nils-herrmann/clean_scirepeval huggingface dataset:
Wikipedia entity expansion and attribute extraction from the web using semi-supervised learning

We develop a new framework to achieve the goal of Wikipedia entity expansion and attribute extraction from the Web. Our framework takes a few existing entities that are automatically collected from a particular Wikipedia category as seed input and explores their attribute infoboxes to obtain clues for the discovery of more entities for this category and the attribute content of the newly discovered entities. One characteristic of our framework is to conduct discovery and extraction from desirable semi-structured data record sets which are automatically collected from the Web. A semi-supervised learning model with Conditional Random Fields is developed to deal with the issues of extraction learning and limited number of labeled examples derived from the seed entities. We make use of a proximate record graph to guide the semi-supervised learning process. The graph captures alignment similarity among data records. Then the semi-supervised learning process can leverage the unlabeled data in the record set by controlling the label regularization under the guidance of the proximate record graph. Extensive experiments on different domains have been conducted to demonstrate its superiority for discovering new entities and extracting attribute content.
Big Data Analysis Using Apache Hadoop

We live in on-demand, on-command Digital universe with data prolifering by Institutions, Individuals and Machines at a very high rate. This data is categories as "Big Data" due to its sheer Volume, Variety and Velocity. Most of this data is unstructured, quasi structured or semi structured and it is heterogeneous in nature. The volume and the heterogeneity of data with the speed it is generated, makes it difficult for the present computing infrastructure to manage Big Data. Traditional data management, warehousing and analysis systems fall short of tools to analyze this data. Due to its specific nature of Big Data, it is stored in distributed file system architectures. Hadoop and HDFS by Apache is widely used for storing and managing Big Data. Analyzing Big Data is a challenging task as it involves large distributed file systems which should be fault tolerant, flexible and scalable. Map Reduce is widely been used for the efficient analysis of Big Data. Traditional DBMS techniques like Joins and Indexing and other techniques like graph search is used for classification and clustering of Big Data. These techniques are being adopted to be used in Map Reduce. In this paper we suggest various methods for catering to the problems in hand through Map Reduce framework over Hadoop Distributed File System (HDFS). Map Reduce is a Minimization technique which makes use of file indexing with mapping, sorting, shuffling and finally reducing. Map Reduce techniques have been studied in this paper which is implemented for Big Data analysis using HDFS.
Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images

Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elementsoriented land-use classification method based on “partlets,” which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed “sparselets,” from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an L0-norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR landuse data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper.
Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text

The task of relation classification in the biomedical domain is complex due to the presence of samples obtained from heterogeneous sources such as research articles, discharge summaries, or electronic health records. It is also a constraint for classifiers which employ manual feature engineering. In this paper, we propose a convolutional recurrent neural network (CRNN) architecture that combines RNNs and CNNs in sequence to solve this problem. The rationale behind our approach is that CNNs can effectively identify coarse-grained local features in a sentence, while RNNs are more suited for long-term dependencies. We compare our CRNN model with several baselines on two biomedical datasets, namely the i2b22010 clinical relation extraction challenge dataset, and the SemEval-2013 DDI extraction dataset. We also evaluate an attentive pooling technique and report its performance in comparison with the conventional max pooling method. Our results indicate that the proposed model achieves state-of-the-art performance on both
Cost Sensitive Credit Card Fraud Detection Using Bayes Minimum Risk

Credit card fraud is a growing problem that affects card holders around the world. Fraud detection has been an interesting topic in machine learning. Nevertheless, current state of the art credit card fraud detection algorithms miss to include the real costs of credit card fraud as a measure to evaluate algorithms. In this paper a new comparison measure that realistically represents the monetary gains and losses due to fraud detection is proposed. Moreover, using the proposed cost measure a cost sensitive method based on Bayes minimum risk is presented. This method is compared with state of the art algorithms and shows improvements up to 23% measured by cost. The results of this paper are based on real life transactional data provided by a large European card processing company.
A bi-directional, isolated, single-stage, DAB-based AC-DC converter with open-loop power factor correction and other advanced features

In this paper, a control method for an AC-DC converter is proposed that simultaneously has the following features: a) galvanic isolation b) bi-directional power flow, c) Zero Current Switching (ZCS) for the primary side switches and Zero Voltage Switching (ZVS) turn-on for the secondary side, d) linear power relationship for easy control implementation, e) unity power factor with open-loop control and f) single-stage power conversion. It is thoroughly analyzed by first assuming it to be a DC-DC converter in a push-pull topology and then extending the results to analyze an AC-DC converter. The conclusions of the analysis are confirmed by simulations.
Hybrid Sample-based Surface Rendering

The performance of rasterization-based rendering on current GPUs strongly depends on the abilities to avoid overdraw and to prevent rendering triangles smaller than the pixel size. Otherwise, the rates at which highresolution polygon models can be displayed are affected significantly. Instead of trying to build these abilities into the rasterization-based rendering pipeline, we propose an alternative rendering pipeline implementation that uses rasterization and ray-casting in every frame simultaneously to determine eye-ray intersections. To make ray-casting competitive with rasterization, we introduce a memory-efficient sample-based data structure which gives rise to an efficient ray traversal procedure. In combination with a regular model subdivision, the most optimal rendering technique can be selected at run-time for each part. For very large triangle meshes our method can outperform pure rasterization and requires a considerably smaller memory budget on the GPU. Since the proposed data structure can be constructed from any renderable surface representation, it can also be used to efficiently render isosurfaces in scalar volume fields. The compactness of the data structure allows rendering from GPU memory when alternative techniques already require exhaustive paging.
A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter

Dynamic neural network (DNN) models provide an excellent means for forecasting and prediction of nonstationary time series. A neural network architecture, known as locally recurrent neural network ((LRNN) , is preferred to the traditional multilayer perceptron (MLP) because the time varying nature of a stock time series can be better represented using LRNN. The use of LRNN has demonstrated superiority in comparison to the widely used neural networks like the multilayered perceptron (MLP) network, radial basis function (RBF) neural network, and wavelet neural networks (WNN), etc. in predicting highly fluctuating time series databases like electrical load, electricity price, and financial markets.
An optimized axial flux variable reluctance resolver with concentric windings

Radial flux variable reluctance resolvers (RFVR2s) are widely used in variety of applications. However, they suffer from an intrinsic error which is called static eccentricity (SE). Although most of the errors in the resolvers can be suppressed using electronic methods in resolver to digital (R/D) converters, static misalignment is an exception. In this paper an axial flux configuration is proposed for variable reluctance (VR) resolvers with concentric windings. Then the proposed structure is optimized to achieve the minimum sensitivity of accuracy against SE. The performance of the presented structure, so called axial flux variable reluctance resolver (AFVR2), is validated using 3-D time stepping finite element method and compared with that of RFVR2. Results show the robustness of the optimized structure against static misalignment.
Ontology Based Expert-System for Suspicious Transactions Detection

The development of an effective mechanism to detect suspicious transactions is a critical problem for financial institutions in their endeavor to prevent anti-money laundering activities. This research addresses this problem by proposing an ontology based expert-system for suspicious transaction detection. The ontology consists of domain knowledge and a set of (SWRL) rules that together constitute an expert system. The native reasoning support in ontology is used to deduce new knowledge from the predefined rules about suspicious transactions. The presented expert-system has been tested on a real data set of more than 8 million transactions of a commercial bank. The novelty of the approach lies in the use of ontology driven technique that not only minimizes the data modeling cost but also makes the expert-system extendable and reusable for different applications.
THz 3-D Image Formation Using SAR Techniques: Simulation, Processing and Experimental Results

An active frequency-modulated continuous wave (FMCW) terahertz (THz) system has been developed to image objects in millimeter-scale resolution in three dimensions. The two-dimensional (2-D) aperture synthesis enables the improvement of cross range resolution, and its high range resolution is achieved through the use of the broadband sweep signal, whose frequency ranges from 514 to 565 GHz. The 3-D data are sufficiently focused by the wavenumber domain approach derived for the dechirped data. We present the THz 3-D imaging strategy using synthetic aperture radar (SAR) techniques, the related theoretical background and the experimental results in this paper.
Analysis of Perceptron-Based Active Learning

We start by showing that in an active learning setting, the Perceptron algorithm needs Ω( 1 2 ) labels to learn linear separators within generalization error . We then present a simple selective sampling algorithm for this problem, which combines a modification of the perceptron update with an adaptive filtering rule for deciding which points to query. For data distributed uniformly over the unit sphere, we show that our algorithm reaches generalization error after asking for just Õ(d log 1 ) labels. This exponential improvement over the usual sample complexity of supervised learning has previously been demonstrated only for the computationally more complex query-by-committee algorithm.
Threat implications of the Internet of Things

There are currently more objects connected to the Internet than there are people in the world. This gap will continue to grow, as more objects gain the ability to directly interface with the Internet or become physical representations of data accessible via Internet systems. This trend toward greater independent object interaction in the Internet is collectively described as the Internet of Things (IoT). As with previous global technology trends, such as widespread mobile adoption and datacentre consolidation, the changing operating environment associated with the Internet of Things represents considerable impact to the attack surface and threat environment of the Internet and Internet-connected systems. The increase in Internet-connected systems and the accompanying, non-linear increase in Internet attack surface can be represented by several tiers of increased surface complexity. Users, or groups of users, are linked to a non-linear number of connected entities, which in turn are linked to a non-linear number of indirectly connected, trackable entities. At each tier of this model, the increasing population, complexity, heterogeneity, interoperability, mobility, and distribution of entities represents an expanding attack surface, measurable by additional channels, methods, and data items. Further, this expansion will necessarily increase the field of security stakeholders and introduce new manageability challenges. This document provides a framework for measurement and analysis of the security implications inherent in an Internet that is dominated by non-user endpoints, content in the form of objects, and content that is generated by objects without direct user involvement.
A collaborative filtering similarity measure based on singularities

Recommender systems play an important role in reducing the negative impact of information overload on those websites where users have the possibility of voting for their preferences on Ítems. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to calcúlate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corrobórate the excellent behaviour of the singularity measure proposed.
A Fabula Model for Emergent Narrative

In this paper we present our continuing efforts to generate narrative using a character-centric approach. In particular we discuss the advantages of explicitly representing the emergent event sequence in order to be able to exert influence on it and generate stories that ‘retell’ the emergent narrative. Based on a narrative distinction between fabula, plot and presentation, we make a first step by presenting a model based on story comprehension that can capture the fabula, and show how it can be used for the automatic creation of stories.
Determining feature extractors for unsupervised learning on satellite images

Advances in satellite imagery presents unprecedented opportunities for understanding natural and social phenomena at global and regional scales. Although the field of satellite remote sensing has evaluated imperative questions to human and environmental sustainability, scaling those techniques to very high spatial resolutions at regional scales remains a challenge. Satellite imagery is now more accessible with greater spatial, spectral and temporal resolution creating a data bottleneck in identifying the content of images. Because satellite images are unlabeled, unsupervised methods allow us to organize images into coherent groups or clusters. However, the performance of unsupervised methods, like all other machine learning methods, depends on features. Recent studies using features from pre-trained networks have shown promise for learning in new datasets. This suggests that features from pre-trained networks can be used for learning in temporally and spatially dynamic data sources such as satellite imagery. It is not clear, however, which features from which layer and network architecture should be used for learning new tasks. In this paper, we present an approach to evaluate the transferability of features from pre-trained Deep Convolutional Neural Networks for satellite imagery. We explore and evaluate different features and feature combinations extracted from various deep network architectures, and systematically evaluate over 2,000 network-layer combinations. In addition, we test the transferability of our engineered features and learned features from an unlabeled dataset to a different labeled dataset. Our feature engineering and learning are done on the unlabeled Draper Satellite Chronology dataset, and we test on the labeled UC Merced Land dataset to achieve near state-of-the-art classification results. These results suggest that even without any or minimal training, these networks can generalize well to other datasets. This method could be useful in the task of clustering unlabeled images and other unsupervised machine learning tasks.
MaaT: Effective and scalable coordination of distributed transactions in the cloud

The past decade has witnessed an increasing adoption of cloud database technology, which provides better scalability, availability, and fault-tolerance via transparent partitioning and replication, and automatic load balancing and fail-over. However, only a small number of cloud databases provide strong consistency guarantees for distributed transactions, despite decades of research on distributed transaction processing, due to practical challenges that arise in the cloud setting, where failures are the norm, and human administration is minimal. For example, dealing with locks left by transactions initiated by failed machines, and determining a multi-programming level that avoids thrashing without under-utilizing available resources, are some of the challenges that arise when using lock-based transaction processing mechanisms in the cloud context. Even in the case of optimistic concurrency control, most proposals in the literature deal with distributed validation but still require the database to acquire locks during two-phase commit when installing updates of a single transaction on multiple machines. Very little theoretical work has been done to entirely eliminate the need for locking in distributed transactions, including locks acquired during two-phase commit. In this paper, we re-design optimistic concurrency control to eliminate any need for locking even for atomic commitment, while handling the practical issues in earlier theoretical work related to this problem. We conduct an extensive experimental study to evaluate our approach against lock-based methods under various setups and workloads, and demonstrate that our approach provides many practical advantages in the cloud context.
Beyond Prediction: Towards Automatic Intervention in MOOC Student Stop-out

High attrition rates in massive open online courses (MOOCs) have motivated growing interest in the automatic detection of student “stopout”. Stopout classifiers can be used to orchestrate an intervention before students quit, and to survey students dynamically about why they ceased participation. In this paper we expand on existing stop-out detection research by (1) exploring important elements of classifier design such as generalizability to new courses; (2) developing a novel framework inspired by control theory for how to use a classifier’s outputs to make intelligent decisions; and (3) presenting results from a “dynamic survey intervention” conducted on 2 HarvardX MOOCs, containing over 40000 students, in early 2015. Our results suggest that surveying students based on an automatic stopout classifier achieves higher response rates compared to traditional post-course surveys, and may boost students’ propensity to “come back” into the course.
Programming in a Socially Networked World : the Evolution of the Social Programmer

Social media has changed how software developers collaborate, how they coordinate their work, and where they find information. Social media sites, such as the Question and Answer (Q&A) portal Stack Overflow, fill archives with millions of entries that contribute to what we know about software development, covering a wide range of topics. For today’s software developers, reusable code snippets, introductory usage examples, and pertinent libraries are often just a web search away. In this position paper, we discuss the opportunities and challenges for software developers that rely on web content curated by the crowd, and we envision the future of an industry where individual developers benefit from and contribute to a body of knowledge maintained by the crowd using social media.
A Variational Model for Joint Motion Estimation and Image Reconstruction

The aim of this paper is to derive and analyze a variational model for the joint estimation of motion and reconstruction of image sequences, which is based on a time-continuous Eulerian motion model. The model can be set up in terms of the continuity equation or the brightness constancy equation. The analysis in this paper focuses on the latter for robust motion estimation on sequences of twodimensional images. We rigorously prove the existence of a minimizer in a suitable function space setting. Moreover, we discuss the numerical solution of the model based on primal-dual algorithms and investigate several examples. Finally, the benefits of our model compared to existing techniques, such as sequential image reconstruction and motion estimation, are shown.