Miracle-dz/UniFAR-FTUs huggingface dataset:
System Evolution Analytics: Evolution and Change Pattern Mining of Inter-Connected Entities

There are many entities (or components) in a system that keeps on evolving over system states. The connection (or relationship) between entities also keep on evolving over system state, which makes series of evolving networks. Such networks can be studied over evolving state to provide system evolution information for analysis. This can be achieved with the help of hybrid mining approaches. The network rule information can be detected using network rule mining. The network subgraph information can be retrieved using network subgraph mining. The evolution information is detected using evolution mining. In this paper, we introduce a “System Evolution Analytics” model, which is explained using two pattern-mining techniques: network evolution rule mining and network evolution subgraph mining. The first technique retrieves network evolution rules (NERs), and the second technique retrieves network evolution subgraphs (NESs). The two techniques are prototyped as two System Evolution Analytics tools that are used to do experiments on six evolving systems. We demonstrated the application of the tools for the system evolution analysis.
Dosimetry Mapping of Mixed-Field Radiation Environment Through Combined Distributed Optical Fiber Sensing and FLUKA Simulation

Distributed optical fiber (OF) radiation sensing is a technique recently adopted at CERN for the monitoring of radiation dose levels in the proton synchrotron (PS) booster and in the PS. In this paper, we present the progress made on our previous work by benchmarking the rich information contained in the spatially distributed OF dose measurement with the radiation levels simulated with the FLUKA Monte Carlo code. We begin by discussing the experimental results for the qualification/calibration of a new radiation sensitive single-mode OF, currently being deployed in the accelerator tunnels. We performed this calibration tests online under 60Co $\gamma $ -rays source. We then move on to report the results of the online irradiation tests that we carried out in the CERN high energy accelerator mixed-field facility. Finally, we compare the OF radiation maps with the dose levels calculated along the OF cable with FLUKA for two very different configurations of the facility. This paper is a first promising example of combining 3-D Monte Carlo simulations of complex mixed-field environment with the 1-D spatially distributed dose measurements provided by the OF-based dosimetry.
Fine Peak Current Detected Digital Control Switching Power Supply for HVDC System

The data center will consume huge power in proportion to an amount of data. Thus, the energy saving and management are necessary by high performance power supply system. Although the digital control switching power supply is recognized as the important factor for such a system, the control accuracy, which is one of the important issue of digital control, should be improved for practical application. The purpose of this paper is to improve the control accuracy of digital peak current mode control dc-dc converter. The programmable delay line is additionally implemented and can optimize on-time. The operation principle of proposed method is expressed. The proposed method is validated by simulation and experiment.
Success Factor Analysis of Jakarta Siaga 112 Emergency Service Management System

Jakarta Siaga 112 Emergency Service is an emergency call provided by the government of Special Capital Region of Jakarta to handle emergency calls from the citizens. Jakarta Siaga 112 Emergency Service Management System is run by the staffs of Jakarta government to organize the follow-ups of emergency calls. Referring to the importance of this system, a study was administered to evaluate factors affecting its success. The investigation was conducted based on the DeLone and McLean success model. Data were collected using questionnaires distributed to staffs of Jakarta Government Work Units and agencies, who work together on the system. The data were analyzed using Partial Least Squares-Structural Equation Model. The results demonstrate that System Quality, Information Quality, Service Quality, Use, User Satisfaction, and Net Benefits significantly influence the success of this system. However, two out of nine hypotheses are rejected. These are the relationship between System Quality towards Use, as well as Use towards User Satisfaction. Theoretical impact from the research and result is to enrich the diversity of research related to emergency services in the field of information technology.
The Real-Time Detection of Traffic Participants Using YOLO Algorithm

Object detection is one of the key software components in the next generation of autonomous cars. Classical computer vision and machine learning approaches for object detection usually suffer from the slow response time. Modern algorithms and architectures based on artificial neural networks, such as YOLO (You Only Look Once) algorithm, solve this problem without precision losses. In this paper we provide the demonstration of the usage of the newest YOLOv3 algorithm for the detection of traffic participants. We have trained the network for 5 object classes (car, truck, pedestrian, traffic signs, and lights) and have demonstrated the effectiveness of the approach in the variety of the driving conditions (bright and overcast sky, snow, fog, and night).
An Application of Generative Adversarial Networks for Super Resolution Medical Imaging

Acquiring High Resolution (HR) Magnetic Resonance (MR) images requires the patient to remain still for long periods of time, which causes patient discomfort and increases the probability of motion induced image artifacts. A possible solution is to acquire low resolution (LR) images and to process them with the Super Resolution Generative Adversarial Network (SRGAN) to create an HR version. Acquiring LR images requires a lower scan time than acquiring HR images, which allows for higher patient comfort and scanner throughput. This work applies SRGAN to MR images of the prostate to improve the in-plane resolution by factors of 4 and 8. The term 'super resolution' in the context of this paper defines the post processing enhancement of medical images as opposed to 'high resolution' which defines native image resolution acquired during the MR acquisition phase. We also compare the SRGAN to three other models: SRCNN, SRResNet, and Sparse Representation. While the SRGAN results do not have the best Peak Signal to Noise Ratio (PSNR) or Structural Similarity (SSIM) metrics, they are the visually most similar to the original HR images, as portrayed by the Mean Opinion Score (MOS) results.
DGDI: A Dataset for Detecting Glomeruli on Renal Direct Immunofluorescence

With the growing popularity of whole slide scanners, there is a high demand to develop computer aided diagnostic techniques for this new digitized pathology data. The ability to extract effective information from digital slides, which serve as fundamental representations of the prognostic data patterns or structures, provides promising opportunities to improve the accuracy of automatic disease diagnosis. The recent advances in computer vision have shown that Convolutional Neural Networks (CNNs) can be used to analyze digitized pathology images providing more consistent and objective information to the pathologists. In this paper, to advance the progress in developing computer aided diagnosis systems for renal direct immunofluorescence test, we introduce a new benchmark dataset for Detecting Glomeruli on renal Direct Immunofluorescence (DGDI). To build the baselines, we investigate various CNN-based detectors on DGDI. Experiments demonstrate that DGDI well represents the challenges of renal direct immunofluorescence image analysis and encourages the progress in developing new approaches for understanding renal disease.
Interchangeable Hebbian and Anti-Hebbian STDP Applied to Supervised Learning in Spiking Neural Network

This work provides a complete framework, including device, architecture, and algorithm, for implementing bio-inspired supervised spiking neural networks (SNNs) on hardware. An analog synapse with atypical dual bipolar resistive-switching (D-BRS) modes demonstrates interchangeable Hebbian spiking-timing-dependent plasticity (STDP) and anti-Hebbian STDP, and it is capable of implementing supervised ReSuMe SNNs in crossbar arrays. By using an “exchange” update scheme, accurate supervised learning (∼96% for MNIST) is achieved in a compact network.
On the Rate Distortion Function of Arbitrarily Varying Remote Sources

We study a lossy source coding problem for an arbitrarily varying remote source (AVRS) which was proposed in a prior work. An AVRS transmits symbols, each generated in an independent and identically distributed manner, which are sought to be estimated at the decoder. These symbols are remotely generated, and the encoder and decoder observe noise corrupted versions received through a two-output noisy channel. This channel is an arbitrarily varying channel controlled by a jamming adversary. We assume that the adversary knows the coding scheme as well as the source data non-causally, and hence, can employ malicious jamming strategies correlated to them. Our interest lies in studying the rate distortion function for codes with a stochastic encoder, i.e, when the encoder can privately randomize while the decoder is deterministic. We provide upper and lower bounds on this rate distortion function.
Evaluation of the Effects of Driver Distraction Part 1: Based on Simulator Experiments

In the present study, the effects of driver distraction were investigated using a systematic evaluation method based on simulator experimental data. Mobile devices and in-vehicle systems intended for driver safety and convenience could actually serve as major factors causing driver error by increasing activities that interrupt driving. A total of 11 volunteers participated under different distracted highway driving environment conditions, such as normal driving, visual-manual load, and cognitive overload. Drivers' steering control response, lateral vehicle motion, and eye-tracking response were measured. The following results indicated significant (α=0.05) differences compared to those of the normal driving condition: Under visual-manual load condition, the steering reversal rate, steering angular velocity, lateral vehicle motion, gaze range, and saccade increased, but the percentage of gaze on the road ahead decreased. Under cognitive load condition, the steering reversal rate, lateral vehicle motion, and percentage of gaze on road ahead increased, but the gaze range and saccade decreased. Different effects on driving were observed according to the driving workload modalities. Further research needs to be performed to generalize the results with an increased sample size, diverse driving environments, and accurate sensors.
Design of Circularly Polarized Dielectric Resonator Reflectarray Antenna

A novel hemi-ellipsoidal dielectric resonator (HEDR) element is proposed for designing a millimeter-wave circularly polarized (CP) reflectarray antenna. The reflection phase of the element is changed by varying the major axe of HEDR. By employing the proposed element, a circular polarized $\pmb{11 \times 11}$ full-fledged reflectarray with line polarized feeding is designed by HFSS-MATLAB-API scripts. Simulated results show that the proposed CP dielectric reflectarray antenna is able to achieve a high gain of 21.7 dBi and a 1-dB gain bandwidth of 11.2%. In addition, a 3-dB axial ratio bandwidth of 6.7% and an aperture efficiency of 32.4% can be obtained.
Identifying Racist Social Media Comments in Sinhala Language Using Text Analytics Models with Machine Learning

Racism or the act of discriminating people based on their race, gender, skin colour and other such related factors has been ruling the world since ancient times. With the development of technology and the introduction of various platforms to communicate with each other such as social media, it soon turned in to a platform to spread racial thoughts within communities. This became a serious issue when certain conversations and actions lead to the outbreak and spread of violence within communities. One such incident that happened in China caused banning of Facebook and certain social media inside the country permanently, while in March 2018, another incident that happened in Sri Lanka made the government ban social media for about one week to stop the spreading of false information and racist thoughts that can make the situation worst. Later, the social media authorities released a statement specifying that the reason why they failed to moderate and stop the spread of hatred and racist comments was due to the unavailability of language translators within their organization. Therefore, the requirement for automatic identification of racist comments on social media has become of utmost importance. However, simple keyword spotting techniques cannot be used to accurately identify the exact intent of a comment. In this paper, we address this issue by building a text analytics model with machine learning that can be used to filter racist comments in Sinhala language. A Two-Class Support Vector Machine was trained with a set of carefully chosen comments from Facebook that were labelled as racist and non-racist based on intent. The trained model was then able to classify racist comments with a 70.8% accuracy in our experimental results.
Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data

Clinical data, such as evaluations, treatments, vital sign and lab test results, are usually observed and recorded in hospital systems. Making use of such data to help physicians to evaluate the mortality risk of in-hospital patients provides an invaluable source of information that can ultimately help with improving healthcare services. In particular, quick and accurate predictions of mortality can be valuable for physicians who are making decisions about interventions. In this work we introduce the use of a predictive Deep Learning model to help evaluate the mortality risk for in-hospital patients. Stacked Denoising Autoencoder (SDA) has been trained using a unique time-stamped dataset (King Abdullah International Research Center – KAIMRC) which is naturally imbalanced. The results are compared to those from common deep learning approaches, using different methods for data balancing. The proposed model demonstrated here aims to overcome the problem of imbalanced data, and outperforms common deep learning approaches with an accuracy of 77.13% for the Recall macro
Feedback Nash Equilibrium for Markov Jump Games under Differential-Algebraic Constraints with Application to Robust Control

As a subclass of stochastic differential games with algebraic constraints, this article studies dynamic noncooperative games where the constraints are described by jump Markov differential-algebraic equations (DAEs). Theoretical tools, which require computing the infinitesimal generator and deriving Hamiton-Jacobi-Bellman equation for Markov jump DAEs, are developed. These fundamental results lead to pure feedback optimal strategies to compute the Nash equilibrium in noncooperative setting. In case of quadratic cost and linear dynamics, these strategies are obtained by solving coupled Riccati differential equations. The problem of robust control can be formulated as a two-player zero sum game and is solved by applying the results developed in this paper.
A Low-power Low-noise Reconfigurable Bandwidth BiCMOS Neural Amplifier

Significant advancements in miniaturized implantable electronics has given rise to a new dimension in the study of neuroscience. A low-noise neural amplifier is the first stage of an implantable electrophysiological signal recording system. This paper presents the design of a two-stage BiCMOS operational transconductance amplifier with reconfigurable bandwidth for the neural signal recording applications. The amplifier is designed using the standard 130 nm BiCMOS process. The designed amplifier achieves a closed-loop gain of 55.75 dB with a reconfigurable lower cut-off frequency of 0.13 Hz to 0.33 Hz and a higher cut-off frequency of 1.4 kHz. The reconfigurable bandwidth has been implemented by controlling the gate bias voltage of a pair of triple-well nMOS transistors working as the pseudoresistors in the feedback path. The simulated input-referred noise of the amplifier is 3.89, 3.59, 2.77 μVrms integrated over the 0.13, 0.17 and 0.33 Hz to 1 kHz frequency band respectively. The total power consumption of the amplifier is 1.5 μW with a dc-offset voltage of 12.3 mV. The designed amplifier has a CMRR (common-mode rejection ratio) and PSRR (power supply rejection ratio) of 104.8 dB and 97 dB respectively. The performance of the designed amplifier shows a good compatibility with the low-frequency neural signal recording systems.
Design of an Equilateral Triangular Patch Antenna by Using AgHT-8 with Overlapping Microstrip Feed Line for S-Band Application

In this paper, design and characterization of an equilateral triangular patch antenna by using AgHT-8 fed via a microstrip feed line by overlapping beneath the radiating patch is presented. The antenna is fabricated on glass substrate where feeding is done via a copper microstrip line. Return loss measurements are carried out for different patch position over the given length of the feed line where a better return loss value of −31.26 dB is achieved at a certain position. Radiation pattern measurements are carried out for the antenna with best matching performance over the −10dB bandwidth. Further, the results are compared with an equilateral triangular patch antenna fabricated with copper designed on the same glass substrate without changing the dimension.
Osteoarthritis Disease Prediction Based on Random Forest

Abstract–Osteoarthritis is a disease of knee joint, indicated from the biochemical changes and thinning of the knee joint cartilage, which can be seen using T2Map MRI and Density-weighted Protons sequence. these tools detect the thickness changes that occur in the cartilage layes which can identify the presence of osteoarthritis and its severity. However, the immediacy of the result of these tools, whether the patient has osteoarthritis or not, is quite low. This paper presents the classification of osteoarthritis disease into three classes of severity using the random forest method. This model can be used to predict the accuracy of osteoarthritis data by 86,96% in diagnosing the disease. The data of 33 patients with osteoarthritis in Cipto Mangunkusumo National Hospital of Indonesia were used.
Online Inverse Optimal Control on Infinite Horizons

In this paper, we consider the problem of computing parameters of a discrete-time infinite-horizon optimal control objective function from (possibly finite-length) state and control sequences. To solve this problem, we propose a novel method of inverse optimal control by exploiting a recently established infinite-horizon discrete-time minimum principle. Our proposed method admits a computationally efficient online implementation in which pairs of states and controls from the state and control sequences are processed sequentially without being stored or processed as a batch. We establish conditions guaranteeing the uniqueness of the cost-function parameters computed by our proposed method and illustrate its application in simulation.
L'archi-élève lecteur en progression entre tâche, activité et performance de lecture

Il est question de presenter les premiers resultats d'une recherche sur l'enseignement de la litterature au fil des niveaux scolaires. Y est defini en premiere approximation la notion d'Archi-eleve. La recherche porte sur les pratiques de lecture litteraire de textes reputes litteraires contrastes : une fable de La Fontaine, appretee par des ressources didactiques nombreuses, une nouvelle de Lovay, auteur de Suisse romande.
Extending Negative Imaginary Systems Theory to Nonlinear Systems

In this paper, we extend the negative imaginary systems framework to nonlinear systems using Lyapunov and dissipativity theories. This enables us to extend some existing results from linear negative imaginary systems theory to nonlinear systems. In particular, asymptotic stability of a positive feedback interconnection of nonlinear negative imaginary systems has been established under suitable assumptions.
Factor Analysis of Intention to Use Garuda Indonesia Mobile Application

Recent customers' behavior changes require Garuda Indonesia, as the national flag carrier, to deliver more excellent services. Therefore, Garuda Indonesia extends its reservation channel to a mobile application. However, its use is far below the company's expectation. To address this problem, a study was conducted to determine factors affecting the intention to use it. This investigation was conducted based on the Suki and Suki's model. User Interface (UI) Attractiveness variable is added to accommodate the importance of UI in mobile applications. This is a quantitative research, which collected data using online questionnaires. There were 101 valid questionnaires. The collected data were analyzed using Partial Least Square-Structural Equation Modelling (PLS-SEM) method with Smart-PLS. The results show that subjective norm is the most important factor to influence the intention to use Garuda Indonesia's mobile application. The other important variable are airline image and perceived ease of use. As theoretical implication, this research contributes to confirm the previous related model and as a baseline for research on flight mobile application in Indonesia. Moreover, for practical implication, this research could help Garuda Indonesia to increase the willingness of their potential customers to use its mobile application.
An Impact Ionization MOSFET With Reduced Breakdown Voltage Based on Back-Gate Misalignment

In this paper, we propose a misaligned double-gate p-i-n impact ionization MOS (MIMOS) with a deliberate misalignment between the top and bottom gates. The presence of a misaligned bottom gate leads to band-to-band-tunneling of electrons at the source-intrinsic region interface and increases the number of carriers for impact ionization. The electric field redistribution provides a longer transport path for the carriers. Therefore, carriers gain higher kinetic energy, and the impact ionization rate is enhanced in the MIMOS. This results in a significantly lower avalanche breakdown voltage compared to conventional single-gate IMOS structure. Using calibrated 2-D simulations, we demonstrate that MIMOS exhibits a steep subthreshold slope (~6 mV/dec) at a significantly low-supply voltage of (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}}= {0.59}$ </tex-math></inline-formula> V), which is ~48% lower than that of the corresponding single-gate IMOS (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}}= {1.15}$ </tex-math></inline-formula> V).