Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Extremism Detection in Textual Content: A Systematic Literature Review

Social media networks such as Twitter, Facebook, YouTube, blogs, and discussion forums are becoming powerful tools that extremist groups use to disseminate radical ideologies and propaganda, and to recruit people to their cause. Identifying extremist social media content and profiles is a top priority for counter-terrorist agencies, technology companies, and governments. The main objective of this paper is to provide a better understanding of the definition of extremism, and a detailed review of the current research regarding online extremism in text. To identify gaps in the literature, a systematic literature review (SLR) of 45 studies published between 2015 and 2020 was undertaken, which revealed challenges, technical pitfalls in previous studies, and opportunities for extending and improving prior results in meaningful ways. The systematic review indicates the need for better understanding of the landscape and directions of the online extremism. This study offers a critical analysis of the new area of research.
SCBSS Signal De-Noising Method of Integrating EEMD and ESMD for Dynamic Deflection of Bridges Using GBSAR

Ground-based synthetic aperture radar (GBSAR) technology, as a new measurement technology, has the advantages of noncontact measurements, high precision, and all-weather measurement capability, and it has been widely used for bridge dynamic deflection measurements. In order to reduce the influence of noise in dynamic deflection of bridges obtained using GBSAR, this article proposes a single-channel blind source separation signal (SCBSS) de-noising method to obtain the denoised dynamic deflection of bridges. First, the extreme-point symmetric mode decomposition (ESMD) method and the ensemble empirical mode decomposition (EEMD) method are used to decompose the obtained dynamic deflection—as the original observation signal—into a series of intrinsic mode functions (IMFs) and a residual R. Second, the Spearman's Rho of each IMF with the original observation signal is calculated to remove the dominant IMFs of high-frequency noise. Third, the remaining IMFs and R decomposed by ESMD and EEMD are reconstructed into two sets of new signals, which form a new virtual multichannel data with the original observation signal. Finally, blind source separation is performed on the new virtual multichannel signal to obtain separated signal components. The separate signal components are converted in the frequency domain using the fast Fourier transform algorithm, and the noise signal components are identified using a spectrum analysis, to achieve further removal of noise information. The results of both simulated and on-site experiments show that the SCBSS signal de-noising method has a powerful signal de-noising ability.
Two-dimensional Intrusion Detection System: A New Feature Selection Technique

The rapid increase and expansion of cyber-attacks requires the development of new quality tools to reduce, combat, and eventually stop their risks. In this context, an Intrusion Prevention Systems (IPS) is introduced. It is an extended IDS solutions network security technology characterized by adding the ability to block threats and detect vulnerabilities exploits. Unlike its predecessor, the IPS is placed as an inline security component where it actively processes via analyzing and taking automated actions on all traffic flows that enter the network. Needless to say, speed and efficiency are a necessity in the IPS to avoid degrading network performance. On one hand, signature-based detection and statistical anomaly-based detection, two dominant detection methods mechanisms, are responsible for finding exploits whenever statistical features are extracted to characterize network traffic flows. The computation cost of the classifier, therefore, will be overlarge. On the other hand, the feature selection problem, a critical component in reducing features and improving overall classification accuracy, is about a highly complex NP-hard problem in a Data science. In this paper, a novel intrusion detection system is proposed. It is a double-action system with meta-classifier based feature selection as well as an ensemble learning meta-classifier for stacking via cross-validation to prevent overfitting. Therefore, a better subset of informative features for each type of attacks was selected rather than the features common to all attacks. Finally, this work, through the several experiments, demonstrates that our proposed model not only reaches high detection rates and significantly reduces false alarms, but also accelerates the learning and the testing process thereby directly contributing to the overall ability to work in online mode and scalability.
Using Robotics in Laboratories During the COVID-19 Outbreak: A Review

The clinical and analytical laboratory has been at the forefront of the fight against the COVID-19 pandemic. This is where testing is carried out, guiding hospitalization and providing information on the spread of the disease. It is also central to the development of new medicines and the vaccines that are still being tested. Laboratories make extensive use of robotics and automation to safely improve quality and productivity at an acceptable cost.
Design and Performance Investigation of 3-Slot/2-Pole High Speed Permanent Magnet Machine

High-speed rotating mechanical machinery is a developed, mature and reliable technology for many engineering applications. In high-speed permanent magnet machines, the rotor’s permanent magnet PM is not strong enough to withstand the centrifugal force resulting from excessive rotational speed; thus, the PM material must be protected by a non-magnetic alloy sleeve. This paper presents a novel high-speed PM machine with solid rotor PM covered by a titanium retaining sleeve. The rotor stress condition is simplified as a plane stress problem according to the strength measurement of solid PM rotors in the high-speed PM motors. The analytical formulas for rotor stress are presented based on the polar coordinate displacement method. The proposed model is compared with a conventional model having auxiliary slots in stator teeth. Using a finite element analysis environment, the performance analysis shows that the proposed design has reduced magnet eddy current loss, cogging torque, and iron losses. The initial design is also optimized using a deterministic optimization algorithm that increases output torque by 26% compared to the initial design.
A Novel Deep Learning Character-Level Solution to Detect Language and Printing Style from a Bilingual Scanned Document

Bangla is one of the world’s most widely-spoken languages, but few languages (or "script") automation solutions have been reported for it. To build an OCR system, it is very important to detect the language and type of printing style to run specific character recognition and segmentation modules. This paper presents a novel solution to automatically detect the language (Bangla vs English in terms of the script), and printing style (printed vs handwritten) from any given bilingual scanned document using multiple deep learning models.
Multisection Ultra-Broadband Directional Coupler Designed in MMIC Technology

An ultra-broadband multisection directional coupler designed in Monolithic Microwave Integrated Circuit (MMIC) technology has been presented. It has been shown for the first time that it is possible to achieve bandwidths exceeding one frequency decade, simultaneously taking under account technological constrains. The proposed directional coupler is composed of three coupled-line sections having different values of electrical lengths and coupling coefficients. The strongest coupled section has been designed as an asymmetric three-strip coupled-line section, whereas the rest of the sections have been implemented as symmetric two-strip coupled-line structures. To improve electrical performance of the coupler, a compensation method has been implemented together with loss analysis which has to be considered in lossy inhomogeneous medium. The proposed 3-dB directional coupler has been designed in PH25 process based on gallium arsenide (GaAs) from United Monolithic Semiconductors (UMS), and fabricated. The measurement confirms applicability of the design method in monolithic technology for such networks operating in bandwidth exceeding one frequency decade.
Can High Current Density HTS Magnets be Quench Protected Using Methods Used to Protect High Current Density LTS Magnets?

In the 1970s the Lawrence Berkeley Laboratory (LBL) was testing high current density magnets where the EJ2 limit was up to two orders of magnitude higher than the quench protection methods of the period would permit at the time. LBL was designing a high current density (J) thin solenoid with a large volume and stored energy (E). The accepted EJ2 limit at the time was ∼1023 J A2 m−4. In order to exceed the EJ2 limit by two orders of magnitude, one had to greatly increase the quench protection voltages (V) and magnet current (I). In 1977, LBL tested four quench protection methods on a 2-meter diameter high current density solenoid with a stored energy of 2 MJ. Three of these quench protection methods worked well, but the fourth didn't appear to work. These methods will be discussed as to whether they can be applied to HTS or MgB2 high current density magnets.
AR video presentation using 3D LiDAR information for operator support in mobile robot teleoperation

In this paper, we propose a new teleoperation system of a mobile robot using Augmented Reality(AR) based on 3D LiDAR information for operator support. In the proposed system, an 360-degree camera is equipped with a robot which works in a remote place controlled by an operator, and the captured 360-degree image is displayed to the head mounted display put on the head of the operator. The operator can smoothly control the robot watching what he needs for his task by turning his head. In addition, the provided image around the robot has no blind spot because of the 360-degree image. However, the data size of the 360-degree image is enormous and it causes a large transfer delay or dropped frames which adversely influence the operator performance. In order to solve this problem, it is proposed that an AR image is actually provided to the operator, in which objects generated based on 3D LiDAR information are superimposed on the 360-degree image as AR objects. In this paper, the usefulness of the proposed method is shown through the experimental results on parking the robot remotely.
A Study of Self-Privacy Violations in Online Public Discourse

User engagement in online public discourse often includes self-disclosure – the revelation of personal information. Such disclosures on online public platforms (e.g., news forums) become a shared history, vulnerable to detrimental use by advertisers and malicious parties. Yet, users indulge in self-disclosing behavior to attain strategic goals like relational development, social connectedness, identity clarification, and social control. In this work, we develop supervised models to detect instances of self-disclosure in users’ comments in the context of public discourse. Using three different datasets, we validate the performance of our models. Our detection models achieve an accuracy of 75.8 percent in a news discourse dataset. The performances on evaluation against existing methods on two secondary datasets are on par if not better. We examine the rate at which users self-disclose to understand when and to what extent users abide by group norms of such behavior. Our results show that self-disclosing users are often similar in their alignment or divergence with the group norm. As such, these similarly divergent users in a conversation use similar language in their disclosures. Finally, we reflect o n t he i mplications o f alignment with or divergence from group norms in light of online privacy.
Comparison of LXC and Docker Technologies

The paper deals with problem of separating computer applications into logical namespaces called containerization. It compares and inspects two world leading Linux container platforms - Linux containers (LXC) and Docker. In the first part, it compares virtual machines and containers. Then the paper analyzes LXC and Docker technologies in depth, highlights their pros and cons. An integral part is also an overview of the various tools used within the technologies.
Feature Extraction from Equipment Sensor Signals with Time Series Clustering and Its Application to Defect Prediction

In semiconductor manufacturing processes, it is important to quickly identify any signs of the occurrence of defects. We applied a time-series clustering method to the signal data of processing equipment and obtained information related to the occurrence of defects. By using the information as the feature values of a prediction model, we were able to predict defects more accurately than by using only conventional feature values.
Content Based COVID-19 Chest X-Ray and CT Images Retrieval framework using Stacked Auto-Encoders

Late 2019, the world witnessed a new devastating outbreak: coronavirus (COVID19) that attacks the human lungs and causes respiratory infections like pneumonia pathologies. This disease is spreading rapidly in the globe, causing multiple damages, even on the healthcare sector, economic and social sectors. In this article, we present a framework dedicated to epidemiologists for the investigation of the Patient 0 infected and establish the propagation path in different areas of the country. A Content Based Medical Image Retrieval system is proposed based on the Stacked Auto-encoders for the recognition of the disease characteristics in medical images. Our model search target COVID Chest X-Ray and CT images using similarity measurements learned through an image database of different pathologies as SARS and other viral or bacterial species of pneumonia diseases.
Peak Estimation Recovery and Safety Analysis

Peak Estimation aims to find the maximum value of a state function achieved by a dynamical system. This problem has been previously cast as a convex infinite-dimensional linear program on occupation measures, which can be approximately solved by a converging hierarchy of moment relaxations. In this letter, we present an algorithm to approximate optimal trajectories if the solutions to these relaxations satisfy rank constraints. We also extend peak estimation to maximin and safety analysis problems, providing a certificate that trajectories are bounded away from an unsafe set.
Detection of Anomaly Stock Price Based on Time Series Deep Learning Models

Anomaly detection is a critical task for financial market, investors, and regulatory authorities, where conventional methods employ rule-based models. With the development of machine learning and deep learning techniques, it becomes more promising to detect anomalous trading behaviors from data. Here we present a deep learning model based on time series LSTM model to detect anomalous behaviors in Chinese stock market. The model is composed of 1dConv-LSTM neurons, which can predict time series stock price data from historical data. We analyzed the price of 14 stocks variations ranging from 2015/01/05 to 2019/12/31 and used univariate and multivariate time series models to generate MAE less than 4.0 consistently. The proposed method improved MSE to 0.0171 on validation datasets. Our model successfully predicts the anomalous price behaviors of ‘601318’ stock in the range of 2019-02-13. Our method provides an automatic way of predicting anomaly stock price behavior in Chinese stock market.
Monocular Sequential Images-Based Relative Orbit Determination Method for Near-Range Non-cooperative Target

Near-range relative orbit determination has significant effect on on-orbit service. The relative orbit determination method with the monocular sequential images is proposed based on the cylindric coordinate for the near-range non-cooperative target in three-dimension space. The relative orbit state equation is constructed under the condition that the observing spacecraft is on a near-circular orbit. With the nonlinear measurement equation of the sequential images, the observability is analyzed with the Gram matrix. The simulation results indicate that the method can effectively realize the near-range relative orbit determination for flyby and leader-follower orbits.
Adaptability of 9-Switch Inverter Configurations for Multiphase Induction Motors with Phase Reconfiguring Techniques

The 9 switch inverter (9SI) configuration is capable to control the power for two independent 3-phase loads simultaneously. However, this 9SI has the limitation on modulation index (M.I) since each inverter leg is controlled with 2 reference waves and one carrier wave. This lower M.I result in higher magnitude requirement of DC-link voltage. In addition to this, the load gets 2 level voltage only. For addressing these issues, in this paper, the 9 switch inverter (9SI) configurations for multiphase (>3) Induction motor (MIM) drives is explored. The 9SI configurations with split winding will generate the 3-level voltage across the effective phase winding of 6-phase and 9-phase IM drives. The phase reconfiguring techniques for enhancing the modulation index of proposed 9SI based multilevel inverter (MLI) fed 6-phase as well as 9-phase machines is proposed, where the phase difference between two identical voltage profile coils (IVPC) excited with same Inverter leg is minimized. The possible phase reconfiguring concepts for both symmetrical and asymmetrical configurations of the 6-phase and 9-phase machines are presented. The generalized adaptability and analysis of the proposed 9SI configurations with phase reconfiguring techniques for a machine with any number of phases is presented. The carrier based space vector pulse width modulation is implemented for proposed 9SI based MLI configurations which enhances the M.I further by 15.4%. The 9SI based MLI fed symmetrical 9-phase IM drive is validated by using Ansys Maxwell 2D.
Predictive Analytics for Default of Credit Card Clients

Predictive analytics has a significant potential to support different decision processes. We aimed to compare various machine learning algorithms for the selected task, which predicts credit card clients' default based on the free available data. We chose Random Forest, AdaBoost, XGBoost, and Gradient Boosting algorithm and applied them to a prepared data sample. We experimentally evaluated the classification models within metrics like accuracy, precision, recall, ROC, and AUC. The results show a very similar performance of the selected algorithms on this dataset. The Gradient boosting (0.7828) achieved the best performance within AUC, but the best precision for target class 1 reached the Bagging algorithm (0.72). The simple data processing brought only minimal improvements in individual metrics. Our results are comparable to the mentioned studies instead of MCC metrics that resulted in better value (0.4111) achieved by the Gradient Boosting model.
Comparison of Model Order Reduction Methods for a Switched Reluctance Machine Characterization

In this article, an exhaustive comparison is presented between two types of model order reduction methods, namely the proper orthogonal decomposition (POD) and the orthogonal interpolation method (OIM), for the characterization of a switched reluctance machine. The purpose of the comparison is to find out the best practices for the design of the machine control parameters and strategy. The control design itself is not presented here, but the needed information is at the focus. The comparison is based on the computation time and the complexity of the reduced-order model as well as the accuracy of the needed data as compared with the one obtained from the full-order model. The results show that OIM is more efficient in terms of calculation time, especially when considering a nonlinear problem.
Supervised Operational Change Point Detection using Ensemble Long-Short Term Memory in a Multicomponent Industrial System

Changes in operating conditions, environment, and deterioration of structural health of components over time leads to unplanned outages in industrial equipment. A multicomponent industrial system may fail when one or more of its components deteriorate beyond a certain limit. The deterioration is often a gradual and continuous process, culminating in sudden failure of an equipment. However, the components in a system may show some early signs of deterioration that might not be explicitly apparent even to domain experts. Therefore, advanced algorithms are required for early detection of these signatures of failure to enable corrective actions in time. A set of algorithms is presented here to detect signatures of failure from the continuous sensor data in a multicomponent system. Each system consists of four identical components, each with a different timing of failure. A set of Long Short-Term Memory (LSTM) based algorithms are employed to identify the onset of abnormal behavior. An ensemble framework, which minimizes the frequency of false and missed alarms is proposed and its performance is compared with other stand-alone algorithms. An ensemble approach on top of a set of LSTM-based models performed better than the individual algorithms.
Instability in Peak Current Mode Controlled Coupled SIDO Buck Converter

This paper investigates the switching instability, also known as sub-harmonic oscillation of coupled inductor single-input dual-output (CI-SIDO) buck converter under peak current mode (PCM) control. The instability is predicted by developing an accurate small-signal model for PCM controlled CI-SIDO buck converter. The exact small-signal model is developed by using current modulator equations in the state-space model of CI-SIDO buck converter. The current modulator equations define the duty ratios as a function of the circuit variables and are the one that predicts the instability. It is observed that instability in CI-SIDO buck with PCM occurs when both or any one duty ratios are greater than 0.5. The instability is verified using MATLAB simulation results.
Impact of Self-Heating on Negative-Capacitance FinFET: Device-Circuit Interaction

In this work, we analyze the impact of self-heating effects (SHEs) on 14-nm negative capacitance (NC)-FinFET performance from device to the circuit level. The 3-D thermal TCAD simulations, after careful calibration with measurements, are performed to analyze the impact of SHE in a broad range of frequency. Furthermore, we use the TCAD calibrated BSIM-CMG model to analyze the impact of SHE in NC-FinFET at the circuit level, after including a physics-based model to capture the NC effect. For the first time, we analyze the impact of a nonuniform distribution of temperature dissipated from the channel region to gate-stack in NC-FinFETs. On account of the thermal insulating properties of the gate-stack, the ferroelectric (FE) layer is found to be cooler than the channel region under the impact of SHE. We demonstrate that neglecting that and, hence, using the channel temperature to evaluate the temperature-dependent parameter $\alpha $ (in the Landau–Khalatanikov model of NC effect) of the FE layer result in a significant overestimation of SHE-induced degradations, such as in the NC voltage gain. Based on our TCAD analysis, we propose a relation between gate-stack temperature and the channel temperature and use this to accurately model the $\alpha $ parameter and, hence, SHE in NC-FinFETs. The SHE is found to dominate for both FinFET and NC-FinFET in the gigahertz range, which eventually degrades the performance at the circuit level, which is further confirmed using ring oscillator (RO) simulations.