Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Preserving Social Media: the Problem of Access

As the applications and services made possible through Web 2.0 continue to proliferate and influence the way individuals exchange information, the landscape of social science research, as well as research in the humanities and the arts, has the potential to change dramatically and to be enriched by a wealth of new, user-generated data. In response to this phenomenon, the UK Data Service have commissioned the Digital Preservation Coalition to undertake a 12-month study into the preservation of social media as part of the ‘Big Data Network’ programme funded by the Economic and Social Research Council (ESRC). The larger study focuses on the potential uses and accompanying challenges of data generated by social networking applications. This paper, ‘Preserving Social Media: the Problem of Access’, comprises an excerpt of that longer study, allowing the authors a space to explore in closer detail the issue of making social media archives accessible to researchers and students now and in the future. To do this, the paper addresses use cases that demonstrate the potential value of social media to academic social science. Furthermore, it examines how researchers and collecting institutions acquire and preserve social media data within a context of curatorial and legislative restrictions that may prove an even greater obstacle to access than any technical restrictions. Based on analysis of these obstacles, it will examine existing methods of curating and preserving social media archives, and second, make some recommendations for how collecting institutions might approach the long-term preservation of social media in a way that protects the individuals represented in the data and complies with the conditions of third party platforms. With the understanding that web-based communication technologies will continue to evolve, this paper will focus on the overarching properties of social media, analysing and comparing current methods of curation and preservation that provide sustainable solutions

Keeping Context In Mind: Automating Mobile App Access Control with User Interface Inspection

Recent studies observe that app foreground is the most striking component that influences the access control decisions in mobile platform, as users tend to deny permission requests lacking visible evidence. However, none of the existing permission models provides a systematic approach that can automatically answer the question: Is the resource access indicated by app foreground? In this work, we present the design, implementation, and evaluation of COSMOS, a context-aware mediation system that bridges the semantic gap between foreground interaction and background access, in order to protect system integrity and user privacy. Specifically, COSMOS learns from a large set of apps with similar functionalities and user interfaces to construct generic models that detect the outliers at runtime. It can be further customized to satisfy specific user privacy preference by continuously evolving with user decisions. Experiments show that COSMOS achieves both high precision and high recall in detecting malicious requests. We also demonstrate the effectiveness of COSMOS in capturing specific user preferences using the decisions collected from 24 users and illustrate that COSMOS can be easily deployed on smartphones as a real-time guard with a very low performance overhead.Comment: Accepted for publication in IEEE INFOCOM'201

COSMOS-7: Video-oriented MPEG-7 scheme for modelling and filtering of semantic content

MPEG-7 prescribes a format for semantic content models for multimedia to ensure interoperability across a multitude of platforms and application domains. However, the standard leaves it open as to how the models should be used and how their content should be filtered. Filtering is a technique used to retrieve only content relevant to user requirements, thereby reducing the necessary content-sifting effort of the user. This paper proposes an MPEG-7 scheme that can be deployed for semantic content modelling and filtering of digital video. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user

COSMOS: Towards an integrated and scalable service for analysing social media on demand

The growing number of people using social media to publish their opinions, share expertise, make social connections and promote their ideas to an international audience is creating data on an epic scale. This enables social scientists to conduct research into ethnography, discourse analysis and analysis of social interactions, providing insight into today's society, which is largely augmented by social computing. The tools available for such analysis are often proprietary and expensive, and often non-interoperable, meaning the rapid marshalling of large data-sets through a range of analyses is arduous and difficult to scale. The collaborative online social media observatory (COSMOS), an integrated social media analysis tool is presented, developed for open access within academia. COSMOS is underpinned by a scalable Hadoop infrastructure and can support the rapid analysis of large data-sets and the orchestration of workflows between tools with limited human effort. We describe an architecture and scalability results for the computational analysis of social media data, and comment on the storage, search and retrieval issues associated with massive social media data-sets. We also provide an insight into the impact of such an integrated on-demand service in the social science academic community

A review of GIS-based information sharing systems

GIS-based information sharing systems have been implemented in many of England and Wales' Crime and Disorder Reduction Partnerships (CDRPs). The information sharing role of these systems is seen as being vital to help in the review of crime, disorder and misuse of drugs; to sustain strategic objectives, to monitor interventions and initiatives; and support action plans for service delivery. This evaluation into these systems aimed to identify the lessons learned from existing systems, identify how these systems can be best used to support the business functions of CDRPs, identify common weaknesses across the systems, and produce guidelines on how these systems should be further developed. At present there are in excess of 20 major systems distributed across England and Wales. This evaluation considered a representative sample of ten systems. To date, little documented evidence has been collected by the systems that demonstrate the direct impact they are having in reducing crime and disorder, and the misuse of drugs. All point to how they are contributing to more effective partnership working, but all systems must be encouraged to record how they are contributing to improving community safety. Demonstrating this impact will help them to assure their future role in their CDRPs. By reviewing the systems wholly, several key ingredients were identified that were evident in contributing to the effectiveness of these systems. These included the need for an effective partnership business model within which the system operates, and the generation of good quality multi-agency intelligence products from the system. In helping to determine the future development of GIS-based information sharing systems, four key community safety partnership business service functions have been identified that these systems can most effectively support. These functions support the performance review requirements of CDRPs, operate a problem solving scanning and analysis role, and offer an interface with the public. By following these business service functions as a template will provide for a more effective application of these systems nationally