nils-herrmann/clean_scirepeval huggingface dataset:
An Efficient Boosting Algorithm for Combining Preferences

The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting algorithm for combining preferences called RankBoost. We also describe an efficient implementation of the algorithm for certain natural cases. We discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different WWW search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. Here, we present results comparing RankBoost to nearest-neighbor and regression algorithms. Thesis Supervisor: David R. Karger Title: Associate Professor
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision

Minimum cut/maximum flow algorithms on graphs have emerged as an increasingly useful tool for exactor approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push -relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.
Wrapper Maintenance: A Machine Learning Approach

The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and efficient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wrappers from extracting data correctly. We present an efficient algorithm that learns structural information about data from positive examples alone. We describe how this information can be used for two wrapper maintenance applications: wrapper verification and reinduction. The wrapper verification system detects when a wrapper is not extracting correct data, usually because the Web source has changed its format. The reinduction algorithm automatically recovers from changes in the Web source by identifying data on Web pages so that a new wrapper may be generated for this source. To validate our approach, we monitored 27 wrappers over a period of a year. The verification algorithm correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes, resulting in precision of 0.73 and recall of 0.95. We validated the reinduction algorithm on ten Web sources. We were able to successfully reinduce the wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data extraction task.
A 3-D Split Manufacturing Approach to Trustworthy System Development

Securing the supply chain of integrated circuits is of utmost importance to computer security. In addition to counterfeit microelectronics, the theft or malicious modification of designs in the foundry can result in catastrophic damage to critical systems and large projects. In this letter, we describe a 3-D architecture that splits a design into two separate tiers: one tier that contains critical security functions is manufactured in a trusted foundry; another tier is manufactured in an unsecured foundry. We argue that a split manufacturing approach to hardware trust based on 3-D integration is viable and provides several advantages over other approaches.
Three-Phase Three-Level DC/DC Converter for High Input Voltage and High Power Applications-Adopting Symmetrical Duty Cycle Control

Three-phase dc/dc converters have the superior characteristics including lower current rating of switches, the reduced output filter requirement, and effective utilization of transformers. To further reduce the voltage stress on switches, three-phase three-level (TPTL) dc/dc converters have been investigated recently; however, numerous active power switches result in a complicated configuration in the available topologies. Therefore, a novel TPTL dc/dc converter adopting a symmetrical duty cycle control is proposed in this paper. Compared with the available TPTL converters, the proposed converter has fewer switches and simpler configuration. The voltage stress on all switches can be reduced to the half of the input voltage. Meanwhile, the ripple frequency of output current can be increased significantly, resulting in a reduced filter requirement. Experimental results from a 540-660-V input and 48-V/20-A output are presented to verify the theoretical analysis and the performance of the proposed converter.
Emoji as Emotion Tags for Tweets

In many natural language processing tasks, supervised machine learning approaches have proved most effective, and substantial effort has been made into collecting and annotating corpora for building such models. Emotion detection from text is no exception; however, research in this area is in its relative infancy, and few emotion annotated corpora exist to date. A further issue regarding the development of emotion annotated corpora is the difficulty of the annotation task and resulting inconsistencies in human annotations. One approach to address these problems is to use self-annotated data, using explicit indications of emotions included by the author of the data in question. We present a study of the use of unicode emoji as self-annotation of a Twitter user’s emotional state. Emoji are found to be used far more extensively than hash tags and we argue that they present a more faithful representation of a user’s emotional state. A substantial set of tweets containing emotion indicative emoji are collected and a sample annotated for emotions. The accuracy and utility of emoji as emotion labels are evaluated directly (with respect to annotations) and through trained statistical models. Results are cautiously optimistic and suggest further study of emotji usage.
Robust Unsupervised Feature Selection on Networked Data

Feature selection has shown its effectiveness to prepare high-dimensional data for many data mining and machine learning tasks. Traditional feature selection algorithms are mainly based on the assumption that data instances are independent and identically distributed. However, this assumption is invalid in networked data since instances are not only associated with high dimensional features but also inherently interconnected with each other. In addition, obtaining label information for networked data is time consuming and labor intensive. Without label information to direct feature selection, it is difficult to assess the feature relevance. In contrast to the scarce label information, link information in networks are abundant and could help select relevant features. However, most networked data has a lot of noisy links, resulting in the feature selection algorithms to be less effective. To address the above mentioned issues, we propose a robust unsupervised feature selection framework NetFS for networked data, which embeds the latent representation learning into feature selection. Therefore, content information is able to help mitigate the negative effects from noisy links in learning latent representations, while good latent representations in turn can contribute to extract more meaningful features. In other words, both phases could cooperate and boost each other. Experimental results on realworld datasets demonstrate the effectiveness of the proposed
Age differences in affective decision making as indexed by performance on the Iowa Gambling Task.

Contemporary perspectives on age differences in risk taking, informed by advances in developmental neuroscience, have emphasized the need to examine the ways in which emotional and cognitive factors interact to influence decision making. In the present study, a diverse sample of 901 individuals between the ages of 10 and 30 were administered a modified version of the Iowa Gambling Task, which is designed to measure affective decision making. Results indicate that approach behaviors (operationalized as the tendency to play increasingly from the advantageous decks over the course of the task) display an inverted U-shape relation to age, peaking in mid- to late adolescence. In contrast, avoidance behaviors (operationalized as the tendency to refrain from playing from the disadvantageous decks) increase linearly with age, with adults avoiding disadvantageous decks at higher rates than both preadolescents and adolescents. The finding that adolescents, compared to adults, are relatively more approach oriented in response to positive feedback and less avoidant in response to negative feedback is consistent with recent studies of brain development, as well as epidemiological data on various types of risky behavior, and may have important practical implications for the prevention of adolescent risk taking.
Twitter use by the U.S. Congress

Twitter is a microblogging and social networking service with millions of members and growing at a tremendous rate. With the buzz surrounding the service have come claims of its ability to transform the way people interact and share information, and calls for public figures to start using the service. In this study, we are interested in the type of content that legislators are posting to the service, particularly by members of the United States Congress. We read and coded over 6,000 posts from all members of Congress using the site. Our analysis shows that Congresspeople are primarily using Twitter to post information, particularly links to news articles about themselves and to their blog posts, and to report on their daily activities. These tend not to provide new insights into government or the legislative process or to improve transparency; rather, they are vehicles for self-promotion. However, Twitter is also facilitating direct communication between Congresspeople and citizens, though this is a less popular activity. We report on our findings and analysis, and discuss other uses of Twitter for legislators.
Context-sensitive information retrieval using implicit feedback

A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using implicit feedback, especially the clicked document summaries, can improve retrieval performance substantially.
Why is Mobility in India so Low ? Social Insurance , Inequality , and Growth ∗

This paper examines the hypothesis that the persistence of low spatial and marital mobility in rural India, despite increased growth rates and rising inequality in recent years, is due to the existence of sub-caste networks that provide mutual insurance to their members. Unique panel data providing information on income, assets, gifts, loans, consumption, marriage, and migration are used to link caste networks to household and aggregate mobility. Our key finding, consistent with the hypothesis that local risk-sharing networks restrict mobility, is that among households with the same (permanent) income, those in higher-income caste networks are more likely to participate in caste-based insurance arrangements and are less likely to both out-marry and out-migrate. At the aggregate level, the networks appear to have coped successfully with the rising inequality within sub-castes that accompanied the Green Revolution. The results suggest that caste networks will continue to smooth consumption in rural India for the foreseeable future, as they have for centuries, unless alternative consumption-smoothing mechanisms of comparable quality become available. ∗We are very grateful to Andrew Foster for many useful discussions that substantially improved the paper. We received helpful comments from Jan Eeckhout, Rachel Kranton, Ethan Ligon and seminar participants at Arizona, Chicago, Essex, Georgetown, Harvard, IDEI, ITAM, LEA-INRA, LSE, Ohio State, UCLA, and NBER. Alaka Holla provided excellent research assistance. Research support from NICHD grant R01-HD046940 and NSF grant SES-0431827 is gratefully acknowledged. †Brown University and NBER ‡Yale University
Fusion of doppler radar and geometric attributes for motion estimation of extended objects

A prime requirement for autonomous driving is a fast and reliable estimation of the motion state of dynamic objects in the ego-vehicle's surroundings. An instantaneous approach for extended objects based on two Doppler radar sensors has recently been proposed. In this paper, that approach is augmented by prior knowledge of the object's heading angle and rotation center. These properties can be determined reliably by state-of-the-art methods based on sensors such as LIDAR or cameras. The information fusion is performed utilizing an appropriate measurement model, which directly maps the motion state in the Doppler velocity space. This model integrates the geometric properties. It is used to estimate the object's motion state using a linear regression. Additionally, the model allows a straightforward calculation of the corresponding variances. The resulting method shows a promising accuracy increase of up to eight times greater than the original approach.
Cepstral mean and variance normalization in the model domain

In prior work we have demonstrated the noise robustness of a novel microphone solution, the PARAT earplug communication terminal. Here we extend that work with results for the ETSI Advanced Front-End and segmental cepstral mean and variance normalization (CMVN). We also propose a method for doing CMVN in the model domain. This removes the need to train models on normalized features, which may significantly extend the applicability of CMVN. The recognition results are comparable to those of the traditional approach.
Explicit Document Modeling through Weighted Multiple-Instance Learning

Representing documents is a crucial component in many NLP tasks, for instance predicting aspect ratings in reviews. Previous methods for this task treat documents globally, and do not acknowledge that target categories are often assigned by their authors with generally no indication of the specific sentences that motivate them. To address this issue, we adopt a weakly supervised learning model, which jointly learns to focus on relevant parts of a document according to the context along with a classifier for the target categories. Derived from the weighted multiple-instance regression (MIR) framework, the model learns decomposable document vectors for each individual category and thus overcomes the representational bottleneck in previous methods due to a fixed-length document vector. During prediction, the estimated relevance or saliency weights explicitly capture the contribution of each sentence to the predicted rating, thus offering an explanation of the rating. Our model achieves state-of-the-art performance on multi-aspect sentiment analysis, improving over several baselines. Moreover, the predicted saliency weights are close to human estimates obtained by crowdsourcing, and increase the performance of lexical and topical features for review segmentation and summarization.
CavePainting: a fully immersive 3D artistic medium and interactive experience

CavePainting is an artistic medium that uses a 3D analog of 2D brush strokes to create 3D works of art in a fully immersive Cave environment. Physical props and gestures are used to provide an intuitive interface for artists who may not be familiar with virtual reality. The system is designed to take advantage of the 8 ft. x 8 ft. x 8 ft. space in which the artist works. CavePainting enables the artist to create a new type of art and provides a novel approach to viewing this art after it has been created. In this paper, we describe CavePainting’s 3D brush strokes, color pickers, artwork viewing mode, and interface. We also present several works of art created using the system along with feedback from artists. Artists are excited about this form of art and the gestural, full-body experience of creating it.
On the Recognition of Printed Characters of Any Font and Size

We describe the current state of a system that recognizes printed text of various fonts and sizes for the Roman alphabet. The system combines several techniques in order to improve the overall recognition rate. Thinning and shape extraction are performed directly on a graph of the run-length encoding of a binary image. The resulting strokes and other shapes are mapped, using a shape-clustering approach, into binary features which are then fed into a statistical Bayesian classifier. Large-scale trials have shown better than 97 percent top choice correct performance on mixtures of six dissimilar fonts, and over 99 percent on most single fonts, over a range of point sizes. Certain remaining confusion classes are disambiguated through contour analysis, and characters suspected of being merged are broken and reclassified. Finally, layout and linguistic context are applied. The results are illustrated by sample pages.
Emotion Analysis of Songs Based on Lyrical and Audio Features

In this paper, a method is proposed to detect the emotion of a song based on its lyrical and audio features. Lyrical features are generated by segmentation of lyrics during the process of data extraction. ANEW and WordNet knowledge is then incorporated to compute Valence and Arousal values. In addition to this, linguistic association rules are applied to ensure that the issue of ambiguity is properly addressed. Audio features are used to supplement the lyrical ones and include attributes like energy, tempo, and danceability. These features are extracted from The Echo Nest, a widely used music intelligence platform. Construction of training and test sets is done on the basis of social tags extracted from the last.fm website. The classification is done by applying feature weighting and stepwise threshold reduction on the k-Nearest Neighbors algorithm to provide fuzziness in the classification.
Evolving large-scale neural networks for vision-based TORCS

The TORCS racing simulator has become a standard testbed used in many recent reinforcement learning competitions, where an agent must learn to drive a car around a track using a small set of task-specific features. In this paper, large, recurrent neural networks (with over 1 million weights) are evolved to solve a much more challenging version of the task that instead uses only a stream of images from the driver’s perspective as input. Evolving such large nets is made possible by representing them in the frequency domain as a set of coefficients that are transformed into weight matrices via an inverse Fourier-type transform. To our knowledge this is the first attempt to tackle TORCS using vision, and successfully evolve a neural network controllers of this size.
Appearance-based gaze estimation in the wild

Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild.
An interactive approach to semantic modeling of indoor scenes with an RGBD camera

We present an interactive approach to semantic modeling of indoor scenes with a consumer-level RGBD camera. Using our approach, the user first takes an RGBD image of an indoor scene, which is automatically segmented into a set of regions with semantic labels. If the segmentation is not satisfactory, the user can draw some strokes to guide the algorithm to achieve better results. After the segmentation is finished, the depth data of each semantic region is used to retrieve a matching 3D model from a database. Each model is then transformed according to the image depth to yield the scene. For large scenes where a single image can only cover one part of the scene, the user can take multiple images to construct other parts of the scene. The 3D models built for all images are then transformed and unified into a complete scene. We demonstrate the efficiency and robustness of our approach by modeling several real-world scenes.
Variational Depth Superresolution Using Example-Based Edge Representations

In this paper we propose a novel method for depth image superresolution which combines recent advances in example based upsampling with variational superresolution based on a known blur kernel. Most traditional depth superresolution approaches try to use additional high resolution intensity images as guidance for superresolution. In our method we learn a dictionary of edge priors from an external database of high and low resolution examples. In a novel variational sparse coding approach this dictionary is used to infer strong edge priors. Additionally to the traditional sparse coding constraints the difference in the overlap of neighboring edge patches is minimized in our optimization. These edge priors are used in a novel variational superresolution as anisotropic guidance of the higher order regularization. Both the sparse coding and the variational superresolution of the depth are solved based on a primal-dual formulation. In an exhaustive numerical and visual evaluation we show that our method clearly outperforms existing approaches on multiple real and synthetic datasets.