Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Web Classifier Approach with Possible Research Direction to Detect Cyber Extremists

The internet is ever expanding and online information is booming, making identification and detection of different web information vitally important, particularly those of dark web or Cyber extremists. Webpages with extremist and terrorist content are believed to be main factors in the radicalization and recruitment of disaffected individuals who might be involved in terrorist activities at home or those who fight alongside terrorist groups abroad. In fact, the sheer volume of online data makes it practically impossible for authorities to carry out the individual examination for every webpage, post or conversational thread that might or might not be relevant to terrorism or contain terrorist sympathies. As terrorists exist within every nation and every religion, hence this paper presents a review and systematic analysis of existing webpages on Cyber Terrorists. This include of existing database of Cyber extremists words and existing techniques of web classifier for keywords. Based on this paper systematic analysis, it will be the input for the formation of a new Cyber extremists WorldNet.
Frequency Agile Radar Using Atomic Norm Soft Thresholding with Modulations

Frequency agile radar (FAR) is known to have excellent performance in electronic counter-countermeasures (ECCM). Recently, compressed sensing (CS) algorithms have been applied in the joint estimation of velocity and range for FAR. Grid based CS algorithms suffer from the off-the-grid problem. Atomic norm minimization algorithms solve the off-the-grid problem by estimating parameters in the continuous domain, however, are not compatible with the cross term in the FAR signal model. To handle the cross term, we divide the value space of velocity into grids, and model the phase term related to velocity and the cross term as modulations. Based on atomic norm soft thresholding with modulations, range and velocity are estimated in the continuous and discrete domain, respectively. The proposed algorithm is compatible with the cross term and alleviates the influence of mismatch. The performance of the novel approach is verified by numerical results.
People Entity Recognition in Indonesian Quran Translation with Conditional Random Field Approach

The Quran is the primary source of law for Muslims. Quran has a total of 30 juzs, divided into 144 surahs and arranged of 6,236 verses and in the Quran discuss different topics and have many entities too, so someone sometimes has difficulty understanding the Quran. To make it easier to understand the Quran, we can get identification of essential entities in the Quran such as names of the people in the Quran. One way to do it is by extracting information on essential entities in Quran is with Named Entity Recognition (NER). NER automatically recognizes essential entities such as the people’s names at the Quran. This paper builds a system for identifying the entity of the people in the Quran using CRF techniques with a multiple choice approach where the system will be introduced with a range of possibilities from the entity and adjusted to an input given to be able to detect entities. On system developing for identifying the entity of the people in Quran with Indonesian Quran translation dataset shows, this test produces an average performance of using the F1 generated at 0.77 for the use of training data as many as 36814 data from 954 verses in the Quran.
A Comprehensive Simulation Platform for Testing Autonomous Vehicles in 3D Virtual Environment

A key obstruction to the progress of AV’s is determining how authorities will certify them. When Automated Driving Systems (ADS) reach SAE level 3 automation, it will no longer be adequate to continue the current practice of relying on physical testing alone. To this end this paper discusses the creation of a virtual platform which may test how an ADS responds to challenging situations. The aim is for the platform to be used for independent tests of ADSs and form part of the approval framework which enables the deployment of AVs on public roads in the future.
Research on Android Component Security Testing Method for Mobile Application in Power System

With the rapid development of mobile communication technology, mobile applications have been widely promoted in power system. As an important part of the Android system, Component security has attracted much attention. Based on the current status of power system mobile application's development, this paper proposes a component classification security testing scheme to solve the problem in current imperfect security testing method. First, according to the development mode, the power mobile application is divided into integrated development application and independent development application. For integrated development application, combined with the current popular component's vulnerabilities, a comprehensive security testing method for public components is proposed. For the independent development application, combined with the component function flow analysis method, a security testing method of the private component is proposed with several specific cases. The testing scheme and test method proposed in this paper, could find out the security risk of Android component efficiently, and provides technical guidance for preline safety evaluation. Automated security testing will be researched in the future.
IQ Imbalance Robust and Low PAPR OFDM Radar Waveform

We address jointly the problem of IQ imbalance and PAPR in OFDM radars. IQ imbalance is known to cause severe distortion in OFDM receivers, manifesting itself as interference from one side of the received spectrum to the other side and vice versa. Various methods have been designed in the wireless communications context to estimate and compensate the receiver IQ imbalance. We adopt here a different approach where the radar waveform is designed to be robust against IQ imbalance. We derive an analytical criterion to achieve this robustness. In addition, we also show that this waveform design can be adapted to keep the OFDM waveform PAPR below 3dB, regardless of the number of sub-carriers, thereby relaxing the usual OFDM power amplifier back-off requirement. We validate the effectiveness of this joint design with simulation results.
Passive Radar Illuminator Identification in Single Frequency Networks

Knowing the properties of illuminators is crucial for the successful operation of passive radar systems. The identification of the transmitters in a single frequency network is often challenging due to the operator-imposed artificial network delays. We demonstrate a method based on time of arrival measurements on a mobile platform to both identify the transmitters and measure their network delays very accurately.
Optimal Design of Transformer Windings for Full-Bridge LLC Resonant Converters

High frequency is the development trend of switching power supplies to achieve miniaturization density of switching power supplies. However, the increase in frequency makes the leakage inductance parameter of the transformer in the switching power supply more and more important to the performance of the circuit. In this paper, the leakage inductance and winding loss of high frequency transformer in LLC resonant converter are analyzed in detail. An optimized design scheme of high frequency transformer based on winding structure is introduced, which can effectively reduce the leakage inductance and winding losses of high frequency transformer, and improve stability and efficiency of the transformer resonant converter. Finite-element analysis simulation validate the design scheme.
Enhance cross-subject MEG decoding by combining Riemannian Geometry feature extraction and multi-task learning

Many brain decoding algorithms are based on the assumption that the training and test data are in the same feature space and have the same distribution. However, in many practical applications, the statistical distribution of brain signal varies across subjects as well as sessions, restricting the transferability of training data or training models between different subjects or sessions. In this study, we focus on this problem in magnetoencephalography (MEG) signal. By reviewing the transfer learning literatures that have achieved satisfactory results in brain decoding, we proposed a new method for MEG decoding across subjects. First, a new kernel is derived by establishing a connection between the Riemannian geometry and the sample covariance matrix (SCM), which is then applied to MEG transfer decoding by using an improved multi-task learning framework. The experiments on an MEG dataset of face vs. scramble decoding task demonstrated that the proposed approach is superior to other comparable methods.
An Efficient MIMO Borehole Radar Imaging Method Based on High Degree of Freedom

In this paper, an efficient multiple-input-multiple-output (MIMO) borehole radar imaging method based on high degree of freedom is presented for subsurface sensing. The variable separations between different transmitter and receiver pairs in MIMO borehole radar are considered and introduced as imaging coefficients into the expanded unified MIMO sample set, which gives the MIMO sample set desired characteristics of high degree of freedom. By exploiting the high degree of freedom in the unified MIMO expanded sample set, the sample interpolation and energy migration of target reflections can be done in once efficient imaging processing for all transmitters of the MIMO radar system, and finally accurate target image with low sidelobe level can be delivered. The computational cost of the proposed method will barely increase with the number of transmitters in the MIMO radar survey, which enables the proposed method to handle MIMO borehole radar imaging in an accurate and efficient manner. The imaging properties of the proposed method are proven to be superior to the conventional imaging methods in sidelobe level and computational cost, which is suitable for large MIMO borehole imaging surveys in subsurface sensing applications.
Denoising of UHF Partial Discharge Signals by Using VMD Based on Shannon Entropy and Kurtosis-Approximation Entropy

The ultra-high frequency (UHF) partial discharge (PD) monitoring method can effectively monitor the internal insulation state of the power equipment. When, however, there are some different types of noise in the substation, it's difficult to simultaneously suppress them with the existed methods. Therefore, Two methods are proposed for PD signal denoising in this paper, namely the VMD based on Shannon entropy and the VMD based on kurtosis-approximation entropy (APEN). The variational mode decomposition (VMD) algorithm is firstly used to decompose the original PD signal into several modes. Then, the Shannon entropy value and kurtosis-approximation entropy value are calculated respectively. Next, the Shannon-based rule and the APEN-based rule are designed to identify the effective components. Finally, the signal is reconstructed by adding the selected modes. Experiments were carried out under mixed noise interference of different intensities. Furthermore, output signal-to-noise ratio (SNR), root mean squared error (RMSE), signal distortion ratio (SDR), and noise rejection ratio (NRR) were also used as indexes to evaluate the denoising performance of the algorithms. Denoising results based on these indexes for simulative and experimental data show that the proposed two methods can filter various noises on a large scale. Compared with EMD, EEMD and CMSE-based VMD, the two methods can obviously perform better, and the latter has the best denoising effect.
Application of the Variational Mode Decomposition-Based Time and Time–Frequency Domain Analysis on Series DC Arc Fault Detection of Photovoltaic Arrays

Series DC arc fault can cause fire hazards in the photovoltaic(PV) array. This paper proposes a time and time-frequency domain analysis method combining the loop current and the PV-side voltage for detecting the series DC arc fault. The fusion of the two different signals can enhance the anti-interference ability of the algorithm. In the time domain analysis, the conductance is put forward to represent the the circuit states. In comparison with some common feature characteristics including current and voltage, the changes of the conductance are more obvious when the arc occurs. In the time-frequency domain analysis, the Variational Mode Decomposition (VMD) is firstly adopted to extract the characteristic frequency band of the arc current signals. VMD can improve the quality of the frequency bands by conquering the modal aliasing and endpoints effects of some traditional modal decomposition algorithms. Then, shannon entropy of the corresponding frequency-band signals is calculated to reflect the variation of the signal complexity. Finally, the two optimal detection variable with rectangle window and the proper time window length are established to achieve the best identification results. In the experimental phase, the experimental results validate that the presented algorithm can not only detect the arc faults timely and accurately but also avoid the nuisance trips caused by the start and shutdown operation of grid-connected inverter with MPPT, the dynamic changes of load and shadow occlusion.
Improving The Accuracy of Fuzzy Vault Scheme in Fingerprint Biometric

At present, authentication techniques using fingerprint biometrics have been widely used in various fields. This is because the authentication techniques using biometrics are safer and more comfortable than using traditional passwords. In order to realize this, a technique in the biometric cryptosystem is proposed in the research, called the fuzzy vault scheme. Although the fingerprint data in the form of minutiae can be protected with a fuzzy vault scheme compared to traditional authentication systems, it can reduce user convenience. Previous studies proposed a distance-based method in the fuzzy vault scheme. The distance-based method is proposed because it is no need to align and rotate the fingerprint image during registration or authentication. Then with the distance-based method also does not produce a helper data that can lead to information leakage that can be exploited by impostor. In the research, the distance-based method is proposed with several modifications, which are the minutiae filter and candidate points identification techniques. The previous method produces FRR 13.4375% and FAR 0.4515% and the proposed method produced FRR 8.9475% and FAR 0.3520%.
An Overview of the Cardiorespiratory Hypothesis and Its Potential Contribution to the Care of Neurodegenerative Disease in Africa

One hypothesis that could explain the beneficial effects of physical exercise on cognitive function is the cardiorespiratory hypothesis. This hypothesis proposes that improved cognitive functioning may be in part a result of the physiological processes that occur after physical exercise such as: Increased cerebral perfusion and regional cerebral blood flow. These processes ensure increased oxygenation and glucose transportation to the brain, which together can improve cognitive function. The objective of this narrative review is to examine the contribution of this hypothesis in the care of African older adults with neurodegenerative conditions (i.e., dementia (Alzheimer’s disease)) or with mild cognitive impairments. Although studies in developed countries have examined people of African descent (i.e., with African Americans), only the limited findings presented in this review reflect how these conditions are also important for the African continent. This review revealed that no studies have examined the effects of cardiorespiratory fitness on neurodegenerative disease in Africa. African nations, like many other developing countries, have an aging population that is growing and will face an increased risk of neurodegenerative declines. It is therefore imperative that new research projects be developed to explore the role of the cardiorespiratory fitness in neurodegenerative disease prevention in African nations.
Competition for membrane receptors: norovirus detachment via lectin attachment.

Virus internalization into the host cells occurs via multivalent interaction with laterally mobile receptor molecules in the plasma membrane. Because of analytical and experimental limitations this complex type of interaction is still poorly understood and quantified. Herein, the multivalent interaction of norovirus-like particles (noroVLPs) with H or B type 1 glycosphingolipid receptors embedded in a supported phospholipid bilayer is investigated by following the competition between noroVLPs and a lectin (from Ralstonia solanacearum) upon binding to these receptors. Changes in noroVLP and lectin coverage, caused by competition, were monitored for both receptor types and at different concentrations of the receptors using quartz crystal microbalance with dissipation monitoring, yielding information about i) the minimum receptor concentration needed for noroVLPs to achieve firm attachment to the bilayer prior to competition, and to ii) remain firmly attached to the bilayer during competition. We show that these two concentrations are almost identical for the H type 1-noroVLP interaction, but differ for B type 1, indicating an accumulation of B type 1 receptors in the noroVLP-bilayer interaction area. Furthermore, the receptor concentration required for firm attachment is significantly larger for H type 1 than for B type 1, indicating a higher affinity of noroVLP towards B type 1. This finding is supported by extracting the energy of single noroVLP-H type 1 and noroVLP-B type 1 bonds from the competition kinetics, which were estimated to be 5 and 6 kcal/mol, respectively. This demonstrates the potential of utilizing competitive receptor-binding kinetics to analyse multivalent interactions, which has remained difficult to quantify using conventional approaches.
Climate-Conscious Urban Growth Mitigates Urban Warming: Evidence from Shenzhen, China.

Urban growth comes with significant warming impacts and related increases in air pollution concentrations, so many cities have implemented growth management to minimize 'sprawl' and its environmental consequences. However, controlling the amount of growth is costly. Therefore, in this paper, we focus on urban warming and investigate whether climate-conscious urban growth planning (CUGP), i.e., urban growth with the same magnitude but optimized spatial arrangements, brings significant mitigation effects. First, the classical spatial multi-objective land use optimization (SMOLA) model is improved by integrating the spatially-, diurnally-, and compositionally-varying associations between land use and their warming impacts. Then, we solve the improved model using the non-dominated genetic algorithm (NSGA-II) to generate urban growth plans with minimal warming impacts and minimal cost of change without reducing the amount of urban growth. Results show that climate-conscious urban growth brings 33.3±4.6% less warming impacts compared to unplanned urban growth in Shenzhen, China, and suggest a compact and spatially equalized development pattern. This study provides evidence that spatial planning tools such as the CUGP can help mitigate human impacts on the environment. Meanwhile, the improved SMOLA model could be applied to balance urban development and other environmental consequences such as air pollution.
Postdiagnosis Loss of Skeletal Muscle, but Not Adipose Tissue, Is Associated with Shorter Survival of Patients with Advanced Pancreatic Cancer

Background: Pancreatic cancer is associated with development of cachexia, a wasting syndrome thought to limit survival. Few studies have longitudinally quantified peripheral tissues or identified biomarkers predictive of future tissue wasting. Methods: Adipose and muscle tissue were measured by computed tomography (CT) at diagnosis and 50 to 120 days later in 164 patients with advanced pancreatic cancer. Tissue changes and survival were evaluated by Cox proportional hazards regression. Baseline levels of circulating markers were examined in relation to future tissue wasting. Results: Compared with patients in the bottom quartile of muscle change per 30 days (average gain of 0.8 ± 2.0 cm2), those in the top quartile (average loss of 12.9 ± 4.9 cm2) had a hazard ratio (HR) for death of 2.01 [95% confidence interval (CI), 1.12–3.62]. Patients in the top quartile of muscle attenuation change (average decrease of 4.9 ± 2.4 Hounsfield units) had an HR of 2.19 (95% CI, 1.18–4.04) compared with those in the bottom quartile (average increase of 2.4 ± 1.6 Hounsfield units). Changes in adipose tissue were not associated with survival. Higher plasma branched chain amino acids (BCAA; P = 0.004) and lower monocyte chemoattractant protein-1 (MCP-1; P = 0.005) at diagnosis were associated with greater future muscle loss. Conclusions: In patients with advanced pancreatic cancer, muscle loss and decrease in muscle density in 2 to 4 months after diagnosis were associated with reduced survival. BCAAs and MCP-1 levels at diagnosis were associated with subsequent muscle loss. Impact: BCAAs and MCP-1 levels at diagnosis could identify a high-risk group for future tissue wasting.
A Neurologist's View of Lyme Disease and Other Tick-Borne Infections

Abstract Tick-borne infections—including tick-borne encephalitis viruses, represented in the United States by rare infections with Powassan and deer tick viruses, and more often Lyme disease—are of increasing importance to neurologists. Lyme neuroborreliosis (LNB) causes all or part of a triad including meningitis, radiculoneuritis, and cranial neuritis. Rarely, parenchymal brain and spinal cord involvement occur, with focal findings on examination and magnetic resonance imaging (MRI). LNB diagnosis requires plausible exposure, objective evidence of nervous system involvement, and, generally, positive two-tier serology. Central nervous system (CNS) LNB is almost always accompanied by abnormal cerebrospinal fluid (CSF) (cells, protein), often with intrathecal antibody production, which is determined by concentration-adjusted comparison of serum and CSF antibody. Measuring CSF antibody in isolation and nucleic acid-based testing of CSF are not useful in LNB and should be avoided. LNB treatment is highly effective with a 2- to 3-week course of antibiotics. Increasing evidence suggests that LNB not involving the CNS parenchyma can be treated successfully with oral doxycycline.