Miracle-dz/UniFAR-FTUs huggingface dataset:
US-Scrum: A Methodology for Developing Software with Enhanced Correctness, Usability and Security

In this modern era customized software especially web applications are given more importance because of global and simultaneous access. Software quality is an important concern of the software developers and customers. Certain number of software quality attributes such as availability, integrity, interoperability, correctness, maintainability, performance, reliability, reusability, scalability, security, testability and usability are defined for quality assessment. Agile software development methods though flexible in coping with changes and organize the software development around functional requirements. Due to advancements in web technology security threats and usability issues have also increased. Agile involves the limelight for ensuring correctness of software while ignoring important quality attributes like security and usability. The purpose of this research is to propose a hybrid model based on Feature Driven Development (FDD) and Scrum principles to accommodate quality focus in terms of correctness, security and usability. The proposed model is organized around multiple sprints, specialized managers and specialized teams. The proposed model provides the benefits of being more user focused and increased customer satisfaction by employing comprehensive verification and validation process.
Tolerance and Accumulation of Heavy Metals by Descurainia sophia L.

Today, biosphere pollution has accelerated strongly with start of industrial revolution by toxicity of heavy metals. One of existing pollution is soil pollution. Unfortunately, soil pollution by metals is as intensive environmental stress for plant hence for human. Plants, which are able to store heavy metals in their organs, can be used for phytoremediation of polluted soils and utilization of these plants is effective for phytoremediation as a cheap and economic method. In this research, the absorption rate of Cd (II), Ni (II) by Descurainia sophia was considered in hydroponic conditions. Plants were grown in Hoagland media containing different concentrations of Cd (II), Ni (II). An experiment in a completely randomized design with three replications was conducted. Two weeks after treatment of plants the sample were gathered and metal concentration was measured by atomic absorption spectroscopy. Besides, the content of chlorophyll and proline was measured. The results showed the chlorophyll content in high concentrations of the metals (Cd (II), Ni (II)) was decreased in plants that were sign of pigment degradation in presence of heavy metals. Similarly, the proline content in plants was increased under stress which was sign of damage of heavy metal stress on plant and activation of defensive mechanisms in this condition. The effects of toxic concentration of nickel and cadmium on metal accumulation in these plants showed that roots were able to absorb more than shoots, which is sign of elements connection to root cell wall.
Comparative Analysis of Fake News Detection System

Enormous amount of information is published daily via online and print media, but it is not easy to tell whether the information is a true or false. The extensive spread of fake news has the potential for extremely negative impacts on individual and society. Therefore, fake news detection has become an emerging research that is attracting tremendous attention. The purpose of the proposed system is to detect fake news with the help of text analysis using n-gram features and machine learning classification techniques. We investigate the feature extraction techniques of term frequency, term frequency –inverse document frequency. Classification of fake or real news is performed using Passive Aggressive Classifier (PAC), Naïve Bayes (NB) and Support Vector Machine (SVM) classifiers. The proposed system is evaluated using three publicly available datasets. Performance of the different classifiers is measured with precision, recall, f-measure and accuracy score. According to the analysis upon the three different datasets with three classifiers, PAC is the strongest classifier among the other two, SVM is stronger than NB.
Automated Waste Segregation using Convolution Neural Network

The key to efficient waste management is to ensure segregation of waste and resource recovery. Waste is usually segregated on the basis of whether it is biodegradable or non biodegradable. A major challenge with respect to this is that waste materials are not segregated before collection and discarded nonetheless. These end up as huge piles of waste in dump yards and need to be segregated. Waste segregation is currently being done manually by the Municipal Corporation. This results in unsanitary working conditions for the people who need to perform this task. Despite being provided with the necessary equipment, they run the risk of catching infections from the waste they work with. Automation of this process will be beneficial for the people who work on this by reducing health hazards.
Multivariate GWAS: Generalized Linear Models, Prior Weights, and Double Sparsity

Results: We extend and efficiently implement iterative hard thresholding (IHT) for multivariate regression. Our extensions accommodate generalized linear models (GLMs), prior information on genetic variants, and grouping of variants. In our simulations, IHT recovers up to 30% more true predictors than SNP-by-SNP association testing, and exhibits a 2 to 3 orders of magnitude decrease in false positive rates compared to lasso regression. These advantages capitalize on IHT’s ability to recover unbiased coefficient estimates. We also apply IHT to the Northern Finland Birth Cohort of 1966 and find that IHT recovers plausible variants associated with HDL and LDL.
Physicochemical Characterizes of Argan Oil (Argania spinosa L.) Affected by Roasting and Pulping by Goats

Present study investigates the influence of roasting and pulping by goats on the quality of argan oil (Argania spinosa L.). To carry out this work, 4 different samples selected by their method of extraction (by mechanical pressing from argan fruits pulped by goats). The study of the physicochemical characteristics of the 4 samples shows that the roasting of the almonds of the argan fruit and the pulping by the goat appear as a parameter can increase the value of the peroxide index and the acidity. It’s also found that pulping by goats can decrease the value of fatty acids (linoleic C18: 2) and the percentage of triglycerides (LLL, LOO and OOO) in argan oil. The result of sterols clearly shows that roasting is reduced the total sterols. The study of the concentration of benzo-α-pyrene suggests that roasting does not produce significant amounts of benzo-α-pyrene. Also, present findings show that roasting and pulping by goats appears as a parameter influencing the acid value of argan oil. In fact, the acidity value is higher in the argan oil samples prepared from roasted almonds and pulped by goats. Finally, the present study indicated that the high quality of argan oil can be extracted by mechanical pressing and which is not pulped by goats and, therefore, current results may support the marketing of the oil argan.
Prevalence of malaria and soil-transmitted helminth infections in healthy school children in Cameroon

Malaria and soil-transmitted helminths are common parasitic diseases found in school children in subSaharan Africa. We determined the prevalence and intensities of these infections in apparently healthy schoolchildren living in Mfou health district, where malaria and intestinal infections are among the first causes of morbidity. In a cross-sectional study involving 503 schoolchildren, anthropometric parameters were measured. Finger-prick blood and fresh stool samples were collected for malaria parasites determination, assessment of anaemia and detection of helminths’ eggs. Logistic regression analysis was used to investigate the association between these infections and other factors. Overall, 40.6 and 29.6% of children harboured malaria parasites and worms respectively. Prevalences of mild, moderate and severe undernutrition were respectively 22.2, 2.3 and 0.5% for underweight, and 21.9, 7.6 and 2.0% for stunting. In logistic regression analysis, anaemia (OR=2.64, 95% CI: 1.71-4.07) and infection with Ascaris lumbricoides (OR=1.72, 95% CI: 1.01-2.91) were significantly associated with malaria infection. Infection with Trichuris trichiura was significantly associated with increased risks of underweight (OR=2.11, 95% CI: 1.11-4.01). Moreover, rural schoolchildren showed increased chances of carrying worms, compared to their urban counterparts (OR=2.60, 95% CI: 1.75-3.86). Malaria prevention and school-based deworming activities should be re-enforced in Mfou health district to reduce the burden of these infections in children.
V VENKATESWARA RAO: EXPLORING THE RELATIONSHIP BETWEEN HRD CLIMATE PRACTICES AND JOB SATISFACTION ACROSS SELECTED SOFTWARE COMPANIES IN HYDERABAD-A STUDY

This study aims to examine the relationship between Top Management Commitment (TMC) and organizational performance in ISO 9001 certified manufacturing organizations. Further, the study tries to investigate the impact of TMC on indicators of organizational performance. In addition, the study examines the effect of company size on the relationship between TMC and organizational performance in ISO 9001 certified manufacturing organizations. For this purpose, a questionnaire was administered to 128 ISO 9001 certified manufacturing organizations and the response was collected from 455 quality management personnel. Karl Pearson’s coefficient of correlation was employed to examine the relation between TMC and organizational performance in ISO 9001 certified manufacturing organizations. Multiple regression analysis was performed to investigate the impact of TMC on organizational performance in ISO 9001 certified manufacturing organizations. Analysis of Variance (ANOVA) was used to examine whether the perception of TMC on ISO 9001 standard would differ across company size. The study reveals that there is a high degree of correlation between TMC and organizational performance. The study further shows that there is a significant impact of TMC on indicators of organizational performance. The study demonstrates that the perception of TMC on ISO 9001 standard differs according to company size.
Researchmethods inmanagement : advances and applications

Introduction Methodology should not be seen as a static and boring section that lands somewhere in a piece of empirical research as a kind of mandatory duty that most researchers slightly adapt from previous work without enthusiasm. Actually, the methodology section has a lot to tell about the nature of each research project, which is inserted in a determined context and history. More than a set of procedures, rules or techniques, methodology embodies the interests, values and principles of researchers. It reveals a worldview, what reality and knowledge production means for a given group sharing the very same ontology and epistemology (Pozzebon, 2018). If theory could be seen as the soul of a piece of research, methodology could be seen as its body, with eyes, ears, hands and a beating heart. Because we see methodology as something that is alive, we claim it represents the way we relate to the world. This is why this special issue seeks to push our readers to discover innovative methodological approaches that might be applied to the management field. It is a bridge between grounded empirical material and new theories and understandings. Although most methodological articles are preoccupied with rigor and precision, we are looking for imagination and creative thinking. In line with Alvesson and Karreman’s (2011, Preface) recent book on “mystery as a method”, the set of articles that form this special issue aim at emphasizing “how empirical studies can be used to come up with unexpected ideas and lines of thinking” and “to inspire less predictable and more exciting theoretical work triggering by empirical material” therefore offering inspiration. In the next section, we introduce 12 articles that put forward advances and new applications in terms of methodological approaches, most of them in form of essays. We present four qualitative articles, six quantitative articles and two that present a mixedmethod approach.
Efficacy and Safety of Postmenopausal Osteoporosis Treatments: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

Although a range of pharmacological interventions is available, it remains uncertain which treatment for osteoporosis is more effective. This network meta-analysis study aimed to compare different drug efficacy and safety in randomized controlled trials (RCTs) for the treatment of postmenopausal osteoporosis. PubMed, EMBASE, MEDLINE, Clinicaltrial.gov, Cochrane library, Google scholar were searched up to 31 October 2020. Randomized placebo-controlled trials that reported measures of bone mineral density (BMD) percentage change and/or numbers of adverse events of postmenopausal osteoporosis patients were included. Network meta-analysis was conducted using frequentist approach. Ninety-four RCTs comprising 15,776 postmenopausal osteoporosis females were included in the network meta-analysis. Compared with placebo, most interventions showed increase in BMD change. According to surfaces under the cumulative ranking curves (SUCRAs), strontium ranelate, fluoride, and hormone replacement therapy were most effective in increasing total hip, lumbar spine, and distal radius BMD, respectively. Parathyroid hormone (PTH) was most effective in preventing new hip fracture. When taking into account all anatomic sites, bisphosphonate (BP), monoclonal antibody (mAb), and fluoride have a balanced efficacy in increasing BMD at all sites. Considering both the effectiveness of increasing BMD and preventing hip fracture, mAb, BP, and PTH are more favorable among all interventions. The treatment effects of different medications on BMD percentage change are anatomic site-dependent. After weighing anti-osteoporosis treatment efficacy against risk of complications, BP and mAb are the more favorable interventions to increase BMD at all sites and reduce the risks of hip fracture and death.
GLOBAL ASPECTS OF INTEGRABLE SURFACE GEOMETRY

The study of surfaces in 3-space has certainly been pivotal in the development of differential geometry and geometric analysis. Real dimension two is of course special in a number of ways: the possible topological types are easy to describe, one can take advantage of complex analysis in dimension one, and visualization of the objects of study in computer experiments allows many new conjectures to be formulated and tested. It is fair to say that nowadays surface geometry serves as a terrain where one can quickly migrate between diverse areas of mathematics, such as integrable systems, moduli spaces of connections and holomorphic bundles, surface group representations, algebraic geometry of special varieties, non-linear variational problems, mathematical physics, and computational methods and visualization, bringing the ideas and techniques of one to bear on another. This article attempts to describe some of these topics and their relevance to classical problems in surface geometry in a conceptual manner. We maintain an informal style with the hope of leaving the reader with some impressions of the subject and a snapshot of some methods under current development. There is a long tradition of physics motivating advances in surface geometry. Early on Riemann found that the limiting isotherms of the heat of a candle placed under a surface provide it with conformal coordinates. The same method can be used to construct energy minimizing (harmonic) maps from surfaces into higher dimensional (non-positively curved) target spaces. Material properties of surfaces in space, like surface tension or bending energy, give rise to equilibrium shapes and interface models such as constant mean curvature and Willmore surfaces. And from a more esoteric perspective, string theory studies surfaces in appropriate target spaces subject to variational constraints. It is perhaps not surprising then that the notion of a quantum field theory (albeit so far only in its classical formulation) offers a unifying perspective on surface geometry. Roughly speaking, a d-dimensional QFT is described by a d-dimensional space-time M , a space of fields F over M , and an action functional L : F → R whose critical points are the classical field configurations. The objective is to calculate the partition function ∫ F e iL(f) or its mathematically more rigorous variant ∫ F e −L(f). In either case the critical points of L on F—its classical solutions—can be used for perturbative calculations. Zero dimensional QFTs are Gaussian integrals whereas one dimensional QFTs give rise to the path integral formulation of quantum mechanics when applied to the space of curves describing particle evolution in some target space. We are concerned with dimension two in which there are a number of geometrically interesting action functionals:
Recurrent Concept Drift Detection Mechanism for Email Streams Filtering

Due to the essential requirement of the transmission of large volume data streams in cyber security environments, the improvements of anti-spam filtering methods become hot spot research fields in today technology periods. The emails in today transactions becomes non-stationary data, and disturbed by the spammers in various ways and the normal data stream mining mechanisms need to be improved to get acceptable performance and concept drip mechanism are popular research interest area in big data era. Therefore, this paper proposed an ensemble mechanism for email spam filtering procedure based on the concept drift detection technique recurrently, and Random Forest Classifier adaptively. The experiments are carried out by using the standard real-world datasets of enorn dataset and apache assassin dataset. The proposed system attained better performance than the popular data stream classification algorithms due to the recurrently and adaptively detection of concepts. The experimental results are evolved by using different assembling of classifiers and different detectors.
Can EU Judicial Intervention Increase Polity Scepticism? Experimental Evidence from Spain

The mantra of “take back control” has become a staple within the rhetorical repertoire of eurosceptics on both the left and right across member states of the EU. At the centre of the slogan’s message is a call to arms against the (perceived) challenge that membership of the EU represents for national state sovereignty. In this paper, we argue that decisions taken by the European Court of Justice (ECJ) can act as a cue for citizens regarding the extent of EU integration on the dilution of national sovereignty and lead to increased ‘polity scepticism’: increased opposition to the EU and decreased satisfaction with national democracy. Empirically, we leverage a natural experiment in Spain to assess how ECJ rulings impact both euroscepticism and dissatisfaction with democracy. Our results provide strong evidence that ECJ rulings can have a negative causal effect on support for the EU and satisfaction with democracy. The implications of our findings suggest that EU institutions seeking to ensure compliance with the rule of law and EU norms should proceed with caution. Interventionist action may backfire by increasing the scrutiny of the EU’s legitimacy and undermining polity support.
Gold-Conjugated Nanobodies for Targeted Imaging Using High-Resolution Secondary Ion Mass Spectrometry

Nanoscale imaging with the ability to identify cellular organelles and protein complexes has been a highly challenging subject in the secondary ion mass spectrometry (SIMS) of biological samples. This is because only a few isotopic tags can be used successfully to target specific proteins or organelles. To address this, we generated gold nanoprobes, in which gold nanoparticles are conjugated to nanobodies. The nanoprobes were well suited for specific molecular imaging using NanoSIMS at subcellular resolution. They were demonstrated to be highly selective to different proteins of interest and sufficiently sensitive for SIMS detection. The nanoprobes offer the possibility of correlating the investigation of cellular isotopic turnover to the positions of specific proteins and organelles, thereby enabling an understanding of functional and structural relations that are currently obscure.
DEEP-INTRUSION DETECTION SYSTEM WITH ENHANCED UNSW-NB15 DATASET BASED ON DEEP LEARNING TECHNIQUES

Growth in the number of devices and data has raised serious security concerns, that have increased the importance of the development of advanced intrusion detection systems (IDS). Deep learning can handle big data and in various fields has shown a great performance. Consequently, security specialists are aiming to adopt deep learning in an intrusion detection system. Numerous studies have been done on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In addition, UNSW-NB15 dataset was developed in different separated files and labelled based on binary classification, in this research, we aim to merge the whole dataset to be in one file so it can test models once, instead of test models separately for each file. then used attacks families in the dataset as new label so that it will develop multi-classification labelled dataset. We investigated the performance of deep learning with the enhanced dataset, within two classification categories (Binary and Multi-Class). We compared our proposed deep learning model results with related works. We have used accuracy and loss to evaluate the efficiency of deep learning and machine learning models in the enhanced dataset. Our proposed Deep learning models Performed yielded accuracy of 99.59% in multi-class classification and 99.26% in binary classification.
LOAD BALANCING OF VIRTUAL MACHINE SERVERS IN CLOUD COMPUTING USING GENETIC-BASED APPROCHES FOR LOAD PREDICTION

1Professor, Department of Computer Science and Engineering, Mahendra Engineering College, Mahendrapuri, 2,3,4UG Scholar (B.E), Department of Computer Science and Engineering, Mahendra Engineering College, Mahendrapuri ---------------------------------------------------------------------***---------------------------------------------------------------------ABSTRACT: This paper offers a two-stage genetic mechanism for the migration-based load stability of digital computing device hosts (VMHs) in cloud computing. Previous techniques generally expect this difficulty as a job-assignment optimization trouble and solely reflect on consideration on the modern-day VMHs’ loads; however, except thinking about hundreds of VMHs after balancing, these techniques can solely achieve confined effectiveness in actual environments. In this study, two genetic-based strategies are built-in and presented. First, overall performance fashions of digital machines (VMs) are extracted from their developing parameters and corresponding overall performance measured in a cloud computing environment. The gene expression programming (GEP) is utilized for producing symbolic regression fashions that describe the overall performance of VMs and are used for predicting masses of VMHs after load-balance. Secondly, with the VMH masses estimated via GEP, the genetic algorithm considers the present day and the future hundreds of VMHs and decides an most beneficial VM-VMH undertaking for migrating VMs and performing load-balance. The overall performance of the proposed strategies is evaluated in a actual cloudcomputing environment, Jnet, whereby these techniques are applied as a centralized load balancing mechanism. The experimental effects exhibit that our approach outperforms preceding methods, such as heuristics and records regression.