Reviewerly/exHarmony huggingface dataset:
Comprehensive-Factor Authentication in Edge Devices in Smart Environments: A Case Study

The objective of this paper is to introduce a scheme of comprehensive-factor authentication in edge computing, focusing on a case study of time attendance in smart environments. This authentication scheme deploys all possible factors to maximize security while maintaining usability at a specific smart context. The factors used include three classic elements: something you know, something you have, and something you are, plus an additional location factor. The usability issue involves the ability to reduce time used and to minimize the human actions required throughout the authentication process. The results show that all factors should be authenticated at once in background, and a user can successfully complete the authentication process by performing one or two actions simultaneously. Since user role in a smart environment can be more complicated than roles in other smart offices, role classification at an early stage is highly recommended. The case study reveals that the same setting can require varying levels of security and usability for each user.
A Metric for Multi-Target Continuous-Time Trajectory Evaluation

Existing target trackers, as well as the corresponding evaluation metrics, are based on discrete-time point-state estimates. An emerging approach to target tracking is to estimate the continuous-time trajectories that are given by a state function of time and contain more information than discrete-time point estimates, for which a proper metric is still missing. In this study, a fundamental metric called the integral multi-target trajectory assignment (IMTA) distance that is suitable for evaluating the continuous-time curve trajectories is proposed. Based on optimal matching between the estimated and ground-truth trajectories, the localization distance consists of the integral for the time-consistent trajectory parts and the penalty for the trajectory time-inconsistent parts. Furthermore, the cardinality error is also defined to account for the false alarm and mis-detection in the level of a whole trajectory. Theoretical analysis and numerical examples are presented to demonstrate the performance of the proposed metric.
Estimation of Real-Time Wheeled Mobile Robot (Differential Drive) Motion &amp; Pose with Obstacle Avoidance

This study involves four various steps for the estimation of wheeled mobile robot pose and motion. Our approach involves implementation of specific poses on RVIZ (Visual Simulator). Later step includes the involvement of one real-time robot (TurtleBot3) is used in a differential drive manner. The controlled movement of this robot has been visualized in four different steps. The first phase of the experiment is feed forward control, secondly circular motion control whereas third is a closed loop advancement and final one is a specific pose motion of the robot. Platform used for this task is python with a TurtleBot mobile robot with its navigation software. Experiment first done on the simulation software. Later this experiment has been done in a real time environment. For final pose motion of a robot simulation result is compared with actual real time experiment of TurtleBot. Results shows maximum efficiency and accuracy of our real time TurtleBot.
Modeling and identification of power forecasting scheme for real PV system using Grey box neural network based NARMAX model

This paper presents photovoltaic power forecasting using the novel grey box neural network-based Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) model. First, the NARMAX based power forecasting model is developed using the meteorological parameters. The NARMAX model is then transformed into grey box neural network(GBNN) to identify the parameters of the forecasting model. The unique structure of NARMAX based GBNN has an advantage of taking less training time than the conventional neural network based schemes. The effectiveness of proposed scheme is demonstrated by experimental data obtained from 20 MW grid photovoltaic power generation system in Dongying, China. The performance of the proposed power forecasting scheme was compared with state of the art neural network scheme in current literature using different assessment metrics. The results demonstrate that the proposed scheme forecasts the power of the photovoltaic plant very effectively with prediction error within the tolerance range.
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers

Great progress has been achieved in the community of autonomous driving in the past few years. As a safety-critical problem, however, anomaly detection is a huge hurdle towards a large-scale deployment of autonomous vehicles in the real world. While many approaches, such as uncertainty estimation or segmentation-based image resynthesis, are extremely promising, there is more to be explored. Especially inspired by works on anomaly detection based on image resynthesis, we propose a novel approach for anomaly detection through style transfer. We leverage generative models to map an image from its original style domain of road traffic to an arbitrary one and back to generate pixelwise anomaly scores. However, our experiments have proven our hypothesis wrong, and we were unable to produce significant results. Nevertheless, we want to share our findings, so that others can learn from our experiments.
Frailty Onset Predictions Using Sleep Analysis

The importance of sleep analysis lies in the preventive detection of health problems. Several types of research and scientific studies have shown that memory problems and cognitive problems are the direct results of poor quality sleep. Cognitive frailty is not the only frailty influenced by the lack or poor quality of sleep. Frailty can be defined as a lack of physiological reserves that no longer allow us to respond to needs or too stressful situations (illness, hospitalization, fall, etc.). In this article, we will present a part of our research work that aims to have a collaborative data analysis approach for the detection of entry into a state of fragility that will lead to becoming a resident in a nursing home, by using two different machine learning methods; supervised and non-supervised classification. We will use as inputs several sleep data.
Blind Image Clustering Based on PRNU With Reduced Computational Complexity

The blind clustering of images without any prior information is always challenging for forensics experts. The problem gets severe in the case of a large dataset where the computational cost increases significantly. The proposed technique is one of the potential solutions for image clustering, especially for large-scale clustering. The technique clusters images based on camera fingerprints using two-stage clustering and additional processes. The algorithm is tested on the sub-datasets of the well-known Dresden dataset. The performance of the clustering framework is either better or comparable with the state-of-the-art clustering algorithms. The algorithm generates good-quality clusters with a significantly lower computational complexity.
White blood cells segmentation and classification using U-Net CNN and hand-crafted features

Haematological disorders can be diagnosed based on the white blood cells (WBCs) count. However, determining WBCs count manually from a microscopic blood smear image is a tedious task and can be done through automatic detection and classification of WBCs using image processing techniques. In this paper, an efficient WBCs detection and classification method is presented. Towards ameliorating this, a modified U - N et CNN segmentation architecture is utilized to detect and segment out the WBCs from red blood cells and platelets. The segmented WBCs are classified into five types based on their texture and shape features using an SVM classifier. Results are presented for the LISC dataset using a 5-fold cross-validation scheme. The performance measures indicate that the proposed method can perform better than similar techniques.
Geochemistry of the Transitional beds between Disang and Barail Successions of the Imphal Valley, Indo-Myanmar Ranges

Major and trace element concentrations were studied from the tansitional beds between Disang and Barail Successions of the Imphal valley, Indo-Myanmar Ranges. Various major and trace element ratios and discrinant diagrams were used to descipher paleoclimate, paleo-redox condition and sedimentary depositional environment of the Disang-Barail Transitional beds. SiO2 vs Al2O3+K2O+ Na2O diagram and Rb/Sr ratios indicates that paleoclimate during the deposition of the sediments changed from arid to semi-arid and humid climate. Ni/Co, V/Cr, V/(Ni+V), V/Sc ratios suggest that these sediments were deposited in oxic, suboxic to anoxic conditions. V/(Ni+V) vs V/Cr diagram suggests paleo-redox environment dominated by sub-reduction to oxidization during Disang –Barail Transitional deposition. The sediments were deposited in transitional to marine depositional environment.
Discriminant Feature Trace Transform for Predictive Object Rotation

This paper presents how to modify the proposed Discriminant Feature Trace Transform (DFTF) algorithm for object rotation.This proposed DFTF algorithm based on the Trace Transform (TF) domain can collect data for the formation of distinctive features and is suitable for predicting the rotational direction of an object with the supervised machine learning.TF domain is used after removing the background image that transformation properties can be generated a feature referred to the rotation features of objects inside.After that, the proposed DFTF algorithm transforms the two-dimensional data of each image from the TF domain into the one-dimensional data that can indicate the direction of object rotation within the image.The results of the DFTF algorithm, which is a 1D data vector from each image, are then generated into the labeled datasets for machine learning algorithms including with the Naïve Bayes (NB) for predicting the rotation direction and Random Forest (RF) to reinforce the predicted values from the NB in the form of quadrant in which the interested object is rotated.The simulation experiments are conducted with two types of databases.The first experiment combines with the three different datasets which the results are very effective and can provide the average accuracy rate for all databases up to 99.8%.The second experiment is derived from the visualization of the water bottle production line.It measures the accuracy of the bottled water transfer to the second station.The proposed approach is an accuracy rate up to 92.2%.
Design a Lightweight Authentication Encryption Based on Stream Cipher and Chaotic Maps with Sponge Structure for Internet of Things Applications

The design of a cipher system is essential and required with the development of the technology era.The internet of things concept is modern technology requiring lightweight, secure algorithms to investigate the requirement of the light device.Furthermore, IoT devices have limited resources (IoT devices should minimize battery life, limited memory size, and response time).A stream cipher system is faster than a block cipher system and has an easy feature with hardware and software implementation.This paper designs a lightweight authentication encryption algorithm based on stream cipher and chaotic maps with sponge structure for IoT applications.Currently, the suggested approach uses text data for encryption and authorization.Additionally, the suggested algorithm will be able to use it with image data in the future.Analysis results of the proposed algorithm based on NIST randomness tests, execution time, memory space, and functional features.The results are compared with other previous works which used sponge structure (e.g.Ascon, Elephant, ISAP, Photon-Beetle, and Xoodyak, etc.).Randomness tests show that the proposed system is random and secure with investigate all the functional features except the parallelization feature which will work it in the future.And also it has good fast speed and less memory space.
High Level Security of Image Transmission through STBC-COFDM System

In this paper, the security of image transmission through space time block coding (STBC)-coded orthogonal frequency division multiplexing (COFDM) is enhanced using two stage algorithms.In the first-stage, the samples scrambling algorithm using one dimensional (1D) chaotic map is proposed.In the second stage, the bits scrambling algorithm using chaotic map is proposed.The sample scrambling algorithm is designed depending on three types of 1D chaotic maps that are logistic-sine-cosine (LSC), sine-tent-cosine (STC), and tent-logistic-cosine (TLC) maps.In the bit scrambling algorithm, the stream bits are XORed with a pseudo-random bit generator (PRBG) produced by these chaotic maps using threshold method.Simulation results demonstrate that the image is retrieved with high quality for STBC-COFDM system and low signal to noise ratio (SNR).The image is unrecovered when the correct key is unavailable.For one level of security, the system using TLC chaotic map has the best performance compared with other chaotic maps.The bit error rate (BER) performance of the suggested system achieved 10 -4 at SNR=1, and 2 dB for (TLC, LSC) and STC, respectively.For two stages of security, the proposed system using STC chaotic map improves the BER performance with a gain of about 2 dB at BER=10 -4 compared with one stage of security.
Design of a New Agile Methodology : Eclectic – An Approach to Overcome Existing Challenges

Agile development has proven its benefits over traditional software life cycle development models for more than a decade.Through offering agility as responsiveness to change, its practitioners maintain flexibility to accommodate change even at later stage of development process and simultaneously depict progress through iterative development.This scope creep and rapidly emerging market needs is placing more stringent constraints on agile projects in terms of meeting timelines, containing cost and adhering to quality.Though methodologies offered by agile have spawned several practices that are claimed to be efficacious, but still empirical validation and evolvement of them is required before adopting them as is for any project.This paper proposes and provides the design of a new methodology named as "Eclectic Agile Methodology".The key concept of eclectic methodology is to provide numerous advantages to mid and large scale organizations through enabling them to utilize novel and efficient ways of processes, tools and validations to assess the vast benefits before choosing any methodology upfront.The inspiration for designing this methodology is to address and overcome various untouched challenges in the current existing agile methodologies like but not limited to tightly coupling with upstream and downstream applications, sprint encountering frequent unplanned severity 1 issues which need immediate attention, shared hosted environments along with schema, dependencies outside the project, cross track impacts and distributed team.
Blockchain for Economy of Scale in Wind Industry: A Demo Case

This paper summarizes the key findings of a qualitative study based on feedbacks of experts from the wind industry followed by a demo case of blockchain technology. This study includes investigation on mapping of supply chain and commodity products related end-to-end life cycle associated data and operational events. Furthermore, identification and blueprinting of the requirements have been pursued for enabling traceability at various stages of their life cycle utilizing blockchain. In this study context, blockchain offers digital traceability of operational events and associated data sharing with complete immutability, ownership, confidentiality, trust, and transparency across distributed supply chain which comprises of multiple stakeholders. In addition, digital technology intervention like IoT has been leveraged to support quality of operations in quantitative manner through real time data driven digitized operations. Thereby, providing economy of scale over operations execution on commodity products in wind industry. This study has focused only on bolts and fasteners associated commodity products and related supply chain. However, this provides a steppingstone foundation for future which can be scaled and mapped to any other commodity product and related supply chain in wind industry. Finally, this study also presents a demonstrator developed in a controlled lab environment to demystify the use of blockchain technology in related manufacturing and supply chain setups of the wind industry.
An Overview of Swarm Intelligence-Based Algorithms

Since the inception and introduction of swarm intelligence (SI)-based algorithms to the field of optimization, these methods have emerged as an effective tool to deal with increasingly complex problems. Their prominence and success have been attributed to their self-learning capabilities, flexibility, and adaptability to external and internal variations. The chapter presents a critical analysis of swarm optimization algorithms, highlighting their key features and discussing their advantages and limitations. It also offers a comprehensive review of particle swarm optimization (PSO) and artificial bee colony (ABC), two of the most commonly used SI-based computational techniques, detailing the stages involved in their implementation. A thorough investigation of the observations and results obtained during their execution on benchmark problems has also been conducted by carrying out a fixed iteration test, where the performances of the algorithms have been compared on the basis of certain predetermined parameters, including statistical measures like mean and standard deviation.
IMPROVING STUDENTS’ SPEAKING SKILL THROUGH RETELLING TECHNIQUE BY USING YOUTUBE VIDEO

The aim of this study was to find out whether or not there was any significant difference speaking skills between students’ who were taught through retelling technique by using YouTube videos and those who were not at tenth graders of SMK Muhammadiyah Pagar Alam. The population of this research was 370 students which divided in to eleventh classes at tenth graders of SMK Muhammadiyah Pagar Alam. The sample was taken from 35 students of OTKP 1 and 35 students from OTKP 2 by using purposive sampling. This research used Quasi-experimental design as the research design. The data was taken from oral test which used 2 raters, and the data was analyzed by using SPSS. The result of this study was revealed that there was significant difference between students who were taught through retelling technique by using YouTube videos and those who were not at tenth graders of SMK Muhammadiyah Pagar Alam. It proves that the use of YouTube videos (narrative story) and retelling technique as strategy to support the main action was effective and could improve students speaking skills.
Diagnosis of contamination discharge state of porcelain insulators based on GA-CNN

Porcelain insulators play an important role in power transmission lines. It is of great significance to improve the accuracy of diagnosis of porcelain insulators’ discharge state and ensure the reliability of power supply. Therefore, this paper presents a diagnosis method of polluted discharge state of porcelain insulator based on GA-optimised CNN network structure. Firstly, the artificial pollution discharge test of porcelain insulator is carried out. According to the characteristics of leakage current, the discharge development process is divided into five stages: normal state, initial discharge, through discharge, flashover and flashover completion. GA algorithm is used to optimise the parameters of CNN model, and several single models are established simultaneously to compare the progress with the proposed model. The results show that GA has the advantages of global optimisation, less adjustment parameters, etc. It can automatically select the best structure of CNN network, avoid the problem of poor performance of artificial selection of CNN network structure, reduce the time required for parameter selection, and improve the accuracy of diagnosis of polluted discharge state of porcelain insulators, with the diagnosis accuracy as high as 99.2%. The results show that the discharge state of porcelain insulator surface can be judged by leakage current.
A Hybrid Multi-user Based Data Replication and Access Control Mechanism for Cloud Data Security

Data replication-based cloud data access control mechanism plays a major role in real-time cloud computing environment due to high computational cost and memory. Hybrid data replication models play a vital role in the cloud-based applications for data recovery and security. Machine learning tools and techniques play an essential role in the medical field and cloud computing applications. Most of the traditional machine learning models use static data partitioning and replication methods in order to recover patterns from multiple virtual machines in cloud computing environment. In this work, a hybrid data replication and multi-user data access mechanism is developed to provide strong data recovery and security in cloud computing environment. In this work, a machine learning based patterns are used for data partitioning and security in the cloud server for cloud data security. Experimental results show that the hybrid data replication model has better data replication time and storage space than the conventional data replication models in cloud computing environment.
Is there a Need to Change the Basic Principles of Histology? Educational, Functional and Embryological Perspective

Although it is not an easy task to classify cells into different types, or in turn cell types into tissue types, a clear, understandable, didactically and clinically relevant tissue classification is indispensable for undergraduate medical education, expert discussions in biomedical research as well as for clinical practice. From the earliest discovery of the light microscope on, tissue classification has been a dynamic process. Historically, it was not a rare occurrence that different textbooks offered different tissue classifications. Nowadays, classifications have almost become uniform - the most common is the histological classification into four basic tissue types (epithelial, connective, muscle, nervous), which is recognized by the majority of modern histology and pathology textbooks. The reason is that, with some exceptions, this classification seems to be the most relevant not only for educational purposes but also from an embryological perspective and clinical-histopathological practice. Recently, attempts have been made to abandon this established classification and replace it with a new one. Any new classification, which would improve the presently used is welcomed. However, if the proposed innovation does not satisfy the needs of modern education and clinical practice, it should be handled with great caution or reconsidered.
Face Kinship Verification Based VGG16 and new Gabor Wavelet Features

Kinship verification from face images is a motivating field of study in computer vision, involving many researches works because-of its importance in many potential applications, such as forensics and finding missing children. This application of automatically determining whether persons share a blood re-lationship by examining their facial characteristics, i.e., features. In this work, we develop an efficient method named Hist-Gabor based on the histogram features extracted from basic Gabor wavelet in order to represent face images with high discriminate power. Indeed, we examine the use of deep features collected from a convolutional neural network model called VGG-face and shallow features by our new Gabor wavelet invoking a powerful dimensionality reduction method named Tensor Cross-view Quadratic Analysis (TXQDA). Empirically, our experiments demonstrate that the proposed approach outperforms the pre-vious state-of-the-art in the challenging datasets Cornell and TSKinFace.