nils-herrmann/clean_scirepeval huggingface dataset:
Development of measures of online privacy concern and protection for use on the Internet

As the Internet grows in importance, concerns about online privacy have arisen. We describe the development and validation of three short Internet-administered scales measuring privacy related attitudes ('Privacy Concern') and behaviors ('General Caution' and 'Technical Protection'). Internet Privacy Scales 1 In Press:  2006 Wiley Periodicals, Inc. Running Head: INTERNET PRIVACY SCALES Development of measures of online privacy concern and protection for use on the
On Detecting Spatial Outliers

The ever-increasing volume of spatial data has greatly challenged our ability to extract useful but implicit knowledge from them. As an important branch of spatial data mining, spatial outlier detection aims to discover the objects whose non-spatial attribute values are significantly different from the values of their spatial neighbors. These objects, called spatial outliers, may reveal important phenomena in a number of applications including traffic control, satellite image analysis, weather forecast, and medical diagnosis. Most of the existing spatial outlier detection algorithms mainly focus on identifying single attribute outliers and could potentially misclassify normal objects as outliers when their neighborhoods contain real spatial outliers with very large or small attribute values. In addition, many spatial applications contain multiple non-spatial attributes which should be processed altogether to identify outliers. To address these two issues, we formulate the spatial outlier detection problem in a general way, design two robust detection algorithms, one for single attribute and the other for multiple attributes, and analyze their computational complexities. Experiments were conducted on a real-world data set, West Nile virus data, to validate the effectiveness of the proposed algorithms.
Robust speaker recognition on mobile devices

In this paper we are exploring different models and methods for improving the performance of text independent speaker identification system for mobile devices. The major issues in speaker recognition for mobile devices are (i) presence of varying background environment, (ii) effect of speech coding introduced by the mobile device, and (iii) impairments due to wireless channel. In this paper, we are proposing multi-SNR multi-environment speaker models and speech enhancement (preprocessing) methods for improving the performance of speaker recognition system in mobile environment. For this study, we have simulated five different background environments (Car, Factory, High frequency, pink noise and white Gaussian noise) using NOISEX data. Speaker recognition studies are carried out on TIMIT, cellular, and microphone speech databases. Autoassociative neural network models are explored for developing these multi-SNR multi-environment speaker models. The results indicate that the proposed multi-SNR multi-environment speaker models and speech enhancement preprocessing methods have enhanced the speaker recognition performance in the presence of different noisy environments.
Robust Face Detection Using the Hausdorff Distance

The localization of human faces in digital images is a fundamental step in the process of face recognition. This paper presents a shape comparison approach to achieve fast, accurate face detection that is robust to changes in illumination and background. The proposed method is edge-based and works on grayscale still images. The Hausdorff distance is used as a similarity measure between a general face model and possible instances of the object within the image. The paper describes an efficient implementation, making this approach suitable for real-time applications. A two-step process that allows both coarse detection and exact localization of faces is presented. Experiments were performed on a large test set base and rated with a new validation measurement.
Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings

We consider the problem of learning general-purpose, paraphrastic sentence embeddings, revisiting the setting of Wieting et al. (2016b). While they found LSTM recurrent networks to underperform word averaging, we present several developments that together produce the opposite conclusion. These include training on sentence pairs rather than phrase pairs, averaging states to represent sequences, and regularizing aggressively. These improve LSTMs in both transfer learning and supervised settings. We also introduce a new recurrent architecture, the GATED RECURRENT AVERAGING NETWORK, that is inspired by averaging and LSTMs while outperforming them both. We analyze our learned models, finding evidence of preferences for particular parts of speech and dependency relations.
Joint 2D-3D-Semantic Data for Indoor Scene Understanding

We present a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric annotations. The dataset covers over 6,000 m and contains over 102,000 RGB images, along with the corresponding depths, surface normals, semantic annotations, global XYZ images (all in forms of both regular and 360◦ equirectangular images) as well as camera information. It also includes registered raw and semantically annotated 3D meshes and point clouds. The dataset enables development of joint and cross-modal learning models and potentially unsupervised approaches utilizing the regularities present in large-scale indoor spaces.
A SURVEY ON NEURAL NETWORK MODELS FOR HEART DISEASE PREDICTION

The healthcare organization (hospitals, medical centers) should provide quality services at affordable costs. Quality of service implies diagnosing patients accurately and suggesting treatments that are effective. To achieve a correct and cost effective treatment, computer-based information and/or decision support Systems can be developed to full-fill the task. The generated information systems typically consist of large amount of data. Health care organizations must have ability to analyze these data. The Health care system includes data such as resource management, patient centric and transformed data. Data mining techniques are used to explore, analyze and extract these data using complex algorithms in order to discover unknown patterns. Many data mining techniques have been used in the diagnosis of heart disease with good accuracy. Neural Networks have shown great potential to be applied in the development of prediction system for various type of heart disease. This paper investigates the benefits and overhead of various neural network models for heart disease prediction.
Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic

Massive open online courses (MOOCs) attract a large number of student registrations, but recent studies have shown that only a small fraction of these students complete their courses. Student dropouts are thus a major deterrent for the growth and success of MOOCs. We believe that understanding student engagement as a course progresses is essential for minimizing dropout rates. Formally defining student engagement in an online setting is challenging. In this paper, we leverage activity (such as posting in discussion forums, timely submission of assignments, etc.), linguistic features from forum content and structural features from forum interaction to identify two different forms of student engagement (passive and active) in MOOCs. We use probabilistic soft logic (PSL) to model student engagement by capturing domain knowledge about student interactions and performance. We test our models on MOOC data from Coursera and demonstrate that modeling engagement is helpful in predicting student performance.
A Neural Network Integrated Decision Support System for Condition-Based Optimal Predictive Maintenance Policy

This paper develops an integrated neural-network-based decision support system for predictive maintenance of rotational equipment. The integrated system is platform-independent and is aimed at minimizing expected cost per unit operational time. The proposed system consists of three components. The first component develops a vibration-based degradation database through condition monitoring of rolling element bearings. In the second component, an artificial neural network model is developed to estimate the life percentile and failure times of roller bearings. This is then used to construct a marginal distribution. The third component consists of the construction of a cost matrix and probabilistic replacement model that optimizes the expected cost per unit time. Furthermore, the integrated system consists of a heuristic managerial decision rule for different scenarios of predictive and corrective cost compositions. Finally, the proposed system can be applied in various industries and different kinds of equipment that possess well-defined degradation characteristics
Research on keyword extraction based on Word2Vec weighted TextRank

In this paper, we do a research on the keyword extraction method of news articles. We build a candidate keywords graph model based on the basic idea of TextRank, use Word2Vec to calculate the similarity between words as transition probability of nodes' weight, calculate the word score by iterative method and pick the top N of the candidate keywords as the final results. Experimental results show that the weighted TextRank algorithm with correlation of words can improve performance of keyword extraction generally.
Structured Adversarial Training for Unsupervised Monocular Depth Estimation

The problem of estimating scene-depth from a single image has seen great progress lately. Recent unsupervised methods are based on view-synthesis and learn depth by minimizing photometric reconstruction error. In this paper, we introduce Structured Adversarial Training (StrAT) to this problem. We generate multiple novel views using depth (or disparity), with the stereo-baseline changing in an increasing order. Adversarial training that goes from easy examples to harder ones produces richer losses and better models. The impact of StrAT is shown to exceed traditional data augmentation using random new views. The combination of an adversarial framework, multiview learning, and structured adversarial training produces state-of-the-art performance on unsupervised depth estimation for monocular images. The StrAT framework can benefit several problems that use adversarial training.
Workshop track-ICLR 2018 A SPECT-BASED Q UESTION G ENERATION

Asking questions is an important ability for a chatbot. Although there are existing works on question generation with a piece of descriptive text, it remains to be a very challenging problem. In this paper, we consider a new question generation problem which also requires the input of a target aspect in addition to a piece of descriptive text. The key reason for this new problem is that it has been found from practical applications that useful questions need to be targeted toward some relevant aspects. One almost never asks a random question in a conversation. Due to the fact that given a descriptive text, it is often possible to ask many types of questions, generating a question without knowing what it is about is of limited use. in order to solve this problem, we propose a novel neural network which is able to generate aspect-based questions. One major advantage of this model is that it can be trained directly using a question-answering corpus without requiring any additional annotations like annotating aspects in the questions or answers. Experimental results show that our proposed model outperforms the state-of-theart question generation methods.
Isogeometric analysis : CAD , finite elements , NURBS , exact geometry and mesh refinement

The concept of isogeometric analysis is proposed. Basis functions generated from NURBS (Non-Uniform Rational B-Splines) are employed to construct an exact geometric model. For purposes of analysis, the basis is refined and/or its order elevated without changing the geometry or its parameterization. Analogues of finite element hand p-refinement schemes are presented and a new, more efficient, higher-order concept, k-refinement, is introduced. Refinements are easily implemented and exact geometry is maintained at all levels without the necessity of subsequent communication with a CAD (Computer Aided Design) description. In the context of structural mechanics, it is established that the basis functions are complete with respect to affine transformations, meaning that all rigid body motions and constant strain states are exactly represented. Standard patch tests are likewise satisfied. Numerical examples exhibit optimal rates of convergence for linear elasticity problems and convergence to thin elastic shell solutions. A k-refinement strategy is shown to converge toward monotone solutions for advection–diffusion processes with sharp internal and boundary layers, a very surprising result. It is argued that isogeometric analysis is a viable alternative to standard, polynomial-based, finite element analysis and possesses several advantages.
PointNet: A 3D Convolutional Neural Network for real-time object class recognition

During the last few years, Convolutional Neural Networks are slowly but surely becoming the default method solve many computer vision related problems. This is mainly due to the continuous success that they have achieved when applied to certain tasks such as image, speech, or object recognition. Despite all the efforts, object class recognition methods based on deep learning techniques still have room for improvement. Most of the current approaches do not fully exploit 3D information, which has been proven to effectively improve the performance of other traditional object recognition methods. In this work, we propose PointNet, a new approach inspired by VoxNet and 3D ShapeNets, as an improvement over the existing methods by using density occupancy grids representations for the input data, and integrating them into a supervised Convolutional Neural Network architecture. An extensive experimentation was carried out, using ModelNet - a large-scale 3D CAD models dataset - to train and test the system, to prove that our approach is on par with state-of-the-art methods in terms of accuracy while being able to perform recognition under real-time constraints.
Big Data Analysis Using Modern Statistical and Machine Learning Methods in Medicine

In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.
A critique of using grounded theory as a research method

Grounded Theory is a powerful research method for collecting and analysing research data. It was ‘discovered’ by Glaser & Strauss (1967) in the 1960s but is still not widely used or understood by researchers in some industries or PhD students in some science disciplines. This paper demonstrates the steps in the method and describes the difficulties encountered in applying Grounded Theory (GT). A fundamental part of the analysis method in GT is the derivation of codes, concepts and categories. Codes and coding are explained and illustrated in Section 3. Merging the codes to discover emerging concepts is a central part of the GT method and is shown in Section 4. Glaser and Strauss’s constant comparison step is applied and illustrated so that the emerging categories can be seen coming from the concepts and leading to the emergent theory grounded in the data in Section 5. However, the initial applications of the GT method did have difficulties. Problems encountered when using the method are described to inform the reader of the realities of the approach. The data used in the illustrative analysis comes from recent IS/IT Case Study research into configuration management (CM) and the use of commercially available computer products (COTS). Why and how the GT approach was appropriate is explained in Section 6. However, the focus is on reporting GT as a research method rather than the results of the Case Study.
Deep Multitask Learning for Semantic Dependency Parsing

We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms. By using efficient, nearly arc-factored inference and a bidirectional-LSTM composed with a multi-layer perceptron, our base system is able to significantly improve the state of the art for semantic dependency parsing, without using hand-engineered features or syntax. We then explore two multitask learning approaches—one that shares parameters across formalisms, and one that uses higher-order structures to predict the graphs jointly. We find that both approaches improve performance across formalisms on average, achieving a new state of the art. Our code is open-source and available at https://github.com/ Noahs-ARK/NeurboParser.
Learning to Generate with Memory

Memory units have been widely used to enrich the capabilities of deep networks on capturing long-term dependencies in reasoning and prediction tasks, but little investigation exists on deep generative models (DGMs) which are good at inferring high-level invariant representations from unlabeled data. This paper presents a deep generative model with a possibly large external memory and an attention mechanism to capture the local detail information that is often lost in the bottom-up abstraction process in representation learning. By adopting a smooth attention model, the whole network is trained end-to-end by optimizing a variational bound of data likelihood via auto-encoding variational Bayesian methods, where an asymmetric recognition network is learnt jointly to infer high-level invariant representations. The asymmetric architecture can reduce the competition between bottom-up invariant feature extraction and top-down generation of instance details. Our experiments on several datasets demonstrate that memory can significantly boost the performance of DGMs on various tasks, including density estimation, image generation, and missing value imputation, and DGMs with memory can achieve state-ofthe-art quantitative results.
Automatic Detection of Sentence Fragments

We present and evaluate a method for automatically detecting sentence fragments in English texts written by non-native speakers. Our method combines syntactic parse tree patterns and parts-of-speech information produced by a tagger to detect this phenomenon. When evaluated on a corpus of authentic learner texts, our best model achieved a precision of 0.84 and a recall of 0.62, a statistically significant improvement over baselines using non-parse features, as well as a popular grammar checker.
Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a Novel Neural Network

Recent works have been shown effective in using neural networks for Chinese word segmentation. However, these models rely on large-scale data and are less effective for low-resource datasets because of insufficient training data. Thus, we propose a transfer learning method to improve low-resource word segmentation by leveraging high-resource corpora. First, we train a teacher model on high-resource corpora and then use the learned knowledge to initialize a student model. Second, a weighted data similarity method is proposed to train the student model on low-resource data with the help of highresource corpora. Finally, given that insufficient data puts forward higher requirements for feature extraction, we propose a novel neural network which improves feature learning. Experiment results show that our work significantly improves the performance on low-resource datasets: 2.3% and 1.5% F-score on PKU and CTB datasets. Furthermore, this paper achieves state-of-the-art results: 96.1%, and 96.2% F-score on PKU and CTB datasets1. Besides, we explore an asynchronous parallel method on neural word segmentation to speed up training. The parallel method accelerates training substantially and is almost five times faster than a serial mode.
Efficient Component Labeling of Images of Arbitrary Dimension Represented by Linear Bintrees

An algorithm is presented to perform connected component labeling of images of arbitrary dimension that are represented by a linear bintree. The bintree is a generalization of the quadtree data structure that enables dealing with images of arbitrary dimension. The linear bintree is a pointerless representation. The algorithm uses an active border which is represented by linked lists instead of arrays. This results in a significant reduction in the space requirements, thereby making it feasible to process threeand higher dimensional images. Analysis of the execution time of the algorithm shows almost linear behavior with respect to the number of leaf nodes in the image, and empirical tests are in agreement. The algorithm can be modified easily to compute a ( d 1)-dimensional boundary measure (e.g., perimeter in two dimensions and surface area in three dimensions) with linear
Antecedents of Self-Regulation : A Developmental Perspective

The emerging ability to comply with caregivers' dictates and to monitor one's own behavior accordingly signifies a major growth of early childhood. However, scant attention has been paid to the developmental course of self-initiated regulation of behavior. This article summarizes the literature devoted to early forms of control and highlights the different philosophical orientations in the literature. Then, focusing on the period from early infancy to the beginning of the preschool years, the author proposes an ontogenetic perspective tracing the kinds of modulation or control the child is capable of along the way. The developmental sequence of monitoring behaviors that is proposed calls attention to contributions made by the growth of cognitive skills. The role of mediators (e.g., caregivers) is also discussed.