Miracle-dz/UniFAR-FTUs huggingface dataset:
Lattice-based Cryptography for IoT in A Quantum World: Are We Ready?

The impending realization of scalable quantum computers has led to active research in Post Quantum Cryptography (PQC). The challenge is harder for embedded IoT (edge) devices, due to their pervasive diffusion in today’s world as well as their stricter resources (tight area and energy budgets). Amongst various classes of quantum-resistant cryptography schemes, Lattice- based Cryptography (LBC) is emerging as one of the most viable, almost half of the ‘survivors’ of second round of the NIST’s PQC competition are lattice-based in construction. This paper surveys the practicality of deployment of these schemes. In this context, the state-of-the-art LBC implementations on the constrained devices (including low-power FPGAs and embedded microprocessors), leading in terms of low-power footprint, small area, compact bandwidth requirements and high performance is fairly evaluated and bench-marked. The work concludes by identifying a suite of some favorite LBC schemes in terms of various IoT critical performance bench-marks.
INVESTIGATION OF COPPER ADDITION ON THE MECHANICAL PROPERTIES AND CORROSION RESISTANCE OF COMMERCIALLY PURE ALUMINUM

صئاصخلا ضافخنا ىلإ يدؤت يتلاو تامعنملا بايغ يف ةريبك تابيبح لكش ىلع دمجتت هكئابس مظعمو موينمللأا ،ةيرھجملا ةينبلا ىلع يقنلا موينمللأا يلإ ساحنلا ةفاضإ ريثأت ةسارد مت لمعلا اذھ يف ،حطسلا ةدوجو ةيكيناكيملا نم تانيع ةثلاث ريضحت مت دقلو . لكآتلا ةمواقمو ةيكيناكيملا صئاصخلا ،مدصلا ةقاط ،تابيبحلا مجح ،ةدلاصلا ساحنلا يف ةدايزلا تدأ ثيح ايئايميكو اكيناكيم اھتسارد متو ساحن %9 ،%6 ،%3 بسنب ساحنلا-موينمللأا ةكيبس صئاصخلا يف نسحتو تابيبحلا يف ظوحلم ميعنت ىلاو ةناتملا يف ليلق ضافخناو ةدلاصلا يف ةيطخ ةدايز يلإ تناك لكآتلا ةمواقمو ةيكيناكيملا صئاصخلا ،مدصلا ةمواقم نم ةفلتخملا صئاصخلا . لكآتلا ةمواقمو ةيكيناكيملا . موينمللاا ىلا ساحن %6 ةبسن ةفاضا دنع لضفلاا
The influence of commercial enzymes on wine clarification and on the sensory characteristics of wines made from three banana cultivars 1

The purpose of this study was to apply enzyme preparations to banana pulp to improve wine clarity, alcohol yields and to assess sensory properties. The ultimate goal is to produce quality and safe banana wine with a stable shelf life. Commercial enzymes were applied to banana (Musa spp.) pulp. The materials used in the study included four selected commercial enzyme preparations and banana juices extracted from pulps of three banana cultivars. Compared with the control, the wine turbidity was lowered significantly in the wines prepared from juices that were extracted from commercial enzyme treated pulps. The effect of proteases on protein hazes was also investigated after periods of one week and four weeks. The addition of proteases had a significant effect on the protein hazes (p<0.05). A longer incubation period resulted in greater reduction in turbidity. The alcohol yields were significantly better (p<0.05) in the wines prepared from juices extracted from pectinolytic enzyme treated pulps than in the untreated pulps. The general organoleptic quality of the wine did not show significant difference (p>0.05) from the control according to the ranking attributes assessed by the panellists. The categories that were consensually derived by the panel of tasters in the flavour profile of the banana wines prepared from three cultivars included wine sweetness, acidity, sourness, fruitiness, astringency, aromatic and dryness. Overall acceptance of the banana wines by the panel scored five and a half points (5.5/9) on the nine-point hedonic scale.
Energy and protein requirements of pigs and the utilization of fibrous feedstuffs in Nigeria : A review

A sound nutrition programme, which allows the understanding of the interaction between nutrients and requirements for different levels of production, is essential for raising swine profitably. Nutrient requirement is that amount of each essential nutrient that will result in maximum production with a minimum of feeding. Hence, high quality feeds containing the essential nutrients in the amounts necessary to meet the animal’s requirements must be provided, in order to attain an optimal rate and efficiency of growth from birth to market. The formulation of balanced diets that provide the correct amounts and proportions of these nutrients is essential to support the requirements for maintenance, growth and reproduction of the animal. It is only then that the feeding programme can be said to have a major impact on the performance and the overall profitability of the swine herd. The scarcity of conventional feeds has hindered the growth and development of the livestock industry in Nigeria. The general shortage of energy and protein feeds appear to be more severe for non-ruminants that depend to a great extent on compounded feeds, especially pigs, which are bulk feeders. There are a number of agro-industrial by-products, farm waste or crop residues that have been exploited as alternative feed sources for the high energy cereals. This has made a substantial contribution towards better and more economic feeding of non-ruminants. However, fibrousness, a feature of most locally available agroindustrial by-products and wastes has limited their use.
Study of Production of Green Conjugates of Silver Nanoparticles for Determination of Antimicrobial Potential against Biofilm Producing Staphylococcus aureus

Nanotechnology is a latest field of modern research dealing with synthesis, design and manipulation of nanosized particles. Remarkable development in nanotechnology has opened a novel and very wide frontier of application that includes the synthesis of International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 6 Number 8 (2017) pp. 2280-2286 Journal homepage: http://www.ijcmas. com
The Universal Networking Language in Action in English-Arabic Machine Translation

This paper presents an interlingua approach to the machine translation of lengthy documents. This appr oach is based on encoding the source text in the form of universa l semantic networks, using the Universal Networking Language, UNL interlingua, which can then be decoded back int o any natural language. This UNL technology has bee n applied to 1000 pages from the Encyclopedia of Life support Systems (EOLSS) in order to be translated into the six official languages of the UNESCO. This paper summarizes the overall strategy adopted with a focus on the decod ing of UNL documents into Arabic.
Earnings Smoothing, Momentum and Statistical Arbitrage: Global Evidence

We employ the notion of statistical arbitrage to investigate the relationship between earnings smoothing and returns from momentum trading of stocks and explore the role that the level of investor sophistication may play in the smoothing-return calculus. To do so, we exploit the observation that both earnings smoothing and momentum profits are related to the cross-sectional variation in returns. We analyze the relevant data of 25 developed and emerging economies. Our results confirm the proposition that momentum profits as indicated by statistical arbitrage measures are inversely related to earnings smoothing but only in those markets where investors are more sophisticated and are able to take advantage of liquidity traders, who are often uninformed.
Paid Employment Participation After Leaving Full-Time Education the First Time

This paper addresses the question: What factors are associated with, or influence, a partnered male’s participation in paid employment over his lifetime? Analyses in this paper are restricted to males who are aged 25 to 64 and have a partner. Of particular interest is whether a non-intact family background has a negative impact on an individual’s paid employment participation later in life, when the effects of other covariates are adjusted for in our statistical models. In order to answer these questions, two different parametric statistical models are applied to the first wave data from the survey of Household, Income and Labour Dynamics in Australia (HILDA).
Stability Analysis of Chili ( Capsicum frutescens L.) Genotypes in the Rift Valley Areas of Ethiopia

Five chili genotypes were evaluated for two years at three locations to evaluate the performance and yield stability. Stability differences were assessed based on linear regression of the genotype on environmental index. Subsequently, the genotype by environment interaction (GEI) was analyzed using the AMMI statistical model. The combined analysis showed that fruit yield over six environments ranged from 21 to 33 q/ha with overall mean yield of 26.8 q/ha and genotype PBC-586, PBC-142A and PBC-401 gave the highest mean yield above the grand mean. The combined analysis of variance showed significant (p<0.05) genotype and genotype by environment effects on total fruit yield. The regression coefficient for fruit yield ranged from 0.77 to 1.2. The regression coefficient of the two high yielding genotypes (PBC-586 and PBC-142A) was above 1, and their performance was high in high yielding environments where growing conditions were favorable in (MARC-2014, Wonji-2013 and Wonji-2014. Thus, both genotype was found the best genotypes with their top ranks in PCA1 and PCA2 that showed their wide adaptability to different environments and was released with local names 'Melk-Ddera and Melka-Oli' for PBC-586 and PBC-142A respectively. to be grown in the Rift Valley and similar agro-ecologies in Ethiopia. Moreover, the potential of chili production in the short rainy season can consistently augment hot pepper production in the off season under irrigated condition so that chili and pepper products can be available throughout the year.
THE INVESTIGATION OF ELECTRONIC AND THERMO-ELASTIC PROPERTIES OF LANTHANUM ORTHOFERRITE ( LaFeO 3 ) PEROVSKITE

Lanthanum orthoferrite LaFeO3 perovskite whose crystal structure is orthorhombic and conforms to Pnma space group (space group no: 62), has been investigated by using Generalized Gradient Spin Approximation (GGSA) in the Density Functional Theory (DFT) as implemented in VASP (Vienna Ab initio Simulation Package) software. The thermo-elastic properties and electronic band structure of this material have been examined after the optimization process. On-site Coulomb interaction is also included in calculations (GGSA + U). The calculated electronic band structure and total electronic density of state (DOS) of LaFeO3 compound within GGSA+U (Ueff =5.3 eV) show that this material have halfmetallic character since a band gap is observed in spin-down states. Therefore, this compound can be used applications in spintronics. The calculated elastic constants and estimated mechanical properties depended on these constants indicate mechanical stability of this material.
The Impact of Market Size and Composition on Health Insurance Premiums: Evidence from the First Year of the ACA

Under the Affordable Care Act, individual states have discretion in how they define coverage regions, within which insurers must charge the same premium to buyers of the same age, family structure, and smoking status. We exploit variation in these definitions to investigate whether the size of the coverage region affects outcomes in the ACA marketplaces. We find large consequences for small and rural markets. When states combine small counties with neighboring urban areas into a single region, the included rural markets see . 6 to . 8 more active insurers, on average, and savings in annual premiums of between $200 and $300.
Support vector machine based orthographic disambiguation

Orthographic variation can be a serious problem for many natural language-processing applications. Japanese in particular contains orthographic variation, because the large quantity of transliteration from other languages causes many possible spelling variations. To manage this problem, this paper proposes a support vector machine (SVM)-based classifier that can determine whether two terms are equivalent. We automatically collected both positive examples (sets of equivalent term pairs) and negative examples (sets of inequivalent term pairs). Experimental results yielded high levels of accuracy (87.8%), demonstrating the feasibility of the proposed approach.
Opportunities and challenges when 3D accelerating mobile user interfaces

This paper addresses new techniques and challenges for user interface design for small-screen devices made possible by the recent availability of 3D graphics hardware. A survey of current research on the topic for desktop systems is presented and applications of these techniques for small screen devices are proposed. Also general differences in user interface design between large and small screen devices are highlighted.
Unsupervised machine learning to detect patient subgroups in electronic health records

The use of Electronic Health Records (EHR) for reporting patient data has been widely adopted by healthcare providers. This data can encompass many forms of medical information such as disease symptoms, results from laboratory tests, ICD-10 classes and other information from patients. Structured EHR data is often high-dimensional and contain many missing values, which impose a complication to many computing problems. Detecting meaningful structures in EHR data could provide meaningful insights in diagnose detection and in development of medical decision support systems. In this work, a subset of EHR data from patient questionnaires is explored through two well known clustering algorithms: K-Means and Agglomerative Hierarchical. The algorithms were tested on different types of data, primarily raw data and data where missing values have been imputed using different imputation techniques. The primary evaluation index for the clustering algorithms was the silhouette value using euclidean and cosine distance measures. The result showed that natural groupings most likely exist in the data set. Hierarchical clustering created higher quality clusters than k-means, and the cosine measure yielded a good interpretation of distance. The data imputation imposed large effects to the data and likewise to the clustering results, and other or more sophisticated techniques are needed for handling missing values in the data set.
Evaluation and diagnostics of the CERA-20 C climate reanalysis ensemble P

CERA-20C is a coupled ocean-atmosphere climate reanalysis produced by the European Centre for Medium Range Weather Forecasts (ECMWF) and it covers the years 1901 to 2010. The atmospheric data assimilation is produced with a 4D-Var technique and assimilates surface pressure observations and wind speed at 10 metres over sea. It runs as an Ensemble of Data Assimilations (EDA) with 10 ensemble members. This report investigates different aspects of the behaviour of the atmospheric ensemble throughout the century. By looking at how the analysis ensemble spread evolves with maps and time series it can be seen that the spread gradually decreases during the century as more observations are being assimilated into the system. It also shows that the surface observations have a substantial impact on the atmospheric state up to around 300hPa. This is especially noticeable in the northern hemisphere in the later part of the century. Ensemble reliability studies have been performed for 2010 using observations of surface pressure as a reference for the truth. These show that the ensemble spread can indeed predict the error in the analysis ensemble mean state. It captures the spatial and temporal variations of the error but it is under dispersive (spread too small) in the extra-tropics. The larger the error, the larger the mismatch between spread and error; there is a conditional bias between the spread and the error. Compared to the operational EDA at ECMWF, the spread and error in CERA-20C are much larger and the operational EDA (which has 25 members) is more reliable. Lastly, the ensemble mean and spread were compared to the climatological spread and variability by using a metric called Relative Entropy (RE). RE takes into account how both the ensemble mean and spread compares to the climate distribution and is used to assess the information content in the CERA-20C reanalysis. Maps from the beginning and the end of the century show that the information content increase with time and that the analyses in southern hemisphere in the beginning of century are close to the climate distribution.
Antecedents of Brand Loyalty in Leading Supermarket Chains in Kenya: The Mediating Role of Customer Satisfaction

The purpose of the study was to establish the extent to which customer satisfaction mediates the relationship between service quality, brand image, customer perceived value with brand loyalty in leading supermarket chains in Kenya. The population of interest comprised of customers of supermarkets in Kenya. A supermarket store sample of 30 stores from Nairobi and Nakuru counties was picked at random from the list of the stores of the four main supermarkets (Nakumatt, Uchumi, Naivas & Tusky’s). A sample of 384 customer respondents was interviewed. The study used multiple linear regression analysis in a four step process which established that customer satisfaction significantly affects brand loyalty. The study also shows that customer satisfaction fully mediates the relationship between service quality, brand image, customer perceived value with brand loyalty in leading supermarkets in Kenya. The dimensions of brand Image, service quality and customer perceived value dimensions are antecedent to brand loyalty and are a significant marketing tool for retail stores that wish to enhance the customers repurchase intention and the intention to recommend. A higher level of customer satisfaction leads to brand loyalty which is paramount to being competitive in the marketplace. The study recommends that supermarkets must strive towards increasing customer satisfaction with a view of enhancing brand loyalty and market share retention. Keywords: Brand Loyalty, Brand Image, Service Quality, Customer Perceived value, Customer Satisfaction Supermarkets
ROV NAVIGATION WITH MECHANICALLY SCANNED SONAR , DVL AND GYROSCOPE

The task of navigating a Remotely Operated underwater Vehicles (ROV) during inspection of man-made structures is performed mostly by visual references and occasionally a magnetic compass. Yet, some environments present a combination of low visibility and ferromagnetic anomalies that negates this approach. This paper, motivated by the development of a ROV designed to work on such environment, proposes a navigation method for this kind of vehicle. As the modeling of the system is nonlinear, the method proposed uses a particle filter to represent the vehicle state that is a nonparametric implementation of the Bayes filter. This method to work needs a priori knowledge of the environment map and to make the data association with this map, a mechanically scanned sonar is used as the main sensor. Besides the sonar, are also part of the navigation system, a Doppler Velocity Log ( DVL ) and a gyroscope. As the ROV is in its early stages of development, a floating platform was used in its place and because of that the estimation of the position in this work was restricted to the horizontal plane (since ROVs are usually statically stable on pitch and roll, this is a good approximation). The tests were conducted in a rectangular tank of know size, and even without a ground truth to validate the results, was possible to conclude that the propose navigation system can estimates planar position of the vehicle with
Effects of some plant growth regulators on stem anatomy of radish seedlings grown under saline (NaCl) conditions

In this work, the effects of gibberellic acid, ethylene, 24-epibrassinolide, triacontanol and polyamine (cadaverine, putrescine, spermidine, spermine) pretreatments on the leaf anatomy of radish seedlings grown under saline conditions were studied. Salt stress decreased the stomata number, epidermis cell number and width, leaf thickness and distance between vascular bundles in the varying degrees in the control seedlings non-pretreated with the growth regulators, in comparison with leaves of the ones in distilled water medium. On the other hand, it was observed that the growth regulator pretreatments affected in different degrees on the leaf anatomy of radish seedlings, and this difference was signifi cant.
The US Inflation-Unemployment Tradeoff: Methodological Issues and Further Evidence

This paper addresses the various methodological issues surrounding vector autoregressions, simultaneous equations, and chain reactions, and provides new evidence on the long-run inflation-unemployment tradeoff in the US. It is argued that money growth is a superior indicator of the monetary environment than the federal funds rate and, thus, the focus is on the inflation/unemployment responses to money growth shocks. SVAR (structural vector autoregression) and GMM (generalised method of moments) estimations confirm earlier findings in Karanassou, Sala and Snower (2005, 2008b) obtained from chain reaction structural models: the slope of the US Phillips curve is far from vertical, even in the long-run, which implies that the nominal and real sides of the economy are symbiotic. In the light of the significant and robust long-run inflation-unemployment tradeoffs, policy makers should reconsider the classical dichotomy thesis.
Numerical Weather Prediction The benefits of multi-analysis and poorman ’ s ensembles NWP

We propose a new approach to probabilistic forecasting, based on the generation of an ensemble of equally likely analyses of the current state of the atmosphere. The rationale behind this approach is to mimic a poor-man’s ensemble, which combines the deterministic forecasts from national met services around the world. The multi-analysis ensemble aims to generate a series of forecasts that are as skilful as each other and the control forecast. This produces an ensemble mean forecast which is superior not only to the ensemble members, but to the control forecast in the short range. This is something that it is not possible with traditional ensemble methods, which perturb a central analysis. Our results show that the multi-analysis ensemble is more skilful than the Met Office’s high-resolution forecast by 4% over the first three days (on average as measured for RMSE). In contrast, the ensemble mean for the ensemble currently run by the Met Office performs 2% worse than the high-resolution forecast (similar results are found for the ECMWF ensemble). We argue that the multi-analysis approach is therefore superior to current ensemble methods. Crown Copyright c � 2007
An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural Network

Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead of backpropagation as training algorithm for artificial neural network (ANN). Some other recent training-based Neural Networks, SVM, and KNN classifiers are used for comparison with the proposed classifier. The classifiers are utilized to classify image as normal or abnormal MRI human brain image. The results show that the proposed classifier is outperformed the other competing classifiers.