nils-herrmann/clean_scirepeval huggingface dataset:
Matching Handwritten Document Images

Given a pair of handwritten documents written by different individuals, compute a document similarity score irrespective of (i) handwritten styles, (ii) word forms, word ordering and word overflow. •  Introducing a large scale synthetic corpus of handwritten word images for enabling deep architectures. •  A deep CNN architecture for state of the art handwritten word spotting in multi-writer scenarios.  Measure of document similarity score irrespective of word forms, ordering and paraphrasing of the content. • Applications in  Comparing handwritten assignments, searching through instructional videos. 2.
Making Usable, Useful, Productivity-Enhancing Computer Applications

lmost a decade has passed since we started advocating a process of usability design [20-22]. This article is a status report about the value of this process and, mainly, a description of new ideas for enhancing the use of the process. We first note that, when followed , the process leads to usable, useful, likeable computer systems and applications. Nevertheless, experience and observational evidence show that (because of the way development work is organized and carried out) the process is often not followed, despite designers' enthusiasm and motivation to do so. To get around these organizational and technical obstacles, we propose a) greater reliance on existing methodologies for establishing test-able usability and productivity-enhancing goals; b) a new method for identifying and focuging attention on long-term, trends about the effects that computer applications have on end-user productivity; and c) a new approach, now under way, to application development, particularly the development of user interfaces. The process consists of four activities [18, 20-22]. Early Focus On Users. Designers should have direct contact with intended or actual users-via interviews , observations, surveys, partic-ipatory design. The aim is to understand users' cognitive, behav-ioral, attitudinal, and anthropomet-ric characteristics-and the characteristics of the jobs they will be doing.  All aspects of usability (e.g., user interface, help system, training plan, documentation) should evolve in parallel, rather than be defined sequentially, and should be under one management. Early~And Continual~User Testing. The only presently feasible approach to successful design is an empirical one, requiring observation and measurement of user behavior , careful evaluation of feedback , insightful solutions to existing problems, and strong motivation to make design changes. Iterative Design. A system under development must be modified based upon the results of behav-ioral tests of functions, user interface , help system, documentation, training approach. This process of implementation, testing, feedback, evaluation, and change must be repeated to iteratively improve the system. We, and others proposing similar ideas (see below), have worked hard at spreading this process of usabil-ity design. We have used numerous channels to accomplish this: frequent talks, workshops, seminars, publications, consulting, addressing arguments used against it [22], conducting a direct case study of the process [20], and identifying methods for people not fully trained as human factors professionals to use in carrying out this process [18]. The Process Works. Several lines of evidence indicate that this usabil-ity design process leads to systems, applications, and products …
Joint spectrum sensing and primary user localization for cognitive radio via compressed sensing

Radio spectrum has become a precious resource, and it has long been the dream of wireless communication engineers to maximize the utilization of the radio spectrum. Dynamic Spectrum Access (DSA) and Cognitive Radio (CR) have been considered promising to enhance the efficiency and utilization of the spectrum. In current overlay cognitive radio, spectrum sensing is first performed to detect the spectrum holes for the secondary user to harness. However, in a more sophisticated cognitive radio, the secondary user needs to detect more than just the existence of primary users and spectrum holes. For example, in a hybrid overlay/underlay cognitive radio, the secondary use needs to detect the transmission power and localization of the primary users as well. In this paper, we combine the spectrum sensing and primary user power/localization detection together, and propose to jointly detect not only the existence of primary users but the power and localization of them via compressed sensing. Simulation results including the miss detection probability (MDP), false alarm probability (FAP) and reconstruction probability (RP) confirm the effectiveness and robustness of the proposed method.
Exposing DeepFake Videos By Detecting Face Warping Artifacts

In this work, we describe a new deep learning based method that can effectively distinguish AI-generated fake videos (referred to as DeepFake videos hereafter) from real videos. Our method is based on the observations that current DeepFake algorithm can only generate images of limited resolutions, which need to be further warped to match the original faces in the source video. Such transforms leave distinctive artifacts in the resulting DeepFake videos, and we show that they can be effectively captured by convolutional neural networks. Our method is evaluated on a set of DeepFake videos for its effectiveness in practice.
Is the Distribution of Stock Returns Predictable ?

A large literature has considered predictability of the mean or volatility of stock returns but little is known about whether the distribution of stock returns more generally is predictable. We explore this issue in a quantile regression framework and consider whether a range of economic state variables are helpful in predicting different quantiles of stock returns representing left tails, right tails or shoulders of the return distribution. Many variables are found to have an asymmetric effect on the return distribution, affecting lower, central and upper quantiles very differently. Out-of-sample forecasts suggest that upper quantiles of the return distribution can be predicted by means of economic state variables although the center of the return distribution is more difficult to predict. Economic gains from utilizing information in time-varying quantile forecasts are demonstrated through portfolio selection and option trading experiments. ∗We thank Torben Andersen, Tim Bollerslev, Peter Christoffersen as well as seminar participants at HEC, University of Montreal, University of Toronto, Goldman Sachs and CREATES, University of Aarhus, for helpful comments.
Biomedical ontologies in action: role in knowledge management, data integration and decision support.

To provide typical examples of biomedical ontologies in action, emphasizing the role played by biomedical ontologies in knowledge management, data integration and decision support.



Biomedical ontologies selected for their practical impact are examined from a functional perspective. Examples of applications are taken from operational systems and the biomedical literature, with a bias towards recent journal articles.



The ontologies under investigation in this survey include SNOMED CT, the Logical Observation Identifiers, Names, and Codes (LOINC), the Foundational Model of Anatomy, the Gene Ontology, RxNorm, the National Cancer Institute Thesaurus, the International Classification of Diseases, the Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). The roles played by biomedical ontologies are classified into three major categories: knowledge management (indexing and retrieval of data and information, access to information, mapping among ontologies); data integration, exchange and semantic interoperability; and decision support and reasoning (data selection and aggregation, decision support, natural language processing applications, knowledge discovery).



Ontologies play an important role in biomedical research through a variety of applications. While ontologies are used primarily as a source of vocabulary for standardization and integration purposes, many applications also use them as a source of computable knowledge. Barriers to the use of ontologies in biomedical applications are discussed.
UPAR7: A knowledge-based system for headline sentiment tagging

For the Affective Text task at SemEval2007, University Paris 7’s system first evaluates emotion and valence on all words of a news headline (using enriched versions of SentiWordNet and a subset of WordNetAffect). We use a parser to find the head word, considering that it has a major importance. We also detect contrasts (between positive and negative words) that shift valence. Our knowledge-based system achieves high accuracy on emotion and valence annotation. These results show that working with linguistic techniques and a broad-coverage lexicon is a viable approach to sentiment analysis of headlines.
Next-Generation Sweep Tool: A Method for Generating All-Hex Meshes on Two-and-One-Half Dimensional Geometries

Placement of interior node points is a crucial step in the generation of quality meshes in sweeping algorithms. Two new algorithms were devised for node point placement and implemented in Sweep Tool, the first based on the use of linear transformations between bounding node loops and the second based on smoothing. Examples are given that demonstrate the effectiveness of these algorithms.
ECG-based heartbeat classification for arrhythmia detection: A survey

An electrocardiogram (ECG) measures the electric activity of the heart and has been widely used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing the electrical signal of each heartbeat, i.e., the combination of action impulse waveforms produced by different specialized cardiac tissues found in the heart, it is possible to detect some of its abnormalities. In the last decades, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, we survey the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. In addition, we describe some of the databases used for evaluation of methods indicated by a well-known standard developed by the Association for the Advancement of Medical Instrumentation (AAMI) and described in ANSI/AAMI EC57:1998/(R)2008 (ANSI/AAMI, 2008). Finally, we discuss limitations and drawbacks of the methods in the literature presenting concluding remarks and future challenges, and also we propose an evaluation process workflow to guide authors in future works.
A Comparison of Rhythmic Similarity Measures

Measuring the similarity between rhythms is a fundamental problem in computational music theory, with many applications such as music information retrieval and copyright infringement resolution. A common way to represent a rhythm is as a binary sequence where a zero denotes a rest (silence) and a one represents a beat or note onset. This paper compares various measures of rhythm similarity including the Hamming distance, the Euclidean interval-vector distance, the interval-difference distance measure of Coyle and Shmulevich, the swap distance, and the chronotonic distance measures of Gustafson and Hofmann-Engl. Traditionally, rhythmic similarity measures are compared according to how well rhythms may be recognized with them, how efficiently they can be retrieved from a data base, or how well they model human perception and cognition of rhythms. In contrast, here similarity measures are compared on the basis of how much insight they provide about the structural inter-relationships that exist within families of rhythms, when phylogenetic trees and graphs are computed from the distance matrices determined by these similarity measures. For two collections of rhythms, namely the 4/4 time and 12/8 time clave-bell time lines used in traditional African and Afro-American music, the chronotonic and swap distances appear to be superior to the other measures, and each has its own atractive features. The similarity measures are also compared according to their computational complexity.
Clustering with t-SNE, provably

t-distributed Stochastic Neighborhood Embedding (t-SNE), a clustering and visualization method proposed by van der Maaten&Hinton in 2008, has rapidly become a standard tool in a number of natural sciences. Despite its overwhelming success, there is a distinct lack of mathematical foundations and the inner workings of the algorithm are not well understood. The purpose of this paper is to prove that t-SNE is able to recover well-separated clusters; more precisely, we prove that t-SNE in the `early exaggeration' phase, an optimization technique proposed by van der Maaten&Hinton (2008) and van der Maaten (2014), can be rigorously analyzed. As a byproduct, the proof suggests novel ways for setting the exaggeration parameter $\alpha$ and step size $h$. Numerical examples illustrate the effectiveness of these rules: in particular, the quality of embedding of topological structures (e.g. the swiss roll) improves. We also discuss a connection to spectral clustering methods.
Motion control of an omnidirectional mobile robot

This paper focuses on the motion control problem of an omnidirectional mobile robot. A new control method based on the inverse input-output linearized kinematic model is proposed. As the actuator saturation and actuator dynamics have important impacts on the robot performance, this control law takes into account these two aspects and guarantees the stability of the closed-loop control system. Real-world experiments with an omnidirectional middle-size RoboCup robot verifies the performance of this proposed control algorithm.
Bracketing Compound Nouns for Logic Form Derivation

Wepresenthereamethodfor bracketingcoordinatedcompoundnounsin thelargercontext of deriving first orderlogic forms. The methodconsistsof two phases:(1) detection extract candidatecompoundnounsfrom a parsetreeand (2) interpretation provide a bracketing solution for each candidate.Bracketingperformanceon 525coordinationsis 87.42%. This comparesfavorably with a 52.35%baseline whenadefault split by thecoordinationis applied.
Effect of Inductive Coil Shape on Sensing Performance of Linear Displacement Sensor Using Thin Inductive Coil and Pattern Guide

This paper discusses the effect of inductive coil shape on the sensing performance of a linear displacement sensor. The linear displacement sensor consists of a thin type inductive coil with a thin pattern guide, thus being suitable for tiny space applications. The position can be detected by measuring the inductance of the inductive coil. At each position due to the change in inductive coil area facing the pattern guide the value of inductance is different. Therefore, the objective of this research is to study various inductive coil pattern shapes and to propose the pattern that can achieve good sensing performance. Various shapes of meander, triangular type meander, square and circle shape with different turn number of inductive coils are examined in this study. The inductance is measured with the sensor sensitivity and linearity as a performance evaluation parameter of the sensor. In conclusion, each inductive coil shape has its own advantages and disadvantages. For instance, the circle shape inductive coil produces high sensitivity with a low linearity response. Meanwhile, the square shape inductive coil has a medium sensitivity with higher linearity.
Automatic Keyword Extraction for Text Summarization in e-Newspapers

Summarization is the process of reducing a text document to create a summary that retains the most important points of the original document. Extractive summarizers work on the given text to extract sentences that best convey the message hidden in the text. Most extractive summarization techniques revolve around the concept of finding keywords and extracting sentences that have more keywords than the rest. Keyword extraction usually is done by extracting relevant words having a higher frequency than others, with stress on important ones'. Manual extraction or annotation of keywords is a tedious process brimming with errors involving lots of manual effort and time. In this paper, we proposed an algorithm to extract keyword automatically for text summarization in e-newspaper datasets. The proposed algorithm is compared with the experimental result of articles having the similar title in four different e-Newspapers to check the similarity and consistency in summarized results.
Ontology-based Software Architecture Documentation

A common approach to software architecture documentation in industry projects is the use of file-based documents. This approach offers a single-dimensional perspective on the architectural knowledge contained. Knowledge retrieval from file-based architecture documentation is efficient if the perspective chosen fits the needs of the readers, it is less so if the perspective does not match the needs of the readers. In this paper we describe an approach aimed at addressing architecture documentation retrieval issues. We have employed a software ontology in a semantic wiki optimized for architecture documentation. We have evaluated this ontology-based approach in a controlled industry experiment involving software professionals. The efficiency and effectiveness of the proposed approach is found to be better than that of the file-based approach.
Software-defined caching: managing caches in multi-tenant data centers

In data centers, caches work both to provide low IO latencies and to reduce the load on the back-end network and storage. But they are not designed for multi-tenancy; system-level caches today cannot be configured to match tenant or provider objectives. Exacerbating the problem is the increasing number of un-coordinated caches on the IO data plane. The lack of global visibility on the control plane to coordinate this distributed set of caches leads to inefficiencies, increasing cloud provider cost.
 We present Moirai, a tenant- and workload-aware system that allows data center providers to control their distributed caching infrastructure. Moirai can help ease the management of the cache infrastructure and achieve various objectives, such as improving overall resource utilization or providing tenant isolation and QoS guarantees, as we show through several use cases. A key benefit of Moirai is that it is transparent to applications or VMs deployed in data centers. Our prototype runs unmodified OSes and databases, providing immediate benefit to existing applications.
Fractional Fourier transform as a signal processing tool: An overview of recent developments

Fractional Fourier transform (FRFT) is a generalization of the Fourier transform, rediscovered many times over the past 100 years. In this paper, we provide an overview of recent contributions pertaining to the FRFT. Specifically, the paper is geared toward signal processing practitioners by emphasizing the practical digital realizations and applications of the FRFT. It discusses three major topics. First, the manuscripts relates the FRFT to other mathematical transforms. Second, it discusses various approaches for practical realizations of the FRFT. Third, we overview the practical applications of the FRFT. From these discussions, we can clearly state that the FRFT is closely related to other mathematical transforms, such as timeâ€“frequency and linear canonical transforms. Nevertheless, we still feel that major contributions are expected in the field of the digital realizations and its applications, especially, since many digital realizations of a b Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.
3D cost aggregation with multiple minimum spanning trees for stereo matching.

Cost aggregation is one of the key steps in the stereo matching problem. In order to improve aggregation accuracy, we propose a cost-aggregation method that can embed minimum spanning tree (MST)-based support region filtering into PatchMatch 3D label search rather than aggregating on fixed size patches. However, directly combining PatchMatch label search and MST filtering is not straightforward, due to the extremely high complexity. Thus, we develop multiple MST structures for cost aggregation on plenty of 3D labels, and design the tree-level random search strategy to find possible 3D labels of each pixel. Extensive experiments show that our method reaches higher accuracy than the other existing cost-aggregation and global-optimization methods such as the 1D MST, the PatchMatch and the PatchMatch Filter, and currently ranks first on the Middlebury 3.0 benchmark.
Agile human-centered software engineering

We seek to close the gap between software engineering (SE) and human-computer interaction (HCI) by indicating interdisciplinary interfaces throughout the different phases of SE and HCI lifecycles. As agile representatives of SE, Extreme Programming (XP) and Agile Modeling (AM) contribute helpful principles and practices for a common engineering approach. We present a cross-discipline user interface design lifecycle that integrates SE and HCI under the umbrella of agile development. Melting IT budgets, pressure of time and the demand to build better software in less time must be supported by traveling as light as possible. We did, therefore, choose not just to mediate both disciplines. Following our surveys, a rather radical approach best fits the demands of engineering organizations.
Undertaking a literature review: a step-by-step approach.

Nowadays, most nurses, pre- and post-qualification, will be required to undertake a literature review at some point, either as part of a course of study, as a key step in the research process, or as part of clinical practice development or policy. For student nurses and novice researchers it is often seen as a difficult undertaking. It demands a complex range of skills, such as learning how to define topics for exploration, acquiring skills of literature searching and retrieval, developing the ability to analyse and synthesize data as well as becoming adept at writing and reporting, often within a limited time scale. The purpose of this article is to present a step-by-step guide to facilitate understanding by presenting the critical elements of the literature review process. While reference is made to different types of literature reviews, the focus is on the traditional or narrative review that is undertaken, usually either as an academic assignment or part of the research process.
Automatic Recognition of Exudative Maculopathy using Fuzzy C- Means Clustering and Neural Networks

Retinal exudates are typically manifested as spatially random yellow/white patches of varying sizes and shapes. They are a characteristic feature of retinal diseases such as diabetic maculopathy. An automatic method for the detection of exudate regions is introduced comprising image colour normalisation, enhancing the contrast between the objects and background, segmenting the colour retinal image into homogenous regions using Fuzzy C-Means clustering, and classifying the regions into exudates and non exudates patches using a neural network. Experimental results indicate that we are able to achieve 92% sensitivity and 82% specificity.