nils-herrmann/clean_scirepeval huggingface dataset:
Staying well during pregnancy and the postpartum: A pilot randomized trial of mindfulness-based cognitive therapy for the prevention of depressive relapse/recurrence.

Clinical decision-making regarding the prevention of depression is complex for pregnant women with histories of depression and their health care providers. Pregnant women with histories of depression report preference for nonpharmacological care, but few evidence-based options exist. Mindfulness-based cognitive therapy has strong evidence in the prevention of depressive relapse/recurrence among general populations and indications of promise as adapted for perinatal depression (MBCT-PD). With a pilot randomized clinical trial, our aim was to evaluate treatment acceptability and efficacy of MBCT-PD relative to treatment as usual (TAU).



Pregnant adult women with depression histories were recruited from obstetric clinics at 2 sites and randomized to MBCT-PD (N = 43) or TAU (N = 43). Treatment acceptability was measured by assessing completion of sessions, at-home practice, and satisfaction. Clinical outcomes were interview-based depression relapse/recurrence status and self-reported depressive symptoms through 6 months postpartum.



Consistent with predictions, MBCT-PD for at-risk pregnant women was acceptable based on rates of completion of sessions and at-home practice assignments, and satisfaction with services was significantly higher for MBCT-PD than TAU. Moreover, at-risk women randomly assigned to MBCT-PD reported significantly improved depressive outcomes compared with participants receiving TAU, including significantly lower rates of depressive relapse/recurrence and lower depressive symptom severity during the course of the study.



MBCT-PD is an acceptable and clinically beneficial program for pregnant women with histories of depression; teaching the skills and practices of mindfulness meditation and cognitive-behavioral therapy during pregnancy may help to reduce the risk of depression during an important transition in many women's lives.
THE SCIENTIFIC MODEL OF CAUSALITY

Causality is a very intuitive notion that is difficult to make precise without lapsing into tautology. Two ingredients are central to any definition: (1) a set of possible outcomes (counterfactuals) generated by a function of a set of ‘‘factors’’ or ‘‘determinants’’ and (2) a manipulation where one (or more) of the ‘‘factors’’ or ‘‘determinants’’ is changed. An effect is realized as a change in the argument of a stable function that produces the same change in the outcome for a class of interventions that change the ‘‘factors’’ by the same amount. The outcomes are compared at different levels of the factors or generating variables. Holding all factors save one at a constant level, the change in the outcome associated with manipulation of the varied factor is called a causal effect of the manipulated factor. This definition, or some version of it, goes back to Mill (1848) and Marshall (1890). Haavelmo’s (1943) made it more precise within the context of linear equations models. The phrase ‘ceteris paribus’ (everything else held constant) is a mainstay of economic analysis
Eye-Movement Analysis: A New Tool for Evaluating the Design of Visually Administered Instruments (Paper and Web) By

A data collection instrument that a respondent self-completes through the visual channel, such as on paper or over the Web, is visually administered. Although insightful in many ways, traditional methods of evaluating questionnaires, such as cognitive interviewing, usability testing, and experimentation may be insufficient when it comes to evaluating the design of visually administered questionnaires because these methods cannot directly identify information respondents perceive or the precise order in which they observe the information (Redline et al 1998). In this paper, we present the results of a study that was conducted to explore whether eye-movement analysis might prove a promising new tool for evaluating the design of visually administered questionnaires. Eye tracking hardware and software, which were originally developed at the Human for use with computer monitors, were adapted to track the eye movements of respondents answering three versions of a paper questionnaire. These versions were chosen for study because differences in the design of their branching instructions were hypothesized to affect eye-movements, which in turn may affect the accuracy of following the branching instructions (Redline and Dillman Forthcoming).  Eye-movement analysis has been used in other fields, most notably reading and scene perception, to study cognitive processing (e.g., Rayner 1992; Rayner 1983). However, survey design research grew out of the interviewer-administered realm, which has been primarily focused on respondents' comprehension of the spoken language of questionnaires. Therefore, the mechanism by which respondents perceive information presented on paper questionnaires or over the Web, the eyes and their movements, has not received much attention until recently. Other reasons for the lack of eye-movement research in the survey field are its cost and relative difficulty. As others have noted, eye-movement research requires specialized knowledge, equipment and expertise to operate the equipment. In addition,
Using API Sequence and Bayes Algorithm to Detect Suspicious Behavior

Computer viruses have become the main threat of the safety and security of industry. Unfortunately, no mature products of anti-virus can protect computers effectively. This paper presents an approach of virus detection which is based on analysis and distilling of representative behavior characteristic and systemic description of the suspicious behaviors indicated by the sequences of APIs which called under Windows. Based on decompilation analysis, according to the determinant of Bayes Algorithm, and by the validation of abundant sample space, the technique implements the virus detection by suspicious behavior identification.
Fitting Multiple Curves to Point Clouds with Complicated Topological Structures

We present an automatic method for fitting multiple B-spline curves to unorganized planar points. The method works on point clouds which have complicated topological structures and a single curve is insufficient for fitting the shape. A divide-and-merge algorithm is developed for dividing the unorganized data points into several groups while each group represents a smooth curve. Each point group is then fitted with a B-spline curve by the SDM method. Our algorithm also sets up automatically the control polygon of initial B-spline curves. Experiments demonstrate the capability of the presented algorithm in accurate reconstruction of topological structures of point clouds.
Ranking Domain-Specific Highlights by Analyzing Edited Videos

We present a fully automatic system for ranking domainspecific highlights in unconstrained personal videos by analyzing online edited videos. A novel latent linear ranking model is proposed to handle noisy training data harvested online. Specifically, given a search query (domain) such as “surfing”, our system mines the Youtube database to find pairs of raw and corresponding edited videos. Leveraging the assumption that edited video is more likely to contain highlights than the trimmed parts of the raw video, we obtain pair-wise ranking constraints to train our model. The learning task is challenging due to the amount of noise and variation in the mined data. Hence, a latent loss function is incorporated to robustly deal with the noise. We efficiently learn the latent model on a large number of videos (about 700 minutes in all) using a novel EM-like self-paced model selection procedure. Our latent ranking model outperforms its classification counterpart, a motion analysis baseline , and a fully-supervised ranking system that requires labels from Amazon Mechanical Turk. Finally, we show that impressive highlights can be retrieved without additional human supervision for domains like skating, surfing, skiing, gymnastics, parkour, and dog activity in unconstrained personal videos.
Automatic calibration and removal of distortion from scenes ofstructured environmentsFrédéric

Most algorithms in 3-D Computer Vision rely on the pinhole camera model because of its simplicity, whereas video optics, especially low-cost wide-angle or fish-eye lens, generate a lot of non-linear distortion which can be critical. To find the distortion parameters of a camera, we use the following fundamental property: a camera follows the pinhole model if and only if the projection of every line in space onto the camera is a line. Consequently, if we find the transformation on the video image so that every line in space is viewed in the transformed image as a line, then we know how to remove the distortion from the image. The algorithm consists of first doing edge extraction on a possibly distorted video sequence, then doing polygonal approximation with a large tolerance on these edges to extract possible lines from the sequence, and then finding the parameters of our distortion model that best transform these edges to segments. Results are presented on real video images, compared with distortion calibration obtained by a full camera calibration method which uses a calibration grid.
Measurement-based quantum computation on cluster states

We give a detailed account of the one-way quantum computer, a scheme of quantum computation that consists entirely of one-qubit measurements on a particular class of entangled states, the cluster states. We prove its universality, describe why its underlying computational model is different from the network model of quantum computation, and relate quantum algorithms to mathematical graphs. Further we investigate the scaling of required resources and give a number of examples for circuits of practical interest such as the circuit for quantum Fourier transformation and for the quantum adder. Finally, we describe computation with clusters of finite size.
Mode-detection via median-shift

Median-shift is a mode seeking algorithm that relies on computing the median of local neighborhoods, instead of the mean. We further combine median-shift with Locality Sensitive Hashing (LSH) and show that the combined algorithm is suitable for clustering large scale, high dimensional data sets. In particular, we propose a new mode detection step that greatly accelerates performance. In the past, LSH was used in conjunction with mean shift only to accelerate nearest neighbor queries. Here we show that we can analyze the density of the LSH bins to quickly detect potential mode candidates and use only them to initialize the median-shift procedure. We use the median, instead of the mean (or its discrete counterpart - the medoid) because the median is more robust and because the median of a set is a point in the set. A median is well defined for scalars but there is no single agreed upon extension of the median to high dimensional data. We adopt a particular extension, known as the Tukey median, and show that it can be computed efficiently using random projections of the high dimensional data onto 1D lines, just like LSH, leading to a tightly integrated and efficient algorithm.
Self-tuned deep super resolution

Deep learning has been successfully applied to image super resolution (SR). In this paper, we propose a deep joint super resolution (DJSR) model to exploit both external and self similarities for SR. A Stacked Denoising Convolutional Auto Encoder (SDCAE) is first pre-trained on external examples with proper data augmentations. It is then fine-tuned with multi-scale self examples from each input, where the reliability of self examples is explicitly taken into account. We also enhance the model performance by sub-model training and selection. The DJSR model is extensively evaluated and compared with state-of-the-arts, and show noticeable performance improvements both quantitatively and perceptually on a wide range of images.
RGBD Salient Object Detection via Deep Fusion

Numerous efforts have been made to design various low-level saliency cues for RGBD saliency detection, such as color and depth contrast features as well as background and color compactness priors. However, how these low-level saliency cues interact with each other and how they can be effectively incorporated to generate a master saliency map remain challenging problems. In this paper, we design a new convolutional neural network (CNN) to automatically learn the interaction mechanism for RGBD salient object detection. In contrast to existing works, in which raw image pixels are fed directly to the CNN, the proposed method takes advantage of the knowledge obtained in traditional saliency detection by adopting various flexible and interpretable saliency feature vectors as inputs. This guides the CNN to learn a combination of existing features to predict saliency more effectively, which presents a less complex problem than operating on the pixels directly. We then integrate a superpixel-based Laplacian propagation framework with the trained CNN to extract a spatially consistent saliency map by exploiting the intrinsic structure of the input image. Extensive quantitative and qualitative experimental evaluations on three data sets demonstrate that the proposed method consistently outperforms the state-of-the-art methods.
Heidi matrix: nearest neighbor driven high dimensional data visualization

Identifying patterns in large high dimensional data sets is a challenge. As the number of dimensions increases, the patterns in the data sets tend to be more prominent in the subspaces than the original dimensional space. A system to facilitate presentation of such subspace oriented patterns in high dimensional data sets is required to understand the data.
 Heidi is a high dimensional data visualization system that captures and visualizes the closeness of points across various subspaces of the dimensions; thus, helping to understand the data. The core concept behind Heidi is based on prominence of patterns within the nearest neighbor relations between pairs of points across the subspaces.
 Given a d-dimensional data set as input, Heidi system generates a 2-D matrix represented as a color image. This representation gives insight into (i) how the clusters are placed with respect to each other, (ii) characteristics of placement of points within a cluster in all the subspaces and (iii) characteristics of overlapping clusters in various subspaces.
 A sample of results displayed and discussed in this paper illustrate how Heidi Visualization can be interpreted.
Towards a Gamification Framework for Software Process Improvement Initiatives: Construction and Validation

One of the main reasons for failure in Software Process Improvement (SPI) initiatives is the lack of motivation of the professionals involved. Therefore, motivation should be encouraged throughout the software process. Gamification allows us to define mechanisms that motivate people to develop specific tasks. A gamification framework was adapted to the particularities of an organization and software professionals to encourage motivation. Thus, it permitted to facilitate the adoption of SPI improvements and a higher success rate. The objective of this research was to validate the framework presented and increase the actual implementation of gamification in organizations. To achieve this goal, a qualitative research methodology was employed through interviews that involved a total of 29 experts in gamification and SPI. The results of this study confirm the validity of the framework presented, its relevance in the field of SPI and its alignment with the standard practices of gamification implementation within organizations.
Randomized Iterative Methods for Linear Systems

We develop a novel, fundamental and surprisingly simple randomized iterative method for solving consistent linear systems. Our method has six different but equivalent interpretations: sketch-and-project, constrain-and-approximate, random intersect, random linear solve, random update and random fixed point. By varying its two parameters—a positive definite matrix (defining geometry), and a random matrix (sampled in an i.i.d. fashion in each iteration)—we recover a comprehensive array of well known algorithms as special cases, including the randomized Kaczmarz method, randomized Newton method, randomized coordinate descent method and random Gaussian pursuit. We naturally also obtain variants of all these methods using blocks and importance sampling. However, our method allows for a much wider selection of these two parameters, which leads to a number of new specific methods. We prove exponential convergence of the expected norm of the error in a single theorem, from which existing complexity results for known variants can be obtained. However, we also give an exact formula for the evolution of the expected iterates, which allows us to give lower bounds on the convergence rate.
Scalable Learning of Non-Decomposable Objectives

Modern retrieval systems are often driven by an underlying machine learning model. The goal of such systems is to identify and possibly rank the few most relevant items for a given query or context. Thus, such systems are typically evaluated using a ranking-based performance metric such as the area under the precision-recall curve, the Fβ score, precision at fixed recall, etc. Obviously, it is desirable to train such systems to optimize the metric of interest. In practice, due to the scalability limitations of existing approaches for optimizing such objectives, large-scale retrieval systems are instead trained to maximize classification accuracy, in the hope that performance as measured via the true objective will also be favorable. In this work we present a unified framework that, using straightforward building block bounds, allows for highly scalable optimization of a wide range of ranking-based objectives. We demonstrate the advantage of our approach on several real-life retrieval problems that are significantly larger than those considered in the literature, while achieving substantial improvement in performance over the accuracyobjective baseline.  2017, Fort Lauderdale, Florida, USA.  W&CP volume 54.
Crowdsourcing a Large Corpus of Clickbait on Twitter

Clickbait has become a nuisance on social media. To address the urging task of clickbait detection, we constructed a new corpus of 38,517 annotated Twitter tweets, the Webis Clickbait Corpus 2017. To avoid biases in terms of publisher and topic, tweets were sampled from the top 27 most retweeted news publishers, covering a period of 150 days. Each tweet has been annotated on 4-point scale by five annotators recruited at Amazon’s Mechanical Turk. The corpus has been employed to evaluate 12 clickbait detectors submitted to the Clickbait Challenge 2017.
Anomaly Detection for Discrete Sequences: A Survey

This survey attempts to provide a comprehensive and structured overview of the existing research for the problem of detecting anomalies in discrete/symbolic sequences. The objective is to provide a global understanding of the sequence anomaly detection problem and how existing techniques relate to each other. The key contribution of this survey is the classification of the existing research into three distinct categories, based on the problem formulation that they are trying to solve. These problem formulations are: 1) identifying anomalous sequences with respect to a database of normal sequences; 2) identifying an anomalous subsequence within a long sequence; and 3) identifying a pattern in a sequence whose frequency of occurrence is anomalous. We show how each of these problem formulations is characteristically distinct from each other and discuss their relevance in various application domains. We review techniques from many disparate and disconnected application domains that address each of these formulations. Within each problem formulation, we group techniques into categories based on the nature of the underlying algorithm. For each category, we provide a basic anomaly detection technique, and show how the existing techniques are variants of the basic technique. This approach shows how different techniques within a category are related or different from each other. Our categorization reveals new variants and combinations that have not been investigated before for anomaly detection. We also provide a discussion of relative strengths and weaknesses of different techniques. We show how techniques developed for one problem formulation can be adapted to solve a different formulation, thereby providing several novel adaptations to solve the different problem formulations. We also highlight the applicability of the techniques that handle discrete sequences to other related areas such as online anomaly detection and time series anomaly detection.
Coherence-Enhancing Shock Filters

Shock filters are based in the idea to apply locally either a dilation or an erosion process, depending on whether the pixel belongs to the influence zone of a maximum or a minimum. They create a sharp shock between two influence zones and produce piecewise constant segmentations. In this paper we design specific shock filters for the enhancement of coherent flow-like structures. They are based on the idea to combine shock filtering with the robust orientation estimation by means of the structure tensor. Experiments with greyscale and colour images show that these novel filters may outperform previous shock filters as well as coherence-enhancing diffusion filters.
Linking Named Entities to Any Database

Existing techniques for disambiguating named entities in text mostly focus on Wikipedia as a target catalog of entities. Yet for many types of entities, such as restaurants and cult movies, relational databases exist that contain far more extensive information than Wikipedia. This paper introduces a new task, called Open-Database Named-Entity Disambiguation (Open-DB NED), in which a system must be able to resolve named entities to symbols in an arbitrary database, without requiring labeled data for each new database. We introduce two techniques for Open-DB NED, one based on distant supervision and the other based on domain adaptation. In experiments on two domains, one with poor coverage by Wikipedia and the other with near-perfect coverage, our Open-DB NED strategies outperform a state-of-the-art Wikipedia NED system by over 25% in accuracy.
ASSESSING PUBLIC OPINIONS OF AND INTEREST IN NEW VEHICLE 1 TECHNOLOGIES : AN AUSTIN PERSPECTIVE

Technological advances are bringing connected and autonomous vehicles (CAVs) to the everevolving transportation system. Anticipating the public acceptance and adoption of these technologies is important. A recent internet-based survey was conducted polling 347 Austinites to understand their opinions on smart-car technologies and strategies. Ordered-probit and other model results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7,253) appears to be much higher than that for adding partial (Level 3) automation ($3,300) to their current vehicles. This study estimates the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding such automations and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, home-location decisions after AVs and SAVs become a common mode of transport, and preferences regarding how congestion-toll revenues are used. Higherincome, technology-savvy males, living in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.