Miracle-dz/UniFAR-FTUs huggingface dataset:
Integration of Information System Success Models to Explain End User Satisfaction of Debtor Information Systems

Acceptance Models by Davis (1988). This study integrates the information system quality variable, information quality variable, and end-user satisfaction variable from Information System Success. The population in this study were all employees user SID of the financial institution, the samples were 166 employees of finance institution. The sampling technique was nonprobability sampling with purposive sampling. The type of data was primary data by the method of questionnaire data collection. This research uses Structural Equation Model to analysis the method and analysis tool used was PLS. The results of this study show that information system quality significantly affects the perceived usefulness, information system quality significantly affects the perceived ease of use, information quality significantly affects the perceived usefulness, information quality significantly affects the perceived ease of use, information quality significantly affects the end-user satisfaction, perceived ease of use significantly affects the perceived usefulness, perceived usefulness significant affects the end-user satisfaction, and perceived ease of use significantly affects the end-user satisfaction. But, the examine the information quality didn’t significantly affect end-user satisfaction. DOI: https://doi.org/10.26905/afr.v2i1. 3260
Design And Practical Implementation Of Harmonic-Transponder Sensors

DESIGN AND PRACTICAL IMPLEMENTATION OF COMPACT MICROSTRIP ANTENNAS FOR HARMONIC TRANSPONDERS by NASSER S ALKHALDI May 2019 Advisor: Dr. Pai-Yen Chen Major: Electrical Engineering Degree: Doctor of Philosophy Harmonic radar is a nonlinear detection technology that transmits and receives radiofrequency (RF) signals at orthogonal frequencies, so as to suppress the undesired clutters, echoes and electromagnetic interreferences due to multipath scattering. Its implementation generally comprises a nonlinear tag (i.e, a harmonic transponder), which picks the interrogation signal at specific fundamental frequency ( f0) and converts it into a high/subharmonic signal (n f0). Such a technology has been successfully applied to tracking small insects and detection of electrically-small objects in the rich-scattering environment. Similarly, a harmonic sensor is used to interrogate electrically-small and passive sensors, of which the magnitude and peak frequency of output harmonics (e.g., second harmonic) are functions of the parameter to be sensed. A harmonic tag or sensor comprises one or multiple antennas, a frequency modulator, a sensor, a microchip and matching networks. Here, we propose and experimentally validate compact, low-cost, low-profile, and conformal hybrid-fed microstrip antennas for the harmonics-based radar and sensor systems. The proposed microstrip antennas are based on a simple single-layered and hybrid-feed structure. By optimizing the feed position and the geometry of microstrip patch, the fundamental mode and particular higher-order modes can be excited at the fundamental frequency and the second harmonic. We have derived the analytical expressions for calculating the
Jammer-Aided Secure Communications for Cooperative NOMA Systems

This letter investigates a jammer-aided cooperative non-orthogonal multiple access (NOMA) system, where one relay is used to deliver information and other relays are acted as jammers. Two simple relay selection (RS) strategies, e.g., random RS and max-min RS, are considered in this letter. Analytical and asymptotical expressions for secrecy outage probability (SOP) under both RS schemes are developed. Simulation results show that NOMA system with jammer achieves a lower SOP than the one without jammer in the moderate to high SNR range. Moreover, by adopting max-min RS strategy, the secrecy outage performance can be further improved in the low SNR region, while still tends to a constant in high SNR region.
Research Design and Major Issues in Developing Dynamic Theories by Secondary Analysis of Qualitative Data

This article discusses the use of the Grounded Theory approach to the analysis of qualitative data to develop dynamic theories. The main objective of the article is to explore the major issues in bringing Grounded Theory and System Dynamics approaches together. The discussion employs an example of secondary analysis of qualitative data as a mixed-method research design for System Dynamics researchers. Findings from this example are organized in a table by research stages. Some of the reported major issues have divided researchers in their fields, such as the theoretical sensitivity issue in the Grounded Theory field and the qualitative and quantitative modeling issue in the System Dynamics field. Other major issues, such as the nature and richness of the dataset, can limit the data analysis and the research outputs. Researchers using the mixed-method should address these issues, and the present article offers solutions for them.
A Surface Defect Detection Framework for Glass Bottle Bottom Using Visual Attention Model and Wavelet Transform

Glass bottles must be thoroughly inspected before they are used for packaging. However, the vision inspection of bottle bottoms for defects remains a challenging task in quality control due to inaccurate localization, the difficulty in detecting defects in the texture region, and the intrinsically nonuniform brightness across the central panel. To overcome these problems, we propose a surface defect detection framework, which is composed of three main parts. First, a new localization method named entropy rate superpixel circle detection (ERSCD), which combines least-squares circle detection and entropy rate superpixel (ERS) with an improved randomized circle detection, is proposed to accurately obtain the region of interest (ROI) of the bottle bottom. Then, according to the structure-property, the ROI is divided into two measurement regions: central panel region and annular texture region. For the former, a defect detection method named frequency-tuned anisotropic diffusion super-pixel segmentation (FTADSP) that integrates frequency-tuned salient region detection (FT), anisotropic diffusion, and an improved superpixel segmentation is proposed to precisely detect the regions and boundaries of defects. For the latter, a defect detection strategy called wavelet transform multiscale filtering (WTMF) based on a wavelet transform and a multiscale filtering algorithm is proposed to reduce the influence of texture and to improve the robustness to localization error. The proposed framework is tested on four data sets obtained by our designed vision system. The experimental results demonstrate that our framework achieves the best performance compared with many traditional methods.
Applications of dynamic functional connectivity to pain and its modulation.

Since early work attempting to characterize the brain's role in pain, it has been clear that pain is not generated by a specific brain region, but rather by coordinated activity across a network of brain regions, the "neuromatrix. " The advent of noninvasive whole-brain neuroimaging, including functional magnetic resonance imaging, has provided insight on coordinated activity in the pain neuromatrix and how correlations in activity between regions, referred to as "functional connectivity," contribute to pain and its modulation. Initial functional connectivity investigations assumed interregion connectivity remained stable over time, and measured variability across individuals. However, new dynamic functional connectivity (dFC) methods allow researchers to measure how connectivity changes over time within individuals, permitting insights on the dynamic reorganization of the pain neuromatrix in humans. We review how dFC methods have been applied to pain, and insights afforded on how brain connectivity varies across time, either spontaneously or as a function of psychological states, cognitive demands, or the external environment. Specifically, we review psychophysiological interaction, dynamic causal modeling, state-based dynamic community structure, and sliding-window analyses and their use in human functional neuroimaging of acute pain, chronic pain, and pain modulation. We also discuss promising uses of dFC analyses for the investigation of chronic pain conditions and predicting pain treatment efficacy and the relationship between state- and trait-based pain measures. Throughout this review, we provide information regarding the advantages and shortcomings of each approach, and highlight potential future applications of these methodologies for better understanding the brain processes associated with pain.
A Process Mining Approach to Understand Self Regulated- Learning In Moodle Environment

This paper proposes the use of Fuzzy Miner, a process mining technique to map the student-regulated learning (SRL) behaviour on Moodle online learning environment. The activity logs from 90 undergraduate students was extracted using the process mining technique. Finding shows process mining was able not only to identify the SRL patterns but also able to highlight the students’ SRL behaviour. Understanding the cyclical nature in SRL and the students’ SRL behavior online as outlined in this study would help the educational stakeholders in motivating the students’ efforts to learn.
Influence Analysis in Evolving Networks: A Survey

Influence analysis aims at detecting influential vertices in networks and utilizing them in cost-effective business strategies. Influence analysis in large-scale networks is a key technique in many important applications ranging from viral marketing and online advertisement to recommender systems, and thus has attracted great interest from both academia and industry. Early investigations on influence analysis often assume static networks. However, it is well recognized that real networks like social networks and the web network are not static but evolve rapidly over time. Thus, to make the results of influence analysis in real networks up-to-date, we have to take network evolution into consideration. Incorporating evolution of networks into influence analysis raises many new challenges, since an evolving network often updates at a fast rate and, except for the network owner, the evolution is usually even not entirely known to people. In this survey, we provide an overview on recent research in influence analysis in evolving networks, which has not been systematically reviewed in literature. We first revisit mathematical models of evolving networks and commonly used influence models. Then, we review recent research in five major tasks of evolving network influence analysis. We also discuss some future directions to explore.
Processing Speed for Action and Semantic Memory

concrete decisions because the shoebox task asks participants to make an action decision. In Experiment 2, the same 81 images from Experiment 1 were used. Participants were presented with a prime and then a visual image (target). Participants were again asked to decide if the presented stimuli could fit inside a shoebox. To determine how much visual information is needed to facilitate responding to various types of primes, pictures were manipulated so that some images appeared clear, slightly blurry, or very blurry. To further investigate how much information is needed to recognize an object was examined in Experiment 3. I wanted to discover what is the minimum amount of visual information that is necessary to recognize an object, and whether less information is needed to process affordances than semantic content. Participants were first presented with a prime word (affordance, semantic feature, or nonassociate). They were then presented with an artificially darkened screen so that features of the object were not visible initially, but then gradually became clearer. Participants were asked to identify the object as quickly as possible as the picture was becoming clearer. In sum, I argued that 1) affordances are optimally processed via perceptual information, not linguistically, 2) the quality of the perceptual information influences 10 processing of affordances, and 3) visual object recognition is faster and more efficient when primed with affordance features compared to semantic features.
Analytical Models and Control Design Approaches for a 6 DOF Motion Test Apparatus

Wind tunnels play an indispensable role in the process of aircraft design, providing a test bed to produce valuable, accurate data that can be extrapolated to actual flight conditions. Historically, time-averaged data has made up the bulk of wind tunnel research, but modern flight design necessitates the use of dynamic wind tunnel testing to provide time-accurate data for high frequency motion. This research explores the use of a 6 degree of freedom (DOF) motion test apparatus (MTA) in the form of a robotic arm to allow models inside a subsonic wind tunnel to track prescribed trajectories to obtain time-accurate force and moment coefficients. Specifically, different control laws were designed, simulated, and integrated into a 2 DOF model representative of the elbow pitch and wrist pitch joints of the MTA system to decrease positional tracking error for a desired end-effector trajectory. Stability of the closed-loop systems was proven via Lyapunov analysis for all of the control laws, and the control laws proved to decrease tracking error during the trajectory case studies. An adaptive sliding mode control scheme was chosen as most suitable to simulate on the 6 DOF model due to the small tracking error as compared to the other control schemes and the availability of parameters of the actual MTA system when subject to the time-varying aerodynamics of the wind tunnel.
Correlation Analysis of Reliability Surrogate Measures in Real Size Water Distribution Networks

A stochastic reliability assessment of a water distribution system requires a high computational effort. Surrogate measures are indices of easy evaluation that in theory represent one or many aspects of reliability, allowing a faster comparison between different design/rehabilitation alternatives. Two types of reliability surrogate measures (RSM) can be found in the research literature: 1. Power-based indices (e.g. resilience index, networks resilience index, available power index) and 2. Entropy-based indices (e.g. flow entropy). These four RSMs are used to optimize five water distribution systems and then a correlation analysis of the different designs of a given network is performed. Results suggest that all Power-based indices are highly correlated, and they all represent the same aspect of network reliability, this is likely related to the functionality of the system under uncertain demands. Meanwhile, the flow entropy does not have any significant correlation with the other indices which indicates that it can better represent the other aspect of network reliability like the functionality of the system under pipe failure scenarios. Further examination of these relationships using samples of pipe-failure and demand variation scenarios is needed.
Using Topic Specific Features for Argument Stance Recognition

Argument detection and its representation through ontologies are important parts of today’s attempt in automated recognition and processing of useful information in the vast amount of constantly produced data. However, due to the highly complex nature of an argument and its characteristics, its automated recognition is hard to implement. Given this overall challenge, as part of the objectives of the RecomRatio project, we are interested in the traceable, automated stance detection of arguments, to enable the construction of explainable pro/con argument ontologies. In our research, we design and evaluate an explainable machine learning based classifier, trained on two publicly available data sets. The evaluation results proved that explainable argument stance recognition is possible with up to . 96 F1 when working within the same set of topics and . 6 F1 when working with entirely different topics. This informed our hypothesis, that there are two sets of features in argument stance recognition: General features and topic specific features.
Wireless Grid Integration of Electric Vehicles with Two-Way Power Flow: New Concepts

With the emergence of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) concepts, electric vehicles (EVs) have been proposed as energy storage devices for dynamic demand management. Bidirectional contactless grid interfaces, based on inductive power transfer (IPT) technology, are gaining popularity as an attractive solution for V2G and G2V systems. Such systems typically include an AC-DC and DC-AC two-stage energy conversion process with a large electrolytic capacitor to maintain a stiff DC-link. In order to minimize the ripple of the grid current, a large input inductor is also employed. Inclusion of these two energy storage devices leads to an expensive, bulky and less reliable converter system. This thesis proposes a single-phase matrix converter (MC) that directly generates voltages at high frequency from the utility grid to drive resonant networks of the bidirectional contactless interface. An MC is a direct AC-AC converter with inherent bidirectional power flow capabilities. Hence, large energy storage elements are not employed in the proposed MC based bidirectional contactless grid interface. A comprehensive mathematical model that predicts the steady state voltages, currents as well as the power drawn from the mains grid is developed to gain an insight into the operation of the proposed MC based interface. Utilizing the mathematical model, a modulation strategy is also proposed to attenuate low order harmonics in the grid current. Experimental results, obtained from a 1 kW prototype, show excellent agreement with those obtained from the mathematical model, validating the accuracy of the mathematical model. Experimental results of total harmonic distortion (THD), power factor, active power delivery and efficiency have been presented to evaluate the performance of the MC. A three-phase to single-phase matrix converter (TSMC) based bidirectional contactless grid interface is also proposed in this thesis for high power EV charging and discharging applications. Simulation results of voltages, currents of converters and
Blockchain-Based Privacy-Preserving Healthcare Architecture

Since the introduction of Internet of Things (IoT), e-health has become one of the main research topics. Due to the sensitivity of patient data, preserving the privacy of patients appears to be challenging. In healthcare applications, patient data are usually stored in the cloud, which makes it difficult for the users to have enough control over their data. However, due to the General Data Protection Regulation (GDPR), it is the data subject’s right to know where and how his data has been stored, who can access his data and to what extent. In this paper, we propose a blockchain-based architecture for e-health applications which provides an efficient privacy-preserving access control mechanism. We take advantage of Blockchain (BC) special features, i.e., immutability and anonymity of users, while modifying the classic blockchain structure in order to overcome its challenges in IoT applications (i.e., low throughput, high overhead and latency). To this end, we cluster the miners of BC, store and process data at the nearest cluster to the patient. While our proposal is a work in progress, we provide a security analysis of our proposed architecture.
Aspect Extraction from Bangla Reviews Through Stacked Auto-Encoders

Interactions between online users are growing more and more in recent years, due to the latest developments of the web. People share online comments, opinions, and reviews about many topics. Aspect extraction is the automatic process of understanding the topic (the aspect) of such comments, which has obtained huge interest from commercial and academic points of view. For instance, reviews available in webshops (like eBay, Amazon, Aliexpress, etc.) can help the customers in purchasing products and automatic analysis of reviews would be useful, as sometimes it is almost impossible to read all the available ones. In recent years, aspect extraction in the Bangla language has been regarded more and more as a task of growing importance. In the previous literature, a few methods have been introduced to classify Bangla texts according to the aspect they were focused on. This kind of research is limited mainly due to the lack of publicly available datasets for aspect extraction in the Bangla language. We take into account the only two publicly available datasets, recently published, collected for the task of aspect extraction in the Bangla language. Then, we introduce several classification methods based on stacked auto-encoders, as far as we know never exploited in the task of aspect extraction in Bangla, and we achieve better aspect classification performance with respect to the state-of-the-art: the experiments show an average improvement of 0.17 , 0.31 and 0.30 (across the two datasets), respectively in precision, recall and F1-score, reported in the state-of-the-art works that tackled the problem.
Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks

Mobile Edge Computing (MEC) is a promising technology to extend the diverse services to the edge of Internet of Things (IoT) system. However, the static edge server deployment may cause “service hole” in IoT networks in which the location and service requests of the User Equipments (UEs) may be dynamically changing. In this paper, we firstly explore a vehicle edge computing network architecture in which the vehicles can act as the mobile edge servers to provide computation services for nearby UEs. Then, we propose as vehicle-assisted offloading scheme for UEs while considering the delay of the computation task. Accordingly, an optimization problem is formulated to maximize the long-term utility of the vehicle edge computing network. Considering the stochastic vehicle traffic, dynamic computation requests and time-varying communication conditions, the problem is further formulated as a semi-Markov process and two reinforcement learning methods: $Q$-learning based method and deep reinforcement learning (DRL) method, are proposed to obtain the optimal policies of computation offloading and resource allocation. Finally, we analyze the effectiveness of the proposed scheme in the vehicular edge computing network by giving numerical results.
New Methodology for Backward Analysis of Reconfigurable Event Control Systems using R-TNCESs

This paper deals with reconfigurable discrete event control systems (RDECSs). We model RDECSs using reconfigurable timed net condition/event systems (R-TNCESs) formalism which is an extension from Petri nets to deal with reconfiguration properties. Model-based diagnosis algorithms are widely used in academia and industry to detect faulty components and ensure systems safety. The application of these methods on reconfigurable systems is impossible due to their special behavior. In this paper, we propose accomplishing techniques of backward reachability to make reconfigurable systems model-based diagnosis possible using R-TNCESs. The flexibility among reconfigurable systems like RDECSs allows them to challenge recent requirements of markets. However, such properties and complicated behavior make their verification task being complex and sometimes impossible. We deal with the previous problem by proposing a new methodology based on backward reachability of RDECSs using (R-TNCESs) formalism including improvement methods. The proposed methodology serves to reduce as much as possible redundant computations and gives a package to be used in model-based diagnosis algorithms. The paper’s contribution is applied to a benchmark modular production system. Finally, a performance evaluation is achieved for different sizes of the problem to study benefits and limits of the proposed methodology among large-scale systems.