Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparkle: adaptive sample based scheduling for cluster computing

Traditional centralised scheduling has becoming unsuitable to analytics clusters with ever growing workload. As a promising alternative, sample based scheduling is highly scalable and, due to its decentralised nature, immune from becoming potential system bottleneck. However, the existing design could only function well in very specific application scenarios. Specifically, we argue that the performance of the baseline sample based scheduling method is sensitive to workload heterogeneity and the cluster's individual worker strength. In this work, we propose a novel method to reduce these sensitivities. We implement our method in the Sparkle scheduler and demonstrate our scheduler is capable of adapting to a much wider range of scenarios. Instead of introducing extra system costs, Sparkle's improved performance is gained by cutting unnecessary wastes and reducing the number sub-optimal scheduling decisions. Hence it could also serve as a foundation model for further studies in decentralised scheduling.
A good trade-off performance between the code rate and PMEPR for OFDM signals using generalized Rudin-Shapiro polynomials

Generalized Golay complementary sequences and multiple-shift complementary sequences have recently been introduced to encode orthogonal frequency division multiplexing (OFDM) signals, reducing the peak-to-mean envelope power ratio (PMEPR). Certain classes of these complementary sequences have been identified as a subset of second order cosets of the first order Reed-Muller codes. Since the code rates of these encoding schemes are prohibitively low for a large number of sub-carriers, it is necessary to find an efficient algebraic way to produce sufficient number of codewords such that the code rate of the encoding scheme is high enough. In this paper, we introduce generalized Rudin-Shapiro polynomials, a subset generalized Golay complementary sequences, to encode OFDM signals. In our encoding scheme, a matrix equation recursively produces a sufficient number of Rudin-Shapiro polynomials such that the code rate increases linearly with respect to the PMEPR. Therefore, it offers an excellent trade-off performance between the code rate and the PMEPR.
GISualization: visualized integration of multiple types of data for knowledge co-production

ABSTRACT Urban planning deals with multiple layers of information stemming from concurrent activities and stakeholders intervening in urban development. For a better management of complexity more comprehensiveness and data integration are needed. This study develops an adaptive and iterative mixed-method approach for knowledge production in urban transformation processes. Specific research questions relate to data integration from different sources and facilitation of co-production of knowledge beyond triangulation. A new multi-layer framework, GISualization, has been developed in the context of a research project exploring compact city qualities. The framework is structured through five data layers, representing different methods for data collection and different grades of complexity, richness and interpretation: basic statistics; advanced statistics; exogenous quali-quantitative descriptions; exogenous qualitative descriptions; and endogenous qualitative descriptions. Thus, data stem from both quantitative and qualitative sources. Our study has proven that GISualization is a methodological framework that enables analysis and visualization of complex data in a rich format. The approach is closely related to analytical eclecticism and abductivity. It embodies a collaborative communication platform that provides a language to navigate between heterogeneous data, information and methods. The GISualization framework opens up for broader stakeholder involvement and community participation extending research into the domain of transdisciplinary knowledge production.
Privacy-Preserving For Collaborative Data Publishing

This paper mainly deals with the issue of privacy preserving in data mining while collaborating n number of parties and trying to maintain confidentiality of all data providers details while collaborating their database. Here two type of attacks are addressed “insider attack” and “outsider attack”. In insider attack, the data providers use their own records and try to retrieve other data provider details. Formal protection model k-Anonymity, l-diversity, t-closeness are used to protect privacy. Here notion of m-privacy algorithm is used to maintain privacy and secure multiparty computation protocol can also be used for privacy preserving. Keywords—Privacy preserving, Anonymization, SMC,
Coverage and Throughput Analysis with a Non-Uniform Small Cell Deployment

Small cell network (SCN) offers, for the first time, a low-cost and scalable mechanism to meet the forecast data-traffic demand. In this paper, we propose a non-uniform SCN deployment scheme. The small cell base stations (BSs) in this scheme will not be utilized in the region within a prescribed distance away from any macrocell BSs, defined as the inner region. Based upon the analytical framework provided in this work, the downlink coverage and single user throughput are precisely characterized. Provided that the inner region size is appropriately chosen, we find that the proposed non-uniform SCN deployment scheme can maintain the same level of cellular coverage performance even with 50% less small cell BSs used than the uniform SCN deployment, which is commonly considered in the literature. Furthermore, both the coverage and the single user throughput performance will significantly benefit from the proposed scheme, if its average small cell density is kept identical to the uniform SCN deployment. This work demonstrates the benefits obtained from a simple non-uniform SCN deployment, thus highlighting the importance of deploying small cells selectively.
Elimination of the outliers from Aster GDEM data

Digital Elevation Models (DEM) provided by stereo matching techniques often contain unwanted outliers due to the mismatched points. Detected outliers are candidate for wrong data that may otherwise adversely lead to model misspecification, biased parameter estimation and incorrect results. Therefore detection of outlying observations and elimination of them from the data is considered as one of the first steps towards obtaining a refined DEM. Several statistical and non-statistical methods are available for analyzing the point cloud and extracting the outliers. The statistical methods often assume that the data follow a Gaussian normal distribution and hence, they look for the observations which are represented doubtful based on mean and standard deviation. In Digital Elevation Models generated from ASTER data the outliers, i.e., artifacts and anomalies, are represented as the following types: residual clouds, and errors due to different stack numbers: it occurs when generating the final GDEM from variable number of individual ASTER DEMs. In this paper a segment-based algorithm is proposed for detecting and eliminating the outliers from ASTER GDEM images. Furthermore, a water mask is produced as an additional layer representing the lower quality of the pixels located on inland water bodies. In the segment-based algorithm, the potential regions are iteratively extracted and evaluated using geometrical feature descriptors to classify the outliers and non-outlier regions. After eliminating the location of the outliers in original ASTER GDEM, an interpolation procedure is employed to fill the gaps. The quality of the final product is compared to the SRTM data and quality parameters are measured.
이색성 기울기와 이색성 선 공간을 이용한 조명 색도 추정 = Illumination chromaticity estimation using dichromatic-slope and dichromatic line space

In this paper, we present a novel dichromatic-based method for estimating illuminant chromaticity. The proposed method is essentially based on the Dichromatic Slope and Dichromatic Line Space that we introduce. Dichromatic slope is the slope of the dichromatic line that passes the image chromaticity of a pixel in chromaticity space. It is specularity-invariant and can be computed for each pixel. Accordingly, each pixel can be projected into a three-dimensional dichromatic line space defined in terms of image chromaticity and dichromatic slope. Reliable dichromatic lines can then be obtained by detecting local maxima in dichromatic line space. Illuminant chromaticity is easily estimated by finding the intersections of the resulting dichromatic lines. The proposed method has some advantages. It does not require precise color segmentation, and therefore, can deal with highly textured images. In addition, the proposed method does not suffer from nearly parallel dichromatic lines and it can estimate artificial illuminant chromaticity.
FXPAL Experiments for TRECVID 2004

For shot boundary detection, our approach combines pairwise similarity analysis and supervised classification. Using primitive low-level image features, we build secondary features based on inter-frame dissimilarity. The secondary features are motivated by prior work on media segmentation in which a kernel function is correlated along the main diagonal of a similarity matrix to construct a frameindexed novelty measure. In contrast to many previous approaches, the kernel functions combine all pairwise dissimilarity information in a neighborhood of L frames around the current frame. These secondary features are used as input to an efficient k-Nearest-Neighbor (kNN) classifier. The classifier labels each frame as a shot boundary or non-boundary, and the classifier outputs are minimally processed to determine the final segmentation.
Effect of Chronic Treatment with Vitamin E on Endothelial Dysfunction in a Type I in Vivo Diabetes Mellitus Model and in Vitro

Diabetes mellitus often leads to generalized vasculopathy. Because of the pathophysiological role of free radicals we investigated the effects of vitamin E. Twenty-eight rats were rendered diabetic by streptozotocin injection and were fed either with a diet with low (10 mg/kg of chow), medium (75 mg/kg of chow) or high amounts of vitamin E (1300 mg/kg of chow). Nine age-matched nondiabetic rats receiving 75 mg of vitamin E/kg chow served as controls. After 7 months, mesenteric microcirculation was investigated. Smooth muscle contractile function was not altered in diabetic versus nondiabetic vessels. Endothelial function was significantly reduced in diabetics; relaxation upon 1 μM acetylcholine was reduced by 50% in diabetics with a medium and high vitamin E diet. In vitamin E-deprived rats, a complete loss of endothelium-dependent relaxation was observed, and instead, acetylcholine elicited vasoconstriction.l-N G-Nitro-arginine-induced vasoconstriction was reduced in small arteries in diabetics, which was not prevented by vitamin E, but was aggravated by vitamin E deprivation. In a subchronic endothelial cell culture model, cells were cultivated with 5 or 20 mM d-glucose for an entire cell culture passage (4 days) with or without vitamin E (20 mg/l versus 0.01 mg/l). Hyperglycemia led to significant reduction in basal and ATP-stimulated nitric oxide (NO)-production. Hyperglycemia-induced reduction in basal NO-release was significantly prevented by vitamin E, whereas reduction in stimulated NO-release was not influenced. NADPH-diaphorase activity was reduced by 40% by hyperglycemia, which was completely prevented by vitamin E. We conclude that 1) vitamin E has a potential to prevent partially hyperglycemia-induced endothelial dysfunction, 2) under in vivo conditions vitamin E deficiency enhanced diabetic endothelial dysfunction dramatically, and 3) positive effects of vitamin E may be attenuated with a longer disease duration.
A confirmatory study of on-line communication satisfaction

The process of message deliver to organizational clients has changed from traditional communication channels to Web-based Communication Mode. As a result, the widely used communication satisfaction measurement instrument Communication Satisfaction Questionnaire, may require adaptation for use in the Internet age. Three models were developed and two new dimensions were recognized for model modification. The new models revealed a comparatively better absolute index data fit, and the shifts were confirmed and significant.
A Solution to the Vertex Correspondence Problem in 2 D Polygon Morphing

This thesis deals with an approach, based on human perception to solve the vertex correspondence problem in two dimensional polygon morphing. Correspondences are established between points of high curvature and their local neighborhood in both source and target shape. To compare local neighborhoods, several criteria are introduced which compose a cost function, assigning costs depending on the similarity of local neighborhoods in source and target. The optimal correspondence will be assumed as the correspondence with the least costs overall. Later on implementation details of these ideas will be shown in the Java programming language with the help of exemplary source code. The ideas of a perceptually based solution for the vertex correspondence problem presented here are elaborations of an algorithm proposed by Liu et al. presented during PG’04 (11).
Towards Component-Based System Integration Testing Framework

The success of a Component Based System (CBS) relies on integrating individual components. CBS integration testing encounters challenges similar to traditional testing; and aims to prioritize test cases with a potential benefit of improving the rate of fault detection. In this paper, we present a CBS in- tegration testing framework to identify integration test criteria and prioritize test cases. We also present an application of CBS integration testing framework to the Information Management System (IMS). risk assessment which can be used in the early phases of the software life cycle. We believe that the CBS complexity measures can enable a system analyst to identify fault-prone components; and can be used as a measure to prioritize test suites and improve the rate of fault detection. In this paper, we present a process to identify CBS integration test criteria and prioritize test suites based on the complexity metrics. The framework uses the structural control flow coverage criteria as adequacy criteria for CBS integration testing. Next, we use complexity measures (14) to select adequate test criteria for interactions in a CBS. The complexity measures enable a system analyst to identify fault-prone components and associated interactions; and are used as a measure to improve the rate of fault detection. Finally, suitable test cases are identified for a chosen test criteria. To demonstrate the usefulness of this method, it has been applied to the Information Management System (IMS) application. The rest of the paper is organized as follows. Section II reviews related literature. In Section III, we present the framework. Section IV describes the application of the method, while Section V presents results of the case study. We conclude the paper and discuss future work in Section VI.
Personality Traits and Behavioral Syndromes in Differently Urbanized Populations of House Sparrows (Passer domesticus)

Urbanization creates novel environments for wild animals where selection pressures may differ drastically from those in natural habitats. Adaptation to urban life involves changes in various traits, including behavior. Behavioral traits often vary consistently among individuals, and these so-called personality traits can be correlated with each other, forming behavioral syndromes. Despite their adaptive significance and potential to act as constraints, little is known about the role of animal personality and behavioral syndromes in animals' adaptation to urban habitats. In this study we tested whether differently urbanized habitats select for different personalities and behavioral syndromes by altering the population mean, inter-individual variability, and correlations of personality traits. We captured house sparrows (Passer domesticus) from four different populations along the gradient of urbanization and assessed their behavior in standardized test situations. We found individual consistency in neophobia, risk taking, and activity, constituting three personality axes. On the one hand, urbanization did not consistently affect the mean and variance of these traits, although there were significant differences between some of the populations in food neophobia and risk taking (both in means and variances). On the other hand, both urban and rural birds exhibited a behavioral syndrome including object neophobia, risk taking and activity, whereas food neophobia was part of the syndrome only in rural birds. These results indicate that there are population differences in certain aspects of personality in house sparrows, some of which may be related to habitat urbanization. Our findings suggest that urbanization and/or other population-level habitat differences may not only influence the expression of personality traits but also alter their inter-individual variability and the relationships among them, changing the structure of behavioral syndromes.
A model for association rules based on clustering

Association rules and clustering are fundamental data mining techniques used for different goals. We propose a unifying theory by proving association support and rule confidence can be bounded and estimated from clusters on binary dimensions. Three support metrics are introduced: lower, upper and average support. Three confidence metrics are proposed: lower, upper and average confidence. Clusters represent a simple model that allows understanding and approximating association rules, instead of searching for them in a large transaction data set.
Audience Segments in Environmental and Science Communication: Recent Findings and Future Perspectives

People differ. Not only do they vote for different parties, buy different products, have different hobbies and use different media. They also differ in their interest in, attitudes on, and behavior towards scientific and environmental issues. This has been shown for people’s general assessments of environmental issues (e.g. Eurobarometer, 2017) and of science and research (for an overview see Besley, 2013). It has also been demonstrated for people’s attitudes towards more specific topics such as global warming (for an overview see Nisbet & Myers, 2007), nuclear energy (e.g. Kristiansen, Bonfadelli, & Kovic, 2016), nanotechnology (e.g. Scheufele, Corley, Shih, Dalrymple, & Ho, 2009), or biotechnology (e.g. Bonfadelli, 2017). These differences are not randomly distributed across populations. Researchers from the social and behavioral sciences have identified the organizing logics that underlie this diversity, and identified factors which help explain the variation of people’s attitudes. They have shown, for example, that perceptual and behavioral differences with regards to scientific and environmental issues are related to people’s education, their age, gender, or socio-economic status, among other factors (see, e.g. Besley, 2013; Kawamoto, Nakayama, & Saijo, 2013; Nisbet & Myers, 2007; Research Councils UK, 2008).
EXIT Chart-Based Power Allocation for Iterative Frequency Domain MIMO Detector

Transmission power allocation in single-carrier multiple-input multiple-output (MIMO) systems with iterative frequency-domain (FD) soft cancellation (SC) minimum mean-squared error (MMSE) equalization is considered. A novel framework for transmission power minimization subject to equalizer convergence constraints, referred as convergence constrained power allocation (CCPA) method, is proposed based on extrinsic information transfer (EXIT) chart analysis. The proposed method decouples the spatial interference between the streams using singular value decomposition (SVD), and minimizes the transmission power while achieving the target mutual information for each stream after iterations at the receiver. We show that the transmission power optimization can be formulated as a convex optimization problem. Three CCPA methods, one approximately optimal, and other two heuristic methods inspired by the Lagrange duality are derived. The numerical results demonstrate that the proposed scheme outperforms the existing linear precoding schemes. Moreover, the proposed heuristic schemes can achieve performance close with that of the approximately optimal method in terms of the equalizer convergence properties as well as transmission power.
Tracking Progress in Reducing Mercury Air Emissions

A 1997 study in Minnesota indicated a relationship between mercury deposition trends and regional coal combustion (as well as other possible sources) spanning a period of two centuries (Engstrom and Swain 1997). Sediment core sampling from rural lakes in eastern Minnesota indicated a growth in mercury deposited from the air since the first half of the 1800s, with the amount peaking in the 1960s and 1970s at 4.0 to 5.2 times higher than pre-industrial levels. Deposition then declined during the 1980s to levels 3.0 to 3.4 times greater than pre-industrial levels. By comparison, sediment core samples from remote coastal lakes in southeastern Alaska showed a general increase in mercury deposition since 1850 that continued increasing after the 1960s with no downward trend. If mercury deposition trends are largely driven by a global pool, one would expect to see the same trend profiles in the remote Alaska lakes as in the rural Minnesota lakes. Because the remote Alaska lakes showed no general decrease in mercury deposition, the downward trend in the Minnesota lakes likely occurred due to changes in regional rather than global mercury emissions. The Minnesota study identified two changes related to regional coal combustion that occurred at the same time as the decrease in mercury deposition to the lakes. These were the increase in use of technologies at coal-fired power plants (as well as metal smelters) that incidentally reduced mercury air emissions and a shift away from coal to natural gas and oil for commercial and residential heating. (Other changes that may have contributed to the decline in mercury emissions include the reduced industrial use of mercury and a decrease in emissions from waste incineration.)
BM: An iterative algorithm to learn stable non-linear dynamical systems with Gaussian mixture models

We model the dynamics of non-linear point-to-point robot motions as a time-independent system described by an autonomous dynamical system (DS). We propose an iterative algorithm to estimate the form of the DS through a mixture of Gaussian distributions. We prove that the resulting model is asymptotically stable at the target. We validate the accuracy of the model on a library of 2D human motions and to learn a control policy through human demonstrations for two multi-degrees of freedom robots. We show the real-time adaptation to perturbations of the learned model when controlling the two kinematically-driven robots.