nils-herrmann/clean_scirepeval huggingface dataset:
Ranking Through Clustering: An Integrated Approach to Multi-Document Summarization

Multi-document summarization aims to create a condensed summary while retaining the main characteristics of the original set of documents. Under such background, sentence ranking has hitherto been the issue of most concern. Since documents often cover a number of topic themes with each theme represented by a cluster of highly related sentences, sentence clustering has been explored in the literature in order to provide more informative summaries. For each topic theme, the rank of terms conditional on this topic theme should be very distinct, and quite different from the rank of terms in other topic themes. Existing cluster-based summarization approaches apply clustering and ranking in isolation, which leads to incomplete, or sometimes rather biased, analytical results. A newly emerged framework uses sentence clustering results to improve or refine the sentence ranking results. Under this framework, we propose a novel approach that directly generates clusters integrated with ranking in this paper. The basic idea of the approach is that ranking distribution of sentences in each cluster should be quite different from each other, which may serve as features of clusters and new clustering measures of sentences can be calculated accordingly. Meanwhile, better clustering results can achieve better ranking results. As a result, ranking and clustering by mutually and simultaneously updating each other so that the performance of both can be improved. The effectiveness of the proposed approach is demonstrated by both the cluster quality analysis and the summarization evaluation conducted on the DUC 2004-2007 datasets.
A Survey of Game Theory in Wireless Sensor Networks Security

Wireless Sensor Networks (WSNs) are becoming an integral part of our lives. There are not widespread applications of WSNs without ensuring WSNs security. Due to the limited capabilities of sensor nodes in terms of computation, communication, and energy, providing security to WSNs is challenging. In fact, the process of implementing WSNs security is adaptive and dynamic, which evolves continually. The essence of attack-defend in WSNs security can be expressed by mutual strategies of interdependence while game theory can be used for the purpose of accounting for interactions among strategies of rational decision makers. Therefore, studying WSNs security with game theory has higher scientificity and rationality. This paper presents a survey of security approaches based on game theory in WSNs. According to different applications, a taxonomy is proposed, which divides current existing typical game-theoretic approaches for WSNs security into four categories: preventing Denial of Services (DoS) attacks, intrusion detection, strengthening security, and coexistence with malicious sensor nodes. The main ideas of each approach are overviewed while advantages and disadvantages of various approaches are discussed. Then, this paper overviews related work and highlights the difference from other surveys, and points out some future research areas for ensuring WSNs security based on game theory, including Base Station (BS) credibility, Intrusion Detection System (IDS) efficiency, WSNs mobility, WSNs Quality of Service (QoS), real-world applicability, energy consumption, sensor nodes learning, and expanding game theory applications and different games. Thus, a global view of WSNs security approaches based on game theory is provided. To our best knowledge of knowing, it is the first paper centrally focusing on game theory in WSNs security. It will make the researchers a better understanding of game-theoretic solutions to WSNs security and further research directions.
A New Pose Estimation Algorithm Using a Perspective-Ray-Based Scaled Orthographic Projection with Iteration

Pose estimation aims at measuring the position and orientation of a calibrated camera using known image features. The pinhole model is the dominant camera model in this field. However, the imaging precision of this model is not accurate enough for an advanced pose estimation algorithm. In this paper, a new camera model, called incident ray tracking model, is introduced. More importantly, an advanced pose estimation algorithm based on the perspective ray in the new camera model, is proposed. The perspective ray, determined by two positioning points, is an abstract mathematical equivalent of the incident ray. In the proposed pose estimation algorithm, called perspective-ray-based scaled orthographic projection with iteration (PRSOI), an approximate ray-based projection is calculated by a linear system and refined by iteration. Experiments on the PRSOI have been conducted, and the results demonstrate that it is of high accuracy in the six degrees of freedom (DOF) motion. And it outperforms three other state-of-the-art algorithms in terms of accuracy during the contrast experiment.
An Introduction to Variable and Feature Selection

Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variabl es are available. These areas include text processing of internet documents, gene expression arr ay nalysis, and combinatorial chemistry. The objective of variable selection is three-fold: improvi ng the prediction performance of the predictors, providing faster and more cost-effective predict ors, and providing a better understanding of the underlying process that generated the data. The contrib utions of this special issue cover a wide range of aspects of such problems: providing a better definit ion of the objective function, feature construction, feature ranking, multivariate feature sele ction, efficient search methods, and feature validity assessment methods.
Security Enhancements for Mobile Ad Hoc Networks With Trust Management Using Uncertain Reasoning

The distinctive features of mobile ad hoc networks (MANETs), including dynamic topology and open wireless medium, may lead to MANETs suffering from many security vulnerabilities. In this paper, using recent advances in uncertain reasoning that originated from the artificial intelligence community, we propose a unified trust management scheme that enhances the security in MANETs. In the proposed trust management scheme, the trust model has two components: trust from direct observation and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation, which is also called secondhand information that is obtained from neighbor nodes of the observer node, the trust value is derived using the Dempster-Shafer theory (DST), which is another type of uncertain reasoning when the proposition of interest can be derived by an indirect method. By combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in MANETs. We then evaluate our scheme under the scenario of MANET routing. Extensive simulation results show the effectiveness of the proposed scheme. Specifically, throughput and packet delivery ratio (PDR) can be improved significantly with slightly increased average end-to-end delay and overhead of messages.
A methodical approach to extracting interesting objects from dynamic web pages

This paper presents a fully automated object extraction system for web documents. Our methodology consists of a layered framework and a suite of algorithms. A distinct feature of our approach is the full automation of both the extraction of data object regions from dynamic Web pages and the identification of correct object boundary separators. We implemented the methodology in the XWRAPElite object extraction system and evaluated the system using more than 3,200 pages over 75 diverse web sites. Our experiments show three important and interesting results: First, our algorithms for identifying the minimal object rich subtree achieves 96% success rate over all web pages we have tested. Second, our algorithms for discovering and extracting object separator tags reach the success rate of 95%. Most significantly, the overall system achieves precision between 96% and 100% (returns only correct objects) and excellent recall (between 95% and 96%, with very few significant objects left out). The minimal subtree extraction algorithms and the object boundary identification algorithms are fast, about 87 milliseconds per page with an average page size of 30KB.
Finding News Citations for Wikipedia

An important editing policy in Wikipedia is to provide citations for added statements in Wikipedia pages, where statements can be arbitrary pieces of text, ranging from a sentence to a paragraph. In many cases citations are either outdated or missing altogether.
 In this work we address the problem of finding and updating news citations for statements in entity pages. We propose a two-stage supervised approach for this problem. In the first step, we construct a classifier to find out whether statements need a news citation or other kinds of citations (web, book, journal, etc.). In the second step, we develop a news citation algorithm for Wikipedia statements, which recommends appropriate citations from a given news collection. Apart from IR techniques that use the statement to query the news collection, we also formalize three properties of an appropriate citation, namely: (i) the citation should entail the Wikipedia statement, (ii) the statement should be central to the citation, and (iii) the citation should be from an authoritative source.
 We perform an extensive evaluation of both steps, using 20 million articles from a real-world news collection. Our results are quite promising, and show that we can perform this task with high precision and at scale.
Comparison of synchronous PM machine types for wide constant-power speed range operation

This paper presents a detailed comparison of the high-speed operating characteristics of four synchronous PM machines for applications that require wide speed ranges of constant-power operation. These machines include surface PM machines with both distributed and fractional-slot concentrated windings, and two interior PM machine with distributed windings. These two versions of the interior PM machine include one with and a tight constraint on the machine's back-emf voltage at top speed and one without this constraint The target application is an automotive direct-drive starter/alternator requiring a very wide 10:1 constant power speed ratio (CPSR). Detailed comparisons of the performance characteristics of the machines are presented that include important issues such as the back-emf voltage at top speed, machine mass and cost, and eddy current losses in the magnets. Analytical results are verified using finite element analysis (FEA). Guidelines are developed to help drive designers decide which type of machine is most suitable for high-CPSR applications. Tradeoffs associated with choosing each of these machines are presented.
Online crowdsourcing: Rating annotators and obtaining cost-effective labels

Labeling large datasets has become faster, cheaper, and easier with the advent of crowdsourcing services like Amazon Mechanical Turk. How can one trust the labels obtained from such services? We propose a model of the labeling process which includes label uncertainty, as well a multi-dimensional measure of the annotators' ability. From the model we derive an online algorithm that estimates the most likely value of the labels and the annotator abilities. It finds and prioritizes experts when requesting labels, and actively excludes unreliable annotators. Based on labels already obtained, it dynamically chooses which images will be labeled next, and how many labels to request in order to achieve a desired level of confidence. Our algorithm is general and can handle binary, multi-valued, and continuous annotations (e.g. bounding boxes). Experiments on a dataset containing more than 50,000 labels show that our algorithm reduces the number of labels required, and thus the total cost of labeling, by a large factor while keeping error rates low on a variety of datasets.
Dermatologists' accuracy in early diagnosis of melanoma of the nail matrix.

To measure and compare the accuracy of 4 different clinical methods in the diagnosis of melanoma in situ of the nail matrix among dermatologists with different levels of clinical experience.



Twelve cases of melanonychias (5 melanomas and 7 nonmelanomas) were presented following 4 successive steps: (1) clinical evaluation, (2) evaluation according to the ABCDEF rule, (3) dermoscopy of the nail plate, and (4) intraoperative dermoscopy. At each step, the dermatologists were asked to decide if the lesion was a melanoma.



The test was administered at 2 dermatological meetings in 2008.



A total of 152 dermatologists, including 11 nail experts, 53 senior dermatologists, and 88 junior dermatologists.



The answers were evaluated as percentage of right answers for each diagnostic step according to the different grade of expertise. Differences among the percentage of right answers in the different steps were evaluated with the z test at a 5% level of significance. The agreement was investigated using Cohen kappa statistic.



The only method that statistically influenced the correct diagnosis for each category (experts, seniors, and juniors) was intraoperative dermoscopy (z test; P < .05). Cohen kappa statistic showed a moderate interobserver agreement.



Overall accuracy of dermatologists in the diagnosis of nail matrix melanoma in situ is low because the percentages of physicians who indicated the correct diagnosis during each of the first 3 clinical steps of the test ranged from 46% to 55%. The level of expertise did not statistically influence the correct diagnosis.
QuizRDF: search technology for the semantic Web

An information-seeking system is described which combines traditional keyword querying of WWW resources with the ability to browse and query against RDF annotations of those resources. RDF(S) and RDF are used to specify and populate an ontology and the resultant RDF annotations are then indexed along with the full text of the annotated resources. The resultant index allows both keyword querying against the full text of the document and the literal values occurring in the RDF annotations, along with the ability to browse and query the ontology. We motivate our approach as a key enabler for fully exploiting the semantic Web in the area of knowledge management and argue that the ability to combine searching and browsing behaviours more fully supports a typical information-seeking task. The approach is characterised as "low threshold, high ceiling" in the sense that where RDF annotations exist they are exploited for an improved information-seeking experience but where they do not yet exist, a search capability is still available.
Towards Portable Learning Analytics Dashboards

This paper proposes a novel approach to build and deploy learning analytics dashboards in multiple learning environments. Existing learning dashboards are barely portable: once deployed on a learning platform, it requires considerable effort to deploy the dashboard elsewhere. We suggest constructing dashboards from lightweight web applications, namely widgets. Our approach allows to port dashboards with no additional cost between learning environments that implement open specifications (Open Social and Activity Streams) for data access and use widget APIs. We propose to facilitate reuse by sharing the dashboards and widgets via a centralized analytics repository.
Dual Switches DC/DC Converter With Three-Winding-Coupled Inductor and Charge Pump

In order to obtain a high step-up voltage gain, high-efficiency converter, this paper proposed a dual switches dc/dc converter with three-winding-coupled inductor and charge pump. The proposed converter composed of dual switches structure, three-winding-coupled inductor, and charge pump. This combination facilitates realization of high step-up voltage gain with a low voltage/current stress on the power switches. Meanwhile, the voltage across the diodes is low and the diode reverse-recovery problem is alleviated by the leakage inductance of the three-winding-coupled inductor. Taking all these into consideration, the efficiency can be high. This paper illustrated the operation principle of the proposed converter; discussed the effect of leakage inductance on voltage gain; the conditions of zero current shutting off of the diodes are illustrated; the voltage and current stress of the power devices are shown; a comparison between the performance of the proposed converter and previous high step-up converters was conducted. Finally, a prototype rated at 500 W has been established, and the experimental results verify the correctness of the analysis.
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

Numerical simulation of large-scale dynamical systems plays a fundamental role in studying a wide range of complex physical phenomena; however, the inherent large-scale nature of the models often leads to unmanageable demands on computational resources. Model reduction aims to reduce this computational burden by generating reduced models that are faster and cheaper to simulate, yet accurately represent the original large-scale system behavior. Model reduction of linear, nonparametric dynamical systems has reached a considerable level of maturity, as reflected by several survey papers and books. However, parametric model reduction has emerged only more recently as an important and vibrant research area, with several recent advances making a survey paper timely. Thus, this paper aims to provide a resource that draws together recent contributions in different communities to survey the state of the art in parametric model reduction methods. Parametric model reduction targets the broad class of problems for which the equations governing the system behavior depend on a set of parameters. Examples include parameterized partial differential equations and large-scale systems of parameterized ordinary differential equations. The goal of parametric model reduction is to generate low-cost but accurate models that characterize system response for different values of the parameters. This paper surveys state-of-the-art methods in projection-based parametric model reduction, describing the different approaches within each class of methods for handling parametric variation and providing a comparative discussion that lends insights to potential advantages and disadvantages in applying each of the methods. We highlight the important role played by parametric model reduction in design, control, optimization, and uncertainty quantification—settings that require repeated model evaluations over different parameter values.
A logical calculus of the ideas immanent in nervous activity. 1943.

Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under the other and gives the same results, although perhaps not in the same time. Various applications of the calculus are discussed.
Statistical matching in the presence of anonymization and obfuscation: Non-asymptotic results in the discrete case

Many popular applications use traces of user data to tune services to their users but come with a significant risk to user privacy. In particular, even if user traces are anonymized, statistical matching of these traces to prior user behavior can be used to identify the user and their behavior. Because of this threat, there has been significant recent work exploring the theoretical foundations of this problem in the limit of a large number of users and/or observations, where the asymptotic nature of the approaches allows for clean analytical results. In this paper, we turn attention to exact performance analysis for a finite number of users and observations. We consider the case where a user is distributed over a discrete set of states according to a probability distribution drawn at random, which we assume is known to the adversary based on his/her analysis of past user behavior. The finite-length traces are then anonymized and obfuscated at a cost in user utility. We analyze the ability of the adversary to correctly identify user data samples as a function of the rate of anonymization and degree of obfuscation, and we arrive at complicated yet readily numerically evaluated expressions. These results allow us to investigate interesting questions left open by the asymptotic nature of previous work.
Agile in Public Administration: Oxymoron or Reality? An Experience Report

In the last 10 years, Agile methods and practices have emerged as an alternative for software development. Different "flavors" of Agile have appeared ranging from project management to tests organization. These approaches have being gaining popularity and involve now a solid option for organizations developing software, but what about Public Administrations? Is Agile a suitable option for developing software in Public Administrations? Even if Public Administrations have been traditionally regarded as changeresistant, Agile approach can also provide them with the benefits of quick adaptation and frequent value delivery. This paper presents the results of two different projects, which use an Agile framework based on Scrum, developed by a Spanish Public Administration. Additionally, after considering the obtained results, it takes out some relevant learned lessons on the suitability of applying Agile approaches to Public Administration environments.
Social cognitive theory of gender development and differentiation.

Human differentiation on the basis of gender is a fundamental phenomenon that affects virtually every aspect of people's daily lives. This article presents the social cognitive theory of gender role development and functioning. It specifies how gender conceptions are constructed from the complex mix of experiences and how they operate in concert with motivational and self-regulatory mechanisms to guide gender-linked conduct throughout the life course. The theory integrates psychological and sociostructural determinants within a unified conceptual structure. In this theoretical perspective, gender conceptions and roles are the product of a broad network of social influences operating interdependently in a variety of societal subsystems. Human evolution provides bodily structures and biological potentialities that permit a range of possibilities rather than dictate a fixed type of gender differentiation. People contribute to their self-development and bring about social changes that define and structure gender relationships through their agentic actions within the interrelated systems of influence.