Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating Different Deep learning Models for Classification of Folliculitis

Folliculitis is a common skin condition that happens when a hair follicle(s) become inflamed. The cause of inflammation can be bacterial or fungal infection, ingrown hair due to hair removal etc. Based on the cause of inflammation in follicle, folliculitis can be categorized into eight different types. If left untreated, it may spread, cause deep infections which can further cause permanent hair loss, scarring, cellulitis and even pass into the bloodstream and become fatal. Dermatologists can usually diagnose folliculitis only by glancing at a patient’s skin. However, in order to find the cause of folliculitis, dermatologists may recommend taking a tissue sample, skin swab and having laboratory tests done. Additionally, a sample of the extracted hair from the troubled areas may be obtained for testing. Using potassium hydroxide, hair samples are microscopically examined to identify a potentially infectious cause. In this paper, we investigate how accurate CNNs are in identifying the type of folliculitis. We will use different Convolutional Neural Network (CNN) models like AlexNet, DenseNet201, GoogLeNet, InceptionV3, ResNet50, VGG19 and Xception. The results show that GoogLeNet performs best in identifying the type of folliculitis.
Opinions of Accounting Companies Managers Regarding the Digitalization Process of Accounting

Digitalization is a process that accounting firms are also subjected to if they want to remain competitive and meet the needs of real-time communication with customers. The purpose of this paperwork was to know the opinions of the managers of these companies on the adoption and implementation of artificial intelligence. The main objectives of the paper are the impact of digitalization on professional accountants, the challenges to which they are subjected, and the advantages/ disadvantages of digitalizing accounting and communication with clients. The research method used falls into the category of qualitative research, i.e. the semi-directive in-depth interview.
The determinants of corporate political activity in Australia

ABSTRACT Large corporations in Australia have shifted from limited to more fulsome engagement in political activity (CPA) in recent decades. The paper argues that this reflects corporate institutional change in response to wider changes in the environment that have included growing government intervention, increased industry concentration and intra-sectoral competition, and a more complex and challenging discursive and issues environment. Quantitative and regression-based analysis of the drivers of CPA are employed and the analysis is extended and deepened with material drawn from interviews with twenty-five corporate government relations professionals.
Improvement of Copa: Behaviors and Friendliness of Delay-Based Congestion Control Algorithm

Delay-based congestion control has drawn a lot of attention in both academics and industry recently. Specifically, the Copa algorithm proposed in NSDI can achieve consistent high performance under various network environments and has already been deployed on Facebook. In this paper, we theoretically analyze Copa and reveal its large queuing delay and poor fairness issue under certain conditions. The root cause is that Copa fails to achieve its expected behaviors, i.e., clear the bottleneck buffer occupancy periodically. Moreover, we also reveal that the pathological competitive mode of Copa fails to guarantee friendliness. To address these issues, we propose Copa+, which enhances Copa with a parameter adaptation mechanism and an optimized competitive mode. Designed based on our theoretical analysis, Copa+ can adaptively clear the bottleneck buffer occupancy and become friendly to Cubic in the competitive mode. As a result, Copa+ inherits the advantages of Copa but achieves lower queuing delay and better fairness under different environments, as confirmed by real-world experiments and simulations. Specifically, Copa+ has the highest average throughput over different Internet links among different cloud nodes, compared to Cubic, BBR, PCC Vivace, Remy, and Indigo. Meanwhile, Copa+ has an 8.1% increase in throughput and similar low queuing delay compared to Copa. Moreover, Copa+ achieves 14.6% lower queuing delay and 2.4% higher throughput compared to Sprout over emulated cellular links.
Pizzza: A Joint Sector Shape and Minimum Spanning Tree-Based Clustering Scheme for Energy Efficient Routing in Wireless Sensor Networks

The widespread employment of wireless sensor networks in various fields necessitates the urgent creation of methods for prevailing over known shortcomings of this network category. Energy shortage as one of the most restrictive deficiencies of the employed sensors in this network category has encouraged many researchers from both academic and industry communities to propose efficient solutions to contribute to efforts done with the aim of decreasing energy consumption and consequently increasing the wireless sensor networks’ lifetime. Among bunches of schemes proposed in this regard, cluster-based routing protocols have demonstrated promising results so far. Plenty of these schemes have improved network communication and minimized delay, however, they still need to be improved in the crucial aspects for which they were proposed, namely energy consumption reduction and network lifetime prolongation. Considering all these pivotal points, a novel cluster-based hierarchical routing protocol, named Pizzza, is introduced in this paper. Pizzza is creatively designed by forming minimum spanning trees among communicating nodes in each sector-shape cluster, where only eligible nodes from the first level of the architecture can undertake cluster head leading role. Employment of this innovative scheme has concluded in the prolongation of the network lifetime through the reduction in energy wastage resulting from the elimination of reverse data flow from BS, data transmission to the nearest neighbors, and balanced energy consumption in the network. The efficient energy consumption in Pizzza has resulted in a 65.52% prolongation in the network lifetime and a 77.05% enhancement in the residual energy of the network compared to a selected set of popular and efficient protocols.
Diagramación e Ilustración digital para el Diseño de Interfaces de un Agente Conversacional en Software educativo

INTRODUCCIÓN. El diseño como disciplina proyectual permite construir referentes y contextos desde la comunicación visual, estos a su vez forman parte de interfaces en productos virtuales que generan interacciones y comunicación. Crear estos espacios depende de la Diagramación que configura textos e imágenes en estructuras funcionales, que se ejecutan mediante software especializado para la creación de imágenes. OBJETIVO. Desarrollar una interfaz de usuario amigable para la enseñanza de la programación a niños, basado en el acompañamiento de un agente conversacional. MÉTODO. El enfoque del estudio es mixto, se recopila información de estudiantes como de docentes a través de encuestas y del diseño participativo en el codiseño del agente conversacional. RESULTADOS. Se encontraron acciones afirmativas con respecto al uso y el interés por el agente conversacional y las actividades que se desarrollan, además se identificó los requerimientos que los usuarios tienen frente a una interfaz para la enseñanza de programación. DISCUSIÓN Y CONCLUSIONES. La participación de los estudiantes en el codiseño de una interfaz constituye un factor clave, porque permite identificar la ruta a seguir para el diseño y diagramación de la interfaz con la finalidad de que esta sea de fácil uso y amigable con los usuarios. En el estudio se ha identificado las características que debe tener la interfaz y el agente conversacional en función de las necesidades del entorno educativo.
Collaborative Intrusion Detection System for SDVN: A Fairness Federated Deep Learning Approach

With the continuous innovations and development in communication technology and intelligent transportation systems, a new generation of vehicular ad hoc networks (VANETs) has become increasingly popular, making VANET communication security increasingly important. An intrusion detection system (IDS) is an important tool for detecting network attacks and is an effective means of improving network security. However, existing IDSs encounter several problems involving inaccurate detections, low detection efficiencies, and incomplete detections owing to extensive changes in vehicle locations in VANETs. This study explores federated learning in software-defined VANETs and designs an efficient and accurate collaborative intrusion detection system (CIDS) model. The model utilizes the collaboration among local software-defined networks (SDNs) to jointly train the CIDS model without directly exchanging local network data flows to improve the expansibility and globality of IDSs. To reduce the model difference between different SDN clients and improve the detection accuracy, this study regards the prediction loss for each SDN client as an objective from the perspective of constrained multi-objective optimization. By optimizing a surrogate maximum function containing all the objectives, the method adopts two-stage gradient optimization to achieve Pareto optimality for SDN clients with the worst fairness constraint maximization performance. In addition, this study evaluates the training model using two open-source datasets and compares it with the latest methods. Experimental results reveal that the proposed model ensures local data privacy and demonstrates high accuracy and efficiency in detecting attacks and is thus superior to the current schemes.
Command filter‐based adaptive control of flexible‐joint manipulator with input saturation and output constraints

This paper focuses on the topic of adaptive neural tracking control for flexible‐joint manipulator systems with output restrictions and input saturation. With the aid of the error compensation mechanism, command filter‐based adaptive neural control is proposed for robotic systems driven by permanent magnet synchronous machine (PMSM). An auxiliary signal produced by the first‐order linear system designed by the property of unmodeled dynamics is used to erase the dynamical uncertainties. The input saturation of system is estimated by tanh$$ \tanh $$ ‐function with mathematical transformation. Using barrier Lyapunov function (BLF), each component constraint of output is tackled. During the virtual control design phase, radial basis function neural networks (RBFNNs) may reliably assess the unknown nonlinear continuous function. All the variables in the closed‐loop system are proved to be semiglobally uniform ultimate bounded (SGUUB) by integrating all compensation signals into the overall Lyapunov function and using the defined compact set in the stability analysis of dynamic surface control (DSC). An applied example on robotic system is used to verify the effectiveness of the constructed design idea.
Reliability-oriented Component Criticality Analysis of Distribution Systems Based on Graph Theory

Severe power outages of distribution systems are usually first caused by the failure of a single component. Hence the analysis of critical components is very important for the reliable operation of distribution systems. This paper proposed a reliability-oriented component criticality analysis method for distribution systems based on graph theory. Specifically, a framework of analyzing the component criticality is proposed firstly based on the Monte Carlo simulation. The sensitivity of distribution system reliability indices to the fault parameters of vulnerable components is used to formulate the component criticality indices. Then, combining with the distribution system graph model, the topology structure of distribution systems is converted into a graph model. The zone partitioning and minimal path searching is applied to access the reliability-oriented criticality of components. Finally, the proposed method is applied to Roy Billinton Test System (RBTS) bus2 to validate the effectiveness of our proposed method.
The Possible Contribution of Neural Plasticity to ON, OFF and Steady-State Responses Elicited by Brief Trains of Repetitive Stimulation

The possible contribution of neural plasticity to ON, OFF and steady state responses elicited by brief, repetitive trains of stimulation was investigated in the intact human subject with the use of the electroencephalogram (EEG). Experiment One implemented trains of stimulation at three different repetition rates, 1.5Hz, 4Hz and 13Hz. The goal was to investigate the nature of the ON, OFF and steady state responses evoked at these repetition rates. The experiment was carried out in three modalities: visual (n=13), auditory (n=lO) and somatosensory (n=12). The main result was that the ON and OFF responses were enhanced at 13Hz compared to the lower repetition rates. Experiment Two sought to answer the question of whether enhancement depended on the repetition rate or the increased experience provided by the higher frequencies. The number of stimuli in the 13Hz trains was reduced to equal the 1.5Hz condition from Experiment One. Graded exposure was then provided to the 13Hz stimulation. This procedure was implemented in two groups of subjects: Replication One (n=12) used 13Hz stimulation and Replication Two (n=24) used 14Hz stimulation. A subset (n=IO) of the Replication Two subjects returned for a second session (Day 2) 24 hours after the first. An assessment of effects was made after minutes and hours. There were four main results. The OFF response was observed after nine 13Hz pulses and did not change over the course of the experiment. The ON response increased with exposure to the 13Hz trains. Steady state responses diminished and showed a phase shift over the experimental session. Results for Day 1 and Day 2 were not different. Within session changes, as a result of exposure to the stimulus, were seen. These effects were not long lasting.
Anxiety Levels in COVID-19 Patients Undergoing Upper Arm and Breathing Exercises

: The number of COVID-19 cases in Indonesia has reached more than 1.5 million, of which 121,011 people are still in treatment. Stress and anxiety are the dominant psychological aspects of COVID-19 pandemic and have been shown to affect overall quality of life. Therefore, this research aims to assess the level of anxiety in severe COVID-19 patients before and after undergoing upper arm exercises and breathing exercises. This study is a clinical trial using a two-group pretest-posttest quasi-experimental study design on 36 patients who diagnosed with confirmed COVID-19 severe severity who were treated in isolation room for 6 months period who met the inclusion and exclusion criteria. Then, baseline examinations were conducted such as: vital signs, laboratories, and Hospital Anxiety and Depression Scale (HADS) in the emergency room for COVID-19 patients before undergoing physiotherapy. A comparison of the results of HADS measurements was made between before and after the intervention (upper arm exercises and breathing exercises). If the data were normally distributed, paired t-test was used (p<0.05). If the data is not normally distributed, Wilcoxon's test were used. The study sample was dominated by men with a total of 23 people (63.9%) compared to 13 women (36.1%). Conditions before exercise, found 18 people (50%) moderate cases, 17 people (47.2%) severe cases and 1 person (2.8%) mild cases. In the post-exercise condition, there were 15 people (41.7%) with moderate cases, 12 people (33.3%) severe cases and 9 people (25%) mild cases. Out Of 53 severe COVID-19 patients who performed upper arm exercises and breathing exercises, the average HADS measurement results before exercise was 14.27 + 2.02 and after exercise 12.19 + 3.20. It was found that mean difference before and after exercise was 2.0833 + 2.11. After t paired test was done, a significance p value was found = 0.000 (p<0.05). Anxiety level in severe COVID-19 patients significantly difference before and after undergoing upper arm exercises and breathing exercises.
Rethink Physical Security: Protecting Vehicles via Battery-Enabled Sensing and Control [Point of View]

Cyberization is the foundation of vehicle electrification and automation, requiring the deployment of ever-increasing on-board sensing, communication, and computing services. However, vehicle cyberization also introduces new cyber vulnerabilities. In this article, we discuss the opportunities and challenges of using the common 12/24V automotive batteries as sensors and actuators to provide vehicles with three-pronged physical security protection: driver authentication, vehicle access control, and vehicle intrusion detection.
Spectral differentiation: Integration and inversion

Pseudospectral differentiation matrices suffer from large round-off error, and give rise to illconditioned systems used to solve differential equations numerically. This thesis presents two types of matrices designed to precondition these systems and improve robustness towards this round-off error for spectral methods on Chebyshev-Gauss-Lobatto points. The first of these is a generalization of a pseudospectral integration matrix described by Wang et al. [18]. The second uses this integration matrix to construct the matrix representing the inverse operator of the differential equation. Comparison is made between expected and calculated eigenvalues. Both preconditioners are tested on several examples. In many cases, accuracy is improved over the standard methodology by several orders of magnitude. Using these matrices on general sets of points is briefly discussed.
Holographic interferometry in a disturbed environment

Digital holographic interferometry (DHI) is an optical measurement technique that can be used to measure very small deformations and/or movements of an object. When the changes of an object are slow the measurement time has to be increased and vibrations of the components in the setup together with motion of the surrounding air start to interfere with the measurement. This thesis investigates the possibility of improving the quality of long time DHI measurements in a disturbed environment by post processing of the data. The approach has been to use the spatial correlation that exists in the noise by measuring a reference noise on a small stationary surface that is introduced in the field of view of the camera. This reference noise is then used in an RLS noise reduction filter to remove some of the noise from the other points in the image. The method has proven successful in removing noise caused by vibration where the spatial correlation is high within the noise. With air disturbances, only noise in the neighbourhood of the reference point is significantly reduced. The size of this region depends on the spatial scales present in the air disturbance.
Comparative Evaluation of Antimicrobial Efficacy of Fluoride-Based and Self-Assembling Peptide P11-4-based Tooth Remineralization Agents on Streptococcus mutans: A Microbiological Study

Background: Dental caries is a biofilm-related oral disease that continues to afflict the majority of the world's population. The disease results from an interaction between specific bacteria and dietary constituents within a biofilm known as dental plaque. Among the cariogenic microorganisms, Streptococcus mutans (S. mutans) plays pivotal role in caries-inducing processes. Objectives: Evaluate and compare the antimicrobial efficacy of self-assembling peptide P11-4-based tooth remineralization agents on S. mutans. Materials and Methods: An in vitro microbiological study. The antibacterial efficacy of self-assembling peptide P11-4 gel (Group 1), fluoride enhanced hydroxyapatite gel (Group 2), acidulated phosphate fluoride gel (Group 3), chlorhexidine gluconate gel 1.0% w/w (Group 4; positive control), and normal saline (Group 5; negative control) was performed using time-kill assay over a period of 24 h and the number of S. mutans colony-forming units (CFUs) were calculated. Statistical Analysis: Statistical analysis was done using Kruskal–Wallis test and Mann–Whitney post hoc Test. The level of significance was set at P < 0.05. Results: Group 1 showed mean CFUs (× 103) of 841.33 ± 3.51, Group 2 showed 10035.33 ± 60.68, Group 3 showed 1058.00 ± 56.96, Group 4 showed 0.00 ± 0.00, and Group 5 showed mean CFUs with 15226.67 ± 96.67. The difference in the mean CFUs (× 103) between different groups was statistically significant at P < 0.001. Conclusion: The self-assembling peptide P11-4-based tooth remineralization agent exhibited an inhibitory influence on S. mutans and hence formation of cariogenic bacteria dominant biofilm can thus be affected by its application.
Future pHealth Ecosystem-Holistic View on Privacy and Trust

Modern pHealth is an emerging approach to collecting and using personal health information (PHI) for personalized healthcare and personalized health management. For its products and services, it deploys advanced technologies such as sensors, actuators, computers, mobile phones, etc. Researchers have shown that today’s networked information systems, such as pHealth ecosystems, miss appropriate privacy solutions, and trust is only an illusion. In the future, the situation will be even more challenging because pHealth ecosystems will be highly distributed, dynamic, increasingly autonomous, and multi-stakeholder, with the ability to monitor the person’s regular life, movements, emotions, and health-related behavior in real time. In this paper, the authors demonstrate that privacy and trust in ecosystems are system-level problems that need a holistic, system-focused solution. To make future pHealth ethically acceptable, privacy-enabled, and trustworthy, the authors have developed a conceptual five-level privacy and trust model as well as a formula that describes the impact of privacy and trust factors on the level of privacy and trust. Furthermore, the authors have analyzed privacy and trust challenges and possible solutions at each level of the model. Based on the analysis performed, a proposal for future ethically acceptable, trustworthy, and privacy-enabled pHealth is developed. The solution combines privacy as personal property and trust as legally binding fiducial duty approaches and uses a blockchain-based smart contract agreement to store people’s privacy and trust requirements and service providers’ promises.
Factor structure of school readiness skills: conceptual vs. statistical distinctions

Introduction School readiness skills are a broad set of abilities that children develop in early childhood that support achievement once they enter formal schooling. Three components of school readiness skills are of focus in the current study: executive function (EF), language/literacy, and mathematics. The current study examines to what extent 13 direct assessments of these skills statistically align with theoretical models for distinct construct- and timepoint-specific latent factors. Methods The sample included 684 children (52.34% male; 42% Black/African American; Mage = 4.80 years in the fall of prekindergarten) assessed in the fall and spring of the prekindergarten year. Results Factor analyses revealed the most statistical support for a model with a latent random intercept across timepoints and constructs, along with timepoint-specific latent factors in the fall and spring of prekindergarten (independent of the random intercept). The timepoint-specific latent factors primarily consisted of early literacy and mathematics assessments. Discussion These findings challenge commonly held practices of creating construct-specific latent factors in early childhood research and, to a lesser extent, timepoint-specific latent factors without consideration of the substantial shared variance across different constructs and timepoints. Implications for the factor structure and developmental theory of school readiness skills are considered, as well as practical considerations for future research.