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Review Authorship Attribution in a Similarity Space

Journal of computer science & Technology 2015年第1期30卷 200-213页

作者：钱铁云刘兵李青司建锋 State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China Department of Computer Science University of Illinois at Chicago Chicago 60607 U.S.A. Multimedia Software Engineering Research Centre and Department of Computer Science City University of Hong Kong Hong Kong China Data Analytics Department Institute for Infocomm Research Singapore 138632 Singapore

Authorship attribution, also known as authorship classification, is the problem of identifying the authors （reviewers） of a set of documents （reviews）. The common approach is to build a classifier using supervised learning. This approach has several issues which hurts its applicability. First, supervised learning needs a large set of documents from each author to serve as the training data. This can be difficult in practice. For example, in the online review domain, most reviewers （authors） only write a few reviews, which are not enough to serve as the training data. Second, the learned classifier cannot be applied to authors whose documents have not been used in training. In this article, we propose a novel solution to deal with the two problems. The core idea is that instead of learning in the original document space, we transform it to a similarity space. In the similarity space, the learning is able to naturally tackle the issues. Our experiment results based on online reviews and reviewers show that the proposed method outperforms the state-of-the-art supervised and unsupervised baseline methods significantly.

关键词： authorship attribution supervised learning similarity space

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How hard is this query? Measuring the semantic complexity of schema-agnostic queries 11

How hard is this query? Measuring the semantic complexity of...

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11th International Conference on Computational Semantics, IWCS 2015

作者： Freitas, André Sales, Juliano Efson Handschuh, Siegfried Curry, Edward Department of Computer Science and Mathematics University of Passau Germany Insight Centre for Data Analytics National University of Ireland Galway Ireland

ISBN: (纸本)9781941643334

The growing size, heterogeneity and complexity of databases demand the creation of strategies to facilitate users and systems to consume data. Ideally, query mechanisms should be schema-agnostic, i.e. they should be able to match user queries in their own vocabulary and syntax to the data, abstracting data consumers from the representation of the data. This work provides an informationtheoretical framework to evaluate the semantic complexity involved in the query-database communication, under a schema-agnostic query scenario. Different entropy measures are introduced to quantify the semantic phenomena involved in the user-database communication, including structural complexity, ambiguity, synonymy and vagueness. The entropy measures are validated using natural language queries over Semantic Web databases. The analysis of the semantic complexity is used to improve the understanding of the core semantic dimensions present at the query-data matching process, allowing the improvement of the design of schema-agnostic query mechanisms and defining measures which can be used to assess the semantic uncertainty or difficulty behind a schema-agnostic querying task. © 2015 Association for Computational Linguistics.

关键词： Entropy

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Validation of expressive XML keys with XML Schema and XQuery 11

Validation of expressive XML keys with XML Schema and XQuery

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11th Asia-Pacific Conference on Conceptual Modelling, APCCM 2015

作者： Liu, Bo Link, Sebastian Muñoz, Emir Department of Computer Science The University of Auckland New Zealand Insight Centre for Data Analytics National University of Ireland Galway Ireland

ISBN: (纸本)9781921770470

The eXtensible Markup Language (XML) is the defacto industry standard for exchanging data on the Web and elsewhere. While the relational model of data enjoys a well-accepted definition of a key, several competing notions of keys exist in XML. These have complementary properties and therefore serve different applications domains. In a nutshell, XML keys allow us to capture important domain semantics in XML documents and thereby advance data processing in most applications. In this paper we propose how to validate XML documents against an expressive class of XML keys using XML Schema and XQuery, respectively. It is somewhat surprising how simple it is to express sophisticated notions of XML keys in these off-the-shelf tools. Experiments show that our simple validation technique works well for real-world data of reasonable size. For large-scale data, however, dedicated tools must be developed. © 2015, Australian computer Society, Inc.

关键词： XML

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Demonstrating a linked data platform for finite element biosimulations of cochlear mechanics

Demonstrating a linked data platform for finite element bios...

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ISWC 2015 Posters and Demonstrations Track, ISWC-P and D 2015 - co-located with the 14th International Semantic Web Conference, ISWC 2015

作者： Mehdi, Muntazir Khan, Yasar Freitas, Andre Jares, Joao Hasapis, Panagiotis Sahay, Ratnesh Insight Centre for Data Analytics NUI Galway Ireland Computer Science Department University of Passau Germany INTRASOFT International Luxembourg Luxembourg

Biosimulations employ Finite Element Method (FEM) to simulate complex biological systems in order to understand different aspects of human organs. The applications of FEM biosimulations range from human ear cochlear mechanics, cardiovascular, to neurovascular research. Performing these large Finite Element (FE) simulations, comparison and analysis of multiple simulation experiment results, and visualization of large FEM numerical data is a time consuming hectic job for domain experts-mainly because simulations are performed in an isolated environment. In this paper, we demonstrate a linked data platform to dispatch simulations on biosimulation models for the inner-ear (cochlear) mechanics in an integrated non-isolated environment. Copyright © 2015 for the individual papers by the papers' authors.

关键词： Linked data

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A concurrent k-NN search algorithm for R-tree 15

A concurrent k-NN search algorithm for R-tree

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8th ACM COMPUTE INDIA Conference, Compute 2015

作者： Challa, Jagat Sesh Goyal, Poonam Nikhil, S. Balasubramaniam, Sundar Goyal, Navneet Advanced Data Analytics and Parallel Technologies Laboratory Department of Computer Science and Information Systems Birla Institute of Technology and Science - Pilani Pilani Campus Pilani India

ISBN: (纸本)9781450336505

K-nearest neighbor (k-NN) search is one of the commonly used query in database systems. It has its application in various domains like data mining, decision support systems, information retrieval, multimedia and spatial databases, etc. When k-NN search is performed over large data sets, spatial data indexing structures such as R-trees are commonly used to improve query efficiency. The best-first k-NN (BF-kNN) algorithm is the fastest known k-NN over R-trees. We present CBF-k NN, a concurrent BF-kNN for R-trees, which is the first concurrent version of k-NN we know of for R-trees. CBF-k NN uses one of the most efficient concurrent priority queues known as mound. CBF-k NN overcomes the concurrency limitations of priority queues by using a tree-parallel mode of execution. CBF-kNN has an estimated speedup of 0(p/k) for p threads. Experimental results on various real datasets show that the speedup in practice is close to this estimate. © 2015 ACM.

关键词： data mining

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PennyLane: Automatic differentiation of hybrid quantum-classical computations

arXiv

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arXiv 2018年

作者： Bergholm, Ville Izaac, Josh Schuld, Maria Gogolin, Christian Ahmed, Shahnawaz Ajith, Vishnu Alam, M. Sohaib Alonso-Linaje, Guillermo AkashNarayanan, B. Asadi, Ali Arrazola, Juan Miguel Azad, Utkarsh Banning, Sam Blank, Carsten Bromley, Thomas R. Cordier, Benjamin A. Ceroni, Jack Delgado, Alain Matteo, Olivia Di Dusko, Amintor Garg, Tanya Guala, Diego Hayes, Anthony Hill, Ryan Ijaz, Aroosa Isacsson, Theodor Ittah, David Jahangiri, Soran Jain, Prateek Jiang, Edward Khandelwal, Ankit Kottmann, Korbinian Lang, Robert A. Lee, Christina Loke, Thomas Lowe, Angus McKiernan, Keri Meyer, Johannes Jakob Montañez-Barrera, J.A. Moyard, Romain Niu, Zeyue O’Riordan, Lee James Oud, Steven Panigrahi, Ashish Park, Chae-Yeun Polatajko, Daniel Quesada, Nicolás Roberts, Chase Sá, Nahum Schoch, Isidor Shi, Borun Shu, Shuli Sim, Sukin Singh, Arshpreet Strandberg, Ingrid Soni, Jay Száva, Antal Thabet, Slimane Vargas-Hernández, Rodrigo A. Vincent, Trevor Vitucci, Nicola Weber, Maurice Wierichs, David Wiersema, Roeland Willmann, Moritz Wong, Vincent Zhang, Shaoming Killoran, Nathan Xanadu 777 Bay Street Toronto Canada Wallenberg Centre for Quantum Technology Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg412 96 Sweden Indian Institute of Information Technology Kottayam India NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States data cybernetics Martin-Kolmsperger-Str 26 Landsberg86899 Germany Department of Medical Informatics and Clinical Epidemiology Oregon Health and Science University PortlandOR97202 United States Dept. of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Department of Physics Indian Institute of Technology Roorkee Uttarakhand Roorkee India qBraid 5235 South Harper Court ChicagoIL60615 United States Factal Analytics Level 2 Chimes Building Plot 61 Sector - 44 Haryana Gurgaon122003 India Centre for High Energy Physics Indian Institute of Science Karnataka Bengaluru560012 India ICFO - Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Av. Carl Friedrich Gauss 3 Castelldefels Barcelona08860 Spain Chemical Physics Theory Group Department of Chemistry University of Toronto Canada DUG Technology 76 Kings Park Rd West PerthWA6005 Australia Rigetti Computing 2919 Seventh Street BerkeleyCA94710 United States Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Advanced Simulation Jülich Supercomputing Centre Forschungszentrum Jülich Jülich52425 Germany University of Amsterdam Netherlands School of Physical Sciences National Institute of Science Education and Research HBNI Odisha Jatni752 050 India Cervest United Kingdom Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 Rio de Janeiro22290-180 Brazil Zurich8092 Switzerland Neo4j UK Ltd United Kingdom Zapata Computing Inc. United States ITC Infotech Bangalore India Department of Microtechnology and Nanoscienc

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane’s core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Factorizing time-aware multi-way tensors for enhancing semantic wearable sensing 21

Factorizing time-aware multi-way tensors for enhancing seman...

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Wang, Peng Smeaton, Alan F. Gurrin, Cathal National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University China Insight Centre for Data Analytics Dublin City University Glasnevin Dublin 9 Ireland

ISBN: (纸本)9783319144443

Automatic concept detection is a crucial aspect of automatically indexing unstructured multimedia archives. However, the current prevalence of one-per-class detectors neglect inherent concept relationships and operate in isolation. This is insufficient when analyzing content gathered from wearable visual sensing, in which concepts occur with high diversity and with correlation depending on context. This paper presents a method to enhance concept detection results by constructing and factorizing a multi-way concept detection tensor in a time-aware manner. We derived a weighted non-negative tensor factorization algorithm and applied it to model concepts’ temporal occurrence patterns and show how it boosts overall detection performance. The potential of our method is demonstrated on lifelog datasets with varying levels of original concept detection accuracies. © Springer International Publishing Switzerland 2015.

关键词： Tensors

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FAST: Fast annotation with synchronized threads

FAST: Fast annotation with synchronized threads

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Dongjae Kim Aria S. Hahn Niels W. Hanson Kishori M. Konwar Steven J. Hallam Department of Computer Science University of British Columbia Vancouver BC Canada Koonkie Inc. Menlo Park CA USA Department of Microbiology & Immunology University of British Columbia Vancouver BC Canada Data & Analytics KPMG LLC Seattle WA USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Graduate Program in Bioinformatics University of British Columbia Vancouver BC Canada

ISBN: (纸本)9781509000128

FAST is a multi-threaded, I/O optimized Seed-and-Extend alignment program. FAST is extensible to nucleotide sequences making it comparable to both BLASTn and BLASTp, and also features several new usage flags reporting only HSPs meeting user defined e-value cut-offs. FASTs threaded database construction allows fast, low memory database construction e.g., RefSeq (9.4GB) can be indexed in under 5 minutes using 20 threads. The threaded database construction gives users the ability to create custom databases on demand which is useful for tasks requiring self-alignment, such as network construction [30]. Finally, FAST supports incremental database construction enabling users to keep their databases up-to-date by adding sequences without suffering the cost of reformatting the entire database. The FAST source is freely available through GitHub https:// ***/hallamlab/FAST and test datasets can be found on Dropbox https://***/sh/xvvavweuzgqybc4/ AAAWCrRnol67ZsXi2qLQWUuOa?

关键词： Instruction sets Synchronization Random access memory Algorithm design and analysis data structures Indexes

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Improving the classification of quantified self activities and behaviour using a fisher kernel

Improving the classification of quantified self activities a...

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ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable computers, UbiComp and ISWC 2015

作者： Wang, Peng Yang, Shiqiang Sun, Lifeng Smeaton, Alan F. National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Haidian District Beijing China Insight Centre for Data Analytics Dublin City University Glasnevin Dublin 9 Ireland

ISBN: (纸本)9781450335751

Visual recording of everyday human activities and behaviour over the long term is now feasible and with the widespread use of wearable devices embedded with cameras this offers the potential to gain real insights into wearers' activities and behaviour. To date we have concentrated on automatical- ly detecting semantic concepts from within visual lifelogs yet identifying human activities from such lifelogged images or videos is still a major challenge if we are to use lifelogs to maximum benefit. In this paper, we propose an activity classification method from visual lifelogs based on Fisher kernels, which extract discriminative embeddings from Hid- den Markov Models (HMMs) of occurrences of semantic concepts. By using the gradients as features, the resulting classifiers can better distinguish different activities and from that we can make inferences about human behaviour. Ex- periments show the effectiveness of this method in improv- ing classification accuracy, especially when the semantic concepts are initially detected with low degrees of accuracy. © 2015 ACM.

关键词： Semantics

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Collective biobjective optimization algorithm for parallel test paper generation 24

Collective biobjective optimization algorithm for parallel t...

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24th International Joint Conference on Artificial Intelligence, IJCAI 2015

作者： Nguyen, Minh Luan Hui, Siu Cheung Fong, Alvis C.M. Data Analytics Department Institute for Infocomm Research Singapore Singapore School of Computer Engineering Nanyang Technological University Singapore Singapore School of Computing Science University of Glasgow United Kingdom

ISBN: (纸本)9781577357384

Parallel Test Paper Generation (k-TPG) is a biobjective distributed resource allocation problem, which aims to generate multiple similarly optimal test papers automatically according to multiple userspecified criteria. Generating high-quality parallel test papers is challenging due to its NP-hardness in maximizing the collective objective functions. In this paper, we propose a Collective Biobjective Optimization (CBO) algorithm for solving k-TPG. CBO is a multi-step greedy-based approximation algorithm, which exploits the submodular property for biobjective optimization of k-TPG. Experiment results have shown that CBO has drastically outperformed the current techniques in terms of paper quality and runtime efficiency.

关键词： Approximation algorithms

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