Volume
OASIcs, Volume 66
2018 Imperial College Computing Student Workshop (ICCSW 2018)
- Part of: Series: Open Access Series in Informatics (OASIcs)
Publication Details
- published at: 2019-01-25
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- ISBN: 978-3-95977-097-2
- DBLP: db/conf/iccsw/iccsw2018
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Document
Complete Volume
OASIcs, Volume 66, ICCSW'18, Complete Volume
Abstract
OASIcs, Volume 66, ICCSW'18, Complete Volume
Cite as
2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@Proceedings{pirovano_et_al:OASIcs.ICCSW.2018,
title = {{OASIcs, Volume 66, ICCSW'18, Complete Volume}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018},
URN = {urn:nbn:de:0030-drops-102298},
doi = {10.4230/OASIcs.ICCSW.2018},
annote = {Keywords: General and reference, General conference proceedings}
}
Document
Front Matter
Front Matter, Table of Contents, Preface, Conference Organization
Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as
2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 0:i-0:x, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@InProceedings{pirovano_et_al:OASIcs.ICCSW.2018.0,
author = {Pirovano, Edoardo and Graversen, Eva},
title = {{Front Matter, Table of Contents, Preface, Conference Organization}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
pages = {0:i--0:x},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.0},
URN = {urn:nbn:de:0030-drops-101816},
doi = {10.4230/OASIcs.ICCSW.2018.0},
annote = {Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Speeding Up BigClam Implementation on SNAP
Abstract
We perform a detailed analysis of the C++ implementation of the Cluster Affiliation Model for Big Networks (BigClam) on the Stanford Network Analysis Project (SNAP). BigClam is a popular graph mining algorithm that is capable of finding overlapping communities in networks containing millions of nodes. Our analysis shows a key stage of the algorithm - determining if a node belongs to a community - dominates the runtime of the implementation, yet the computation is not parallelized. We show that by parallelizing computations across multiple threads using OpenMP we can speed up the algorithm by 5.3 times when solving large networks for communities, while preserving the integrity of the program and the result.
Cite as
C. H. Bryan Liu and Benjamin Paul Chamberlain. Speeding Up BigClam Implementation on SNAP. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 1:1-1:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@InProceedings{liu_et_al:OASIcs.ICCSW.2018.1,
author = {Liu, C. H. Bryan and Chamberlain, Benjamin Paul},
title = {{Speeding Up BigClam Implementation on SNAP}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
pages = {1:1--1:13},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.1},
URN = {urn:nbn:de:0030-drops-101829},
doi = {10.4230/OASIcs.ICCSW.2018.1},
annote = {Keywords: BigClam, Community Detection, Parallelization, Networks}
}
Document
THRIFTY: Towards High Reduction In Flow Table memorY
Abstract
The rapid evolution of information technology has compelled the ubiquitous systems and computing to adapt with this expeditious development. Because of its rigidity, computer networks failed to meet that evolvement for decades, however, the recently emerged paradigm of software-defined networks gives a glimpse of hope for a new networking architecture that provides more flexibility and adaptability. Fault tolerance is considered one of the key concerns with respect to the software-defined networks dependability. In this paper, we propose a new architecture, named THRIFTY, to ease the recovery process when failure occurs and save the storage space of forwarding elements, which is therefore aims to enhance the fault tolerance of software-defined networks. Unlike the prevailing concept of fault management, THRIFTY uses the Edge-Core technique to forward the incoming packets. THRIFTY is tailored to fit the only centrally controlled systems such as the new architecture of software-defined networks that interestingly maintain a global view of the entire network. The architecture of THRIFTY is illustrated and experimental study is reported showing the performance of the proposed method. Further directions are suggested in the context of scalability towards achieving further advances in this research area.
Cite as
Ali Malik, Benjamin Aziz, and Chih-Heng Ke. THRIFTY: Towards High Reduction In Flow Table memorY. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 2:1-2:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@InProceedings{malik_et_al:OASIcs.ICCSW.2018.2,
author = {Malik, Ali and Aziz, Benjamin and Ke, Chih-Heng},
title = {{THRIFTY: Towards High Reduction In Flow Table memorY}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
pages = {2:1--2:9},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.2},
URN = {urn:nbn:de:0030-drops-101830},
doi = {10.4230/OASIcs.ICCSW.2018.2},
annote = {Keywords: Source Routing, Resiliency, Fault Tolerance, SDN, TCAM}
}
Document
Data-Driven Chinese Walls
Abstract
Security policy and access control models are often based on qualitative attributes, e.g. security labels, cryptographic credentials. In this paper, we enrich one such model, namely the Chinese Walls model, with quantitative attributes derived from data. Therefore, we advocate a data-driven approach that considers a quantitative definition of access we term, working relations.
Cite as
Gulsum Akkuzu and Benjamin Aziz. Data-Driven Chinese Walls. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 3:1-3:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@InProceedings{akkuzu_et_al:OASIcs.ICCSW.2018.3,
author = {Akkuzu, Gulsum and Aziz, Benjamin},
title = {{Data-Driven Chinese Walls}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
pages = {3:1--3:8},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.3},
URN = {urn:nbn:de:0030-drops-101841},
doi = {10.4230/OASIcs.ICCSW.2018.3},
annote = {Keywords: Access Control, Big Data, Security Policies, Chinese Walls Model}
}
Document
Comparison of Platforms for Recommender Algorithm on Large Datasets
Abstract
One of the challenges our society faces is the ever increasing amount of data. Among existing platforms that address the system requirements, Hadoop is a framework widely used to store and analyze "big data". On the human side, one of the aids to finding the things people really want is recommendation systems. This paper evaluates highly scalable parallel algorithms for recommendation systems with application to very large data sets. A particular goal is to evaluate an open source Java message passing library for parallel computing called MPJ Express, which has been integrated with Hadoop. As a demonstration we use MPJ Express to implement collaborative filtering on various data sets using the algorithm ALSWR (Alternating-Least-Squares with Weighted-lambda-Regularization). We benchmark the performance and demonstrate parallel speedup on Movielens and Yahoo Music data sets, comparing our results with two other frameworks: Mahout and Spark. Our results indicate that MPJ Express implementation of ALSWR has very competitive performance and scalability in comparison with the two other frameworks.
Cite as
Christina Diedhiou, Bryan Carpenter, and Ramazan Esmeli. Comparison of Platforms for Recommender Algorithm on Large Datasets. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 4:1-4:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@InProceedings{diedhiou_et_al:OASIcs.ICCSW.2018.4,
author = {Diedhiou, Christina and Carpenter, Bryan and Esmeli, Ramazan},
title = {{Comparison of Platforms for Recommender Algorithm on Large Datasets}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
pages = {4:1--4:10},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.4},
URN = {urn:nbn:de:0030-drops-101852},
doi = {10.4230/OASIcs.ICCSW.2018.4},
annote = {Keywords: HPC, MPJ Express, Hadoop, Spark, Mahout}
}
Document
Towards Context-Aware Syntax Parsing and Tagging
Abstract
Information retrieval (IR) has become one of the most popular Natural Language Processing (NLP) applications. Part of speech (PoS) parsing and tagging plays an important role in IR systems. A broad range of PoS parsers and taggers tools have been proposed with the aim of helping to find a solution for the information retrieval problems, but most of these are tools based on generic NLP tags which do not capture domain-related information. In this research, we present a domain-specific parsing and tagging approach that uses not only generic PoS tags but also domain-specific PoS tags, grammatical rules, and domain knowledge. Experimental results show that our approach has a good level of accuracy when applying it to different domains.
Cite as
Alaa Mohasseb, Mohamed Bader-El-Den, and Mihaela Cocea. Towards Context-Aware Syntax Parsing and Tagging. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 5:1-5:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@InProceedings{mohasseb_et_al:OASIcs.ICCSW.2018.5,
author = {Mohasseb, Alaa and Bader-El-Den, Mohamed and Cocea, Mihaela},
title = {{Towards Context-Aware Syntax Parsing and Tagging}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
pages = {5:1--5:9},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.5},
URN = {urn:nbn:de:0030-drops-101866},
doi = {10.4230/OASIcs.ICCSW.2018.5},
annote = {Keywords: Information Retrieval, Natural Language Processing, PoS Tagging, PoS Parsing, Machine Learning}
}
Document
Evaluation of Rule-Based Learning and Feature Selection Approaches For Classification
Abstract
Feature selection is typically employed before or in conjunction with classification algorithms to reduce the feature dimensionality and improve the classification performance, as well as reduce processing time. While particular approaches have been developed for feature selection, such as filter and wrapper approaches, some algorithms perform feature selection through their learning strategy. In this paper, we are investigating the effect of the implicit feature selection of the PRISM algorithm, which is rule-based, when compared with the wrapper feature selection approach employing four popular algorithms: decision trees, naïve bayes, k-nearest neighbors and support vector machine. Moreover, we investigate the performance of the algorithms on target classes, i.e. where the aim is to identify one or more phenomena and distinguish them from their absence (i.e. non-target classes), such as when identifying benign and malign cancer (two target classes) vs. non-cancer (the non-target class).
Cite as
Fatima Chiroma, Mihaela Cocea, and Han Liu. Evaluation of Rule-Based Learning and Feature Selection Approaches For Classification. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 6:1-6:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@InProceedings{chiroma_et_al:OASIcs.ICCSW.2018.6,
author = {Chiroma, Fatima and Cocea, Mihaela and Liu, Han},
title = {{Evaluation of Rule-Based Learning and Feature Selection Approaches For Classification}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
pages = {6:1--6:6},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.6},
URN = {urn:nbn:de:0030-drops-101872},
doi = {10.4230/OASIcs.ICCSW.2018.6},
annote = {Keywords: Feature Selection, Prism, Rule-based Learning, Wrapper Approach}
}
Document
The iBUG Eye Segmentation Dataset
Abstract
This paper presents the first dataset for eye segmentation in low resolution images. Although eye segmentation has long been a vital preprocessing step in biometric applications, this work is the first to focus on low resolutions image that can be expected from a consumer-grade camera under conventional human-computer interaction and / or video-chat scenarios. Existing eye datasets have multiple limitations, including: (a) datasets only contain high resolution images; (b) datasets did not include enough pose variations; (c) a utility landmark ground truth did not be provided; (d) high accurate pixel-level ground truths had not be given. Our dataset meets all the above conditions and requirements for different segmentation methods. Besides, a baseline experiment has been performed on our dataset to evaluate the performances of landmark models (Active Appearance Model, Ensemble Regression Tree and Supervised Descent Method) and deep semantic segmentation models (Atrous convolutional neural network with conditional random field). Since the novelty of our dataset is to segment the iris and the sclera areas, we evaluate above models on sclera and iris only respectively in order to indicate the feasibility on eye-partial segmentation tasks. In conclusion, based on our dataset, deep segmentation methods performed better in terms of IOU-based ROC curves and it showed potential abilities on low-resolution eye segmentation task.
Cite as
Bingnan Luo, Jie Shen, Yujiang Wang, and Maja Pantic. The iBUG Eye Segmentation Dataset. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 7:1-7:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@InProceedings{luo_et_al:OASIcs.ICCSW.2018.7,
author = {Luo, Bingnan and Shen, Jie and Wang, Yujiang and Pantic, Maja},
title = {{The iBUG Eye Segmentation Dataset}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
pages = {7:1--7:9},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.7},
URN = {urn:nbn:de:0030-drops-101883},
doi = {10.4230/OASIcs.ICCSW.2018.7},
annote = {Keywords: dataset, eye, segmentation, landmark, pixel-level}
}
Document
Anomaly Detection for Big Data Technologies
Abstract
The main goal of this research is to contribute to automated performance anomaly detection for large-scale and complex distributed systems, especially for Big Data applications within cloud computing. The main points that we will investigate are:
- Automated detection of anomalous performance behaviors by finding the relevant performance metrics with which to characterize behavior of systems.
- Performance anomaly localization: To pinpoint the cause of a performance anomaly due to internal or external faults.
- Investigation of the possibility of anomaly prediction. Failure prediction aims to determine the possible occurrences of catastrophic events in the near future and will enable system developers to utilize effective monitoring solutions to guarantee system availability.
- Assessment for the potential of hybrid methods that combine machine learning with traditional methods used in performance for anomaly detection.
The topic of this research proposal will offer me the opportunity to more deeply apply my interest in the field of performance anomaly detection and prediction by investigating and using novel optimization strategies. In addition, this research provides a very interesting case of utilizing the anomaly detection techniques in a large-scale Big Data and cloud computing environment. Among the various Big Data technologies, in-memory processing technology like Apache Spark has become widely adopted by industries as result of its speed, generality, ease of use, and compatibility with other Big Data systems. Although Spark is developing gradually, currently there are still shortages in comprehensive performance analyses that specifically build for Spark and are used to detect performance anomalies. Therefore, this raises my interest in addressing this challenge by investigating new hybrid learning techniques for anomaly detection in large-scale and complex systems, especially for in-memory processing Big Data platforms within cloud computing.
Cite as
Ahmad Alnafessah and Giuliano Casale. Anomaly Detection for Big Data Technologies. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, p. 8:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@InProceedings{alnafessah_et_al:OASIcs.ICCSW.2018.8,
author = {Alnafessah, Ahmad and Casale, Giuliano},
title = {{Anomaly Detection for Big Data Technologies}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
pages = {8:1--8:1},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.8},
URN = {urn:nbn:de:0030-drops-101899},
doi = {10.4230/OASIcs.ICCSW.2018.8},
annote = {Keywords: Performance anomalies, Apache Spark, Neural Network, Resilient Distributed Dataset (RDD)}
}
Document
A Novel Method for Event Detection using Wireless Sensor Networks
Abstract
Reliable event detection is one of the hottest research areas in the wireless sensor networks field these days. Battlefield monitoring, fire detection, nuclear and chemical attack, and gas leak detection are examples of the event detection applications. One of the main goals to WSNs is transmitting the sensed data to the sink (Base station) in an efficient way with minimum energy usage to achieve high degree of event detection reliability. Thus, Its very important to determine the reliability degree to know the number of data that are required to receive at the sink to achieve the desired reliability.
Most of the previous research works proposed different solutions for reliable event detection. The idea of all these solutions is based on increasing the amount of the transmitted data to the sink by controlling the sources reporting rate. However, rising the reporting rate may lead to losing the transmitted data due to the network congestion and packets collision, and this is related to the restricted resources capacity of the network's sensor nodes.
Therefore, in this paper, a new indoor method to achieve quality based event reliability for critical event detection have been implemented using hardware sensor nodes (Waspmote). The idea of this method is depending on sending the sensed data to the sink using a node called Cluster Head (CH) in a sequence according to their priority from the high to the low. The network nodes have been deployed in the experiment area into clusters, and each cluster have a CH node which work on collecting the cluster members readings and reorder it in descending order to send it next to the sink. The probability to deliver the important data to detect the event to the sink will increase by using this new method. The proposed mechanism intends to improve the event detection reliability, minimize the end-to-end delay, and increase the network lifetime. Experiments results show that the proposed method achieved a good the performance in terms of packets delivery, event detection, and end-to-end delay.
Cite as
Ameer A. Al-Shammaa and A. J. Stocker. A Novel Method for Event Detection using Wireless Sensor Networks. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, p. 9:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@InProceedings{alshammaa_et_al:OASIcs.ICCSW.2018.9,
author = {Al-Shammaa, Ameer A. and Stocker, A. J.},
title = {{A Novel Method for Event Detection using Wireless Sensor Networks}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
pages = {9:1--9:1},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.9},
URN = {urn:nbn:de:0030-drops-101904},
doi = {10.4230/OASIcs.ICCSW.2018.9},
annote = {Keywords: Waspmote nodes, Critical events, Wireless Sensor Networks, Clustering}
}
Document
Context-Aware Adaptive Biometrics System using Multiagents
Abstract
Traditional biometric systems are designed and configured to operate in predefined circumstances to address the needs of a particular application. The performance of such biometrics systems tend to decrease because when they encounter varying conditions as they are unable to adapt to such variations. Many real-life scenarios require identification systems to recognise uncooperative people in uncontrolled environments. Therefore, there is a real need to design biometric systems that are aware of their context and be able to adapt to changing conditions.
The context-awareness and adaptation of a biometric system are based on a set of factors that include: the application (e.g. healthcare system, border control, unlock smart devices), environment (e.g. quiet/noisy, indoor/outdoor), desired and pre-defined requirements (e.g. speed, usability, reliability, accuracy, robustness to high/low quality samples), user of the system (e.g. cooperative or non-cooperative), the chosen modality (e.g. face, speech, gesture signature), and used techniques (e.g. pre-processing to normalise and clean biometrics data, feature extraction and classification). These factors are linked and might affect each other, hence the system has to work adaptively to meet its overall aim based to its operational context.
The aim of this research is to develop a multiagent based framework to represent a context-aware adaptive biometric system. This is to improve the decision making process at each processing step of traditional biometric identification systems. Agents will be used to provide the system with intelligence, adaptation, flexibility, automation, and reliability during the identification process. The framework will accommodate at least five agents, one for each of the five main processing steps of a typical biometric system (i.e. data capture, pre-processing, feature extraction, classification and decision). Each agent can contribute differently towards its designated goal to achieve the best possible solution by selecting/ applying the best technique. For example, an agent can be used to assess the quality of the input biometric sample to ensure the important features can be extracted and processed in further steps. Another agent can be used to pre-process the biometric sample if necessary. A third agent is used to select the appropriate set of features followed by another to select a suitable classifier that works well in a given condition.
Cite as
Fatina Shukur and Harin Sellahewa. Context-Aware Adaptive Biometrics System using Multiagents. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, p. 10:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@InProceedings{shukur_et_al:OASIcs.ICCSW.2018.10,
author = {Shukur, Fatina and Sellahewa, Harin},
title = {{Context-Aware Adaptive Biometrics System using Multiagents}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
pages = {10:1--10:1},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.10},
URN = {urn:nbn:de:0030-drops-101910},
doi = {10.4230/OASIcs.ICCSW.2018.10},
annote = {Keywords: Biometrics, Multiagents, Context-Aware}
}
Document
Harnessing AI For Research
Abstract
Artificial Intelligence is increasingly being used to both augment existing fields of research and open up new avenues of discovery. From quality control for imaging flow cytometry to computational musicology, modern AI is an exciting new tool for research and thus knowing how to engineer AI systems in a research context is a vital new skill for RSEs to acquire. In this talk, I will outline four different areas of AI: supervised learning, unsupervised learning, interactive learning, and Bayesian learning. For each of these approaches, I will discuss how they typically map to different research problems and explore best practices for RSEs via specific use cases. At the end of the talk, you will have received a high-level overview of AI technologies and their use in research, have seen some cool examples of how AI has been used in a wide range of research areas, and have a good sense of where to go to learn more.
Cite as
Matthew Johnson. Harnessing AI For Research. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, p. 11:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
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@InProceedings{johnson:OASIcs.ICCSW.2018.11,
author = {Johnson, Matthew},
title = {{Harnessing AI For Research}},
booktitle = {2018 Imperial College Computing Student Workshop (ICCSW 2018)},
pages = {11:1--11:1},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-097-2},
ISSN = {2190-6807},
year = {2019},
volume = {66},
editor = {Pirovano, Edoardo and Graversen, Eva},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.11},
URN = {urn:nbn:de:0030-drops-101922},
doi = {10.4230/OASIcs.ICCSW.2018.11},
annote = {Keywords: Artificial intelligence}
}