Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Ambiguous Phishing Certificates using Machine Learning

Recent phishing attacks have started to migrate to HTTP over TLS (HTTPS), making a phishing web page appear safe to the user’s browser despite its malicious purpose. This paper benefits from both digital certificates and domains related data features to propose machine learning-based solutions to predict digital certificates involved in HTTPS as phishing or benign certificates. In contrast to previous works that consider this a binary classification problem, we take into account that a certificate can be partially benign and phishy simultaneously. We propose a multi-class classifier and a regressor to classify these ambiguous certificates, in addition to benign and phishing certificates, where the ‘phishyness’ of a certificate is expressed as a value between 0 and 1 for the regressor. We apply our method to a set of certificates obtained from certificate transparency logs and show that we can classify them with high performance. We extend our validation by evaluating the performance of the model over time, showing that our model generalizes over time on our training data set.
Electrical insulation design and numerical simulation analysis of 1 MV compact repetitive Marx generator

An electrical insulation optimum design and electric-field numerical simulation of a 1 MV compact repetitive Marx generator filled with sulfur hexafluoride gas was performed.Two critical insulating regions in Marx generator,modular switch cavity and outer shell were well-designed with compact dimension base on the analysis of sulfur hexafluoride insulating property in static and pulse high-voltage environment,3D electric-field numerical simulation results of the interested regions indicate that the current design meets the insulating requests of sulfur hexafluoride at 0.3 MPa.
Multi-way Time Series Join on Multi-length Patterns

This paper introduces a new pattern mining task that considers aligning or joining a set of time series based on an arbitrary number of subsequences (i.e., patterns) with arbitrary lengths. Joining multiple time series along common patterns can be pivotal in clustering and summarizing large time series datasets. An exact algorithm to join hundreds of time series based on multi-length patterns is impractical due to the high computational costs. This paper proposes a fast algorithm named MultiPAL to join multiple time series at interactive speed to summarize large time series datasets. The algorithm exploits Matrix Profiles of the individual time series to enable a greedy search over possible joins. The algorithm is orders of magnitude faster than the exact solution and can utilize hundreds of Matrix Profiles. We evaluate our algorithm for sequential mining on data from various real-world domains, including power management and bioacoustics monitoring.
Toward Sustainable Cycling: Modelling and Visualization Issues of Cycle Paths for IoT-based Sensing

The rise of distributed sensors-based monitoring applications has allowed the gathering of important information in urban areas, strongly supporting the maturation of smart cities. In those scenarios, alternative transportation has been leveraged as viable solutions for the complex challenges of urban mobility, bringing cycling to a new setting. Actually, when assessing the quality of cycling paths, safety and quality parameters can be retrieved from distributed sensor nodes attached to bikes, allowing better perceptions of the adverse conditions faced by cyclists in modern cities. However, such IoT-based procedures require proper modelling of cycling paths, since quality parameters are expected to have spatiotemporal significance. Therefore, this paper discusses and proposes solutions for some important modelling and visualization issues related to the quality monitoring of cycle paths. Such solutions are concerned with the mathematical modelling of this problem, as well as the proposal of a generic computer-assisted solution to handle environmental parameters retrieved by sensors attached to bikes. Doing so, it is defined a uniform representation model and an expected data flow that can be exploited by any monitoring application in urban areas. As an important step in that direction, a reference implementation is described, allowing formal representation, structured processing and graphical visualization of bike-centric monitoring parameters, potentially supporting new developments in the area of smart cycling.
Enhancing Physical Layer Security in Internet of Things via Feedback: Revisit

Physical layer security (PLS) in the down-link transmission of Internet of Things (IoT) systems is modeled as the compound wiretap channel (C-WTC). Recently, it has been shown that channel feedback helps to enhance the secrecy capacity of the C-WTC, where the channel feedback is used to generate secret keys protecting the transmitted message from being eavesdropped by the eavesdropper. In this paper, we show that for the Gaussian compound wiretap channel (GC-WTC), a hybrid feedback scheme that combines the existing secret-key type feedback and a variation of the classical Schalkwijk-Kailath (SK) type feedback scheme, performs better than the already existing one in the literature. The study of this paper offers a new option for the enhancement of the PLS in IoT systems.
A Lightweight Beak-Like Sensing System for Grasping Tasks of Flapping Aerial Robots

Many sensor systems in robotics are bio-inspired by similar mechanisms in living creatures. Birds frequently use their beaks to grasp and manipulate objects. This work proposes a very lightweight sensor system that emulates a bird’s beak, thus allowing flapping aerial robots to interact with the environment, as e.g. to perform grasping or manipulation tasks. The sensor system is composed of a flexible link (beam) actuated by a micro servomotor, two strain gauges placed on different points and a rigid link opposed. Additionally, a new algorithm is also developed that estimates the instant at which the beak impacts with an object, the contact position and the exerted force. Our sensor system outperforms the existing designs in robotics applications, because it is lightweight, small, cheap, with very low computational load and without any complementary perception. It is demonstrated that the adequate placement of two strain gauges allow the estimation of the contact point and the force exerted between the beam and an object, and the accuracy achieved is enough to reckon properties of the object and develop force control systems. The validation has been made and reported through finite-element simulations and experiments, and the results illustrate the efficiency of the prototype and the proposed algorithm.
Model Predictive Control for PMSM Drive with Dead Time Compensation

In order to reduce voltage error and stator current distortion caused by dead time in the model predictive control (MPC). Based on the permanent magnet synchronous motor driver and model predictive current control (MPCC), an novel control algorithm is proposed to correct the negative influence caused by dead time to improve its steady-state performance of model predictive control. The correctness of the proposed algorithm is verified by simulation.
Land Subsidence Monitoring by SBAS-InSAR Technique with the Conversion to Horizontal-Vertical Displacements in Semarang

The main factors of land subsidence are excessive pumping of groundwater for industry, agriculture, snow melting, cooling, pumping of natural gas, and mines’ tunnel excavation. The city of Semarang, the capital of Central Java province, is located on the northern coast of Java island in Indonesia. Land subsidence in this city has enormous potentials for many hazards threatening people and urban infrastructures, such as buildings, roads, or whatever necessary for livelihoods. To accurately measure the land subsidence in Semarang, time series analysis of Small Baseline Subset (SBAS) or Permanent/Persistent Scatterers (PS) and Distributed Scatterers (DS) InSAR analysis is essential. This study shows the recent land subsidence volume in Semarang city with the SBAS technique and compares it to historical InSAR analysis. We also conducted the 2.5-dimensional analysis to convert the satellite line-of-site (LOS) displacement into the horizontal and vertical direction to see if there are differences between ascending and descending data. The SBAS analysis result showed the displacement volume in a range of - 18.2cm subsided and +6.8cm uplifted. The displacement rate was estimated to be substantially constant over time without any apparent seasonal effect. There is no significant difference in the results between ascending and descending datasets. The 2.5-dimensional analysis identified the horizontal and vertical displacements that we corrected any noise and inclined directional values using incidence angle.
The CLEAR Framework to Implement Active Learning in STEM Education

Despite rapidly increasing research evidence, the implementation of active learning practices in science, technology, engineering, and mathematics (STEM) classrooms has been slow. Effective translation of this new paradigm into teaching activities necessitates the development of an actionable course design framework that can explicitly guide the implementation of active learning by teachers. This study proposes a novel conceptual framework by the author-Create, Learn, Extend, Apply, and Remember (CLEAR)-to fostering a digital change in faculty development programs and implementing active learning in undergraduate-level STEM education. The CLEAR framework has both theory and practice layers. CLEAR template courses are introduced as an online faculty development model, and its nested course structure will allow learners to experience active learning at both course and lesson levels. Future works are introduced, and further efforts are suggested.
Teaching Visualization-first for Novices to Understand Programming

Teaching computer programming is always thought to be “hard” and a challenge. In order to reduce novice's cognitive load, this research aimed to explore how teaching, employing the integration of variable and visualization would impact the performance of novices' learning outcomes in computer programming. The proposed experimental method provides visualization-first at both design and implement level, which was evaluated through a survey and comparison of performance from two group students. The contribution of this research is that we have successfully employed the debugger from the programming IDE for visualization of both variables and coding. Through the visualization-first method, students also learned debugging and testing at the early stage of programming, which contributes to build up fundamental skills for them towards further study.
Capacity Limitation of Small Cell Densification

Deployment of small cells is considered as an easy approach for adding capacity to the system. However, it is important to realize that in a non-noise limited system, each additional cell increases the interference in the system. The target of this paper is to show the capacity limitation of the cellular network with an increasing number of small cells in a network. Ultra-dense deployment of small cells implies a high probability of line of sight (LOS) transmission between the transmitter (TX) and receiver (RX). The LOS transmission helps in enhancing the received signal strength, whereas, on the other hand, the interference power significantly grows with small cell densification. This paper presents the analytical analysis of bad cell border area for one-, and two-dimensional grid of small cells. The lamp post solution for the small cell deployment along the street is studied through simulations. The acquired results show that the overall interference in the system and the bad signal to interference plus noise ratio (SINR) cell border area grows with cell densification. System capacity saturates and then starts to collapse as the capacity loss due to the additional cell interference becomes dominant over the gain of cell densification after the saturation point.
Structural and Functional Brain Abnormalities in Internet Gaming Disorder and Attention-Deficit/Hyperactivity Disorder: A Comparative Meta-Analysis

Background: Patients with Internet gaming disorder (IGD) and attention-deficit/hyperactivity disorder (ADHD) have high comorbidity but it is still unknown whether these disorders have shared and distinctive neuroimage alterations. Objective: The aim of this meta-analysis was to identify shared and disorder-specific structural, functional, and multimodal abnormalities between IGD and ADHD. Methods: A systematic literature search was conducted for whole-brain voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies comparing people with IGD or ADHD with healthy controls. Regional gray matter volume (GMV) and fMRI differences were compared over the patient groups and then a quantitative comparison was performed to find abnormalities (relative to controls) between IGD and ADHD using seed-based d mapping meta-analytic methods. Result: The meta-analysis contained 14 IGD VBM studies (contrasts covering 333 IGDs and 335 HCs), 26 ADHD VBM studies (1,051 patients with ADHD and 887 controls), 30 IGD fMRI studies (603 patients with IGD and 564 controls), and 29 ADHD fMRI studies (878 patients with ADHD and 803 controls). Structurally, VBM analysis showed disorder-specific GMV abnormality in the putamen among IGD subjects and orbitofrontal cortex in ADHD and shared GMV in the prefrontal cortex. Functionally, fMRI analysis discovered that IGD-differentiating increased activation in the precuneus and shared abnormal activation in anterior cingulate cortex, insular, and striatum. Conclusion: IGD and ADHD have shared and special structural and functional alterations. IGD has disorder-differentiating structural alterations in the putamen and ADHD has alterations in the orbitofrontal cortex. Disorder-differentiating fMRI activations were predominantly observed in the precuneus among IGD subjects and shared impairing function connection was in the rewards circuit (including ACC, OFC, and striatum).
DIAGNOSTIC APPROACHES AND FORECASTING IN THE INTENSIVE CARE

Introduction . Uncertainty about approaches to optimizing monitoring and tactics of hemodynamic correction today directly affects the effectiveness of interventions. This problem is especially pronounced in the case of patients with high cardiac risk, as well as in the case of rapidly developing intraoperative hemodynamic disorders, which are a specific feature of some cardiac surgical interventions, such as myocardial revascularization operations on a beating heart. In such a situation, the key factor for the success of the operation is the coordination of the work of the surgeon and the anesthesiologist. Objectives . To determine the main approaches to monitoring and assessing the volemic status of a patient during direct myocardial revascularization operations on a working heart and to evaluate the possibility of transesophageal echocardiography as such a monitoring tool. Materials and methods . The search was carried out in the electronic databases PubMed, Scopus, Web of Science Core Collection; the analysis was carried out based on works published in the period from 2001 to 2021. Results . The resulting material, which includes validated peer-reviewed studies, randomized clinical trials, major systematic reviews and meta-analyzes, provides an overview of global experience in optimizing fluid loading and its characteristics in cardiac surgery patients. According to the analyzed data, there is no consensus among specialists on the above issues, and many important factors remain outside the framework of everyday clinical practice. For high-quality interaction, a combination of highly informative monitoring and proven tactics of conducting perioperative infusion therapy in response to the dynamically changing
EMPLOYEE PROFESSIONAL AND PERSONAL DEVELOPMENT: A SOURCE OF COMPETITIVE ADVANTAGE

Successful organizations never forget that their employees have been fulfilling a number of different roles both inside their work life and as part of their own definition of living their lives. Consequently, they approach their employees as a whole and not just as tools fulfilling specific functions and job related tasks. Consequently, to achieve sustained competitive advantage organizations must manage their employees as portfolios of assets fulfilling work-life roles. To do so, such entities need to capitalize on their employees’ previous and future personal development, and to further contribute to their professional development in order to ensure the efficiency and effectiveness of their strategies.
The 15 th International Scientific Conference “DEFENSE RESOURCES MANAGEMENT IN THE 21st CENTURY”

: Smart devices have become the perfect accessories of the knowledge-based society. Military organizations, like civilians, try to benefit from the usefulness of social media applications that surround us today, but the main feature of these tools, to share data and user-generated media content, is a challenge to managing the information resource in the military field. The paper analyzes the effects generated by the use of the Strava Maps application inside the military bases and the security breach generated by the online posting of StravaMaps.
Quantization of Images based on Color Levels in Histogram

In this paper, developed an algorithm of image quantization based on histogram distribution. Aim of study presented an algorithm that working on separating regions of images depending on real brightness ranges of colors in the histogram representation and color intensity distribution. The new way of image quantization represented lead to avoiding overlap of regions and reducing calculation’s complexities for image segmentation that followed image quantization stage, where it’s acts main steps in Image analysis and preprocessing. The proposed algorithm providing high accuracy of image quantization comparing with the methods of quantization presented recently.
FOXD1 Regulates the Sensitivity of Cetuximab by Regulating the Expression of EGFR in Head and Neck Squamous Cell Cancer

Objectives Previous experiments have shown that growth factor receptors play important role in tumor proliferation, metastasis, and therapeutic effect of chemotherapeutic drugs. At the same time, forkhead box D1 (FOXD1) plays an important role in a variety of signal transmission, but its expression profile was known little about head and neck cancer. The purpose of this experiment was to explore the regulation of FOXD1 on the tumor progression of head and neck cancer and to explore the correlation of FOXD1 on the expression of growth factor receptors (EGFR). Methods The bioinformatics online database analyzed the expression of FOXD1 and EGFR in tumor tissues and nontumor tissues. Real-time quantitative PCR was used to detect the FOXD1 and EGFR expression in 45 tumor tissues and 15 nontumor tissues. The plasmid was used to construct FOXD1 overexpressing head and neck squamous cell cancer lines and observe the clonal formation and invasion of tumor cells under the intervention of EGFR-specific antibody—cetuximab. Results The expression of FOXD1 and EGFR in tumor tissues was higher than that in nontumor tissues. The higher expression of FOXD1 and EGFR was not conducive to the prognosis of patients. The expression of FOXD1 and EGFR was positively correlated, and immunohistochemical analysis showed the high expression of FOXD1 and EGFR has close relation to the advanced stage of the tumor. In vitro cell experiments proved that overexpression of FOXD1 can partially offset the cloning ability of cetuximab on head and neck tumor cells. Conclusion FOXD1 has an important regulatory role in the progression of head and neck cancer, and its abnormally high expression was not conducive to the prognosis of cancer patients. FOXD1 can regulate the expression of growth factor receptors in head and neck cancer, which provides a new idea for the better use of tumor growth factor receptor-specific antibodies for collaborative therapy.
Manganese excess and deficiency affects photosynthesis and metabolism in Marchantia polymorpha

Manganese is an essential metal for plant growth. The most important Mn-containing enzyme is the Mn4CaO5 cluster that catalyses water oxidation in Photosystem II. Mn deficiency primarily affects photosynthesis, while Mn excess is generally toxic. Mn excess and deficiency were studied in the liverwort Marchantia polymorpha, an emerging model ideally suited for analysis of metal stress since it accumulates rapidly toxic substances due to the absence of well-developed vascular and radicular systems and a reduced cuticle. We established growth conditions for Mn excess and deficiency, performed analysis of metal content in thalli and isolated chloroplasts and determined metabolites. Metabolome analysis revealed a strong accumulation of N-methylalanine upon exposure to Mn excess and a different response of Marchantia to heavy metal stress than that known for higher plants. We investigated photosynthetic performance by chlorophyll fluorescence at room temperature and at 77K, P700 absorption and by studying the susceptibility of thalli to photoinhibition. In vivo super-resolution fluorescence microscopy was used to visualize changes in the organization of the thylakoid membrane under Mn excess and deficiency. Non-optimal Mn concentrations changed the ratio of photosystem I to photosystem II and altered the organisation of thylakoid membranes. Mn deficiency seems to favour cyclic electron flow around photosystem I protecting thereby photosystem II against photoinhibition.
Militarismo y militarización: el caso venezolano

En este análisis se desarrollan tres puntos centrales. En el primero, se identifican cuatro indicadores principales de la militarización de la sociedad venezolana. En el segundo, se describe el contexto internacional, en general, y se consideran las relaciones con la Unión Europea (UE) y España, en particular. En el tercero, se presentan los escenarios previstos a corto plazo, con especial énfasis en el papel de los militares en un posible proceso de transición y en las negociaciones que se iniciaron en agosto de 2021 en México entre el gobierno de Venezuela y la Plataforma Unitaria de Venezuela, suspendidas en octubre por decisión unilateral del gobierno.
Deep Learning-based Smart Predictive Evaluation for Interactive Multimedia-enabled Smart Healthcare

Two-dimensional1 arrays of bi-component structures made of cobalt and permalloy elliptical dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a self-aligned shadow deposition technique. Brillouin light scattering has been exploited to study the frequency dependence of thermally excited magnetic eigenmodes on the intensity of the external magnetic field, applied along the easy axis of the elements. This study aims to enhance the security for people's health, improve the medical level further, and increase the confidentiality of people's privacy information. Under the trend of wide application of deep learning algorithms, the convolutional neural network (CNN) is modified to build an interactive smart healthcare prediction and evaluation model (SHPE model) based on the deep learning model. The model is optimized and standardized for data processing. Then, the constructed model is simulated to analyze its performance. The results show that accuracy of the constructed system reaches 82.4%, which is at least 2.4% higher than other advanced CNN algorithms and 3.3% higher than other classical machine algorithms. It is proved based on comparison that the accuracy, precision, recall, and F1 of the constructed model are the highest. Further analysis on error shows that the constructed model shows the smallest error of 23.34 pixels. Therefore, it is proved that the built SHPE model shows higher prediction accuracy and smaller error while ensuring the safety performance, which provides an experimental reference for the prediction and evaluation of smart healthcare treatment in the later stage.