nils-herrmann/clean_scirepeval huggingface dataset:
Frame-Semantic Parsing

Frame semantics is a linguistic theory that has been instantiated for English in the FrameNet lexicon. We solve the problem of frame-semantic parsing using a two-stage statistical model that takes lexical targets (i.e., content words and phrases) in their sentential contexts and predicts frame-semantic structures. Given a target in context, the first stage disambiguates it to a semantic frame. This model uses latent variables and semi-supervised learning to improve frame disambiguation for targets unseen at training time. The second stage finds the target's locally expressed semantic arguments. At inference time, a fast exact dual decomposition algorithm collectively predicts all the arguments of a frame at once in order to respect declaratively stated linguistic constraints, resulting in qualitatively better structures than naïve local predictors. Both components are feature-based and discriminatively trained on a small set of annotated frame-semantic parses. On the SemEval 2007 benchmark data set, the approach, along with a heuristic identifier of frame-evoking targets, outperforms the prior state of the art by significant margins. Additionally, we present experiments on the much larger FrameNet 1.5 data set. We have released our frame-semantic parser as open-source software.
Image segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE-OED)

A novel method based on MLE–OED is proposed for unsupervised image segmentation of multiple objects with fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function. The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method is validated by experiments on both synthetic and real images. The experimental results show that the proposed method outperformed two popular methods.
Multi-dimensional trade-off considerations of the 750V micro pattern trench IGBT for electric drive train applications

The EDT2 750V uses a micro pattern trench cell with a narrow mesa for reducing the on-state losses with a tailored channel width for short circuit robustness. To account for high system stray inductances (Lstray) and currents for Full or Hybrid Electric Vehicle inverter applications, it features a 750V voltage rating compared to the predecessor IGBT3 650V by an optimized vertical structure and proper plasma shaping. This plasma distribution not only determines the performance tradeoff between on-state and switching losses, but at the same time defines the surge voltage for a given Lstray*I in the application as visualized in a switch-off loss vs. surge voltage trade-off diagram. Shaping of the feedback capacitance Cgc optimizes the tunability of the switching slopes by means of an external gate resistor for an easier adaption to a wider range of system inductances with low losses.
REALM: A rule-based evolutionary computation agent that learns to play Mario

REALM is a rule-based evolutionary computation agent for playing a modified version of Super Mario Bros. according to the rules stipulated in the Mario AI Competition held in the 2010 IEEE Symposium on Computational Intelligence and Games. Two alternate representations for the REALM rule sets are reported here, in both hand-coded and learned versions. Results indicate that the second version, with an abstracted action set, tends to perform better overall, but the first version shows a steeper learning curve. In both cases, learning quickly surpasses the hand-coded rule sets.
A Survey of Dimension Reduction Techniques

Advances in data collection and storage capabilities during the past decades have led to an information overload in most sciences. Researchers working in domains as diverse as engineering, astronomy, biology, remote sensing, economics, and consumer transactions, face larger and larger observations and simulations on a daily basis. Such datasets, in contrast with smaller, more traditional datasets that have been studied extensively in the past, present new challenges in data analysis. Traditional statistical methods break down partly because of the increase in the number of observations, but mostly because of the increase in the number of variables associated with each observation. The dimension of the data is the number of variables that are measured on each observation. High-dimensional datasets present many mathematical challenges as well as some opportunities, and are bound to give rise to new theoretical developments [11]. One of the problems with high-dimensional datasets is that, in many cases, not all the measured variables are “important” for understanding the underlying phenomena of interest. While certain computationally expensive novel methods  can construct predictive models with high accuracy from high-dimensional data, it is still of interest in many applications to reduce the dimension of the original data prior to any modeling of the data. In mathematical terms, the problem we investigate can be stated as follows: given the p-dimensional random variable x = (x1, . . . , xp) T , find a lower dimensional representation of it, s = (s1, . . . , sk) T with k ≤ p, that captures the content in the original data, according to some criterion. The components of s are sometimes called the hidden components. Different fields use different names for the p multivariate vectors: the term “variable” is mostly used in statistics, while “feature” and “attribute” are alternatives commonly used in the computer science and machine learning literature. Throughout this paper, we assume that we have n observations, each being a realization of the pdimensional random variable x = (x1, . . . , xp) T with mean E(x) = μ = (μ1, . . . , μp) T and covariance matrix E{(x − μ)(x− μ) } = Σp×p. We denote such an observation matrix by X = {xi,j : 1 ≤ i ≤ p, 1 ≤ j ≤ n}. If μi and σi = √
Scaling big data mining infrastructure: the twitter experience

The analytics platform at Twitter has experienced tremendous growth over the past few years in terms of size, complexity, number of users, and variety of use cases. In this paper, we discuss the evolution of our infrastructure and the development of capabilities for data mining on "big data". One important lesson is that successful big data mining in practice is about much more than what most academics would consider data mining: life "in the trenches" is occupied by much preparatory work that precedes the application of data mining algorithms and followed by substantial effort to turn preliminary models into robust solutions. In this context, we discuss two topics: First, schemas play an important role in helping data scientists understand petabyte-scale data stores, but they're insufficient to provide an overall "big picture" of the data available to generate insights. Second, we observe that a major challenge in building data analytics platforms stems from the heterogeneity of the various components that must be integrated together into production workflows---we refer to this as "plumbing". This paper has two goals: For practitioners, we hope to share our experiences to flatten bumps in the road for those who come after us. For academic researchers, we hope to provide a broader context for data mining in production environments, pointing out opportunities for future work.
Direct Sparse Odometry

Direct Sparse Odometry (DSO) is a visual odometry method based on a novel, highly accurate sparse and direct structure and motion formulation. It combines a fully direct probabilistic model (minimizing a photometric error) with consistent, joint optimization of all model parameters, including geometry-represented as inverse depth in a reference frame-and camera motion. This is achieved in real time by omitting the smoothness prior used in other direct methods and instead sampling pixels evenly throughout the images. Since our method does not depend on keypoint detectors or descriptors, it can naturally sample pixels from across all image regions that have intensity gradient, including edges or smooth intensity variations on essentially featureless walls. The proposed model integrates a full photometric calibration, accounting for exposure time, lens vignetting, and non-linear response functions. We thoroughly evaluate our method on three different datasets comprising several hours of video. The experiments show that the presented approach significantly outperforms state-of-the-art direct and indirect methods in a variety of real-world settings, both in terms of tracking accuracy and robustness.
The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks

The diffusion decision model allows detailed explanations of behavior in two-choice discrimination tasks. In this article, the model is reviewed to show how it translates behavioral dataaccuracy, mean response times, and response time distributionsinto components of cognitive processing. Three experiments are used to illustrate experimental manipulations of three components: stimulus difficulty affects the quality of information on which a decision is based; instructions emphasizing either speed or accuracy affect the criterial amounts of information that a subject requires before initiating a response; and the relative proportions of the two stimuli affect biases in drift rate and starting point. The experiments also illustrate the strong constraints that ensure the model is empirically testable and potentially falsifiable. The broad range of applications of the model is also reviewed, including research in the domains of aging and neurophysiology.
Digitizing paper forms with mobile imaging technologies

In low-resource settings in developing countries, most records are still captured and maintained using paper forms. Despite a recent proliferation of digital data collection systems, paper forms remain a trusted, low-cost and ubiquitous medium that will continue to be utilized in these communities for years to come. However, it can be challenging to aggregate, share, and analyze the data collected using paper forms. This paper presents mScan, a mobile smartphone application that uses computer vision to capture data from paper forms that use a multiple choice or bubble format. The initial mScan implementation targets the task of digitizing paper forms used to record vaccine statistics in rural health centers in Mozambique. We have evaluated the accuracy and performance of mScan under a variety of different environmental conditions, and our results show that mScan is a robust tool that is capable of accurately capturing and digitizing data from paper forms.
Visual Parameter Space Analysis: A Conceptual Framework

Various case studies in different application domains have shown the great potential of visual parameter space analysis to support validating and using simulation models. In order to guide and systematize research endeavors in this area, we provide a conceptual framework for visual parameter space analysis problems. The framework is based on our own experience and a structured analysis of the visualization literature. It contains three major components: (1) a data flow model that helps to abstractly describe visual parameter space analysis problems independent of their application domain; (2) a set of four navigation strategies of how parameter space analysis can be supported by visualization tools; and (3) a characterization of six analysis tasks. Based on our framework, we analyze and classify the current body of literature, and identify three open research gaps in visual parameter space analysis. The framework and its discussion are meant to support visualization designers and researchers in characterizing parameter space analysis problems and to guide their design and evaluation processes.
Stack-based scheduling of realtime processes

The Priority Ceiling Protocol (PCP) of Sha, Rajkumar and Lehoczky is a policy for locking binary semaphores that bounds priority inversion (i.e., the blocking of a job while a lower priority job executes), and thereby improves schedulability under fixed priority preemptive scheduling. We show how to extend the PCP to handle: multiunit resources, which subsume binary semaphores and reader-writer locks; dynamic priority schemes, such as earliest-deadline-first (EDF), that use static “preemption levels”; sharing of runtime stack space between jobs. These extensions can be applied independently, or together. The Stack Resource Policy (SRP) is a variant of the SRP that incorporates the three extensions mentioned above, plus the conservative assumption that each job may require the use of a shared stack. This avoids unnecessary context switches and allows the SRP to be implemented very simply using a stack. We prove a schedulability result for EDF scheduling with the SRP that is tighter than the one proved previously for EDF with a dynamic version of the PCP. The Minimal SRP (MSRP) is a slightly more complex variant of the SRP, which has similar properties, but imposes less blocking. The MSRP is optimal for stack sharing systems, in the sense that it is the least restrictive policy that strictly bounds priority inversion and prevents deadlock for rate monotone (RM) and earliest-deadline-first (EDF) scheduling.
RuleViz: a model for visualizing knowledge discovery process

We propose an interactive model, RuleViz, for visualizing the entire process of knowledge discovery and data mining. The model consists of ve components according to the main ingredients of the knowledge discovery process: original data visualization, visual data reduction, visual data preprocess, visual rule discovery, and rule visualization. The RuleViz model for visualizing the process of knowledge discovery is introduced and each component is discussed. Two aspects are emphasized, human-machine interaction and process visualization. The interaction helps the KDD system navigate through the enormous search spaces and recognize the intentions of the user, and the visualization of the KDD process helps users gain better insight into the multidimensional data, understand the intermediate results, and interpret the discovered patterns. According to the RuleViz model, we implement an interactive system, CViz, which exploits \parallel coordinates" technique to visualize the process of rule induction. The original data is visualized on the parallel coordinates, and can be interactively reduced both horizontally and vertically. Three approaches for discretizing numerical attributes are provided in the visual data preprocessing. CViz learns classi cation rules on the basis of a rule induction algorithm and presents the result as the algorithm proceeds. The discovered rules are nally visualized on the parallel coordinates with each rule being displayed as a directed \polygon", and the rule accuracy and quality are used to render the \polygons" and control the choice of rules to be displayed to avoid clutter. The CViz system has been experimented with the UCI data sets and synthesis data sets, and the results demonstrate that the RuleViz model and the implemented visualization system are useful and helpful for understanding the process of knowledge discovery and interpreting the nal results.
Distributed Representations for Biological Sequence Analysis

Biological sequence comparison is a key step in inferring the relatedness of various organisms and the functional similarity of their components. Thanks to the Next Generation Sequencing efforts, an abundance of sequence data is now available to be processed for a range of bioinformatics applications. Embedding a biological sequence – over a nucleotide or amino acid alphabet – in a lower dimensional vector space makes the data more amenable for use by current machine learning tools, provided the quality of embedding is high and it captures the most meaningful information of the original sequences. Motivated by recent advances in the text document embedding literature, we present a new method, called seq2vec, to represent a complete biological sequence in an Euclidean space. The new representation has the potential to capture the contextual information of the original sequence necessary for sequence comparison tasks. We test our embeddings with protein sequence classification and retrieval tasks and demonstrate encouraging outcomes.
Fast Random Walk with Restart and Its Applications

How closely related are two nodes in a graph? How to compute this score quickly, on huge, disk-resident, real graphs? Random walk with restart (RWR) provides a good relevance score between two nodes in a weighted graph, and it has been successfully used in numerous settings, like automatic captioning of images, generalizations to the "connection subgraphs", personalized PageRank, and many more. However, the straightforward implementations of RWR do not scale for large graphs, requiring either quadratic space and cubic pre-computation time, or slow response time on queries. We propose fast solutions to this problem. The heart of our approach is to exploit two important properties shared by many real graphs: (a) linear correlations and (b) block- wise, community-like structure. We exploit the linearity by using low-rank matrix approximation, and the community structure by graph partitioning, followed by the Sherman- Morrison lemma for matrix inversion. Experimental results on the Corel image and the DBLP dabasets demonstrate that our proposed methods achieve significant savings over the straightforward implementations: they can save several orders of magnitude in pre-computation and storage cost, and they achieve up to 150x speed up with 90%+ quality preservation.
Modeling Motivation in a Social Network Game Using Player-Centric Traits and Personality Traits

People are drawn to play different types of videogames and find enjoyment in a range of gameplay experiences. Envisaging a representative game player or persona allows game designers to personalize game content; however, there are many ways to characterize players and little guidance on which approaches best model player behavior and preference. To provide knowledge about how player characteristics contribute to game experience, we investigate how personality traits as well as player styles from the BrianHex model moderate the prediction of player motivation with a social network game. Our results show that several player characteristics impact motivation, expressed in terms of enjoyment and effort. We also show that player enjoyment and effort, as predicted by our models, impact players’ in-game behaviors, illustrating both the predictive power and practical utility of our models for guiding user adaptation.
Bayesian Nonparametric Matrix Factorization for Recorded Music

Recent research in machine learning has focused on breaking audio spectrograms into separate sources of sound using latent variable decompositions. These methods require that the number of sources be specified in advance, which is not always possible. To address this problem, we develop Gamma Process Nonnegative Matrix Factorization (GaP-NMF), a Bayesian nonparametric approach to decomposing spectrograms. The assumptions behind GaP-NMF are based on research in signal processing regarding the expected distributions of spectrogram data, and GaP-NMF automatically discovers the number of latent sources. We derive a mean-field variational inference algorithm and evaluate GaP-NMF on both synthetic data and recorded music.
Skew detection and correction in document images bsed on straight-line fitting

During document scanning, skew is inevitably introduced into the incoming document image. Since the algorithms for layout analysis and character recognition are generally very sensitive to the page skew, skew detection and correction in document images are the critical steps before layout analysis. In this paper, a novel skew detection method based on straight-line fitting is proposed. And a concept of eigen-point is introduced. After the relations between the neighboring eigen-points in every text line within a suitable sub-region were analyzed, the eigen-points most possibly laid on the baselines are selected as samples for the straight-line fitting. The average of these baseline directions is computed, which corresponds to the degree of skew of the whole document image. Then a fast skew correction method based on the scanning line model is also presented. Experiments prove that the proposed approaches are fast and accurate.
Supervised hashing with latent factor models

Due to its low storage cost and fast query speed, hashing has been widely adopted for approximate nearest neighbor search in large-scale datasets. Traditional hashing methods try to learn the hash codes in an unsupervised way where the metric (Euclidean) structure of the training data is preserved. Very recently, supervised hashing methods, which try to preserve the semantic structure constructed from the semantic labels of the training points, have exhibited higher accuracy than unsupervised methods. In this paper, we propose a novel supervised hashing method, called latent factor hashing(LFH), to learn similarity-preserving binary codes based on latent factor models. An algorithm with convergence guarantee is proposed to learn the parameters of LFH. Furthermore, a linear-time variant with stochastic learning is proposed for training LFH on large-scale datasets. Experimental results on two large datasets with semantic labels show that LFH can achieve superior accuracy than state-of-the-art methods with comparable training time.
A Scalable Concurrent malloc(3) Implementation for FreeBSD

The FreeBSD project has been engaged in ongoing work to provide scalable support for multi-processor computer systems since version 5. Sufficient progress has been made that the C library’s malloc(3) memory allocator is now a potential bottleneck for multi-threaded applications running on multiprocessor systems. In this paper, I present a new memory allocator that builds on the state of the art to provide scalable concurrent allocation for applications. Benchmarks indicate that with this allocator, memory allocation for multi-threaded applications scales well as the number of processors increases. At the same time, single-threaded allocation performance is similar to the previous allocator implementation.
Sensor-based stroke detection and stroke type classification in table tennis

In this paper we present a sensor-based table tennis stroke detection and classification system. We attached inertial sensors to table tennis rackets and collected data of 8 different basic stroke types from 10 amateur and professional players. Firstly, single strokes were detected by a event detection algorithm. Secondly, features were computed and used as input for stroke type classification. Multiple classifiers were compared regarding classification rates and computational effort. The overall sensitivity of the stroke detection was 95.7% and the best classifier reached a classification rate of 96.7%. Therefore, our presented approach is able to detect table tennis strokes in time-series data and to classify each stroke into correct stroke type categories. The system has the potential to be implemented as an embedded real-time application for other racket sports, to analyze training exercises and competitions, to present match statistics or to support the athletes' training progress. To our knowledge, this is the first paper that addresses a wearable support system for table tennis, and our future work aims at using the presented results to build a complete match analysis system for this sport.