Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

Ontological approach to development of computing with words based systems

AbstractComputing with words introduced by Zadeh becomes a very important concept in processing of knowledge represented in the form of propositions. Two aspects of this concept – approximation and personalization – are essential to the process of building intelligent systems for human-centric computing.For the last several years, Artificial Intelligence community has used ontology as a means for representing knowledge. Recently, the development of a new Internet paradigm – the Semantic Web – has led to introduction of another form of ontology. It allows for defining concepts, identifying relationships among these concepts, and representing concrete information. In other words, an ontology has become a very powerful way of representing not only information but also its semantics.The paper proposes an application of ontology, in the sense of the Semantic Web, for development of computing with words based systems capable of performing operations on propositions including their semantics. The ontology-based approach is very flexible and provides a rich environment for expressing different types of information including perceptions. It also provides a simple way of personalization of propositions. An architecture of computing with words based system is proposed. A prototype of such a system is described

An agility-oriented and fuzziness-embedded semantic model for collaborative cloud service search, retrieval and recommendation

Cloud computing enables a revolutionary paradigm of consuming ICT services. However, due to the inadequately described service information, users often feel confused while trying to find the optimal services. Although some approaches are proposed to deal with cloud service semantic modelling and recommendation issues, they would only work for certain restricted scenarios in dealing with basic service specifications. Indeed, the missing extent is that most cloud services are "agile" whilst there are many vague service terms and descriptions. This paper proposes an agility-oriented and fuzziness-embedded ontology model, which adopts agility-centric design along with OWL2 (Web Ontology Language) fuzzy extensions. The captured cloud service specifications are maintained in an open and collaborative manner, as the fuzziness in the model accepts rating updates from users on the fly. The model enables comprehensive service specification by capturing cloud concept details and their interactions, even across multiple service categories and abstraction levels. Utilizing the model as a knowledge base, a service recommendation system prototype is developed. Case studies demonstrate that the approach can outperform existing practices by achieving effective service search, retrieval and recommendation outcomes

A semantic framework for unified cloud service search, recommendation, retrieval and management

Cloud computing (CC) is a revolutionary paradigm of consuming Information and Communication Technology (ICT) services. However, while trying to find the optimal services, many users often feel confused due to the inadequacy of service information description. Although some efforts are made in the semantic modelling, retrieval and recommendation of cloud services, existing practices would only work effectively for certain restricted scenarios to deal for example with basic and non-interactive service specifications. In the meantime, various service management tasks are usually performed individually for diverse cloud resources for distinct service providers. This results into significant decreased effectiveness and efficiency for task implementation. Fundamentally, it is due to the lack of a generic service management interface which enables a unified service access and manipulation regardless of the providers or resource types.To address the above issues, the thesis proposes a semantic-driven framework, which integrates two main novel specification approaches, known as agility-oriented and fuzziness-embedded cloud service semantic specifications, and cloud service access and manipulation request operation specifications. These consequently enable comprehensive service specification by capturing the in-depth cloud concept details and their interactions, even across multiple service categories and abstraction levels. Utilising the specifications as CC knowledge foundation, a unified service recommendation and management platform is implemented. Based on considerable experiment data collected on real-world cloud services, the approaches demonstrate distinguished effectiveness in service search, retrieval and recommendation tasks whilst the platform shows outstanding performance for a wide range of service access, management and interaction tasks. Furthermore, the framework includes two sets of innovative specification processing algorithms specifically designed to serve advanced CC tasks: while the fuzzy rating and ontology evolution algorithms establish a manner of collaborative cloud service specification, the service orchestration reasoning algorithms reveal a promising means of dynamic service compositions

Online annotations tools for micro-level human behavior labeling on videos

Abstract. Successful machine learning and computer vision approach generally require significant amounts of annotated data for learning. These methods including identification, retrieval, classification of events, and analysis of human behavior from a video. Micro-level human behavior analysis usually requires laborious efforts for obtaining the precise labels. As the quantity of online video grows, the crowdsourcing approach provides a method for workers without a professional background to complete the annotation task. These workers require training to understand implicit knowledge of human behavior. The motivation of this study was to enhance the interaction between annotation workers for training purposes. By observing experienced local researchers in Oulu, the key problem with annotation is the precision of the results. The goal of this study was to provide training tools for people to improve the label quality, it illustrates the importance of training. In this study, a new annotation tool was developed to test workers’ performance in reviewing other annotations. This tool filters very noisy input by comment and vote feature. The result indicated that users were more likely to annotate micro behavior and time that refer to other opinions, and it was a more effective and reliable way to train. Besides, this study reported the development process with React and Firebase, it emphasized the use of more Web resources and tools to develop annotation tools

Towards an aspect weaving BPEL engine

This position paper proposes the use of dynamic aspects and the visitor design pattern to obtain a highly configurable and extensible BPEL engine. Using these two techniques, the core of this infrastructural software can be customised to meet new requirements and add features such as debugging, execution monitoring, or changing to another Web Service selection policy. Additionally, it can easily be extended to cope with customer-specific BPEL extensions. We propose the use of dynamic aspects not only on the engine itself but also on the workflow in order to tackle the problems of Web Service hot deployment and hot fixes to long running processes. In this way, composing aWeb Service "on-the-fly" means weaving its choreography interface into the workflow

PowerAqua: supporting users in querying and exploring the semantic web

With the continued growth of online semantic information, the processes of searching and managing this massive scale and heterogeneous content have become increasingly challenging. In this work, we present PowerAqua, an ontology basedcQuestion Answering system that is able to answer queries by locating and integrating information, which can be massively distributed across heterogeneous semantic resources. We provide a complete overview of the system including: the research challenges that it addresses, its architecture, the evaluations that have been conducted to test it and a deep discussion showing how PowerAqua effectively supports users in querying and exploring the Semantic Web content