nils-herrmann/clean_scirepeval huggingface dataset:
Language to Completion: Success in an Educational Data Mining Massive Open Online Class

Completion rates for massive open online classes (MOOCs) are notoriously low, but learner intent is an important factor. By studying students who drop out despite their intent to complete the MOOC, it may be possible to develop interventions to improve retention and learning outcomes. Previous research into predicting MOOC completion has focused on click-streams, demographics, and sentiment analysis. This study uses natural language processing (NLP) to examine if the language in the discussion forum of an educational data mining MOOC is predictive of successful class completion. The analysis is applied to a subsample of 320 students who completed at least one graded assignment and produced at least 50 words in discussion forums. The findings indicate that the language produced by students can predict with substantial accuracy (67.8 %) whether students complete the MOOC. This predictive power suggests that NLP can help us both to understand student retention in MOOCs and to develop automated signals of student success.
Simple Resolver Demodulation

A simple method to demodulate the resolver signal is introduced in this article. The proposed method can provide the demodulated resolver quadrature signals, sine and cosine, without requiring the resolver excitation signal as the carrier signal. Therefore, the phase delay between the stator winding and the rotor winding is avoided. The configuration of this approach consists of commercial avariable circuit building blocks such as comparator, simple and hold (S/H) and monostable multivibrators. Thus, the proposed approach is obtained to the economical attraction. The operation of the proposed demodulator and its performances are confirmed by simulation and experimental results.
The mechanisms, diagnosis, and management of severe asthma in adults

There has been a recent increase in the prevalence of asthma worldwide; however, the 5-10% of patients with severe disease account for a substantial proportion of the health costs. Although most asthma cases can be satisfactorily managed with a combination of anti-inflammatory drugs and bronchodilators, patients who remain symptomatic despite maximum combination treatment represent a heterogeneous group consisting of those who are under-treated or non-adherent with their prescribed medication. After excluding under-treatment and poor compliance, corticosteroid refractory asthma can be identified as a subphenotype characterised by a heightened neutrophilic airway inflammatory response in the presence or absence of eosinophils, with evidence of increased tissue injury and remodelling. Although a wide range of environmental factors such as allergens, smoking, air pollution, infection, hormones, and specific drugs can contribute to this phenotype, other features associated with changes in the airway inflammatory response should be taken into account. Aberrant communication between an injured airway epithelium and underlying mesenchyme contributes to disease chronicity and refractoriness to corticosteroids. The importance of identifying underlying causative factors and the recent introduction of novel therapeutic approaches, including the targeting of immunoglobulin E and tumour necrosis factor alpha with biological agents, emphasise the need for careful phenotyping of patients with severe disease to target improved management of the individual patient's needs.
Towards end-to-end speech recognition for Chinese Mandarin using long short-term memory recurrent neural networks

End-to-end speech recognition systems have been successfully designed for English. Taking into account the distinctive characteristics between Chinese Mandarin and English, it is worthy to do some additional work to transfer these approaches to Chinese. In this paper, we attempt to build a Chinese speech recognition system using end-to-end learning method. The system is based on a combination of deep Long Short-Term Memory Projected (LSTMP) network architecture and the Connectionist Temporal Classification objective function (CTC). The Chinese characters (the number is about 6,000) are used as the output labels directly. To integrate language model information during decoding, the CTC Beam Search method is adopted and optimized to make it more effective and more efficient. We present the first-pass decoding results which are obtained by decoding from scratch using CTC-trained network and language model. Although these results are not as good as the performance of DNN-HMMs hybrid system, they indicate that it is feasible to choose Chinese characters as the output alphabet in the end-toend speech recognition system.
FAST: a roc-based feature selection metric for small samples and imbalanced data classification problems

The class imbalance problem is encountered in a large number of practical applications of machine learning and data mining, for example, information retrieval and filtering, and the detection of credit card fraud. It has been widely realized that this imbalance raises issues that are either nonexistent or less severe compared to balanced class cases and often results in a classifier's suboptimal performance. This is even more true when the imbalanced data are also high dimensional. In such cases, feature selection methods are critical to achieve optimal performance. In this paper, we propose a new feature selection method, Feature Assessment by Sliding Thresholds (FAST), which is based on the area under a ROC curve generated by moving the decision boundary of a single feature classifier with thresholds placed using an even-bin distribution. FAST is compared to two commonly-used feature selection methods, correlation coefficient and RELevance In Estimating Features (RELIEF), for imbalanced data classification. The experimental results obtained on text mining, mass spectrometry, and microarray data sets showed that the proposed method outperformed both RELIEF and correlation methods on skewed data sets and was comparable on balanced data sets; when small number of features is preferred, the classification performance of the proposed method was significantly improved compared to correlation and RELIEF-based methods.
A survey on information visualization: recent advances and challenges

Information visualization (InfoVis), the study of transforming data, information, and knowledge into interactive visual representations, is very important to users because it provides mental models of information. The boom in big data analytics has triggered broad use of InfoVis in a variety of domains, ranging from finance to sports to politics. In this paper, we present a comprehensive survey and key insights into this fast-rising area. The research on InfoVis is organized into a taxonomy that contains four main categories, namely empirical methodologies, user interactions, visualization frameworks, and applications, which are each described in terms of their major goals, fundamental principles, recent trends, and state-of-the-art approaches. At the conclusion of this survey, we identify existing technical challenges and propose directions for future research.
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.

Methods of evaluating and comparing the performance of diagnostic tests are of increasing importance as new tests are developed and marketed. When a test is based on an observed variable that lies on a continuous or graded scale, an assessment of the overall value of the test can be made through the use of a receiver operating characteristic (ROC) curve. The curve is constructed by varying the cutpoint used to determine which values of the observed variable will be considered abnormal and then plotting the resulting sensitivities against the corresponding false positive rates. When two or more empirical curves are constructed based on tests performed on the same individuals, statistical analysis on differences between curves must take into account the correlated nature of the data. This paper presents a nonparametric approach to the analysis of areas under correlated ROC curves, by using the theory on generalized U-statistics to generate an estimated covariance matrix.
A REVIEW ON IMAGE SEGMENTATION TECHNIQUES WITH REMOTE SENSING PERSPECTIVE

With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper, different image segmentation techniques applied on optical remote sensing images are reviewed. The selection of papers include sources from image processing journals, conferences, books, dissertations and thesis out of more than 3000 journals, books and online research databases available at UNB. The conceptual details of the techniques are explained and mathematical details are avoided for simplicity. Both broad and detailed categorisations of reviewed segmentation techniques are provided. The state of art research on each category is provided with emphasis on developed technologies and image properties used by them. The categories defined are not always mutually independent. Hence, their interrelationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application ∗ Corresponding author.
IT Capabilities, Process-Oriented Dynamic Capabilities, and Firm Financial Performance

Research Article Gimun Kim Konyang University gmkim@konyang.ac.kr Bongsik Shin San Diego State University bshin@mail.sdsu.edu Kyung Kyu Kim Yonsei University kyu.kim@yonsei.ac.kr Ho Geun Lee Yonsei University h.lee@yonsei.ac.kr More and more publications are highlighting the value of IT in affecting business processes. Recognizing firmlevel dynamic capabilities as key to improved firm performance, our work examines and empirically tests the influencing relationships among IT capabilities (IT personnel expertise, IT infrastructure flexibility, and IT management capabilities), process-oriented dynamic capabilities, and financial performance. Processoriented dynamic capabilities are defined as a firm’s ability to change (improve, adapt, or reconfigure) a business process better than the competition in terms of integrating activities, reducing cost, and capitalizing on business intelligence/learning. They encompass a broad category of changes in the firm’s processes, ranging from continual adjustments and improvements to radical one-time alterations. Although the majority of changes may be incremental, a firm’s capacity for timely changes also implies its readiness to execute radical alterations when the need arises. Grounded on the theoretical position, we propose a research model and gather a survey data set through a rigorous process that retains research validity. From the analysis of the survey data, we find an important route of causality, as follows: IT personnel expertise  IT management capabilities  IT infrastructure flexibility  process-oriented dynamic capabilities  financial performance. Based on this finding, we discuss the main contributions of our study in terms of the strategic role of IT in enhancing firm performance.
Resource management for OFDMA based next generation 802.11 WLANs

Recently, IEEE 802.11ax Task Group has adapted OFDMA as a new technique for enabling multi-user transmission. It has been also decided that the scheduling duration should be same for all the users in a multi-user OFDMA so that the transmission of the users should end at the same time. In order to realize that condition, the users with insufficient data should transmit null data (i.e. padding) to fill the duration. While this scheme offers strong features such as resilience to Overlapping Basic Service Set (OBSS) interference and ease of synchronization, it also poses major side issues of degraded throughput performance and waste of devices' energy. In this work, for OFDMA based 802.11 WLANs we first propose practical algorithm in which the scheduling duration is fixed and does not change from time to time. In the second algorithm the scheduling duration is dynamically determined in a resource allocation framework by taking into account the padding overhead, airtime fairness and energy consumption of the users. We analytically investigate our resource allocation problems through Lyapunov optimization techniques and show that our algorithms are arbitrarily close to the optimal performance at the price of reduced convergence rate. We also calculate the overhead of our algorithms in a realistic setup and propose solutions for the implementation issues.
jMetal: A Java framework for multi-objective optimization

This paper describes jMetal, an object-oriented Java-based framework aimed at the development, experimentation, and study of metaheuristics for solving multi-objective optimization problems. jMetal includes a number of classic and modern state-of-the-art optimizers, a wide set of benchmark problems, and a set of well-known quality indicators to assess the performance of the algorithms. The framework also provides support to carry out full experimental studies, which can be configured and executed by using jMetal’s graphical interface. Other features include the automatic generation of statistical information of the obtained results, and taking advantage of the current availability of multi-core processors to speed-up the running time of the experiments. In this work, we include two case studies to illustrate the use of jMetal in both solving a problem with a metaheuristic and designing and performing an experi-
A New Tool for Measuring Student Behavioral Engagement in Large University Classes

Vol. 44, No. 6, 2015 We developed a classroom observation protocol for quantitatively measuring student engagement in large university classes. The Behavioral Engagement Related to Instruction (BERI) protocol can be used to provide timely feedback to instructors as to how they can improve student engagement in their classrooms. We tested BERI on seven courses with different instructors and pedagogy. BERI achieved excellent interrater agreement (>95%) with a one-hour training session with new observers. It also showed consistent patterns of variation in engagement with instructor actions and classroom activity. Most notably, it showed that there was substantially higher engagement among the same group of students when interactive teaching methods were used compared with more traditional didactic methods. The same general variations in student engagement with instructional methods were present in all parts of the room and for different instructors. A New Tool for Measuring Student Behavioral Engagement in Large University Classes
Rivulet: Exploring Participation in Live Events through Multi-Stream Experiences

Live streaming has recently emerged as a growing form of participatory social media. While current live streaming practice focuses on single stream experiences, there are increasing instances of events covered by multiple live streams. In order to explore how to support communication and participation in multi-stream experiences, we present the design and evaluation of Rivulet, an end-to-end mobile live streaming system designed to support participatory multi-stream experiences. Rivulet affords simultaneously watching multiple live streams and incorporates existing feedback mechanisms of text chat and hearts with a novel push-to-talk audio modality. By recruiting viewers through Mechanical Turk, we were able to conduct a study of Rivulet at scale. We found that Rivulet afforded new engaging experiences for participants and led to an impromptu sense of community.
Animated Pose Templates for Modeling and Detecting Human Actions

This paper presents animated pose templates (APTs) for detecting short-term, long-term, and contextual actions from cluttered scenes in videos. Each pose template consists of two components: 1) a shape template with deformable parts represented in an And-node whose appearances are represented by the Histogram of Oriented Gradient (HOG) features, and 2) a motion template specifying the motion of the parts by the Histogram of Optical-Flows (HOF) features. A shape template may have more than one motion template represented by an Or-node. Therefore, each action is defined as a mixture (Or-node) of pose templates in an And-Or tree structure. While this pose template is suitable for detecting short-term action snippets in two to five frames, we extend it in two ways: 1) For long-term actions, we animate the pose templates by adding temporal constraints in a Hidden Markov Model (HMM), and 2) for contextual actions, we treat contextual objects as additional parts of the pose templates and add constraints that encode spatial correlations between parts. To train the model, we manually annotate part locations on several keyframes of each video and cluster them into pose templates using EM. This leaves the unknown parameters for our learning algorithm in two groups: 1) latent variables for the unannotated frames including pose-IDs and part locations, 2) model parameters shared by all training samples such as weights for HOG and HOF features, canonical part locations of each pose, coefficients penalizing pose-transition and part-deformation. To learn these parameters, we introduce a semi-supervised structural SVM algorithm that iterates between two steps: 1) learning (updating) model parameters using labeled data by solving a structural SVM optimization, and 2) imputing missing variables (i.e., detecting actions on unlabeled frames) with parameters learned from the previous step and progressively accepting high-score frames as newly labeled examples. This algorithm belongs to a family of optimization methods known as the Concave-Convex Procedure (CCCP) that converge to a local optimal solution. The inference algorithm consists of two components: 1) Detecting top candidates for the pose templates, and 2) computing the sequence of pose templates. Both are done by dynamic programming or, more precisely, beam search. In experiments, we demonstrate that this method is capable of discovering salient poses of actions as well as interactions with contextual objects. We test our method on several public action data sets and a challenging outdoor contextual action data set collected by ourselves. The results show that our model achieves comparable or better performance compared to state-of-the-art methods.
Parenting stress and psychological functioning among mothers of preschool children with autism and developmental delay.

Parents of children with developmental disabilities, particularly autism spectrum disorders (ASDs), are at risk for high levels of distress. The factors contributing to this are unclear. This study investigated how child characteristics influence maternal parenting stress and psychological distress. Participants consisted of mothers and developmental-age matched preschool-aged children with ASD (N = 51) and developmental delay without autism (DD) ( N = 22). Evidence for higher levels of parenting stress and psychological distress was found in mothers in the ASD group compared to the DD group. Children's problem behavior was associated with increased parenting stress and psychological distress in mothers in the ASD and DD groups. This relationship was stronger in the DD group. Daily living skills were not related to parenting stress or psychological distress. Results suggest clinical services aiming to support parents should include a focus on reducing problem behaviors in children with developmental disabilities.
Interpersonal evaluations following threats to self: role of self-esteem.

In 2 studies, the authors used dyadic interactions to assess the influence of ego threat on likability as a function of self-esteem. In both studies, 2 naive participants engaged in a structured conversation; in half of the dyads, 1 participant received an ego threat prior to the interaction. In the 1st study, threatened high self-esteem participants were rated as less likable than were threatened low self-esteem participants. The 2nd study confirmed that ego threats are associated with decreased liking for those with high self-esteem and with increased liking for those with low self-esteem. A mediational analysis demonstrated that decreased liking among high self-esteem participants was due to being perceived as antagonistic. Study 2 also indicated that the findings could not be explained by trait levels of narcissism. These patterns are interpreted in terms of differential sensitivity to potential interpersonal rejection.
Scalable extension of the high efficiency video coding SHEVC performance study

Scalable High Efficiency Video Coding (SHVC) is the extension of the High Efficiency Video Coding (HEVC). This standard is developed to ameliorate the coding efficiency for the spatial and quality scalability. In this paper, we investigate a survey for SHVC extension. We describe also its types and explain the different additional coding tools that further improve the Enhancement Layer (EL) coding efficiency. Furthermore, we assess through experimental results the performance of the SHVC for different coding configurations. The effectiveness of the SHVC was demonstrated, using two layers, by comparing its coding adequacy compared to simulcast configuration and HEVC for enhancement layer using HM16 for several test sequences and coding conditions.
Fuzzy logic control vs. conventional PID control of an inverted pendulum robot

This paper addresses some of the potential benefits of using fuzzy logic controllers to control an inverted pendulum system. The stages of the development of a fuzzy logic controller using a four input Takagi-Sugeno fuzzy model were presented. The main idea of this paper is to implement and optimize fuzzy logic control algorithms in order to balance the inverted pendulum and at the same time reducing the computational time of the controller. In this work, the inverted pendulum system was modeled and constructed using Simulink and the performance of the proposed fuzzy logic controller is compared to the more commonly used PID controller through simulations using Matlab. Simulation results show that the fuzzy logic controllers are far more superior compared to PID controllers in terms of overshoot, settling time and response to parameter changes.
Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions

We consider the least-squares regression problem and provide a detailed asymptotic analysis of the performance of averaged constant-step-size stochastic gradient descent. In the strongly-convex case, we provide an asymptotic expansion up to explicit exponentially decaying terms. Our analysis leads to new insights into stochastic approximation algorithms: (a) it gives a tighter bound on the allowed step-size; (b) the generalization error may be divided into a variance term which is decaying as O(1/n), independently of the step-size γ, and a bias term that decays as O(1/γn); (c) when allowing non-uniform sampling of examples over a dataset, the choice of a good sampling density depends on the trade-off between bias and variance: when the variance term dominates, optimal sampling densities do not lead to much gain, while when the bias term dominates, we can choose larger step-sizes that lead to significant improvements.
Where shall we go today?: planning touristic tours with tripbuilder

In this paper we propose TripBuilder, a new framework for personalized touristic tour planning. We mine from Flickr the information about the actual itineraries followed by a multitude of different tourists, and we match these itineraries on the touristic Point of Interests available from Wikipedia. The task of planning personalized touristic tours is then modeled as an instance of the Generalized Maximum Coverage problem. Wisdom-of-the-crowds information allows us to derive touristic plans that maximize a measure of interest for the tourist given her preferences and visiting time-budget. Experimental results on three different touristic cities show that our approach is effective and outperforms strong baselines.