nils-herrmann/clean_scirepeval huggingface dataset:
Critical Inquiry in a Text-Based Environment: Computer Conferencing in Higher Education

The purpose of this study is to provide conceptual order and a tool for the use of computermediated communication (CMC) and computer conferencing in supporting an educational experience. Central to the study introduced here is a model of community inquiry that constitutes three elements essential to an educational transactionÐcognitive presence, social presence, and teaching presence. Indicators (key words/phrases) for each of the three elements emerged from the analysis of computer-conferencing transcripts. The indicators described represent a template or tool for researchers to analyze written transcripts, as well as a guide to educators for the optimal use of computer conferencing as a medium to facilitate an educational transaction. This research would suggest that computer conferencing has considerable potential to create a community of inquiry for educational purposes.
Sinusoidal pulse width modulation (SPWM) with variable carrier synchronization for multilevel inverter controllers

Voltage or current converters generate discrete output waveforms, which require large inductances connected in series with the respective load to generate the desired current waveform. Mostly, neither the voltage nor the current waveforms are as expected and also have distorted voltages and currents waveforms produces harmonic contamination, additional power losses, and high frequency noise. In this paper a method of minimization of THD with near to reference current generation is proposed based on multilevel inverter. A sinusoidal pulse width modulation scheme is developed for the multilevel inverter.
Classes of small-world networks.

We study the statistical properties of a variety of diverse real-world networks. We present evidence of the occurrence of three classes of small-world networks: (a) scale-free networks, characterized by a vertex connectivity distribution that decays as a power law; (b) broad-scale networks, characterized by a connectivity distribution that has a power law regime followed by a sharp cutoff; and (c) single-scale networks, characterized by a connectivity distribution with a fast decaying tail. Moreover, we note for the classes of broad-scale and single-scale networks that there are constraints limiting the addition of new links. Our results suggest that the nature of such constraints may be the controlling factor for the emergence of different classes of networks.
Unsupervised Sparse Vector Densification for Short Text Similarity

Sparse representations of text such as bag-ofwords models or extended explicit semantic analysis (ESA) representations are commonly used in many NLP applications. However, for short texts, the similarity between two such sparse vectors is not accurate due to the small term overlap. While there have been multiple proposals for dense representations of words, measuring similarity between short texts (sentences, snippets, paragraphs) requires combining these token level similarities. In this paper, we propose to combine ESA representations and word2vec representations as a way to generate denser representations and, consequently, a better similarity measure between short texts. We study three densification mechanisms that involve aligning sparse representation via many-to-many, many-to-one, and oneto-one mappings. We then show the effectiveness of these mechanisms on measuring similarity between short texts.
P-Rank: a comprehensive structural similarity measure over information networks

With the ubiquity of information networks and their broad applications, the issue of similarity computation between entities of an information network arises and draws extensive research interests. However, to effectively and comprehensively measure "how similar two entities are within an information network" is nontrivial, and the problem becomes even more challenging when the information network to be examined is massive and diverse. In this paper, we propose a new similarity measure, P-Rank (Penetrating Rank), toward effectively computing the structural similarities of entities in real information networks. P-Rank enriches the well-known similarity measure, SimRank, by jointly encoding both in- and out-link relationships into structural similarity computation. P-Rank is proven to be a unified structural similarity framework, under which all state-of-the-art similarity measures, including CoCitation, Coupling, Amsler and SimRank, are just its special cases. Based on its recursive nature of P-Rank, we propose a fixed point algorithm to reinforce structural similarity of vertex pairs beyond the localized neighborhood scope toward the entire information network. Our experimental studies demonstrate the power of P-Rank as an effective similarity measure in different information networks. Meanwhile, under the same time/space complexity, P-Rank outperforms SimRank as a comprehensive and more meaningful structural similarity measure, especially in large real information networks.
Categorical Data Analysis

Modeling and inferential procedures (estimation and precision assessment) are discussed for a single proportion, thereafter in the context of cross-classified data, i.e., contingency tables, is discussed. First, the situation of two by two tables is given some treatment and then the more general context of R by C tables is considered. Particular emphasis is put on testing the null hypothesis of no association (independence) between the row and column classification. Next, the setting of multiple contingency tables is considered. Attention is given to the log-linear model and to the multinomial regression model. When the covariate is continuous, these methods naturally extend to logistic regression, a method at the intersection of generalized linear modeling and categorical data analysis. When the outcome is a count, one often uses Poisson regression. Finally, correlated categorical data settings are briefly discussed.
How Much of the Corporate-Treasury Yield Spread is Due to Credit Risk ? 1

How Much of the Corporate-Treasury Yield Spread Is Due to Credit Risk? No consensus has yet emerged from the existing credit risk literature on how much of the observed corporate-Treasury yield spreads can be explained by credit risk. In this paper, we propose a new calibration approach based on historical default data and show that one can indeed obtain consistent estimate of the credit spread across many different economic considerations within the structural framework of credit risk valuation. We find that credit risk accounts for only a small fraction of the observed corporate-Treasury yield spreads for investment grade bonds of all maturities, with the fraction smaller for bonds of shorter maturities; and that it accounts for a much higher fraction of yield spreads for junk bonds. We obtain these results by calibrating each of the models – both existing and new ones – to be consistent with data on historical default loss experience. Different structural models, which in theory can still generate a very large range of credit spreads, are shown to predict fairly similar credit spreads under empirically reasonable parameter choices, resulting in the robustness of our conclusion.
Overview of the 2nd International Competition on Plagiarism Detection

This paper overviews 18 plagiarism detectors that have been developed and evaluated within PAN’10. We start with a unified retrieval process that summarizes the best practices employed this year. Then, the detectors’ performances are evaluated in detail, highlighting several important aspects of plagiarism detection, such as obfuscation, intrinsic vs. external plagiarism, and plagiarism case length. Finally, all results are compared to those of last year’s competition. Martin Braschler and Donna Harman (Eds.): Notebook Papers of CLEF 2010 LABs and Workshops, 22-23 September, Padua, Italy. ISBN 978-88-904810-0-0. 2010.
The optical character recognition of Urdu-like cursive scripts

We survey the optical character recognition (OCR) literature with reference to the Urdu-like cursive scripts. In particular, the Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts. Before detaining the OCR works, the peculiarities of the Urdu-like scripts are outlined, which are followed by the presentation of the available text image databases. For the sake of clarity, the various attempts are grouped into three parts, namely: (a) printed, (b) handwritten, and (c) online character recognition. Within each part, the works are analyzed par rapport a typical OCR pipeline with an emphasis on the preprocessing, segmentation, feature extraction, classification, and
Dynamic personalized pagerank in entity-relation graphs

Extractors and taggers turn unstructured text into entity-relation(ER) graphs where nodes are entities (email, paper, person,conference, company) and edges are relations (wrote, cited,works-for). Typed proximity search of the form <B>type=personNEAR company~"IBM", paper~"XML"</B> is an increasingly usefulsearch paradigm in ER graphs. Proximity search implementations either perform a Pagerank-like computation at query time, which is slow, or precompute, store and combine per-word Pageranks, which can be very expensive in terms of preprocessing time and space. We present HubRank, a new system for fast, dynamic, space-efficient proximity searches in ER graphs. During preprocessing, HubRank computesand indexes certain "sketchy" random walk fingerprints for a small fraction of nodes, carefully chosen using query log statistics. At query time, a small "active" subgraph is identified, bordered bynodes with indexed fingerprints. These fingerprints are adaptively loaded to various resolutions to form approximate personalized Pagerank vectors (PPVs). PPVs at remaining active nodes are now computed iteratively. We report on experiments with CiteSeer's ER graph and millions of real Cite Seer queries. Some representative numbers follow. On our testbed, HubRank preprocesses and indexes 52 times faster than whole-vocabulary PPV computation. A text index occupies 56 MB. Whole-vocabulary PPVs would consume 102GB. If PPVs are truncated to 56 MB, precision compared to true Pagerank drops to 0.55; incontrast, HubRank has precision 0.91 at 63MB. HubRank's average querytime is 200-300 milliseconds; query-time Pagerank computation takes 11 seconds on average.
True Online Temporal-Difference Learning

The temporal-difference methods TD(λ) and Sarsa(λ) form a core part of modern reinforcement learning. Their appeal comes from their good performance, low computational cost, and their simple interpretation, given by their forward view. Recently, new versions of these methods were introduced, called true online TD(λ) and true online Sarsa(λ), respectively (van Seijen and Sutton, 2014). Algorithmically, these true online methods only make two small changes to the update rules of the regular methods, and the extra computational cost is negligible in most cases. However, they follow the ideas underlying the forward view much more closely. In particular, they maintain an exact equivalence with the forward view at all times, whereas the traditional versions only approximate it for small step-sizes. We hypothesize that these true online methods not only have better theoretical properties, but also dominate the regular methods empirically. In this article, we put this hypothesis to the test by performing an extensive empirical comparison. Specifically, we compare the performance of true online TD(λ)/Sarsa(λ) with regular TD(λ)/Sarsa(λ) on random MRPs, a real-world myoelectric prosthetic arm, and a domain from the Arcade Learning Environment. We use linear function approximation with tabular, binary, and non-binary features. Our results suggest that the true online methods indeed dominate the regular methods. Across all domains/representations the learning speed of the true online methods are often better, but never worse than that of the regular methods. An additional advantage is that no choice between traces has to be made for the true online methods. We show that new true online temporal-difference methods can be derived by making changes to the real-time forward view and then rewriting the update equations.
Cloud computing security requirements: A systematic review

Many publications have dealt with various types of security requirements in cloud computing but not all types have been explored in sufficient depth. It is also hard to understand which types of requirements have been under-researched and which are most investigated. This paper's goal is to provide a comprehensive and structured overview of cloud computing security requirements and solutions. We carried out a systematic review and identified security requirements from previous publications that we classified in nine sub-areas: Access Control, Attack/Harm Detection, Non-repudiation, Integrity, Security Auditing, Physical Protection, Privacy, Recovery, and Prosecution. We found that (i) the least researched sub-areas are non-repudiation, physical protection, recovery and prosecution, and that (ii) access control, integrity and auditability are the most researched sub-areas.
Teaching Computational Thinking in K-6: The CSER Digital Technologies MOOC

In recent decades, ICT curriculum in K-10 has typically focussed on ICT as a tool, with the development of digital literacy being the key requirement. Areas such as computer science (CS) or computational thinking (CT) were typically isolated into senior secondary programs, with a focus on programming and algorithm development, when they were considered at all. New curricula introduced in England, and currently awaiting minister endorsement within Australia, have identified the need to educate for both digital literacy and CS, and the need to promote both from the commencement of schooling. This has presented significant challenges for teachers within this space, as they generally do not have the disciplinary knowledge to teach new computing curriculum and pedagogy in the early years is currently underdeveloped. In this paper, we introduce the CSER Digital Technologies MOOC, assisting teachers in the development of the fundamental knowledge of CT and the Australian Digital Technologies curriculum component. We describe our course structure, and key mechanisms for building a learning community within a MOOC context. We identify key challenges that teachers have identified in mastering this new curriculum, highlighting areas of future research in the teaching and learning of CT in K-6.
kR^X: Comprehensive Kernel Protection against Just-In-Time Code Reuse

The abundance of memory corruption and disclosure vulnerabilities in kernel code necessitates the deployment of hardening techniques to prevent privilege escalation attacks. As more strict memory isolation mechanisms between the kernel and user space, like Intel's SMEP, become commonplace, attackers increasingly rely on code reuse techniques to exploit kernel vulnerabilities. Contrary to similar attacks in more restrictive settings, such as web browsers, in kernel exploitation, non-privileged local adversaries have great flexibility in abusing memory disclosure vulnerabilities to dynamically discover, or infer, the location of certain code snippets and construct code-reuse payloads. Recent studies have shown that the coupling of code diversification with the enforcement of a "read XOR execute" (R^X) memory safety policy is an effective defense against the exploitation of userland software, but so far this approach has not been applied for the protection of the kernel itself.
 In this paper, we fill this gap by presenting kR^X: a kernel hardening scheme based on execute-only memory and code diversification. We study a previously unexplored point in the design space, where a hypervisor or a super-privileged component is not required. Implemented mostly as a set of GCC plugins, kR^X is readily applicable to the x86-64 Linux kernel and can benefit from hardware support (e.g., MPX on modern Intel CPUs) to optimize performance. In full protection mode, kR^X incurs a low runtime overhead of 4.04%, which drops to 2.32% when MPX is available.
Internet of Things for Smart Cities

The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.
mTCP: a Highly Scalable User-level TCP Stack for Multicore Systems

Scaling the performance of short TCP connections on multicore systems is fundamentally challenging. Although many proposals have attempted to address various shortcomings, inefficiency of the kernel implementation still persists. For example, even state-of-the-art designs spend 70% to 80% of CPU cycles in handling TCP connections in the kernel, leaving only small room for innovation in the user-level program. This work presents mTCP, a high-performance userlevel TCP stack for multicore systems. mTCP addresses the inefficiencies from the ground up—from packet I/O and TCP connection management to the application interface. In addition to adopting well-known techniques, our design (1) translates multiple expensive system calls into a single shared memory reference, (2) allows efficient flowlevel event aggregation, and (3) performs batched packet I/O for high I/O efficiency. Our evaluations on an 8-core machine showed that mTCP improves the performance of small message transactions by a factor of 25 compared to the latest Linux TCP stack and a factor of 3 compared to the best-performing research system known so far. It also improves the performance of various popular applications by 33% to 320% compared to those on the Linux stack.
It Depends: Dependency Parser Comparison Using A Web-based Evaluation Tool

The last few years have seen a surge in the number of accurate, fast, publicly available dependency parsers. At the same time, the use of dependency parsing in NLP applications has increased. It can be difficult for a non-expert to select a good “off-the-shelf” parser. We present a comparative analysis of ten leading statistical dependency parsers on a multi-genre corpus of English. For our analysis, we developed a new web-based tool that gives a convenient way of comparing dependency parser outputs. Our analysis will help practitioners choose a parser to optimize their desired speed/accuracy tradeoff, and our tool will help practitioners examine and compare parser output.
Multi-Modal Sensor Calibration Using a Gradient Orientation Measure

This paper presents a new metric for automated registration of multi-modal sensor data. The metric is based on the alignment of the orientation of gradients formed from the two candidate sensors. Data registration is performed by estimating the sensors’ extrinsic parameters that minimises the misalignment of the gradients. The metric can operate in a large range of applications working on both 2D and 3D sensor outputs and is suitable for both (i) single scan data registration and (ii) multi-sensor platform calibration using multiple scans. Unlike traditional calibration methods, it does not require markers or other registration aids to be placed in the scene. The effectiveness of the new method is demonstrated with experimental results on a variety of camera-lidar and camera-camera calibration problems. The novel metric is validated through comparisons with state of the art methods. Our approach is shown to give high quality registrations under all tested conditions.
Raw Waveform-based Audio Classification Using Sample-level CNN Architectures

Music, speech, and acoustic scene sound are often handled separately in the audio domain because of their different signal characteristics. However, as the image domain grows rapidly by versatile image classification models, it is necessary to study extensible classification models in the audio domain as well. In this study, we approach this problem using two types of sample-level deep convolutional neural networks that take raw waveforms as input and uses filters with small granularity. One is a basic model that consists of convolution and pooling layers. The other is an improved model that additionally has residual connections, squeeze-and-excitation modules and multi-level concatenation. We show that the sample-level models reach state-of-the-art performance levels for the three different categories of sound. Also, we visualize the filters along layers and compare the characteristics of learned filters.
Active former of monocycle high-voltage subnanosecond pulses

Experiments have been performed to investigate the operating modes of a generator of 2/spl times/500-ps bipolar high-voltage, nanosecond pulses with the double amplitude (270 kV) close to that of the charge pulse of the RADAN-303 nanosecond driver. The generator contains an additional peaker shortening the risetime of the starting pulse and a pulse-forming line with two untriggered gas gaps operating with a total jitter of 200 ps.
What is a Good Linear Element? Interpolation, Conditioning, and Quality Measures

When a mesh of simplicial elements (triangles or tetrahedra) is used to form a piecewise linear approximation of a function, the accuracy of the approximation depends on the sizes and shapes of the elements. In finite element methods, the conditioning of the stiffness matrices also depends on the sizes and shapes of the elements. This paper explains the mathematical connections between mesh geometry, interpolation errors, and stiffness matrix conditioning. These relationships are expressed by error bounds and element quality measures that determine the fitness of a triangle or tetrahedron for interpolation or for achieving low condition numbers. Unfortunately, the quality measures for these two purposes do not agree with each other; for instance, small angles are bad for matrix conditioning but not for interpolation. Several of the upper and lower bounds on interpolation errors and element stiffness matrix conditioning given here are tighter than those that have appeared in the literature before, so the quality measures are likely to be unusually precise indicators of element fitness.
A risk minimization framework for information retrieval

This paper presents a probabilistic information retrieval fr mework in which the retrieval problem is formally treated as a statistical decision probl em. In this framework, queries and documents are modeled using statistical language model s, user preferences are modeled through loss functions, and retrieval is cast as a risk m nimization problem. We discuss how this framework can unify existing retrieval models and a ccommodate systematic development of new retrieval models. As an example of using the framework to model nontraditional retrieval problems, we derive retrieval model s for subtopic retrieval, which is concerned with retrieving documents to cover many differen t subtopics of a general query topic. These new models differ from traditional retrieval m odels in that they relax the traditional assumption of independent relevance of documents .