Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud Framework for Wireless Sensor Networks

Sensor technology is becoming more powerful at lower costs, encouraging the large-scale deployment of wireless sensor networks (WSN) in urban environments. WSN are becoming a vehicle to the Internet of Things. However, significant research work done to date has focused on in-network support for wireless sensor network applications and not the backend, which recently has seen a surge of works concerning the integration of machine-to-machine (M2M) solutions on cloud computing platforms. The virtualization of resources brought by cloud computing platforms raises new opportunities for infrastructure deployment in urban environments, such as large scale deployment of sensors, communicating through wireless networks. This paper presents a reviews storage and processing solutions for supporting WSN, which are critical to support the growing communication and processing needs on metropolitan areas, and, considering the increased interest that the emergent Cloud Computing technology has been receiving, presents a solution for the backend of WSN that is, by itself, capable of increasing or decreasing the resources it requires according to the load of the system, guarantees that the data stored in the Cloud is protected through encryption and that is agnostic to the Cloud Computing provider. Keywords—Cloud Computing; Resource Elasticity; Large-Scale Sensing; Storage and Processing; Wireless Sensor Networks.
FinnWordNet - WordNet på finska via översättning

FinnWordNet is a WordNet for Finnish that conforms to the framework given in Fellbaum (1998) and Vossen (1998). FinnWord- Net1 is open source and currently contains 117,000 synsets. A classic WordNet consists of synsets, or sets of partial synonyms whose shared meaning is described and exemplified by a gloss, a common part of speech and a hyperonym. Synsets in a WordNet are arranged in hierarchical partial orderings according to semantic relations like hyponymy/hyperonymy. Together the gloss, part of speech and hyperonym fix the meaning of a word and constrain the possible translations of a word in a given synset. The Finnish group has opted for translating Princeton WordNet 3.0 synsets wholesale into Finnish by professional translators, because the translation process can be controlled with regard to quality, coverage, cost and speed of translation. The project was financed by FIN-CLARIN at the University of Helsinki. According to our preliminary evaluation, the translation process was diligent and the quality is on a par with the original Princeton WordNet.
Reduction of Torque Ripple in 3 Phase SRM Drive using Hybrid ( Intelligent ) Controller

Now a days, Switched Reluctance Motors (SRMs) attract additional and additional attention. The Switched Reluctance Motor is easy to construct. It not solely options a salient pole mechanical device with focused coils, that permits earlier winding and shorter finish returns than different forms of motors, but additionally options a salient pole rotor, that has no conductors or magnets and is therefore the best of all electrical machine rotors which merely makes the SRM cheap and reliable, and alongside its high speed capability and high torsion force to inertia magnitude relation, makes it a superior selection in several applications. The target of this paper is to accomplish craved speed execution of 6/4 Switched Reluctance Motor (SRM) with decreased drive swells. to acknowledge enhanced speed execution, PI+FUZZY controller was presented. Twofold shut circle framework is utilized for the higher speed execution qualities. to weaken the torsion compel swell, 3 entirely unexpected administration ways (PI, fuzzy and PI in addition to fuzzy) which are anticipated. Reproduction result comes about reassert the adequacy inside the parts of higher speed execution and torsion compel swell decrease. Keywords–Switched reluctance motor drives, device topology,
THE CEREVOICE SPEECH SYNTHESISER

This paper describes the CereVoice text-to-speech system developed by Cereproc Ltd, and its use for the generation of the test sentences for the Albayzin 2008 TTS evaluation. Also, the building procedure of a Cerevoice-compatible voice for the Albayzin 2008 evaluation using the provided database and the Cerevoice VCK, a Cereproc tool for fast and fully automated creation of voices, is described.
Journey to Becoming a Thai English Teacher : A New Perspective on Investigating Teacher Shortage 1

In this study, the author provides a unique perspective on teacher shortage by focusing on teacher retention, in terms of why teachers stay in the teaching profession, rather than focusing on teacher attrition, or why teachers leave the teaching profession. The change in perspective created an opportunity to study the journey of how teachers chose to become a teacher, which is a bridge between inspirations of becoming with inspirations of staying in the teaching profession. The findings revealed that teachers attributed their decisions to enter the teaching profession to destiny, pride, schooling, and motherly qualities. They also reported that they stayed in the teaching profession because of the students, a sense of belonging, a mission, and their hometown. This study contributes to teacher education programs because it raises awareness of how teacher educators could use these rich histories to educate student teachers that endeavor to enter the teaching profession.
Response of Two Wheat Cultivars to Foliar Application with Amino Acids under Low Levels of Nitrogen Fertilization

This study was conducted in the two successive winter seasons of 2014/2015 and 2015/2016 at the Agricultural Experimental Farm of Al-Azhar Univ. at Assiut, to study the response of two cultivars of wheat (Sids-1 and Sids-12) to foliar application of amino acids (control, 2.5 and 5 ml/L) and different nitrogen fertilizer rates (30, 45 and 60 kg N /fed.). The experiments were performed in a split-split plot design with three replicates; where wheat cultivars were assigned to the main plot, while nitrogen rates and amino acids were distributed randomly in the sub and sub-sub plot, respectively. The obtained results showed that wheat cultivars exhibited significant differences in flag leaf area in both seasons, except plant height, number of tillers/plant and number of grains/spike in the first season only, as well as number of leaves/plant and 1000-grains weight in the second season only. On other hand, number of spikes/m, grain yield/fed. , straw yield/fed. , protein and carbohydrate percentage didn't show significant differences in both seasons. Sids-12 cultivar was superior to Sids-1 in all studied traits except of grain yield/fed. , in the first season only Sids-1 was superior to Sids-12. Increasing the level of nitrogen fertilizer from 30 or 45 to 60 kg N /fed. caused a significant increases in all traits under study in both seasons. Foliar application with amino acids significant increases in all studied characters, i.e. plant height, number of leaves/plant, number of tillers/plant, flag leaf area, number of spikes/m, number of grains/spike and 1000-grains weight (g) as well as grain and straw yields/fed. Also, such treatment increased protein and carbohydrate percentages of wheat grains. The interaction between nitrogen rates and foliar application of amino acids had a significant effect on plant height, number of leaves/plant, flag leaf area, number of spikes/m and number of grains/spike in both seasons and 1000-grains weight and protein percentage in the second season only. Concerning the interaction between cultivars and nitrogen fertilizer rates were significant on flag leaf area in the first season only. The interaction between cultivars and foliar application of amino acids were significant on plant height in both seasons. In general, the highest value of grain yield/ fed. was obtained from Sids-1 and Sids-12 cultivars when received 60 kg N/fed. and foliar application of amino acids under Assiut conditions. It is recommended to use foliar application of amino acids with a reduced level of chemical nitrogen fertilizer to improve growth and yield of wheat and to decrease the environmental pollution resulting from using high amount of large chemical fertilizers.
Recent Advances in Visualization of Volumetric Data

In the past few years, there have been key advances in the three main approachesto the visualization of volumetric data: isosurfacing, slicing and volume rendering, which together make up the ﬁeld of volume visualization. In this report we set the scene by describing the fundamental techniques for each of these approaches, using this to motivate the range of advances which have evolved over the past few years. In isosurfacing, we see how the original marching cubes algorithm has matured,with improvements in robustness, topological consistency, accuracy and performance. In the performance area, we look in detail at pre-processing steps which help identify data which contributes to the particular isosurface required.In slicing too, there are performance gains from identifying active cells quickly. In volume rendering, we describe the two main approaches of ray casting and projection. Both approaches have evolved technically over the past decade, and the holy grail of real-time volume rendering has arguably been reached. Theaimof this Eurographics 2000 STAR is to pull these developments together in a coherent review of recent advances in volume visualization.
Acoustic characteristics of English diphthongs produced by Japanese learners

This study aimed to characterize the English diphthongs produced by the Japanese learners of English, compared to the native speakers of English. The target diphthongs were . The duration, trajectory length and spectral rate of change were measured. It was revealed that  was problematic to the Japanese learners.
Learning Computationally Efficient Metrics for Large Scale Person Identification

Nearest neighbor (NN) classifiers rely on a distance metric either a priori fixed or previously estimated through metric learning. When such metric learning occurs, a natural objective is to minimize the classification errors of the NN classifier. This learning procedure is however commonly performed without any regard to the computational efficiency of the NN classifier at test time. In this work, we propose to formulate the metric learning problem as a multi-objective trade-off between classification performance and computational efficiency at test time. This is illustrated here in the context of person identification. Specifically, a Mahalanobis metric learning scheme is cast as a convex optimization problem over a set of positive semi-definite matrices, and solved through projected gradient descent. Experimental results are presented on semiartificial data, representative of a profile-based person identification task at a large scale (≥ 10 individuals). Our method shows a significant improvement of the search efficiency of a NN classifier, compared to standard soft-margin maximization metrics. In presence of hard time and space constraints, it leads to a drastic enhancement of the identification performance.
Industrial development : Some stylized facts and policy directions

Structural issues were once at the core of thinking on economic development policies. Development economists of the “old school” understood well the fundamental role that structural transformation played in the course of development. In their thinking, the movement of labour from traditional activities in agriculture and other primary sectors to “modern” industry was the key to raising the economy’s saving and investment rates and to fostering economic growth. The faster the rate at which labour would move from traditional agriculture and low-productivity informal activities to the modern sector, the more rapid the rate of economic growth. Of course not all modern activities need to take place within manufacturing industries. The expansion of non-traditional agriculture can play an important role in development (as it has notably in Chile). And the modernization of traditional agriculture can be a significant source of productivity gains (as with the green revolution). But historically rapid growth is associated first and foremost with the expansion of industrial activities. Economic globalization has greatly increased the premium on manufacturing, particularly of the exportable kind. In recent decades rapidly growing developing countries have been able to grow much faster than earlier antecedents (Britain during the industrial revolution, the United States during its catch-up with Britain in the late 19 century, or European recovery in the post-war period). The reason for this is that world markets provide nearlimitless demand for manufactured exports from developing countries. An expansion of non-tradables is self-limiting, as the domestic terms of trade eventually turns against non-tradables, choking off further investment and growth. And there are natural limits to export-led growth based on primary products, as country after country has discovered. Developing countries exporting manufactured products do not face such limits as long as they can latch on to new activities which face dynamic demand in rich countries’ markets.
Does Surgeon Sex Matter ? s of Female and Male Surgeons Practice Patterns and Outcome

Objective: We sought to compare postoperative outcomes of female surgeons (FS) and male surgeons (MS) within general surgery. Summary of Background Data: FS in the workforce are increasing in number. Female physicians provide exceptional care in other specialties. Differences in surgical outcomes of FS and MS have not been examined. Methods: We linked the AMA Physician Masterfile to discharge claims from New York, Florida, and Pennsylvania (2012 to 2013) to examine practice patterns and to compare surgical outcomes of FS and MS. We paired FS and MS operating at the same hospital using cardinality matching with refined balance and compared inpatient mortality, any postoperative complication, and prolonged length of stay (pLOS) in FS and MS. Results: Overall practice patterns differed between the 663 FS and 3219 MS. We identified 2462 surgeons (19% FS, 81% MS) at 429 hospitals who met inclusion criteria for outcomes analysis. FS were younger (mean age SD FS: 48.5 8.4 years, MS: 54.3 9.4y; P < 0.001) with less clinical experience (mean years SD FS: 11.6 8.3 y, MS: 17.6 10.0 years; P < 0.001) than MS before matching. FS had lower rates of inpatient mortality (FS: 1.51%, MS: 2.30%; P < 0.001), any postoperative complication (FS: 12.6%, MS: 16.1%; P < 0.001), and pLOS (FS: 18.4%, MS: 20.7%; P < 0.001) before matching. After matching, FS and MS outcomes were equivalent. Conclusion: Surgeon practice patterns vary by sex and experience. FS and MS with similar characteristics who treat similar patients at the same hospital have equivalent rates of inpatient morality, postoperative complications, and prolonged length of hospital stay. Patients should select the surgeon who is the best fit for them regardless of sex.
A Rough RBF Neural Networks Optimized by the Genetic Algorithm

The large-scale data parallelism processing is an inherent characteristic of artificial neural networks, but the networks bring the efficiency problems of data processing. As one of the artificial neural networks, Radial Basis Function (RBF) neural networks have the same problem. Therefore, how to reduce the scale of data to improve the efficiency of data processing has been a hot issue among the artificial intelligence scholars. Based on the traditional RBF neural networks, this paper puts forward a method which determines the important degree of the sample attributes based on knowledge entropy of Rough set by analyzing the relationship between the knowledge entropy and the weight of the sample attributes, and assesses the importance of the sample attributes between the input layer and the hidden layer, namely the attribution reduction, so as to achieve reduce the scale of data processing. The ultimate aim of training RBF neural networks is to seek a set of suitable networks parameters which makes the sample output error achieve the minimum or required accuracy, while Genetic Algorithm (GA) has the properties of finding out the optimal solution through multiplepoint random search in the solution space, so Genetic Algorithm is used to optimize the centers, the widths and the weights between the hidden layer and the output layer of RBF neural networks in training the networks. Finally, a model about A Rough RBF Neural Networks Optimized by the Genetic Algorithm (GA-RS-RBF) is proposed in this paper. The simulation results show that the rough RBF neural network optimized by the Genetic Algorithm is better than the traditional RBF neural networks in classification about Iris datasets.
How do Humans Interact with Algorithms? Experimental Evidence from Health Insurance

Algorithms increasingly assist consumers in making their purchase decisions across a variety of markets; yet little is known about how humans interact with algorithmic advice. We examine how algorithmic, personalized information affects consumer choice among complex financial products using data from a randomized, controlled trial of decision support software for choosing health insurance plans. The intervention significantly increased plan switching, cost savings, time spent choosing a plan, and choice process satisfaction, particularly when individuals were exposed to an algorithmic expert recommendation. We document systematic selection - individuals who would have responded to treatment the most were the least likely to participate. A model of consumer decision-making suggests that our intervention affected consumers’ signals about both product features (learning) and utility weights (interpretation).
Attention Based Hierarchical RNN Model

Inspired by recent work in soft attention and hierarchical recurrent neural network models, we propose an attention based hierarchical model for image captioning and video recognition. Our model extracts features from different layers of the Convolutional Neural Network and enables implicitly regularizing the network based on previous attentions. We show that our model achieves state-of-the-art performance on MS COCO while learning where to focus at different time steps.
Comparison of renal dynamic imaging and two plasma sample method in determining glomerular filtration rate in patients with diabetic nephropathy

Objective: This study aims to compare the application significance of renal dynamic imaging (RDI) and two plasma sample (TPS) method in determining glomerular filtration rate (GFR) in patients with diabetic nephropathy. Methods: A total of 68 patients who received medical service in the Endocrinology Department of our hospital were enrolled into this study. These patients were divided into three groups: early nephropathy group (DN1 group, n=23), clinical nephropathy group (DN2 group, n=23) and uremia group (DN3 group, n=22). In addition, 22 healthy subjects were enrolled and assigned as control group (DN0 group). GFRs of all subjects were determined by both RDI and TPS method, and the correlation between results and determination methods were analyzed. Results: GFRs (gGFRs and tGFRs) in the DN0, DN1 and DN2 groups were determined by RDI and TPS, and the difference was not statistically significant (P>0.05). Differences between gGFR and tGFR in the DN3 group was statistically significant (P<0.05). Correlation coefficients between GFRs determined by RDI and TPS in the DN0, DN1, DN2 and DN3 groups were 0.999, 0.947, 0.925 and 0.999, respectively. All values exhibited a significant positive correlation (P<0.05). Conclusions: Both RDI and TPS can sensitively reveal GFR changes in early and clinical nephropathy. However, RDI revealed poor accuracy and low sensitivity in the diagnosis of diabetic nephropathy in the uremic phase, and medical history combined with other examination methods should be applied in the diagnosis.
Best-Worst Scaling: Theory, Methods and Applications

Preface Acknowledgments Theory and Methods: 1. Introduction and overview of the book 2. The BWS object case 3. The BWS profile case 4. The BWS multi-profile case 5. Basic models 6. Looking forward Applications - Case 1: 7. BWS object case application: attitudes towards end-of-life care Terry N. Flynn, Elisabeth Huynh and Charles Corke 8. How consumers choose wine: using best-worst scaling across countries Larry Lockshin and Eli Cohen 9. Best-worst scaling: an alternative to ratings data Geoffrey N. Soutar, Jillian C. Sweeney and Janet R. McColl-Kennedy Applications - Case 2: 10. When the ayes don't have it: supplementing an accept/reject DCE with a case 2 best-worst scaling task Richard T. Carson and Jordan J. Louviere 11. BWS profile case application: preferences for treatment in dentistry Emma McIntosh and Terry N. Flynn 12. BWS profile case application: preferences for quality of life in Australia Terry N. Flynn and Elisabeth Huynh Applications - Case 3: 13. The stability of aggregate-level preferences in longitudinal discrete choice experiments Towhidul Islam and Jordan J. Louviere 14. Case 3 best-worst analysis using delivered pizza and toothpaste examples Bart D. Frischknecht and Jordan J. Louviere 15. Using alternative-specific DCE designs and best and worst choices to model choices Jordan J. Louviere References Subject index Author index.
The accuracy assessment of presepsin (sCD14-ST) for mortality prediction in adult patients with sepsis and a head-to-head comparison to PCT: a meta-analysis

Objective: The soluble cluster of differentiation 14 subtype (sCD14-ST) or presepsin has recently been identified as a promising biomarker in sepsis. The present meta-analysis is performed to assess the prognostic value of presepsin in septic patients. Further, we compare the prognostic performance between presepsin and procalcitonin (PCT) in predicting all-cause mortality in these patients. Methods: A systemic and comprehensive search was conducted in PubMed, Embase and Cochrane databases by using Exploded Medical Subject Headings and appropriate corresponding keywords. Studies were eligible if they assessed the prognostic value of presepsin in sepsis and provided sufficient information to construct a 2×2 contingency table. A bivariate meta-analysis model was used to calculate the pooled sensitivity, specificity, positive/negative likelihood ratios and diagnostic odds ratio. The Chi-square and I2 index were used to assess the heterogeneity and inconsistency. The Deek’s funnel plot asymmetry test was used to assess the likelihood of publication bias. Results: Nine publications, comprising 1,561 patients, were included in this study. The overall area under the receiver operating characteristic curve (AUROC) of presepsin was 0.77 (95% CI, 0.73–0.81) with a pooled prognostic sensitivity (SEN) and specificity (SPE) of 0.83 (95% CI, 0.72–0.90) and 0.69 (95% CI, 0.63–0.74), respectively. Additionally, the PLR, NLR and DOR of presepsin were 2.6 (95% CI, 2.1–3.3), 0.25 (95% CI, 0.15–0.44) and 10 (95% CI, 5–22), respectively. The AUROC of PCT was 0.81 (95% CI, 0.78–0.84) with a pooled SEN of 0.76 (95% CI, 0.55–0.89) and SPE of 0.74 (95% CI, 0.33–0.94). There is no statistically significant difference in the performance of pooled SEN and SPE between presepsin and PCT, with a p value of 0.39 and 0.71, respectively. Conclusions: Based on the results of this meta-analysis, both presepsin and PCT are promising biomarkers for the prognosis of mortality in sepsis.