Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting Text Regions from Scene Images using Weighted Median Filter and MSER

The natural scene images contain valuable information about themselves in the form of textual matter present in them. The process of extracting text regions can be used to understand the context of the image, that can be of great help in many applications helpful for humanity. The extraction of text regions from natural scene images, which is a daunting task due to variation in text elements in the form of size, orientation, colors, low contrast images and complicated background. In this paper, we propose a method based on Maximal Stable Extremal Region (MSER) and weighted median filter along with three text specific traits to identify and extract text regions by creating bounding box around them in natural scene images. The image is passed through a weighted median filter to preserve and smoothen the edges followed by candidate region extraction by MSER. Heuristics rules filter the non-text components. Finally, the classification process is carried out with the help of classifiers (using adaboost.m1 and k-nn) to classify candidate text regions and non-text regions based on three text specific traits, followed by the grouping of text components in text line using clustering. The method aims to extract text regions robustly from low contrast images. The performance of the method is checked on ICDAR 2011 testing dataset to prove its efficiency concerning precision, recall, and f-measure.
Fake News Filtering: Semantic Approaches

In 2016, the attention to the fake news phenomenon drastically increased. Mobile devices such as cellular phones and sources of information such as social networks are instruments that enable individuals to receive news, publish posts, communicate with peers, watch videos, listen to music, etc. In today’s highly mobile society, this is a current trend. The uncontrolled freedom and simplicity in publications on the Internet result in overwhelming users receiving news that are fake and hoaxes. Detecting and filtering such information is a challenging problem. This paper discusses different approaches to combat fake news. They are used to a) determine text features utilizing linguistic natural language processing methods (it is necessary to create a profile of the text document), b) detect spam bots in social networks to isolate those using machine-learning methods (it is crucial to reduce the number of analyzed documents), and c) confirm the facts in online documents by applying techniques used in search engines (it is very much important to select trusted documents). A system combining these mechanisms may demonstrate a high level of accuracy in filtering fake news.
Security Issues and Challenges in Perception Layer of Smart Healthcare

This paper mainly focuses on the security issues and challenges that we can face during operating of any e-healthcare devices. In this paper, functions of various applications in the domain of internet of things when combined with cloud computing platform are shown. The four layer architecture of perception, transportation, support, and application has been implemented in the IoT domain for healthcare sector. It focuses on how it can make everyone’s life easy and secure and will help healthcare center to keep record of their patients. Security issues and challenges at the perception layer of smart healthcare is the initial step to recognize the problem of the health sector. This paper will also deliberate upon the security issues in the perception layer of the smart healthcare. This paper will help to make changes that people face in health care sector
Cloning the $\lambda$ Switch: Digital and Markov Representations

The lysis-lysogeny switch in E. coli due to infection from lambda phage has been extensively studied and explained by scientists of molecular biology. The bacterium either survives with the viral strand of deoxyribonucleic acid (DNA) or dies producing hundreds of viruses for propagation of infection. Many proteins transcribed after infection by $\lambda $ phage take part in determining the fate of the bacterium, but two proteins that play a key role in this regard are the cI and cro dimers, which are transcribed off the viral DNA. This paper presents a novel modeling mechanism for the lysis-lysogeny switch, by transferring the interactions of the main proteins, the lambda right operator and promoter regions and the ribonucleic acid (RNA) polymerase, to a finite state machine (FSM), to determine cell fate. The FSM, and thus derived is implemented in field-programmable gate array (FPGA), and simulations have been run in random conditions. A Markov model has been created for the same mechanism. Steady state analysis has been conducted for the transition matrix of the Markov model, and the results have been generated to show the steady state probability of lysis with various model values. In this paper, it is hoped to lay down guidelines to convert biological processes into computing machines.
Analysis of MPPT Methods: P & O, INC and Fuzzy Logic (CLF) for a PV System

The MPPT (Maximum Power Point Tracking) control unit is an essential part for the photovoltaic system optimal operation. In addition to the protection function, this command ensures the continuation of the maximum power point (MPPT) and allows the PV generator to deliver its maximum power regardless of the variation in climatic conditions (sunshine and) temperature). This work intends to provide a comparative study of different techniques to track the maximum power point (MPP) from the photovoltaic array. Disturbs and observes P&O, incremental conductance INC and fuzzy logic based FLC strategies are analyzed and their simulation under unsteady state conditions has been performed using MATLAB SIMULINK for the purpose of showing the advantages of each of them
A Modular Approach For Facial Expression Recognition using Euler Principal Component Analysis (e-PCA)

Though spectacular recognition rates are achieved with numerous techniques underneath the controlled face image capturing surroundings, creating recognition a lot of reliable underneath uncontrolled surroundings remains an excellent challenge. Security and surveillance images, pictures, captured in open uncontrolled environments, ar seemingly subjected to extreme lighting conditions like underexposed, and overexposed areas that scale back the number of helpful details obtainable within the collected face images. Euler Principal Component Analysis uses a difference live to extend the variations between objects although the face images area unit below the influence of visual variation. In this paper, we investigate e-PCA that can be used as the facial expression recognition in modular way. 1-NN classifier is used for our recognition purpose. Experiments area unit done on JAFFE, VIDEO and CK+ databases and it shown that there area unit enhancements within the expression recognition rate using Euler Principal Component Analysis (e-PCA) under certain circumstances.
Experimentation and Analysis of Dynamic Checkpoint on Apache Hadoop with Failure Scenarios

The growth of reliability problems on high performance systems has motivated searches for fault tolerance mechanisms. The Apache Hadoop framework, created to store and process large amounts of data, implements Checkpoint and Recovery to help on recovery process of its distributed file system (Hadoop Distributed File System - HDFS) in presence of failure. However, once configuration attributes can not be changed at runtime, bad choices may cause performance and reliability problems. This work uses a dynamic configuration mechanism for checkpoint on Hadoop and evaluates its performance on scenarios with induced fault on the master element of HDFS.
State Feedback controller in a closed poultry house system

The poultry house model is governed through the nonlinear behavior of psychrometric mechanisms. In order to discern the dynamics of broiler house and to construct a convenient controller, we examine in this work, the problem of stabilizing temperature and humidity in poultry house model during the winter climate. The specific aim of this research is to analyze the application of the state feedback-Integrator controller to a multivariable system (MIMO). For this purpose, the designed control strategy is executed to adjust the required conditions of an optimal growth of the broilers. The proposed controller was implemented to maintain the relative humidity and temperature inside a poultry house under the cold conditions related to the Moroccan climate. The simulation results shows a good performance in terms of the state error and settling time.
Fast Algebraic Immunity of <inline-formula> <tex-math notation="LaTeX">$2^m+2$ </tex-math></inline-formula> & <inline-formula> <tex-math notation="LaTeX">$2^m+3$ </tex-math></inline-formula> Variables Majority Function

Boolean functions should possess high fast algebraic immunity (FAI) when it is used in stream ciphers in order to stand up to fast algebraic attacks. However, in previous research, the FAI of Boolean functions was usually calculated by computer. But, as everyone knows, it is very difficult to calculate the FAI of a given Boolean function with high algebraic degree when the variable number is greater than 18. In 2016, Tang <italic>et al.</italic> gave that the exact value of FAI of the majority function on <inline-formula> <tex-math notation="LaTeX">$2^{m}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2^{m}+1$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$m \ge 2$ </tex-math></inline-formula>) variables is <inline-formula> <tex-math notation="LaTeX">$2^{m-1}+2$ </tex-math></inline-formula>. This paper proves that the FAI of <inline-formula> <tex-math notation="LaTeX">$2^{m}+2$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2^{m}+3$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$m \ge 2$ </tex-math></inline-formula>) variables majority function equals <inline-formula> <tex-math notation="LaTeX">$2^{m-1}+4$ </tex-math></inline-formula>.
ECG Signal Spectral Estimation and Noise Filtering

A time changeable signal which is applicable to analyze various cardiovascular diseases or calculate patient's situation is electrocardiogram (ECG). To diagnose a patient feature points, P, Q, R, S, and T are used in ECG. Patient's skin situation, from improper link between electrodes, muscle contractions; baseline itinerant, and many other traits can corrupt the signal. This paper presents the power spectral density (PSD) estimates of ECG signal s using four different PSD estimation techniques. Hence, Multirate filter is designed for filtering out the noise component from the noise corrupted ECG signal using MATLAB.
On Linear Quadratic Optimal Control for Time-Varying Multimodal Linear Systems With Time-Triggered Jumps

This letter details the solution to the linear quadratic (LQ) optimal control problem over a finite interval for time-varying multimodal linear systems with time-triggered jumps. By multimodal, we mean the possibility for the system state to change dimension after every jump. To this end, we introduce the multimodal jumping differential Riccati equation (MJDRE) and we show the equivalence between the solvability of the optimal control problem and the existence of a finite solution of the MJDRE. The MJDRE can be used to compute optimal tracking gains for hybrid system with state-triggered jumps, whose state dimension changes after each jump (multimodal hybrid system). This is demonstrated, in simulation, on a 2DOF dual-mass spring-damper system.
Quality Management of Infrastructure Project Life-Cycle

For improving the performance of infrastructure project execution, the quality of construction products is of concern to the government and related stakeholders, especially for Indonesian construction industry is facing the tight competition of national and global construction markets. Therefore, quality assurance at planning, constructing, and controlling of infrastructure project is necessary, to integrate all parties’ tasks and responsibilities throughout the project stages. By using two rounds Delphi study, this paper discusses the effectiveness of the implementation of quality management indicators by certified QMS-ISO 9001 Indonesian planning consultants, contractors, and supervising consultants in the three stages of infrastructure project life-cycle. It is found that the quality system indicators are effectively implemented during the planning, constructing, and controlling of infrastructure project execution. These profiles are beneficial in synergizing the construction services providers’ roles in the project life-cycle, as well as strengthening their communication and coordination, as an attempt to produce qualified and sustainable infrastructure projects.
An Enhanced Recommendation System using proposed Efficient K Means User-based Clustering Algorithm

With the blossoming of electronic commerce, recommendation system helps people to find captivating things easily. Plenty of recommendation systems use collaborative filtering technique which has been evidenced to be one of the most efficacious technique in recommendation systems. But the existing technique suffers from the efficiency issue, as it has to determine the similarity index between each pair of users to find out their neighborhood and scalability issue, where many recommendations has to be executed per second for millions of users and products. Also, the another major problem in existing user based recommendation system is to calculate distances which includes large amount of mathematical operations for finding the best neighbors. This paper presents an implementation of proposed efficient K-means clustering algorithm which shows experimentally the comparison with the existing collaborative filtering technique and existing k means technique in recommendation system in terms of computational time, iterations and root mean square error. The Experimental results shows that proposed recommendation system using efficient k means clustering is more effective, scalable and accurate.
Compression of Multilead Electrocardiogram Using Principal Component Analysis and Machine Learning Approach

In this work, a multi-lead Electrocardiogram (ECG) data compression using principal component analysis (PCA) combined with a machine learning technique is proposed to achieve a high compression ratio (CR) with low reconstruction error (within 2% percentage root mean squared difference, or, PRD). The beat detection procedure was inspired by the Pan-Tompkins algorithm with some necessary modifications. A lead-wise PCA decomposition was performed for dimensionality reduction with a single beat from each lead at a time using a fixed energy reconstruction criteria. The optimal quantization levels of the principal components were allocated using multi-layer perceptron neural network (MLP-NN) using lead clinical features as the input. This MLP-NN was tuned offline by a particle swarm optimization (PSO) generated data for quantization level of coefficients of PC as the reference. The proposed technique was evaluated using 8 types of cardiac abnormalities record from multi-lead ECG data from the PTB Diagnostic ECG database, with an average CR, PRD and PRDN of 16.2, 1.47% and 1.84% respectively. The reconstructed records were clinically acceptable. The proposed technique provides superior performance than few recent published works on multilead ECG compression.
An Artificial Intelligence Driven Multi-Feature Extraction Scheme for Big Data Detection

The Internet improves the speed of information dissemination, and the scale of unstructured text data is expanding and increasingly being used for mass communication. Although these large amounts of data meet the infinite demand, it is difficult to find public focus in a timely manner. Therefore, information extraction from big data has become an important research issue, and there are many published studies on big data processing at home and abroad. In this paper, we propose a multi-feature keyword extraction method, and based on this, an artificial intelligence driven big data MFE scheme is designed, then an application example of the general scheme is expanded and detailed. Taking news as the carrier, this scheme is applied to the algorithm design of hot event detection. As a result, a multi-feature fusion clustering algorithm is proposed based on user attention with two main stages. In the first stage, a multi-feature fusion model is developed to evaluate keywords, and this model combines the term frequency and part of speech features. We use it to extract keywords for representing news and events. In the second stage, we perform clustering and detect hot events in accordance with the procedure, and during the composition of news clusters, we analyze several variadic parameters in order to explore the optimal effectiveness. Then, experiments on the news corpus are conducted, and the results show that the approach presented herein performs well.
SMCis: Scientific Applications Monitoring Tool for HPC Environments

The performance evaluation in HPC, understanding the computational requirements of scientific applications, its relation with power consumption is a fundamental task to overcome the current barriers and to achieve the computational exascale. However, this imposes some challenging tasks, such as to monitor a wide range of parameters in heterogeneous environments, to enable fine grained profiling and power consumed across different components, to be language independent and to avoid code instrumentation. Considering these challenges, this work proposes the SMCis, an application monitoring tool developed with the goal of to collect all these aspects in an effective and accurate way, as well as to correlate these data graphically, with the environment of analysis and visualization.
Intersection Navigation Under Dynamic Constraints Using Deep Reinforcement Learning

In this study, we present a unified motion planner with low- level controller for continuous control of a differential drive mobile robot. Deep reinforcement agent takes 10 dimensional state vector as input and calculates each wheel’s torque value as a 2 dimensional output vector. These torque values are fed into the dynamic model of the robot, and lastly steering commands are gathered. In previous studies, navigation problem solutions that uses deep - RL methods, have not been considered with agent’s own dynamic constraints, but it has been done by only considering kinematic models. This is not reliable enough for real-world scenarios. In this paper, deep-RL based motion planning is performed by considering both kinematic and dynamic constraints. According to the simulations in a dynamic environment, the agent succesfully navigates through the intersection with 99.6% success rate.
Noise in Functional Magnetic Resonance Imaging

Functional magnetic resonance imaging (fMRI) is a method for analyses and to capture dynamic patterns of process occurring in the brain of a human being. The fMRI measurement is based on the tissue parameters and physics behind MRI. fMRI produce highly dimensional, sparse and noise in data which are crucial to envision, analyse and monitor. The method of smoothing by spatial domain is frequently used in fMRI studies because fMRI data represent the correlations in spatial domain due to similarities of brain regions and soften of the vascular system. In this paper, time - series noise in fMRI is pre-processed by filters and their SNR are compared. Different parameters of noise have been studied.
Long-Time Self-Body Image Acquisition and Its Application to the Control of Musculoskeletal Structures

The tendon-driven musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex muscle and bone structures is difficult and conventional model-based controls cannot realize intended movements. Therefore, a learning control mechanism that acquires nonlinear relationships between joint angles, muscle tensions, and muscle lengths from the actual robot is necessary. In this study, we propose a system which runs the learning control mechanism for a long time to keep the self-body image of the musculoskeletal humanoid correct at all times. Also, we show that the musculoskeletal humanoid can conduct position control, torque control, and variable stiffness control using this self-body image. We conduct a long-time self-body image acquisition experiment lasting 3 h, evaluate variable stiffness control using the self-body image, etc., and discuss the superiority and practicality of the self-body image acquisition of musculoskeletal structures, comprehensively.
Classification of Polyps in Capsule Endoscopic Images using CNN

Capsule endoscopy (CE) is an reliable diagnostic modality for detecting any abnormality in the gastrointestinal (GI) tract. Polyp is one such abnormalities which need to be detected early before it becomes malignant. A high accuracy in detecting and predicting polyp type during colonoscopy would help an endoscopists a great deal. In this paper, an automated framework for efficient classification of such polyps as malignant or benign in endoscopic images has been devised. However, The classification has put many challenges because of difference in geometry, lighting condition and other factors. A deep convolutional neural network (CNN) with Fully connected layer has been employed to train a network and subsequently used for classification of polyp types. Since, there is no availability of labelled data, we manually labelled each frame as benign or malignant using kudo’s classification. Data augmentation was done to increase the number of images for training. The proposed network architecture and settings of hyper parameters shows a competitive results. The performance of the proposed method in classifying the polyps in-comparison to handcrafted feature based method is superior.
Adaptively Dense Feature Pyramid Network for Object Detection

We propose a novel one-stage object detection network, called adaptively dense feature pyramid network (ADFPNet), to detect objects cross various scales. The proposed network is developed on single shot multibox detector (SSD) framework with a new proposed ADFP module, which is consisted of two components: a dense multi scales and receptive fields block (DMSRB) and an adaptively feature calibration block (AFCB). Specifically, DMSRB block extracts rich semantic information in a dense way through atrous convolutions with different atrous rates to extract dense features in multi scales and receptive fields; the AFCB block calibrate the dense features to retain features contributing more and depress features contributing less. The extensive experiments have been conducted on VOC 2007, VOC 2012, and MS COCO dataset to evaluate our method. In particular, we achieve the new state of the art accuracy with the mAP of 82.5 on VOC 2007 test set and the mAP of 36.4 on COCO test-dev set using a simple VGG-16 backbone. When testing with a lower resolution ( $300\times 300$ ), we achieve an mAP of 81.1 on VOC 2007 test set with an FPS of 62.5 on an NVIDIA 1080ti GPU, which meets the requirement for real-time detection.