nils-herrmann/clean_scirepeval huggingface dataset:
MarkIt: privacy markers for protecting visual secrets

The increasing popularity of wearable devices that continuously capture video, and the prevalence of third-party applications that utilize these feeds have resulted in a new threat to privacy. In many situations, sensitive objects/regions are maliciously (or accidentally) captured in a video frame by third-party applications. However, current solutions do not allow users to specify and enforce fine grained access control over video feeds.
 In this paper, we describe MarkIt, a computer vision based privacy marker framework, that allows users to specify and enforce fine grained access control over video feeds. We present two example privacy marker systems -- PrivateEye and WaveOff. We conclude with a discussion of the computer vision, privacy and systems challenges in building a comprehensive system for fine grained access control over video feeds.
Flexible Domain Adaptation for Automated Essay Scoring Using Correlated Linear Regression

Most of the current automated essay scoring (AES) systems are trained using manually graded essays from a specific prompt. These systems experience a drop in accuracy when used to grade an essay from a different prompt. Obtaining a large number of manually graded essays each time a new prompt is introduced is costly and not viable. We propose domain adaptation as a solution to adapt an AES system from an initial prompt to a new prompt. We also propose a novel domain adaptation technique that uses Bayesian linear ridge regression. We evaluate our domain adaptation technique on the publicly available Automated Student Assessment Prize (ASAP) dataset and show that our proposed technique is a competitive default domain adaptation algorithm for the AES task.
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

The reparameterization trick enables the optimization of large scale stochastic computation graphs via gradient descent. The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution. After refactoring, the gradients of the loss propagated by the chain rule through the graph are low variance unbiased estimators of the gradients of the expected loss. While many continuous random variables have such reparameterizations, discrete random variables lack continuous reparameterizations due to the discontinuous nature of discrete states. In this work we introduce concrete random variables – continuous relaxations of discrete random variables. The concrete distribution is a new family of distributions with closed form densities and a simple reparameterization. Whenever a discrete stochastic node of a computation graph can be refactored into a one-hot bit representation that is treated continuously, concrete stochastic nodes can be used with automatic differentiation to produce low-variance biased gradients of objectives (including objectives that depend on the log-likelihood of latent stochastic nodes) on the corresponding discrete graph. We demonstrate their effectiveness on density estimation and structured prediction tasks using neural networks.
Stochastic gradient descent with differentially private updates

Differential privacy is a recent framework for computation on sensitive data, which has shown considerable promise in the regime of large datasets. Stochastic gradient methods are a popular approach for learning in the data-rich regime because they are computationally tractable and scalable. In this paper, we derive differentially private versions of stochastic gradient descent, and test them empirically. Our results show that standard SGD experiences high variability due to differential privacy, but a moderate increase in the batch size can improve performance significantly.
Design and Development of a 50kW Z-Source Inverter for Fuel Cell Vehicles

A detailed design process of the Z-source inverter is presented in this paper. A DC rail clamp circuit is used to reduce the overshoot of the device during turn off. The thermal and 3-D design process is gone through, and the loss calculation of the inverter is discussed, which is different from traditional PWM inverters. A 50 kW inverter for fuel cell vehicle is developed to demonstrate the validity of the design process. Experimental results confirmed the design process and demonstrated the high efficiency characteristic of the Z-source inverter
Radiometric CCD camera calibration and noise estimation

Changes in measured image irradiance have many physical causes and are the primary cue for several visual processes, such as edge detection and shape from shading. Using physical models for charged-coupled device ( C C D ) video cameras and material reflectance, we quantify the variation in digitized pixel values that is due to sensor noise and scene variation. This analysis forms the basis of algorithms for camera characterization and calibration and for scene description. Specifically, algorithms are developed for estimating the parameters of camera noise and for calibrating a camera to remove the effects of fixed pattern nonuniformity and spatial variation in dark current. While these techniques have many potential uses, we describe in particular how they can be used to estimate a measure of scene variation. This measure is independent of image irradiance and can be used to identify a surface from a single sensor band over a range of situations. Experimental results confirm that the models presented in this paper are useful for modeling the different sources of variation in real images obtained from video cameras. Index T e m s C C D cameras, computer vision, camera calibration, noise estimation, reflectance variation, sensor modeling.
Sleep deprivation in critical illness: its role in physical and psychological recovery.

Critically ill patients frequently experience poor sleep, characterized by frequent disruptions, loss of circadian rhythms, and a paucity of time spent in restorative sleep stages. Factors that are associated with sleep disruption in the intensive care unit (ICU) include patient-ventilator dysynchrony, medications, patient care interactions, and environmental noise and light. As the field of critical care increasingly focuses on patients' physical and psychological outcomes following critical illness, understanding the potential contribution of ICU-related sleep disruption on patient recovery is an important area of investigation. This review article summarizes the literature regarding sleep architecture and measurement in the critically ill, causes of ICU sleep fragmentation, and potential implications of ICU-related sleep disruption on patients' recovery from critical illness. With this background information, strategies to optimize sleep in the ICU are also discussed.
Glaserian and Straussian grounded theory: similar or completely different?

Grounded Theory is growing in popularity as a research method in ICT research areas such as Information Systems and Software Engineering. Although there are two distinct methods, namely the Glaserian and the Straussian versions, a substantial number of research articles tend to ignore the difference and just claim that they are using grounded theory. To a researcher new to the grounded theory method, the two methods look very similar. Because the Straussian method is more prescriptive, most opt to follow this method, without investigating the Glaserian version. The few who try to use a hybrid of the two methods (not appreciating that the two methods are substantially different), only realize after a significant investment in time that the methods are not reconcilable and that either the one or the other should be followed. To contribute towards eliminating the confusion, this paper investigates the differences between the two methods. This will, hopefully, enable ICT researchers to make a more informed decision on which method to follow.
Compressive sensing over graphs

In this paper, motivated by network inference and tomography applications, we study the problem of compressive sensing for sparse signal vectors over graphs. In particular, we are interested in recovering sparse vectors representing the properties of the edges from a graph. Unlike existing compressive sensing results, the collective additive measurements we are allowed to take must follow connected paths over the underlying graph. For a sufficiently connected graph with n nodes, it is shown that, using O(k log(n)) path measurements, we are able to recover any k-sparse link vector (with no more than k nonzero elements), even though the measurements have to follow the graph path constraints. We mainly show that the computationally efficient ℓ1 minimization can provide theoretical guarantees for inferring such k-sparse vectors with O(k log(n)) path measurements from the graph.
Comparison of the response of doubly fed and fixed-speed induction generator wind turbines to changes in network frequency

Synchronous and fixed-speed induction generators release the kinetic energy of their rotating mass when the power system frequency is reduced. In the case of doubly fed induction generator (DFIG)-based wind turbines, their control system operates to apply a restraining torque to the rotor according to a predetermined curve with respect to the rotor speed. This control system is not based on the power system frequency and there is negligible contribution to the inertia of the power system. A DFIG control system was modified to introduce inertia response to the DFIG wind turbine. Simulations were used to show that with the proposed control system, the DFIG wind turbine can supply considerably greater kinetic energy than a fixed-speed wind turbine.
SenTube: A Corpus for Sentiment Analysis on YouTube Social Media

In this paper we present SenTube – a dataset of user-generated comments on YouTube videos annotated for information content and sentiment polarity. It contains annotations that allow to develop classifiers for several important NLP tasks: (i) sentiment analysis, (ii) text categorization (relatedness of a comment to video and/or product), (iii) spam detection, and (iv) prediction of comment informativeness. The SenTube corpus favors the development of research on indexing and searching YouTube videos exploiting information derived from comments. The corpus will cover several languages: at the moment, we focus on English and Italian, with Spanish and Dutch parts scheduled for the later stages of the project. For all the languages, we collect videos for the same set of products, thus offering possibilities for multiand cross-lingual experiments. The paper provides annotation guidelines, corpus statistics and annotator agreement details.
A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration

We present a new method for solving total variation (TV) minimization problems in image restoration. The main idea is to remove some of the singularity caused by the nondifferentiability of the quantity |∇u| in the definition of the TV-norm before we apply a linearization technique such as Newton’s method. This is accomplished by introducing an additional variable for the flux quantity appearing in the gradient of the objective function, which can be interpreted as the normal vector to the level sets of the image u. Our method can be viewed as a primal-dual method as proposed by Conn and Overton [A Primal-Dual Interior Point Method for Minimizing a Sum of Euclidean Norms,  1994] and Andersen [Ph.D. thesis, Odense University, Denmark, 1995] for the minimization of a sum of Euclidean norms. In addition to possessing local quadratic convergence, experimental results show that the new method seems to be globally convergent.
General autonomic components of motion sickness.

This report refers to a body of investigations performed in support of experiments aboard the Space Shuttle, and designed to counteract the symptoms of Space Adapatation Syndrome, which resemble those of motion sickness on Earth. For these supporting studies we examined the autonomic manifestations of earth-based motion sickness. Heart rate, respiration rate, finger pulse volume and basal skin resistance were measured on 127 men and women before, during and after exposure to nauseogenic rotating chair tests. Significant changes in all autonomic responses were observed across the tests (p<.05). Significant differences in autonomic responses among groups divided according to motion sickness susceptibility were also observed (p<.05). Results suggest that the examination of autonomic responses as an objective indicator of motion sickness malaise is warranted and may contribute to the overall understanding of the syndrome on Earth and in Space. DESCRIPTORS: heart rate, respiration rate, finger pulse volume, skin resistance, biofeedback, motion sickness.
The two faces of transformational leadership: empowerment and dependency.

Followers' identification with the leader and the organizational unit, dependence on the leader, and empowerment by the leader are often attributed to transformational leadership in organizations. However, these hypothesized outcomes have received very little attention in empirical studies. Using a sample of 888 bank employees working under 76 branch manages, the authors tested the relationships between transformational leadership and these outcomes. They found that transformational leadership was positively related to both followers' dependence and their empowerment and that personal identification mediated the relationship between transformational leadership and followers' dependence on the leader, whereas social identification mediated the relationship between transformational leadership and followers' empowerment. The authors discuss the implications of these findings to both theory and practice.
Depth Matters: Influence of Depth Cues on Visual Saliency

Most previous studies on visual saliency have only focused on static or dynamic 2D scenes. Since the human visual system has evolved predominantly in natural three dimensional environments, it is important to study whether and how depth information influences visual saliency. In this work, we first collect a large human eye fixation database compiled from a pool of 600 2D-vs-3D image pairs viewed by 80 subjects, where the depth information is directly provided by the Kinect camera and the eye tracking data are captured in both 2D and 3D free-viewing experiments. We then analyze the major discrepancies between 2D and 3D human fixation data of the same scenes, which are further abstracted and modeled as novel depth priors. Finally, we evaluate the performances of state-of-the-art saliency detection models over 3D images, and propose solutions to enhance their performances by integrating the depth priors.
A Smart Educational Game to Model Personality Using Learning Analytics

Various research work have highlighted the importance of modeling learner's personality to provide a personalized computer based learning. In particular, questionnaire is the most used method to model personality which can be long and not motivating. This makes learners unwilling to take it. Therefore, this paper refers to Learning Analytics (LA) to implicitly model learners' personalities based on their traces generated during the learning-playing process. In this context, an LA system and an educational game were developed. Forty five participants (34 learners and 11 teachers) participated in an experiment to evaluate the accuracy level of the learners' modeling results and the teachers' satisfaction degree towards this LA system. The obtained results highlighted that the LA system has a high level of accuracy and a "good" agreement degree compared to the questionnaire paper. Besides, the teachers found the LA system easy to use, useful and they were willing to use it in the future.
Motivational variables in second-language acquisition.

1 During the summer of 2005, I discovered that there was not a copy of my dissertation available from the library at McGill University. I was, however, able to obtain a copy of it on microfilm from another university that had initially obtained it on interlibrary loan. I am most grateful to Vicki Galbraith who typed this version from that copy, which except for some minor variations due to differences in type size and margins (plus this footnote, of course) is identical to that on the microfilm. ACKNOWLEDGEMENTS 1 The writer is grateful to Dr. J. T. McIlhone, Associate General Director in Charge of English Classes of the Montreal Catholic School Board, for his kind cooperation in making subjects available, and to the Principals and French teachers of each high school for their assistance and cooperation during the testing programs. advice on the statistical analysis. In addition, the writer would like to express his appreciation to Mr. K. Tunstall for his assistance in the difficult task of interviewing the parents of each student. Finally, the writer would like to express his gratitude to Janet W. Gardner for her invaluable assistance in all phases of the research program.
Sentiment Flow - A General Model of Web Review Argumentation

Web reviews have been intensively studied in argumentation-related tasks such as sentiment analysis. However, due to their focus on content-based features, many sentiment analysis approaches are effective only for reviews from those domains they have been specifically modeled for. This paper puts its focus on domain independence and asks whether a general model can be found for how people argue in web reviews. Our hypothesis is that people express their global sentiment on a topic with similar sequences of local sentiment independent of the domain. We model such sentiment flow robustly under uncertainty through abstraction. To test our hypothesis, we predict global sentiment based on sentiment flow. In systematic experiments, we improve over the domain independence of strong baselines. Our findings suggest that sentiment flow qualifies as a general model of web review argumentation.
Low cost crowd counting using audio tones

With mobile devices becoming ubiquitous, collaborative applications have become increasingly pervasive. In these applications, there is a strong need to obtain a count of the number of mobile devices present in an area, as it closely approximates the size of the crowd. Ideally, a crowd counting solution should be easy to deploy, scalable, energy efficient, be minimally intrusive to the user and reasonably accurate. Existing solutions using data communication or RFID do not meet these criteria. In this paper, we propose a crowd counting solution based on audio tones, leveraging the microphones and speaker phones that are commonly available on most phones, tackling all the above criteria. We have implemented our solution on 25 Android phones and run several experiments at a bus stop, aboard a bus, within a cafeteria and a classroom. Experimental evaluations show that we are able to achieve up to 90% accuracy and consume 81% less energy than the WiFi interface in idle mode.
Improved Arabic Dialect Classification with Social Media Data

Arabic dialect classification has been an important and challenging problem for Arabic language processing, especially for social media text analysis and machine translation. In this paper we propose an approach to improving Arabic dialect classification with semi-supervised learning: multiple classifiers are trained with weakly supervised, strongly supervised, and unsupervised data. Their combination yields significant and consistent improvement on two different test sets. The dialect classification accuracy is improved by 5% over the strongly supervised classifier and 20% over the weakly supervised classifier. Furthermore, when applying the improved dialect classifier to build a Modern Standard Arabic (MSA) language model (LM), the new model size is reduced by 70% while the English-Arabic translation quality is improved by 0.6 BLEU point.
Highly Parameterized K-means Clustering on FPGAs: Comparative Results with GPPs and GPUs

K-means clustering has been widely used in processing large datasets in many fields of studies. Advancement in many data collection techniques has been generating enormous amount of data, leaving scientists with the challenging task of processing them. Using General  Processors or GPPs to process large datasets may take a long time, therefore many acceleration methods have been proposed in the literature to speed-up the processing of such large datasets. In this work, we propose a parameterized Field Programmable Gate Array (FPGA) implementation of the Kmeans algorithm and compare it with previous FPGA implementation as well as recent implementations on Graphics Processing Units (GPUs) and with GPPs. The proposed FPGA implementation has shown higher performance in terms of speed-up over previous FPGA GPU and GPP implementations, and is more energy efficient.
A Bayesian approach to adaptive video super resolution

Although multi-frame super resolution has been extensively studied in past decades, super resolving real-world video sequences still remains challenging. In existing systems, either the motion models are oversimplified, or important factors such as blur kernel and noise level are assumed to be known. Such models cannot deal with the scene and imaging conditions that vary from one sequence to another. In this paper, we propose a Bayesian approach to adaptive video super resolution via simultaneously estimating underlying motion, blur kernel and noise level while reconstructing the original high-res frames. As a result, our system not only produces very promising super resolution results that outperform the state of the art, but also adapts to a variety of noise levels and blur kernels. Theoretical analysis of the relationship between blur kernel, noise level and frequency-wise reconstruction rate is also provided, consistent with our experimental results.
Hybrid neural network models for bankruptcy predictions

The objective of this paper is to develop the hybrid neural network models for bankruptcy prediction. The proposed hybrid neural network models are (1) a MDA-assisted neural network, (2) an ID3-assisted neural network, and (3) a SOFM(self organizing feature map)-assisted neural network. Both the MDA-assisted neural network and the ID3-assisted neural network are the neural network models operating with the input variables selected by the MDA method and ID3 respectively. The SOFM-assisted neural network combines a backpropagation model (supervised learning) with a SOFM model (unsupervised learning). The performance of the hybrid neural network model is evaluated using MDA and ID3 as a benchmark. Empirical results using Korean bankruptcy data show that hybrid neural network models are very promising neural network models for bankruptcy prediction in terms of predictive accuracy and adaptability.
Adaptation and Evaluation of Recommendations for Short-term Shopping Goals

An essential characteristic in many e-commerce settings is that website visitors can have very specific short-term shopping goals when they browse the site. Relying solely on long-term user models that are pre-trained on historical data can therefore be insufficient for a suitable next-basket recommendation. Simple "real-time" recommendation approaches based, e.g., on unpersonalized co-occurrence patterns, on the other hand do not fully exploit the available information about the user's long-term preference profile. In this work, we aim to explore and quantify the effectiveness of using and combining long-term models and short-term adaptation strategies. We conducted an empirical evaluation based on a novel evaluation design and two real-world datasets. The results indicate that maintaining short-term content-based and recency-based profiles of the visitors can lead to significant accuracy increases. At the same time, the experiments show that the choice of the algorithm for learning the long-term preferences is particularly important at the beginning of new shopping sessions.