Ontology-based Activity Recognition Framework and Services

This paper introduces an ontology-based integrated framework for activity modeling, activity recognition and activity model evolution. Central to the framework is ontological activity modeling and semantic-based activity recognition, which is supported by an iterative process that incrementally improves the completeness and accuracy of activity models. In addition, the paper presents a service-oriented architecture for the realization of the proposed framework which can provide activity context-aware services in a scalable distributed manner. The paper further describes and discusses the implementation and testing experience of the framework and services in the context of smart home based assistive living

Automatic Metadata Generation Through Analysis of Narration Within Instructional Videos

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Current activity recognition based assistive living solutions have adopted relatively rigid models of inhabitant activities. These solutions have some deficiencies associated with the use of these models. To address this, a goal-oriented solution has been proposed. In a goal-oriented solution, goal models offer a method of flexibly modelling inhabitant activity. The flexibility of these goal models can dynamically produce a large number of varying action plans that may be used to guide inhabitants. In order to provide illustrative, video-based, instruction for these numerous actions plans, a number of video clips would need to be associated with each variation. To address this, rich metadata may be used to automatically match appropriate video clips from a video repository to each specific, dynamically generated, activity plan. This study introduces a mechanism of automatically generating suitable rich metadata representing actions depicted within video clips to facilitate such video matching. This performance of this mechanism was evaluated using eighteen video files; during this evaluation metadata was automatically generated with a high level of accuracy

Test Coverage Analysis by Diffing Production Logs Against Integration Test Logs

This disclosure describes tools and techniques to analyze input and configuration coverage of software by performing large scale analysis of production and integration test data in a privacy-compliant manner. Test coverage analysis is made possible for highly complex input schema or for input schema that evolve with time. The techniques uncover the input-subspace that has been thus far integration-tested and the input-subspace that remains untested (known as test-gaps). When gathering data on how a specific software input/configuration is used in production, the techniques determine not only if they are used, but also how often they are used. Such data can help a developer evaluate the severity associated with a specific test gap

Tritrophic genetic interactions in Acyrthosiphon pisum

Anthropogenic climate change is putting ecosystems under threat and so it has become increasingly important to understand community structure in order to protect the integrity of remaining global ecosystems. One understudied element of community structuring is the role of intraspecific variation (ITV) and how it interacts with indirect genetic effects (IGEs). IGEs can act across multiple trophic levels and by altering these effects, ITV can have a cascading role across ecosystems. A small community across several trophic levels is therefore ideal to study the effect ITV has on IGEs and the well-studied pea aphid (Acyrthosiphon pisum) provides this. A. pisum is entirely dependent on its host plant for nutrition. The aphid also has a parasitoid wasp (Aphidius ervi) and a bacterial symbiont (Hamiltonella defensa) providing protection against the wasp. Strong coevolutionary pressures within this community would be expected. To explore the effects of intra- and interspecific variation across different trophic levels I looked at aphid fecundity and resistance to A. ervi using a variety of aphid, Hamiltonella and host plant genotypes as well as plant species. Host plant species altered innate aphid parasitoid resistance as well as symbiont mediated protection and plant genotype significantly interacted with aphid genotype to affect A. ervi susceptibility. Aphid fecundity was affected by host plant species and Hamiltonella genotype but surprisingly there was no variation in fecundity between aphid clones. Hamiltonella also offered a different protective effect across two different aphid genotypes. The observed role of host plants in parasitoid resistance was novel and raises interesting questions about the evolutionary pressures governing plant-aphid-symbiont-parasitoid interactions. Aphid host plants altering the aphid genotype effect on Hamiltonella mediated protection provides evidence for the importance of multitrophic ITV in communities. Thus, future work into understanding the role native and irregular plant hosts have on aphid-parasitoid defences would be insightful

From Activity Recognition to Intention Recognition for Assisted Living Within Smart Homes

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The global population is aging; projections show that by 2050, more than 20% of the population will be aged over 64. This will lead to an increase in aging related illness, a decrease in informal support, and ultimately issues with providing care for these individuals. Assistive smart homes provide a promising solution to some of these issues. Nevertheless, they currently have issues hindering their adoption. To help address some of these issues, this study introduces a novel approach to implementing assistive smart homes. The devised approach is based upon an intention recognition mechanism incorporated into an intelligent agent architecture. This approach is detailed and evaluated. Evaluation was performed across three scenarios. Scenario 1 involved a web interface, focusing on testing the intention recognition mechanism. Scenarios 2 and 3 involved retrofitting a home with sensors and providing assistance with activities over a period of 3 months. The average accuracy for these three scenarios was 100%, 64.4%, and 83.3%, respectively. Future will extend and further evaluate this approach by implementing advanced sensor-filtering rules and evaluating more complex activities

Goal Lifecycles and Ontological Models for Intention Based Assistive Living within Smart Environments

Current ambient assistive living solutions have adopted a traditional sensor-centric approach, involving data analysis and activity recognition to provide assistance to individuals. The reliance on sensors and activity recognition in this approach introduces issues with scalability and ability to model activity variations. This study introduces a novel approach to assistive living which intends to address these issues via a paradigm shift from a sensor centric approach to a goal-oriented one. The goal-oriented approach focuses on identification of user goals in order to pro-actively offer assistance by either pre-defined or dynamically constructed instructions. This paper introduces the architecture of this goal-oriented approach and describes an ontological goal model to serve as its basis. The use of this approach is illustrated in a case study which focuses on assisting a user with activities of daily living