Miracle-dz/UniFAR-FTUs huggingface dataset:
The impacts of knowledge management enablers and knowledge management processes on university performance in Vietnam

ABSTRACT Despite the abundance of literature on university performance (UP), few studies have examined the impacts of knowledge management (KM) enablers and KM processes on UP. This study introduces a model explaining the variance in UP, including KM enablers, which include organisational enablers (OEs) and personal enablers (PEs) as indirect determinants, and KM processes as direct determinants. Data from 296 Vietnamese university lecturers and managerial staff indicated that KM processes mediate the link between KM and UP. However, OEs and PEs had unequal impacts on KM processes. Of the three OE components, organisational rewards and culture had a direct impact on KM, while organisational leadership had an indirect impact through the two other processes. Of the two PE components, knowledge self-efficacy had a significant influence on KM. The findings indicate ways for Vietnamese universities to enhance performance and thus develop sustainably.
Improving Image Compression With Adjacent Attention and Refinement Block

Recently, learned image compression algorithms have shown incredible performance compared to classic hand-crafted image codecs. Despite its considerable achievements, the fundamental disadvantage is not optimized for retaining local redundancies, particularly non-repetitive patterns, which have a detrimental influence on the reconstruction quality. This paper introduces the autoencoder-style network-based efficient image compression method, which contains three novel blocks, i.e., adjacent attention block, Gaussian merge block, and decoded image refinement block, to improve the overall image compression performance. The adjacent attention block allocates the additional bits required to capture spatial correlations (both vertical and horizontal) and effectively remove worthless information. The Gaussian merge block assists the rate-distortion optimization performance, while the decoded image refinement block improves the defects in low-resolution reconstructed images. A comprehensive ablation study analyzes and evaluates the qualitative and quantitative capabilities of the proposed model. Experimental results on two publicly available datasets reveal that our method outperforms the state-of-the-art methods on the KODAK dataset (by around 4dB and 5dB) and CLIC dataset (by about 4dB and 3dB) in terms of PSNR and MS-SSIM.
Personality Classification Model of Social Network Profiles based on their Activities and Contents

—Social networks have become an important part of everyday life, especially after the latest technologies such as smartphones, tablets, and laptops have become widespread. Individuals spend a lot of time on social media and express their feelings and opinions through statuses, comments, and updates which could be a way to understand and classify their personalities. The personalities in psychological science are divided into five classes according to the Big-five model (Openness, Extraversion, Consciousness, Agreeableness, and Neurotic). This model shows the key features with their weights for each personality. In this paper, a proposed model is developed for detecting the personality features from users’ activities in social networks. In this model, machine learning techniques are used for predicting the personalities with a score for each Big-five model type and sorting them in descending order. The personality classification model will be useful in developing a better understanding of the user profile and specifically targeting users with appropriate advertising. Any social media network user's personality can be predicted by using their posts and status updates to get better accuracy. The experimental results of the model in this study provide an enhancement because it can predict the precise score of one user in each factor of the Big-five. The proposed model was tested on a dataset extracted from Facebook and manually classified by experts, and it achieved 89.37% accuracy.
Design of Compressive Sensing Adaptive Taylor-Fourier Comb Filters for Harmonic Synchrophasor Estimation

In modern power systems, phasor measurements are expected to deal with challenging conditions, e.g., fast dynamics and high distortion levels. Taylor-Fourier Multifrequency models represent a promising solution, but their performance is strongly related to the accurate extraction of the signal spectral support. In this context, this paper proposes an enhanced method for support recovery that exploits the inherent block-sparsity properties of electrical signals. The proposed method is fully characterized in diverse and distorted test conditions, inspired by reference standards and real-world scenarios. The comparison against another Compressive Sensing based approach confirms the significant improvement in terms of both recovered support exactness and synchrophasor measurement accuracy.
Secure Command and Control for Internet of Battle Things Using Novel Network Paradigms

Current military operations require flexible and adaptable troops, which must be able to cooperate with civilian agencies and coalitions in open field and urban scenarios. These challenges require informed decisions not only at headquarters, but also at the edge of the battlefield, together with network-enabled skills and agile Command and Control (C2) Systems. The C2 system should ensure a timely and secure dissemination of information, obtained from interconnected devices, such as radars, unmanned aerial vehicles (UAV), sensors, armored combat vehicles and wearable devices. These networked sensors and effectors constitute the Internet of Battle Things (IoBT). This work investigates a secure network strategy to achieve C2 agility in the IoBT heterogeneous environment, by coupling the application and network with a multi-layer, defense-in-depth cybersecurity mechanism at the hardware, network and application level. The planned framework uses the Software-Defined Networking (SDN) paradigm to orchestrate network services, by means of semantics-oriented data from technologies such as Information Centric Networks (ICN) and Delay-Tolerant Networks (DTN). The results from preliminary simulated scenarios showed a significant improvement in network metrics, as well as network resilience to withstand cyber-attacks.
Isogeometric Finite Element Analysis based on Bézier Extraction of NURBS and T-splines

This thesis will give a theoretical overview of B-splines, as well as NURBS and T-splines which are based on B-splines, and also the concept of Bezier decomposition of these spline functions. Bezier decomposition will decompose the splines into Bernstein polynomials which are defined over the domain of one quadrature element. This theoretical background will then be used to implement a Matlab isogeometric finite element analysis program. Two different choices for implementation are explored, a isogeometric finite element solver built from scratch for use of NURBS, and the use of Bezier extraction to implement isogeometric analysis with NURBS and T-splines in an already existing finite element solver. The main focus will be on use of Bezier extraction, which will signicantly ease the implementation. Numerical studies are performed with problems of linear elasticity and heat conduction, to study the convergence of an isogeometric analysis.The accuracy of isogeometric analysis will prove to be better than for a traditional FEA for the analyzed problems
The Bacillus anthracis S-layer is an exoskeleton-like structure that imparts mechanical and osmotic stabilization to the cell wall

Abstract Surface layers (S-layers) are 2D paracrystalline protein monolayers covering the cell envelope of many prokaryotes and archaea. Proposed functions include a role in cell support, as scaffolding structure, as molecular sieve, or as virulence factor. Bacillus anthracis holds two S-layers, composed of Sap or EA1, which interchange in early and late exponential growth phase. We previously found that acute disruption of B. anthracis Sap S-layer integrity, by means of nanobodies, results in severe morphological cell surface defects and cell collapse. Remarkably, this loss of function is due to the destruction of the Sap lattice structure rather than detachment of monomers from the cell surface. Here, we combine force nanoscopy and light microscopy observations to probe the contribution of the S-layer to the mechanical, structural, and functional properties of the cell envelope, which have been so far elusive. Our experiments reveal that cells with a compromised S-layer lattice show a decreased compressive stiffness and elastic modulus. Furthermore, we find that S-layer integrity is required to resist cell turgor under hypotonic conditions. These results present compelling experimental evidence indicating that the S-layers can serve as prokaryotic exoskeletons that support the cell wall in conferring rigidity and mechanical stability to bacterial cells.
Sub-synchronous oscillation analysis of complicated power grid incorporating wind farms with different types

: A complicated test system with integration of doubly-fed induction generator (DFIG)-based and permanent magnet synchronous generator (PMSG)-based wind farms is built up in this study to explore and exploit the interactions between DFIG unit and PMSG unit on the issue of sub-synchronous oscillation (SSO). With detailed modelling of wind farm units, transmission lines and loads, the SSO modes are elaborately analysed through eigenvalue analysis and participation factor methods. The level and position of series compensated lines are discussed leading to some useful comments and suggestions. Besides, the power outputs of the two wind farm types are compared to in-vestigate their potential in ﬂ uences on the oscillation, and the most in ﬂ uential proportional – integral parameters producing the potential risks of oscillation are chosen out.
Building A Learning Pathway: An Interactive How-to Guide to Course Design

Abstract : Not all that long ago, one would have had great difficulty finding a book about how to design college courses. And, given the paradigm that seemingly dominated within higher education for much of the 20th century, it is no wonder. College-level teaching has historically lived within what has been called an instruction-focused paradigm (Barr and Tagg, 1995). Within this paradigm, a college professor's primary task in the classroom was to deliver instruction literally to profess his or her knowledge of a particular discipline to students, who were expected to absorb the to-be-learned material.
Pathological Evaluation by Microtomography of Ranula. Clinical Case Report

This study aims to demonstrate, through a clinical case report, the applicability of the use of microtomography (μCT) in the histopathological evaluation of a ranula lesion on the oral floor and to evaluate the use of 2 % elemental iodine solution as a contrast agent, in order to obtain a better contrast effect in a tissue sample, thus facilitating the identification of anatomical structures, the histomorphological evaluation and the potential use of μCT in the imaging diagnosis of lesions. Different parameters were evaluated for obtaining images in SkyScan 1172, in a biopsied piece, when impregnated in a 10 % formalin solution and in a 2 % elemental iodine solution, when impregnated for 24h and 48h. Contrast agent impregnation was evaluated using the Hounsfield unit. The use of μCT allowed the identification of sialoliths dispersed inside the biopsy specimen, while the impregnation with Elemental iodine 2 % for 24h resulted in a better contrast when compared to the other conditions. The use of the Hounsfield unit allowed an adequate evaluation of the contrast obtained when the different parameters of impregnation and image acquisition were applied. The comparison between the 3D images with and without a specific marker highlighted a better evidencing of the soft tissues, with an improvement in the contrast of the images, also allowing the identification of the glandular duct obstructed by the sialoliths, allowing a conclusive histopathological evaluation of the biopsied lesion.
How Digital Skills Shift Intentions in a Smart Senior Care Context

Aging is an irresistible trend. How to deal with the problems derived from aging has become an extremely important issue among government departments, academia and senior care industry. The objective of this study was to velidate the relationship among perceived digital skills, digital knowledge, attitude and behavioral intentions to use smart senior care platform among the elderly. A total of 403 valid samples were collected through online and onsite questionnaires survey. The results of structural equition model analysis showed that, without considering digital skills, respondents' digital knowledge significantly affected their attitude and behavioral intentions to use the smart senior care platform; and respondents with low digital skills will significantly affect the relationship between their digital knowledge and attitude while considering digital skills. It is suggested that the government and the industry should continue to strengthen digital education and training of digital skills, and the families of the elderly should also pay attention to the digital skills and knowledge of their elders.
Results from the CACTI experiment: Air-Cerenkov and particle measurements of PeV air showers at Los Alamos

An array of six wide angle Cerenkov detectors was constructed amongst the scintillator and muon detectors of the CYGNUS II array at Los Alamos National Laboratory to investigate cosmic ray composition in the PeV region through measurements of the shape of Cerenkov lateral distributions. Data were collected during clear, moonless nights over three observing periods in 1995. Estimates of depths of shower maxima determined from the recorded Cerenkov lateral distributions align well with existing results at higher energies and suggest a mixed to heavy composition in the PeV region with no significant variation observed around the knee. The accuracy of composition determination is limited by uncertainties in the expected levels of depth of maximum predicted using different Monte-Carlo shower simulation models.
A Pricing Mechanism for Ride-Hailing Systems in the Presence of Driver Acceptance Uncertainty

Freelance drivers in ride-hailing systems may strategically accept or reject ride requests based on their projection of the profitability of the assigned rides. This driver acceptance uncertainty is mainly caused by the flat rate payment and the blind ride acceptance rule adopted by most ride-hailing platforms. As a result, a high driver rejection rate has been observed, causing a negative impact on the service quality and matching efficiency for the ride-hailing systems. In this paper, we propose a pricing mechanism to improve drivers’ average ride acceptance rate by offering personalized payments computed based on the characteristics of individual riders and the estimated acceptance rates of the drivers. Specifically, we model and predict the drivers’ ride acceptance rates through a binary choice model and incorporate it into the stochastic optimization problem for the ride-hailing system. This provides personalized payment for each driver in connection with the characteristics of the assigned ride and the preferences of the drivers. We then evaluate the performance of the proposed pricing mechanism through extensive numerical experiments based on RideAustin trip data from June 2016 to April 2017. The results suggest that our proposed pricing mechanism improves the drivers’ average acceptance rate by an average of 60% compared to some commonly used pricing schemes. It also significantly increases the platform’s expected profit and matching rate. This implies a strong potential for the proposed pricing mechanism to improve service reliability and quality in ride-hailing systems.
Boosted Locality Sensitive Hashing: Discriminative, Efficient, and Scalable Binary Codes for Source Separation

We propose a novel adaptive boosting approach to learn discriminative binary hash codes, boosted locality sensitive hashing (BLSH), that can represent audio spectra efficiently. We aim to use the learned hash codes in the single-channel speech denoising task by designing a nearest neighborhood search method that operates in the hashed feature space. To achieve the optimal denoising results given the highly compact binary feature representation, our proposed BLSH algorithm learns simple logistic regressors as the weak learners in an incremental way (i.e., one by one) so that each weak learner is trained to complement the mistake its predecessors have made. Upon testing, their binary classification results transform each spectrum of noisy speech into a bit string, where the bits are ordered based on their significance, adding scalability to the denoising system. Simple bitwise operations calculate Hamming distance to find the $\boldsymbol{K}$-nearest matching hashed frames in the dictionary of training noisy speech spectra, whose associated ideal binary masks are averaged to estimate the denoising mask for that test mixture. In contrast to the locality sensitive hashing method's random projections, our proposed supervised learning algorithm trains the projections such that the distance between the self-similarity matrix of the hash codes and that of the original spectra is minimized. Likewise, the process conceptually aligns to the Adaboost algorithm, although ours is specialized in learning binary features for source separation rather than classification. Experimental results on speech denoising suggest that the BLSH algorithm learns more discriminative representations than Fourier or mel spectra and the nonlinear kernels derived from them. Our compact binary representation is expected to facilitate model deployment onto resource-constrained environments, where comprehensive models (e.g., deep neural networks) are unaffordable.
Machine-Learning-Based Reconstruction of Turbulent Vortices From Sparse Pressure Sensors in a Pump Sump

Getting access to the state of turbulent flow from limited sensor measurements in engineering systems is a major challenge. Development of technologies to accurately estimate the state of the flow is now possible with the use of machine learning. We present a supervised machine learning technique to reconstruct turbulent vortical structures in a pump sump from sparse surface pressure measurements. For the current flow reconstruction technique, a combination of multi-layer perceptron and three-dimensional convolutional neural network is utilized. This technique provides accurate flow estimation from only a few sensor measurements, identifying the presence of adverse vortices. The dependence of the model performance on the amount of training data, the number of input sensors, and the noise levels are investigated. The present machine learning-based flow estimator supports safe operations of pumps and can be extended to a broad range of applications for industrial fluid-based systems.
Competition patterns and biodegradation results of multiple fungal species on Moso bamboo (Phyllostachys edulis)

ABSTRACT The biodegradation of lignocellulosic materials in nature is a complex process. To investigate the fungal competition patterns and degradation on Moso bamboo (Phyllostachys edulis), four wood decay fungi, including white-rot and brown-rot fungi, were selected as the test fungi in this research. Bamboo blocks were pre-colonized by different fungi then paired to form dual cultures. The results showed different competition outcomes among these fungi on potato dextrose agar, forest soil, and bamboo. Whit-rot fungus Trametes versicolor was highly competitive on bamboo where it could replace brown-rot fungi, and the biodegradation of bamboo was more severe in dual cultures, especially in the combination of Trametes versicolor and Gloeophyllum trabeum. The degradation of the bamboo components also differed with the monocultures and dual cultures. In monocultures of brown-rot fungi, cellulose was the most severely degraded component among the three primary components; while in monocultures of white-rot fungi, lignin was the most severely degraded component instead. The degradation of hemicellulose and lignin was more significant than that of cellulose in the dual cultures containing white-rot fungi. These results suggest that dual culturing of decay fungi has the potential to improve biomass utilization of bamboo and reduce the chemical treatment cost of raw materials.
Charismatic experiences in the Congo Evangelistic Mission Churches: A review of some practices

divisions within the CEM churches have become more frequent and thereby making it difficult for many pastors to lead their members, especially those with charismatic gifts (prophets, Balombi). Contribution: Focusing specifically on charismatic gifts, this article establishes theological guidelines that would revive the Church’s missiological impulse. The guidelines for dealing with these practices will better equip Pentecostal church members to practice the charismatic gifts in light of the New Testament perspectives.
Fuzzy Clustering Analysis of Power Incomplete Data based on Improved IVAEGAN Model

—The scale of data generated by the complex and huge power system during operation is also very large. With the data acquisition of various information systems, it is easy to form the situation of incomplete power data information, which cannot guarantee the efficiency and quality of work, and reduce the security and reliability of the entire power grid. When incomplete data and incomplete data sets are caused by data storage failure or data acquisition errors, fuzzy clustering of data will face great difficulties. The fuzzy clustering of incomplete data of the power equipment is divided into the processing of incomplete data and the clustering analysis of "recovered" complete data. This paper proposes an IVAEGAN-IFCM interval fuzzy clustering algorithm, which uses interval data sets to fill in the incomplete data, and then completes the clustering of interval data. At the same time, the whole numerical data set is transformed into a complete interval data set. The final clustering result is obtained by interval fuzzy mean clustering analysis of the whole interval data set. Finally, the algorithm proposed in this paper and other machine learning training data sets is made for experimental analysis. The experimental results show that the algorithm proposed in this paper can complete incomplete data sets with high precision clustering. Compared with other contrast methods, it shows higher clustering accuracy. Compared with the numerical clustering algorithm, the clustering accuracy is improved by more than 4.3%, and it has better robustness. It also shows better generalization on the artificial data sets and other complex data sets. It is helpful to improve the technical level of the existing power grid and has important theoretical research value and engineering practice significance.
Modern Privacy-Preserving and Security Schemes in Social Networks: A Review

Online Social Network (OSN) is a popular application to exchange messages over the internet. However, millions of users are still under threat because of protection drawbacks. Many papers have proposed security methods, including firewalls, protocols, cryptography, statistical analysis, even learning algorithms. This paper provides an overview of privacy and security issues and describes multiple OSN protection techniques. We present various security schemes in OSNs and outline existing solutions to mitigate those attacks. This paper also discusses future research directions regarding OSN security problems and techniques.