A platform for semantic web studies

The Semantic Web can be seen as a large, heterogeneous network of ontologies and semantic documents. Characterizing these ontologies, the way they relate and the way they are organized can help in better understanding how knowledge is produced and published online. It also provides new ways to explore and exploit this large collection of ontologies. In this paper, we present the foundation of a research platform for characterizing the Semantic Web, relying on the collection of ontologies and the functionalities provided by the Watson Semantic Web search engine. We more specifically focus on formalizing and monitoring relationships between ontologies online, considering a variety of different relations (similarity, versioning, agreement, modularity) and how they can help us obtaining meaningful overviews of the current state of the Semantic Web

Tracking decision-making during architectural design

There is a powerful cocktail of circumstances governing the way decisions are made during the architectural design process of a building project. There is considerable potential for misunderstandings, inappropriate changes, change which give rise to unforeseen difficulties, decisions which are not notified to all interested parties, and many other similar problems. The paper presents research conducted within the frame of the EPSRC funded ADS project aiming at addressing the problems linked with the evolution and changing environment of project information to support better decision-making. The paper presents the conceptual framework as well as the software environment that has been developed to support decision-making during building projects, and reports on work carried out on the application of the approach to the architectural design stage. This decision-tracking environment has been evaluated and validated by professionals and practitioners from industry using several instruments as described in the paper

A Survey on Array Storage, Query Languages, and Systems

Since scientific investigation is one of the most important providers of massive amounts of ordered data, there is a renewed interest in array data processing in the context of Big Data. To the best of our knowledge, a unified resource that summarizes and analyzes array processing research over its long existence is currently missing. In this survey, we provide a guide for past, present, and future research in array processing. The survey is organized along three main topics. Array storage discusses all the aspects related to array partitioning into chunks. The identification of a reduced set of array operators to form the foundation for an array query language is analyzed across multiple such proposals. Lastly, we survey real systems for array processing. The result is a thorough survey on array data storage and processing that should be consulted by anyone interested in this research topic, independent of experience level. The survey is not complete though. We greatly appreciate pointers towards any work we might have forgotten to mention.Comment: 44 page

Repairing Deep Neural Networks: Fix Patterns and Challenges

Significant interest in applying Deep Neural Network (DNN) has fueled the need to support engineering of software that uses DNNs. Repairing software that uses DNNs is one such unmistakable SE need where automated tools could be beneficial; however, we do not fully understand challenges to repairing and patterns that are utilized when manually repairing DNNs. What challenges should automated repair tools address? What are the repair patterns whose automation could help developers? Which repair patterns should be assigned a higher priority for building automated bug repair tools? This work presents a comprehensive study of bug fix patterns to address these questions. We have studied 415 repairs from Stack overflow and 555 repairs from Github for five popular deep learning libraries Caffe, Keras, Tensorflow, Theano, and Torch to understand challenges in repairs and bug repair patterns. Our key findings reveal that DNN bug fix patterns are distinctive compared to traditional bug fix patterns; the most common bug fix patterns are fixing data dimension and neural network connectivity; DNN bug fixes have the potential to introduce adversarial vulnerabilities; DNN bug fixes frequently introduce new bugs; and DNN bug localization, reuse of trained model, and coping with frequent releases are major challenges faced by developers when fixing bugs. We also contribute a benchmark of 667 DNN (bug, repair) instances

ArrayBridge: Interweaving declarative array processing with high-performance computing

Scientists are increasingly turning to datacenter-scale computers to produce and analyze massive arrays. Despite decades of database research that extols the virtues of declarative query processing, scientists still write, debug and parallelize imperative HPC kernels even for the most mundane queries. This impedance mismatch has been partly attributed to the cumbersome data loading process; in response, the database community has proposed in situ mechanisms to access data in scientific file formats. Scientists, however, desire more than a passive access method that reads arrays from files. This paper describes ArrayBridge, a bi-directional array view mechanism for scientific file formats, that aims to make declarative array manipulations interoperable with imperative file-centric analyses. Our prototype implementation of ArrayBridge uses HDF5 as the underlying array storage library and seamlessly integrates into the SciDB open-source array database system. In addition to fast querying over external array objects, ArrayBridge produces arrays in the HDF5 file format just as easily as it can read from it. ArrayBridge also supports time travel queries from imperative kernels through the unmodified HDF5 API, and automatically deduplicates between array versions for space efficiency. Our extensive performance evaluation in NERSC, a large-scale scientific computing facility, shows that ArrayBridge exhibits statistically indistinguishable performance and I/O scalability to the native SciDB storage engine.Comment: 12 pages, 13 figure

Versioning, Brand-Stretching, and the Evolution of e-Commerce Markets

This paper offers an analysis of the evolution of e-commerce markets. We develop a model in which an initial group of small, no-name click firms create such markets by offering horizontally differentiated customized or versioned products and competing in prices. Subsequently, a traditional brick firm enters by stretching its brand name into the digital marketplace. Such entry causes many initial entrants to exit. Contrary to much popular and formal literature, we show that the volume of initial entry may well be inefficiently low despite the anticipated later exit. In addition, the conventional relationship between sunk cost and market structure is substantially weakened.versioning, brand-stretching, price discrimination, market structure

CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements. The most computationally expensive step in the simulation pipeline of a typical experiment at the Large Hadron Collider (LHC) is the detailed modeling of the full complexity of physics processes that govern the motion and evolution of particle showers inside calorimeters. We introduce \textsc{CaloGAN}, a new fast simulation technique based on generative adversarial networks (GANs). We apply these neural networks to the modeling of electromagnetic showers in a longitudinally segmented calorimeter, and achieve speedup factors comparable to or better than existing full simulation techniques on CPU ($100\times$-$1000\times$) and even faster on GPU (up to $\sim10^5\times$). There are still challenges for achieving precision across the entire phase space, but our solution can reproduce a variety of geometric shower shape properties of photons, positrons and charged pions. This represents a significant stepping stone toward a full neural network-based detector simulation that could save significant computing time and enable many analyses now and in the future.Comment: 14 pages, 4 tables, 13 figures; version accepted by Physical Review D (PRD