Lirolem: A virtual studio/Institutional Repository for the University of Lincoln

Gives an account of the Lirolem project at the University of Lincoln which was to build a repository capable of handling multimedia material as well as providing a repository for the University's research output

Intelligent Coordination and Automation for Smart Home Accessories

Smarthome accessories are rapidly becoming more popular. Although many companies are making devices to take advantage of this market, most of the created smart devices are actually unintelligent. Currently, these smart home devices require meticulous, tedious configuration to get any sort of enhanced usability over their analog counterparts. We propose building a general model using machine learning and data science to automatically learn a user\u27s smart accessory usage to predict their configuration. We have identified the requirements, collected data, recognized the risks, implemented the system, and have met the goals we set out to accomplish

The Knowledge Application and Utilization Framework Applied to Defense COTS: A Research Synthesis for Outsourced Innovation

Purpose -- Militaries of developing nations face increasing budget pressures, high operations tempo, a blitzing pace of technology, and adversaries that often meet or beat government capabilities using commercial off-the-shelf (COTS) technologies. The adoption of COTS products into defense acquisitions has been offered to help meet these challenges by essentially outsourcing new product development and innovation. This research summarizes extant research to develop a framework for managing the innovative and knowledge flows. Design/Methodology/Approach – A literature review of 62 sources was conducted with the objectives of identifying antecedents (barriers and facilitators) and consequences of COTS adoption. Findings – The DoD COTS literature predominantly consists of industry case studies, and there’s a strong need for further academically rigorous study. Extant rigorous research implicates the importance of the role of knowledge management to government innovative thinking that relies heavily on commercial suppliers. Research Limitations/Implications – Extant academically rigorous studies tend to depend on measures derived from work in information systems research, relying on user satisfaction as the outcome. Our findings indicate that user satisfaction has no relationship to COTS success; technically complex governmental purchases may be too distant from users or may have socio-economic goals that supersede user satisfaction. The knowledge acquisition and utilization framework worked well to explain the innovative process in COTS. Practical Implications – Where past research in the commercial context found technological knowledge to outweigh market knowledge in terms of importance, our research found the opposite. Managers either in government or marketing to government should be aware of the importance of market knowledge for defense COTS innovation, especially for commercial companies that work as system integrators. Originality/Value – From the literature emerged a framework of COTS product usage and a scale to measure COTS product appropriateness that should help to guide COTS product adoption decisions and to help manage COTS product implementations ex post

Correlating Architecture Maturity and Enterprise Systems Usage Maturity to Improve Business/IT Alignment

This paper compares concepts of maturity models in the areas of Enterprise Architecture and Enterprise Systems Usage. We investigate whether these concepts correlate, overlap and explain each other. The two maturity models are applied in a case study. We conclude that although it is possible to fully relate constructs from both kinds of models, having a mature architecture function in a company does not imply a high Enterprise Systems Usage maturity

FORGE: An eLearning Framework for Remote Laboratory Experimentation on FIRE Testbed Infrastructure

The Forging Online Education through FIRE (FORGE) initiative provides educators and learners in higher education with access to world-class FIRE testbed infrastructure. FORGE supports experimentally driven research in an eLearning environment by complementing traditional classroom and online courses with interactive remote laboratory experiments. The project has achieved its objectives by defining and implementing a framework called FORGEBox. This framework offers the methodology, environment, tools and resources to support the creation of HTML-based online educational material capable accessing virtualized and physical FIRE testbed infrastruc- ture easily. FORGEBox also captures valuable quantitative and qualitative learning analytic information using questionnaires and Learning Analytics that can help optimise and support student learning. To date, FORGE has produced courses covering a wide range of networking and communication domains. These are freely available from FORGEBox.eu and have resulted in over 24,000 experiments undertaken by more than 1,800 students across 10 countries worldwide. This work has shown that the use of remote high- performance testbed facilities for hands-on remote experimentation can have a valuable impact on the learning experience for both educators and learners. Additionally, certain challenges in developing FIRE-based courseware have been identified, which has led to a set of recommendations in order to support the use of FIRE facilities for teaching and learning purposes

Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin

Adapting Sequence to Sequence models for Text Normalization in Social Media

Social media offer an abundant source of valuable raw data, however informal writing can quickly become a bottleneck for many natural language processing (NLP) tasks. Off-the-shelf tools are usually trained on formal text and cannot explicitly handle noise found in short online posts. Moreover, the variety of frequently occurring linguistic variations presents several challenges, even for humans who might not be able to comprehend the meaning of such posts, especially when they contain slang and abbreviations. Text Normalization aims to transform online user-generated text to a canonical form. Current text normalization systems rely on string or phonetic similarity and classification models that work on a local fashion. We argue that processing contextual information is crucial for this task and introduce a social media text normalization hybrid word-character attention-based encoder-decoder model that can serve as a pre-processing step for NLP applications to adapt to noisy text in social media. Our character-based component is trained on synthetic adversarial examples that are designed to capture errors commonly found in online user-generated text. Experiments show that our model surpasses neural architectures designed for text normalization and achieves comparable performance with state-of-the-art related work.Comment: Accepted at the 13th International AAAI Conference on Web and Social Media (ICWSM 2019