32,909 research outputs found
A Preliminary Study on Methods for Retaining Data Quality Problems in Automatically Generated Test Data
Data in an organisation often contains business secrets that organisations do not want to release. However, there are occasions when it is necessary for an organisation to release its data such as when outsourcing work or using the cloud for Data Quality (DQ) related tasks like data cleansing. Currently, there is no mechanism that allows organisations to release their data for DQ tasks while ensuring that it is suitably protected from releasing business related secrets. The aim of this paper is therefore to present our current progress on determining which methods are able to modify secret data and retain DQ problems. So far we have identified the ways in which data swapping and the SHA-2 hash function alterations methods can be used to preserve missing data, incorrectly formatted values, and domain violations DQ problems while minimising the risk of disclosing secrets
Future Directions for Optimizing Compilers
As software becomes larger, programming languages become higher-level, and
processors continue to fail to be clocked faster, we'll increasingly require
compilers to reduce code bloat, eliminate abstraction penalties, and exploit
interesting instruction sets. At the same time, compiler execution time must
not increase too much and also compilers should never produce the wrong output.
This paper examines the problem of making optimizing compilers faster, less
buggy, and more capable of generating high-quality output
SwarMAV: A Swarm of Miniature Aerial Vehicles
As the MAV (Micro or Miniature Aerial Vehicles) field matures, we expect to see that the platform's degree of autonomy, the information exchange, and the coordination with other manned and unmanned actors, will become at least as crucial as its aerodynamic design. The project described in this paper explores some aspects of a particularly exciting possible avenue of development: an autonomous swarm of MAVs which exploits its inherent reliability (through redundancy), and its ability to exchange information among the members, in order to cope with a dynamically changing environment and achieve its mission. We describe the successful realization of a prototype experimental platform weighing only 75g, and outline a strategy for the automatic design of a suitable controller
A Survey on Artificial Intelligence and Data Mining for MOOCs
Massive Open Online Courses (MOOCs) have gained tremendous popularity in the
last few years. Thanks to MOOCs, millions of learners from all over the world
have taken thousands of high-quality courses for free. Putting together an
excellent MOOC ecosystem is a multidisciplinary endeavour that requires
contributions from many different fields. Artificial intelligence (AI) and data
mining (DM) are two such fields that have played a significant role in making
MOOCs what they are today. By exploiting the vast amount of data generated by
learners engaging in MOOCs, DM improves our understanding of the MOOC ecosystem
and enables MOOC practitioners to deliver better courses. Similarly, AI,
supported by DM, can greatly improve student experience and learning outcomes.
In this survey paper, we first review the state-of-the-art artificial
intelligence and data mining research applied to MOOCs, emphasising the use of
AI and DM tools and techniques to improve student engagement, learning
outcomes, and our understanding of the MOOC ecosystem. We then offer an
overview of key trends and important research to carry out in the fields of AI
and DM so that MOOCs can reach their full potential.Comment: Working Pape
Intelligent Word Embeddings of Free-Text Radiology Reports
Radiology reports are a rich resource for advancing deep learning
applications in medicine by leveraging the large volume of data continuously
being updated, integrated, and shared. However, there are significant
challenges as well, largely due to the ambiguity and subtlety of natural
language. We propose a hybrid strategy that combines semantic-dictionary
mapping and word2vec modeling for creating dense vector embeddings of free-text
radiology reports. Our method leverages the benefits of both
semantic-dictionary mapping as well as unsupervised learning. Using the vector
representation, we automatically classify the radiology reports into three
classes denoting confidence in the diagnosis of intracranial hemorrhage by the
interpreting radiologist. We performed experiments with varying hyperparameter
settings of the word embeddings and a range of different classifiers. Best
performance achieved was a weighted precision of 88% and weighted recall of
90%. Our work offers the potential to leverage unstructured electronic health
record data by allowing direct analysis of narrative clinical notes.Comment: AMIA Annual Symposium 201
Evaluation of the electrochemical O2 concentrator as an O2 compressor
A program was successfully completed to analytically and experimentally evaluate the feasibility of using an electrochemical oxygen (O2) concentrator as an O2 compressor. The electrochemical O2 compressor (EOC) compresses 345 kN/sq m (50 psia) O2 generated on board the space vehicle by the water electrolysis subsystem (WES) in a single stage to 20,700 kN/sq m (3000 psia) to refill spent extravehicular equipment O2 bottles and to eliminate the need for high pressure O2 storage. The single cell EOC designed, fabricated, and used for the feasibility testing is capable of being tested at O2 pressures up to 41,400 kN/sq m (6000 psia). A ground support test facility to test the EOC cell was designed, fabricated, and used for the EOC feasibility testing. A product assurance program was established, implemented, and maintained which emphasized safety and materials compatibility associated with high pressure O2 operation. A membrane development program was conducted to develop a membrane for EOC application. Data obtained using a commercially available membrane were used to guide the development of the membranes fabricated specifically for an EOC. A total of 15 membranes were fabricated
Feasibility study of an Integrated Program for Aerospace-vehicle Design (IPAD) system. Volume 2: Characterization of the IPAD system, phase 1, task 1
The aircraft design process is discussed along with the degree of participation of the various engineering disciplines considered in this feasibility study
Learning Modulo Theories for preference elicitation in hybrid domains
This paper introduces CLEO, a novel preference elicitation algorithm capable
of recommending complex objects in hybrid domains, characterized by both
discrete and continuous attributes and constraints defined over them. The
algorithm assumes minimal initial information, i.e., a set of catalog
attributes, and defines decisional features as logic formulae combining Boolean
and algebraic constraints over the attributes. The (unknown) utility of the
decision maker (DM) is modelled as a weighted combination of features. CLEO
iteratively alternates a preference elicitation step, where pairs of candidate
solutions are selected based on the current utility model, and a refinement
step where the utility is refined by incorporating the feedback received. The
elicitation step leverages a Max-SMT solver to return optimal hybrid solutions
according to the current utility model. The refinement step is implemented as
learning to rank, and a sparsifying norm is used to favour the selection of few
informative features in the combinatorial space of candidate decisional
features.
CLEO is the first preference elicitation algorithm capable of dealing with
hybrid domains, thanks to the use of Max-SMT technology, while retaining
uncertainty in the DM utility and noisy feedback. Experimental results on
complex recommendation tasks show the ability of CLEO to quickly focus towards
optimal solutions, as well as its capacity to recover from suboptimal initial
choices. While no competitors exist in the hybrid setting, CLEO outperforms a
state-of-the-art Bayesian preference elicitation algorithm when applied to a
purely discrete task.Comment: 50 pages, 3 figures, submitted to Artificial Intelligence Journa
Highly focused document retrieval in aerospace engineering : user interaction design and evaluation
Purpose – This paper seeks to describe the preliminary studies (on both users and data), the design and evaluation of the K-Search system for searching legacy documents in aerospace engineering. Real-world reports of jet engine maintenance challenge the current indexing practice, while real users’ tasks require retrieving the information in the proper context. K-Search is currently in use in Rolls-Royce plc and has evolved to include other tools for knowledge capture and management.
Design/methodology/approach – Semantic Web techniques have been used to automatically extract information from the reports while maintaining the original context, allowing a more focused retrieval than with more traditional techniques. The paper combines semantic search with classical information retrieval to increase search effectiveness. An innovative user interface has been designed to take advantage of this hybrid search technique. The interface is designed to allow a flexible and
personal approach to searching legacy data.
Findings – The user evaluation showed that the system is effective and well received by users. It also shows that different people look at the same data in different ways and make different use of the same system depending on their individual needs, influenced by their job profile and personal attitude.
Research limitations/implications – This study focuses on a specific case of an enterprise working in aerospace engineering. Although the findings are likely to be shared with other engineering domains (e.g. mechanical, electronic), the study does not expand the evaluation to different settings.
Originality/value – The study shows how real context of use can provide new and unexpected challenges to researchers and how effective solutions can then be adopted and used in organizations.</p
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Towards Rapid Generation and Visualisation of Large 3D Urban Landscapes for Mobile Device Navigation
In this paper a procedural 3D modelling solution for mobile devices is presented based on scripting algorithms allowing for both the automatic and also semi-automatic creation of photorealistic quality virtual urban content. The combination of aerial images, GIS data, 2D ground maps and terrestrial photographs as input data coupled with a user-friendly customized interface permits the automatic and interactive generation of large-scale, accurate, georeferenced and fully-textured 3D virtual city content, content that can be specially optimized for use with mobile devices but also with navigational tasks in mind. Furthermore, a user-centred mobile virtual reality (VR) visualisation and interaction tool operating on PDAs (Personal Digital Assistants) for pedestrian navigation is also discussed. Via this engine, the import and display of various navigational file formats (2D and 3D) is supported, including a comprehensive front-end user-friendly graphical user interface providing immersive virtual 3D navigation
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