Volume
OASIcs, Volume 60
2017 Imperial College Computing Student Workshop (ICCSW 2017)
- Part of: Series: Open Access Series in Informatics (OASIcs)
Event
ICCSW 2017, September 26-27, 2017, London, UKEditors
Publication Details
- published at: 2018-02-21
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- ISBN: 978-3-95977-059-0
- DBLP: db/conf/iccsw/iccsw2017
Access Numbers
- Detailed Access Statistics available here
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Document
Complete Volume
OASIcs, Volume 60, ICCSW'17, Complete Volume
Abstract
OASIcs, Volume 60, ICCSW'17, Complete Volume
Cite as
2017 Imperial College Computing Student Workshop (ICCSW 2017). Open Access Series in Informatics (OASIcs), Volume 60, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
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@Proceedings{leahy_et_al:OASIcs.ICCSW.2017,
title = {{OASIcs, Volume 60, ICCSW'17, Complete Volume}},
booktitle = {2017 Imperial College Computing Student Workshop (ICCSW 2017)},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-059-0},
ISSN = {2190-6807},
year = {2018},
volume = {60},
editor = {Leahy, Fergus and Franco, Juliana},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2017},
URN = {urn:nbn:de:0030-drops-85425},
doi = {10.4230/OASIcs.ICCSW.2017},
annote = {Keywords: Concurrent Programming, Design Tools and Techniques, Performance measures, Software Architectures, Language Constructs and Features}
}
Document
Front Matter
Front Matter, Table of Contents, Preface, Conference Organization
Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as
2017 Imperial College Computing Student Workshop (ICCSW 2017). Open Access Series in Informatics (OASIcs), Volume 60, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
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@InProceedings{leahy_et_al:OASIcs.ICCSW.2017.0,
author = {Leahy, Fergus and Franco, Juliana},
title = {{Front Matter, Table of Contents, Preface, Conference Organization}},
booktitle = {2017 Imperial College Computing Student Workshop (ICCSW 2017)},
pages = {0:i--0:xii},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-059-0},
ISSN = {2190-6807},
year = {2018},
volume = {60},
editor = {Leahy, Fergus and Franco, Juliana},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2017.0},
URN = {urn:nbn:de:0030-drops-84421},
doi = {10.4230/OASIcs.ICCSW.2017.0},
annote = {Keywords: Front Matter, Table of Contents, Preface, Conference Organization, External Reviewers}
}
Document
How to Write a Great Research Paper
Abstract
Writing papers is a core research skill for any researcher, but they aren't easy. Writing is not just a way to report on great research; it's a way to do great research. Yet many papers are so badly written that, even if they describe excellent work, the work has much less impact than it should. In this talk I'll give you seven simple, actionable guidelines that will, I hope, help you to write better papers, and have more fun at the same time. I don’t have all the answers—far from it—and I hope that the presentation will evolve into a discussion in which you share your own insights, rather than a lecture.
Cite as
Simon Peyton Jones. How to Write a Great Research Paper. In 2017 Imperial College Computing Student Workshop (ICCSW 2017). Open Access Series in Informatics (OASIcs), Volume 60, p. 1:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
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@InProceedings{peytonjones:OASIcs.ICCSW.2017.1,
author = {Peyton Jones, Simon},
title = {{How to Write a Great Research Paper}},
booktitle = {2017 Imperial College Computing Student Workshop (ICCSW 2017)},
pages = {1:1--1:1},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-059-0},
ISSN = {2190-6807},
year = {2018},
volume = {60},
editor = {Leahy, Fergus and Franco, Juliana},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2017.1},
URN = {urn:nbn:de:0030-drops-84436},
doi = {10.4230/OASIcs.ICCSW.2017.1},
annote = {Keywords: Academia, Research, Writing}
}
Document
Optimizing the Unoptimizable: A Whirlwind Tour of JavaScript
Abstract
A whirlwind tour through the history and state-of-the-art of JavaScript execution and optimization, with a focus on the V8 engine used by Chrome and Node.js, and how a 10-day prototype became one of the most important programming languages in the world.
Cite as
Leszek Swirski. Optimizing the Unoptimizable: A Whirlwind Tour of JavaScript. In 2017 Imperial College Computing Student Workshop (ICCSW 2017). Open Access Series in Informatics (OASIcs), Volume 60, p. 2:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
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@InProceedings{swirski:OASIcs.ICCSW.2017.2,
author = {Swirski, Leszek},
title = {{Optimizing the Unoptimizable: A Whirlwind Tour of JavaScript}},
booktitle = {2017 Imperial College Computing Student Workshop (ICCSW 2017)},
pages = {2:1--2:1},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-059-0},
ISSN = {2190-6807},
year = {2018},
volume = {60},
editor = {Leahy, Fergus and Franco, Juliana},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2017.2},
URN = {urn:nbn:de:0030-drops-84448},
doi = {10.4230/OASIcs.ICCSW.2017.2},
annote = {Keywords: Javascript, NodeJS}
}
Document
Improving the Latency and Throughput of ZooKeeper Atomic Broadcast
Abstract
ZooKeeper is a crash-tolerant system that offers fundamental services to Internet-scale applications, thereby reducing the development and hosting of the latter. It consists of >3 servers that form a replicated state machine. Maintaining these replicas in a mutually consistent state requires executing an Atomic Broadcast Protocol, Zab, so that concurrent requests for state changes are serialised identically at all replicas before being acted upon.
Thus, ZooKeeper performance for update operations is determined by Zab performance. We contribute by presenting two easy-to-implement Zab variants, called ZabAC and ZabAA. They are designed to offer small atomic-broadcast latencies and to reduce the processing load on the primary node that plays a leading role in Zab. The former improves ZooKeeper performance and the latter enables ZooKeeper
to face more challenging load conditions.
Cite as
Ibrahim EL-Sanosi and Paul Ezhilchelvan. Improving the Latency and Throughput of ZooKeeper Atomic Broadcast. In 2017 Imperial College Computing Student Workshop (ICCSW 2017). Open Access Series in Informatics (OASIcs), Volume 60, pp. 3:1-3:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
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@InProceedings{elsanosi_et_al:OASIcs.ICCSW.2017.3,
author = {EL-Sanosi, Ibrahim and Ezhilchelvan, Paul},
title = {{Improving the Latency and Throughput of ZooKeeper Atomic Broadcast}},
booktitle = {2017 Imperial College Computing Student Workshop (ICCSW 2017)},
pages = {3:1--3:10},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-059-0},
ISSN = {2190-6807},
year = {2018},
volume = {60},
editor = {Leahy, Fergus and Franco, Juliana},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2017.3},
URN = {urn:nbn:de:0030-drops-84452},
doi = {10.4230/OASIcs.ICCSW.2017.3},
annote = {Keywords: Atomic Broadcast, Server Replication, Protocol Latency, Throughput}
}
Document
Demand for Medical Care by the Elderly: A Nonparametric Variational Bayesian Mixture Approach
Abstract
Outpatient care is a large share of total health care spending, making analysis of data on outpatient utilization an important part of understanding patterns and drivers of health care spending growth. Common features of outpatient utilization measures include zero-inflation, over-dispersion, and skewness, all of which complicate statistical modeling. Mixture modeling is a popular approach because it can accommodate these features of health care utilization data. In this work, we add a nonparametric clustering component to such models. Our fully Bayesian model framework allows for an unknown number of mixing components, so that the data, rather than the researcher, determine the number of mixture components. We apply the modeling framework to data on visits to physicians by elderly individuals and show that each subgroup has different characteristics that allow easy interpretation and new insights.
Cite as
Christoph F. Kurz and Rolf Holle. Demand for Medical Care by the Elderly: A Nonparametric Variational Bayesian Mixture Approach. In 2017 Imperial College Computing Student Workshop (ICCSW 2017). Open Access Series in Informatics (OASIcs), Volume 60, pp. 4:1-4:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
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@InProceedings{kurz_et_al:OASIcs.ICCSW.2017.4,
author = {Kurz, Christoph F. and Holle, Rolf},
title = {{Demand for Medical Care by the Elderly: A Nonparametric Variational Bayesian Mixture Approach}},
booktitle = {2017 Imperial College Computing Student Workshop (ICCSW 2017)},
pages = {4:1--4:7},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-059-0},
ISSN = {2190-6807},
year = {2018},
volume = {60},
editor = {Leahy, Fergus and Franco, Juliana},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2017.4},
URN = {urn:nbn:de:0030-drops-84462},
doi = {10.4230/OASIcs.ICCSW.2017.4},
annote = {Keywords: machine learning, health care utilization, Bayesian statistics}
}
Document
Discriminative and Generative Models for Clinical Risk Estimation: An Empirical Comparison
Abstract
Linear discriminative models, in the form of Logistic Regression, are a popular choice within the clinical domain in the development of risk models. Logistic regression is commonly used as it offers explanatory information in addition to its predictive capabilities. In some examples the coefficients from these models have been used to determine overly simplified clinical risk scores. Such models are constrained to modeling linear relationships between the variables and the class despite it known that this relationship is not always linear. This paper compares the conditions under which linear discriminative and linear generative models perform best. This is done through comparing logistic regression and naïve Bayes on real clinical data. The work shows that generative models perform best when the internal representation of the data is closer to the true distribution of the data and when there is a very small difference between the means of the classes. When looking at variables such as sodium it is shown that logistic regression can not model the observed risk as it is non-linear in its nature, whereas naïve Bayes gives a better estimation of risk. The work concludes that the risk estimations derived from discriminative models such as logistic regression need to be considered in the wider context of the true risk observed within the dataset.
Cite as
John Stamford and Chandra Kambhampati. Discriminative and Generative Models for Clinical Risk Estimation: An Empirical Comparison. In 2017 Imperial College Computing Student Workshop (ICCSW 2017). Open Access Series in Informatics (OASIcs), Volume 60, pp. 5:1-5:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
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@InProceedings{stamford_et_al:OASIcs.ICCSW.2017.5,
author = {Stamford, John and Kambhampati, Chandra},
title = {{Discriminative and Generative Models for Clinical Risk Estimation: An Empirical Comparison}},
booktitle = {2017 Imperial College Computing Student Workshop (ICCSW 2017)},
pages = {5:1--5:9},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-059-0},
ISSN = {2190-6807},
year = {2018},
volume = {60},
editor = {Leahy, Fergus and Franco, Juliana},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2017.5},
URN = {urn:nbn:de:0030-drops-84475},
doi = {10.4230/OASIcs.ICCSW.2017.5},
annote = {Keywords: Discriminative, Generative, Na\"{i}ve Bayes, Logistic Regression, Clinical Risk}
}
Document
Hey there's DALILA: a DictionAry LearnIng LibrAry
Abstract
Dictionary Learning and Representation Learning are machine learning methods for decomposition, denoising and reconstruction of data with a wide range of applications such as text recognition, image processing and biological processes understanding. In this work we present DALILA, a scientific Python library for regularised dictionary learning and regularised representation learning that allows to impose prior knowledge, if available. DALILA, differently from the others available libraries for this purpose, is flexible and modular. DALILA is designed to be easily extended for custom needs. Moreover, it is compliant with the most widespread ML Python library and this allows for a straightforward usage and integration.
We here present and discuss the theoretical aspects and discuss its strength points and implementation.
Cite as
Veronica Tozzo, Vanessa D'Amario, and Annalisa Barla. Hey there's DALILA: a DictionAry LearnIng LibrAry. In 2017 Imperial College Computing Student Workshop (ICCSW 2017). Open Access Series in Informatics (OASIcs), Volume 60, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
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@InProceedings{tozzo_et_al:OASIcs.ICCSW.2017.6,
author = {Tozzo, Veronica and D'Amario, Vanessa and Barla, Annalisa},
title = {{Hey there's DALILA: a DictionAry LearnIng LibrAry}},
booktitle = {2017 Imperial College Computing Student Workshop (ICCSW 2017)},
pages = {6:1--6:14},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-059-0},
ISSN = {2190-6807},
year = {2018},
volume = {60},
editor = {Leahy, Fergus and Franco, Juliana},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2017.6},
URN = {urn:nbn:de:0030-drops-84483},
doi = {10.4230/OASIcs.ICCSW.2017.6},
annote = {Keywords: Machine learning, dictionary learning, representation learning, alternating proximal gradient descent, parallel computing}
}
Document
Faster Concurrent Range Queries with Contention Adapting Search Trees Using Immutable Data
Abstract
The need for scalable concurrent ordered set data structures with linearizable range query support is increasing due to the rise of multicore computers, data processing platforms and in-memory databases. This paper presents a new concurrent ordered set with linearizable range query support. The new data structure is based on the contention adapting search tree and an immutable data structure.
Experimental results show that the new data structure is as much as three times faster compared to related data structures. The data structure scales well due to its ability to adapt the sizes of its immutable parts to the contention level and the sizes of the range queries.
Cite as
Kjell Winblad. Faster Concurrent Range Queries with Contention Adapting Search Trees Using Immutable Data. In 2017 Imperial College Computing Student Workshop (ICCSW 2017). Open Access Series in Informatics (OASIcs), Volume 60, pp. 7:1-7:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
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@InProceedings{winblad:OASIcs.ICCSW.2017.7,
author = {Winblad, Kjell},
title = {{Faster Concurrent Range Queries with Contention Adapting Search Trees Using Immutable Data}},
booktitle = {2017 Imperial College Computing Student Workshop (ICCSW 2017)},
pages = {7:1--7:13},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-059-0},
ISSN = {2190-6807},
year = {2018},
volume = {60},
editor = {Leahy, Fergus and Franco, Juliana},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2017.7},
URN = {urn:nbn:de:0030-drops-84492},
doi = {10.4230/OASIcs.ICCSW.2017.7},
annote = {Keywords: linearizability, concurrent data structures, treap}
}
Document
Gesture Recognition and Classification using Intelligent Systems
Abstract
Gesture Recognition is defined as non-verbal human motions used as a method of communication in HCI interfaces. In a virtual reality system, gestures can be used to navigate, control, or interact with a computer. Having a person make gestures formed in specific ways to be detected by a device, like a camera, is the foundation of gesture recognition. Finger tracking is an interesting principle which deals with three primary parts of computer vision: segmentation of the finger, detection of finger parts, and tracking of the finger. Fingers are most commonly used in varying gesture recognition systems.
Finger gestures can be detected using any type of camera; keeping in mind that different cameras will yield different resolution qualities. 2-dimensional cameras exhibit the ability to detect most finger motions in a constant surface called 2-D. While the image processes, the system prepares to receive the whole image so that it may be tracked using image processing tools. Artificial intelligence releases many classifiers, each one with the ability to classify data, that rely on its configuration and capabilities. In this work, the aim is to develop a system for finger motion acquisition in 2-D using feature extraction algorithms such as Wavelets transform (WL) and Empirical Mode Decomposition (EMD) plus Artificial Neural Network (ANN) classifier.
Cite as
Norah Alnaim and Maysam Abbod. Gesture Recognition and Classification using Intelligent Systems. In 2017 Imperial College Computing Student Workshop (ICCSW 2017). Open Access Series in Informatics (OASIcs), Volume 60, p. 8:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
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@InProceedings{alnaim_et_al:OASIcs.ICCSW.2017.8,
author = {Alnaim, Norah and Abbod, Maysam},
title = {{Gesture Recognition and Classification using Intelligent Systems}},
booktitle = {2017 Imperial College Computing Student Workshop (ICCSW 2017)},
pages = {8:1--8:1},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-059-0},
ISSN = {2190-6807},
year = {2018},
volume = {60},
editor = {Leahy, Fergus and Franco, Juliana},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2017.8},
URN = {urn:nbn:de:0030-drops-84508},
doi = {10.4230/OASIcs.ICCSW.2017.8},
annote = {Keywords: Wavelets, Empirical Model Decomposition, Artificial Neural Network, Gesture Recognition, HCI}
}
Document
KubeNow: A Cloud Agnostic Platform for Microservice-Oriented Applications
Abstract
KubeNow is a platform for rapid and continuous deployment of microservice-based applications over cloud infrastructure. Within the field of software engineering, the microservice-based architecture is a methodology in which complex applications are divided into smaller, more narrow services. These services are independently deployable and compatible with each other like building blocks. These blocks can be combined in multiple ways, according to specific use cases. Microservices are designed around a few concepts: they offer a minimal and complete set of features, they are portable and platform independent, they are accessible through language agnostic APIs and they are encouraged to use standard data formats. These characteristics promote separation of concerns, isolation and interoperability, while coupling nicely with test-driven development. Among many others, some well-known companies that build their software around microservices are: Google, Amazon, PayPal Holdings Inc. and Netflix [11].
Cite as
Marco Capuccini, Anders Larsson, Salman Toor, and Ola Spjuth. KubeNow: A Cloud Agnostic Platform for Microservice-Oriented Applications. In 2017 Imperial College Computing Student Workshop (ICCSW 2017). Open Access Series in Informatics (OASIcs), Volume 60, pp. 9:1-9:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
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@InProceedings{capuccini_et_al:OASIcs.ICCSW.2017.9,
author = {Capuccini, Marco and Larsson, Anders and Toor, Salman and Spjuth, Ola},
title = {{KubeNow: A Cloud Agnostic Platform for Microservice-Oriented Applications}},
booktitle = {2017 Imperial College Computing Student Workshop (ICCSW 2017)},
pages = {9:1--9:2},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-059-0},
ISSN = {2190-6807},
year = {2018},
volume = {60},
editor = {Leahy, Fergus and Franco, Juliana},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2017.9},
URN = {urn:nbn:de:0030-drops-84511},
doi = {10.4230/OASIcs.ICCSW.2017.9},
annote = {Keywords: Microservices, Cloud computing, Infrastructure as Code, Docker, Kubernetes}
}