Miracle-dz/UniFAR-FTUs huggingface dataset:
Consumer Privacy Concerns over Free Cloud Services

We, as consumers, use and appreciate a lot of the mobile apps that are free and help us in our daily lives. These apps range from online stores, navigation apps, social media, mobile banking, games and a lot more. We don't even wonder what the terms of service and privacy policy of these services say when we sign up for them. We don't even know what and when can these apps access. Some apps and services misuse this trust and collect more data than they should so that they can sell that data later. This generation of apps and services run mostly on our smartphones but as more and more devices get “Smart” and connect to the internet, these concerns are going to increase if not checked right now. This paper provides a framework which can help consumers and organisations avoid these concerns and function smoothly.
Novel Machine Learning Technique for Intrusion Detection in Recent Network-based Attacks

Intrusion Detection is a vastly growing area. Traditionally supervised learning techniques were used for detecting intrusions in the network traffic data. But nowadays not only the rate of traffic has increased tremendously, but the nature of network attacks is also changing. Detecting these new types of attacks requires improvements in detection techniques. Machine learning algorithms are well researched by researchers for detecting anomalies in the network traffic. New datasets have been added in the public repositories. In this paper, we have used novel feature reduction based machine learning algorithms for detecting anomalous patterns in the recently provided dataset. High accuracy of 86.15 percent has been achieved.
Play and Speech Therapy in Schools: Toward a Model of Interprofessional Collaborative Practice

Research over the past eight decades has demonstrated strong correlations between the development of play and the development of language in young children. Play and speech therapists share a common child-centered, play-based approach to therapeutic interventions. The growing trend of service delivery within the school setting, along with professional ethical requirements, has led to the necessity of play therapists, speech therapists, teachers, and parents to work collaboratively in promoting holistic, comprehensive intervention plans that serve children’s best interests. Based on the healthcare field’s model of interprofessional collaborative practice, the purpose of this chapter is to define areas of common practice among speech and play therapists and to propose a model of interprofessional collaborative practice within a school-based context.
Genome-Wide Scan for Copy Number Variation Association with Biomarker Quantitative Trait Loci in Aging

Biomarkers are emerging as important tools in the detection and monitoring of various diseases. A major limitation and challenge to effectively utilize biomarker signals is the limited understanding of factors contributing to their variance. As genetic variation is a major contributor to phenotypic variation, exploring genetic contributions is of great importance. Copy number variants (CNVs) offer an alternative genomic framework to understand contributions to phenotypic variance. A copy-number variation genome-wide association study was performed using 116 serum inflammatory biomarkers as quantitative trait in elderly normal controls to test the hypothesis that CNVs contribute to the phenotypic heterogeneity of serum biomarkers. Three chromosomal regions were associated with four biomarkers in trans. Transforming growth factor alpha (TG-alpha) serum levels were associated with CNV dosage at chr11:5,788 kb, soluble levels of receptor for advanced glycation endproducts (sRAGE) was associated with CNV dosage at chr8:40,183 kb and both thrombospondin-1 and tissue inhibitor of metalloproteinase 1 (TIMP-1) were associated with CNV dosage at chr11:18,961 kb. The CNV at chr11:5,788 kb harbors 2 olfactory genes and the introns of Tripartite motif-containing (TRIM) gene cluster TRIM5&22 while the CNV at chr11:18,961 includes the Mas-related G-protein coupled receptor member X1. These trans associations may identify novel relationships in the relevant pathways and suggest that genetic variation can contribute to biomarker levels. The detected trans-association between MRGPRX1 and thrombospondin-1/TIMP-1 could implicate a novel pathway between pain/itching and inflammation. Cataloguing all genetic variants with an effect on biomarkers will serve as a tool to interpret epidemiological studies and establish causal relationships through Mendelian randomization.
Wavelet transform based approach for EEG feature selection of motor imagery data for braincomputer interfaces

The Electroencephalogram (EEG) signals recorded during motor imagery (MI) tasks are often used as input in the brain-computer interface (BCI) applications. The adaptation of these EEG signals to control signals depend on the selection of an accurate feature that can provide good classification. The study compared various features of alpha frequency band adapting wavelet-based time-frequency analysis approach. The mean, variance, wavelet energy, Shannon entropy, log energy entropy, kurtosis, and skewness feature were extracted from the EEG signals recorded from symmetrical electrode locations of the motor cortex and their performance accuracies were evaluated using SVM and KNN classification algorithms. In results, the Shannon entropy showed the highest classification accuracy of 86.4 % with SVM classifier. Findings of this study establish Shannon entropy as a potential feature to use in EEG-based BCI analysis along with SVM classification algorithm.
Performance Analysis for Various Source Oriented Cascaded Multilevel Inverter

The performance analysis for a cascaded multilevel inverter with various combinations of source magnitudes are presented in this paper. A uni-polar voltage levels based basic module consisting of two source and four switches is developed. The H-bridge is used across the module to generate the bipolar voltage levels at the load. The module can be cascaded for higher power applications with various configurations of source magnitudes. The two modules of cascaded structure are considered here for the analysis. The module based three different source configurations of 1:1, 1:2 and 1:3 ratios are considered here to generate 9, 13 and 17 output voltage levels, respectively. The cascaded module is generalized with various mathematical equations. The gate pulses required to operate the module switches are generated with the help of sinusoidal pulse width modulation (SPWM) technique. The simulation of various analyses is executed in MATLAB/SIMULINK and verified with hardware results. Due to the cascading nature of the module and various source magnitude oriented structure, it is suitable for solar PV applications.
Performance Analysis of Multicasting Over Rician-K Fading Channels: A Secrecy Tradeoff

A secure multicasting framework is considered in this paper to investigate the secrecy performance of a single-input multiple-output (SIMO) system in which a single antenna source transmits private message to multiple receivers in the presence of multiple eavesdroppers. All the receivers and eavesdroppers are equipped with multiple antennas. The perfect secrecy is obtained when evasedroppers can not be able to decode any transmitted information. Considering independent and identically distributed (i.i.d) Rician-K fading channels, the mathematical expressions for the probability of non-zero secrecy multicast capacity, the ergodic secrecy multicast capacity and the secure outage probability for multicasting are derived in closed-forms. Numerical results are provided to show the impact of the diversity provided by the multiple antennas at the receivers. In addition, the impact of Rician factor, K on the secrecy capacity and outage behaviour of the proposed multicasting scenario is also demonstrated. The performance analysis includes the Rayleigh fading scenario as a special case of Rician-K fading channel and focuses the performance comparison between Rician-K and Rayleigh fading channels.
Multi-Temporal Landsat Data Automatic Cloud Removal Using Poisson Blending

Cloud and cloud shadow are common issues in optical satellite imagery, which greatly reduce the usage of data archive. As for the Landsat data, great advances have been made on detecting cloud and cloud shadow. However, few studies were performed on Landsat cloud removal for large areas. To facilitate land cover dynamics studies with high temporal resolution, we present an automatic cloud removal algorithm in this paper. Specifically, For Landsat Collection 1 Level-1 surface reflectance products, the algorithm first builds a cloud mask from the Quality Assessment (QA) band, and then reconstructs cloud-contaminated portions based on multi-temporal Landsat images with temporal similarity. To further eliminate radiation differences between cloud-free and reconstructed regions, a Poisson blending algorithm is adopted. Besides, the efficiency of gradient-domain compositing is accelerated by the quad-tree approach. Experiments have been performed to process more than 50,000 Landsat 8 Operational Land Imager (OLI) images covering China from 2013 to 2017, which yield promising results in terms of radiometric accuracy and consistency for experimental images with cloud coverage less than 80%. The produced Landsat time series images with cloud removal can be further used for analyzing land cover and land change dynamics in China, and the proposed algorithm should be easily employed to produce cloud-free Landsat time series for other areas.
Comparative Analysis of various Techniques for Heart Disease Prediction

With the increasing death counts due to heart diseases, a system is required in place for accurately predicting heart disease. Various researchers have proposed a number of models for predicting heart diseases using different technologies such as artificial neural networks, machine learning, data mining, etc. This paper analyses the work done by various researchers on the accuracy of heart disease prediction through the different approaches. A detail literature review has been provided in the study. The analysis has also been presented on the basis on technology used.
Cross-Language Plagiarism Detection using Word Embedding and Inverse Document Frequency (IDF)

The purpose of cross-language textual similarity detection is to approximate the similarity of two textual units in different languages. This paper embeds the distributed representation of words in cross-language textual similarity detection using word embedding and IDF. The paper introduces a novel cross-language plagiarism detection approach constructed with the distributed representation of words in sentences. To improve the textual similarity of the approach, a novel method is used called CL-CTS-CBOW. Consequently, adding the syntax feature to the approach is improved by a novel method called CL-WES. Afterward, the approach is improved by the IDF weighting method. The corpora used in this study are four Arabic-English corpora, specifically books, Wikipedia, EAPCOUNT, and MultiUN, which have more than 10,017,106 sentences and uses with supported parallel and comparable assemblages. The proposed method in this paper combines different methods to confirm their complementarity. In the experiment, the proposed system obtains 88% English-Arabic similarity detection at the word level and 82.75% at the sentence level with various corpora.
A Systematic Review of Complimentary Therapies to Treat Symptoms of Post-Traumatic Stress Disorder in the Aftermath of Domestic Abuse

3 Introduction.5 Method.6 IPV Impact on Health.18 IPV Current Treatments.19 Review of Complimentary Treatments.21 Conclusion of Results.35 References.37 A SYSTEMATIC REVIEW OF COMPLIMENTARY THERAPIES TO TREAT SYMPTOMS OF POST TRAUMATIC STRESS DISORDER IN THE AFTERMATH OF DOMESTIC ABUSE
Methods for Modeling and Evaluating Construction Disputes: A Critical Review

Cost and time are the targeted outcomes of any successful construction project, and disputes over these two key factors constitute a major obstacle to successful project outcomes. As escalated levels of dispute are becoming increasingly unavoidable, the construction industry is aiming to develop dispute identification strategies to reduce and eliminate them during construction. However, existing research on construction disputes appears to give more consideration to dispute resolution than it does to avoiding conflict and preventing disputes from arising in the first place. This paper aims to minimize disputes during construction by addressing the causes of disputes during the pre-construction phase. As an integral part of ongoing research, it presents the results of a thorough study encompassing a critical review of previous research on construction disputes. Several conflicts and disputes are categorized and analyzed to allow for the future determination of their direct and/or indirect links to the pre-construction phase. This review also elaborates on the different methods of research adopted in the literature and the relevant research tools utilized. The research highlights the use of fuzzy logic coupled with structural equation modeling (SEM) as a recognized and valid modeling tool in construction projects, as it models and establishes an appropriate framework for dispute modeling and evaluation. The findings of this review therefore call for a further investigation of and deep research into the relationship between the characteristics of the pre-construction phase and the types of disputes and their likelihood of taking place during the construction phase itself. These findings can be utilized to develop an operational framework for predicting dispute occurrences during construction. The paper concludes by providing a developed hybrid fuzzy-SEM model to quantify the probability of dispute occurrence in construction projects, thereby enabling project stakeholders to predict, identify, and properly manage dispute occurrences during the pre-construction phase.
Trade off between Droop and Virtual Synchronous Control for PV Synchronous Generator

Microgrid (MG) is emerging as a modern power system which can be operated in islanded as well as grid-connected modes. The evolution of power electronic technology in the power system gives forward direction to smart grid and microgrid. A major challenge in a microgrid is the absence of rotational inertia as distributed energy resources (DER) are interfaced with the grid through power electronic converters. Converters are operated under droop control for power-sharing conventionally. The concept of the virtual synchronous generator(VSG) can be a suitable approach for controlling these converters in low inertia microgrids. VSG implementation with a solar photovoltaic system is called photovoltaic synchronous generator. This paper discusses the trade-off between conventional droop control and VSG control of photovoltaic generation (PV).
Study on Power Generation Performance of Quad-Stable Wake Galloping Energy Harvester

Advances in MEMS technology have greatly reduced the power consumption of the sensor, and it is possible to power it with environmental energy. In this paper, a quad-stable cantilever piezoelectric wake-galloping energy harvester is proposed by introducing nonlinear quad-stable vibration into the system. The mechanical model of the harvester system is established, and governing equations are obtained. The effect laws of the potential well depth on the harvesting performance of the quad-stable wake-galloping energy harvester are studied. The above research results can provide some reference for the study of wake-galloping energy harvesters.
Neural Network based Vehicle Number Plate Recognition System

In this paper, we have developed Vehicle Number Plate Recognition (VNPR) system, where can be installed in the tollgate and high security surveillance area. The main aim of this work is implemented as an autonomous system to control the process of payment fee and parking allocation in tollgates and shopping mall. The novelty of this work is the neural network based genetic algorithm derived to detect and recognize the vehicle number plate details as an digital image. Identifying the vehicle through the recognition of its VNPR using low level digital image processing algorithms and neural network algorithms.
SSD: Cache or Tier an Evaluation of SSD Cost and Efficiency using MapReduce

Solid-State Drives (SSDs) play a crucial role in today's storage systems. They are appended into the Hard-Disk Drives (HDDs) storage systems to improve performance. They provide high IO rate and low latency, which makes them a perfect candidate for analytic-based workloads such as MapReduce. Defining an efficient SSD deployment strategies for MapReduce workloads is a challenging task: SSDs are costly and have limited capacity, the workloads have a big process data size, and the platform has a unique workflow nature. The goal of the work is to establish performance and cost relationship between SSD approaches and MapReduce workloads. In our setup, MapReduce workloads were executed with two SSD approaches of tiering and caching each with two setups: compress and uncompress. Our results showed that by using SSD as a tier, MapReduce workload performs better by up to 66% and increased SSD lifespan by around 20% when comparing with cache approach. We also observed that applying compression on the tier approach enhanced the lifespan by 60% but reduced lifespan of cache tier by 50%.
Isolated Multiport Converter for Solar PV Systems and Energy Storage Systems for DC Microgrid

Multiport converters (MPC) are the cost-effective and efficient solution for integrating multiple energy sources and loads. This paper presents an isolated four port bidirectional multiport structure for interfacing Solar PV and storage devices with DC microgrid (DCMG). Solar PV is used as the preliminary energy source whereas the battery along with the supercapacitor (SC) work as an energy buffer for the system. The converter is based on a triple active bridge, which is coupled with an additional half bridge bidirectional switching cells to form the four port topology. In the developed scheme, a decoupled phase-shifted control strategy is employed to control the power flow between the ports. The decoupled control strategy eliminates the manipulation of possible interdependency in the control of different converter ports. The MPC and the control strategy has been validated through simulation studies under various conditions.
Reconfiguration Based Single Stage PV Water Pumping System

The market for PV is growing rapidly around the world in last few years due to its diverse applications. The output power of PV cells depends on various factors. Among them, Partial Shading is commonly encountered issue which yields in reduction of output power. This reduction of power reduces the output of the pump. This paper proposed a reconfiguration based PV water pumping system. The proposed algorithm firstly generates maximum power output under Partial shading conditions. Secondly, the MPPT algorithm tracks the maximum power. Further $\mathrm{V}/f$ control and SAZE PWM are used to operate the Induction motor effectively. The Performance of the system is analyzed using MATLAB simulations.
Context Save and Restore of Partial Reconfiguration Regions for Xilinx FPGAs

Preemption is an important technique used by modern operating systems to achieve multitasking. When FPGAs need to be integrated into the preemptive multitasking mechanisms of an operating system, a context switch of the design configured into the FPGA is required. Several proposals on how to take advantage of such a context switch by saving and restoring the internal state of an FPGA have been presented in the past. Unfortunately, these discussions give neither much of the technical details of the save and restore mechanism itself, nor are they available for modern FPGAs like Ultrascale-Series. This paper summarizes the technical details of these officially unsupported techniques to save and restore the internal state of modern Xilinx FPGAS (namely 7-Series and Ultrascale-Series) which are currently scattered over several other publications and technical manuals.
Control Strategy for Receiving-End Power Grid after UHVDC Transmission System Blocking Fault Based on Apriori Algorithm

Once blocking fault occurs in high-capacity ultra-high-voltage direct current (UHVDC) transmission system, it seriously affects the stability of the receiving-end power grid. In allusion to the receiving-end power grid of UHVDC power transmission system, this paper uses the dynamic power flow algorithm to simulate blocking fault of UHVDC power transmission lines. Then association between the tie line section of the power grid and control strategy after the fault is analyzed by Apriori algorithm, the intrinsic connection between the overloaded section and the unit and load control strategy is mined and provides overloaded sections control strategies for DC blocking fault. Finally, the proposed method is validated through the simulation experiment of Jinping-Suzhou DC bipolar blocking fault, the result shows that association analysis based on Apriori algorithm can obtain the connection between the overloaded sections and the unit and load adjustment strategies based on support and confidence, and provides technical support for dispatcher’s decisionmaking.
Performance Comparison of Cepstral Features for Language Identification using Convolutional Recurrent Neural Networks

In this paper, we consider the problem of automatic language identification, and propose the viability of front-end features such as MFCC, and the cepstral features derived with multitapered magnitude (MTMAG) and multitapered modified group delay function (MTMOGDF), using the recently proposed convolutional recurrent neural network (CRNN)-based image classifiers. The CRNN exploits both the spatial and temporal information between the image blocks, and within a given image, for the classification task. The images to these CRNN-based classifiers are constructed based on the considered cepstral-domain features. We conduct a detailed set of experiments on the news database with five International languages, along with the Akashvani news data base with ten Indian languages. The insights from the resultant confusion matrices are inconclusive in the favor of a particular feature. In other words, each of the chosen features perform well for only a set of the chosen languages. However, evaluating the average performance metrics such as equal error rate (EER) and F1 score shows that the MTMOGDF-driven CRNN outperforms those that were designed using MFCC and MTMAG features. In particular, it is seen that the MTMOGDF-driven CRNN achieves nearly 3 dB reduction in EER, with a slight loss of performance in F1 score.