nils-herrmann/clean_scirepeval huggingface dataset:
SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation

Expertise in C++, C, Perl, Haskell, Linux system administration. Technical experience in compiler design and implementation, release engineering, network administration, FPGAs, hardware design, probabilistic inference, machine learning, web search engines, cryptography, datamining, databases (SQL, Oracle, PL/SQL, XML), distributed knowledge bases, machine vision, automated web content generation, 2D and 3D graphics, distributed computing, scientific and numerical computing, optimization, virtualization (Xen, VirtualBox). Also experience in risk analysis, finance, game theory, firm behavior, international economics. Familiar with Java, C++ Standard Template Library, Java Native Interface, Java Foundation Classes, Android development, MATLAB, CPLEX, NetPBM, Cascading Style Sheets (CSS), Tcl/Tk, Windows system administration, Mac OS X system administration, ElasticSearch, modifying the Ubuntu installer.
Sentence comprehension and simulation of object temporary, canonical and stable affordances

Two experiments investigate the effects of language comprehension on affordances. Participants read a sentence composed by either an observation or an action verb (Look at/Grasp) followed by an object name. They had to decide whether the visual object following the sentence was the same as the one mentioned in the sentence. Objects graspable with either a precision or a power grip were presented in an orientation affording action (canonical) or not. Action sentences were faster than observation sentences, and power grip objects were faster than precision grip objects. Moreover, faster RTs were obtained when orientation afforded action. Results indicate that the simulation activated during language comprehension leads to the formation of a "motor prototype" of the object. This motor prototype encodes information on temporary/canonical and stable affordances (e.g., orientation, size), which can be possibly referred to different cognitive and neural systems (dorsal, ventral systems).
Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions

While computer and communication technologies have provided effective means to scale up many aspects of education, the submission and grading of assessments such as homework assignments and tests remains a weak link. In this paper, we study the problem of automatically grading the kinds of open response mathematical questions that figure prominently in STEM (science, technology, engineering, and mathematics) courses. Our data-driven framework for mathematical language processing (MLP) leverages solution data from a large number of learners to evaluate the correctness of their solutions, assign partial-credit scores, and provide feedback to each learner on the likely locations of any errors. MLP takes inspiration from the success of natural language processing for text data and comprises three main steps. First, we convert each solution to an open response mathematical question into a series of numerical features. Second, we cluster the features from several solutions to uncover the structures of correct, partially correct, and incorrect solutions. We develop two different clustering approaches, one that leverages generic clustering algorithms and one based on Bayesian nonparametrics. Third, we automatically grade the remaining (potentially large number of) solutions based on their assigned cluster and one instructor-provided grade per cluster. As a bonus, we can track the cluster assignment of each step of a multistep solution and determine when it departs from a cluster of correct solutions, which enables us to indicate the likely locations of errors to learners. We test and validate MLP on real-world MOOC data to demonstrate how it can substantially reduce the human effort required in large-scale educational platforms.
HLAer : a System for Heterogeneous Log Analysis

Logs are ubiquitous for system monitoring and debugging. However, there lacks a comprehensive system that is capable of performing heterogeneous log organization and analysis for various purposes with very limited domain knowledge and human surveillance. In this manuscript, a novel system for heterogeneous log analysis is proposed. The system, denoted as Heterogeneous Log Analyzer (HLAer), achieves the following goals concurrently: 1) heterogeneous log categorization and organization; 2) automatic log format recognition and 3) heterogeneous log indexing. Meanwhile, HLAer supports queries and outlier detection on heterogeneous logs. HLAer provides a framework which is purely dataoriented and thus general enough to adapt to arbitrary log formats, applications or systems. The current implementation of HLAer is scalable to Big Data.
Semantic trajectories modeling and analysis

Focus on movement data has increased as a consequence of the larger availability of such data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement suitable for the specific purposes of the application. This trend has promoted semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This survey provides the definitions of the basic concepts about mobility data, an analysis of the issues in mobility data management, and a survey of the approaches and techniques for: (i) constructing trajectories from movement tracks, (ii) enriching trajectories with semantic information to enable the desired interpretations of movements, and (iii) using data mining to analyze semantic trajectories and extract knowledge about their characteristics, in particular the behavioral patterns of the moving objects. Last but not least, the article surveys the new privacy issues that arise due to the semantic aspects of trajectories.
Ellipse Fitting Using the Finite Rate of Innovation Sampling Principle

Standard approaches for ellipse fitting are based on the minimization of algebraic or geometric distance between the given data and a template ellipse. When the data are noisy and come from a partial ellipse, the state-of-the-art methods tend to produce biased ellipses. We rely on the sampling structure of the underlying signal and show that the x - and y -coordinate functions of an ellipse are finite-rate-of-innovation (FRI) signals, and that their parameters are estimable from partial data. We consider both uniform and nonuniform sampling scenarios in the presence of noise and show that the data can be modeled as a sum of random amplitude-modulated complex exponentials. A low-pass filter is used to suppress noise and approximate the data as a sum of weighted complex exponentials. The annihilating filter used in FRI approaches is applied to estimate the sampling interval in the closed form. We perform experiments on simulated and real data, and assess both objective and subjective performances in comparison with the state-of-the-art ellipse fitting methods. The proposed method produces ellipses with lesser bias. Furthermore, the mean-squared error is lesser by about 2 to 10 dB. We show the applications of ellipse fitting in iris images starting from partial edge contours, and to free-hand ellipses drawn on a touch-screen tablet.
Apache Storm, a Hands on Tutorial

Apache Storm is a popular low latency distributed stream processing framework. Apache Storm is used everywhere at Yahoo and at many other companies from automatically tagging every image uploaded to Flickr and analyzing trending search queries to monitoring production servers looking for problems. This hands-on tutorial is divided into two parts. The first part covers the basics of Storm, its architecture, and walks you through writing a simple application (not just word count). The second part looks more at how to modify Storm and will walk you through adding in a new feature.
SMART Electronic Legal Discovery Via Topic Modeling

Electronic discovery is an interesting sub problem of information retrieval in which one identifies documents that are potentially relevant to issues and facts of a legal case from an electronically stored document collection (a corpus). In this paper, we consider representing documents in a topic space using the well-known topic models such as latent Dirichlet allocation and latent semantic indexing, and solving the information retrieval problem via finding document similarities in the topic space rather doing it in the corpus vocabulary space. We also develop an iterative SMART ranking and categorization framework including human-in-the-loop to label a set of seed (training) documents and using them to build a semi-supervised binary document classification model based on Support Vector Machines. To improve this model, we propose a method for choosing seed documents from the whole population via an active learning strategy. We report the results of our experiments on a real dataset in the electronic
State of the Art Report: Audio-Based Music Structure Analysis

Humans tend to organize perceived information into hierarchies and structures, a principle that also applies to music. Even musically untrained listeners unconsciously analyze and segment music with regard to various musical aspects, for example, identifying recurrent themes or detecting temporal boundaries between contrasting musical parts. This paper gives an overview of state-of-theart methods for computational music structure analysis, where the general goal is to divide an audio recording into temporal segments corresponding to musical parts and to group these segments into musically meaningful categories. There are many different criteria for segmenting and structuring music audio. In particular, one can identify three conceptually different approaches, which we refer to as repetition-based, novelty-based, and homogeneitybased approaches. Furthermore, one has to account for different musical dimensions such as melody, harmony, rhythm, and timbre. In our state-of-the-art report, we address these different issues in the context of music structure analysis, while discussing and categorizing the most relevant and recent articles in this field.
Returnn: The RWTH extensible training framework for universal recurrent neural networks

In this work we release our extensible and easily configurable neural network training software. It provides a rich set of functional layers with a particular focus on efficient training of recurrent neural network topologies on multiple GPUs. The source of the software package is public and freely available for academic research purposes and can be used as a framework or as a standalone tool which supports a flexible configuration. The software allows to train state-of-the-art deep bidirectional long short-term memory (LSTM) models on both one dimensional data like speech or two dimensional data like handwritten text and was used to develop successful submission systems in several evaluation campaigns.
On Bitcoin Security in the Presence of Broken Crypto Primitives

Digital currencies like Bitcoin rely on cryptographic primitives to operate. However, past experience shows that cryptographic primitives do not last forever: increased computational power and advanced cryptanalysis cause primitives to break frequently, and motivate the development of new ones. It is therefore crucial for maintaining trust in a crypto currency to anticipate such breakage. We present the first systematic analysis of the effect of broken primitives on Bitcoin. We identify the core cryptographic building blocks and analyze the various ways in which they can break, and the subsequent effect on the main Bitcoin security guarantees. Our analysis reveals a wide range of possible effects depending on the primitive and type of breakage, ranging from minor privacy violations to a complete breakdown of the currency. Our results lead to several observations on, and suggestions for, the Bitcoin migration plans in case of broken cryptographic primitives.
E2MD for reduction of motion artifacts from photoplethysmographic signals

Most of the intensive care units (ICU) are equipped with commercial pulse oximeters for monitoring arterial blood oxygen saturation (SpO2) and pulse rate (PR). Photoplethysmographic (PPG) data recorded from pulse oximeters usually corrupted by motion artifacts (MA), resulting in unreliable and inaccurate estimated measures of SpO2. In this paper, a simple and efficient MA reduction method based on Ensemble Empirical Mode Decomposition (E2MD) is proposed for the estimation of SpO2 from processed PPGs. Performance analysis of the proposed E2MD is evaluated by computing the statistical and quality measures indicating the signal reconstruction like SNR and NRMSE. Intentionally created MAs (Horizontal MA, Vertical MA and Bending MA) in the recorded PPGs are effectively reduced by the proposed one and proved to be the best suitable method for reliable and accurate SpO2 estimation from the processed PPGs.
Convolutional Oriented Boundaries

We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is computationally efficient, because it requires a single CNN forward pass for contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. Particularly, we show that learning to estimate not only contour strength but also orientation provides more accurate results. We perform extensive experiments on BSDS, PASCAL Context, PASCAL Segmentation, and MS-COCO, showing that COB provides state-of-the-art contours, region hierarchies, and object proposals in all datasets.
Automatic multimedia cross-modal correlation discovery

Given an image (or video clip, or audio song), how do we automatically assign keywords to it? The general problem is to find correlations across the media in a collection of multimedia objects like video clips, with colors, and/or motion, and/or audio, and/or text scripts. We propose a novel, graph-based approach, "MMG", to discover such cross-modal correlations.Our "MMG" method requires no tuning, no clustering, no user-determined constants; it can be applied to any multimedia collection, as long as we have a similarity function for each medium; and it scales linearly with the database size. We report auto-captioning experiments on the "standard" Corel image database of 680 MB, where it outperforms domain specific, fine-tuned methods by up to 10 percentage points in captioning accuracy (50% relative improvement).
Failure-Atomic Slotted Paging for Persistent Memory

The slotted-page structure is a database page format commonly used for managing variable-length records. In this work, we develop a novel "failure-atomic slotted page structure" for persistent memory that leverages byte addressability and durability of persistent memory to minimize redundant write operations used to maintain consistency in traditional database systems. Failure-atomic slotted paging consists of two key elements: (i) in-place commit per page using hardware transactional memory and (ii) slot header logging that logs the commit mark of each page. The proposed scheme is implemented in SQLite and compared against NVWAL, the current state-of-the-art scheme. Our performance study shows that our failure-atomic slotted paging shows optimal performance for database transactions that insert a single record. For transactions that touch more than one database page, our proposed slot-header logging scheme minimizes the logging overhead by avoiding duplicating pages and logging only the metadata of the dirty pages. Overall, we find that our failure-atomic slotted-page management scheme reduces database logging overhead to 1/6 and improves query response time by up to 33% compared to NVWAL.
Keepin' it real: pushing the desktop metaphor with physics, piles and the pen

We explore making virtual desktops behave in a more physically realistic manner by adding physics simulation and using piling instead of filing as the fundamental organizational structure. Objects can be casually dragged and tossed around, influenced by physical characteristics such as friction and mass, much like we would manipulate lightweight objects in the real world. We present a prototype, called BumpTop, that coherently integrates a variety of interaction and visualization techniques optimized for pen input we have developed to support this new style of desktop organization.
The Esterel Synchronous Programming Language: Design, Semantics, Implementation

We present the ESTEREL programming language which is especially designed to program reactive systems, that is systems which maintain a permanent interaction with their environment: real-time process controllers, communication protocols, man-machine interface drivers, etc. ESTEREL is a deterministic concurrent programming language. It differs from classical asynchronous languages by its synchrony hypothesis: the outputs of a system are conceptually synchronous with its inputs. The synchrony hypothesis permits a high-level modular programming style simpler and more rigorous than its asynchronous counterpart. We present the imperative primitives of ESTEREL and the temporal manipulations they permit. We give a small programming example. We present two mathematical semantics of ESTEREL, which are given by conditional rewrite rules and related by a correctness theorem. The behavioral semantics defines the behavior of programs in an uneffective way as the solution of tixpoint equations. The effective execution semantics computes actions to be performed by a conceptually infinitely fast execution machine. To relate the two semantics, we solve the causality problems that are inherent in synchronous formalisms. We show how the ESTEREL v2 and ESTEREL v3 compilers efficiently translate concurrent ESTEREL programs into efficient equivalent sequential automata that can be implemented in conventional sequential languages. We discuss the quality of this object code and the practical adequacy of the synchrony hypothesis.
A Compact Quadrature Hybrid Based on High-Pass and Low-Pass Lumped Elements

In this letter, a novel compact quadrature hybrid using low-pass and high-pass lumped elements is proposed. This proposed topology enables significant circuit size reduction in comparison with former approaches applying microstrip branch line or Lange couplers. In addition, it provides wider bandwidth in terms of operational frequency, and provides more convenience to the monolithic microwave integrated circuit layout since it does not have any bulky via holes as compared to those with lumped elements that have been published. In addition, the simulation and measurement of the fabricated hybrid implemented using PHEMT processes are evidently good. With the operational bandwidth ranging from 25 to 30 GHz, the measured results of the return loss are better than 17.6 dB, and the insertion losses of coupled and direct ports are approximately 3.4plusmn0.7 dB, while the relative phase difference is approximately 92.3plusmn1.4deg. The core dimension of the circuit is 0.4 mm times 0.15 mm.
Acted vs. natural frustration and delight: Many people smile in natural frustration

This work is part of research to build a system to combine facial and prosodic information to recognize commonly occurring user states such as delight and frustration. We create two experimental situations to elicit two emotional states: the first involves recalling situations while expressing either delight or frustration; the second experiment tries to elicit these states directly through a frustrating experience and through a delightful video. We find two significant differences in the nature of the acted vs. natural occurrences of expressions. First, the acted ones are much easier for the computer to recognize. Second, in 90% of the acted cases, participants did not smile when frustrated, whereas in 90% of the natural cases, participants smiled during the frustrating interaction, despite self-reporting significant frustration with the experience. This paper begins to explore the differences in the patterns of smiling that are seen under natural frustration and delight conditions, to see if there might be something measurably different about the smiles in these two cases, which could ultimately improve the performance of classifiers applied to natural expressions.
A Weighted Correlation Index for Rankings with Ties

Understanding the correlation between two different scores for the same set of items is a common problem in graph analysis and information retrieval. The most commonly used statistics that quantifies this correlation is Kendall's tau; however, the standard definition fails to capture that discordances between items with high rank are more important than those between items with low rank. Recently, a new measure of correlation based on average precision has been proposed to solve this problem, but like many alternative proposals in the literature it assumes that there are no ties in the scores. This is a major deficiency in a number of contexts, and in particular when comparing centrality scores on large graphs, as the obvious baseline, indegree, has a very large number of ties in social networks and web graphs. We propose to extend Kendall's definition in a natural way to take into account weights in the presence of ties. We prove a number of interesting mathematical properties of our generalization and describe an O(n\log n) algorithm for its computation. We also validate the usefulness of our weighted measure of correlation using experimental data on social networks and web graphs.
Interactive cluster analysis of diverse types of spatiotemporal data

We suggest an approach to exploratory analysis of diverse types of spatiotemporal data with the use of clustering and interactive visual displays. We can apply the same generic clustering algorithm to different types of data owing to the separation of the process of grouping objects from the process of computing distances between the objects. In particular, we apply the densitybased clustering algorithm OPTICS to events (i.e. objects having spatial and temporal positions), trajectories of moving entities, and spatial distributions of events or moving entities in different time intervals. Distances are computed in a specific way for each type of objects; moreover, it may be useful to have several different distance functions for the same type of objects. Thus, multiple distance functions available for trajectories support different analysis tasks. We demonstrate the use of our approach by example of two datasets from the VAST Challenge 2008: evacuation traces (trajectories of moving entities) and landings and interdictions of migrant boats (events).
Unmodified Device Driver Reuse and Improved System Dependability via Virtual Machines

We propose a method to reuse unmodified device drivers and to improve system dependability using virtual machines. We run the unmodified device driver, with its original operating system, in a virtual machine. This approach enables extensive reuse of existing and unmodified drivers, independent of the OS or device vendor, significantly reducing the barrier to building new OS endeavors. By allowing distinct device drivers to reside in separate virtual machines, this technique isolates faults caused by defective or malicious drivers, thus improving a system’s dependability. We show that our technique requires minimal support infrastructure and provides strong fault isolation. Our prototype’s network performance is within 3–8% of a native Linux system. Each additional virtual machine increases the CPU utilization by about 0.12%. We have successfully reused a wide variety of unmodified Linux network, disk, and PCI device drivers.