLung employs a combination of: (1) natural windowing of audio data from the patient respiration cycle captured by the inertial sensors, (2) in-phone speech detection and filtering by a lightweight classifier for patient privacy, and (3) in-cloud lung and confounding sound classification by a heavyweight and expert supervised classifier. This paper describes the design and architecture of mLung and using novel lung activity data collected by smartphone from 131 patients and healthy subjects, provides empirical evidence that mLung is 15%–25% more accurate in detecting lung sounds when compared to a state-of-the-art phone based internal body sound detection system using specialized microphone hardware, with a best f-1 score of 98%.
Implementation of STDP Learning for Non-volatile Memory-based Spiking Neural Network using Comparator Metastability

This paper presents a circuit for spike-timing dependent plasticity (STDP) learning of a non-volatile memory (NVM) based spiking neural network (SNN). Unlike conventional hardware for implementation of STDP learning, the proposed circuit does not require additional memory, amplifiers, or an STDP spike generator. Instead, the circuit utilizes the comparison time information of the dynamic comparator to implement a non-linear transfer curve of STDP learning. The circuit includes a dynamic comparator, NVM device, and some digital circuitry to write the conductance of NVM according to the STDP learning rule. Finally, the conductance response model and designed circuit for the STDP learning are used to compare the simulation results of STDP with mathematical STDP. Applications of the proposed circuit are in the design of NVM-based SNN hardware or other bio-inspired hardware systems.
Gender Differences in Students' Continuous Adoption of Mobile Learning in Saudi Higher Education

With the rapid advance in the internet, mobile information systems have turned into a promised sector within the universal information and communication technology. Saudi ministry of higher education exerts effort to integrate IS and technology into learning especially with the Saudi vision 2030 toward knowledge investment. However, it has been observed that researches overlook the investigation of gender differences in the continuous adoption of M-learning. This study aims to examine the effect of gender on student's perceptions and evaluations of M-learning systems. A total of 640 subjects participated in the experiment. The data analysis illustrates females and males had significant differences among four hypotheses. Males' continuance intention toward using M-learning was stronger than females. Females' attitude towards extrinsic motivations was stronger than males. This study discussed the implications of the results and provided recommendations to enhance students' continuance intention to use M-learning by considering the moderating effect of gender.
A Fast and Efficient Broadcast Protocol With a Mobile Sink Node in Asynchronous Wireless Sensor Networks

We investigate a broadcast scenario that a mobile sink disseminating broadcast packets while moving in asynchronous wireless sensor networks (WSNs), to prolong the network lifetime and reduce the broadcast delay. Such a scenario may yield multiple logical sink nodes in WSNs and the moving speed and trajectory of the mobile sink have the significant impact on the speed and efficiency of the broadcast. Thus, the conventional broadcast protocols may not fit for the mobile sink scenario. We propose a fast and efficient broadcast (FEB) protocol with a mobile sink in asynchronous WSNs. We analyze the moving pattern as well as the travel speed of the mobile sink. We jointly consider the broadcast process, the node location, and the neighbor coverage information within two-hops and then propose an efficient broadcast scheme together with a heuristic moving strategy for the mobile sink. The proposed broadcast scheme needs to exchange node coverage information before the transmission begins, thus to avoid redundant packet transmissions, and the proposed moving pattern strategy decreases the network broadcast delay by moving the mobile sink to monitoring areas, where fewer nodes are covered by the broadcast process. The simulation results show that our proposed protocol significantly reduces the broadcast delay and energy consumption.
A New Similarity Measure for Web Information Retrieval using PSO Approach

The drastic rise of data in the World Wide Web (WWW) causes the problem for the data organization and usability of data in real life. Through constant innovations, the WWW has become a part of our everyday life and is routinely used to reach other people through various web-based applications. This paper reveals a novel similarity measure which helps to retrieve more relevant documents from the repository thus contributing considerably to the effectiveness of Web Information Retrieval (WIR) system. Bio-inspired PSO methodology is used to retrieve the top ranked documents with a reduced response time and optimizes WIR process, hence contributes to the efficiency of the system. This paper also demonstrates a comparative study of the proposed system with the existing method. Experiments are conducted on CACM collections. Better precision-recall rates are achieved than the existing system.
A VLC-Based Location System for Mobile Application

: Visible light communication is the new technology with more benefits over an existing radio frequency communication system such as high security, high energy, efficiency and long life. The primary aim of LED is illumination which is important requirement of VLC system. White light can be used in both indoor and outdoor applications for illumination as well as data transmission because colours of objects as seen closely resemble under both the white light and natural light. The proposed VLC system enables indoor applications by allowing illumination as well as data transmission both of which not affected on each other. The transmitter comprises On-Off keying (OOK) modulation and LED to send data by flashing the light at a flicker-free frequency and speed undetectable to the human eye. In order to avoid flickering in lighting, the designated system should work above 200Hz. The receiver comprises photodiode and its amplification system followed by Bluetooth module to ensure its connectivity to a smart phone.PWM signals are modulated (Single carrier modulation technique) by the user data and the led driver drive the LED according to the modulated PWM signal.VLC is the peer to peer communication. Therefore, it is enables for several other applications such as home, office, airplane etc.
Contra-Directional 3dB 90° Hybrid Coupler in Ridge Waveguides Using Even and Odd TE Modes

A novel compact wide-band contra-directional single conductor ridge waveguide coupler is presented. It exhibits 50% fractional bandwidth with equal power split between the through and coupled ports while occupying a very compact volume. The design can be exploited in versatile applications, such as beam forming, or high-power combiners. The methodology to design the coupler is presented. It starts with designing the coupling section in a modified double ridge waveguide that supports two independent propagating TE modes. Proper choice of the cross section allows the designer to achieve the required impedance levels for tight coupling values, not common for standard waveguide contra-directional couplers. An S-band proof of concept prototype was synthesized, simulated, fabricated and tested demonstrating excellent measured results without any tuning.
Characteristic Mode Analysis of Circular Microstrip Patch Antennas

The modal analysis of circular patch antenna gives an intuition on interaction of near field and their dependencies in antenna performance. Various optimization algorithms fail to address this interactions and dependencies as the design formulas are languished. CM has an ability to characterize radiation and scattering properties of conducting bodies based on object geometry and material properties. CM theory is alternate approach to study the EM interactions on patch antenna. The characteristic mode analysis is based solely on patch shape on the contrary the efficiency and bandwidth are determined based on substrate properties, thickness and permitivity. The impact of parameter variations like Surface field, feed location, Substrate permitivity, Substrate height, ground radius could help us to speculate a Physical interpretation of circular patch. The significance of slotting on the patch to excite the desired modes is presented in CMA perspective.
A Highly Linear FMCW Radar Chipset in H-Band with 50 GHz Bandwidth

In this paper we present a transmit and receive MMIC for FMCW radar. The transmitter consisting of a frequency multiplier-by-three and a power amplifier featuring a high output power of 7 dBm with a 60 GHz 3-dB RF-bandwidth. The receiver is designed to be highly linear over a LO and RF bandwidth from 235 to 285 GHz. It employs a frequency tripler and a power amplifier as driver stage for a passive I/Q downconverter which enables an image reject architecture. To ensure linear operation and improve the overall receiver noise an input amplifier stage with an input referred 1-dB compression point exceeding -3 dBm is also integrated. The chipset is realized in a 35 nm metamorphic high electron mobility transistor technology.
Multi-rotor Robot Learning to Fly in a Bio-inspired Way Using Reinforcement Learning

Bio-inspired artificial intelligence—reinforcement learning, artificial neural network and bio-inspired methods— is used in this paper in order to design an algorithm capable of learning to flight autonomously.In this paper, we introduced and explained about our algorithm called FE-TRPO (Flight Enhanced TRPO) and tested it in Gazebo simulator by an AscTec Pelican simulated model where the multi-rotor shows the capability of learning to fly by learning from its own actions and the rewards receiving from the environment in an autonomous way, where the only controller is the reinforcement learning based algorithm and no conventional controllers such as PID, MPC, etc is used. Finally, our algorithm learns to fly using only the data generated from IMU. As a result, our algorithm can be used by a multi-rotor at any unknown environment where no guiding system such as Vicon exist. A video clip of our work can be viewed in: https://www.youtube. com/watch? v=Jj42A5q-YA
Delivering ICT Security Courses over 5.944 km : Experience of Czech-Kenyan Co-operation

This paper presents practical experience with remote delivery of ICT courses taught by Czech teachers for students at Strathmore University in Kenya. Although e-learning and remote course delivery technologies have been successfully applied to distance education many years ago, their application for ICT courses requires additional instruments as virtualized labs, on-site boot camps, etc. that help students in achieving practical skills. The paper demonstrates different teaching strategies that were implemented to deliver ICT security courses for Kenyan students in 2015-2017. The paper discusses the implementation from the perspective of the learner, mode of delivery, and expected learning outcomes.
Impact of Learning Functions on Prediction of Stock Data in Neural Network

Digitization has made a vast impact on the modern society. Financial sector is one field where a huge revolution has been experienced because of digitization. Financial data especially time series data is being stored in the digital repositories where it can be used for prediction and analysis. One such data is a stock market data which is a time series data and is generated in a huge amount every second. The stock market data is of great importance as the proper analysis and prediction of data can transform the fate of the global market. Thus the companies and the individuals are looking forward for the development of the automated techniques that can predict stock market data accurately in a real time. In this regard, many researchers developed machine learning techniques such as use of neural network for prediction of stock data. The most common learning function used in neural network is sigmoid function. However, we found that there are many learning functions are available for building neural network. In this paper we are studying the impact of four different learning functions in estimating/predicting the stock value. From the experimental study we found that unipolar sigmoid learning function produced an accuracy of 95.65%, bipolar sigmoid produced an accuracy of 91.34%, tan hyperbolic equation produced an accuracy of 91.02%, and radial base equation produced an accuracy of 87.53%. Clearly, unipolar sigmoid function emerged as the best learning function to build stock data prediction model. The main reason behind its out-performance of unipolar sigmoid is its less complex structure and the 0 to 1 range.
Predicting Natural Gas Spot Prices Using Artificial Neural Network

Natural gas is a major global energy commodity. Gas prices around the world face substantial volatility, inducing major downside market risks. Forecasting accuracy is thus a major concern for the consumers. Traditional econometrics models do not perform well due to inherent nonlinear and nonstationary gas price data. We thus propose an Autoregressive Neural Network (ARNN) model for forecasting daily spot gas prices. The model is benchmarked against the traditional Autoregressive Integrated Moving Average (ARIMA) model. Using a cross validation study, the ARNN model showed an improvement of around 33% over ARIMA in terms of mean squared error. This improvement is significant when price forecasts are used in gas purchase decisions.
A Customized Convolutional Neural Network Design Using Improved Softmax Layer for Real-time Human Emotion Recognition

This paper proposes an improved softmax layer algorithm and hardware implementation, which is applicable to an effective convolutional neural network of EEG-based real-time human emotion recognition. Compared with the general softmax layer, this hardware design adds threshold layers to accelerate the training speed and replace the Euler’s base value with a dynamic base value to improve the network accuracy. This work also shows a hardware-friendly way to implement batch normalization layer on chip. Using the EEG emotion DEAP[7] database, the maximum and mean classification accuracy were achieved as 96.03% and 83.88% respectively. In this work, the usage of improved softmax layer can save up to 15% of training model convergence time and also increase by 3 to 5% the average accuracy.
Low-Energy ECG Processing for Accurate Features' Extraction in Wireless Body Sensor Networks

Wireless body sensor networks (WBSNs) represent an attractive low-cost infrastructure for e-health applications. In depth, these wireless tiny devices can be deployed to gather, to process and transmit bio-signals providing remote real-time monitoring of patient health signs. Among applications that can be implemented using these sensors, the ECG signal processing for features' extraction to detect arrhythmia disease is attracting the focus of several recent research activities. In this context, the detection of Atrial Fibrillation episodes in ECG signal requires accurate analysis of RR irregularity and efficient detection of the absence of P-waves. However, the weak processing bandwidth and the limited energy in these sensors strongly limit their adequacy to be deployed for such application. This paper focuses on the design of a low-energy ECG processing scheme, for WBSN implementation, that accurately extracts the relevant ECG features required for atrial fibrillation detection and notify a remote server. The paper presents the performance analysis of this approach and shows the efficiency of the proposed scheme for energy aware ECG processing and its adequacy to be implemented in WBSN.
Performance Improvement of Dynamic Response for Parallel Operation of Inverters Under Line Impedance Mismatch

In this paper, the internal model controller (IMC)-based universal droop controller is proposed to improve the dynamic response of parallel operated inverters as compared to conventional droop controller when there is a mismatch in line impedance. The proposed controller is demonstrated on two converters, 3-bus system, and results are compared with proportional integral (PI)-based conventional droop controller. The effectiveness of the proposed controller is verified from the dynamic response of the system. Also, the voltage and frequency of the system can be maintained within an acceptable range with the proposed controller.
A Fully Integrated C-band GaN MMIC Doherty Power Amplifier with High Gain and High Efficiency for 5G Application

This paper presents a fully integrated C-band GaN MMIC Doherty power amplifier (DPA) for 5G application. A driver stage is employed in both the main and auxiliary branches to boost the power gain. A compact low-loss output network is designed to realize high efficiency and minimize chip size. The fabricated DPA exhibits a power gain of 17 dB, a saturated output power of 41.3 dBm, a saturated PAE of 61%, and a 6-dB back-off PAE of 47% at 4.7 GHz, with a compact size of 3 mm × 2.5 mm. Moreover, the 6-dB back-off PAE is better than 40% across a large bandwidth from 4.4 GHz to 5.1 GHz. Using a 40-MHz LTE signal at the carrier frequency of 4.7 GHz, the measured adjacent channel power ratio is -45 dBc at the average output power of 33.7 dBm after applying digital predistortion.
Comparative Analysis on Requirement Engineering Modelling Techniques Case Study of Personal Al-Haj E-Guide

Requirement Engineering is a framework of requirement elicitation, analysis, documentation, validation and requirements management. Object oriented, dynamic and functional modeling techniques are used for requirement engineering process. Many approaches can be adapted during this phase of software development life cycle. UML based, Model oriented, Goal oriented and Business process oriented approaches are discussed in the paper. Modeling techniques are linked with each other but approaches are not. Similarly requirement engineering approaches are not dependent on modeling techniques. Any approach can be used with any modeling technique and this is purely decided by requirement engineers with the help of various constraints like kind of project, its domain, scope, business environment, target end users, stakeholder's need and many more that aggregate as deciding factors of modeling technique and RE approach to be used for that selected modeling technique. Paper has shown comparative analysis of modeling techniques and requirement engineering approaches with the help of case study of Personal A-Hajj E-guide. Data flow diagram, sequence diagram, collaboration diagram and context level data flow diagram are drawn and its impact on approaches is being discussed. Whatever technique and approach is being selected for RE phase, it should be effective enough to produce complete, concise, unambiguous SRS. Planned and managed requirement engineering leads towards successfulness of the project with implementation of defined quality process where modeling techniques play their own vital role. So technique should be well approached to competently achieve quality software products successfully.
Modern Architecture Style Transfer for Ruin Buildings

In this work, we focus on building style transfer, which transforms ruin buildings to modern architecture. Inspired by Gaty’s and Goodfellow’s style transfer and generative adversarial network (GAN), we use CycleGAN to conquer this type of problem. To avoid the artifacts and generate better images, we add “perception loss” into the network, which is the feature loss extracted by VGG pre-trained model. We also adjust cycle loss by changing the ratio of weighting parameters. Finally, we collect images of both ruin and modern architecture from websites and use unsupervised learning to train the model. The experimental results show our proposed method indeed realize the modern architecture style transfer for ruin buildings.
A Performance Comparison Between $\beta$ -Ga2O3 and GaN HEMTs

We report on the quantitative estimates of various metrics of performance for <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub>-based high electron mobility transistors (HEMTs) for RF and power applications and compare them with III-nitride devices. Device parameters such as electron velocity and current density are estimated based on an optical phonon model reported earlier. 2-D simulation using an appropriate velocity-field relationship was employed to study the device characteristics and to assess the RF performance. It is found that despite a lower cutoff frequency, <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>0</sub> HEMTs are likely to provide higher RF output power compared to GaN HEMTs in the low-frequency regime. However, the thermal resistance (TR) and the channel temperature of <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> HEMTs are expected to be significantly higher than those of GaN HEMTs which will pose serious limitations on heat dissipation. <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> modulation doped field effect transistor on extremely thinned substrates will have similar TR s as compared to GaN devices on GaN substrates. The cutoff frequency was found to drop by 50% as the power dissipation increases from 1 to 7 W/mm. On the other hand, for estimates of dc power switching performance, we estimate the net losses as a function of device periphery and find that <inline-formula> <tex-math notation="LaTeX">$\sim {8}\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${10}\times $ </tex-math></inline-formula> lower electron mobility in Ga<sub>2</sub>O<sub>3</sub> devices compared to that in AlGaN/GaN HEMTs will limit its dc switching as well as its ON- state performance in terms of efficiency, loss, and current carrying capability although the blocking voltage can be much higher than in GaN.