Miracle-dz/UniFAR-FTUs huggingface dataset:
Work Engagement at A Transforming Tertiary Institution: A Reflection of NUST Academic Staff, Namibia

Engagement of employees is a concern for most institutions because a lack of engagement can be costly. Employees are regarded as engaged when they have a sense of commitment, exhibit discretionary behaviour, see their organisation as a good place to work and will go the extra mile. The aim of the study was to determine the work engagement levels of the academic staff at NUST, Namibia. To achieve the aims of the study a self-administered questionnaire, The Questionnaire was administered to full-time academic staff members across the six faculties and a total of 201 academic staff members participated in the study. Descriptive analysis was carried out to determine the distribution levels of engagement of academic staff at NUST. Furthermore, inferential analysis such as chi-square and correlation analysis were used to determine the strength of the relationships between engagement and job resources and job demands. Findings indicated that the academic staff at NUST is only moderately engaged on aspects of vigour, dedication and absorption. This implies that most academics perform duties required by their job descriptions and do not go beyond what is expected of them. The results prompted recommendations on measures needed to shift the engagement levels from moderate to high. IJARBAS Accepted 13 August 2019 Published 14 August 2019 DOI: 10.5281/zenodo.3368285
Energy Consumption Using IoT and Big Data Analytics Approach in Smart Home

: One of the major attribute of the Smart home automation and energy management systems integrate IOT and renewable energy resources at the consumption premises. With advancement of Automation technology, today’s world prefers Automatic systems over manual systems. This paper presents the design, implementation and testing of an embedded system using arduino that integrates solar energy resources and energy management to a smart home incorporated with IOT. The proposed system provides and manages a smart home energy requirement by installing renewable energy and scheduling the power flow during peak and off-peak period. IOT (Internet of things) is the latest and emerging internet technology. Increasing cost and demand of energy has led many organizations to find smart ways for monitoring, controlling and saving energy. A smart Energy Management System (EMS) can contribute towards cutting the costs while still meeting energy demand. The emerging technologies of Internet of Things (IoT) and Big Data can be utilized to better manage energy consumption in residential, commercial, and industrial sectors. This paper presents an Energy Management System (EMS) for smart homes. In this system, each home device is interfaced with a data acquisition module that is an IoT object with a unique IP address resulting in a large mesh wireless network of devices. The data acquisition System on Chip (SoC) module collects energy consumption data from each device of each smart home and transmits the data to a centralized server for further processing and analysis. This information from all residential areas accumulates in the utility’s server as Big Data. The proposed EMS utilizes off-the-shelf Business Intelligence (BI) and Big Data analytics software packages to better manage energy consumption and to meet consumer demand. Since air conditioning contributes to 60% of electricity consumption in Arab Gulf countries, HVAC (Heating, Ventilation and Air Conditioning) Units have been taken as a case study to validate the proposed system. A prototype was built and tested in the lab to mimic small residential area HVAC systems1.
Annotating the SIRO model and discovering experimental protocols

We are manually annotating a corpus of 100 full-text experimental protocols in cell biology, neuroscience, developmental biology, microbiology and molecular biology. We are gathering the protocols from repositories like bio-protocols, Cold Spring Harbor Protocols and Nature protocols. Our annotation focuses on: the SIRO model, namely: i) the Sample tested, ii) the Instruments employed, iii) the Reagents used and, iv) the overall Objective of the protocol. The SIRO model represents the minimal information for describing an experimental protocol. Our manual annotation experience illustrates how to use annotations from domain experts in the enrichment of an ontology; we are identifying key terms in the text and relating these to concepts in the ontology. By the same token, our annotation experience is supporting our NLP infrastructure; the identiﬁcation of speciﬁc facets, S I R O, allows us to enrich our gazetteers and better tailor our JAPE rules. Our approach supports answering queries such as we support queries such as “transformation protocol (objective)”, of “ Saccharomyces cerevisiae (sample)” by using “lithium acetate/single-stranded carrier (reagent)”.
Colonoscopic post-polypectomy bleeding in patients on uninterruptedclopidogrel therapy: A systematic review and meta-analysis

Current guidelines recommend temporary cessation of clopidogrel for 7-10 days for patients on clopidogrel undergoing colonoscopy with polypectomy. However, recent prospective randomized controlled trials have advocated for uninterrupted clopidogrel, due to similar post-polypectomy bleeding (PPB) rates with and without continued clopidogrel therapy. Thus, a meta-analysis was conducted to assess the risk of PPB rate in patients on continued clopidogrel therapy. Systemically identified publications were used to compare the rate of PPB in patients on continued clopidogrel therapy with those who had interrupted clopidogrel therapy. The primary outcome was the incidence of PPB. The secondary outcomes were immediate PPB, delayed PPB and serious cardio-thrombotic events. This study has been registered in PROSPERO (no. CRD42018118325). A total of five studies were identified, which included 655 patients in the continued clopidogrel group and 6620 patients in the control group. There was an increased risk of PPB with continued clopidogrel [P=0.0003; risk ratio (RR), 1.96; 95% confidence interval (CI), 1.36-2.83). The rate of immediate PPB was slightly higher in the continued clopidogrel group (5.77% vs. 1.77%, respectively), but was not statistically significant (P=0.06; RR, 1.57; 95%CI, 0.98-2.51). The rate of delayed PPB was increased in the continued clopidogrel group (P=0.0008; RR, 3.10; 95%CI, 1.60-5.98). However, no significant difference in serious cardio-thrombotic events was observed within 30 days (P=0.74; RR, 0.78; 95%CI, 0.18-3.40). Although continued clopidogrel therapy decreased the incidence of serious cardio-thrombotic events, the risk of delayed PPB was increased. Therefore, endoscopists should make all preparations to prevent bleeding in the perioperative period for patients at high thrombotic risk and on continued clopidogrel therapy, if polypectomy cannot be reasonably postponed.
A Comparison between WIMAX and LTE Based on Multicarrier Technology

: Worldwide Interoperability for Microwave Access ( WIMAX) is realized on IEEE 802.16,while LTE is realized on 3GPP,Both providing high speed data over a wide area. 4G is a promising wireless broadband technology with providing data rates up to 1Gbps. WIMAX and LTE (LongTerm Evolution standards) have realized on a multiple access technique called OFDMA.The two main competitors in the 4G wireless system are WIMAX and LTE.OFDMA has utilized multiuser diversity of LTE, whereas the frequencydiversity is utilized in WIMAX.LTI reacts faster than the WIMAX according to the traffic demand, but the design of WIMAX is easier than LTE.
Evaluation of Intrusion Detection using a Novel Composite Metric

: In this technological age, a lot of data that include secrets have been given away to the internet. The exponential growth in the E-Commerce, the challenge of security of the secret data is a huge problem to provide security to data for individuals, companies and governments. It is very important that the data should reach safely to the destination and only the intended person should able to access the data without any intrusions over the network. But in these days, where there is a continuous increase in immorality and decrease in ethical values, cyber crimes have become regular news items in our televisions and news papers. Researchers have been contributing various solutions towards addressing this problem and Network Intrusion Detection Systems (NIDS) is one of the solutions against these cyber crimes and network attacks. However, the performance of these Network Intrusion Detection Systems needs to be measured by effective metrics. There are various primitive metrics available such as Accuracy, Detection Rate and False Alarms Rate etc., but there is no bench mark performance metric available in the literature to evaluate the performance of the NIDSs. On the other hand performance metrics play a very major role in data security because the performance of the NID systems is evaluated through these metrics. In this paper, a novel Composite Performance Metric, called ‘DAFAR’ has been proposed to evaluate the performance of different Network Intrusion Detection systems. Various experiments were carried out on the benchmark data set (ISCX 2012) [1] to test the efficacy of the NID systems’ performance. The experimental results show that the proposed metric gives better direction while evaluating the performance of different Network Intrusion Detection systems.
Cascaded H-Bridge Multilevel Inverter Based D STATCOM for Power Quality Improvement

Cascaded multilevel configuration of the inverter has the advantage of its simplicity and modularity over the configurations of the diode-clamped and flying capacitor multilevel inverters. This paper presents a threephase, five-level and seven level cascaded multilevel voltage source inverter based active filter for power line conditioning to improve power quality in the distribution network. The DSTATCOM helps to improve the power factor and eliminate the Total Harmonics Distortion (THD) drawn from a Non-Liner Diode Rectifier Load (NLDRL). The compensation process is based on concept of p-q theory. A CHB Inverter is considered for shunt compensation of a 11 kV distribution system. Finally a level shifted PWM (LSPWM) and phase shifted PWM (PSPWM) techniques are adopted to investigate the performance of CHB Inverter. The results are obtained through Matlab/Simulink software package.
A Survey Disease Detection Mechanism for Cotton Leaf: Training & Precaution Based Approach

The large number of people depends on cotton crop. The recognition of cotton leaf disease are of the major important as they have a cogent and momentous impact on quality and production of cotton. Cotton disease identification is an art and science. The start with collecting the images. We will consider two diseases they are Foliar, and Alternaria of cotton leaves. We have extracted the features and compare those features with the features that are extracted from the input test image they can like grey scaling, thresholding, cropping for detecting the boundary of image. Colour feature like HSV features are extracted from the output of segmentation and (ANN) artificial neural network is trained by choosing the feature value that could distinguish the healthy and disease sample. Experimental result showed that classification performance by ANN taking feature set is better with an accuracy of 80%. The present work proposes a methodology for detecting cotton leaf disease early, using image processing techniques and artificial neural network (ANN). We are also work with the current and future precaution for the cotton tree to protects it from future disease & maintain it to improve its good production as well as life.
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior

In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields. These methods are mostly based on L1-regularized optimization which has a number of disadvantages such as the inability to assess model uncertainty and expensive crossvalidation to find the optimal regularization parameter. Moreover, the model's predictive performance may degrade dramatically with a suboptimal value of the regularization parameter (which is sometimes desirable to induce sparseness). We propose a fully Bayesian approach based on a "spike and slab" prior (similar to L0 regularization) that does not suffer from these shortcomings. We develop an approximate MCMC method combining Langevin dynamics and reversible jump MCMC to conduct inference in this model. Experiments show that the proposed model learns a good combination of the structure and parameter values without the need for separate hyper-parameter tuning. Moreover, the model's predictive performance is much more robust than L1-based methods with hyper-parameter settings that induce highly sparse model structures.
Web Usage Mining in Ranking Web Pages

: With the rapid increase in the size of the web data new methods and techniques have been emerging which help in accessing the information easily. Ranking of the pages are being done which help in finding relevant information as per need of the user. In order to facilitate the ranking process various algorithms are being designed from time to time which help to rank the pages in order of priorities. The ranking algorithms follow different web mining techniques like structural links, content format and usage pattern in order to rank the web pages. Web Usage Mining can be quite helpful in the process of ranking as they consider the web log files which store the usage pattern of the users. It helps to track the visiting pages on the basis of query. The navigational behavior of the users can be captured which ease the process of retrieval. The paper focus on basic web mining techniques and ranking algorithms and throws light on the effect of web usage mining in the ranking process and its overall strategy.
using MrBayes

MrBayes is a software program for inferring phylogenetic parameters in a Bayesian statistical framework (Huelsenbeck and Ronquist, 2001; 2005; Ronquist et al., 2012). This program relies on the Markov chain Monte Carlo (MCMC) numerical method to approximate the joint posterior probability distribution of model parameters (Metropolis et al., 1953; Hastings, 1970). The current version, v3.2, allows you to perform several different types of analyses:
Nonvolatile and Energy-Efficient FeFET-Based Multiplier for Energy-Harvesting Devices

Energy-harvesting internet-of-things devices must deal with unstable power input. Nonvolatile processors (NVPs) can offer an effective solution. Compact and low-energy arithmetic circuits that can efficiently switch between computation and backup operations are highly desirable for NVP design. This paper introduces a nonvolatile ferroelectric field-effect transistors (FeFET)-based sequential multiplier with the ability to do continued calculation after a power outage, thus achieving zero backup overhead. We exploit the unique characteristics of FeFETs to construct key components of a sequential multiplier. The multiplier relies on a FeFET-based adder and a new FeFET-based latch to achieve compact area and low operating energy. Moreover, it uses the hysteretic characteristic of FeFETs to realize the storage capability, and hence is able to store, at no extra cost, the intermediate data of an operation in a nonvolatile manner. This property provides support for continued computation when power supplies may be intermittent. Simulation results show that, assuming the same technology node, the proposed FeFET-based multiplier saves up to 21% and 19% area than a conventional CMOS-based sequential multiplier of 4-bits and 8-bits, respectively. It also saves 32% and 73% less area compared with a CMOS-based array multiplier. Furthermore, the proposed design can offer up to 32%/23% energy saving per operation compared with a 4/8-bit CMOS-based sequential multiplier.
Broadcast Mechanism Based on Hybrid Wireless/Wired NoC for Efficient Barrier Synchronization in Parallel Computing

Parallel computing is essential to achieve the manycore architecture performance potential, since it utilizes the parallel nature provided by the hardware for its computing. These applications will inevitably have to synchronize its parallel execution: for instance, broadcast operations for barrier synchronization. Conventional network-on-chip architectures for broadcast operations limit the performance as the synchronization is affected significantly due to the critical path communications that increase the network latency and degrade the performance drastically. A Wireless network-on-chip offers a promising solution to reduce the critical path communication bottlenecks of such conventional architectures by providing hardware broadcast support. We propose efficient barrier synchronization support using hybrid wireless/wired NoC to reduce the cost of broadcast operations. The proposed architecture reduces the barrier synchronization cost up to 42.79% regarding network latency and saves up to 42.65% communication energy consumption for a subset of applications from the PARSEC benchmark.
DC-Link Quasi-Switched Boost Inverter With Improved PWM Strategy and its Comparative Evaluation

Low voltage stress on the impedance source network devices is the main reason for the advent of DC-linked type quasi-switched boost inverters (DqSBIs). However, the DqSBI has become less attractive due to its low voltage gain. This paper presents a novel PWM control strategy for the DqSBI and its comparative performance evaluation to the conventional inverters. The modified PWM control strategy based DqSBI improves the voltage gain and reduces the conduction loss and the inductor current ripple. Circuit analysis and comparison study between the DqSBI, the quasi-Z-source inverters, and the two-stage inverter with a boost DC-DC converter are presented. 1-kVA three-phase inverter prototypes were set up to compare and evaluate the performance of the DqSBI with the improved PWM method for voltage gains of 0.85 to 1.6. The measured efficiency and total harmonic distortion (THD) values are also presented.
FPGA Implementation of Stepping Correlation Acquisition Algorithm for Beidou II B1 Signal

This paper based on the time domain serial acquisition principle, a multi-channel step correlation acquisition algorithm is designed and implemented by FPGA. According to the principle of modular design, the whole acquisition system is divided into local carrier NCO module, local code NCO module, and operation unit module. Their functions are to generate local in-phase and quadrature carriers, generate local pseudo code, and down-conversion of intermediate frequency signal, then carry out related cumulative calculation, and fmally rerunning threshold judgment. Every module is implemented in Quartus, and the function simulation is achieved in Modelsim, Through the simulation of the real time Beidou satellite digital IF signal in the whole acquisition module, and compared with the Matlab simulation results based on FFT parallel code phase acquisition, The results show that the algorithm can correctly and effectively capture the Beidou satellite signals it can improve the acquisition efficiency and reduce the utilization ratio of resources.
Predicting Diabetes using Distributed Machine Learning based on Apache Spark*

Diabetes mellitus is a long-standing disease. It constitutes a severe challenge for public health worldwide. As stated by the International Diabetes Federation, there are presently about 246 million diabetic people around the world, and this number is anticipated to increase to around 380 million by the year 2025. More than this, 3.8 million death cases occur annually due to diabetes complications. The primary objective of this work is developing an applicable system to predict diabetes using distributed machine learning based on big data platforms such as Spark. In this context, this study aims to develop models using distributed machine learning based on Apache Spark to predict diabetes. Five machine learning classification methods were used like Decision Tree, Support Vector Machine, Logistic Regression Classifier, Naive Bayes, and Random Forest Classifier. Comparison between different algorithms was calculated using three measures, which are accuracy, recall, and precision. The experimental results proposed that LR achieved the highest percentage of accuracy, recall, and precision,82%, 92%, and 82%, respectively.
Multi-Resolution Intrinsic Texture Geometry-Based Local Binary Pattern for Texture Classification

In this paper, we propose a new hybrid Local Binary Pattern (LBP) based on Hessian matrix and Attractive Center-Symmetric LBP (ACS-LBP), called Hess-ACS-LBP.d The Hessian matrix provides the directional derivative information of different texture regions, while ACS-LBP reveals the local texture features efficiently.d To obtain the macro- and micro-structure textural changes, Hessian matrix is calculated in a multiscale schema.d Multiscale Hessian matrix presents the intrinsic local geometry of the texture changes.d The magnitude information of the Hessian matrix is used in the ACS-LBP method.d A cross-scale joint coding strategy is used to construct Hess-ACS-LBP descriptor.d Finally, histogram concatenation is carried out.d Extensive experiments on eight texture databases of CUReT, USPTex, KTH-TIPS2b, MondialMarmi, OuTeX TC_00013, XU HR, ALOT and STex validate the efficiency of the proposed method.d The proposed Hess-ACS-LBP method achieves about 20% improvement over the original LBP method and 1%–11% improvement over the other state-of-the-art hand-crafted LBP methods in terms of classification accuracy.d Besides, the experimental results show that the proposed method achieves up to 32% better results than the state-of-the-art deep learning based methods.d Especially, the performance of the proposed method on ALOT and STex datasets containing many classes is remarkable.
Enhancing the Accuracy of Partial Discharge Localization in Power Transformers Using the UHF Measurement Technique

Reliable operation of power systems depends highly on the condition of power transformers, hence making their condition monitoring top priority. Partial discharge (PD) is among the most common and deteriorative faults in a power transformer. PDs cause progressive degradation of the insulation system of transformers, and may lead to catastrophic failures, if no countermeasure is taken. The ultrahigh frequency (UHF) measurement technique is capable of detecting and localizing PDs inside a power transformer. In this method, electromagnetic waves emitted from the PD fault are captured by UHF probes. From the difference in arrival times of the signals, captured by the probes, the location of the PD fault can be obtained. To date, several researches have attempted to introduce a method for arrival time detection. However, these methods are not capable of providing an acceptable location for the PD inside the transformer tank for each set of the received signals. This reduction in the provided PD locations by the signal sets is exacerbated when the electromagnetic waves encounter more barriers in their path to the UHF probes. This contribution is aimed at optimizing the number of selected signal sets that yield an acceptable location for the PD, in order to improve the localization accuracy. The PD signals are denoised using wavelet filtering, and the arrival time detection method is then applied to the denoised signals. The obtained PD locations are subsequently assessed to estimate the PD fault position with the highest possible accuracy. This research is accompanied by experimental measurements to acquire the location of a PD source inside a specially designed transformer tank and evaluate the proposed algorithm. A novel approach regarding the optimal selection of the captured signal sets is presented and the results are compared.