Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning for Object Labelling

Identification of objects from an image using machine learning is an emerging research topic. It is known to the scientific community that the labelling of objects by human intelligence in the current scenario of high volume data consumes a large amount of time and produces an error of 5% as observed in the case of ImageNet. In this regard, machine learning methods have become more popular and effective as they produce better results. However, the performance of different machine learning methods vary while identifying objects from images. To address this fact, an object labelling problem has been formulated by considering the images that contain objects of 10 different classes either in the form of solid, hollow and mixed. In order to label the objects in such three types of images, two different approaches are followed. First, a training dataset is created of size 240 samples where 24 samples are present in each of the 10 classes. Here each class contains equal number of solid and hollow objects. In the second approach, entropy is used to reduce the number of samples in the training dataset needed to achieve similar performance as obtained in the first approach. Thereafter, both the training datasets are applied separately to the seven well-known machine learning methods and one classical method to obtain the final results on three test images. The performance of the methods is demonstrated in terms of accuracy as well as by providing annotated objects in images. A software named ObLab2018 has also been developed to annotate the objects from images. The software and the datasets are provided online at http://www.nitttrkol. ac.in/indrajit/projects/ObLab2018/.
Symmetric Jumbling-Salting Encryption Algorithm for Files

This paper presents an improvised approach for file encryption in the server's database using jumbling-salting algorithm. One of the most import concepts used here is randomization using a randomly generated character set. Dictionary attacks and brute force attacks while sending a file from the client to the server are common ways of security breaches. Here, the Jumbling-Salting algorithm has been used to augment the security aspect by dividing the file at hand into chunks of a set size (explicitly by the client) and then encrypting the chunks individually. The length of the cipher text is considerably increased in this process. The algorithm defined in the paper defines a character set from where it selects a random character and integrates it with the plaintext using a modulus function. This encompasses the process of Jumbling. Following that is salting process which requires the algorithm to append a random string to the jumbled string. Ultimately, the chunks that are encrypted are sent to the server, in the same order, where rearrangement is done. Randomized version of JS algorithm ensures that there is increase in time to crack the cipher text chunks, by forming a highly secured version of encrypted file.
Accelerating Drug Discovery Using Convolution Neural Network Based Active Learning

Drug discovery is an expensive and time consuming process, especially in the era of new technology, such as personalized medicine where tremendous experiments and analysis are needed before bringing new drugs to the market. While In vivo and In vitro experiments are expensive, In silico methods become important and they can reduce the cost in drug discovery by prioritizing the experiments in more efficient ways. In this paper, we propose a new convolution neural network based active learning model which helps to reduce the number of experiments needed in drug discovery. Using the drugs performance on other cell lines as assisting information, our model can precisely select the most promising drug from those candidates for a new cell line. Our model uses a deep neural network structure where there are two CNN channels for drugs and cell lines respectively, which are followed by a fulled connected network. The experimental results show that our model can achieve significant better performance than the existing methods.
Improving the Testing of Java Garbage Collection Through an Efficient Benchmark Generation

Garbage Collection (GC) is a core feature of multiple modern technologies (e.g., Java, Android). On one hand, it offers significant software engineering benefits over explicitly memory management, like preventing most types of memory leaks. On the other hand, GC is a known cause of performance degradation. However, it is considerably challenging to understand its exact impact on the overall application performance. This is because the non-deterministic nature of GC makes very complex to properly model it and evaluate its performance impacts. To help tackling these problems, we present an engine to generate realistic GC benchmarks by enabling to effectively capture the GC/memory behaviours experienced by real-world Java applications. We also demonstrate, through a comprehensive experimental evaluation, how such benchmarks can be useful to strengthen the evaluation of GC-related advancements.
Energy Efficient Analog Synapse/Neuron Circuit for Binarized Neural Networks

Energy efficiency is one of the most important factors to make deep neural networks viable in embedded systems. In this paper, we propose an analog synapse circuit using resistive random access memory (ReRAM) which operates with a switched capacitor neuron for binarized neural networks (BNNs). Thanks to the compact and energy efficient ReRAM synapses, the circuit simulation results of an MLP implemented with the proposed synapse and neuron circuits show 2.5ns classification latency and very high energy efficiency of 1536TOPS/W on 32nm technology.
Implementation of 3D Hand Gesture Recognition System using FPGA

In this paper, a VLSI design with dual-camera to construct the depth map and recognize hand gestures was proposed. The proposed system adopts adaptive depth filter which can separate foreground to segment the interesting object under complicated environment. We also proposed dynamic gesture recognition by using depth and coordinate information. The system contains one static gesture and two dynamic gestures and has been implemented in FPGA and the average accuracy with each gesture is 83.98%.
Modelling and Simulation of Handover in Light Fidelity (Li-Fi) Network

The demand of a faster and more secure wireless communication system leads to the development of a new and innovated network in future. Light Fidelity (Li-Fi) is being researched to provide a better wireless network communication. In this communication technology, light from Light Emitting Diodes (LEDs) has been used for data transmission. The purpose of this research work is to investigate the performance of handover algorithms in a Li-Fi network. Two handover algorithms are Closest Access Point (AP) (CAP) and Maximum Channel Gain (MCG). MATLAB simulation results are presented to evaluate those two types of handover algorithms and to show the impacts of UE’s rotation and movement on handover performance.
A neural-based approach for impoving the magnification of degraded document images from mobile terminals

Optical reading of degraded and low-resolution text requires the application of an effective super-resolution approach to improve the recognition rate. In this context, we propose a new approach to improve the finesse and reduce the complexity of an existing non-linear super-resolution method, which is inefficient on low-resolution degraded texts from mobile terminals. In order to preserve the readability of fine line text on this type of images, it is important to increase the local neighborhood perimeter without increasing computational complexity. Our approach is a new way of using of a neural architecture based on multi-layer perceptron. It represents a new technique of using a neural architecture by learning, linearizing and extending a limited super-resolution algorithm, non-linear, complex and inefficient on degraded character images. The obtained results demonstrate, also, the effectiveness of our approach compared to conventional approaches based on a direct neuronal learning of low / high resolution transformation.
Validation of Cuffless Blood Pressure Monitoring Using Wearable Device

Ambulatory blood pressure monitoring (ABPM) has become essential to diagnose the hypertension. In the fast few decades, commercialized blood pressure monitor used oscillometric cuff-based methods which is cumbersome and unsuitable for ABPM due to the discomfort it cause to the patients. In this study, we developed the prototype of cuff-less blood pressure monitor to validate cuff-less BP estimation algorithm. Sixteen subjects were participated in the experiment to test the accuracy of the device compared to the cuff-based BP monitor using oscillometric method. Experimental results using Bland-Altman plot showed that the mean and standard deviation of error of systolic BP (SBP) and diastolic BP (DBP) were 0.3±6.6 and 1.7±9.1 respectively.
The Fractional Fourier Transform on Graphs: Sampling and Recovery

Signal processing on graphs expands discrete signal processing theory and techniques to signals supported on graphs. In this paper, we study the sampling and recovery of graph signals under the graph fractional Fourier transform. We show that a-bandlimited signals in the graph fractional Fourier domain can be perfectly recovered. Experimentally designed sampling strategy is used to generate optimal fractional sampling operators on graphs. We give numerical examples, and test the semi-supervised classification of online blogs and handwritten digits using fractional sampling on graphs, and compare it with GFT sampling. We find that fractional sampling on graphs can lead to better classification accuracy at an optimal fractional order.
Empirical Study on Specification Metrics to Predict Volatility and Software Defects

Successful software implementation needs high-quality software requirement specifications (SRSs). However, SRSs are not only difficult to evaluate quantitatively, but there is not an effective indicator to predict which SRSs are prone to modifications. Moreover, few studies have investigated the impact of SRS quality on software quality. Herein we use two specification metrics for SRSs to evaluate their effectiveness to predict future modifications in two actual developments. The results show that our metrics are useful to predict future specification modifications and our specifications are closely related with software quality.
Adaptive Subspace OMP for Infrared Small Target Image

This paper presents an adaptive subspace OMP method based on target attributions for reconstruction of infrared small target image. The proposed method makes use of a prior knowledge of target size and coherence of target distribution to change the structure of subspace adaptively. Firstly, the maximal projection value of redundancy vector on dictionary space is computed according to certain relevance principle. Then, according to a prior knowledge of target size and coherence of target distribution, the corresponding pixel coordinate of maximal value of projection is set as the center point, and column stack of the surrounding pixels acts as an index set, in which the atom set could be selected. The obtained atom set is used to expand the retained one during iteration and then the subspace is obtained in this iteration, which most closely matches the redundant vector. This process changes the structure of the subspace adaptively, and more importantly, it improves the performance of reconstruction by taking advantages of OMP, SP and SAMP, which could obtain the same quality of reconstruction with less time consuming.
Characterization of Magnetic Materials: an Alternative for Power Transformers

Este trabajo presenta una alternativa para la caracterización materiales que forman parte de la estructura magnética de un transformador existente. La propuesta busca generar una alternativa a los métodos normalizados (IEC 60404-2:2008 e IEC 60404-3:2009) en los casos en los cuales no se posee una muestra del material a analizar. Se efectúa un modelado matemático que permite obtener la densidad de flujo magnético (B) y la intensidad de campo magnético (H), a partir de las variables eléctricas. Los resultados obtenidos experimentalmente demuestran que la técnica presentada resulta apropiada para caracterizar materiales magnéticos.
Trajectory Tracking Control Design for Dual-Arm Robots Using Dynamic Surface Controller

This paper presents a dynamic surface controller (DSC) for dual arm manipulator (DAM) tracking desired trajectories. The DSC algorithm is based on backstepping technique and multiple sliding surface control principle, but with an important addition. In the design of DSC, low-pass filters are included which prevent the complexity arising due to the “explosion of terms” i.e. the number of terms in the control law rapidly gets out of hand. The proposed controller is simulated on complex kinetic dynamic model of a four degrees of freedom (DOF) dual-arm robot. Moreover, the stability of the control system is proved by using Lyapunov theory. The simulation results show the effectiveness of the controller which provide precise tracking performance of the manipulator.
Application of Learning Analytics in Higher Educational Institutions

This article considers how teachers and university administrators can use a significant amount of data stored in the information systems of institutions. Intelligent analysis of these learning processes is of great use in the higher education system. The use of learning analytics (LA) by a large number of higher educational institutions shows the interest and participation of universities in this matter. Learning analytics can tell a lot about the progress of students and the environment in which learning takes place. Intellectualization of educational analytics will help provide predictive models that can serve as a basis for quality assurance and quality improvement. This article gives an idea of the current level of LA development at the international level. The article also draws conclusions about the problems and limitations associated with learning analytics (LA). The existing experience has been studied, and the conclusion have been made about the existing limitations that prevent the wider use of LA.
Sparse Orthogonal Tucker Decomposition for 2D+3D Facial Expression Recognition

Multimodal facial expression recognition (FER) has been becoming a promising research hotspot in recent years. In this paper, we propose a novel sparse orthogonal Tucker decomposition for 2D+3D facial expression recognition (FER). Firstly, we construct a 4D tensor model to explore the efficient structure and complement information between 2D and 3D data. Secondly, based on orthogonal Tucker decomposition, the tensor optimization model, which impose sparse constraint on the involved core tensor and factor matrices, are utilized to find strong interactions between the core tensor and factor matrices for better classification prediction. Finally, a alternating direction method is employed to effectively solve the proposed optimization problem. Numerical experiments are implemented on the BU-3DFE database to indicate the effectiveness of the proposed approach.
Dynamic voltage Drop induced Path Delay Analysis for STV and NTV Circuits during At-speed Scan Test

The NTV circuit has been introduced as a new low power design concept, which increases energy efficiency significantly. However, delay sensitivity of the NTV circuit is a major challenge. In addition, this problem can be more critical during at-speed scan test because of the dynamic voltage drop issue. In this paper, we propose a comparison of dynamic voltage drop induced path delay between STV and NTV circuits during at-speed scan test. To the best knowledge of the authors, it is the first time to analyze the voltage drop induced path delay during the NTV circuit scan test. Experimental results show that the path delay increment of NTV is larger than that of STV although the dynamic voltage drop of NTV is smaller than that of STV.
Distilling Deep Neural Networks for Robust Classification with Soft Decision Trees

Recent deep neural networks have achieved impressive performance in image classification. However, these networks are sensitive to the attack of adversarial examples, leading to a sharp drop in accuracy. To address this issue, this paper proposes a learning approach to improve the robustness by distilling deep neural networks with soft decision trees. This approach learns a decision tree in a softening manner by jointly using data and the predictions of a well-trained deep neural network. In this way, the resulting soft decision tree can distil the knowledge from deep neural network when preserving the efficiency of decision tree. Experimental results show that the proposed approach has better robustness again adversarial examples than deep neural networks and decision trees.
Cellulose Nanofibril (CNF) Films and Xylan from Hot Water Extracted Birch Kraft Pulps

The effects of xylan extraction from birch kraft pulp on the manufacture and properties of cellulose nanofibril (CNF) films were here investigated. Hot water extractions of bleached and unbleached kraft pulps were performed in a flow-through system to remove and recover the xylan. After the extraction, the pulps were oxidized with 2,2,6,6-tetramethylpiperidine-1-oxyl radical (TEMPO) and fibrillated in a high-pressure microfluidizer. Compared to CNF from bleached kraft pulp, the CNF dispersions obtained from water-extracted pulps were less viscous and generally contained a higher amount of microfiber fragments, although smaller in size. In all cases, however, smooth and highly transparent films were produced from the CNF dispersions after the addition of sorbitol as plasticizer. The CNF films made from water-extracted pulps showed a lower tensile strength and ductility, probably due to their lower xylan content, but the stiffness was only reduced by the presence of lignin. Interestingly, the CNF films from water-extracted bleached pulps were less hydrophilic, and their water vapour permeability was reduced up to 25%. Therefore, hot water extraction of bleached birch kraft pulp could be used to produce CNF films with improved barrier properties for food packaging, while obtaining a high-purity xylan stream for other high-value applications.
Vehicle Classification Using AKAZE and Feature Matching Approach and Artificial Neural Network

This research proposes a method in order to classify vehicles in a highly congested roads , a robust technique for vehicle classification with low computational power must be used. So, a proposed solution is to embed an AKAZE feature matching extraction which is ran in an artificial neural network will be used. AKAZE was used because it is faster than SIFT. The features extracted from the AKAZE algorithm will be grouped according to the type of vehicle where it was used and be placed to an Artificial Neural Network (ANN) for the training of the network itself. The results yielded good for real-time Vehicle Classification.
Investigation of the Temperature Effect on the Electrical Parameters of a Photovoltaic Module at Ouargla City

Abstract To predict the I-V characteristics of the photovoltaic module, five parameters photovoltaic model Abstract: To predict the I-V characteristics of the photovoltaic module, five parameters photovoltaic model was utilized. The most influential parameters in the photovoltaic module are the solar irradiance level (E) and the temperature (T). The present research was conducted due to the high-temperature values in Ouargla city (can reach 60 °C in the hot season), which will affect remarkably the installed PV installations in this region. The experimental was done in several days cause the investigation need a constant irradiance values with different temperature. The temperature of a photovoltaic module varies according to other conditions, the temperature measurements made on the rear face of the PV module may not be indicative due to a temperature gradient in the material of the rear face of the module. Unfortunately, photovoltaic systems manufacturers do not take into consideration these environmental circumstances which negatively influence the module parameters and yielded deterioration in the system efficiency. The aim of this paper is to investigate the effect of the temperature term on the electrical performances such as the open circuit voltage (Voc), short circuit current (Isc), optimal power (Pm) and Fill Factor. The temperature distribution is non-uniform temperature on the surface of PV modules joined to that of the quality of temperature measurements affects the values of temperature coefficients found. To validate a model allows the researcher to get approximately the I-V characteristic similar to the experiment values. It use the conventional technique (Newton Raphson method) and it was compared by an artificial intelligent method which is the PSO technique, the five parameters estimated (Iph, Is, Rs, Rp, n). This proposed approach can be utilized to model any marketable PV module based on given datasheet parameters only. Statistical indicators were adopted to evaluate the performance of the proposed models; where the relative error of the PSO method comes more less the conventional method.
De-duplicating Encrypted Data using ABE & ECC for Secured Cloud Environment

Attribute-Based Encryption (ABE) was widely used in cloud environments, where owner outsource data to cloud service with an encrypted format and share his data with other users with some specific credentials or attributes. In an existing ABE, Data secure deduplication was not involved only attribute based encryption was involved. In our paper, we have used a deduplication technique with a secured data attribute based encryption using ECC(Elliptic Curve Cryptography) in multi cloud setting. Where a duplicate detection is responsible by private cloud and storage space is managed by public cloud. Compared to other systems we have designed data confidentiality with settings of access policy avoiding the share of decryption keys. And also we have created a system that create only one cipher text as per access policy for a plaintext for different owners data with a different access policy and without revealing the plain text to users.
Drowsiness Detection and Monitoring the Sleeping Pattern using Brainwaves Technology and IoT

In this paper, a drowsiness detection system based on a brain-computer interface (BCI) headset having 3 electrodes is proposed. The headset is mounted on the user’s head to compute the brain wave frequencies. The signal received from BCI headset is processed to remove the external noise. The computed frequencies is then compared by the threshold frequencies of the brain state and a particular decision like whether an individual is in an active state or in a drowsy state can be taken. If an individual is in a drowsy state, a particular alarm is generated on his/her Android phone to wake the person up. An individual user’s sleeping pattern while working is stored on the cloud so that it can be used for analyzing an individual’s brain state while working. This project is proposed with an aim of getting the work done within a particular time period.