nils-herrmann/clean_scirepeval huggingface dataset:
Essentia: An Audio Analysis Library for Music Information Retrieval

We present Essentia 2.0, an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set of spectral, temporal, tonal and high-level music descriptors. The library is also wrapped in Python and includes a number of predefined executable extractors for the available music descriptors, which facilitates its use for fast prototyping and allows setting up research experiments very rapidly. Furthermore, it includes a Vamp plugin to be used with Sonic Visualiser for visualization purposes. The library is cross-platform and currently supports Linux, Mac OS X, and Windows systems. Essentia is designed with a focus on the robustness of the provided music descriptors and is optimized in terms of the computational cost of the algorithms. The provided functionality, specifically the music descriptors included in-the-box and signal processing algorithms, is easily expandable and allows for both research experiments and development of large-scale industrial applications.
FasTC: accelerated fixed-rate texture encoding

We present a new algorithm for encoding low dynamic range images into fixed-rate texture compression formats. Our approach provides orders of magnitude improvements in speed over existing publicly-available compressors, while generating high quality results. The algorithm is applicable to any fixed-rate texture encoding scheme based on Block Truncation Coding and we use it to compress images into the OpenGL BPTC format. The underlying technique uses an axis-aligned bounding box to estimate the proper partitioning of a texel block and performs a generalized cluster fit to compute the endpoint approximation. This approximation can be further refined using simulated annealing. The algorithm is inherently parallel and scales with the number of processor cores. We highlight its performance on low-frequency game textures and the high frequency Kodak Test Image Suite.
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multiinstance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training.
Misuse Cases: Use Cases with Hostile Intent

Eliciting security requirements Security requirements exist because people and the negative agents that they create (such as computer viruses) pose real threats to systems. Security differs from all other specification areas in that someone is deliberately threatening to break the system. Employing use and misuse cases to model and analyze scenarios in systems under design can improve security by helping to mitigate threats. Some misuse cases occur in highly specific situations, whereas others continually threaten systems. For instance, a car is most likely to be stolen when parked and unattended, whereas a Web server might suffer a denial-of-service attack at any time. You can develop misuse and use cases recursively, going from system to subsystem levels or lower as necessary. Lower-level cases can highlight aspects not considered at higher levels, possibly forcing another analysis. The approach offers rich possibilities for exploring, understanding, and validating the requirements in any direction. Drawing the agents and misuse cases explicitly helps focus attention on the elements of the scenario. Let’s compare Figure 1 to games such as chess or Go. A team’s best strategy consists of thinking ahead to the other team’s best move and acting to block it. In Figure 1, the use cases appear on the left; the misuse cases are on the right. The misuse threat is car theft, the use-case player is the lawful driver, and the misuse-case player the car thief. The driver’s freedom to drive the car is at risk if the thief can steal the car. The driver must be able to lock the car—a derived requirement—to mitigate the threat. This is at the top level of analysis. The next level begins when you consider the thief’s response. If he breaks the door lock and shorts the ignition, this requires another mitigating approach, such as locking the transmission. In this focus
Maiter: An Asynchronous Graph Processing Framework for Delta-Based Accumulative Iterative Computation

Myriad of graph-based algorithms in machine learning and data mining require parsing relational data iteratively. These algorithms are implemented in a large-scale distributed environment to scale to massive data sets. To accelerate these large-scale graph-based iterative computations, we propose delta-based accumulative iterative computation (DAIC). Different from traditional iterative computations, which iteratively update the result based on the result from the previous iteration, DAIC updates the result by accumulating the “changes” between iterations. By DAIC, we can process only the “changes” to avoid the negligible updates. Furthermore, we can perform DAIC asynchronously to bypass the high-cost synchronous barriers in heterogeneous distributed environments. Based on the DAIC model, we design and implement an asynchronous graph processing framework, Maiter. We evaluate Maiter on local cluster as well as on Amazon EC2 Cloud. The results show that Maiter achieves as much as 60 × speedup over Hadoop and outperforms other state-of-the-art frameworks.
Digital image processing using MATLAB

All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, without permission in writing from the publisher. The author and publisher of this book have used their best efforts in preparing this book. These efforts include the development, research, and testing of the theories and programs to determine their effectiveness. The author and publisher shall not be liable in any event for incidental or consequential damages with, or arising out of, the furnishing, performance, or use of these programs. 1 1 Introduction Preview Digital image processing is an area characterized by the need for extensive experimental work to establish the viability of proposed solutions to a given problem. In this chapter we outline how a theoretical base and state-of-the-art software can be integrated into a prototyping environment whose objective is to provide a set of well-supported tools for the solution of a broad class of problems in digital image processing.  An important characteristic underlying the design of image processing systems is the significant level of testing and experimentation that normally is required before arriving at an acceptable solution. This characteristic implies that the ability to formulate approaches and quickly prototype candidate solutions generally plays a major role in reducing the cost and time required to arrive at a viable system implementation. Little has been written in the way of instructional material to bridge the gap between theory and application in a well-supported software environment. The main objective of this book is to integrate under one cover a broad base of theoretical concepts with the knowledge required to implement those concepts using state-of-the-art image processing software tools. The theoretical underpinnings of the material in the following chapters are mainly from the leading textbook in the field: Digital Image Processing, by Gonzalez and Woods,  The software code and supporting tools are based on the leading software package in the field: The MATLAB Image Processing Toolbox, † 1.1 † In the following discussion and in subsequent chapters we sometimes refer to Digital Image Processing by Gonzalez and Woods as " the Gonzalez-Woods book, " and to the Image Processing Toolbox as " IPT " or simply as the " toolbox. " 2 Chapter 1 I Introduction from The MathWorks, Inc. (see Section 1.3). The material in the present book shares the same design, notation, and style of presentation …
An obstacle-based rapidly-exploring random tree

Tree-based path planners have been shown to be well suited to solve various high dimensional motion planning problems. Here we present a variant of the Rapidly-Exploring Random Tree (RRT) path planning algorithm that is able to explore narrow passages or difficult areas more effectively. We show that both workspace obstacle information and C-space information can be used when deciding which direction to grow. The method includes many ways to grow the tree, some taking into account the obstacles in the environment. This planner works best in difficult areas when planning for free flying rigid or articulated robots. Indeed, whereas the standard RRT can face difficulties planning in a narrow passage, the tree based planner presented here works best in these areas
A framework of knowledge management systems: issues and challenges for theory and practice

As the basis of value creation increasingly depends on the leverage of the intangible assets of firms, knowledge management systems (KMS) are emerging as powerful sources of competitive advantage. However, the general recognition of the importance of such systems seems to be accompanied by a technology-induced drive to implement systems with inadequate consideration of the fundamental knowledge problems that the KMS are likely to solve. This paper contributes to the stream of research on knowledge management systems by proposing an inductively developed framework for this important class of information systems, classifying KMS based on the locus of the knowledge and the a priori structuring of contents. This framework provides a means to explore issues related to KMS and unifying dimensions underlying different types of KMS. The contingencies that we discuss—the size and diversity of networks, the maintenance of knowledge flows and the long term effects of the use of KMS—provide a window into work in a number of reference disciplines that would enrich the utility of KMS and also open up fruitful areas for future research.
Multiclass SVM-based automated diagnosis of diabetic retinopathy

Diabetes related eye disease is growing as a major health concern worldwide. Diabetic retinopathy is an infirmity due to higher level of glucose in the retinal capillaries, resulting in cloudy vision and blindness eventually. With regular screening, pathology can be detected in the instigating stage and if intervened with in time medication could prevent further deterioration. This paper develops an automated diagnosis system to recognize retinal blood vessels, and pathologies, such as exudates and microaneurysms together with certain texture properties using image processing techniques. These anatomical and texture features are then fed into a multiclass support vector machine (SVM) for classifying it into normal, mild, moderate, severe and proliferative categories. Advantages include, it processes quickly a large collection of fundus images obtained from mass screening which lessens cost and increases efficiency for ophthalmologists. Our method was evaluated on two publicly available databases and got encouraging results with a state of the art in this area.
Visualizing work processes in software engineering with developer rivers

Work processes involving dozens or hundreds of collaborators are complex and difficult to manage. Problems within the process may have severe organizational and financial consequences. Visualization helps monitor and analyze those processes. In this paper, we study the development of large software systems as an example of a complex work process. We introduce Developer Rivers, a timeline-based visualization technique that shows how developers work on software modules. The flow of developers' activity is visualized by a river metaphor: activities are transferred between modules represented as rivers. Interactively switching between hierarchically organized modules and workload metrics allows for exploring multiple facets of the work process. We study typical development patterns by applying our visualization to Python and the Linux kernel.
An Entity Based Model for Coreference Resolution

Recently, many advanced machine learning approaches have been proposed for coreference resolution; however, all of the discriminatively-trained models reason over mentions rather than entities. That is, they do not explicitly contain variables indicating the “canonical” values for each attribute of an entity (e.g., name, venue, title, etc.). This canonicalization step is typically implemented as a post-processing routine to coreference resolution prior to adding the extracted entity to a database. In this paper, we propose a discriminatively-trained model that jointly performs coreference resolution and canonicalization, enabling features over hypothesized entities. We validate our approach on two different coreference problems: newswire anaphora resolution and research paper citation matching, demonstrating improvements in both tasks and achieving an error reduction of up to 62% when compared to a method that reasons about mentions only.
Forecasting dynamic public transport Origin-Destination matrices with long-Short term Memory recurrent neural networks

A considerable number of studies have been undertaken on using smart card data to analyse urban mobility. Most of these studies aim to identify recurrent passenger habits, reveal mobility patterns, reconstruct and predict passenger flows, etc. Forecasting mobility demand is a central problem for public transport authorities and operators alike. It is the first step to efficient allocation and optimisation of available resources. This paper explores an innovative approach to forecasting dynamic Origin-Destination (OD) matrices in a subway network using long Short-term Memory (LSTM) recurrent neural networks. A comparison with traditional approaches, such as calendar methodology or Vector Autoregression is conducted on a real smart card dataset issued from the public transport network of Rennes Métropole, France. The obtained results show that reliable short-term prediction (over a 15 minutes time horizon) of OD pairs can be achieved with the proposed approach. We also experiment with the effect of taking into account additional data about OD matrices of nearby transport systems (buses in this case) on the prediction accuracy.
Data Mining Techniques in Fraud Detection

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models.
Estimating Cosmological Parameters from the Dark Matter Distribution

A grand challenge of the 21 century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach in estimating the cosmological parameters is to use the large scale matter distribution of the Universe. Galaxy surveys provide the means to map out cosmic large-scale structure in three dimensions. Information about galaxy locations is typically summarized in a “single” function of scale, such as the galaxy correlation function or powerspectrum. We show that it is possible to estimate these cosmological parameters directly from the distribution of matter. This paper presents the application of deep 3D convolutional networks to volumetric representation of dark-matter simulations as well as the results obtained using a recently proposed distribution regression framework, showing that machine learning techniques are comparable to, and can sometimes outperform, maximum-likelihood point estimates using “cosmological models”. This opens the way to estimating the parameters of our Universe with higher accuracy.
Short links under attack: geographical analysis of spam in a URL shortener network

URL shortener services today have come to play an important role in our social media landscape. They direct user attention and disseminate information in online social media such as Twitter or Facebook. Shortener services typically provide short URLs in exchange for long URLs. These short URLs can then be shared and diffused by users via online social media, e-mail or other forms of electronic communication. When another user clicks on the shortened URL, she will be redirected to the underlying long URL. Shortened URLs can serve many legitimate purposes, such as click tracking, but can also serve illicit behavior such as fraud, deceit and spam. Although usage of URL shortener services today is ubiquituous, our research community knows little about how exactly these services are used and what purposes they serve. In this paper, we study usage logs of a URL shortener service that has been operated by our group for more than a year. We expose the extent of spamming taking place in our logs, and provide first insights into the planetary-scale of this problem. Our results are relevant for researchers and engineers interested in understanding the emerging phenomenon and dangers of spamming via URL shortener services.
Relating ROC and CMC curves via the biometric menagerie

In the academic literature, the matching accuracy of a biometric system is typically quantified through measures such as the Receiver Operating Characteristic (ROC) curve and Cumulative Match Characteristic (CMC) curve. The ROC curve, measuring verification performance, is based on aggregate statistics of match scores corresponding to all biometric samples, while the CMC curve, measuring identification performance, is based on the relative ordering of match scores corresponding to each biometric sample (in closed-set identification). In this study, we determine whether a set of genuine and impostor match scores generated from biometric data can be reassigned to virtual identities, such that the same ROC curve can be accompanied by multiple CMC curves. The reassignment is accomplished by modeling the intra- and inter-class relationships between identities based on the “Doddington Zoo” or “Biometric Menagerie” phenomenon. The outcome of the study suggests that a single ROC curve can be mapped to multiple CMC curves in closed-set identification, and that presentation of a CMC curve should be accompanied by a ROC curve when reporting biometric system performance, in order to better understand the performance of the matcher.
Hashing for Similarity Search: A Survey

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space. Index Terms —Approximate Nearest Neighbor Search, Similarity Search, Hashing, Locality Sensitive Hashing, Learning to Hash, Quantization.
End-to-End Relation Extraction using Markov Logic Networks

The task of end-to-end relation extraction consists of two sub-tasks: i) identifying entity mentions along with their types and ii) recognizing semantic relations among the entity mention pairs. It has been shown that for better performance, it is necessary to address these two sub-tasks jointly [22,13]. We propose an approach for simultaneous extraction of entity mentions and relations in a sentence, by using inference in Markov Logic Networks (MLN) . We learn three different classifiers : i) local entity classifier, ii) local relation classifier and iii) “pipeline” relation classifier which uses predictions of the local entity classifier. Predictions of these classifiers may be inconsistent with each other. We represent these predictions along with some domain knowledge using weighted first-order logic rules in an MLN and perform joint inference over the MLN to obtain a global output with minimum inconsistencies. Experiments on the ACE (Automatic Content Extraction) 2004 dataset demonstrate that our approach of joint extraction using MLNs outperforms the baselines of individual classifiers. Our end-to-end relation extraction performance is better than 2 out of 3 previous results reported on the ACE 2004 dataset.
0.37mW/Gb/s low power SLVS transmitter for battery powered applications

A power-efficient voltage-mode transmitter for battery powered mobile applications was designed. The proposed transmitter consists of a power optimized 16:2 serializer, a data-aligner, 2:1 multiplexing pre-driver, and an I/O driver with segment control for impedance matching. With scalable low-voltage signaling (SLVS) with the output swing of 200mVp-p and power optimization for digital blocks, 0.37mW/Gb/s power consumption was achieved. The test chip for 4.8 Gb/s interfaces was designed in a 65nm 1.2V supply CMOS logic process. The proposed I/O specifications are appropriate for MIPI M-PHY systems.
SIFT-Bag kernel for video event analysis

In this work, we present a SIFT-Bag based generative-to-discriminative framework for addressing the problem of video event recognition in unconstrained news videos. In the generative stage, each video clip is encoded as a bag of SIFT feature vectors, the distribution of which is described by a Gaussian Mixture Models (GMM). In the discriminative stage, the SIFT-Bag Kernel is designed for characterizing the property of Kullback-Leibler divergence between the specialized GMMs of any two video clips, and then this kernel is utilized for supervised learning in two ways. On one hand, this kernel is further refined in discriminating power for centroid-based video event classification by using the Within-Class Covariance Normalization approach, which depresses the kernel components with high-variability for video clips of the same event. On the other hand, the SIFT-Bag Kernel is used in a Support Vector Machine for margin-based video event classification. Finally, the outputs from these two classifiers are fused together for final decision. The experiments on the TRECVID 2005 corpus demonstrate that the mean average precision is boosted from the best reported 38.2% in [36] to 60.4% based on our new framework.