Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cost Model for SPARK SQL

In this paper, we propose a novel cost model for Spark SQL. The cost model covers the class of Generalized Projection, Selection, Join (GPSJ) queries. The cost model keeps into account the network and IO costs as well as the most relevant CPU costs. The execution cost is computed starting from a physical plan produced by Spark. The set of operations adopted by Spark when executing a GPSJ query are analytically modeled based on the cluster and application parameters, together with a set of database statistics. Experimental results carried out on three benchmarks and on two clusters of different sizes and with different computation features show that our model can estimate the actual execution time with about the 20 percent of errors on the average. Such an accuracy is good enough to let the system choose the most effective plan even when the execution time differences are limited. The error can be reduced to 14 percent, if the analytic model is coupled with our straggler handling strategy.
Spatial Measurement of Residential Segregation in Metropolitan Cape Town

Residential segregation has been studied for more than half a century, due to the negative impact this phenomenon has on the cohesive development of urban societies and integrated infrastructure. Consequently, the measurement of segregation in the city has received increasing attention, with a focus on spatial segregation indices, which allow the study of spatial distribution of population groups. This paper focuses on the spatial distribution of racial segregation in Cape Town through the application of global and local spatial segregation indices.
Modeling asymmetric vocal fold vibrations

Hoarseness in case of unilateral recurrent laryngeal nerve paralysis is caused by irregular, asymmetric vocal fold oscillations [1]. Using a high-speed camera system, these oscillations are observable in real-time. An image processing algorithm extracts the motion of the vocal folds. This work concerns the problem of reproducing the vocal fold dynamic using a biomechanical model in case of one-sided recurrent laryngeal nerve paralysis. Such a procedure, which adapts the model parameters to match the extracted dynamic of the vocal folds is the solving of an inverse problem. The values of the model parameters describe qualitatively the physiological properties of the vocal folds. The results of the inversion procedure are compared with perturbation measures defined for the extracted vocal fold oscillations.
Analysis of Genetic Algorithms and Distinctiveness in Back Propagation Neural Network on Diabetic Retinopathy classification

Neural Networks are of growing interest in the fields of pattern recognition and regression. In this report, the performance of a Back-propagation neural network model is assessed based on its classification accuracy on the Diabetic Retinopathy Debrecen dataset. The efficiency of the implemented model was observed to be similar to other neural network models. In neural networks, determining the hyper-parameters is a tough problem to solve. This paper addresses a solution to that problem by implementing a genetic algorithm to find the best hyper-parameters. Moreover, a pruning technique, distinctiveness, is applied to further reduce the hidden units. Various other performance improvement techniques such as mini-batch training are also explored.
energy transition through Value Designing for Sensitive Design

Designers can do much for a more sustainable future. Sustainability transitions research and empirical assessment of its course in a specific context can be used to identify a relevant space-time for different design initiatives. We explore this reasoning in advancing solar photovoltaics in heritage, where a loss of aesthetic qualities and the heritage value of buildings may curb where solar arrays are sited. By using the Value Sensitive Design framework we illustrate how a working compromise among the seemingly conflicting values involved can be found. The value mix used and the resulting concept informs solar proponents in siting solar in culturally sensitive ways and shows the heritage constituency that solar technology does not categorically mean a misfit with cultural heritage. 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/).
Higher coverage in word-to-word associations for the sampling-based alignment method

The most used method to compute word-toword associations for statistical machine translation is a probabilistic method, based on expectation-maximization, implemented in the tool MGIZA++ [1]. Run in two directions and followed by the grow-diag-final heuristic, it allows to extract aligned phrases from a bilingual parallel corpus. The sampling-based alignment method [5], implemented in Anymalign, is an associative method that was proposed to easily compute wordto-word associations and extract aligned phrases. It has several advantages: it can be interrupted at any time; it is bidirectional, even multilingual by design. It is known that using it in combination with standard statistical machine translation tools results in lower translation accuracy as measured by BLEU, but it has also been shown that it delivers better results than GIZA for lexicon induction [7]. The coverage of a word association method is defined as the percentage of words aligned over the total number of words contained in the training corpus. It has been observed that not all words are necessarily aligned by Anymalign and that coverage increases with larger time-outs. In this paper, we propose a new sampling method to increase the coverage of Anymalign in the wordto-word association task, to increase the number of words aligned. We also show how to keep reasonable processing times by monitoring the sizes of samples.
Predicting International Tourist Arrivals with Corrected Search Engine Query Data

As search engines constitute a leading tool in scheduling vacations, researchers have adopted search engine query data to predict the consumption of tourism products. However, when the prevailing shares of visitors come from countries in different languages and with different dominating search engine platforms, the identification of the aggregate search intensity index to forecast overall international arrivals becomes challenging since two critical sources of bias are involved. After defining the language bias and the platform bias, this study focuses on a destination with a multilingual set of source markets along with different dominating search engine platforms. We analyze monthly data (2004-2015) for Cyprus with two non-causality testing procedures. We find that the corrected aggregate search engine volume index, adjusted for different search languages and different search platforms, is preferable in forecasting international visitor volumes to Cyprus compared to the non-adjusted index.
IV A Part IV Total Hip Arthroplasty Part IV A Navigation : Total Hip Arthroplasty

Chapter 42 Validation of Imageless Total Hip Navigation – 334 J.B. Stiehl, D.A. Heck Chapter 43 Cup and Stem Navigation with the Orthopilot System – 339 H. Kiefer, A. Othman Chapter 44 Imageless Cup and Stem Navigation in Dysplastic Hips with the Navitrack and Vector Vision Systems – 344 J. Babisch, F. Layher, K. Sander Chapter 45 Navigation of Computer-Assisted Designed Hip Arthroplasty – 352 J.-N. Argenson, S. Parratte, X. Flecher, J.-M. Aubaniac Chapter 46 Computer-assisted Hip Resurfacing – 359 H. Bäthis, L. Perlick, M. Tingart, T. Kalteis, J. Grifka Chapter 47 Navigation for Hip Resurfacing – 365 T. Hess Chapter 48 Cup and Stem Navigation with the OrthoPilot System – 372 D. Lazovic Chapter 49 Cup and Stem Navigation with the VectorVision System – 378 L. Perlick, T. Kalteis, M. Tingart, H. Bäthis, C. Lüring
F 2-B : Millimeter-Wave Standoff Detection of Concealed Explosives

The goal of this project is the development and evaluation of an inexpensive, high resolution radar that can distinguish foreign objects hidden on individuals under clothing at a distance, while keeping the radar to a size that can still fit in or on a van. A new 3D system based on an sparse array of transmitting/receiving antennas and a Passive Array of Scatters (PAS) have been studied by our group recently, showing that a resolution of about 7.5 millimeters at 40 meters range can be achieved with this novel configuration at 60 GHz. During the last year, we have also investigated the use of Mechanical Reconfigurable Surfaces (MRS) for providing multiple views of the subject under test at standoff distances.
Multivariate Infrared Signal Processing by Partial Least-Squares Thermography

Abstract. This paper introduces and tests a statistical correlation method for the optimization of the pulsed thermography inspection. The method is based on partial least squares regression, which decomposes the thermographic PT data sequence obtained during the cooling regime into a set of latent variables. The regression method is applied to experimental PT data from a carbon fiber-reinforced composite with simulated defects. Results showed that with the new proposed technique is possible to suppress much of the noise affecting the detection of smaller and deepest defects.
Development of Real-time Environment Recognition System using LiDAR for Autonomous Driving 1

Light detection and ranging (LiDAR) sensors are able to measure the shape, distance, and infrared refection intensity of objects within the range of 100 m, and the performance deterioration is small against the change of the natural environment. In this research, we developed a novel real-time environment-recognition system for autonomous driving using a LiDAR sensor. Moreover, we developed an improved method of long-distance recognition precision of LiDAR by superimposing the measurement data in time-series. In this paper, we describe the developed systems and methods. Furthermore, their effectiveness, such as in recognition precision and processing time as confirmed by several experiments using a car installed with the developed LiDAR system and employed methods, is described. Keywords—Autonomous Driving, Real-Time Measurement, LiDAR
Teacher language learning and residence abroad : What makes a difference ? Perspectives from two case studies

This chapter reports on the experiences and views of two New Zealand language teachers who participated in one-year overseas immersion programmes and the ways in which these experiences were seen to impact on their target language (TL) proficiency. Following Wang’s (2010) recommendation, the chapter seeks to contribute to study and residence abroad research by focusing on the process of language learning alongside the gains achieved. We take a case study approach using data from questionnaires and interviews to provide an in-depth understanding of the teachers’ contexts, their views of changes in their TL proficiency and factors they saw as facilitating and hindering learning while on their immersion programme. Milroy’s (1987) social network framework provides a way to analyse the meaning and utility of interactional opportunities experienced by the two teachers while overseas. Findings indicate overall positive outcomes for the two teachers, but the study also uncovered a sense of missed opportunities for learning. This chapter provides insights for future immersion programmes and makes suggestions that seek to optimise teachers’ learning and thus enhance positive outcomes for their students. We expect this chapter to be of interest to the academic community concerned with language teacher professional development, as well as to language teachers wishing to undertake an immersion programme.
Chapter 8 Identifying verbal multiword expressions with POS tagging and parsing techniques

The chapter describes an extended version (USzeged+) of our previous system (USzeged) submitted to PARSEME’s Shared Task on automatic identification of verbal multiword expressions. USzeged+ exploits POS tagging and dependency parsing to identify singleand multi-token verbal MWEs in text. USzeged competed on nine of the eighteen languages, where USzeged+ aims to identify the VMWEs in all eighteen languages of the shared task and contains fixes for deficiencies of the previously submitted system. Our chapter describes how our system works and gives a detailed error analysis.
Foreground Object Detection and S egmentation B ased on U sing Region Contrast

Foreground object detection is one of the major impotent tasks in the field of computer vision, which attempt to discover important objects in still image or image sequences or locate related targets from the scene. Foreground objects detection is very important for several approaches like object recognition, surveillance, image annotation, and image retrieval, etc. In this work, we have proposed a method for detection and segmentation foreground object from image or video in case when the targets are moving or not moving. Comparisons with general foreground detectors such as background subtraction techniques our approach are able to detect important target for case the target is moving or not and can separate foreground object with high details.
BAYESIAN DEFORMABLE MODELS BUILDING 3 a penalized likelihood ( PL ) or the more recent maximum description length approach in

The problem of the definition and the estimation of generative models based on deformable templates from raw data is of particular importance for modeling nonaligned data affected by various types of geometrical variability. This is especially true in shape modeling in the computer vision community or in probabilistic atlas building in Computational Anatomy. A first coherent statistical framework modeling the geometrical variability as hidden variables was described in Allassonnière, Amit, and Trouvé (2007). The present paper gives a theoretical proof of convergence of effective stochastic approximation expectation strategies to estimate such models and shows the robustness of this approach against noise through numerical experiments in the context of handwritten digit modeling.
Multiresolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments

Abstract The Gaussian process is a standard tool for building emulators for both deterministic and stochastic computer experiments. However, application of Gaussian process models is greatly limited in practice, particularly for large-scale and many-input computer experiments that have become typical. We propose a multiresolution functional ANOVA (MRFA) model as a computationally feasible emulation alternative. More generally, this model can be used for large-scale and many-input nonlinear regression problems. An overlapping group lasso approach is used for estimation, ensuring computational feasibility in a large-scale and many-input setting. New results on consistency and inference for the (potentially overlapping) group lasso in a high-dimensional setting are developed and applied to the proposed MRFA model. Importantly, these results allow us to quantify the uncertainty in our predictions. Numerical examples demonstrate that the proposed model enjoys marked computational advantages. Data capabilities, in terms of both sample size and dimension, meet or exceed best available emulation tools while meeting or exceeding emulation accuracy. Supplementary materials for this article are available online.
Anthropometric changes in diabetic steatohepatitic patients treated pharmacologically versus dietetically

* Department of Complementary Medicine, Medical Division, National Research Center, Cairo, Egypt. ** Department of Tropical Medicine, Faculty of Medicine for Girls, Al-Azhar University, Cairo. ***Digestive Endoscopy Unit, Theodor Bilhariz Institute, Cairo, Egypt. ****Clinical Chemistry Department, Theodor Bilhariz Institute, Cairo, Egypt. ***** Department of Anthropology, Medical Division, National Research Center, Cairo, Egypt. ****** India, Corresponding author. Abstract Non-alcoholic fatty liver disease (NAFLD) encompasses a spectrum of disease ranging from simple steatosis, to inflammatory nonalcoholic steatohepatitis (NASH); with increasing levels of fibrosis and ultimately cirrhosis. Pioglitazone is an insulin sensitizer of the TZD class of Antidiabetic agents that lead to improvement of insulin sensitivity through perosisome proliferators activated receptor and improving the biochemical and histological indices in NASH patients. To evaluate the effect of pioglitazone, calorie restriction and combining the two methods; this study was performed; where 90 patients were subdivided equally among 3 groups. The first group received pioglitazone in a 30 mg daily dose, the second group followed a restricted calorie diet with caloric reduction of (500 -1000 calorie) for females and (1000 -1200 calorie) for males of the original caloric intake, while the third group was subjected to a combination of both medical treatment and calorie restriction in the same doses as the first and second group. Concerning our patients treated with pioglitazone; the mean BMI & Mean abdominal girth significantly increased after the study; while mid arm circumference non significantly increased. Patients on restricted calories diet showed the same anthropometric results. The same was for the third group. The adverse effect of weight gain was proven in both groups A and C; where pioglitazone was a component of the treatment, also caloric restriction failed to control body weight and anthropometric measures; but it may have effect if longer time and stronger adherence to the associated calorie restriction model occurs.
04 04 14 2 v 1 2 5 A pr 2 00 4 Zero-Point cooling and low heating of trapped 111 Cd + ions

We report on ground state laser cooling of single Cd ions confined in radio-frequency (Paul) traps. Heating rates of trapped ion motion are measured for two different trapping geometries and electrode materials, where no effort was made to shield the electrodes from the atomic Cd source. The low measured heating rates suggest that trapped Cd ions may be well-suited for experiments involving quantum control of atomic motion, including applications in quantum information science.
Detecting Anamolies by Particle Swarm Optimization technique for Cancer Dataset

Data mining mechanism has widely been applied in business and manufacturing companies across many industrial sectors. Data mining is defined as “The nontrivial extraction of implicit, previously unknown, and potentially useful information from data”. Data mining encompasses a number of different technical approaches, such as classification, clustering, data summarization, finding dependency networks, analysing changes and detecting anomalies. The proposed system detects the anamolies in cancer dataset using particle swarm optimization and results are taken based on number of data vs. Accuracy.
Energy Informatics in Smart Grids : Agent-based Modelling of Electricity Markets

The electric power systems are undergoing major modernization process due to latest demands that are placed on the electrical grid. A new generation of electric power systems, called the smart grid, emerges through the modernization process. The “smarts” in the smart grid are mainly achieved through vertical extension of the existing electric power systems with the (i) information and communication technology (ICT); and (ii) market layers. This paper provides conceptual study of modelling the latter of these layers, i.e., the smart grid electricity market modelling. The special focus is placed on the agent-based modelling as a viable energy informatics approach for addressing the issue of market modelling, especially in the complex environment such as the smart grid. Keywords—energy informatics; smart grid; agent-based computational economics; electricity markets; market modelling
Experiencing the engineering challenges of game development

Seven years after the introduction of project-based Computer Science (CS) education at Delft University of Technology, its highly instructive and motivating potential has been more than confirmed. Among the projects offered, the second year Games project has justly become the integrator course par excellence of the Computer Science BSc curriculum. More importantly, the Games project brings CS students for the first time to work in a realistic and interdisciplinary game development team, involving fellow students pursuing a Game Design and Development degree at the Utrecht School of the Arts. The reputation and popularity of the Games project are being effectively exploited by the Faculty for the urgent purpose of recruiting new CS students. We describe several project features that we have developed and exploited throughout the years for these purposes, mostly in close collaboration with older CS students and alumni, and with experts from various Dutch game development companies. Among other goals, we seek that candidate students experience the contribution of the various disciplines involved in the development of a computer game, and acquire a no-nonsense view on the surrounding game industry. So far our efforts have been rewarded with a steady trend of an increasing yearly enrolment.
An Analysis of Dual-Polarized Radar Measurables for Rainfall Measurement

In the Rayleigh-Gans scattering case, following the results in the book of Bringi and Chandrasekar (2001), the elements of the backscattering matrix @? A*CB D E can be written as: FHG G IKJ 2 LNM OQP RHSUTWV J ; ) PYX[Z\S J ;A] + (4) F>^_^ I%J 2\LNM` aO#P R X[Z\S J ; ) PYSUTbV J ;A] + (5) c Corresponding author address: V.N. Bringi, Colorado State University, Dept. of Electrical and Computer Engr. , Fort Collins, CO, 80523; e-mail: bringi@engr.colostate. edu F>^ G F G ^ IKJ 2\LNMU d P R 9P e SUTWV e ; f + (6)
Performance Evaluation of Replication-Based Delay-Tolerant Routing Protocols for Underwater Networks

Underwater acoustic channel has extensive and varying propagation delay, high bit error rate and inadequacy of bandwidth. One of the major applications of underwater networks is environmental monitoring and surveillance. To accomplish this task, underwater mobile networks are comprised of Autonomous Underwater Vehicles (AUVs) which roam around the area of surveillance while collecting required data and exchanging among themselves. During their operation the AUVs often suffer from intermittent connectivity as they move around the area and go out of the communication range of other mobile nodes. This feature together with the inherent properties of underwater acoustic channel makes the scenario of underwater mobile networks deployed for monitoring applications appropriate to be modeled as a Delay-Tolerant Network (DTN). Therefore, it is of utmost importance to choose a DTN routing protocol best-suited for underwater mobile networks. In this paper, we have investigated two types of replication-based DTN routing protocols: flooding and Resource Allocation Protocol for Intentional DTN (RAPID) for underwater mobile networks, and their performance is analyzed in terms to packet delivery ratio and average end-to-end delay in different load conditions across various node mobility scenarios.
Dispersion Compensation Techniques : A Review

Dispersion compensation plays a very significant role in controlling the overall chromatic dispersion of a system. It is used to avoid excessive temporal broadening of ultra-short pulses or the distortion of signals. Without dispersion compensation, each symbol gets broadened so much that it would strongly overlap with a number of neighbored symbols. Dispersion bound the bandwidth or information carrying capacity of a fiber. The dispersion compensation techniques discussed in this paper are Dispersion compensating fiber (DCF), Electronic dispersion compensation (EDC), Fiber Bragg grating (FBG), Digital filters. Keywords— equalizer, dispersion, dispersion compensation, dispersion compensating fiber (DCF), digital filters, electronic dispersion compensation (EDC), fiber Bragg grating (FBG).