Lucas’s abstract

“Indoor environment quality has a significant effect on worker productivity through a complex interplay of factors such as temperature, humidity and levels of Volatile Organic Compounds (VOCs).

In this talk I will discuss my Masters project which used off the shelf sensors and Raspberry Pis to collect environmental readings at one minute intervals throughout the Computer Science buildings. The prevalence of erroneous readings due to sensor failure and the strategy used for the identification and correction of such faults will be presented. Identifiable correlations between environmental variables and attempts to model these relationships will be discussed

Past studies identifying the ideal environmental conditions for human comfort and productivity allow for the objective assessment of indoor environmental conditions. An adaptation of Frešer’s environment rating system will be presented, showing how VOC levels can be incorporated into assessments of environment quality and how this can be communicated to building users.”

Nnamdi’s abstract

“Cloud computing is becoming an almost ubiquitous part of the computing landscape. For many companies today, moving their entire infrastructure and workloads to the cloud reduces complexity, time to deployment, and saves money. Spot Instances, a subset of Amazon’s cloud computing infrastructure (EC2), expands on this. They allow a user to bid on spare compute capacity in Amazon’s data centres at heavily discounted prices. If demand was ever to increase such that the user’s maximum bid is exceeded, their compute instance is terminated.

In this work, we conduct one of the first detailed analyses of how location affects the overall cost of deployment of a spot instance. We simultaneously examine the reliability of pricing data of a spot instance, and whether a user can be confident that their instance has a low risk of termination.

We analyse spot pricing data across all available Amazon Web Services regions for 60 days on a variety of instance types. We find that location does play a critical role in spot instance pricing and also that pricing differs depending on the granularity of the location – from a more coarse-grained AWS region to a more fine-grained Availability Zone within a region. We relate the pricing differences we find to the price’s stability, confirming whether we can be confident in the bid prices we make.

We conclude by showing that it is very possible to run workloads on Spot Instances achieving
both a very low risk of termination as well as paying very low amounts per hour.”

Esma’s abstract

The ability to integrate information from different sensory modalities in a social context is crucial for achieving an understanding of social cues and gaining useful social interaction and experience. Recent research has focused on multi-modal integration of social signals from visual, auditory, haptic or physiological data. Different data fusion techniques have been designed and developed; however, the majority have not achieved significant accuracy improvement in recognising social cues compared to uni-modal social signal recognition. One of the possible limitations is that these existing approaches have no sufficient capacity to model various types of interactions between different modalities and have not been able to leverage the advantages of multi-modal signals by considering each of them as complementary to the others. We introduce ideas for creating a decentralised model for social signals integration inspired by computational models of multi-sensory integration in neuroscience and the perception of social signals in the human brain.

Sheriffo’s abstract

The recent boom of big data, coupled with the challenges of its processing and storage gave rise to the development of distributed data processing and storage paradigms like MapReduce, Spark, and NoSQL databases. With the advent of cloud computing, processing and storing such massive datasets on clusters of machines is now feasible with ease. However, there are limited tools and approaches, which users can rely on to gauge and comprehend the performance of their big data applications deployed locally on clusters, or in the cloud. Researchers have started exploring this area by providing benchmarking suites suitable for big data applications. However, many of these tools are fragmented, complex to deploy and manage, and do not provide transparency with respect to the monetary cost of benchmarking an application.

In this talk, I will present Plug And Play Bench PAPB (https://github.com/sneceesay77/papb): an infrastructure aware abstraction built to integrate and simplify the process of big data benchmarking. PAPB automates the tedious process of installing, configuring and executing common big data benchmark workloads by containerising the tools and settings based on the underlying cluster deployment framework. Our proof of concept implementation utilises HiBench as the benchmark suite, HDP as the cluster deployment framework and Azure as the cloud platform. The talk will further illustrate the inclusion of cost metrics based on the underlying Microsoft Azure cloud platform.