nils-herrmann/clean_scirepeval huggingface dataset:
Chameleon: Hierarchical Clustering Using Dynamic Modeling

68 Computer C lustering is a discovery process in data mining. 1 It groups a set of data in a way that maximizes the similarity within clusters and minimizes the similarity between two different clusters. 1,2 These discovered clusters can help explain the characteristics of the underlying data distribution and serve as the foundation for other data mining and analysis techniques. Clustering is useful in characterizing customer groups based on purchasing patterns, categorizing Web documents, 3 grouping genes and proteins that have similar func-tionality, 4 grouping spatial locations prone to earthquakes based on seismological data, and so on. Most existing clustering algorithms find clusters that fit some static model. Although effective in some cases, these algorithms can break down—that is, cluster the data incorrectly—if the user doesn't select appropriate static-model parameters. Or sometimes the model cannot adequately capture the clusters' characteristics. Most of these algorithms break down when the data contains clusters of diverse shapes, densities , and sizes. Existing algorithms use a static model of the clusters and do not use information about the nature of individual clusters as they are merged. Furthermore, one set of schemes (the CURE algorithm and related schemes) ignores the information about the aggregate interconnectivity of items in two clusters. The other set of schemes (the Rock algorithm, group averaging method, and related schemes) ignores information about the closeness of two clusters as defined by the similarity of the closest items across two clusters. (For more information, see the " Limitations of Traditional Clustering Algorithms " sidebar.) By only considering either interconnectivity or close-ness, these algorithms can easily select and merge the wrong pair of clusters. For instance, an algorithm that focuses only on the closeness of two clusters will incorrectly merge the clusters in Figure 1a over those in Figure 1b. Similarly, an algorithm that focuses only on interconnectivity will, in Figure 2, incorrectly merge the dark-blue with the red cluster rather than the green one. Here, we assume that the aggregate interconnec-tivity between the items in the dark-blue and red clusters is greater than that of the dark-blue and green clusters. However, the border points of the dark-blue cluster are much closer to those of the green cluster than those of the red cluster. Chameleon is a new agglomerative hierarchical clustering algorithm that overcomes the limitations of existing clustering algorithms.  (on page 70) provides an overview of the overall approach …
WikiQA: A Challenge Dataset for Open-Domain Question Answering

We describe the WIKIQA dataset, a new publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering. Most previous work on answer sentence selection focuses on a dataset created using the TREC-QA data, which includes editor-generated questions and candidate answer sentences selected by matching content words in the question. WIKIQA is constructed using a more natural process and is more than an order of magnitude larger than the previous dataset. In addition, the WIKIQA dataset also includes questions for which there are no correct sentences, enabling researchers to work on answer triggering, a critical component in any QA system. We compare several systems on the task of answer sentence selection on both datasets and also describe the performance of a system on the problem of answer triggering using the WIKIQA dataset.
A Convolutional Neural Network for Modelling Sentences

The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline.
A functional taxonomy of wireless sensor network devices

With the availability of cheap computing technology and the standardization of different wireless communication paradigms, wireless sensor networks (WSNs) are becoming a reality. This is visible in the increasing amount of research being done in the WSN area, and the growing number of companies offering commercial WSN solutions. In this regard, we realize that there is a need for an unambiguous classification and definition of WSN devices. Such a classification scheme would benefit the WSN research and industrial community. We consider the spectrum of wireless devices ranging from passive RF devices to gateway devices for the classification. The classification scheme is based on functionality of the devices. This scheme is analogous to an object-oriented classification, where an object is classified on the basis of its functionality and is described by its attributes. Attributes of the wireless devices are grouped into five broad categories: communication, sensing, power, memory, and "other features". Each of these are further classified to provide sufficient details that are required for a typical WSN application
Extending the Coverage of DBpedia Properties using Distant Supervision over Wikipedia

DBpedia is a Semantic Web project aiming to extract structured data from Wikipedia articles. Due to the increasing number of resources linked to it, DBpedia plays a central role in the Linked Open Data community. Currently, the information contained in DBpedia is mainly collected from Wikipedia infoboxes, a set of subject-attribute-value triples that represents a summary of the Wikipedia page. These infoboxes are manually compiled by the Wikipedia contributors, and in more than 50% of the Wikipedia articles the infobox is missing. In this article, we use the distant supervision paradigm to extract the missing information directly from the Wikipedia article, using a Relation Extraction tool trained on the information already present in DBpedia. We evaluate our system on a data set consisting of seven DBpedia properties, demonstrating the suitability of the approach in extending the DBpedia coverage.
A Survey of Schema Matching Research using Database Schemas and Instances

Schema matching is considered as one of the essential phases of data integration in database systems. The main aim of the schema matching process is to identify the correlation between schema which helps later in the data integration process. The main issue concern of schema matching is how to support the merging decision by providing the correspondence between attributes through syntactic and semantic heterogeneous in data sources. There have been a lot of attempts in the literature toward utilizing database instances to detect the correspondence between attributes during schema matching process. Many approaches based on instances have been proposed aiming at improving the accuracy of the matching process. This paper set out a classification of schema matching research in database system exploiting database schema and instances. We survey and analyze the schema matching techniques applied in the literature by highlighting the strengths and the weaknesses of each technique. A deliberate discussion has been reported highlights on challenges and the current research trends of schema matching in database. We conclude this paper with some future work directions that help researchers to explore and investigate current issues and challenges related to schema matching in
A Team Formation and Project-based Learning Support Service for Social Learning Networks

The Internet affords new approaches to learning. Geographically dispersed selfdirected learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks do not readily support effective, coherence-creating and motivating learning settings. It is argued that providing project-based learning opportunities and team formation services can help overcome these shortcomings. A review of existing team formation tools evidences that a new design for team formation and the initiation of projectbased learning is required before these can be supported in social learning networks. A design is proposed which identifies ‘knowledge’, ‘personality’ and ‘preferences’ as categories in which data is needed to form teams, and it specifies how the required data are gathered and assessed. The design defines rules deduced from team formation principles from prior team formation research to optimise team formations towards increased productivity, creative solutions or higher learning outcomes. The rules are implemented in three team formation expressions each calculating one of the desired team formations. The expressions are deployed on a set of test data, demonstrating the effectiveness of the team formation service design. The article includes a discussion of the results and provides indications for future research.
Solutions to the GSM Security Weaknesses

Recently, the mobile industry has experienced an extreme increment in number of its users. The GSM network with the greatest worldwide number of users succumbs to several security vulnerabilities. Although some of its security problems are addressed in its upper generations, there are still many operators using 2G systems. This paper briefly presents the most important security flaws of the GSM network and its transport channels. It also provides some practical solutions to improve the security of currently available 2G systems.
Introduction to Information Retrieval Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schtze (Stanford University, Yahoo! Research, and University of Stuttgart) Cambridge: Cambridge University Press, 2008, xxi+482 pp; hardbound, ISBN 978-0-521-86571-5, $60.00

Introduction to Information Retrieval is the first textbook with a coherent treatment of classical and web information retrieval, including web search and the related areas of text classification and text clustering. Written from a computer science perspective, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents and of methods for evaluating systems, along with an introduction to the use of machine learning methods on text collections. Designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also interest researchers and professionals. A complete set of lecture slides and exercises that accompany the book are available on the web.
Leadership in Complex Organizations

This paper asks how complexity theory informs the role of leadership in organizations. Complexity theory is a science of complexly interacting systems; it explores the nature of interaction and adaptation in such systems and how they influence such things as emergence, innovation, and fitness. We argue that complexity theory focuses leadership efforts on behaviors that enable organizational effectiveness, as opposed to determining or guiding effectiveness. Complexity science broadens conceptualizations of leadership from perspectives that are heavily invested in psychology and social psychology (e.g., human relations models) to include processes for managing dynamic systems and interconnectivity. We develop a definition of organizational complexity and apply it to leadership science, discuss strategies for enabling complexity and effectiveness, and delve into the relationship between complexity theory and other currently important leadership theories. The paper concludes with a discussion of possible implications for research strategies in the social
Generalized Methods for the Design of Quasi-Ideal Symmetric and Asymmetric Coupled-Line Sections and Directional Couplers

Generalized methods for the design of quasi-ideal symmetric and asymmetric coupled-line sections have been proposed. Two cases of coupled-line sections, in which the inductive coupling coefficient is either greater or smaller than the capacitive coupling coefficient, have been separately considered. To compensate for the coupling coefficients' inequality, a combination of lumped capacitors and short coupled-line sections has been introduced into the coupled-line section. The proposed methods allow for designing quasi-ideal coupled-line sections in nearly arbitrarily chosen dielectric media and for arbitrarily chosen coupling. The theoretical analyses have been verified by the design and measurement of three compensated coupled-line directional couplers illustrating application of the proposed methods in different dielectric structures.
Knowledge Sharing: Influences of Trust, Commitment and Cost

This paper’s aim is to examine the influence of perceived cost of sharing knowledge and affective trust in colleagues on the relationship between affective commitment and knowledge sharing.  The methodology used was a survey of 496 employees from 15 organizations across ten industries.  Affective trust in colleagues moderates the relationship between affective commitment and knowledge sharing and the relationship between cost of knowledge sharing and knowledge sharing.  Future researchers should operationalize the perceived cost of knowledge sharing construct to include other potential group barriers; for instance, politics and organizational barriers, management commitment and lack of trust.  The findings of this study suggest that employees who value social relationships and social resources tend to view knowledge as a collectively owned commodity. As such, their knowledge sharing behavior reflects the model of reciprocal social exchanges. Social implications – The results of this study indicate that an organizational culture that encourages affect-based trust between colleagues will facilitate knowledge sharing.  The paper bridges the gap between the literature on knowledge sharing, perceived cost of knowledge sharing, affective organizational commitment and trust in a single model.
Backpropagation for Energy-Efficient Neuromorphic Computing

Solving real world problems with embedded neural networks requires both training algorithms that achieve high performance and compatible hardware that runs in real time while remaining energy efficient. For the former, deep learning using backpropagation has recently achieved a string of successes across many domains and datasets. For the latter, neuromorphic chips that run spiking neural networks have recently achieved unprecedented energy efficiency. To bring these two advances together, we must first resolve the incompatibility between backpropagation, which uses continuous-output neurons and synaptic weights, and neuromorphic designs, which employ spiking neurons and discrete synapses. Our approach is to treat spikes and discrete synapses as continuous probabilities, which allows training the network using standard backpropagation. The trained network naturally maps to neuromorphic hardware by sampling the probabilities to create one or more networks, which are merged using ensemble averaging. To demonstrate, we trained a sparsely connected network that runs on the TrueNorth chip using the MNIST dataset. With a high performance network (ensemble of 64), we achieve 99.42% accuracy at 108 μJ per image, and with a high efficiency network (ensemble of 1) we achieve 92.7% accuracy at 0.268 μJ per image.
A Classification and Comparison Framework for Software Architecture Description Languages

ÐSoftware architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which of several possible ADLs is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification, module interconnection, simulation, and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from other modeling notations. The framework is used to classify and compare several existing ADLs, enabling us, in the process, to identify key properties of ADLs. The comparison highlights areas where existing ADLs provide extensive support and those in which they are deficient, suggesting a research agenda for the future. Index TermsÐSoftware architecture, architecture description language, component, connector, configuration, definition, classification, comparison.
Universal approximation bounds for superpositions of a sigmoidal function

Approximation properties of a class of artificial neural networks are established. It is shown that feedforward networks with one layer of sigmoidal nonlinearities achieve inte­ grated squared error of order O(l/n), where n is the number of nodes. The function appruximated is assumed to have a bound on the first moment of the magnitude distribution of the Fourier transform. The nonlinear parameters associated with the sigmoidal nodes, as well as the parameters of linear combination, are adjusted in the approximation. In contrast, it is shown that for series expansions with n terms, in which only the parameters of linear combination are adjusted, the integrated squared approximation error cannot be made smaller than order 1/n2/d uniformly for functions satisfying the same smoothness assumption, where d is the dimension of the input to the function. For the class of functions examined here, the approximation rate and the parsimony of the parameterization of the networks are surprisingly advantageous in high-dimensional settings.
Indian Music Instruments Semantic Knowledge Representation

Semantic Web is an intelligent incarnation and advancement in World Wide Web to store, annotate, index and retrieve information by providing knowledge representation, classification, and categorization, a common understanding among the group of people and structuring the information in machine processable format. To structure the information in machine processable semantic models Semantic Web have introduced the concept of ontology. Ontologies are most widely used in artificial intelligence, semantic web, software engineering, information retrieval, knowledge representation, knowledge sharing, knowledge integration, knowledge reuse, and so on. Music plays a vital role in human life. A vast number of musical instruments are used to produce rhythmic sounds. The rapid growth of technology enables on line music. The instruments used and the terms used to describe the concepts varies from individual groups. To support concept based knowledge representation and semantic retrieval music files music instruments knowledge should be identified and represented. This raises the need for construction of Indian music instruments ontology. This ropes semantic web with more intelligent, capable, relevant, responsive interaction and high performance retrieval system. The Indian music instrument ontology is been constructed and the paper concludes with conclusion and future work.
Natural Language Processing using NLTK and WordNet

Natural Language Processing is a theoretically motivated range of computational techniques for analysing and representing naturally occurring texts at one or more levels of linguistic analysis for the purpose of achieving human-like language processing for a range of tasks or applications . To perform natural language processing a variety of tools and platform have been developed, in our case we will discuss about NLTK for Python.The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python programming language[2]. It provides easy-to-use interfaces to many corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. In this paper we discuss different approaches for natural language processing using NLTK.
Acquiring Axially-Symmetric Transparent Objects Using Single-View Transmission Imaging

We propose a novel, practical solution for high quality reconstruction of axially-symmetric transparent objects. While a special case, such transparent objects are ubiquitous in the real world. Common examples of these are glasses, goblets, tumblers, carafes, etc., that can have very unique and visually appealing forms making their reconstruction interesting for vision and graphics applications. Our acquisition setup involves imaging such objects from a single viewpoint while illuminating them from directly behind with a few patterns emitted by an LCD panel. Our reconstruction step is then based on optimization of the objects geometry and its refractive index to minimize the difference between observed and simulated transmission/refraction of rays passing through the object. We exploit the objects axial symmetry as a strong shape prior which allows us to achieve robust reconstruction from a single viewpoint using a simple, commodity acquisition setup. We demonstrate high quality reconstruction of several common rotationally symmetric as well as more complex n-fold symmetric transparent objects with our approach.
A High Performance CRF Model for Clothes Parsing

 Simple Features  o Logistic regression trained for one-vs-all o Color histograms (RGB and CIE L*a*b) o Texture histograms (Gabor filters) o Location features (relative position to 2D pose)  Person Mask o Foreground/background segmentation by  o Avoids false positives on background  Clothelets o Pose-conditioned garment likelihood o Masks for all garments are averaged based on 2D pose bounding boxes  Shape Features o Logistic regression trained for one-vs-all o Enriched SIFT descriptors with second-order pooling  CRF Model Superpixel labels: yi ∈ {1,...,C} Limb segment labels: lp ∈ {1,...,C} Edgar Simo-Serra1 Sanja Fidler2 Francesc Moreno-Noguer1 Raquel Urtasun2 A High Performance CRF Model for Clothes Parsing