nils-herrmann/clean_scirepeval huggingface dataset:
A semantic web primer

The development of the Semantic Web, with machine-readable content, has the potential to revolutionize the World Wide Web and its use. In A Semantic Web Primer Grigoris Antoniou and Frank van Harmelen provide an introduction and guide to this emerging field, describing its key ideas, languages, and technologies. Suitable for use as a textbook or for self-study by professionals, the book concentrates on undergraduate-level fundamental concepts and techniques that will enable readers to proceed with building applications on their own and includes exercises, project descriptions, and annotated references to relevant online materials. A Semantic Web Primer is the only available book on the Semantic Web to include a systematic treatment of the different languages (XML, RDF, OWL, and rules) and technologies (explicit metadata, ontologies, and logic and inference) that are central to Semantic Web development. The book also examines such crucial related topics as ontology engineering and application scenarios. After an introductory chapter, topics covered in succeeding chapters include XML and related technologies that support semantic interoperability; RDF and RDF Schema, the standard data model for machine-processible semantics; and OWL, the W3C-approved standard for a Web ontology language that is more extensive than RDF Schema; rules, both monotonic and nonmonotonic, in the framework of the Semantic Web; selected application domains and how the Semantic Web would benefit them; the development of ontology-based systems; and current debates on key issues and predictions for the future.
Fast and accurate image upscaling with super-resolution forests

The aim of single image super-resolution is to reconstruct a high-resolution image from a single low-resolution input. Although the task is ill-posed it can be seen as finding a non-linear mapping from a low to high-dimensional space. Recent methods that rely on both neighborhood embedding and sparse-coding have led to tremendous quality improvements. Yet, many of the previous approaches are hard to apply in practice because they are either too slow or demand tedious parameter tweaks. In this paper, we propose to directly map from low to high-resolution patches using random forests. We show the close relation of previous work on single image super-resolution to locally linear regression and demonstrate how random forests nicely fit into this framework. During training the trees, we optimize a novel and effective regularized objective that not only operates on the output space but also on the input space, which especially suits the regression task. During inference, our method comprises the same well-known computational efficiency that has made random forests popular for many computer vision problems. In the experimental part, we demonstrate on standard benchmarks for single image super-resolution that our approach yields highly accurate state-of-the-art results, while being fast in both training and evaluation.
Critical Thinking What is Critical Thinking ?

Political How to Defeat Terrorism with Neuro-Semantics (2) The President Finally Speaks out about Radial Isalism (3) A Call for Responsible Protests (20) Hidden Frames #1 (21) Behind Hidden Frames #2 (22) What’s Behind It All?  The Politics within NLP and Neuro-Semantics Models (24) Ferguson Again? What’s the Real Problem? (35) Give me a Non-Politician (36) Is it Time for Trump to Apologize? (41) Religious Terrorism: The Paris Attacks (49) A Meaningful Antidote for Terrorism (51)
Learning-Based Frequency Estimation Algorithms

Estimating the frequencies of elements in a data stream is a fundamental task in data analysis and machine learning. The problem is typically addressed using streaming algorithms which can process very large data using limited storage. Today’s streaming algorithms, however, cannot exploit patterns in their input to improve performance. We propose a new class of algorithms that automatically learn relevant patterns in the input data and use them to improve its frequency estimates. The proposed algorithms combine the benefits of machine learning with the formal guarantees available through algorithm theory. We prove that our learning-based algorithms have lower estimation errors than their non-learning counterparts. We also evaluate our algorithms on two real-world datasets and demonstrate empirically their performance gains.
The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations

The Parallel Meaning Bank is a corpus of translations annotated with shared, formal meaning representations comprising over 11 million words divided over four languages (English, German, Italian, and Dutch). Our approach is based on cross-lingual projection: automatically produced (and manually corrected) semantic annotations for English sentences are mapped onto their word-aligned translations, assuming that the translations are meaning-preserving. The semantic annotation consists of five main steps: (i) segmentation of the text in sentences and lexical items; (ii) syntactic parsing with Combinatory Categorial Grammar; (iii) universal semantic tagging; (iv) symbolization; and (v) compositional semantic analysis based on Discourse Representation Theory. These steps are performed using statistical models trained in a semisupervised manner. The employed annotation models are all language-neutral. Our first results are promising.
Evaluating Learning Algorithms Composed by a Constructive Meta-Learning Scheme for a Rule Evaluation Support Method

In this paper, we present an evaluation of learning algorithms of a novel rule evaluation support method for postprocessing of mined results with rule evaluation models based on objective indices. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset with noises. To reduce the costs in such rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which learn from objective indices for mined classification rules and evaluations by a human expert for each rule. To enhance adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms. Then, we have done the case study on the meningitis data mining as an actual problem
Non-negative Matrix Factorization with Sparseness Constraints

Non-negative matrix factorization (NMF) is a recently deve loped technique for finding parts-based, linear representations of non-negative data. Although it h as successfully been applied in several applications, it does not always result in parts-based repr esentations. In this paper, we show how explicitly incorporating the notion of ‘sparseness’ impro ves the found decompositions. Additionally, we provide complete MATLAB code both for standard NMF a nd for our extension. Our hope is that this will further the application of these methods to olving novel data-analysis problems.
Map-Based Visual-Inertial Monocular SLAM using Inertial assisted Kalman Filter

In this paper, we present a novel tightly-coupled monocular visual-inertial Simultaneous Localization and Mapping algorithm following an inertial assisted Kalman Filter and reusing the estimated 3D map. By leveraging an inertial assisted Kalman Filter, we achieve an efficient motion tracking bearing fast dynamic movement in the front-end. To enable place recognition and reduce the trajectory estimation drift, we construct a factor graph based non-linear optimization in the back-end. We carefully design a feedback mechanism to balance the front/back ends ensuring the estimation accuracy. We also propose a novel initialization method that accurately estimate the scale factor, the gravity, the velocity, and gyroscope and accelerometer biases in a very robust way. We evaluated the algorithm on a public dataset, when compared to other state-of-the-art monocular Visual-Inertial SLAM approaches, our algorithm achieves better accuracy and robustness in an efficient way. By the way, we also evaluate our algorithm in a MonocularInertial setup with a low cost IMU to achieve a robust and lowdrift realtime SLAM system.
Changes in the Ground Reaction Force, Lower-Limb Muscle Activity, and Joint Angles in Athletes with Unilateral Ankle Dorsiflexion Restriction During A Rebound-Jump Task

This study compared differences between a control group and a group with unilateral ankle dorsiflexion restriction in the ground reaction force (GRF), angles of the lower limbs joints, and muscular activity during a rebound-jump task in athletes who continue to perform sports activities with unilateral ankle dorsiflexion restriction.  The athletes were divided into the following two groups: The dorsiflexion group included those with a difference of ≥7◦ between bilateral ankle dorsiflexion angles (DF), and the control group included those with a difference of <7◦ between the two ankles (C). An ankle foot orthosis was attached to subjects in group C to apply a restriction on the right-angle dorsiflexion angle. The percentage of maximum voluntary contraction (%MVC) of the legs musculature, components of the GRF, and the hip and knee joint angles during the rebound-jump task were compared between groups DF and C.  Group DF showed increased %MVC of the quadriceps muscle, decreased upward component of the GRF, decreased hip flexion, and increased knee eversion angles.  This study highlighted that athletes with ankle dorsiflexion restriction had significantly larger knee eversion angles in the rebound-jump task. The reduced hip flexion was likely caused by the restricted ankle dorsiflexion and compensated by the observed increase in quadriceps muscle activation when performing the jump.
Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Features

We present a multilingual Named Entity Recognition approach based on a robust and general set of features across languages and datasets. Our system combines shallow local information with clustering semi-supervised features induced on large amounts of unlabeled text. Understanding via empirical experimentation how to effectively combine various types of clustering features allows us to seamlessly export our system to other datasets and languages. The result is a simple but highly competitive system which obtains state of the art results across five languages and twelve datasets. The results are reported on standard shared task evaluation data such as CoNLL for English, Spanish and Dutch. Furthermore, and despite the lack of linguistically motivated features, we also report best results for languages such as Basque and German. In addition, we demonstrate that our method also obtains very competitive results even when the amount of supervised data is cut by half, alleviating the dependency on manually annotated data. Finally, the results show that our emphasis on clustering features is crucial to develop robust out-of-domain models. The system and models are freely available to facilitate its use and guarantee the reproducibility of results.
The Korean Wave ( Hallyu ) in East Asia : A Comparison of Chinese , Japanese , and Taiwanese Audiences Who Watch Korean TV Dramas *

The major purpose of this study is to find out what factors are responsible for Hallyu, the flow and popularity of Korean popular cultural products in East Asia, and which direction Hallyu is heading for. To do this, recent structural and institutional changes in the region are examined and the 2008 EASS data analyzed. Opening up of the cultural market and development of the mass media industry, especially television, laid the ground for Hallyu in East Asia. The 2008 EASS data reveal that social proximity and two demographic variables are common factors for the rise of Hallyu in East Asia, but globalization and modernity variables had no effect on Hallyu. Females are more attracted to Korean TV dramas than males, but typical Hallyu fans are youngest in China, followed by Taiwan, and Japan the oldest. Besides these characteristics of Hallyu fans, the quality of Korean cultural products is an important factor in their success abroad. Since the second decade of the new millennium, Hallyu evolves into a new, or second, stage in which its contents, strategy, and media change considerably. Hallyu, representing a case of alternative globalization, signals a new phase in the recent history of globalization.
Nested Named Entity Recognition Revisited

We propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using features extracted from a recurrent neural network. In evaluations on three standard data sets, we show that our approach significantly outperforms existing state-of-the-art methods, which are feature-based. The approach is also efficient: it operates linearly in the number of tokens and the number of possible output labels at any token. Finally, we present an extension of our model that jointly learns the head of each entity mention.
Hierarchical vs. flat n-gram-based text categorization: Can we do better?

Hierarchical text categorization (HTC) refers to assigning a text document to one or more most suitable categories from a hierarchical category space. In this paper we present two HTC techniques based on kNN and SVM machine learning techniques for categorization process and byte n-gram based document representation. They are fully language independent and do not require any text preprocessing steps, or any prior information about document content or language. The effectiveness of the presented techniques and their language independence are demonstrated in experiments performed on five tree-structured benchmark category hierarchies that differ in many aspects: Reuters-Hier1, Reuters-Hier2, 15NGHier and 20NGHier in English and TanCorpHier in Chinese. The results obtained are compared with the corresponding flat categorization techniques applied to leaf level categories of the considered hierarchies. While kNN-based flat text categorization produced slightly better results than kNN-based HTC on the largest TanCorpHier and 20NGHier datasets, SVM-based HTC results do not considerably differ from the corresponding flat techniques, due to shallow hierarchies; still, they outperform both kNN-based flat and hierarchical categorization on all corpora except the smallest Reuters-Hier1 and Reuters-Hier2 datasets. Formal evaluation confirmed that the proposed techniques obtained state-of-the-art results.
Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection

The fast growing deep learning technologies have become the main solution of many machine learning problems for medical image analysis. Deep convolution neural networks (CNNs), as one of the most important branch of the deep learning family, have been widely investigated for various computer-aided diagnosis tasks including long-term problems and continuously emerging new problems. Image contour detection is a fundamental but challenging task that has been studied for more than four decades. Recently, we have witnessed the significantly improved performance of contour detection thanks to the development of CNNs. Beyond purusing performance in existing natural image benchmarks, contour detection plays a particularly important role in medical image analysis. Segmenting various objects from radiology images or pathology images requires accurate detection of contours. However, some problems, such as discontinuity and shape constraints, are insufficiently studied in CNNs. It is necessary to clarify the challenges to encourage further exploration. The performance of CNN based contour detection relies on the state-of-the-art CNN architectures. Careful investigation of their design principles and motivations is critical and beneficial to contour detection. In this paper, we first review recent development of medical image contour detection and point out the current confronting challenges and problems. We discuss the development of general CNNs and their applications in image contours (or edges) detection. We compare those methods in detail, clarify their strengthens and weaknesses. Then we review their recent applications in medical image analysis and point out limitations, with the goal to light some potential directions in medical image analysis. We expect the paper to cover comprehensive technical ingredients of advanced CNNs to enrich the study in the medical image domain. 1E-mail: zizhaozhang@ufl.edu  2018 ar X iv :1 70 8. 07 28 1v 1 [ cs .C V ] 2 4 A ug 2 01 7
TAM or Just Plain Habit: A Look at Experienced Online Shoppers

According to the Technology Acceptance Model (TAM), behavioral intentions to use a new IT are primarily the product of a rational analysis of its desirable perceived outcomes, namely perceived usefulness (PU) and perceived ease of use (PEOU). But what happens with the continued use of an IT among experienced users? Does habit also kick in as a major factor or is continued use only the product of its desirable outcomes? This study examines this question in the context of experienced online shoppers. The data show that, as hypothesized, online shoppers’ intentions to continue using a website that they last bought at depend not only on PU and PEOU, but also on habit. In fact, habit alone can explain a large proportion of the variance of continued use of a website. Moreover, the explained variance indicates that habit may also be a major predictor of PU and PEOU among experienced shoppers. Implications are discussed.
Learning to Detect Tables in Scanned Document Images Using Line Information

This paper presents a method to detect table regions in document images by identifying the column and row line-separators and their properties. The method employs a run-length approach to identify the horizontal and vertical lines present in the input image. From each group of intersecting horizontal and vertical lines, a set of 26 low-level features are extracted and an SVM classifier is used to test if it belongs to a table or not. The performance of the method is evaluated on a heterogeneous corpus of French, English and Arabic documents that contain various types of table structures and compared with that of the Tesseract OCR system.
Mining and summarizing customer reviews

Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.
Grounded theory: an exploration of process and procedure.

Grounded theory, as an evolving qualitative research method, is a product of its history as well as of its epistemology. Within the literature, there have been a number of discussions focusing on the differences between Glaser's (1978, 1992) and Strauss's (1987, 1990) versions of grounded theory. The purpose of this article is to add a level of depth and breadth to this discussion through specifically exploring the Glaser-Strauss debate by comparing the data analysis processes and procedures advocated by Glaser and by Strauss. To accomplish this task, the authors present the article in two sections. First, they provide relevant background information on grounded theory as a research method. Second, they pursue a more in-depth discussion of the positions of Glaser, using Glaser's work, and Strauss, using Strauss's and Strauss and Corbin's (1990) work, regarding the different phases of data analysis, specifically addressing the coding procedures, verification, and the issue of forcing versus emergence.
Cross Language Text Classification by Model Translation and Semi-Supervised Learning

In this paper, we introduce a method that automatically builds text classifiers in a new language by training on already labeled data in another language. Our method transfers the classification knowledge across languages by translating the model features and by using an Expectation Maximization (EM) algorithm that naturally takes into account the ambiguity associated with the translation of a word. We further exploit the readily available unlabeled data in the target language via semisupervised learning, and adapt the translated model to better fit the data distribution of the target language.
Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions

Diagnostic imaging often requires the simultaneous identification of a multitude of findings of varied size and appearance. Beyond global indication of said findings, the prediction and display of localization information improves trust in and understanding of results when augmenting clinical workflow. Medical training data rarely includes more than global image-level labels as segmentations are time-consuming and expensive to collect. We introduce an approach to managing these practical constraints by applying a novel architecture which learns at multiple resolutions while generating saliency maps with weak supervision. Further, we parameterize the Log-Sum-Exp pooling function with a learnable lower-bounded adaptation (LSE-LBA) to build in a sharpness prior and better handle localizing abnormalities of different sizes using only image-level labels. Applying this approach to interpreting chest x-rays, we set the state of the art on 9 abnormalities in the NIH’s CXR14 dataset while generating saliency maps with the highest resolution to date.
Serverless computing: economic and architectural impact

Amazon Web Services unveiled their âLambdaâ platform in late 2014. Since then, each of the major cloud computing infrastructure providers has released services supporting a similar style of deployment and operation, where rather than deploying and running monolithic services, or dedicated virtual machines, users are able to deploy individual functions, and pay only for the time that their code is actually executing. These technologies are gathered together under the marketing term âserverlessâ and the providers suggest that they have the potential to significantly change how client/server applications are designed, developed and operated. 
This paper presents two case industrial studies of early adopters, showing how migrating an application to the Lambda deployment architecture reduced hosting costs â by between 66% and 95% â and discusses how further adoption of this trend might influence common software architecture design practices.