nils-herrmann/clean_scirepeval huggingface dataset:
Optimizing SVMs for complex call classification

Large margin classifiers such as support vector machines (SVM) or Adaboost are obvious choices for natural language document or call routing. However, how to combine several binary classifiers to optimize the whole routing process and how this process scales when it involves many different decisions (or classes) is a complex problem that has only received partial answers. We propose a global optimization process based on an optimal channel communication model that allows a combination of possibly heterogeneous binary classifiers. As in Markov modeling, computational feasibility is achieved through simplifications and independence assumptions that are easy to interpret. Using this approach, we have managed to decrease the call-type classification error rate for AT&T's How May I Help You (HMIHY/sup (sm)/) natural dialog system by 50 %.
Rational Kernels

We introduce a general family of kernels based on weighted transducers or rational relations, rational kernels, that can be used for analysis of variable-length sequences or more generally weighted automata, in applications such as computational biology or speech recognition. We show that rational kernels can be computed efficiently using a general algorithm of composition of weighted transducers and a general single-source shortest-distance algorithm. We also describe several general families of positive definite symmetric rational kernels. These general kernels can be combined with Support Vector Machines to form efficient and powerful techniques for spoken-dialog classification: highly complex kernels become easy to design and implement and lead to substantial improvements in the classification accuracy. We also show that the string kernels considered in applications to computational biology are all specific instances of rational kernels.
A general segmentation scheme for DjVu document compression

We describe the “DjVu” (Déjà Vu) technology: an efficient document image compression methodology, a file format, and a delivery platform that together, enable instant access to high quality documents from essentially any platform, over any connection. Originally developed for scanned color documents, it was recently expanded to electronic documents, so DjVu has now truly become a universal document interchange format. With DjVu, a color magazine page scanned at 300dpi typically occupies between 40KB and 80KB, i.e. approximately 5 to 10 times smaller than JPEG for a similar level of readability (the typical compression ratio is 500:1). Converting electronic documents to DjVu also offers substantial advantages, as described in the paper. The technology relies on a classification of each pixel as either foreground (text, drawing) or background (pictures, paper texture and color), thereby producing a segmentation into layers that are compressed separately. The novel contribution of this paper is a unified approach for segmentation of scanned or electronic documents, using a rigorous approach based on the Minimum Description Length (MDL) principle. The foreground layer is compressed using a pattern matching technique taking advantage of the similarities between character shapes. A progressive, wavelet-based compression technique, combined with a masking algorithm, is then used to compress the background image at lower resolution, while minimizing the number of bits spent on the pixels that are otherwise covered by foreground pixels. Encoders, decoders, and real-time, memory efficient plug-ins for various web browsers are available for all the major platforms. H. Talbot, R. Beare (Eds): Proceedings of ISMM2002 Redistribution rights reserved CSIRO Publishing. ISBN 0 643 06804 X 17
Escaping the Convex Hull with Extrapolated Vector Machines

Maximum margin classifiers such as Support Vector Machines (SVMs) critically depends upon the convex hulls of the training samples of each class, as they implicitly search for the minimum distance between the convex hulls. We propose Extrapolated Vector Machines (XVMs) which rely on extrapolations outside these convex hulls. XVMs improve SVM generalization very significantly on the MNIST [7] OCR data. They share similarities with the Fisher discriminant: maximize the inter-class margin while minimizing the intra-class disparity.
Efficient conversion of digital documents to multilayer raster formats

How can we turn the description of a digital (i.e. electronically produced) document into something that is efficient for multi-layer raster formats? It is first shown that a foreground/background segmentation without overlapping foreground components can be more efficient for viewing or printing. Then, a new algorithm that prevents overlaps between foreground components while optimizing both the document quality and compression ratio is derived from the minimum description length (MDL) criterion. This algorithm makes the DjVu compression format significantly, more efficient on electronically produced documents. Comparisons with other formats are provided.
Djvu: Un systeme de compression d'images pour la distribution reticulaire de documents numerises (Djvu: An image compression system for distributing scanned document on the internet)

Nous presentons une technique nouvelle de compression d'images appelee DjVu. Cette technique est specialement concue pour la compression de documents en couleurs numerises a haute resolution. Un fichier DjVu representant une page typique d'un magazine en couleurs, numerisee a 300 points par pouce (dpi), requiert entre 40 et 80 KB, ce qui est est 5 a 10 fois meilleur qu'un fichier JPEG offrant une lisibilite similaire. Le compresseur DjVu commence par classer chaque pixel de l'image numerisee comme pixel d'avant-plan (texte, dessins au trait) ou pixel d'arriere-plan (images, photos, texture du papier). Cette classification forme une image bitonale qui est compressee grâce a une technique qui tire parti des similitudes de forme entre les divers caracteres composant l'avant-plan. Les images d'avant-plan et d'arriere-plan sont ensuite compressees a l'aide d'un algorithme a base d'ondelettes a resolution reduite. Un algorithme de masquage minimise le nombre de bits utilises pour coder les pixels d'avant-plan ou d'arriere-plan qui ne sont pas visibles dans l'image finale. Un plug-in pour butineur permet de visualiser tres efficacement les images DjVu sur le Web.
DjVu document browsing with on-demand loading and rendering of image components

Image-based digital documents are composed of multiple pages, each of which may be composed of multiple components such as the test, pictures background, and annotations. We describe the image structure and software architecture that allows the DjVu system to load and render the required components on demand while minimizing the bandwidth requirements, and the memory requirements in the client. DjVu document files are merely a list of enriched URLs that point to individual files (or file elements) that contain image components. Image components include :text images, background images, shape dictionaries shared by multiple pages, OCRed text, and several types of annotations. A multithreaded software architecture with smart caching allows individual components to be loaded and pre-decoded and rendered on-demand. Pages are pre-fetched or loaded on demand, allowing users to randomly access pages without downloading the entire document, and without the help of a byte server. Components that are shared across pages (e.g. shape dictionaries, or background layers) are loaded as required and cached. This greatly reduces the overall bandwidth requirements. Shared dictionaries allow 40% typical file size reduction for scanned bitonal documents at 300dpi. Compression ratios on scanned US patents at 300dpi are 5.2 to 10.2 times higher than GroupIV with shared dictionaries and 3.6 to 8.5 times higher than GroupIV without shared dictionaries.
DjVu: analyzing and compressing scanned documents for Internet distribution

DjVu is an image compression technique specifically geared towards the compression of scanned documents in color at high resolution. Typical color magazine pages scanned at 300 dpi are compressed to between 40 and 80 kBytes, or 5 to 10 times smaller than with JPEG for a similar level of subjective quality. The foreground layer, which contains the text and drawings and requires high spatial resolution, is separated from the background layer, which contains pictures and backgrounds and requires less resolution. The foreground is compressed with a bi-tonal image compression technique that takes advantage of character shape similarities. The background is compressed with a new progressive, wavelet-based compression method. A real-time, memory-efficient version of the decoder is available as a plug-in for popular Web browsers.
SVMs for Histogram Based Image Classification

Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that Support Vector Machines (SVM) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form K(x,y) = e−ρ P i |x i −y i | with a ≤ 1 and b ≤ 2 are evaluated on the classification of images extracted from the Corel Stock Photo Collection and shown to far outperform traditional polynomial or Gaussian RBF kernels. Moreover, we observed that a simple remapping of the input xi → x a i improves the performance of linear SVMs to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.  Support Vector Machines, Radial Basis Functions, Image Histogram, Image Classification, Corel.
Support vector machines for histogram-based image classification

Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM's) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form K(x, y) = e(-rho)Sigma(i)/xia-yia/b with a < or = 1 and b < or = 2 are evaluated on the classification of images extracted from the Corel stock photo collection and shown to far outperform traditional polynomial or Gaussian radial basis function (RBF) kernels. Moreover, we observed that a simple remapping of the input x(i)-->x(i)(a) improves the performance of linear SVM's to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.
Color documents on the Web with DjVu

We present a new image compression technique called "DjVu" that is specifically geared towards the compression of scanned documents in color at high resolution. With DjVu, a magazine page in color at 300 dpi typically occupies between 40 KB and 80 KB, approximately 5 to 10 times better than JPEG for a similar level of readability. Using a combination of hidden Markov model techniques and MDL-driven heuristics, DjVu first classifies each pixel in the image as either foreground (text, drawings) or background (pictures, photos, paper texture). The pixel categories form a bitonal image which is compressed using a pattern matching technique that takes advantage of the similarities between character shapes. A progressive, wavelet-based compression technique, combined with a masking algorithm, is then used to compress the foreground and background images at lower resolutions while minimizing the number of bits spent on the pixels that are not visible in the foreground and background planes. Encoders, decoders, and real-time, memory efficient plug-ins for various web browsers are available for all the major platforms.
DjVu: a Compression Method for Distributing Scanned Documents in Color over the Internet

We present a new image compression technique called “DjVu” that is specifically geared towards the compression of scanned documents in color at high revolution. DjVu enable any screen connected to the Internet to access and display images of scanned pages while faithfully reproducing the font, color, drawings, pictures, and paper texture. With DjVu, a typical magazine page in color at 300dpi can be compressed down to between 40 to 60 KB, approximately 5 to 10 times better than JPEG for a similar level of subjective quality. A real-time, memory efficient version of the decoder is available as a plug-in for popular web browsers.
Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks

Signal processing and pattern recognition algorithms make extensive use of convolution. In many cases, computational accuracy is not as important as computational speed. In feature extraction, for instance, the features of interest in a signal are usually quite distorted. This form of noise justifies some level of quantization in order to achieve faster feature extraction. Our approach consists of approximating regions of the signal with low degree polynomials, and then differentiating the resulting signals in order to obtain impulse functions (or derivatives of impulse functions). With this representation, convolution becomes extremely simple and can be implemented quite effectively. The true convolution can be recovered by integrating the result of the convolution. This method yields substantial speed up in feature extraction and is applicable to convolutional neural networks.
Image and video coding-emerging standards and beyond

Discusses coding standards for still images and motion video. We first briefly discuss standards already in use, including: Group 3 and Group 4 for bilevel fax images; JPEG for still color images; and H.261, H.263, MPEG-1, and MPEG-2 for motion video. We then cover newly emerging standards such as JBIG1 and JBIG2 for bilevel fax images, JPEG-2000 for still color images, and H.263+ and MPEG-4 for motion video. Finally, we describe some directions beyond the standards such as hybrid coding of graphics/photo images, MPEG-7 for multimedia metadata, and possible new technologies.
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs

This paper develops a fast method for solving linear SVMs with L2 loss function that is suited for large scale data mining tasks such as text classification. This is done by modifying the finite Newton method of Mangasarian in several ways. Experiments indicate that the method is much faster than decomposition methods such as SVMlight, SMO and BSVM (e.g., 4-100 fold), especially when the number of examples is large. The paper also suggests ways of extending the method to other loss functions such as the modified Huber's loss function and the L1 loss function, and also for solving ordinal regression.
Contextual recommender problems [extended abstract]

The contextual recommender task is the problem of making useful offers, e.g., placing ads or related links on a web page, based on the context information, e.g., contents of the page and information about the user visiting, and information on the available alternatives, i.e., the advertisements or relevant links. In the case of ads for example, the goal is to select ads that result in high click rates, where the (ad) click rate is some unknown function of the attributes of the context and ad. We describe the task and make connections to related problems including recommender and multi-armed bandit problems.
Prioritizing Scientific Data for Transmission

A software system has been developed for prioritizing newly acquired geological data onboard a planetary rover. The system has been designed to enable efficient use of limited communication resources by transmitting the data likely to have the most scientific value. This software operates onboard a rover by analyzing collected data, identifying potential scientific targets, and then using that information to prioritize data for transmission to Earth. Currently, the system is focused on the analysis of acquired images, although the general techniques are applicable to a wide range of data modalities. Image prioritization is performed using two main steps. In the first step, the software detects features of interest from each image. In its current application, the system is focused on visual properties of rocks. Thus, rocks are located in each image and rock properties, such as shape, texture, and albedo, are extracted from the identified rocks. In the second step, the features extracted from a group of images are used to prioritize the images using three different methods: (1) identification of key target signature (finding specific rock features the scientist has identified as important), (2) novelty detection (finding rocks we haven t seen before), and (3) representative rock sampling (finding the most average sample of each rock type). These methods use techniques such as K-means unsupervised clustering and a discrimination-based kernel classifier to rank images based on their interest level.
An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels

In this paper, we give an efficient method for computing the leave-one-out (LOO) error for support vector machines (SVMs) with Gaussian kernels quite accurately. It is particularly suitable for iterative decomposition methods of solving SVMs. The importance of various steps of the method is illustrated in detail by showing the performance on six benchmark datasets. The new method often leads to speedups of 10-50 times compared to standard LOO error computation. It has good promise for use in hyperparameter tuning and model comparison.
Training Invariant Support Vector Machines

Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated into the training procedure. We describe and review all known methods for doing so in support vector machines, provide experimental results, and discuss their respective merits. One of the significant new results reported in this work is our recent achievement of the lowest reported test error on the well-known MNIST digit recognition benchmark task, with SVM training times that are also significantly faster than previous SVM methods.
Techniques for Onboard Prioritization of Science Data for Transmission

Future planetary exploration missions will continue to face an ever-increasing data prioritization problem as instrument data collection rates continue to exceed spacecraft downlink transmission rates. Hard decisions must be made about what data are sent back to Earth and what data are purged without being seen by scientists. In this article, we present a suite of techniques for the prioritization of science data for transmission. These techniques include methods to ensure that data representative of the local geology, as well as any unusual observations, are given the highest priority.  As planetary exploration continues to expand, the combination of more missions, an increased number of instruments, and the advanced capabilities of those instruments will cause an increase in the volume of data to be transmitted back to Earth via the Deep Space Network (DSN). Missions will have to make critical decisions regarding the quantity and quality of the downlinked data, both of which are indirectly affected by, among other things, the availability constraints of the DSN and the limited spacecraft power. Although the DSN’s receiving capability increases every year, the number of missions it must service is growing rapidly. New methods must be developed to maximize the science return for the available bandwidth. Conventional data compression helps in this regard; however, ever-increasing amounts of compression can cause unacceptably high distortion levels. We are developing onboard analysis methods to autonomously prioritize data for downlink as another approach to getting the most out of the limited bandwidth. Data prioritization already implicitly occurs. A priori decisions are made regarding when to turn on an instrument based on the number of data sets that can be transmitted to Earth. Instruments may not be activated because existing scenarios have limited or no ability to make intelligent decisions about the value of data being collected. In contrast, by collecting more data than can be transmitted to Earth and applying onboard data-analysis technology to carefully select which data are sent, the quality of data returned can be increased, maximizing the science return for the available downlink. Selecting the most
The Institute for the Learning Sciences TOWARD A QUALITATIVE THEORY OF SAFETY CONTROL - WHEN AND HOW TO PANIC INTELLIGENTLY

Safe control of a physical system requires the ability to both detect and avoid dangerous situations, while striving to achieve performance goals. State-of-the-art controllers still tend to rely heavily on classical control theory with feedback, occasionally with some limited use of associational-reasoning (expert systems) to help detect threatening situations and recall standard recovery procedures [Dvorak, 1987] . The need for model-based reasoning to control in novel situations and with incomplete data is widely acknowledged, but largely unaddressed to date. Existing relevant work has tended to focus on model-based monitoring to track the system state over time, with little attention to reasoning about control itself. In such work, any provisions for finding safe control actions tend to be mixtures of associational-reasoning and general-purpose planning or qualitative simulation which suffer problems of brittleness and intractability, respectively. To address those shortcomings, I propose a qualitative theory of safety control. The goal is to make explicit the kinds of intuitions that human operators use to focus the task of avoiding dangerous situations . I view the problem of safety control as two major issues: when to worry and how to control. Intuitively, one often worries about the need to control only if the chance of danger has increased assuming the initial state was safe. Furthermore, different styles of control seem most appropriate in different types of situations. For example, when safety seems impossible to maintain it often seems best to try to at least delay the impending doom, in hopes that "buying time" might help . I argue that qualitative physics can be used to robustly capture such reasoning, but only with fundamental advances in how and what to qualitatively simulate .
IQE : An Incremental Qualitative Envisioner

The finite representation of infinite behaviors provided by total envisionments has been shown to be useful for tasks such as model development, explanation, and monitoring . Unfortunately, generating total envisionments of complex systems can be intractable, or at least excessive for highly-focused tasks . Such shortcomings have encouraged some researchers to favor alternative schemes, such as attainable envisioning and history generation . However, we argue in this paper for an incremental means of envisioning that can realize many of the practical advantages of total envisioning while supporting more-focused search to address issues of tractability and relevance . In this paper, we describe our theory of incremental envisioning and compare it with QPC  and QPE [9] . We emphasize how IQE attempts to reason about states at low (i.e ., ) levels of detail when that is sufficient for the overall task . Such reasoning helps avoid the inefficiency of making state distinctions that are irrelevant for the overall task a problem common among most other current qualitative simulators, including QPC and QPE. John W. Collins Qualitative Reasoning Group Beckman Institute University of Illinois 605 N. Mathews St . Urbana, Illinois 61801 email: jcollins@cs.uiuc .edu