nils-herrmann/clean_scirepeval huggingface dataset:
Second Language Acquisition Modeling

We present the task of second language acquisition (SLA) modeling. Given a history of errors made by learners of a second language, the task is to predict errors that they are likely to make at arbitrary points in the future. We describe a large corpus of more than 7M words produced by more than 6k learners of English, Spanish, and French using Duolingo, a popular online language-learning app. Then we report on the results of a shared task challenge aimed studying the SLA task via this corpus, which attracted 15 teams and synthesized work from various fields including cognitive science, linguistics, and machine learning.
Computational Modeling of Top-down Visual Attention in Interactive Environments

Modeling how visual saliency guides the deployment of atten tion over visual scenes has attracted much interest recently — among both computer v ision and experimental/computational researchers — since visual attention is a key function of both machine and biological vision systems. Research efforts in compute r vision have mostly been focused on modeling bottom-up saliency. Strong influences o n attention and eye movements, however, come from instantaneous task demands. Here , w propose models of top-down visual guidance considering task influences. The n ew models estimate the state of a human subject performing a task (here, playing video gam es), and map that state to an eye position. Factors influencing state come from scene gi st, physical actions, events, and bottom-up saliency. Proposed models fall into two categ ori s. In the first category, we use classical discriminative classifiers, including Reg ression, kNN and SVM. In the second category, we use Bayesian Networks to combine all the multi-modal factors in a unified framework. Our approaches significantly outperfor m 15 competing bottom-up and top-down attention models in predicting future eye fixat ions on 18,000 and 75,00 video frames and eye movement samples from a driving and a flig ht combat video game, respectively. We further test and validate our approaches o n 1.4M video frames and 11M fixations samples and in all cases obtain higher prediction s c re that reference models.
Measuring e-learning systems success in an organizational context: Scale development and validation

As a promising solution, electronic learning (e-learning) has been widely adopted by many companies to offer learning-on-demand opportunities to individual employees for reducing training time and cost. While information systems (IS) success models have received much attention among researchers, little research has been conducted to assess the success and/or effectiveness of e-learning systems in an organizational context. Whether traditional information systems success models can be extended to investigating e-learning systems success is rarely addressed. Based on the previous IS success literature, this study develops and validates a multidimensional model for assessing e-learning systems success (ELSS) from employee (e-learner) perspectives. The procedures used in conceptualizing an ELSS construct, generating items, collecting data, and validating a multiple-item scale for measuring ELSS are described. This paper presents evidence of the scale’s factor structure, reliability, content validity, criterion-related validity, convergent validity, and discriminant validity on the basis of analyzing data from a sample of 206 respondents. Theoretical and managerial implications of our results are then discussed. This empirically validated instrument will be useful to researchers in developing and testing e-learning systems theories, as well as to organizations in implementing successful e-learning systems.
Bayesian combination of sparse and non-sparse priors in image super resolution

In this paper the application of image prior combinations to the Bayesian Super Resolution (SR) image registration and reconstruction problem is studied. Two sparse image priors, a Total Variation (TV) prior and a prior based on the `1 norm of horizontal and vertical first order differences (f.o.d.), are combined with a non-sparse Simultaneous Auto Regressive (SAR) prior. Since, for a given observation model, each prior produces a different posterior distribution of the underlying High Resolution (HR) image, the use of variational approximation will produce as many posterior approximations as priors we want to combine. A unique approximation is obtained here by finding the distribution on the HR image given the observations that minimizes a linear convex combination of Kullback-Leibler (KL) divergences. We find this distribution in closed form. The estimated HR images are compared with the ones obtained by other SR reconstruction methods.
Deep learning for undersampled MRI reconstruction

This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting the image-folding problem dictated by the Poisson summation formula. To deal with the localization uncertainty due to image folding, a small number of low-frequency k-space data are added. Training the deep learning net involves input and output images that are pairs of the Fourier transforms of the subsampled and fully sampled k-space data. Our experiments show the remarkable performance of the proposed method; only 29[Formula: see text] of the k-space data can generate images of high quality as effectively as standard MRI reconstruction with the fully sampled data.
Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators

This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model the observed graph as a sample from a manifold endowed with a vector field, and we design an algorithm that separates and recovers the features of this process: the geometry of the manifold, the data density and the vector field. The algorithm is motivated by our analysis of Laplacian-type operators and their continuous limit as generators of diffusions on a manifold. We illustrate the recovery algorithm on both artificially constructed and real data.
Accurate estimation of influenza epidemics using Google search data via ARGO.

Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search-based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people's online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.
Barriers to use: usability and content accessibility on the Web's most popular sites

Content accessibility is a key feature in highly usable Web sites, but reports in the popular press typically report that 95% or more of all Web sites are inaccessible to users with disabilities. The present study is a content accessibility compliance audit of 50 of the Web's most popular sites, undertaken to determine if content accessibility can be conceived and reported in continuous, rather than dichotomous, terms. Preliminary results suggest that a meaningful ordinal ranking of content accessibility is not only possible, but also correlates significantly with the results of independent automated usability assessment procedures.
Design comparison of 6.5 kV Si-IGBT, 6.5kV SiC JBS diode, and 10 kV SiC MOSFETs in megawatt converters for shipboard power system

In this paper a comparative design study has been shown with 6.5kV Si-IGBT/Si-P IN diode, 6.5kV Si-IGBT/SiC-JBS diode, and 10kV SiC-M OSFET/SiC-JBS diode in an act ive front-end (AFE) converter for medium-voltage shipboard application. Megawatt converters based on the aforementioned technologies are being designed and compared at tw o different switching frequencies. In this regard, accurate circuit models for 5-10A die for each (a) silicon 6.5kV IGBT, (b) 6.5kV Si-IGBT incorporating a 6.5kV SiC-JB S Diode, and (c) 10kV SiC MOSFET with 10kV SiC JBS Diode, are paralleled to ma ke a 100A switch and used as conv erter switching devices in SPICE circuit simulation to perform the comparative analysis. Switching waveforms, characteristics, switching power and energy loss measurements are follo wed by an efficiency comparison of a 1MW converter with 7.5kVdc at 1kH z and 5kH z switching frequencies. It is shown that 6.5kV Si-IGBT/SiC-JBS diode, with its high efficiency performance up to 5kHz, is a strong candidate for MW ran ge converters. The 10kV SiC-MOSFET/SiC-JBS diode is an option for h igher switching frequency MW converters.
Emotional intelligence and addictions: a systematic review.

Since the millennium, an expanding number of research articles have examined the relationship between emotional intelligence (EI) and physical and mental health. The relationship between EI and addictive disorders has, however, remained relatively well-hidden. We therefore systematically reviewed and critically evaluated the literature on this relationship. We identified 51 articles on the topic of which 36 fulfilled our inclusion criteria. Results indicate that a lower level of EI is associated with more intensive smoking, alcohol use, and illicit drug use and two components of EI play a key role in addictions: "decoding and differentiation of emotions" and "regulation of emotions."
Generative Partition Networks for Multi-Person Pose Estimation

This paper proposes a new framework, named Generative Partition Network (GPN), for addressing the challenging multi-person pose estimation problem. Different from existing pure top-down and bottom-up solutions, the proposed GPN models the multi-person partition detection as a generative process from joint candidates and infers joint configurations for person instances from each person partition locally, resulting in both low joint detection and joint partition complexities. In particular, GPN designs a generative model based on the Generalized Hough Transform framework to detect person partitions via votes from joint candidates in the Hough space, parameterized by centroids of persons. Such generative model produces joint candidates and their corresponding person partitions by performing only one pass of joint detection. In addition, GPN formulates the inference procedure for joint configurations of human poses as a graph partition problem and optimizes it locally. Inspired by recent success of deep learning techniques for human pose estimation, GPN designs a multi-stage convolutional neural network with feature pyramid branch to jointly learn joint confidence maps and Hough transformation maps. Extensive experiments on two benchmarks demonstrate the efficiency and effectiveness of the proposed GPN.
Efficient Exploration for Dialogue Policy Learning with BBQ Networks & Replay Buffer Spiking

When rewards are sparse and action spaces large, Q-learning with -greedy exploration can be inefficient. This poses problems for otherwise promising applications such as task-oriented dialogue systems, where the primary reward signal, indicating successful completion of a task, requires a complex sequence of appropriate actions. Under these circumstances, a randomly exploring agent might never stumble upon a successful outcome in reasonable time. We present two techniques that significantly improve the efficiency of exploration for deep Q-learning agents in dialogue systems. First, we introduce an exploration technique based on Thompson sampling, drawing Monte Carlo samples from a Bayes-by-backprop neural network, demonstrating marked improvement over common approaches such as -greedy and Boltzmann exploration. Second, we show that spiking the replay buffer with experiences from a small number of successful episodes, as are easy to harvest for dialogue tasks, can make Q-learning feasible when it might otherwise fail.
MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving

For autonomous driving, moving objects like vehicles and pedestrians are of critical importance as they primarily influence the maneuvering and braking of the car. Typically, they are detected by motion segmentation of dense optical flow augmented by a CNN based object detector for capturing semantics. In this paper, our aim is to jointly model motion and appearance cues in a single convolutional network. We propose a novel two-stream architecture for joint learning of object detection and motion segmentation. We designed three different flavors of our network to establish systematic comparison. It is shown that the joint training of tasks significantly improves accuracy compared to training them independently. Although motion segmentation has relatively fewer data than vehicle detection. The shared fusion encoder benefits from the joint training to learn a generalized representation. We created our own publicly available dataset (KITTI MOD) by extending KITTI object detection to obtain static/moving annotations on the vehicles. We compared against MPNet as a baseline, which is the current state of the art for CNN-based motion detection. It is shown that the proposed two-stream architecture improves the mAP score by 21.5% in KITTI MOD. We also evaluated our algorithm on the non-automotive DAVIS dataset and obtained accuracy close to the state-of-the-art performance. The proposed network runs at 8 fps on a Titan X GPU using a basic VGG16 encoder.
Analyzing spatially-varying blur

Blur is caused by a pixel receiving light from multiple scene points, and in many cases, such as object motion, the induced blur varies spatially across the image plane. However, the seemingly straight-forward task of estimating spatially-varying blur from a single image has proved hard to accomplish reliably. This work considers such blur and makes two contributions: a local blur cue that measures the likelihood of a small neighborhood being blurred by a candidate blur kernel; and an algorithm that, given an image, simultaneously selects a motion blur kernel and segments the region that it affects. The methods are shown to perform well on a diversity of images.
A novel LS-SVMs hyper-parameter selection based on particle swarm optimization

The selection of hyper-parameters plays an important role to the performance of least-squares support vector machines (LS-SVMs). In this paper, a novel hyper-parameter selection method for LS-SVMs is presented based on the particle swarm optimization (PSO). The proposed method does not need any priori knowledge on the analytic property of the generalization performance measure and can be used to determine multiple hyper-parameters at the same time. The feasibility of this method is examined on benchmark data sets. Different kinds of kernel families are investigated by using the proposed method. Experimental results show that the best or quasi-best test performance could be obtained by using the scaling radial basis kernel function (SRBF) and RBF kernel functions, respectively. & 2008 Elsevier B.V. All rights reserved.
How Public Is My Private Life?: Privacy in Online Dating

Online dating services let users expand their dating pool beyond their social network and specify important characteristics of potential partners. To assess compatibility, users share personal information — e.g., identifying details or sensitive opinions about sexual preferences or worldviews — in profiles or in one-on-one communication. Thus, participating in online dating poses inherent privacy risks. How people reason about these privacy risks in modern online dating ecosystems has not been extensively studied. We present the results of a survey we designed to examine privacy-related risks, practices, and expectations of people who use or have used online dating, then delve deeper using semi-structured interviews. We additionally analyzed 400 Tinder profiles to explore how these issues manifest in practice. Our results reveal tensions between privacy and competing user values and goals, and we demonstrate how these results can inform future designs.
Static Disassembly of Obfuscated Binaries

Disassembly is the process of recovering a symbolic representation of a program’s machine code instructions from its binary representation. Recently, a number of techniques have been proposed that attempt to foil the disassembly process. These techniques are very effective against state-of-the-art disassemblers, preventing a substantial fraction of a binary program from being disassembled correctly. This could allow an attacker to hide malicious code from static analysis tools that depend on correct disassembler output (such as virus scanners). The paper presents novel binary analysis techniques that substantially improve the success of the disassembly process when confronted with obfuscated binaries. Based on control flow graph information and statistical methods, a large fraction of the program’s instructions can be correctly identified. An evaluation of the accuracy and the performance of our tool is provided, along with a comparison to several state-of-the-art disassemblers.
An innovative methodology for detection and quantification of cracks through incorporation of depth perception

Visual inspection of structures is a highly qualitative method in which inspectors visually assess a structure’s condition. If a region is inaccessible, binoculars must be used to detect and characterize defects. Although several Non-Destructive Testing methods have been proposed for inspection purposes, they are nonadaptive and cannot quantify crack thickness reliably. In this paper, a contact-less remote-sensing crack detection and quantification methodology based on 3D scene reconstruction (computer vision), image processing, and pattern recognition concepts is introduced. The proposed approach utilizes depth perception to detect cracks and quantify their thickness, thereby giving a robotic inspection system the ability to analyze images captured from any distance and using any focal length or resolution. This unique adaptive feature is especially useful for incorporating mobile systems, such as unmanned aerial vehicles, into structural inspection methods since it would allow inaccessible regions to be properly inspected for cracks. Guidelines are presented for optimizing the acquisition and processing of images, thereby enhancing the quality and reliability of the damage detection approach and allowing the capture of even the slightest cracks (e.g., detection of 0.1 mm cracks from a distance of 20 m), which are routinely encountered in realistic field applications where the camera-object distance and image contrast are not controllable.
HAWAII: a domain-based approach for supporting mobility in wide-area wireless networks

Mobile IP is the current standard for supporting macromobility of mobile hosts. However, in the case of micromobility support, there are several competing proposals. In this paper, we present the design, implementation, and performance evaluation of HAWAII, a domain-based approach for supporting mobility. HAWAII uses specialized path setup schemes which install host-based forwarding entries in specific routers to support intra-domain micromobility. These path setup schemes deliver excellent performance by reducing mobility related disruption to user applications. Also, mobile hosts retain their network address while moving within the domain, simplifying quality-of-service (QoS) support. Furthermore, reliability is achieved through maintaining soft-state forwarding entries for the mobile hosts and leveraging fault detection mechanisms built in existing intra-domain routing protocols. HAWAII defaults to using Mobile IP for macromobility, thus providing a comprehensive solution for mobility support in wide-area wireless networks.
"All I know about politics is what I read in Twitter": Weakly Supervised Models for Extracting Politicians' Stances From Twitter

During the 2016 United States presidential election, politicians have increasingly used Twitter to express their beliefs, stances on current political issues, and reactions concerning national and international events. Given the limited length of tweets and the scrutiny politicians face for what they choose or neglect to say, they must craft and time their tweets carefully. The content and delivery of these tweets is therefore highly indicative of a politician’s stances. We present a weakly supervised method for extracting how issues are framed and temporal activity patterns on Twitter for popular politicians and issues of the 2016 election. These behavioral components are combined into a global model which collectively infers the most likely stance and agreement patterns among politicians, with respective accuracies of 86.44% and 84.6% on average.
A convex, smooth and invertible contact model for trajectory optimization

Trajectory optimization is done most efficiently when an inverse dynamics model is available. Here we develop the first model of contact dynamics defined in both the forward and inverse directions. The contact impulse is the solution to a convex optimization problem: minimize kinetic energy in contact space subject to non-penetration and friction-cone constraints. We use a custom interior-point method to make the optimization problem unconstrained; this is key to defining the forward and inverse dynamics in a consistent way. The resulting model has a parameter which sets the amount of contact smoothing, facilitating continuation methods for optimization. We implemented the proposed contact solver in our new physics engine (MuJoCo). A full Newton step of trajectory optimization for a 3D walking gait takes only 160 msec, on a 12-core PC.
Articulated Multi-person Tracking in the Wild

In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos. Our starting point is a model that resembles existing architectures for single-frame pose estimation but is several orders of magnitude faster. We achieve this in two ways: (1) by simplifying and sparsifying the body-part relationship graph and leveraging recent methods for faster inference, and (2) by offloading a substantial share of computation onto a feed-forward convolutional architecture that is able to detect and associate body joints of the same person even in clutter. We use this model to generate proposals for body joint locations and formulate articulated tracking as spatio-temporal grouping of such proposals. This allows to jointly solve the association problem for all people in the scene by propagating evidence from strong detections through time and enforcing constraints that each proposal can be assigned to one person only. We report results on a public MPII Human Pose benchmark and on a new dataset of videos with multiple people. We demonstrate that our model achieves state-of-the-art results while using only a fraction of time and is able to leverage temporal information to improve state-of-the-art for crowded scenes1.