A multi-agent model for assessing electricity tariffs

This paper describes the framework for modelling a multi-agent approach for assessing dynamic pricing of electricity and demand response. It combines and agent-based model with decision-making data, and a standard load-flow model. The multi-agent model described here represents a tool in investigating not only the relation between different dynamic tariffs and consumer load profiles, but also the change in behaviour and impact on low-voltage electricity distribution networks.The authors acknowledge the contribution of the EPSRC Transforming Energy Demand Through Digital Innovation Programme, grant agreement numbers EP/I000194/1 and EP/I000119/1, to the ADEPT project

Innovations and Social Media Analytics in a Digital Society

info:eu-repo/semantics/publishedVersio

Innovations and Social Media Analytics in a Digital Society

Recent advances in digitization are transforming healthcare, education, tourism, information technology, and some other sectors. Social media analytics are tools that can be used to measure innovation and the relation of the companies with the citizens. This book comprises state-ofthe-art social media analytics, and advanced innovation policies in the digitization of society. The number of applications that can be used to create and analyze social media analytics generates large amounts of data called big data, including measures of the use of the technologies to develop or to use new services to improve the quality of life of the citizens. Digitization has applications in fields from remote monitoring to smart sensors and other devices. Integration generates data that need to be analyzed and visualized in an easy and clear way, that will be some of the proposals of the researchers present in this book. This volume offers valuable insights to researchers on how to design innovative digital analytics systems and how to improve information delivery remotely.info:eu-repo/semantics/publishedVersio

Data mining and modelling for sign language

Sign languages have received significantly less attention than spoken languages in the research areas of corpus analysis, machine translation, recognition, synthesis and social signal processing, amongst others. This is mainly due to signers being in a clear minority and there being a strong prior belief that sign languages are simply arbitrary gestures. To date, this manifests in the insufficiency of sign language resources available for computational modelling and analysis, with no agreed standards and relatively stagnated advancements compared to spoken language interaction research. Fortunately, the machine learning community has developed methods, such as transfer learning, for dealing with sparse resources, while data mining techniques, such as clustering can provide insights into the data. The work described here utilises such transfer learning techniques to apply neural language model to signed utterances and to compare sign language phonemes, which allows for clustering of similar signs, leading to automated annotation of sign language resources. This thesis promotes the idea that sign language research in computing should rely less on hand-annotated data thus opening up the prospect of using readily available online data (e.g. signed song videos) through the computational modelling and automated annotation techniques presented in this thesis

An integrated clustering analysis framework for heterogeneous data

Big data is a growing area of research with some important research challenges that motivate our work. We focus on one such challenge, the variety aspect. First, we introduce our problem by defining heterogeneous data as data about objects that are described by different data types, e.g., structured data, text, time-series, images, etc. Through our work we use five datasets for experimentation: a real dataset of prostate cancer data and four synthetic dataset that we have created and made them publicly available. Each dataset covers different combinations of data types that are used to describe objects. Our strategy for clustering is based on fusion approaches. We compare intermediate and late fusion schemes. We propose an intermediary fusion approach, Similarity Matrix Fusion (SMF), where the integration process takes place at the level of calculating similarities. SMF produces a single distance fusion matrix and two uncertainty expression matrices. We then propose a clustering algorithm, Hk-medoids, a modified version of the standard k-medoids algorithm that utilises uncertainty calculations to improve on the clustering performance. We evaluate our results by comparing them to clustering produced using individual elements and show that the fusion approach produces equal or significantly better results. Also, we show that there are advantages in utilising the uncertainty information as Hkmedoids does. In addition, from a theoretical point of view, our proposed Hk-medoids algorithm has less computation complexity than the popular PAM implementation of the k-medoids algorithm. Then, we employed late fusion that aggregates the results of clustering by individual elements by combining cluster labels using an object co-occurrence matrix technique. The final cluster is then derived by a hierarchical clustering algorithm. We show that intermediate fusion for clustering of heterogeneous data is a feasible and efficient approach using our proposed Hk-medoids algorithm

Proceedings of the ECMLPKDD 2015 Doctoral Consortium

ECMLPKDD 2015 Doctoral Consortium was organized for the second time as part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), organised in Porto during September 7-11, 2015. The objective of the doctoral consortium is to provide an environment for students to exchange their ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in machine learning, data mining, and related areas. These proceedings collect together and document all the contributions of the ECMLPKDD 2015 Doctoral Consortium

Cartoons as interdiscourse : a quali-quantitative analysis of social representations based on collective imagination in cartoons produced after the Charlie Hebdo attack

The attacks against Charlie Hebdo in Paris at the beginning of the year 2015 urged many cartoonists – most professionals but some laymen as well – to create cartoons as a reaction to this tragedy. The main goal of this article is to show how traumatic events like this one can converge in a rather limited set of metaphors, ranging from easily recognizable topoi to rather vague interdiscourses that circulate in contemporary societies. To do so, we analyzed 450 cartoons that were produced as a reaction to the Charlie Hebdo attacks, and took a quali-quantitative approach that draws both on discourse analysis and semiotics. In this paper, we identified eight main themes and we analyzed the five ones which are anchored in collective imagination (the pen against the sword, the journalist as a modern hero, etc.). Then, we studied the cartoons at figurative, narrative and thematic levels thanks to Greimas’ model of the semiotic square. This paper shows the ways in which these cartoons build upon a memory-based network of events from the recent past (particularly 9/11), and more generally on a collective imagination which can be linked to Western values.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Evaluation of cloud computing modelling tools: simulators and predictive models

Experimenting with novel algorithms and configurations for the automatic management of Cloud Computing infrastructures is expensive and time consuming on real systems. Cloud computing delivers the benefits of using virtualisation techniques to data centers instead of physical servers for customers. However, it is still complex for researchers to test and run their experiments on data center due to the cost for repeating the experiments. To address this, various tools are available to enable simulators, emulators, mathematical models, statistical models and benchmarking. Despite this, there are different methods used by researchers to avoid the difficulty of conducting Cloud Computing research on actual large data centre infrastructure. However, it is still difficult to chose the best tool to evaluate the proposed research. This research focuses on investigating the level of accuracy of existing known simulators in the field of cloud computing. Simulation tools are generally developed for particular experiments, so there is little assurance that using them with different workloads will be reliable. Moreover, a predictive model based on a data set from a realistic data center is delivered as an alternative model of simulators as there is a lack of their sufficient accuracy. So, this work addresses the problem of investigating the accuracy of different modelling tools by developing and validating a procedure based on the performance of a target micro data centre. Key insights and contributions are: Involving three alternative models for Cloud Computing real infrastructure showing the level of accuracy of selected simulation tools. Developing and validating a predictive model based on a Raspberry Pi small scale data centre. The use of predictive model based on Linear Regression and Artificial Neural Net- works models based on training data set drawn from a Raspberry Pi Cloud infrastructure provides better accuracy