nils-herrmann/clean_scirepeval huggingface dataset:
Identification Of Food Grains And Its Quality Using Pattern Classification

The research work deals with an approach to perform texture and morphological based retrieval on a corpus of food grain images. The work has been carried out using Image Warping and Image analysis approach. The method has been employed to normalize food grain images and hence eliminating the effects of orientation using image warping technique with proper scaling. The images have been properly enhanced to reduce noise and blurring in image. Finally image has segmented applying proper segmentation methods so that edges may be detected effectively and thus rectification of the image has been done. The approach has been tested on sufficient number of food grain images of rice based on intensity, position and orientation. A digital image analysis algorithm based on color, morphological and textural features was developed to identify the six varieties rice seeds which are widely planted in Chhattisgarh region. Nine color and nine morphological and textural features were used for discriminant analysis. A back propagation neural network-based classifier was developed to identify the unknown grain types. The color and textural features were presented to the neural network for training purposes. The trained network was then used to identify the unknown grain types.
Accurate Continuous Sweeping Framework in Indoor Spaces With Backpack Sensor System for Applications to 3-D Mapping

In indoor environments, there exists a few distinctive indoor spaces' features (ISFs). However, up to our knowledge, there is no algorithm that fully utilizes ISF for accurate 3-D SLAM. In this letter, we suggest a sensor system that efficiently captures ISF and propose an algorithm framework that accurately estimates sensor's 3-D poses by utilizing ISF. Experiments conducted in six representative indoor spaces show that the accuracy of the proposed method is better than the previous method. Furthermore, the proposed method shows robust performances in a sense that a set of adjusted parameters of the related algorithms does not need to be recalibrated as target environment changes. We also demonstrate that the proposed method not only generates 3-D depth maps but also builds a dense 3-D RGB-D map.
The Irony of Satire Political Ideology and the Motivation to See What You Want to See in The Colbert Report

This study investigated biased message processing of political satire in The Colbert Report and the influence of political ideology on perceptions of Stephen Colbert. Results indicate that political ideology influences biased processing of ambiguous political messages and source in late-night comedy. Using data from an experiment (N = 332), we found that individual-level political ideology significantly predicted perceptions of Colbert’s political ideology. Additionally, there was no significant difference between the groups in thinking Colbert was funny, but conservatives were more likely to report that Colbert only pretends to be joking and genuinely meant what he said while liberals were more likely to report that Colbert used satire and was not serious when offering political statements. Conservatism also significantly predicted perceptions that Colbert disliked liberalism. Finally, a post hoc analysis revealed that perceptions of Colbert’s political opinions fully mediated the relationship between political ideology and individual-level opinion.
How Lufthansa Capitalized on Big Data for Business Model Renovation

“Big data” is the term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Big data offers unprecedented opportunities for enterprises to use analytics for achieving new levels of competitive advantage, including optimizing operations, customer intelligence and innovation in products and services. It is one of the most significant technology disruptions for businesses since the meteoric rise of the Internet and the digital economy.3 Proponents of big data expect organizations to derive much value from high velocity,4 massive volumes of data originating from “everywhere,” including the Internet of Things (IoT),5
SegAN: Adversarial Network with Multi-scale L 1 Loss for Medical Image Segmentation

Inspired by classic Generative Adversarial Networks (GANs), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN’s discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. Instead, we use a fully convolutional neural network as the segmentor to generate segmentation label maps, and propose a novel adversarial critic network with a multi-scale L 1 loss function to force the critic and segmentor to learn both global and local features that capture long- and short-range spatial relationships between pixels. In our SegAN framework, the segmentor and critic networks are trained in an alternating fashion in a min-max game: The critic is trained by maximizing a multi-scale loss function, while the segmentor is trained with only gradients passed along by the critic, with the aim to minimize the multi-scale loss function. We show that such a SegAN framework is more effective and stable for the segmentation task, and it leads to better performance than the state-of-the-art U-net segmentation method. We tested our SegAN method using datasets from the MICCAI BRATS brain tumor segmentation challenge. Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.
Apollo: Scalable and Coordinated Scheduling for Cloud-Scale Computing

Efficiently scheduling data-parallel computation jobs over cloud-scale computing clusters is critical for job performance, system throughput, and resource utilization. It is becoming even more challenging with growing cluster sizes and more complex workloads with diverse characteristics. This paper presents Apollo, a highly scalable and coordinated scheduling framework, which has been deployed on production clusters at Microsoft to schedule thousands of computations with millions of tasks efficiently and effectively on tens of thousands of machines daily. The framework performs scheduling decisions in a distributed manner, utilizing global cluster information via a loosely coordinated mechanism. Each scheduling decision considers future resource availability and optimizes various performance and system factors together in a single unified model. Apollo is robust, with means to cope with unexpected system dynamics, and can take advantage of idle system resources gracefully while supplying guaranteed resources when needed.
Customer segmentation and analysis of a mobile telecommunication company of Pakistan using two phase clustering algorithm

Pakistan hosts a competitive and fluid telecommunication market and for a company to sustain, create customer value and increase economic efficiency, it needs to better understand its customers. The purpose of clustering or customer segmentation is to deliver actionable results for marketing, product development and business planning. In this paper, we focus on customer segmentation using clustering algorithms on real data of a telecommunication company in Pakistan. After choosing appropriate attributes for clustering, we used the two-step clustering algorithm in order to create different customer segments. Moreover, the insights obtained from each segment were analyzed before suggesting marketing strategies for up-selling and better targeted campaigns.
A survey on unsupervised outlier detection in high-dimensional numerical data

High-dimensional data in Euclidean space pose special challenges to data mining algorithms. These challenges are often indiscriminately subsumed under the term ‘curse of dimensionality’, more concrete aspects being the so-called ‘distance concentration effect’, the presence of irrelevant attributes concealing relevant information, or simply efficiency issues. In about just the last few years, the task of unsupervised outlier detection has found new specialized solutions for tackling high-dimensional data in Euclidean space. These approaches fall under mainly two categories, namely considering or not considering subspaces (subsets of attributes) for the definition of outliers. The former are specifically addressing the presence of irrelevant attributes, the latter do consider the presence of irrelevant attributes implicitly at best but are more concerned with general issues of efficiency and effectiveness. Nevertheless, both types of specialized outlier detection algorithms tackle challenges specific to high-dimensional data. In this survey article, we discuss some important aspects of the ‘curse of dimensionality’ in detail and survey specialized algorithms for outlier detection from both categories.
Novel F-Shaped Quad-Band Printed Slot Antenna for WLAN and WiMAX MIMO Systems

A novel F-shaped microstrip slot antenna for WLAN and WiMAX multiple-input-multiple-output (MIMO) systems is presented. The proposed antenna structure consists of both open-ended and short-ended slots connected by a metal “via” to a microstrip line. The open-ended slot is exploited to obtain resonant mode at 2.4 GHz, and three short-ended slots are aimed to obtain resonant modes at 3.5, 5.2, and 5.8 GHz, whose center frequencies can be adjust by the slot lengths. The parametric studies show that the center frequencies are independent of each other. A four-element array configuration of the proposed antenna for MIMO applications is also studied. The simulation and measurement results of reflection coefficient, mutual coupling, and radiation pattern are presented, which attest to the applicability of antenna.
Cost-aware travel tour recommendation

Advances in tourism economics have enabled us to collect massive amounts of travel tour data. If properly analyzed, this data can be a source of rich intelligence for providing real-time decision making and for the provision of travel tour recommendations. However, tour recommendation is quite different from traditional recommendations, because the tourist's choice is directly affected by the travel cost, which includes the financial cost and the time. To that end, in this paper, we provide a focused study of cost-aware tour recommendation. Along this line, we develop two cost-aware latent factor models to recommend travel packages by considering both the travel cost and the tourist's interests. Specifically, we first design a cPMF model, which models the tourist's cost with a 2-dimensional vector. Also, in this cPMF model, the tourist's interests and the travel cost are learnt by exploring travel tour data. Furthermore, in order to model the uncertainty in the travel cost, we further introduce a Gaussian prior into the cPMF model and develop the GcPMF model, where the Gaussian prior is used to express the uncertainty of the travel cost. Finally, experiments on real-world travel tour data show that the cost-aware recommendation models outperform state-of-the-art latent factor models with a significant margin. Also, the GcPMF model with the Gaussian prior can better capture the impact of the uncertainty of the travel cost, and thus performs better than the cPMF model.
Opinion mining: reviewed from word to document level

Opinion mining is one of the most challenging tasks of the field of information retrieval. Research community has been publishing a number of articles on this topic but a significant increase in interest has been observed during the past decade especially after the launch of several online social networks. In this paper, we provide a very detailed overview of the related work of opinion mining. Following features of our review make it stand unique among the works of similar kind: (1) it presents a very different perspective of the opinion mining field by discussing the work on different granularity levels (like word, sentences, and document levels) which is very unique and much required, (2) discussion of the related work in terms of challenges of the field of opinion mining, (3) document level discussion of the related work gives an overview of opinion mining task in blogosphere, one of most popular online social network, and (4) highlights the importance of online social networks for opinion mining task and other related sub-tasks.
ZVS Phase-Shift PWM-Controlled Single-Stage Boost Full-Bridge AC–AC Converter for High-Frequency Induction Heating Applications

This paper presents a new single-stage utility frequency ac–high-frequency ac resonant power converter for high-frequency induction heating applications. The newly proposed ac–ac converter features a boost converter and a full-bridge resonant inverter integrated circuit with a source voltage sensorless control system. The experimental results of a 3.0 kW–40 kHz prototype are demonstrated on zero-voltage soft switching and steady-state characteristics, then, the validity of the proposed ac–ac converter is revealed from a practical viewpoint.
Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits

Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While current methods offer efficiencies by adaptively choosing new configurations to train, an alternative strategy is to adaptively allocate resources across the selected configurations. We formulate hyperparameter optimization as a pure-exploration non-stochastic infinitely many armed bandit problem where allocation of additional resources to an arm corresponds to training a configuration on larger subsets of the data. We introduce HYPERBAND for this framework and analyze its theoretical properties, providing several desirable guarantees. We compare HYPERBAND with state-ofthe-art Bayesian optimization methods and a random search baseline on a comprehensive benchmark including 117 datasets. Our results on this benchmark demonstrate that while Bayesian optimization methods do not outperform random search trained for twice as long, HYPERBAND in favorable settings offers valuable speedups.
Deep Neural Network Based Feature Representation for Weather Forecasting

This paper concentrated on a new application of Deep Neural Network (DNN) approach. The DNN, also widely known as Deep Learning(DL), has been the most popular topic in research community recently. Through the DNN, the original data set can be represented in a new feature space with machine learning algorithms, and intelligence models may have the chance to obtain a better performance in the “learned” feature space. Scientists have achieved encouraging results by employing DNN in some research fields, including Computer Vision, Speech Recognition, Natural Linguistic Programming and Bioinformation Processing. However, as an approach mainly functioned for learning features, DNN is reasonably believed to be a more universal approach: it may have the potential in other data domains and provide better feature spaces for other type of problems. In this paper, we present some initial investigations on applying DNN to deal with the time series problem in meteorology field. In our research, we apply DNN to process the massive weather data involving millions of atmosphere records provided by The Hong Kong Observatory (HKO)1. The obtained features are employed to predict the weather change in the next 24 hours. The results show that the DNN is able to provide a better feature space for weather data sets, and DNN is also a potential tool for the feature fusion of time series problems.
Traffic Analysis with Deep Learning

Deep Neural Networks (DNN) has obtained enormous attention with its advantageous feature learning and its powerful prediction ability. In this paper, we broadly study the applicability of deep learning to traffic analysis and present its effectiveness on the feature extraction for state-of-the-art machine learning algorithms, website and keyword fingerprinting attacks, and the prediction on the fingerprintability of websites. To the best of our knowledge, this is the first extensive work to introduce various applications using DNN in traffic analysis. With great help of DNN, the quality of cutting edge website fingerprinting attacks is upgraded while the feature dimension becomes much lower. As the classifiers, DNN successfully detects which website the user visited among 100 websites with 91% TPR and 1% FPR against 100,000 background websites, and as the fingerprintability predictors, it almost perfectly determines the fingerprintability of 4,500 website traffic instances with 99% of accuracy.
Information Centric Networking over SDN and OpenFlow: Architectural Aspects and Experiments on the OFELIA Testbed

Information Centric Networking (ICN) is a new networking paradigm in which the ne twork provides users with content instead of communicatio n channels between hosts. Software Defined Networking (SDN) is an approach that promises to enable the co ntinuous evolution of networking architectures. In this paper we propose and discuss solutions to support ICN by using SDN concepts. We focus on an ICN framework called CONET, which groun ds its roots in the CCN/NDN architecture and can interwork with its implementation (CCNx). Altho ugh some details of our solution have been specifically designed for the CONET architecture, i ts general ideas and concepts are applicable to a c lass of recent ICN proposals, which follow the basic mod e of operation of CCN/NDN. We approach the problem in two complementary ways. First we discuss a general and long term solution based on SDN concepts without taking into account specific limit ations of SDN standards and equipment. Then we focus on an experiment to support ICN functionality over a large scale SDN testbed based on OpenFlow, developed in the context of the OFELIA Eu ropean research project. The current OFELIA testbed is based on OpenFlow 1.0 equipment from a v ariety of vendors, therefore we had to design the experiment taking into account the features that ar e currently available on off-the-shelf OpenFlow equipment.
How attributional ambiguity shapes physiological and emotional responses to social rejection and acceptance.

The authors examined White and Black participants' emotional, physiological, and behavioral responses to same-race or different-race evaluators, following rejecting social feedback or accepting social feedback. As expected, in ingroup interactions, the authors observed deleterious responses to social rejection and benign responses to social acceptance. Deleterious responses included cardiovascular (CV) reactivity consistent with threat states and poorer performance, whereas benign responses included CV reactivity consistent with challenge states and better performance. In intergroup interactions, however, a more complex pattern of responses emerged. Social rejection from different-race evaluators engendered more anger and activational responses, regardless of participants' race. In contrast, social acceptance produced an asymmetrical race pattern--White participants responded more positively than did Black participants. The latter appeared vigilant and exhibited threat responses. Discussion centers on implications for attributional ambiguity theory and potential pathways from discrimination to health outcomes.
Learning to rank for top mobile games prediction

Learning to rank as one of machine learning techniques has been widely used in document categorization, information retrieval, text processing, product rating, and other ranking problem domains. Ranking task has become an interesting research in terms of big data and analytics in data mining. Meanwhile, top mobile games prediction is one of the popular topics in terms of increasing games excitement in mobile devices today. This paper introduces an approach for top mobile games prediction using learning to rank. This paper proposes a prediction system which is called MGPrediction. Experiments have been conducted by performing our prediction system and executing various algorithms to our ranking dataset of mobile games using learning to rank in RankLib. Preliminary results present that our system has been successfully applied and we have discovered that MART gives the highest accuracy among others in training model and ranking prediction samples. Thus, MART is the most considered algorithm towards top mobile games predictions problem.
A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations

Dynamic graphs are a powerful way to model an evolving set of objects and their ongoing interactions. A broad spectrum of systems, such as information, communication, and social, are naturally represented by dynamic graphs. Outlier (or anomaly) detection in dynamic graphs can provide unique insights into the relationships of objects and identify novel or emerging relationships. To date, outlier detection in dynamic graphs has been studied in the context of graph streams, focusing on the analysis and comparison of entire graph objects. However, the volume and velocity of data are necessitating a transition from outlier detection in the context of graph streams to outlier detection in the context of edge streams–where the stream consists of individual graph edges instead of entire graph objects. In this paper, we propose the first approach for outlier detection in edge streams. We first describe a highlevel model for outlier detection based on global and local structural properties of a stream. We propose a novel application of the Count-Min sketch for approximating these properties, and prove probabilistic error bounds on our edge outlier scoring functions. Our sketch-based implementation provides a scalable solution, having constant time updates and constant space requirements. Experiments on synthetic and real world datasets demonstrate our method’s scalability, effectiveness for discovering outliers, and the effects of approximation.
A global perspective on child sexual abuse: meta-analysis of prevalence around the world.

Our comprehensive meta-analysis combined prevalence figures of childhood sexual abuse (CSA) reported in 217 publications published between 1980 and 2008, including 331 independent samples with a total of 9,911,748 participants. The overall estimated CSA prevalence was 127/1000 in self-report studies and 4/1000 in informant studies. Self-reported CSA was more common among female (180/1000) than among male participants (76/1000). Lowest rates for both girls (113/1000) and boys (41/1000) were found in Asia, and highest rates were found for girls in Australia (215/1000) and for boys in Africa (193/1000). The results of our meta-analysis confirm that CSA is a global problem of considerable extent, but also show that methodological issues drastically influence the self-reported prevalence of CSA.