Reviewerly/exHarmony huggingface dataset:
Real-time Face Recognition Under Different Environment

The problems related to security and safety have motivated us to add some contribution to face recognition work by making it real-time process with more automation and robustness. As we know, fingerprint scanning is more manual as it needs human intervention. So Face is an unique identity of a person which is used by surveillance system. The aim of these surveillance systems is to provide an automatic interpretation of scenes by identifying correct person for human safety purposes. But due to lots of variation due to unconstrained environment and limitations face recognition becomes difficult task for real-time applications, where we will be considering few main problems like illumination, occlusion, head-pose and facial expressions will be solved in our work to improve real-time face recognition process. We will be using MTCNN and pre-trained Inception- ResNet model of CNN.
GAN-Based Rain Noise Removal from Single-Image Considering Rain Composite Models

Under severe weather conditions, outdoor images or videos captured by cameras can be affected by heavy rain and fog. In this paper, we address a single-image rain removal problem (de-raining). As compared to video-based methods, single-image based methods are challenging because of the lack of temporal information. Although many existing methods have tackled these challenges, they suffer from overfitting, over-smoothing, and unnatural hue change. To solve these problems, we propose a GAN-based de-raining method. The optimal generator is determined by experimental comparisons. To train the generator, we learn the mapping between rainy and residual images from the training dataset. Besides, we synthesize a variety of rainy images to train our network. In particular, we focus on not only the orientations and scales of rain streaks but also the rainy image composite models. Our method also achieves better performance on both synthetic and real-world images than state-of-the-art methods in terms of quantitative and visual performances.
The Use of Hybrid Knowledge Bases in Designing Engineering Systems

frame networks (as tools of the situational approach) are used to draw up technical specifications, the conceptual design of systems and to describe the subject area. In the paper the authors describe modifications of frame models for solving applied tasks. New classes of software system generate new requirements, and researchers must develop and modify conceptual models and instrumental systems for building software. The authors focus on frame models and describe new classes of frames. They are the multimedia frame and the frames with procedures of fuzzy logic. The authors describe instrumental systems and stages of designing software using frame models.
A Further Investigation of Neural Network Players for Game 2048

Game 2048 is a stochastic single-player game. Development of strong computer players for Game 2048 has been based on N-tuple networks trained by reinforcement learning. Some computer players were developed with neural networks, but their performance was poor. In our previous work, we showed that we can develop better policy-network players by supervised learning. In this study, we further investigate neural-network players for Game 2048 in two aspects. Firstly, we focus on the component (i.e., layers) of the networks and achieve better performance in a similar setting. Secondly, we change input and/or output of the networks for better performance. The best neural-network player achieved average score 215 803 without search techniques, which is comparable to N-tuple-network players.
Information security risk assessment based on decomposition probability via Bayesian Network

Well-known approaches to risk analysis suggest considering the level of an information system risk as one frame in a film. This means that we only can perform a risk assessment for the current point in time. This article explores the idea of risk assessment in a future period, as a prediction of what we will see in the film later. In other words, the article presents an approach to predicting a potential future risk and suggests the idea of relying on forecasting the likelihood of an attack on information system assets. To establish the risk level at a selected time interval in the future, one has to perform a mathematical decomposition. To do this, we need to select the required information system parameters for the predictions and their statistical data for risk assessment. This method can be used to ensure more detailed budget planning when ensuring the protection of the information system. It can be also applied in case of a change of the information protection configuration to satisfy the accepted level of risk associated with projected threats and vulnerabilities.
3D-Modeling Dataset Augmentation for Underwater AUV Real-time Manipulations

Underwater real-time object recognition is essential to unmanned underwater drones, namely autonomous underwater vehicles (AUV), cruising in the ocean. As the deep learning technology evolves swiftly lately, the attempt for AUVs to fully understand the surrounding environment becomes an emerging demand for marine or military applications. No matter which approach that deep learning manages to adopt, a large dataset with sufficient number of images for each object is required. In this investigation, a dataset augmentation method based on 3D modeling is proposed to resolve the mentioned problem. By rotating and scaling the target object in 3 dimensions with different backgrounds, the number of underwater object images is increased over 1000 times. Through the proposed method, high quality image data are forged to improve the recognition accuracy of those rare underwater objects, which are very hard to collect enough number of images, by 20% based on real-time video clips' experiment.
Text Classification using Convolutional Neural Network by Genetic Algorithms

In order to improve the performance of Text Classification by Convolutional Neural Network, we propose adopting Genetic Algorithms as the optimization method in Convolutional Neural Network in the domain of Text Classification. We evaluated our method with TREC, SSTI, and SST2 datasets and obtained enlightening results.
Improving the Targets’ Trajectories Estimated by an Automotive RADAR Sensor Using Polynomial Fitting

A way to reduce the uncertainty at the output of a Kalman filter embedded into a tracker connected to an automotive RADAR sensor consists of the adaptive selection of parameters during the tracking process. Different informed strategies for automatically tuning the tracker’s parameters and to jointly learn the parameters and state/output sequence using: expectation maximization; optimization approaches, including the simplex algorithm; coordinate descent; genetic algorithms; nonlinear programming using finite differencing to estimate the gradient; Bayesian optimization and reinforcement learning; automatically tuning hyper-parameters in the least squares, were already proposed. We develop here a different semi-blind post-processing approach, which is faster and more robust. Starting from the conjecture that the trajectory is polynomial in Cartesian coordinates, our method supposes to fit the data obtained at the output of the tracker to a polynomial. We highlight, by simulations, the improvement of the estimated trajectory’s accuracy using the polynomial fitting for single and multiple targets. We propose a new polynomial fitting method based on wavelets in two steps: denoising and polynomial part extraction, which compares favorably with the classical polynomial fitting method. The effect of the proposed post-processing methods is visible, the accuracy of targets’ trajectories estimations being hardly increased.
SRACARE: Secure Remote Attestation with Code Authentication and Resilience Engine

Recent technological advancements have enabled proliferated use of small embedded and IoT devices for collecting, processing, and transferring the security-critical information and user data.This exponential use has acted as a catalyst in the recent growth of sophisticated attacks such as the replay, man-in-the-middle, and malicious code modification to slink, leak, tweak or exploit the security-critical information in malevolent activities.Therefore, secure communication and software state assurance (at run-time and boot-time) of the device has emerged as open security problems.Furthermore, these devices need to have an appropriate recovery mechanism to bring them back to the known-good operational state.Previous researchers have demonstrated independent methods for attack detection and safeguard.However, the majority of them lack in providing onboard system recovery and secure communication techniques.To bridge this gap, this manuscript proposes SRACARE -a framework that utilizes the custom lightweight, secure communication protocol that performs remote/local attestation, and secure boot with an onboard resilience recovery mechanism to protect the devices from the above-mentioned attacks.The prototype employs an efficient lightweight, low-power 32-bit RISC-V processor, secure communication protocol, code authentication, and resilience engine running on the Artix 7 Field Programmable Gate Array (FPGA) board.This work presents the performance evaluation and state-of-the-art comparison results, which shows promising resilience to attacks and demonstrate the novel protection mechanism with onboard recovery.The framework achieves these with only 8% performance overhead and a very small increase in hardware-software footprint.
LAHEL: Lightweight Attestation Hardening Embedded Devices using Macrocells

In recent years, we have seen an advent in software attestation defenses targeting embedded systems which aim to detect tampering with a device's running program. With a persistent threat of an increasingly powerful attacker with physical access to the device, attestation approaches have become more rooted into the device's hardware with some approaches even changing the underlying microarchitecture. These drastic changes to the hardware make the proposed defenses hard to apply to new systems. In this paper, we present and evaluate LAHEL as the means to study the implementation and pitfalls of a hardware-based attestation mechanism. We limit LAHEL to utilize existing technologies without demanding any hardware changes. We implement LAHEL as a hardware IP core which interfaces with the CoreSight Debug Architecture available in modern ARM cores. We show how LAHEL can be integrated to system on chip designs allowing for microcontroller vendors to easily add our defense into their products. We present and test our prototype on a Zynq-7000 SoC, evaluating the security of LAHEL against powerful time-of-check-time-of-use (TOCTOU) attacks, while demonstrating improved performance over existing attestation schemes.
Learning Multi-Sense Word Distributions using Approximate Kullback-Leibler Divergence

Learning word representations has garnered greater attention in the recent past due to its diverse text applications. Word embeddings encapsulate the syntactic and semantic regularities of words among sentences. Modelling word embedding as multi-sense gaussian mixture distributions will additionally capture uncertainty and polysemy of words. We propose to learn the Gaussian mixture representation of words using a Kullback-Leibler (KL) divergence based objective function. The KL divergence based energy function provides a better distance metric which can effectively capture entailment and distribution similarity among the words. Due to the intractability of KL divergence for Gaussian mixture, we go for a KL approximation between Gaussian mixtures. We train on a Wikipedia based dataset and perform qualitative and quantitative experiments on benchmark word similarity and entailment datasets which demonstrate the effectiveness of the proposed approach.
Development and Validation of Smart Education System for Early Childhood

The study purposes to develop a Smart Early Childhood Education Service System (SECESS) including emotion detection, statistical analysis, and training service for kindergartens. The digital content comprises 40 emotion-detection (ED) games and 40 sandwich-dialogue (SD) animations. The findings show the improvement of parents' and teachers' SD ability by using 40 SD animations and a significantly positive correlation between SD ability and children's emotional ability. The application of findings in practice builds a global education farm experience. The application of findings in follow-up research is to predict children's emotions based on parents' and teachers' SD ability.
Random Number Generator Based on Metastabilities of Ring Oscillators and Irregular Sampling

This paper presents a new random number generator based on irregular sampling of regular waveform method. Wakeup uncertainty and shut-down metastabilities are used as additional entropy source to the timing jitter of the ring oscillators. To be able to change the stability of the rings, multiplexer and inverter are added to each ring. 34 ring oscillators are combined with an XOR gate to generate an irregular waveform that is used as the sampling clock. The select input of the multiplexers are controlled with a control unit. A signal that causes stability change with 2MHz frequency is routed from controller to the select inputs of the multiplexers and the XORed signal is masked with 2MHz, and 10ns width pulses. To reduce the speed of sampling, the irregular signal is downsampled that results in a final throughput of 13kbps. All of the circuits mentioned in this paper is implemented in Xilinx Zynq7000 FPGA. Furthermore, NIST 800-22 test suite is used to test the designed RNGs. Data sets of one million bits are collected with a UART module. All the NIST 800-22 test suite requirements are satisfied with the new proposed RNG.
Design of Power Wheelchair Controller

An intelligent power wheelchair motor driver based on Arduino board is proposed in the paper. The system is developed for ensuring safely travel to disabled or elderly people. By using of two ultrasonic and a LIDAR sensor, the system can avoid collisions, including static and moving obstacles. They can be walking people and moving vehicles, as all of them can cause serious harm to people in wheelchairs. The power base contains drive wheels of the wheelchair. For control the wheelchair by the user is used a Standard joystick. The speed of the chair is proportional to the displacement of the control unit, and the wheelchair moves the same direction as the joystick is pointing. In addition proportional control allows moving in any direction within the radius of 360°. The command from the joystick is being sent to the Arduino board where the controller ATMega328p processes the command. After processing the controller send the command in the form of digital signal to the motor driving integrated circuit as it controls the movement of wheelchair. The used brushed DC motors' parameters are 24VDC, 250W which are independently controlled by the joystick.
Target Measurement Method Based on Sparse Disparity for Live Power Lines Maintaining Robot

3D perception has been an indispensable part of the research on many manipulator robots. In this paper, we propose a the new kind of target measurement method to perceive the surrounding environment of robots based on binocular stereo vision theory, which can effectively improves the 3D reconstruction accuracy and effectiveness of the target recognition. The sparse disparity method we apply is different from the dense binocular stereo vision method in common. At the beginning of the article, a series of effective methods of image processing we use to identify and extract target images will be introduce. A novel operator-matching algorithm is proposed to improve the extraction and recognition. The contour-center stereo matching theory is expounded to lay a foundation to the calculation of sparse disparity estimation. To give accurate 3D reconstruction model information, a calibration system with low measurement error is proposed based on dual manipulators of our robot. After coordinate transformation implementation, robots obtain point cloud data of the target and able to perform interactive actions. A wiring experiment is set to test this method by judging work efficiency and success rate.
Design, Implementation &amp; Performance Analysis of Solar Tracking System on Simulink Platform

Abstract This paper presents the idea of tracking framework which was executed on Simulink stage. The variety in current and voltage for static and tracking SPV power plant are appeared in this paper. The relative study at various situating of panel (static force plant) is additionally appeared in this paper. The entire model like sun tracking model, static SPV model, LDR sensor model, DC motor model additionally appeared in the paper. The primary goal of this paper is to break down the outcomes concerning static force plant. The tiltaion edges are 30°, 60° and 90° static force plants. The simulation results and the impact of tiltation point are additionally introduced in this paper. In the closing comment, the improved effectiveness because of tracking method is appeared.
Research on Image Super-Resolution Reconstruction Based on Generative Countermeasure Network

Image super-resolution reconstruction technology is an important image processing technology used to improve image quality and video resolution in the field of computer vision. Nowadays, the main methods of super-resolution reconstruction based on convolutional neural network (CNN) are mostly to get higher peak signal-to-noise ratio (PSNR), which will produce many other problems, such as, the visual sensory effect of the image is not ideal, the gradient back propagation is difficult, the training effect of long training time is not ideal, the image lacks some high-frequency information and texture details, etc. In this paper, an improved image super-resolution reconstruction method based on generative multi anti network (GAN) is proposed. For the traditional generative adversary network, a VGG19 network assistant generator and discriminator model training are added, and the loss function is improved. Combined with the GAN adversary loss, the feature loss of the auxiliary VGG19 network is added. Experimental results show that compared with the existing methods, this method improves the overall visual effect of the reconstructed image, restores more realistic texture effect and higher MOS score under the condition of ensuring PSNR.
An IoT-alert System for Chronic Asthma Patients

Asthma claims thousands of lives every year, especially during the cold season, mostly because of delayed access to first-aid and lack of immediate and adequate medical care. Asthma-related fatalities are preventable by home remedies, exercises, prescribed treatments, and providing rapid response and care to patients in case of asthma attacks. The latter is paramount to many asthmatics who are not able to predict an asthma attack well in advance. This work proposes and deploys an IoT-alert system that a chronic asthma patient (or any asthma patient) can use to trigger an alert to inform emergency contacts in case of a sudden asthma attack. This research, done in Rwanda, aims to alert emergency contacts early enough for a rapid response in order to reduce the number of asthma-related deaths in the country. The proposed system, in its basic form, uses a keypad unit as an interaction point with the users, which when pressed triggers the alert system to send SMS messages to registered emergency contacts and sounds a buzzer to alert few of the immediate neighbours of the asthma patients living close by. The intention is to provide support and help the chronic asthma patients, particularly those living alone, to receive appropriate medical aid within five minutes of alerting the neighbors and emergency contacts during a sudden asthma attack. The keypad interaction unit, which is the primary component of this IoT-alert system, can be wall-mounted in any room so that patients can easily access it. A small functioning prototype of the proposed system has been successfully tested at Kigali and testing has proven its effectiveness in helping the asthma patients.
Data Selection for Bilingual Lexicon Induction from Specialized Comparable Corpora

Narrow specialized comparable corpora are often small in size.This particularity makes it difficult to build efficient models to acquire translation equivalents, especially for less frequent and rare words.One way to overcome this issue is to enrich the specialized corpora with out-ofdomain resources.Although some recent studies have shown improvements using data augmentation, the enrichment method was roughly conducted by adding out-of-domain data with no particular attention given to how to enrich words and how to do it optimally.In this paper, we contrast several data selection techniques to improve bilingual lexicon induction from specialized comparable corpora.We first apply two well-established data selection techniques often used in machine translation that is: Tf-Idf and cross entropy.Then, we propose to exploit BERT for data selection.Overall, all the proposed techniques improve the quality of the extracted bilingual lexicons by a large margin.The best performing model is the cross entropy, obtaining a gain of about 4 points in MAP while decreasing computation time by a factor of 10.
A Real-Time Optimization-Based Trajectory Planning Method in Dynamic and Uncertain Environments

Reducing conservatism while ensuring safety poses great difficulties for real-time trajectory planning in uncertain and cluttered environments. If we view trajectory planning as an optimization problem, the non-convex collision avoidance constraints with uncertain obstacles make trajectory planning challenging and time-consuming. Disjunctive chance constraint-based methods have been one of the most popular stochastic tools for this problem, for they can provide a tighter bound and lead to less conservative trajectories compared with other methods. However, previous work on disjunctive chance constraint-based trajectory planning adopts mixed-integer programming which has exponential complexity. Different from existing work, we propose a new optimization-based trajectory planning method with chance constraints, which turns uncertain obstacles into bounding boxes with tight upper bound collision avoidance constraints. Then, with a proposed time-varying convex feasible sets (TVCFS) algorithm, the original non-convex optimization problem is transferred into a series of convex problems, which can meet real-time requirements. Since the planned trajectory may be dynamically infeasible, we consider vehicle kinematics and formulate an optimal control problem to further smooth the planned trajectory and obtain desired control inputs. Simulation tests demonstrate the effectiveness of the proposed method.
Unsupervised Clustering on Signed Graphs with Unknown Number of Clusters

We consider the problem of unsupervised clustering on signed graphs, i.e., graphs with positive and negative edge weights. Motivated by signed cut minimization, we propose an optimization problem that minimizes the total variation of the cluster labels subject to constraints on the cluster size, augmented with a regularization that prevents clusters consisting of isolated nodes. We estimate the unknown number of clusters by tracking the change of total variation with successively increasing putative cluster numbers. Simulation results indicate that our method yields excellent results for moderately unbalanced graphs.