nils-herrmann/clean_scirepeval huggingface dataset:
Using vanishing points for camera calibration

In this article a new method for the calibration of a vision system which consists of two (or more) cameras is presented. The proposed method, which uses simple properties of vanishing points, is divided into two steps. In the first step, the intrinsic parameters of each camera, that is, the focal length and the location of the intersection between the optical axis and the image plane, are recovered from a single image of a cube. In the second step, the extrinsic parameters of a pair of cameras, that is, the rotation matrix and the translation vector which describe the rigid motion between the coordinate systems fixed in the two cameras are estimated from an image stereo pair of a suitable planar pattern. Firstly, by matching the corresponding vanishing points in the two images the rotation matrix can be computed, then the translation vector is estimated by means of a simple triangulation. The robustness of the method against noise is discussed, and the conditions for optimal estimation of the rotation matrix are derived. Extensive experimentation shows that the precision that can be achieved with the proposed method is sufficient to efficiently perform machine vision tasks that require camera calibration, like depth from stereo and motion from image sequence.
ResearchGate versus Google Scholar: Which finds more early citations?

ResearchGate has launched its own citation index by extracting citations from documents uploaded to the site and reporting citation counts on article profile pages. Since authors may upload preprints to ResearchGate, it may use these to provide early impact evidence for new papers. This article assesses the whether the number of citations found for recent articles is comparable to other citation indexes using 2675 recently-published library and information science articles. The results show that in March 2017, ResearchGate found less citations than did Google Scholar but more than both Web of Science and Scopus. This held true for the dataset overall and for the six largest journals in it. ResearchGate correlated most strongly with Google Scholar citations, suggesting that ResearchGate is not predominantly tapping a fundamentally different source of data than Google Scholar. Nevertheless,  sharing in ResearchGate is substantial enough for authors to take seriously.
Autonomous Driving in Urban Environments: Boss and the Urban Challenge

Chris Urmson, Joshua Anhalt, Drew Bagnell, Christopher Baker, Robert Bittner, M. N. Clark, John Dolan, Dave Duggins, Tugrul Galatali, Chris Geyer, Michele Gittleman, Sam Harbaugh, Martial Hebert, Thomas M. Howard, Sascha Kolski, Alonzo Kelly, Maxim Likhachev, Matt McNaughton, Nick Miller, Kevin Peterson, Brian Pilnick, Raj Rajkumar, Paul Rybski, Bryan Salesky, Young-Woo Seo, Sanjiv Singh, Jarrod Snider, Anthony Stentz, William “Red” Whittaker, Ziv Wolkowicki, and Jason Ziglar Carnegie Mellon University Pittsburgh, Pennsylvania 15213
Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002

James Allan (editor), Jay Aslam, Nicholas Belkin, Chris Buckley, Jamie Callan, Bruce Croft (editor), Sue Dumais, Norbert Fuhr, Donna Harman, David J. Harper, Djoerd Hiemstra, Thomas Hofmann, Eduard Hovy, Wessel Kraaij, John Lafferty, Victor Lavrenko, David Lewis, Liz Liddy, R. Manmatha, Andrew McCallum, Jay Ponte, John Prager, Dragomir Radev, Philip Resnik, Stephen Robertson, Roni Rosenfeld, Salim Roukos, Mark Sanderson, Rich Schwartz, Amit Singhal, Alan Smeaton, Howard Turtle, Ellen Voorhees, Ralph Weischedel, Jinxi Xu, ChengXiang Zhai
Tangible Query Interfaces: Physically Constrained Tokens for Manipulating Database Queries

We present a new approach for using physically constrained tokens to express, manipulate, and visualize parameterized database queries. This method extends tangible interfaces to enable interaction with large aggregates of information. We describe two interface prototypes that use physical tokens to represent database parameters. These tokens are manipulated upon physical constraints, which map compositions of tokens onto interpretations including database queries, views, and Boolean operations. We propose a framework for “ token + constraint” interfaces, and compare one of our prototypes with a comparable graphical interface in a preliminary user study. † Current affiliation: Visualization Dept., Zuse Institute Berlin (ZIB), ullmer@zib.de †† Current affiliation: Computer Science Dept., Tufts University, jacob@cs.tufts.edu
Mean Shift, Mode Seeking, and Clustering

Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some k-means like clustering algorithms its special cases. It is shown that mean shift is a mode-seeking process on a surface constructed with a “shadow” kernel. For Gaussian kernels, mean shift is a gradient mapping. Convergence is studied for mean shift iterations. Cluster analysis is treated as a deterministic problem of finding a fixed point of mean shift that characterizes the data. Applications in clustering and Hough transform are demonstrated. Mean shift is also considered as an evolutionary strategy that performs multistart global optimization.
Efficient Defenses Against Adversarial Attacks

Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of undermining a system. In the case of DNNs, the lack of better understanding of their working has prevented the development of efficient defenses. In this paper, we propose a new defense method based on practical observations which is easy to integrate into models and performs better than state-of-the-art defenses. Our proposed solution is meant to reinforce the structure of a DNN, making its prediction more stable and less likely to be fooled by adversarial samples. We conduct an extensive experimental study proving the efficiency of our method against multiple attacks, comparing it to numerous defenses, both in white-box and black-box setups. Additionally, the implementation of our method brings almost no overhead to the training procedure, while maintaining the prediction performance of the original model on clean samples.
A Stereo Machine for Video-Rate Dense Depth Mapping and Its New Applications

We have developed a video-rate stereo machine that has the capability of generating a dense depth map at the video rate. The performance bench marks of the CMU video-rate stereo machine are: 1) multi image input of up to 6 cameras; 2) throughput of 30 million point × disparity measurement per second; 3) frame rate of 30 frame/sec; 4) a dense depth map of up to 256 × 240 pixels; 5) disparity search range of up to 60 pixels; 6) high precision of depth output up to 8 bits (with interpolation). The capability of passively producing such a dense depth map (3D representation) of a scene at the video rate can open up a new class of applications of 3D vision: merging real and virtual worlds in real time.
Document categories in the ISI Web of Knowledge: Misunderstanding the Social Sciences?

Thomson Reuter’s ISI Web of Knowledge (or ISI for short) is used in the majority of benchmarking analyses and bibliometric research projects. Therefore, it is important to be aware of the limitations of data provided by ISI. This article deals with a limitation that disproportionally affects the Social Sciences: ISI’s misclassification of journal articles containing original research into the “review” or “proceedings paper” category. I report on a comprehensive, 11 year analysis, of document categories for 27 journals in nine Social Science and Science disciplines. I show that although ISI’s “proceedings paper” and “review” classifications seem to work fairly well in the Sciences, they illustrate a profound misunderstanding of research and publication practices in the Social Sciences.
Enhancing Knowledge Graph Embedding with Probabilistic Negative Sampling

Link Prediction using Knowledge graph embedding projects symbolic entities and relations into low dimensional vector space, thereby learning the semantic relations between entities. Among various embedding models, there is a series of translation-based models such as TransE , TransH , and TransR[3]. This paper proposes modifications in the TransR model to address the issue of skewed data which is common in real-world knowledge graphs. The enhancements enable the model to smartly generate corrupted triplets during negative sampling, which significantly improves the training time and performance of TransR. The proposed approach can be applied to other translationbased models.
Knowledge discovery and data mining in toxicology.

Knowledge discovery and data mining tools are gaining increasing importance for the analysis of toxicological databases. This paper gives a survey of algorithms, capable to derive interpretable models from toxicological data, and presents the most important application areas. The majority of techniques in this area were derived from symbolic machine learning, one commercial product was developed especially for toxicological applications. The main application area is presently the detection of structure-activity relationships, very few authors have used these techniques to solve problems in epidemiological and clinical toxicology. Although the discussed algorithms are very flexible and powerful, further research is required to adopt the algorithms to the specific learning problems in this area, to develop improved representations of chemical and biological data and to enhance the interpretability of the derived models for toxicological experts.
Generalized Thompson Sampling for Contextual Bandits

Thompson Sampling, one of the oldest heuristics for solving multi-armed bandits, has recently been shown to demonstrate state-of-the-art performance. The em pirical success has led to great interests in theoretical understanding of this heuristic. In this pap er, we approach this problem in a way very different from existing efforts. In particular, motiv ated by the connection between Thompson Sampling and exponentiated updates, we propose a new family of algorithms called Generalized Thompson Sampling in the expert-learning framework, which includes Thompson Sampling as a special case. Similar to most expert-learning algorithms, Generalized Thompson Sampling uses a loss function to adjust the experts’ weights. General regr et bounds are derived, which are also instantiated to two important loss functions: square loss a nd logarithmic loss. In contrast to existing bounds, our results apply to quite general contextual bandi ts. More importantly, they quantify the effect of the “prior” distribution on the regret bounds.
Temporal Model Adaptation for Person Re-Identification

Lowrank similarity-dissimilarity metric learning method which introduces sparsity inducing regularizers that allow identification and exploitation of the most discriminative dimensions for matching and can be trained in an incremental fashion by means of a stochastic Alternating Directions Methods of Multipliers (ADMM) optimization algorithm A method to reduce the human labelling effort required to properly update the model • An unsupervised graph-based approach identifies only the most relevant gallery persons for every single probe Existing approaches: • Neglected the problem of adapting the features or the learned model over time • Manual labeling of supervised approaches require huge human effort Approach Overview
DNA methylome analysis using short bisulfite sequencing data

Bisulfite conversion of genomic DNA combined with next-generation sequencing (BS-seq) is widely used to measure the methylation state of a whole genome, the methylome, at single-base resolution. However, analysis of BS-seq data still poses a considerable challenge. Here we summarize the challenges of BS-seq mapping as they apply to both base and color-space data. We also explore the effect of sequencing errors and contaminants on inferred methylation levels and recommend the most appropriate way to analyze this type of data.
COID: A cluster–outlier iterative detection approach to multi-dimensional data analysis

Nowadays, most data mining algorithms focus on clustering methods alone. Also, there are a lot of approaches designed for outlier detection. We observe that, in many situations, clusters and outliers are concepts whose meanings are inseparable to each other, especially for those data sets with noise. Thus, it is necessary to treat both clusters and outliers as concepts of the same importance in data analysis. In this paper, we present our continuous work on the cluster–outlier iterative detection algorithm (Shi in SubCOID: exploring cluster-outlier iterative detection approach to multi-dimensional data analysis in subspace. Auburn, pp. 132–135, 2008; Shi and Zhang in Towards exploring interactive relationship between clusters and outliers in multi-dimensional data analysis.  Tokyo, pp. 518–519, 2005) to detect the clusters and outliers in another perspective for noisy data sets. In this algorithm, clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. The adjustment and modification of the clusters and outliers are performed iteratively until a certain termination condition is reached. This data processing algorithm can be applied in many fields, such as pattern recognition, data clustering, and signal processing. Experimental results demonstrate the advantages of our approach.
ABC: An Academic Industrial-Strength Verification Tool

ABC is a public-domain system for logic synthesis and formal verification of binary logic circuits appearing in synchronous hardware designs. ABC combines scalable logic transformations based on And-Inverter Graphs (AIGs), with a variety of innovative algorithms. A focus on the synergy of sequential synthesis and sequential verification leads to improvements in both domains. This paper introduces ABC, motivates its development, and illustrates its use in formal verification.
A framework for the design and integration of collaborative classroom games

The progress registered in the use of video games as educational tools has not yet been successfully transferred to the classroom. In an attempt to close this gap, a framework was developed that assists in the design and classroom integration of educational games. The framework addresses both the educational dimension and the ludic dimension. The educational dimension employs Bloom’s revised taxonomy to define learning objectives and applies the classroom multiplayer presential game (CMPG) pedagogical model while the ludic dimension determines the gaming elements subject to constraints imposed by the educational dimension. With a view to validating the framework, a game for teaching electrostatics was designed and experimentally implemented in a classroom context. An evaluation based on pre/post testing found that the game increased the average number of correct answers by students participating in the experiment from 6.11 to 10.00, a result found to be statistically significant. Thus validated, the framework offers a promising basis for further exploration through the development of other games and fine-tuning of its components.
Towards an implementation framework for business intelligence in healthcare

As healthcare organizations continue to be asked to do more with less, access to information is essential for sound evidence-based decision making. Business intelligence (BI) systems are designed to deliver decision-support information and have been repeatedly shown to provide value to organizations. Many healthcare organizations have yet to implement BI systems and no existing research provides a healthcare-specific framework to guide implementation. To address this research gap, we employ a case study in a Canadian Health Authority in order to address three questions: (1) what are the most significant adverse impacts to the organization’s decision processes and outcomes attributable to a lack of decision-support capabilities? (2) what are the root causes of these impacts, and what workarounds do they necessitate? and (3) in light of the issues identified, what are the key considerations for healthcare organizations in the early stages of BI implementation? Using the concept of co-agency as a guide we identified significant decision-related adverse impacts and their root causes. We found strong management support, the right skill sets and an information-oriented culture to be key implementation considerations. Our major contribution is a framework for defining and prioritizing decision-support information needs in the context of healthcare-specific processes.
Semi-Supervised Learning for Natural Language

Statistical supervised learning techniques have been successful for many natural language processing tasks, but they require labeled datasets, which can be expensive to obtain. On the other hand, unlabeled data (raw text) is often available "for free" in large quantities. Unlabeled data has shown promise in improving the performance of a number of tasks, e.g. word sense disambiguation, information extraction, and natural language parsing. In this thesis, we focus on two segmentation tasks, named-entity recognition and Chinese word segmentation. The goal of named-entity recognition is to detect and classify names of people, organizations, and locations in a sentence. The goal of Chinese word segmentation is to find the word boundaries in a sentence that has been written as a string of characters without spaces. Our approach is as follows: In a preprocessing step, we use raw text to cluster words and calculate mutual information statistics. The output of this step is then used as features in a supervised model, specifically a global linear model trained using the Perceptron algorithm. We also compare Markov and semi-Markov models on the two segmentation tasks. Our results show that features derived from unlabeled data substantially improves performance, both in terms of reducing the amount of labeled data needed to achieve a certain performance level and in terms of reducing the error using a fixed amount of labeled data. We find that sometimes semi-Markov models can also improve performance over Markov models. Thesis Supervisor: Michael Collins Title: Assistant Professor, CSAIL
Alignment by Agreement

We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between the models. Compared to the standard practice of intersecting predictions of independently-trained models, joint training provides a 32% reduction in AER. Moreover, a simple and efficient pair of HMM aligners provides a 29% reduction in AER over symmetrized IBM model 4 predictions. Disciplines Computer Sciences Comments Percy Liang, Ben Taskar, and Dan Klein. 2006. Alignment by agreement. In Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL '06). Association for Computational Linguistics, Stroudsburg, PA, USA, 104-111. DOI=10.3115/1220835.1220849 http://dx.doi.org/10.3115/1220835.1220849  This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, {(2006)} http://doi.acm.org/10.3115/1220835.1220849" Email permissions@acm.org This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/533 Alignment by Agreement Percy Liang UC Berkeley Berkeley, CA 94720 pliang@cs.berkeley.edu Ben Taskar UC Berkeley Berkeley, CA 94720 taskar@cs.berkeley.edu Dan Klein UC Berkeley Berkeley, CA 94720 klein@cs.berkeley.edu
Color-Based Tracking of Heads and Other Mobile Objects at Video Frame Rates

We develop a simple and very fast method for object tracking based exclusively on color information in digitized video images. Running on a Silicon Graphics R4600 Indy system with an IndyCam, our algorithm is capable of simultaneously tracking objects at f i l l frame size (640 x 480 pixels) and video frame rate (30 hs). Robustness with respect to occlusion is achieved via an explicit hypothesis-tree model of the occlusion process. We demonstrate the eficacy of our technique in the challenging task of tracking people, especially tracking human heads and hands.
Can You Hear Me Now? Musical Training Shapes Functional Brain Networks for Selective Auditory Attention and Hearing Speech in Noise

Even in the quietest of rooms, our senses are perpetually inundated by a barrage of sounds, requiring the auditory system to adapt to a variety of listening conditions in order to extract signals of interest (e.g., one speaker's voice amidst others). Brain networks that promote selective attention are thought to sharpen the neural encoding of a target signal, suppressing competing sounds and enhancing perceptual performance. Here, we ask: does musical training benefit cortical mechanisms that underlie selective attention to speech? To answer this question, we assessed the impact of selective auditory attention on cortical auditory-evoked response variability in musicians and non-musicians. Outcomes indicate strengthened brain networks for selective auditory attention in musicians in that musicians but not non-musicians demonstrate decreased prefrontal response variability with auditory attention. Results are interpreted in the context of previous work documenting perceptual and subcortical advantages in musicians for the hearing and neural encoding of speech in background noise. Musicians' neural proficiency for selectively engaging and sustaining auditory attention to language indicates a potential benefit of music for auditory training. Given the importance of auditory attention for the development and maintenance of language-related skills, musical training may aid in the prevention, habilitation, and remediation of individuals with a wide range of attention-based language, listening and learning impairments.
The Application of a Convolution Neural Network on Face and License Plate Detection

In this paper, two detectors, one for face and the other for license plates, are proposed, both based on a modified convolutional neural network (CNN) verifier. In our proposed verifier, a single feature map and a fully connected MLP were trained by examples to classify the possible candidates. Pyramid-based localization techniques were applied to fuse the candidates and to identify the regions of faces or license plates. In addition, geometrical rules filtered out false alarms in license plate detection. Some experimental results are given to show the effectiveness of the approach