Stacked Autoencoder and Meta-Learning based Heterogeneous Domain Adaptation for Human Activity Recognition

The field of human activity recognition (HAR) using machine learning approaches has gained a lot of interest in the research community due to its empowerment of automation and autonomous systems in industries and homes with respect to the given context and due to the increasing number of smart wearable devices. However, it is challenging to achieve a considerable accuracy for recognizing actions with diverse set of wearable devices due to their variance in feature spaces, sampling rate, units, sensor modalities and so forth. Furthermore, collecting annotated data has always been a serious issue in the machine learning community. Domain adaptation is a field that helps to cope with the issue by training on the source domain and labeling the samples in the target domain, however, due to the aforementioned variances (heterogeneity) in wearable sensor data, the action recognition accuracy remains on the lower side. Existing studies try to make the target domain feature space compliant with the source domain to improve the results, but it assumes that the system has a prior knowledge of the feature space of the target domain, which does not reflect real-world implication. In this regard, we propose stacked autoencoder and meta-learning based heterogeneous domain adaptation (SAM- HDD) network. The stacked autoencoder part is trained on the source domain feature space to extract the latent representation and train the employed classifiers, accordingly. The classification probabilities from the classifiers are trained with meta-learner to further improve the recognition performance. The data from tar- get domain undergoes the encoding layers of the trained stacked autoencoders to extract the latent representations, followed by the classification of label from the trained classifiers and meta- learner. The results show that the proposed approach is efficient in terms of accuracy score and achieves best results among the existing works, respectivel

Towards Activity Context using Software Sensors

Service-Oriented Computing delivers the promise of configuring and reconfiguring software systems to address user's needs in a dynamic way. Context-aware computing promises to capture the user's needs and hence the requirements they have on systems. The marriage of both can deliver ad-hoc software solutions relevant to the user in the most current fashion. However, here it is a key to gather information on the users' activity (that is what they are doing). Traditionally any context sensing was conducted with hardware sensors. However, software can also play the same role and in some situations will be more useful to sense the activity of the user. Furthermore they can make use of the fact that Service-oriented systems exchange information through standard protocols. In this paper we discuss our proposed approach to sense the activity of the user making use of software

Activity awareness in context-aware systems using software sensors

Context-aware systems being a component of ubiquitous or pervasive computing environment sense the users’ physical and virtual surrounding to adapt their behaviour accordingly. To achieve activity context tracking devices are common practice. Service Oriented Architecture is based on collections of services that communicate with each other. The communication between users and services involves data that can be used to sense the activity context of the user. SOAP is a simple protocol to let applications exchange their information over the web. Semantic Web provides standards to express the relationship between data to allow machines to process data more intelligently. This work proposes an approach for supporting context-aware activity sensing using software sensors. The main challenges in the work are specifying context information in a machine processable form, developing a mechanism that can understand the data extracted from exchanges of services, utilising the data extracted from these services, and the architecture that supports sensing with software sensors. To address these issues, we have provided a bridge to combine the traditional web services with the semantic web technologies, a knowledge structure that supports the activity context information in the context-aware environments and mapping methods that extract the data out of exchanges occurring between user and services and map it into a context model. The Direct Match, the Synonym Match and the Hierarchical Match methods are developed to put the extracted data from services to the knowledge structure. This research will open doors to further develop automated and dynamic context-aware systems that can exploit the software sensors to sense the activity of the user in the context-aware environments

Activity awareness in context-aware systems using software sensors

Context-aware systems being a component of ubiquitous or pervasive computing environment sense the users’ physical and virtual surrounding to adapt their behaviour accordingly. To achieve activity context tracking devices are common practice. Service Oriented Architecture is based on collections of services that communicate with each other. The communication between users and services involves data that can be used to sense the activity context of the user. SOAP is a simple protocol to let applications exchange their information over the web. Semantic Web provides standards to express the relationship between data to allow machines to process data more intelligently. This work proposes an approach for supporting context-aware activity sensing using software sensors. The main challenges in the work are specifying context information in a machine processable form, developing a mechanism that can understand the data extracted from exchanges of services, utilising the data extracted from these services, and the architecture that supports sensing with software sensors. To address these issues, we have provided a bridge to combine the traditional web services with the semantic web technologies, a knowledge structure that supports the activity context information in the context-aware environments and mapping methods that extract the data out of exchanges occurring between user and services and map it into a context model. The Direct Match, the Synonym Match and the Hierarchical Match methods are developed to put the extracted data from services to the knowledge structure. This research will open doors to further develop automated and dynamic context-aware systems that can exploit the software sensors to sense the activity of the user in the context-aware environments.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Activity awareness in context-aware systems using software sensors

Context-aware systems being a component of ubiquitous or pervasive computing environment sense the users’ physical and virtual surrounding to adapt their behaviour accordingly. To achieve activity context tracking devices are common practice. Service Oriented Architecture is based on collections of services that communicate with each other. The communication between users and services involves data that can be used to sense the activity context of the user. SOAP is a simple protocol to let applications exchange their information over the web. Semantic Web provides standards to express the relationship between data to allow machines to process data more intelligently. This work proposes an approach for supporting context-aware activity sensing using software sensors. The main challenges in the work are specifying context information in a machine processable form, developing a mechanism that can understand the data extracted from exchanges of services, utilising the data extracted from these services, and the architecture that supports sensing with software sensors. To address these issues, we have provided a bridge to combine the traditional web services with the semantic web technologies, a knowledge structure that supports the activity context information in the context-aware environments and mapping methods that extract the data out of exchanges occurring between user and services and map it into a context model. The Direct Match, the Synonym Match and the Hierarchical Match methods are developed to put the extracted data from services to the knowledge structure. This research will open doors to further develop automated and dynamic context-aware systems that can exploit the software sensors to sense the activity of the user in the context-aware environments.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Mapping for activity recognition in the context-aware systems using software sensors

Context-aware systems are concerned with identifying the context of a user and then to either provide that information based on queries or to automatically decide on appropriate actions to be taken. Some context aspects (such as location) are easy to sense through hardware, while the activity of a user has shown to be somewhat elusive to being sensed with hardware sensors. As users use web services more frequently they are exchanging messages with the services through the SOAP protocol. SOAP messages contain data, which is valuable if gathered and interpreted right - especially as this data can be shedding information on the activity of a user that goes beyond "sitting at the computer and typing". We have developed software sensors, essentially based on monitoring SOAP messages and inserting data for further reasoning and querying into a semantic context model. In this paper we consider a solution to map the data from a SOAP message to our OWL ontology model automatically. Specifically, we explain the methodology to map from SOAP messages to an existing structure of knowledge

User-Centric Advertisement using Software Sensors Technique

Contextual advertising is one of the most critical components in the economic system of internet due to increase internet publisher’s income highly dependent on the user-centric advertisement that is displayed on the sites according to the user context during interaction with the multiple sites. Previous contextual advertisement research work generally emphasises on investigating either to the keyword they type, content of the sites or uses any other application from the network hence, this finding has identified work when being extended through the user’s context. In this work we have looked at users’ profile information and user preferences to reach the users according to their context. These smart devices are ready with all capabilities to give useful contexts including information about physical environment, social connection, user internal and external context. These logical contexts beyond just content of the web pages, search keywords, and profile information are well used and organized for user-centric advertising. Here we are also arguing the appearances of the logical contexts which are available on the user browser, profile and visibly define the challenges of results from these logical contexts to improve the advertisement. We present a user-centric advertisement architecture and model that collects to integrate the users’ profile context and activity context to select, generate and to present advertisement with context. Finally, we discuss to implement the aspects of design and one specific application and outline our plans for future

Cyber Threats/Attacks and a Defensive Model to Mitigate Cyber Activities

Nowadays, every internet user is part of cyber world. In this way, millions of users, knowledge seekers, and service provider organizations are connected to each other, a vast number of common people shifted their everyday activities to cyber world as they can save their time, traffic problem and gets effective and costless services by using various services such as, online banking, social networking sites, government services and cloud services. The use of Cyber services, eBusiness, eCommerce and eGovernance increases the usage of online/cyber services also increased the issue of cyber security. Recently, various cases have been reported in the literature and media about the cyber-attacks and crimes which seriously disrupted governments, businesses and personal lives. From the literature. It is noticed that every cyber user is unaware about privacy and security practices and measures. Therefore, cyber user has provided knowledge and fully aware them from the online services and also about cyber privacy and security. This paper presents a review on the recent cybercrimes, threats and attacks reported in the literature and media. In addition, the impact of these cyber breaches and cyber law to deal with cyber security has been discussed. At last, a defensive model is also proposed to mitigate cyber-criminal activities