Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Efficiency of Character-level Convolution Neuron Network and Long Short Term Memory on Malicious URL Detection

Machine learning techniques, especially deep learning neuron networks have been increasingly applied to solve the problems relating to information security and cybersecurity. Malicious URL (Uniform Resource Locator) detection is one of these. It is considered as a binary classification in machine learning, in which a URL or website address is classed as malign or benign. In this work, we implement the experiments on two different datasets to explore the efficiency of three proposed character-level deep neuron networks: (1) CNN (Convolution Neuron Network) based on VGG-16 architecture (Visual Geometry Group), (2) LSTM (Long Short Term Memory), and a fusion of CNN and LSTM for malicious URL detection. The experimental results are promising, especially for the fusion scheme of LSTM and CNN, with above 96% for precision and 98% for recall.
Communication Usage Optimization of Gradient Sparsification with Aggregation in Deep Learning

Communication usage is a bottleneck of scaling workers for distributed deep learning. One solution is to compress the exchanged gradients into sparse format with gradient sparsification. We found that the send cost of server, which is the aggregated size of sparse gradient, can be reduced by the gradient selection from workers. Following an observation that only a few gradients are significantly large and in a short period of time, we proposed several gradient selection algorithms based on different metrics. Experiment showed that our proposed method can reduce the aggregated size for server, and the reduction in time per iteration can make the convergence rate faster than traditional sparsification.
Empirical Approach Towards Video Analysis Using Shot Frontier Detection and Key-Frame Mining

In current scenario yet correspondence innovation, utilization about video based data is expanding step by step. With lowered cost about advanced stockpiling gadget, accessibility concerning greater transmission dimensions through presenting 4-G innovation alongside the improved pressure technique, it is currently achievable after accomplish the computerized video available in imitation of all. Every single video contain partial fundamental facts which lie extricated without losing someone data from the video, a lot deliberation is currently attached according to the video getting ready innovation. Video family actually contain of number concerning still pictures referred to as outlines. Key edges are the casings of the video that comprise partial imperative data. Extraction regarding this edge is certain regarding the essential petition in video preparing. Extraction of these solution edges which mirrors the real substance of the video in extraordinarily essential for originative perusing and removal over the video arrangement from a vast database concerning video. Such kind on activity is extremely beneficial for constructing over the stepped forward advanced video innovation. This paper affords the method over choosing superb key-frames extraction mechanism because video looking where the modern approach is according to extract almost attribute frame. The methodology utilized right here extracts the resolution frames the use of a Pixel-Threshold Technique where the calculated standard deviation is introduced out in contrast with the total block difference on the frames. Experimental effects exhibit that the extracted key-frames can shorten the salient functions or characteristics concerning the video retaining the integrity of the records contained within up to expectation video.
Joint 3D Beamforming and Trajectory Design for UAV-Enabled Mobile Relaying System

The goal of this paper is to investigate the security performance of the multi-antenna unmanned aerial vehicle (UAV)-enabled mobile relaying system with a multi-antenna destination in the presence of a multi-antenna eavesdropper. To improve the secrecy outage probability performance, we propose a location-based three dimensional (3D) beamforming scheme without the channel state information of the destination and the eavesdropper. To this end, we first derive the expression of the secrecy outage probability for uniform linear arrays (ULAs) and uniform circular arrays (UCAs), which is valid for arbitrary values of system parameters. The expression is practicable in the UAV-enabled mobile relaying system as the system parameters are time-variant with the movement of the UAV. Using this expression, we study the secrecy outage probability minimization problem by optimizing the 3D beamforming direction along with the trajectory of the UAV, subject to the mobility constraint and location constraint. To solve this problem, an efficient iterative algorithm is proposed. The numerical results demonstrate the effectiveness of our algorithms by comparing to other benchmark schemes, they also show the system with UCAs outperforms the system with ULAs on secrecy outage probability performance.
Super Resolution of Image Using Sparse Representation of Image Patches with LASSO Approximation on CUDA Platform

Image super resolution refers to increasing the physical dimensions of image to overcome resolution limitations originated from certain imaging sensors such as light sensitivity of sensor, environmental lightning conditions, physical size of sensor and also of each pixel w.r. t the sensor. With the help of sparse code and the dictionary it is possible to reconstruct good quality image with high resolution. Training images are used for obtaining the sample patches which are used for obtaining coefficients of the dictionary. Therefore the process of image super resolution is carried from sparse representation of low resolution image patch generating image of high resolution. Although method is effective, it is slow due to need of processing large number of image patches. Using LASSO (Least Absolute Shrinkage and Selection Operator) approximation which is one of the regression analysis method where the approximation of solution is performed. The implementation is done on MATLAB (CPU) and NVidia CUDA platform. Here NVidia GPU which increases the speed of patch processing thereby overall improvement time consumed to run the algorithm. The algorithm is evaluated using performance parameters SSIM, Running Time, RMSE, PSNR and Entropy.
A 3D-Printable Docking System for Aerial Robots: Controlling Aerial Robotic Manipulators in Outdoor Industrial Applications

This article proposes a novel docking system design for unmanned aerial vehicles (UAVs) that provides measurements of the robot's position at high frequency. These measurements are used to control the aerial robot, enabling it to hover while it performs any kind of manipulation task in GPS-denied industrial environments without causing the UAV to drift or putting at risk the platform and its environment. The novel tool is designed as an arm end effector, preventing the aerial manipulator from colliding while in operation. A cascade controller is proposed to close the position loop. An additional use case for the docking system is described in the "Experimental Validation" section; it consists of performing position-based servoing (PBS) of a second manipulator using the position provided by the docking tool and the manipulator's kinematic model.
Catching the Flow with Locality Sensitive Hashing in Programmable Data Planes

Flow-based load balancing exploits the parallelism of network traffic to improve forwarding performance. However, in programmable data planes, the concept of “flow” has been changed, which undermines the premise of hashing-based load balancing. The current network hashing algorithms such as Toeplitz and CRC16 cannot recognize the flow containing different packets, may resulting in forwarding performance degradation. In this study, we introduced an approach based on the locality sensitive hashing to the failure at flow recognition. We proved that bit sampling achieves a higher probability that packets belonging to the same flow are mapped to the same queue than Toeplitz and random algorithm. To guarantee the load balancing performance of bit sampling, we proposed a method for bits selection. The experimental results showed that bit sampling could improve the probability by at least 55% over current network hashing algorithms while maintaining competitive load-balancing performance.
A Stereo Visual Obstacle Detection Approach Using Fuzzy Logic and Neural Network in Agriculture

This paper describes a stereo visual obstacle detection approach using fuzzy logic and neural network in agriculture. Firstly, by training obstacle and non-obstacle colour features with a 6-input neural network, appearance_based method is applied to extract novel regions in monocular image. We apply improved FAST algorithm to extract features, which adapts illumination variation better. At last, we match feature points in the image pairs and obtain the disparity information. The results show that the algorithm is capable of detecting obstacles such as vehicles, humans, trees, plastic bar and other common obstacles in agricultural environment successfully.
A Novel Method for Estimating Continuous Motion and Time-Varying Stiffness of Human Elbow Joint

This paper presents a method to estimate continuous motion and time-varying stiffness of the human elbow joint based on surface electromyography (sEMG). Firstly, based on the Hill-based muscle model (HMM), a continuous motion estimation model (CMEM) of elbow joint with sEMG signals as input and joint angle as output was established. And the genetic algorithm was used to optimize the unknown parameters in this model. Secondly. , the joint kinetic equation expressed the relationship of skeletal muscle stiffness and the elbow joint stiffness. And the time-varying stiffness estimation model (TVSEM) of elbow joint based on sEMG signals was established. So that the TVSEM of the elbow joint based on sEMG signals was established. Finally. , the accuracy and validity of the CMEM and the TVSEM of elbow joint were presented by the experimental platform.
Fuzzy Based Switched Boost Inverter for Solar PV Application

Power electronics converters are required to interface solar PV panel to load or grid. Currently Z-source Inverter (ZSI) are in application. The ZSI due to their limitations need to be improved. In this paper Switched Boost Inverter (SBI) is proposed with modified PWM technique. SBI produces an output (both ac and dc) which can be either more or less than dc input voltage. In order to boost the voltage shoot-through state of inverter leg switches is allowed in this converter. In proposed SBI for solar application MPPT is also applied and fuzzy controller is applied to regulate output voltage. The proposed converter is validated through MATLAB/Simulink study and results.
The Coefficient of Synonymy

The measurement of synonymy between words is an essential function required by numerous NLP tasks. However, similarity measures often give word similarity values that are very difficult to interpret and compare when applied to popular word embeddings. We introduce a coefficient of synonymy Cs, which uses a new method based on word distance and the density of words around those being compared for measuring synonymy. This method provides consistent, comparable values indicating the level of synonymy between words. We compare measurements of Cswith cosine using four off-the-shelf word embeddings created by Word2Vec and GloVe and two 350,000 word pair datasets.
AI Application to Data Analysis, Automatic File Processing

The state of the art of data engineering and statistical algorithms opens the possibility for automating certain tasks Although field types of columns may not be known in advance, we assume data type is enforced, e.g., that date fields have nothing but dates, in some specific format. Data related tasks include feature engineering, data processing, text recognition and errors detection. For example, of interest to auditors is detection of input errors in financial spreadsheets, fraudulent transactions, abusive insurance claims, etc. Clearly, for a large dataset, an auditor cannot possibly go through the entire set of records, so a way to highlight records of interest may be of great value by increasing audits productivity. Once an auditor has identified a suspect record, he/she may be interested to give “similar” record a closer examination. In this short essay we demonstrate the plausibility of automating the process of feature creation, outlier detection and calculating the distance between any two records in a spreadsheets whereby providing the proverbial auditor the means to quickly identify strange and unusual records.
A Circular Shaped Microstrip patch Antenna for Bluetooth/Wi-Fi/UWB/X-band Applications

In this paper, a circular shaped Microstrip patch antenna is investigated with single band notch characteristics for use in applications in Bluetooth (2.4 to 2.485), Wi- Fi (2.4 to 5.8 GHz), Ultra wideband (UWB) (3.1 to 10.6 GHz) and X-band (8 to 12 GHz) respectively. Proposed Antenna involves a circular radiation patch modified with a circular slot, a crescent shaped slot and a L shaped slot and Ground plane of antenna is defected with two I shaped slots and truncated at its top corners. Proposed antenna impedance bandwidth is from 2.35 to 12.9 GHz range except the frequencies on single notched bands of 2.9 to 3.8 GHz. The proposed antenna is of compact size 32mm × 36mm × 1.6mm. VSWR < 2 for total performing frequency spectrum range except for the single band rejection. We get good return loss, antenna efficiency, VSWR, antenna gain and radiation patterns by doing the simulation.
Fuzzy logic with load compensation for upper limb exoskeleton control based on IMU data fusion

This paper describes an intelligent control approach for upper limb exoskeleton tracking the user's arm. In this work, the authors critically discuss and compare two different control strategies of PD-Fuzzy and FLC-LC (Fuzzy Logic Controller with Load Compensation) to control an upper limb exoskeleton. Two IMU sensors measure the arm movement of the user, whose data are fed to the developed controller with the goal of achieving a successful interactive control in terms of comfortability, flexibility, and arm motion imitation. To evaluate the performance of these two controllers, two experiments are performed, with and without human-robot interaction. The results show that both control strategies are successful, with the FLC-LC outperforming the PD-Fuzzy in terms of motion tracking and robustness.
Active Fault-Tolerant Tracking Control of a Quadrotor UAV

This paper presents a reliable active fault-tolerant tracking control (AFTTC) design method against actuator faults of a quadrotor unmanned aerial vehicle (VAV). The scheme consists of a baseline controller, a fault detection and diagnosis (FDD) estimator and an adaptive fault compensator. In the absence of actuator faults, a state feed-back controller acting as the normal controller is developed by utilizing linear quadratic regulator, so that the system stability and desired tracking performance are guaranteed. When the actuator fails to operate, an adaptive two-stage Kalman filter estimator is exploited for simultaneous state and fault estimation. Using the information from the FDD estimator, an adaptive fault compensator based on model reference adaptive control is designed automatically to mitigate the negative impacts of actuator faults. Simulation results show that the proposed AFTTC method enables the quadrotor UAV to track the desired refere-nce commands in the absence/presence of actuator faults.
Facial expression detection using Six Facial Expressions Hexagon (SFEH) model

Within the diverse field of image processing, facial expression detection is tremendously an interesting part of face recognition. In order to track and locate facial expressions unusual parts of face movements like eyes, nose, mouth, cheeks etc must be observed. In this paper, six basic facial expressions are detected using Six Facial Expressions Hexagon (SFEH) Model. The SFEH model provides the general representation of six facial expressions on six edges of surface hexagon (S-Hex). The S-hex is the outer boundary of face and can be classified into three parts as upper triangle, middle rectangle and lower triangle. The partitioning of facial features into three parts narrows down the processing area of face where expression originates and is very helpful to locate the varied region. Moreover, Facial Action Coding System (FACS) and Facial Animation Parameters System (FAPS) are used as intermediate frame to analyze the proposed SFEH model. The scope of the proposed SFEH model is to customize only the varied region of the face using morphological operations under reasonable computational cost
A Novel Three-Phase Transformerless H-8 Topology With Reduced Leakage Current for Grid-Tied Solar PV Applications

In this paper, a novel transformerless topology is proposed for three-phase grid-connected applications. The proposed topology has eight switches, and it combines the advantages of both dc-bypass and ac-bypass circuit configurations. The modulation strategy for the proposed topology is based on sinusoidal pulsewidth modulation followed by dedicated logic functions enabling control over the full range of modulation index. Logic functions are responsible for producing gate pulses for the eight switches, which are controlled in such a way that the variation in common-mode voltage (CMV) during inverter operation is low. Low variation in CMV throughout the operation results in reduced leakage current. The topology is capable of limiting the leakage current as per the German standard DIN VDE 0126-1-1. The output voltage of the proposed topology is unipolar, which results in low stress on the output filter inductors and low THD. There are two additional switches in the proposed topology compared to the conventional three-phase topology, but due to the special modulation strategy used, the drop in efficiency is insignificant. Theoretical analysis, simulation, and experimental results are presented to validate the concept of the proposed topology. Results show low variation in CMV and low THD in the output grid current.
Fast-Processing Implementation of Current-Ripple-Losses-Optimized Common-Mode Voltage Reduction PWM

In this paper, a fast-processing implementation method of the current-ripple-loss-optimized common-mode voltage reduction pulsewidth modulation (CRLO-CMVRPWM) is studied. With the novel sector definitions and the simplified method to solve the constraint nonlinear programming model, the compute burden of the proposed method is reduced by 82.8% and 50.5% for the case where the modulation index is below and above $4\sqrt 3 /9$, respectively. In addition, the effects of the optimized zero-sequence voltage errors caused by the adopted linear fitting strategy are proven to be negligible by studying the harmonic characteristics difference between the fast-processing and original CRLO-CMVRPWM. Simulated and experimental results are given to validate the effectiveness.
AM-FED+: An Extended Dataset of Naturalistic Facial Expressions Collected in Everyday Settings

Public datasets have played a significant role in advancing the state-of-the-art in automated facial coding. Many of these datasets contain posed expressions and/or videos recorded in controlled lab conditions with little variation in lighting or head pose. As such, the data do not reflect the conditions observed in many real-world applications. We present AM-FED+ an extended dataset of naturalistic facial response videos collected in everyday settings. The dataset contains 1,044 videos of which 545 videos (263,705 frames or 21,859 seconds) have been comprehensively manually coded for facial action units. These videos act as a challenging benchmark for automated facial coding systems. All the videos contain gender labels and a large subset (77 percent) contain age and country information. Subject self-reported liking and familiarity with the stimuli are also included. We provide automated facial landmark detection locations for the videos. Finally, baseline action unit classification results are presented for the coded videos. The dataset is available to download online: https://www.affectiva. com/facial-expression-dataset/
eX2: a framework for interactive anomaly detection

We introduce eX 2 (coined after explain and explore), a framework based on explainable outlier analysis and interactive recommendations that enables cybersecurity researchers to efficiently search for new attacks. We demonstrate the framework with both publicly available and real-world cybersecurity datasets, showing that eX 2 improves the detection capability of stand-alone outlier analysis methods, therefore improving the efficiency of so-called threat hunting activities.
Optimal Design of Planar Magnetic Components for a Two-Stage GaN-Based DC–DC Converter

This paper develops a 200-W wide-input-range (64–160-to-24-V) rail grade dc–dc converter based on gallium nitride devices. A two-stage configuration is proposed. The first regulated stage is a two-phase interleaved buck converter (<inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula>400 kHz), and the second unregulated stage is an <italic>LLC</italic> (2-MHz) dc transformer. In order to achieve high frequency and high efficiency, the critical-mode operation is applied for the buck converter, and the negative coupled inductors are used to reduce the frequency and the conduction losses. Then, a systematical methodology is proposed to optimize the planar-coupled inductors. For the unregulated <italic>LLC</italic> converter, it can always work at its most efficient point, and an analytical model is used to optimize the planar transformer. Finally, the proposed dc–dc converter, built in a quarter brick form factor, is demonstrated with a peak efficiency of 95.8% and a power density of 195 W/in<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula>.
Characterization of Antigen-Presenting Cell Subsets in Human Liver-Draining Lymph Nodes

T-cell immunity in the liver is tightly regulated to prevent chronic liver inflammation in response to antigens and toxins derived from food and intestinal bacterial flora. Since the main sites of T cell activation in response to foreign components entering solid tissues are the draining lymph nodes (LN), we aimed to study whether Antigen-Presenting Cell (APC) subsets in human liver lymph-draining LN show features that may contribute to the immunologically tolerant liver environment. Healthy liver LN, iliac LN, spleen and liver perfusates were obtained from multi-organ donors, while diseased liver LN were collected from explanted patient livers. Inguinal LN were obtained from kidney transplant recipients. Mononuclear cells were isolated from fresh tissues, and immunophenotypic and functional characteristics of APC subsets were studied using flowcytometry and in ex vivo cultures. Healthy liver-draining LN contained significantly lower relative numbers of CD1c+ conventional dendritic cells (cDC2), plasmacytoid DC (PDC), and CD14+CD163+DC-SIGN+ macrophages (MF) compared to inguinal LN. Compared to spleen, both types of LN contained low relative numbers of CD141hi cDC1. Both cDC subsets in liver LN showed a more activated/mature immunophenotype than those in inguinal LN, iliacal LN, spleen and liver tissue. Despite their more mature status, cDC2 isolated from hepatic LN displayed similar cytokine production capacity (IL-10, IL-12, and IL-6) and allogeneic T cell stimulatory capacity as their counterparts from spleen. Liver LN from patients with inflammatory liver diseases showed a further reduction of cDC1, but had increased relative numbers of PDC and MF. In steady state conditions human liver LN contain relatively low numbers of cDC2, PDC, and macrophages, and relative numbers of cDC1 in liver LN decline during liver inflammation. The paucity of cDC in liver LN may contribute to immune tolerance in the liver environment.