SGAI

AI-2016 Thirty-sixth SGAI International Conference on Artificial Intelligence. CAMBRIDGE, ENGLAND 13-15 DECEMBER 2016

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Workshops

The first day of the conference comprises a range of workshops, to be held on Tuesday 13th December. Delegates will find these events to be especially valuable where there is a current need to consider the introduction of new AI technologies into their own organisations.

There will be four half-day workshops, including the Twenty-first UK CBR Workshop. Delegates are free to choose any combination of morning and afternoon sessions to attend. The programme of workshops is shown below. Note that the morning session starts at 11 a.m. to reduce the need for delegates to stay in Cambridge on the previous night. There is a lunch break from 12.30-13.15 and there are refreshment breaks from 14.45-15.15 and from 16.45-17.00.

Workshops organiser: Professor Adrian Hopgood, University of Liege, Belgium


Stream 1 - Morning (11.00-12.30 and 13.15-14.45 Nightingale Room)

Data stream mining

Chair: Dr Frederic Stahl, University of Reading, UK

The four main dimensions of Big Data are known as Volume, referring to the size of the data, Velocity, referring to the data that is generated rapidly, Veracity, referring to uncertainty in data and Variety, referring to data from different kinds of sources such as text, structure and video data. This workshop's focus is on the Velocity dimension of Big Data. The analytics of high velocity data has many applications, such as topic detection in Twitter, traffic control, network intrusion detection, etc. The difference compared with data that is stored on a disk is that real-time data may change its characteristics over time. However, decision support applications rely on the recency of their supporting data, hence, data generated at a high velocity needs to be processed ‘on the fly’. On the other hand, there are applications that are more interested in the actual change of the data, i.e. intrusion detection and network fault detection. Hence there is a need for computationally efficient real-time techniques that take changes of the data into consideration.

The workshop’s aim is to bring together researchers in this field to present their latest work, discuss challenges and future directions of research in Data Stream Mining.

A selection of papers from the workshop are available at the workshop web page.

Programme

11am-12:30pm

  • A Text Mining Framework for Big Data
    Niki Pavlopoulou, Aeham Abushwashi and Vittorio Scibetta (Exonar Ltd., UK)

  • Mining TV Twitter Networks for Adaptive Content Navigation and Community Awareness
    Hugo Hromic, Andrea Barraza-Urbina, Conor Hayes (National University of Ireland Galway, Ireland) and Neal Cantle (Raidió Teilifís Éireann (RTÉ), Ireland)

  • Activity recognition from body worn accelerometers - toward real-time event detection
    Ali K. Mohamed Ali, Rachel King, Balazs Janko (University of Reading, UK) Emma Sack Ann Ashburn, Malcolm Burnett (University of Southampton, UK), Ian Craddock (University of Bristol), William Harwin (University of Reading, UK)

1:15pm-2:45pm

  • Outlier Detection in Random Subspaces over Data Streams: An Approach for Insider Threat Detection
    Diana Haidar, Mohamed Medhat Gaber (Birmingham City University, UK)

  • Towards Real-Time Feature Tracking Technique using Adaptive Micro-Clusters
    Mahmood Shakir Hammoodi, Frederic Stahl, Mark Tennant, Atta Badii (University of Reading, UK)

Stream 1 - Afternoon (15.15-16.45 and 17.00-18.30 Nightingale Room)

Deep learning meets the semantic web

Chair: Dr Mercedes Arguello Casteleiro, University of Manchester, UK

The workshop will use material from the following contributors:
M. Arguello Casteleiro1, W. Read1, G. Demetriou‎1, M.J. Fernandez-Prieto2, D. Maseda-Fernandez3, G. Nenadic1, J. Klein4, J. Keane1 and R. Stevens1

1 School of Computer Science, University of Manchester, UK
2 School of Languages, University of Salford, UK
3 Leighton Hospital, NHS England, UK
4 Institut National de la Sante et de la Recherche Medicale (INSERM), France

Ontologies are the backbone of the Semantic Web and the W3C vision of the Web of linked data. Within the biomedical field, ontologies such as the Gene Ontology (GO) are used to represent the meaning of data. The bioinformatics field is rich in textual sources, both structured and unstructured, where data comes from a variety of resources, such as ‘omics (e.g. genomics, transcriptomics, proteomics and metabolomics) experiments, the biomedical literature, and healthcare systems. Over the years, ontologies such as GO and controlled vocabularies like the Medical Subject Headings (MeSH) emerged as methods to represent the semantic meaning of terms in a way that can support computational tasks such as information extraction, information retrieval and data analysis. A current challenge is how to maintain and keep updated resources such as GO and MeSH considering the continuously growth of biomedical textual repositories, especially with ‘big data’ being continuously produced in the areas of health research and health care.

This workshop examines both traditional methods from knowledge engineering to capture and represent information extracted from texts, and the latest trends from machine learning to automatically derive semantic similarity from large unannotated free text. Recent advances in artificial neural networks make feasible the derivation of words from corpora of billions of words: hence the growing interest in Deep Learning. We demonstrate tasks for which Deep Learning technology can be utilised to support the development and enrichment of ontologies, with a particular focus on biomedical and clinical data.

Please see the group's webpage http://pole-dl.cs.manchester.ac.uk for further information and post-workshop follow-up.


Stream 2 - Morning (11.00-12.30 and 13.15-14.45 Peterhouse Lecture Theatre)

Twenty-first UK Case-Based Reasoning Workshop

Organiser: Professor Miltos Petridis, University of Brighton

Session 1: 11.00-12.30 Lecture Theatre

Chair: Professor Miltos Petridis

Please see the UKCBR webpage for further information and post-workshop follow-up.

Welcome, introductions and housekeeping

Invited talk: Dr Stelios Kapetanakis (University of Brighton)
Big Data in 4D: could CBR meet the needs of 21st century?

Accepted paper: Vani Aul and Thomas Roth-Berghofer
Managing Search Engine Optimisation Experience Using the INRECA Methodology

Session 2: 13.15-14.45 Lecture Theatre

Chair: Dr Stelios Kapetanakis

Invited research project talk: Dr Sadiq Sani (Robert Gordon University)
Data-driven self-management of chronic health conditions

Accepted paper: Maria Leikola, Lotta Rintala, Christian Severin Sauer, Thomas Roth-Berghofer and Mari Lundström
Case-based Reasoning Application: Selection of Cyanide-free Gold Leaching Methods

Stream 2 - Afternoon (15.15-16.45 Peterhouse Lecture Theatre)

Twenty-first UK Case-Based Reasoning Workshop

Organiser: Professor Miltos Petridis, University of Brighton

Session 3: 15.15-16.45

Chair: Professor Nirmalie Wiratunga

Invited PhD Thesis work Presentation: Jose Luis Jorro-Aragoneses (Universidad Complutense de Madrid)
A study of context-aware social recommender systems (45 mins)

Accepted paper: Tariq Saad Al Murayziq, Stelios Kapetanakis and Miltos Petridis
Towards successful Dust storm prediction using Bayesian networks and Case-based Reasoning

Community discussion (15 mins)

SGAI

AI-2016 Thirty-sixth SGAI International Conference on Artificial Intelligence. CAMBRIDGE, ENGLAND 13-15 DECEMBER 2016

home | schedule | technical stream | application stream | poster sessions
workshops | proceedings | exhibition | registration | sponsors | organisers
enquiries | social | visa info | venue | accommodation | panel session | special session
ai open mic | information for speakers | previous conferences | letter of invitation

call for papers | paper submission and info for authors | accepted papers
internet access for delegates | after-dinner speaker

BCS