An architecture for user preference-based IoT service selection in cloud computing using mobile devices for smart campus

The Internet of things refers to the set of objects that have identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social environments and user context. Interconnected devices communicating to each other or to other machines on the network have increased the number of services. The concepts of discovery, brokerage, selection and reliability are important in dynamic environments. These concepts have emerged as an important field distinguished from conventional distributed computing by its focus on large-scale resource sharing, delivery and innovative applications. The usage of Internet of Things technology across different service provisioning environments has increased the challenges associated with service selection and discovery. Although a set of terms can be used to express requirements for the desired service, a more detailed and specific user interface would make it easy for the users to express their requirements using high-level constructs. In order to address the challenge of service selection and discovery, we developed an architecture that enables a representation of user preferences and manipulates relevant descriptions of available services. To ensure that the key components of the architecture work, algorithms (content-based and collaborative filtering) derived from the architecture were proposed. The architecture was tested by selecting services using content-based as well as collaborative algorithms. The performances of the algorithms were evaluated using response time. Their effectiveness was evaluated using recall and precision. The results showed that the content-based recommender system is more effective than the collaborative filtering recommender system. Furthermore, the results showed that the content-based technique is more time-efficient than the collaborative filtering technique

Information-seeking on the Web with Trusted Social Networks - from Theory to Systems

This research investigates how synergies between the Web and social networks can enhance the process of obtaining relevant and trustworthy information. A review of literature on personalised search, social search, recommender systems, social networks and trust propagation reveals limitations of existing technology in areas such as relevance, collaboration, task-adaptivity and trust. In response to these limitations I present a Web-based approach to information-seeking using social networks. This approach takes a source-centric perspective on the information-seeking process, aiming to identify trustworthy sources of relevant information from within the user's social network. An empirical study of source-selection decisions in information- and recommendation-seeking identified five factors that influence the choice of source, and its perceived trustworthiness. The priority given to each of these factors was found to vary according to the criticality and subjectivity of the task. A series of algorithms have been developed that operationalise three of these factors (expertise, experience, affinity) and generate from various data sources a number of trust metrics for use in social network-based information seeking. The most significant of these data sources is Revyu.com, a reviewing and rating Web site implemented as part of this research, that takes input from regular users and makes it available on the Semantic Web for easy re-use by the implemented algorithms. Output of the algorithms is used in Hoonoh.com, a Semantic Web-based system that has been developed to support users in identifying relevant and trustworthy information sources within their social networks. Evaluation of this system's ability to predict source selections showed more promising results for the experience factor than for expertise or affinity. This may be attributed to the greater demands these two factors place in terms of input data. Limitations of the work and opportunities for future research are discussed

Generic adaptation framework for unifying adaptive web-based systems

The Generic Adaptation Framework (GAF) research project first and foremost creates a common formal framework for describing current and future adaptive hypermedia (AHS) and adaptive webbased systems in general. It provides a commonly agreed upon taxonomy and a reference model that encompasses the most general architectures of the present and future, including conventional AHS, and different types of personalization-enabling systems and applications such as recommender systems (RS) personalized web search, semantic web enabled applications used in personalized information delivery, adaptive e-Learning applications and many more. At the same time GAF is trying to bring together two (seemingly not intersecting) views on the adaptation: a classical pre-authored type, with conventional domain and overlay user models and data-driven adaptation which includes a set of data mining, machine learning and information retrieval tools. To bring these research fields together we conducted a number GAF compliance studies including RS, AHS, and other applications combining adaptation, recommendation and search. We also performed a number of real systems’ case-studies to prove the point and perform a detailed analysis and evaluation of the framework. Secondly it introduces a number of new ideas in the field of AH, such as the Generic Adaptation Process (GAP) which aligns with a layered (data-oriented) architecture and serves as a reference adaptation process. This also helps to understand the compliance features mentioned earlier. Besides that GAF deals with important and novel aspects of adaptation enabling and leveraging technologies such as provenance and versioning. The existence of such a reference basis should stimulate AHS research and enable researchers to demonstrate ideas for new adaptation methods much more quickly than if they had to start from scratch. GAF will thus help bootstrap any adaptive web-based system research, design, analysis and evaluation

Agent-oriented constructivist knowledge management

In Ancient Times, when written language was introduced, books and manuscripts were often considered sacred. During these times, only a few persons were able to read and interpret them, while most people were limited in accepting these interpretations. Then, along with the industrial revolution of the XVIII and XIX centuries and especially boosted by the development of the press, knowledge slowly became available to all people. Simultaneously, people were starting to apply machines in the development of their work, usually characterized by repetitive processes, and especially focused in the production of consuming goods, such as furniture, clocks, clothes and so on. Following the needs of this new society, it was finally through science that new processes emerged to enable the transmission of knowledge from books and instructors to learners. Still today, people gain knowledge based on these processes, created to fulfill the needs of a society in its early stages of industrialization, thus not being compatible with the needs of the information society. In the information society, people must deal with an overloading amount of information, by the means of the media, books, besides different telecommunication and information systems technology. Furthermore, people’s relation to work has been influenced by profound changes, for instance, knowledge itself is now regarded as a valuable work product and, thus, the workplace has become an environment of knowledge creation and learning. Modifications in the world economical, political and social scenarios led to the conclusion that knowledge is the differential that can lead to innovation and, consequently, save organizations, societies, and even countries from failing in achieving their main goals. Focusing on these matters is the Knowledge Management (KM) research area, which deals with the creation, integration and use of knowledge, aiming at improving the performance of individuals and organizations. Advances in this field are mainly motivated by the assumption that organizations should focus on knowledge assets (generally maintained by the members of an organization) to remain competitive in the information society’s market. This thesis argues that KM initiatives should be targeted based on a constructivist perspective. In general, a constructivist view on KM focuses on how knowledge emerges, giving great importance to the knowledge holders and their natural practices. With the paragraph above, the reader may already have an intuition of how this work faces and targets Knowledge Management, however, let us be more precise. Research in Knowledge Management has evolved substantially in the past 30 years, coming from a centralized view of KM processes to a distributed view, grounded in organizational and cognitive sciences studies that point out the social, distributed, and subjective nature of knowledge. The first Knowledge Management Systems (KMSs) were centrally based and followed a top-down design approach. The organization managers, supported by knowledge engineers, collected and structured the contents of an organizational memory as a finished product at design time (before the organizational memory was deployed) and then disseminated the product, expecting employees to use it and update it. However, employees often claimed that the knowledge stored in the repository was detached from their real working practices. This led to the development of evolutionary methods, which prescribe that the basic KM system is initially developed and evolves proactively in an on-going fashion. However, most of the initiatives are still based on building central repositories and portals, which assume standardized vocabularies, languages, and classification schemes. Consequently, employees’ lack of trust and motivation often lead to dissatisfaction. In other words, workers resist on sharing knowledge, since they do not know who is going to access it and what is going to be done with it. Moreover, the importance attributed to knowledge may give an impression that these central systems take away a valuable asset from his or her owner, without giving appreciable benefits in return. The problems highlighted in the previous paragraph may be attenuated or even solved if a top-down/bottom-up strategy is applied when proposing a KM solution. This means that the solution should be sought with aim at organizational goals (top-down) but at the same time, more attention should be given to the knowledge holders and on the natural processes they already use to share knowledge (bottom-up). Being active agency such an important principle of Constructivism, this work recognizes that the Agent Paradigm (first defined by Artificial Intelligence and more recently adopted by Software Engineering) is the best approach to target Knowledge Management, taking a technological and social perspective. Capable of modeling and supporting social environments, agents is here recognized as a suitable solution for Knowledge Management especially by providing a suitable metaphor used for modeling KM domains (i.e. representing humans and organizations) and systems. Applying agents as metaphors on KM is mainly motivated by the definition of agents as cognitive beings having characteristics that resemble human cognition, such as autonomy, reactivity, goals, beliefs, desires, and social-ability. Using agents as human abstractions is motivated by the fact that, for specific problems, such as software engineering and knowledge management process modeling, agents may aid the analyst to abstract away from some of the problems related to human complexity, and focus on the important issues that impact the specific goals, beliefs and tasks of agents of the domain. This often leads to a clear understanding of the current situation, which is essential for the proposal of an appropriate solution. The current situation may be understood by modeling at the same time the overall goals of the organization, and the needs and wants of knowledge holders. Towards facilitating the analysis of KM scenarios and the development of adequate solutions, this work proposes ARKnowD (Agent-oriented Recipe for Knowledge Management Systems Development). Systems here have a broad definition, comprehending both technology-based systems (e.g. information system, groupware, repositories) and/or human systems, i.e. human processes supporting KM using non-computational artifacts (e.g. brain stormings, creativity workshops). The basic philosophical assumptions behind ARKnowD are: a) the interactions between human and system should be understood according to the constructivist principle of self-construction, claiming that humans and communities are self-organizing entities that constantly construct their identities and evolve throughout endless interaction cycles. As a result of such interactions, humans shape systems and, at the same time, systems constrain the ways humans act and change; b) KM enabling systems should be built in a bottom-up approach, aiming at the organizational goals, but understanding that in order to fulfill these goals, some personal needs and wants of the knowledge holders (i.e. the organizational members) need to be targeted; and c) there is no “silver bullet��? when pursuing a KM tailoring methodology and the best approach is combining existing agent-oriented approaches according to the given domain or situation. This work shows how the principles above may be achieved by the integration of two existing work on agent-oriented software engineering, which are combined to guide KM analysts and system developers when conceiving KM solutions. Innovation in our work is achieved by supporting topdown/bottom-up approaches to KM as mentioned above. The proposed methodology does that by strongly emphasizing the earlier phases of software development, the so-called requirement analysis activity. In this way, we consider all stakeholders (organizations and humans) as agents in our analysis model, and start by understanding their relations before actually thinking of developing a system. Perhaps the problem may be more effectively solved by proposing changes in the business processes, rather than by making use of new technology. And besides, in addition to humans and organizations, existing systems are also included in the model from start, helping the analyst and designer to understand which functionalities are delegated to these so-called artificial agents. In addition to that, benefits as a result of the application of ARKnowD may be also attributed to our choice of using the proper agent cognitive characteristics in the different phases of the development cycle. With the main purpose of exemplifying the use of the proposed methodology, this work presents a socially-aware recommender agent named KARe (Knowledgeable Agent for Recommendations). Recommender Systems may be defined by those that support users in selecting items of their need from a big set of items, helping users to overcome the overwhelming feeling when facing a vast information source, such as the web, an organizational repository or the like. Besides serving as a case for our methodology, this work also aims at exploring the suitability of the KARe system to support KM processes. Our choice for supporting knowledge sharing through questioning and answering processes is again supported by Constructivism proponents, who understand that social interaction is vital for active knowledge building. This assumption is also defended by some KM theories, claiming that knowledge is created through cycles of transformation between two types of knowledge: tacit and explicit knowledge. Up to now, research on KM has paid much attention to the formalization and exchange of explicit knowledge, in the form of documents or other physical artifacts, often annotated with metadata, and classified by taxonomies or ontologies. Investigations surrounding tacit knowledge have been so far scarce, perhaps by the complexity of the tasks of capturing and integrating such kind of knowledge, defined as knowledge about personal experience and values, usually confined on people’s mind. Taking a flexible approach on supporting this kind of knowledge conversion, KARe relies on the potential of social interaction underlying organizational practices to support knowledge creation and sharing. The global objective of this work is to support knowledge creation and sharing within an organization, according to its own natural processes and social behaviors. In other words, this work is based on the assumption that KM is better supported if knowledge is looked at from a constructivist perspective. To sum up, this thesis aims at: 1) Providing an agent-oriented approach to guide the creation and evolvement of KM initiatives, by analyzing the organizational potentials, behaviors and processes concerning knowledge sharing; 2) Developing the KARe recommender system, based on a semantically enriched Information Retrieval technique for recommending knowledge artifacts, supporting users to ask and answer to each others’ questions. These objectives are achieved as follows: - Defining the principles that characterize a Constructivist KM supporting environment and understanding how they may be used to support the creation of more effective KM solutions; - Providing an agent-oriented approach to develop KM systems. This approach is based on the integration of two different agent-oriented software engineering works, profiting from their strengths in providing a comprehensive methodology that targets both analysis and design activities; - Proposing and designing a socially aware agent-oriented recommender system both to exemplify the application of the proposed approach and to explore its potential on supporting knowledge creation and sharing. - Implementing an Information Retrieval algorithm to support the previously mentioned system in generating recommendations. Besides describing the algorithm, this thesis brings experimental results to prove its effectiveness

Design revolutions: IASDR 2019 Conference Proceedings. Volume 4: Learning, Technology, Thinking

In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 4 includes papers from Learning, Technology and Thinking tracks of the conference

Music information retrieval: conceptuel framework, annotation and user behaviour

Understanding music is a process both based on and influenced by the knowledge and experience of the listener. Although content-based music retrieval has been given increasing attention in recent years, much of the research still focuses on bottom-up retrieval techniques. In order to make a music information retrieval system appealing and useful to the user, more effort should be spent on constructing systems that both operate directly on the encoding of the physical energy of music and are flexible with respect to users’ experiences. This thesis is based on a user-centred approach, taking into account the mutual relationship between music as an acoustic phenomenon and as an expressive phenomenon. The issues it addresses are: the lack of a conceptual framework, the shortage of annotated musical audio databases, the lack of understanding of the behaviour of system users and shortage of user-dependent knowledge with respect to high-level features of music. In the theoretical part of this thesis, a conceptual framework for content-based music information retrieval is defined. The proposed conceptual framework - the first of its kind - is conceived as a coordinating structure between the automatic description of low-level music content, and the description of high-level content by the system users. A general framework for the manual annotation of musical audio is outlined as well. A new methodology for the manual annotation of musical audio is introduced and tested in case studies. The results from these studies show that manually annotated music files can be of great help in the development of accurate analysis tools for music information retrieval. Empirical investigation is the foundation on which the aforementioned theoretical framework is built. Two elaborate studies involving different experimental issues are presented. In the first study, elements of signification related to spontaneous user behaviour are clarified. In the second study, a global profile of music information retrieval system users is given and their description of high-level content is discussed. This study has uncovered relationships between the users’ demographical background and their perception of expressive and structural features of music. Such a multi-level approach is exceptional as it included a large sample of the population of real users of interactive music systems. Tests have shown that the findings of this study are representative of the targeted population. Finally, the multi-purpose material provided by the theoretical background and the results from empirical investigations are put into practice in three music information retrieval applications: a prototype of a user interface based on a taxonomy, an annotated database of experimental findings and a prototype semantic user recommender system. Results are presented and discussed for all methods used. They show that, if reliably generated, the use of knowledge on users can significantly improve the quality of music content analysis. This thesis demonstrates that an informed knowledge of human approaches to music information retrieval provides valuable insights, which may be of particular assistance in the development of user-friendly, content-based access to digital music collections