3,149 research outputs found
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The telematic dress: Evolving garments and distributed proprioception in streaming media and fashion performance
Centered around several short films from streaming performances created in 2005, this paper
explores new ideas for movement technologies and garment design in an arts and digital research
context. The "telematic dress" project, developed at the DAP Lab in Nottingham, involves
transdisciplinary intersections between fashion and live performance, interactive system architecture,
electronic textiles, wearable technologies, choreography, and anthropology.
The concept on an evolving garment design that is materialized (moved) in live performance
originates from DAP Lab's experimentation with telematics and distributed media (http://art.ntu.ac.
uk/performance_research/birringer/dap.htm] addressing "connective tissues" through a study of
perception/proprioception in the wearer (tactile sensory processing) and the dancer/designer/viewer
relationship. This study is conducted as cross-cultural communication with online performance
partners in Europe, the US, Brazil and Japan. The inter-active space is predicated on transcultural
questions: how does the movement with an evolving design and wearable interactive sensors travel,
how does movement - and capturing of movement - allow the design to emerge toward a garment
statement, and how are bodies-in-relation-to sensory fabrics affected by the multidimensional
kinesthetics of a media-rich, responsive environment
Fuzzy personalized wireless information agents
2002-2003 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe
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Stochastic modelling of the effects of interdependencies between critical infrastructure
An approach to Quantitative Interdependency Analysis, in the context of Large Complex Critical Infrastructures, is presented in this paper. A Discrete stateāspace, Continuousātime, Stochastic Process models the operation of critical infrastructure, taking interdependencies into account. Of primary interest are the implications of both model detail (that is, level of model abstraction) and model parameterisation for the study of dependencies. Both of these factors are observed to affect the distribution of cascadeāsizes within and across infrastructure
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection
In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to select small representative exemplars from large
training data. As to be able to computationally compress the larger volume of
training data with scalability, MapReduce based parallelization approach is
then implemented and evaluated for each step of the model compression process
abovementioned, on which common but efficient classification methods can be
directly used. Experimental application study on two publicly available
datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the
classification using the compressed model proposed can effectively speed up the
detection procedure at up to 184 times, most importantly at the cost of a
minimal accuracy difference with less than 1% on average
Comparative Study Of Implementing The On-Premises and Cloud Business Intelligence On Business Problems In a Multi-National Software Development Company
Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNowadays every enterprise wants to be competitive. In the last decade, the data volumes are increased dramatically. As each year data in the market increases, the ability to extract, analyze and manage the data become the backbone condition for the organization to be competitive.
In this condition, organizations need to adapt their technologies to the new business reality in order to be competitive and provide new solutions that meet new requests. Business Intelligence by the main definition is the ability to extract analyze and manage the data through which an organization gain a competitive advantage. Before using this approach, itās important to decide on which computing system it will base on, considering the volume of data, business context of the organization and technologies requirements of the market.
In the last 10 years, the popularity of cloud computing increased and divided the computing Systems into On-Premises and cloud. The cloud benefits are based on providing scalability, availability and fewer costs. On another hand, traditional On-Premises provides independence of software configuration, control over data and high security. The final decision as to which computing paradigm to follow in the organization itās not an easy task as well as depends on the business context of the organization, and the characteristics of the performance of the current On-Premises systems in business processes. In this case, Business Intelligence functions and requires in-depth analysis in order to understand if cloud computing technologies could better perform in those processes than traditional systems.
The objective of this internship is to conduct a comparative study between 2 computing systems in Business Intelligence routine functions. The study will compare the On-Premises Business Intelligence Based on Oracle Architecture with Cloud Business Intelligence based on Google Cloud Services. A comparative study will be conducted through participation in activities and projects in the Business Intelligence department, of a company that develops software digital solutions to serve the telecommunications market for 12 months, as an internship student in the 2nd year of a masterās degree in Information Management, with a specialization in Knowledge Management and Business Intelligence at Nova Information Management School (NOVA IMS)
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