Miracle-dz/UniFAR-FTUs huggingface dataset:
Explaining Continuance Intention of a Cloud-Based Virtual Learning Environment with Psychological Ownership and the Technology Acceptance Model

This study investigated teachers’ intention to continue using a cloud-based Virtual Learning Environment (VLE) by incorporating the theory of psychological ownership and the Technology Acceptance Model (TAM). Psychological ownership refers to a sense of ownership where people feel psychologically attached to a particular object, and this concept is becoming increasingly relevant in technology adoption research. The study proposed that such phenomenon can also occur when teachers use a cloud-based VLE. Hence, a hypothesized model was tested with 402 practicing teachers from Malaysia. Measures were adapted and adopted from established scales used in previous research. Results from Partial Least Squares Structural Equation Modeling approach affirmed the potential effect of psychological ownership and its antecedents as external variables of TAM. Overall, the model explained 69% variance of teachers’ intention in continuing to use the VLE. This study advances the theory of psychological ownership in the technological context, with evidences obtained within the ambit of cloud-based VLE.
A Game Theoretical Based Self-Organization Scheduling Mechanism Mesh Networks in UAVs (unmanned aerial vehicles) cooperative multi-task assignment

The UAVs system is vigorously promoted in several fields. The Information Synchronization of Unmanned Aerial Vehicles is an important factor that affects the coordinated control of UAVs. We proposed a game theoretical based self-organization Scheduling mechanism (GTSS). The GTSS is applied to solve unmanned aerial vehicle (UAVs) cooperative multi-task assignment problem. It mainly sets a repeated game model in the communication scheduling among the UAVs.
The use of prototypes to bridge knowledge boundaries in agile software development

Bridging knowledge boundaries among project team members is essential to prevent delays or complete failure of software development projects. Prior researchers have reported that software prototypes can be used to help bridge knowledge boundaries between team members in traditional software development settings, yet their use in an agile development setting remains unexplored. Agile development centers the interactions between team members on emerging representations of the prototype whose properties are prone to change over time. Therefore, we conducted an in‐depth study of an agile development project to enhance our understanding on how software prototypes are used as boundary objects in a distributed team setting. Our analyses of team member interactions during 46 virtual meetings that took place over a period of 6 months revealed four different prototype use practices (exemplifying, contrasting, relating, framing) that were effective in bridging syntactic, semantic, or pragmatic knowledge boundaries. We also provide empirically grounded evidence of how variations in object properties can afford different use practices, how the use practices take advantage of these properties, and how object properties are reshaped through these use practices as different types of knowledge boundaries are bridged. These findings bear important implications for research on prototypes as boundary objects in software development in particular and boundary object use in general.
Multiply imputing missing values arising by design in transplant survival data

In this article, we address a missing data problem that occurs in transplant survival studies. Recipients of organ transplants are followed up from transplantation and their survival times recorded, together with various explanatory variables. Due to differences in data collection procedures in different centers or over time, a particular explanatory variable (or set of variables) may only be recorded for certain recipients, which results in this variable being missing for a substantial number of records in the data. The variable may also turn out to be an important predictor of survival and so it is important to handle this missing‐by‐design problem appropriately. Consensus in the literature is to handle this problem with complete case analysis, as the missing data are assumed to arise under an appropriate missing at random mechanism that gives consistent estimates here. Specifically, the missing values can reasonably be assumed not to be related to the survival time. In this article, we investigate the potential for multiple imputation to handle this problem in a relevant study on survival after kidney transplantation, and show that it comprehensively outperforms complete case analysis on a range of measures. This is a particularly important finding in the medical context as imputing large amounts of missing data is often viewed with scepticism.
Millimeter wave multi-RAT small cells for heterogeneous mobile services : performance analysis and optimization. (Millimeter wave Multi-RAT small cells pour services hétérogènes : analyse et optimisation des performances)

Future wireless applications anticipate an explosion in the plethora of use-cases and services, which cannot be sustained by incremental improvements on the existing communication schemes. For this, two research directions are particularly attractive: network densification using small cells and millimeter wave (mm-wave) wave communications. In this thesis, we model and evaluate cellular networks consisting of multi-radio access technique (RAT) mm-wave small cells deployed on top of the legacy macro-architecture. First, we mathematically model a homogeneous deployment of multi-RAT small cells and characterize the user and network performance in terms of signal to interference plus noise ratio (SINR) coverage probability, downlink throughput, and the cell overloading probability. Then, we study users association to different tiers and optimal selection of different RATs, so as to optimize these performance metrics. Generally, cellular network models that assume homogeneous deployments of small cells fail to take into account the nuances of urban blockage characteristics. To address this, we model the small cell locations along the roads of a city, and subsequently, we take into consideration the signal blockages due to buildings or moving vehicles on the roads. In this network, we assume that the operator supports three types of services v.i. z. , ultra-reliable low-latency communications (URLLC), massive machine-type communications (mMTC), and enhanced mobile broadband (eMBB) with different requirements. Consequently, we study the optimal RAT selection for these services with varying vehicular blockages. Finally, based on the on-road deployment model of mm-wave small cells, we study a network designed to support positioning and data services simultaneously. We characterize the positioning accuracy based on the localization bounds and then study optimal resource partitioning and beamwidth selection strategies to address varied positioning and data-rate requirements.
SRNoC: An Ultra-Fast Configurable FPGA-Based NoC Simulator Using Switch–Router Architecture

Network-on-chip (NoC) has become one of the most common interconnection architectures to integrate multicore system. However, the performance, hardware costs, and power consumption of NoC are sensitive to many parameters, such as topology, the number and depth of virtual channels (VCs), routing algorithms, and flow control mechanisms. In order to find the best NoC solution for different applications, a fast and flexible NoC simulator is necessary. In this article, we present an ultrafast field programmable gate array (FPGA)-based NoC simulator called SRNoC, which is able to be configured via host on PC. In SRNoC, we proposed switch–router architecture and virtual boundary technology. Switch–router is an architecture which makes SRNoC support irregular or custom NoC topology by configuring the NoC topology in hardware. Virtual boundary is a novel configurable virtualization mechanism which can store boundary traffic and use the stored boundary traffic to simulate large NoC in virtualized mode. By employing the switch–router architecture and virtual boundary, SRNoC demonstrates <inline-formula> <tex-math notation="LaTeX">$45\times -3077\times $ </tex-math></inline-formula> speed-up against Booksim and maintains the same level of simulation accuracy. This simulator has three main advantages: 1) configured switch–router can support irregular and custom topologies in a nonvirtualized mode; 2) virtual boundary can improve the simulation speed in virtualized mode, effectively; and 3) embedded models can provide evaluation of packet latency, power consumption, and temperature distribution. Due to the switch–router architecture and virtual boundary, SRNoC can simulate a 30-node (<inline-formula> <tex-math notation="LaTeX">$5\times 6$ </tex-math></inline-formula>) NoC in nonvirtualized mode and 3072-node (<inline-formula> <tex-math notation="LaTeX">$64\times 48$ </tex-math></inline-formula>) in virtualized mode on a Xilinx Virtex-7 FPGA.
Virtual Indicative Broadband over Power Lines Topologies for Respective Subclasses by Adjusting Channel Attenuation Statistical Distribution Parameters of Statistical Hybrid Models (Class Maps) – Part 1: Theory

Based on a set of indicative overhead and underground medium voltage broadband over power lines (OV and UN MV BPL) topologies, initial statistical hybrid model (iSHM) and modified statistical hybrid model (mSHM) are statistical channel models suitable for the distribution BPL networks. Both iSHM and mSHM statistically process channel attenuation and capacity values of assumed indicative OV and UN MV BPL topologies by exploiting channel attenuation statistical distributions (CASDs). iSHM exploits a set of well-known CASDs (i.e., Gaussian, Lognormal, Wald, Weibull and Gumbel CASDs) while mSHM exploits the Empirical CASD. Each indicative OV and UN MV BPL topology acts as the representative one of a respective OV and UN MV BPL topology class (i.e., rural, suburban, urban and aggravated urban class) that consists of a number of respective statistically equivalent OV and UN MV BPL topologies. The contribution of this paper is the theoretical framework presentation of the creation of new virtual indicative OV and UN MV BPL topologies by appropriately adjusting the parameters of iSHM and mSHM CASDs. These new virtual indicative OV and UN MV BPL topologies will enrich the respective today’s OV and UN MV BPL topology classes with respective OV and UN MV BPL topology subclasses while each subclass will be enriched by a number of respective statistically equivalent OV and UN MV BPL topologies. The procedure of defining new virtual distribution BPL topologies by applying iSHM and mSHM will allow a better capacity study of OV and UN MV BPL topology classes. Apart from the definition procedure of the virtual indicative OV MV and UN MV BPL topologies and their respective virtual subclasses by adjusting CASD parameters of iSHM and mSHM, the contribution of this paper is the class map that analytically describes the taxonomy of distribution BPL topology classes and subclasses. Citation: Lazaropoulos, A. G. (2019). Virtual Indicative Broadband over Power Lines Topologies for Respective Subclasses by Adjusting Channel Attenuation Statistical Distribution Parameters of Statistical Hybrid Models (Class Maps) – Part 1: Theory. Trends in Renewable Energy, 5, 237-257. DOI: 10.17737/tre.2019. 5.3. 0099
Examining face-sensitive brain potentials in natural environments using mobile EEG

Faces are a unique type of stimulus for humans. As such, they are processed differently to other types of stimuli like houses or objects. In the past, laboratory based testing has been used to examine the neural correlates of human face processing. However, viewing faces in a laboratory differs considerably from how it occurs the real world. This thesis examines the neural mechanisms underlying the processing of face images in a naturalistic setting. A custom-design mobile brain and body imaging technique was used to explore the neural mechanisms governing naturalistic face processing. Simultaneous mobile electroencephalography (EEG) and eye-tracking data was recorded from participants while they freely viewed images presented in a mock art gallery. The synchronization of both data streams allowed us to analyse the EEG signal by time-locking markers to naturally occurring visual events captured by the eye tracker. Using this methodology the effects of emotion, familiarity and body posture on the face-sensitive N170 ERP component were investigated. The findings demonstrated, for the first time, the possibility of detecting face-specific brain potentials in freely moving, unrestricted subjects during passive viewing of images, as well as during active interactions with another person. The results present the effects of emotional valence on the N170 amplitude and replicate previous lab-based findings. Furthermore, the effects of body posture on early visual ERPs, but not the face-sensitive N170 contribute new insights to the face processing literature. Finally, the N170 component produced during a dyadic social interaction is described in relation to previous laboratory based reports.
Caching in heterogeneous satellite networks with fountain codes

In this paper, we investigate the performance of caching schemes based on fountain codes in a heterogeneous satellite network. We consider multiple cache‐aided hubs, which are connected to a geostationary satellite through backhaul links. With the aim of reducing the average number of transmissions over the satellite backhaul link, we propose the use of a caching scheme based on fountain codes. We derive a simple analytical expression of the average backhaul transmission rate and provide a tight upper bound on it. Furthermore, we show how the performance of the fountain code‐based caching scheme is similar to that of a caching scheme based on maximum distance separable codes.
Coordinated Control of Distributed Energy Resources Using Features of Voltage Disturbances

Distributed energy resources (DERs) often rely on renewable sources whose random power fluctuations bring about voltage disturbances in distribution networks. Under such circumstances, voltage regulation through centralized control of DERs requires reliable detection and coordination mechanisms. This article proposes a new data-driven approach for event-triggered and coordinated control of DERs based on features of voltage disturbances. Synchrophasor datasets are processed to construct disturbance matrices that quantify spatio-temporal features of voltage disturbances. The estimated features are employed in clustering and control of DERs to suppress incipient events before exceeding a critical time. The proposed approach is tested in the IEEE 123-bus network, which has 15 solar photovoltaic sources with battery energy storage systems. The simulation results validate reliability and efficiency of the proposed method, and confirm that feature extraction combined with coordination of DERs can improve reliable and economic operation of distribution grids with renewables.
Combined Model of Technology Acceptance and Innovation Diffusion Theory for Adoption of Smartwatch

This study examined the factors influencing the intention to use smartwatches using the integrated model of technology acceptance model (TAM) and innovation diffusion theory (IDT). An online survey was conducted and the data were analyzed using the structural equation modeling (SEM). The results showed that the research model had an acceptable fit, and all paths, except for the one from the perceived ease of use to the intention to use, were supported. Regarding paths from IDT to TAM, it was observed that higher the compatibility, the users perceived greater usefulness. Additionally, both observability and trialability influenced the perceived ease of use. However, perceived ease of use affected the intention only through the mediated effect of perceived usefulness. The implication of the study lies on the major focus on the effects of users’ perceptions regarding innovative characteristics of smartwatches on the intention to adopt and attempted to increase the explanatory power of the TAM and IDT by combining both.
Non/Semi-parametric learning from data with complex features

This thesis is mainly focused on developing novel and flexible non/semi-parametric statistical methods dealing with data with complex features. In recent years, advancement of high throughput technologies has made it possible to collect sophisticated high-dimensional datasets, such as microarray data, genome-wide single nucleotide polymorphism (SNP) data, and RNA sequencing (RNA-seq) data. These advances have caused an escalating demand for innovative dimension reduction tools to extract useful information from the huge amount of data, to visualize the underlying structure, and to facilitate the understanding and analysis of the data. The research undertaken in my thesis are described below. In Chapter 2, we consider a semiparametric additive partially linear regression model (APLM) for analyzing ultra-high-dimensional data where both the number of linear components and the number of nonlinear components can be much larger than the sample size. We propose a two-step approach for estimation, selection and simultaneous inference of the components in the APLM. In the first step, the nonlinear additive components are approximated using polynomial spline basis functions, and a doubly penalized procedure is proposed to select nonzero linear and nonlinear components based on adaptive LASSO. In the second step, local linear smoothing is then applied to the data with the selected variables to obtain the asymptotic distribution of the estimators of the nonparametric functions of interest. The proposed method selects the correct model with probability approaching one under regularity conditions. The estimators of both the linear part and nonlinear part are consistent and asymptotically normal, which enables us to construct confidence
Cross Audio-Visual Speaker Identification in the Wild Using Deep Learning

Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the extracted information from one modality to improve the recognition ability of the other modality by complementing the missing information. The essential problem is to find the correspondence between the audio and visual streams, which is the goal of this work. We propose the use of a coupled 3D Convolutional Neural Network (CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio-visual streams using the learned multimodal features. The proposed architecture will incorporate both spatial and temporal information jointly to effectively find the correlation between temporal information for different modalities. By using a relatively small network architecture and much smaller dataset for training, our proposed method surpasses the performance of the existing similar methods for audio-visual matching which use CNNs for feature representation. We also demonstrate that an effective pair selection method can significantly increase the performance. The proposed method achieves relative improvements over 28% on the Equal Error Rate (EER) and over 7% on the Average Precision (AP) in comparison to the state-of-the-art method.
Does abusive supervision always promote employees to hide knowledge? From both reactance and COR perspectives

PurposeKnowledge hiding as an important topic in knowledge management field might be triggered by abusive supervision, but few studies discussed how to alleviate the effect of abusive supervision on knowledge hiding. Drawing on both reactance theory and conservation of resources (COR) theory, this study aims to build a moderated mediation framework to examine effects of abusive supervision on knowledge hiding via job insecurity and under moderation of motivational climate (including mastery climate and performance climate). Design/methodology/approachThe paper uses a two-wave survey study among 155 knowledge workers from educational and manufacturing industries.FindingsResults show that abusive supervision is not significantly related to knowledge hiding directly but indirectly via job insecurity. Abusive supervision’s interaction with mastery climate is negatively related to knowledge hiding, but its interaction with performance climate is positively related to knowledge hiding. The indirect relation of abusive supervision to knowledge hiding via job insecurity is significantly moderated by mastery climate but not by performance climate.Research limitations/implicationsDespite contributions, this study also has some limitations. Variables rated from the same source (i.e. employees) may have common method bias although the two-wave design does help alleviate this concern.Practical implicationsThe paper highlights important reasons why people hide knowledge at work (because of abusive supervision and job insecurity) and identifies a boundary condition (mastery climate) which will reduce abusive supervision’s influence on knowledge hiding.Originality/valueThis paper contributes to knowledge hiding literature which is an important part of knowledge management from the perspective of abusive supervision based on both reactance theory and COR theory.
Twenty‐five years of the Information Systems Journal: A bibliometric and ontological overview

The Information Systems Journal (ISJ) published its first issue in 1991, and in 2015, the journal celebrated its 25th anniversary. This study presents an overview of the leading research trends in the papers that the journal has published during its first quarter of a century via a bibliometric and ontological analysis. From a bibliometric perspective, the analysis considers the publication and citation structure of the journal. The study then develops a graphical analysis of the bibliographic material by using visualization of similarities software that employs bibliographic coupling and cocitation analysis. The work produces an ontological framework of impact and analyses the journal papers to assess qualitatively ISJ's impact. The results indicate that the journal has grown significantly over time and is now recognized as one of the leading journals in information systems. Yet challenges remain if the journal is to meet its aims in impacting and setting the agenda for the development of the Information Systems field.
Condição funcional, força muscular respiratória e qualidade de vida em pacientes cirróticos

BACKGROUND: Liver diseases are responsible for metabolic disorders and loss of muscle mass and function that affect functional status and quality of life (QoL). OBJECTIVE: To compare exercise capacity, respiratory muscle strength, and QoL in liver transplant candidates with cirrhosis of the following etiologies: hepatitis C virus (HCV), hepatitis B virus (HBV), and alcoholic cirrhosis (AC). METHODS: Cross-sectional study comprising 86 patients divided into three groups: HCV (40 patients), HBV (14 patients), and AC (32 patients). Patients were evaluated using the Six-Minute Walk Test (6MWT), manometry, and the QoL questionnaire SF-36. RESULTS: The AC group showed the lowest performance in the 6MWT (meters) compared to the HBV and HCV groups (373.50±50.48, 464.16±32, and 475.94±27.84, respectively, p=0.001). In the domains of the SF-36, the AC group had lower scores for functional capacity and physical limitations when compared to the HBV and HCV groups (p=0.001). In the comparison of respiratory muscle strength, the AC group had lower MIP (cmH2O) compared to the HBV and HCV groups (-65.54±11.28, -71.61±6.96, -82.44±13.71, respectively, p=0.001). The MEP (cmH2O) in the AC group was also lower than in the HBV and HCV groups (65.13±10.74, 82.44±13.87, 83.44±12.20, respectively, p=0.001). CONCLUSION: The AC group showed worse exercise capacity, respiratory muscle strength, and QoL compared to patients with HCV and HBV.