Demonstration of Software Agents Prototype System : [AIMM] Agents for Improving Maintenance Management

This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. • Refining the development of a multi agent system for data mining in virtual environments (Active Worlds) by developing and implementing a filtering agent on the results obtained from applying data mining techniques on the maintenance data. • Integrating the filtering agent within the multi agents system in an interactive networked multi-user 3D virtual environment. • Populating maintenance data and discovering new rules of knowledge.

Collaborative Filtering Recommender Systems based on Popular Tags

The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences for making personalized recommendations. However, the uncontrolled vocabulary causes a lot of problems to profile users accurately, such as ambiguity, synonyms, misspelling, low information sharing etc. To solve these problems, this paper proposes to use popular tags to represent the actual topics of tags, the content of items, and also the topic interests of users. A novel user profiling approach is proposed in this paper that first identifies popular tags, then represents users’ original tags using the popular tags, finally generates users’ topic interests based on the popular tags. A collaborative filtering based recommender system has been developed that builds the user profile using the proposed approach. The user profile generated using the proposed approach can represent user interests more accurately and the information sharing among users in the profile is also increased. Consequently the neighborhood of a user, which plays a crucial role in collaborative filtering based recommenders, can be much more accurately determined. The experimental results based on real world data obtained from Amazon.com show that the proposed approach outperforms other approaches.

Website physical activity interventions : preferences of potential users

Information and communication technologies (particularly websites and e-mail) have the potential to deliver health behavior change programs to large numbers of adults at low cost. Controlled trials using these new media to promote physical activity have produced mixed results. User-centered development methods can assist in understanding the preferences of potential participants for website functions and content, and may lead to more effective programs. Eight focus group discussions were conducted with 40 adults after they had accessed a previously trialed physical activity website. The discussions were audio taped, transcribed and interpreted using a themed analysis method. Four key themes emerged: structure, interactivity, environmental context and content. Preferences were expressed for websites that include simple interactive features, together with information on local community activity opportunities. Particular suggestions included online community notice boards, personalized progress charts, e-mail access to expert advice and access to information on specific local physical activity facilities and services. Website physical activity interventions could usefully include personally relevant interactive and environmentally focused features and services identified through a user-centered development process.

Integrating collaborative filtering and matching-based search for product recommendation

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and thus help them in making good decisions about which product to buy from the vast number of product choices available to them. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based recommender system approaches. These approaches are not suitable for recommending luxurious and infrequently purchased products as they rely on a large amount of ratings data that is not usually available for such products. This research aims to explore novel approaches for recommending infrequently purchased products by exploiting user generated content such as user reviews and product click streams data. From reviews on products given by the previous users, association rules between product attributes are extracted using an association rule mining technique. Furthermore, from product click streams data, user profiles are generated using the proposed user profiling approach. Two recommendation approaches are proposed based on the knowledge extracted from these resources. The first approach is developed by formulating a new query from the initial query given by the target user, by expanding the query with the suitable association rules. In the second approach, a collaborative-filtering recommender system and search-based approaches are integrated within a hybrid system. In this hybrid system, user profiles are used to find the target user’s neighbour and the subsequent products viewed by them are then used to search for other relevant products. Experiments have been conducted on a real world dataset collected from one of the online car sale companies in Australia to evaluate the effectiveness of the proposed recommendation approaches. The experiment results show that user profiles generated from user click stream data and association rules generated from user reviews can improve recommendation accuracy. In addition, the experiment results also prove that the proposed query expansion and the hybrid collaborative filtering and search-based approaches perform better than the baseline approaches. Integrating the collaborative-filtering and search-based approaches has been challenging as this strategy has not been widely explored so far especially for recommending infrequently purchased products. Therefore, this research will provide a theoretical contribution to the recommender system field as a new technique of combining collaborative-filtering and search-based approaches will be developed. This research also contributes to a development of a new query expansion technique for infrequently purchased products recommendation. This research will also provide a practical contribution to the development of a prototype system for recommending cars.

Chaotic soul - messy heart : the phenomenon of experiencing auditory hallucinations

Globally, it is estimated that 24 million people live with schizophrenia (WHO, 2008), while 1.2 million people have been diagnosed with schizophrenia in Indonesia. Auditory hallucinations are a key symptom of schizophrenia according to the DSM IV-TR (Frances, First, & Pincus, 2002). It is estimated that the prevalence of auditory hallucinations in people with schizophrenia range from 64.3% to 83.4% (Thomas et al., 2007). Until recently, the majority of studies were conducted in Western societies the primary focus of which, has been on the causes and treatments of auditory hallucinations (Walton, 1999) and on the biological and cognitive aspects of the phenomenon (Changas, Garcia-Montes, de Lemus & Olivencia, 2003). While a few studies have explored the lived experience of people with schizophrenia, there is little research about the experience of auditory hallucinations. Therefore, the focus of this study was on an exploration of the experience of auditory hallucinations as described by Indonesian people living with schizophrenia. Based on the available literature, there have been no published qualitative studies relating to the lived experience of auditory hallucinations as described by Indonesian people diagnosed with schizophrenia. Husserlian descriptive phenomenological approach was applied in explicating the phenomenon of auditory hallucinations in this study. In-depth audio-taped interviews were conducted with 13 participants. Analysis of participant transcripts was undertaken using Colaizzi.s (1973) approach. Eight major themes were explicated: Feeling more like a robot than a human being - feeling compelled to respond to auditory hallucinations; voices of contradiction - a point of confusion; a frightening experience, the voices emerged at times of loss and grief; disruption to daily living; tattered relationships and family disarray; finding a personal path to living with auditory hallucinations; seeking relief in Allah through prayer and ritual. Experiencing auditory hallucinations for people diagnosed with schizophrenia is a journey of challenges as each individual struggles to understand their now changed life-world, reconstruct a sense of meaning within their illness experience, and to carve out a pathway to wellness. The challenge for practitioners is to learn from those who have experienced auditory hallucinations, to be with them in their journey of recovery and wellness, and to apply a person-centered approach to care within the context of a multidisciplinary team.

Integrating collaborative filtering and matching-based search for product recommendations

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based approaches. These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the ecommerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user’s query are retrieved and recommended. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user profiling approach is proposed to generate user’s preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similarminded users (i.e., neighbours) accurately. Two recommendation approaches are proposed, namely Round- Robin fusion algorithm (CFRRobin) and Collaborative Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user’s query to search for products as normal search based systems do, the CFRRobin technique uses the attributes of the products in which the target user’s neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of the products that the user’s neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are widely applied by the current e-commerce applications.

Developing a dashboard for real-time data stream composition and visualization

Technological advances have led to an influx of affordable hardware that supports sensing, computation and communication. This hardware is increasingly deployed in public and private spaces, tracking and aggregating a wealth of real-time environmental data. Although these technologies are the focus of several research areas, there is a lack of research dealing with the problem of making these capabilities accessible to everyday users. This thesis represents a first step towards developing systems that will allow users to leverage the available infrastructure and create custom tailored solutions. It explores how this notion can be utilized in the context of energy monitoring to improve conventional approaches. The project adopted a user-centered design process to inform the development of a flexible system for real-time data stream composition and visualization. This system features an extensible architecture and defines a unified API for heterogeneous data streams. Rather than displaying the data in a predetermined fashion, it makes this information available as building blocks that can be combined and shared. It is based on the insight that individual users have diverse information needs and presentation preferences. Therefore, it allows users to compose rich information displays, incorporating personally relevant data from an extensive information ecosystem. The prototype was evaluated in an exploratory study to observe its natural use in a real-world setting, gathering empirical usage statistics and conducting semi-structured interviews. The results show that a high degree of customization does not warrant sustained usage. Other factors were identified, yielding recommendations for increasing the impact on energy consumption.

FixVegas : facilitating multi-directional communication between government officials and citizens to support urban planning in the city of Brisbane

The goal of this project was to develop a mobile application for the iOS platform, that would support the partner of this project, the Brisbane City Council, in stronger engage citizens in participating in urban planning and development projects. The resulting application is an extended version of FixVegas, a system that allows citizens to report maintenance request to the Brisbane City Council through their smartphone. The new version of the system makes all incoming requests publicly available within the application, allows users to support, comment or disapprove of these. As an addition, the concept of the idea has been introduced. Citizens can submit suggestions for improving the city to the municipality, discuss them with other fellow citizens and, ideally, also with Council representatives. The city officials as well are provided with the ability of publishing development project as an idea and let citizens deliberate it. This way, bidirectional communication between these two parties is created. A web interface complements the iPhone application. The system has been developed after the principle of User Centered Design, by assessing user needs, creating and evaluating prototypes and conducting a user study. The study showed that FixVegas2 has been perceived as an enhancement compared to the previous version, and that the idea concept has been received on a positive note. Indepth questions, such as the influence the system could have on community dynamics or the public participation in urban planning projects could only hardly investigated. However, these findings can be achieved by the alternative study designs that have been proposed.

Supporting collaboration on very large-scale interactive wall surfaces

In this paper we describe CubIT, a multi-user presentation and collaboration system installed at the Queensland University of Technology’s (QUT) Cube facility. The ‘Cube’ is an interactive visualisation facility made up of five very large-scale interactive multi-panel wall displays, each consisting of up to twelve 55-inch multi-touch screens (48 screens in total) and massive projected display screens situated above the display panels. The paper outlines the unique design challenges, features, implementation and evaluation of CubIT. The system was built to make the Cube facility accessible to QUT’s academic and student population. CubIT enables users to easily upload and share their own media content, and allows multiple users to simultaneously interact with the Cube’s wall displays. The features of CubIT were implemented via three user interfaces, a multi-touch interface working on the wall displays, a mobile phone and tablet application and a web-based content management system. Each of these interfaces plays a different role and offers different interaction mechanisms. Together they support a wide range of collaborative features including multi-user shared workspaces, drag and drop upload and sharing between users, session management and dynamic state control between different parts of the system. The results of our evaluation study showed that CubIT was successfully used for a variety of tasks, and highlighted challenges with regards to user expectations regarding functionality as well as issues arising from public use.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Life is too short to RTFM : how users relate to documentation and excess features in consumer products

This paper addresses two common problems that users of various products and interfaces encounter— over-featured interfaces and product documentation. Over-featured interfaces are seen as a problem as they can confuse and over-complicate everyday interactions. Researchers also often claim that users do not read product documentation, although they are often exhorted to ‘RTFM’(read the field manual).We conducted two sets of studies with users which looked at the issues of both manuals and excess features with common domestic and personal products. The quantitative set was a series of questionnaires administered to 170 people over 7 years. The qualitative set consisted of two 6-month longitudinal studies based on diaries and interviews with a total of 15 participants. We found that manuals are not read by the majority of people, and most do not use all the features of the products that they own and use regularly. Men are more likely to do both than women, and younger people are less likely to use manuals than middle-aged and older ones. More educated people are also less likely to read manuals. Over-featuring and being forced to consult manuals also appears to cause negative emotional experiences. Implications of these findings are discussed.

A lightweight, modular robotic vehicle for the sustainable intensification of agriculture

This paper describes a lightweight, modular and energy efficient robotic vehicle platform designed for broadacre agriculture - the Small Robotic Farm Vehicle (SRFV). The current trend in farming is towards increasingly large machines that optimise the individual farmer’s productivity. Instead, the SRFV is designed to promote the sustainable intensification of agriculture by allowing farmers to concentrate on more important farm management tasks. The robot has been designed with a user-centred approach which focuses the outcomes of the project on the needs of the key project stakeholders. In this way user and environmental considerations for broadacre farming have informed the vehicle platform configuration, locomotion, power requirements and chassis construction. The resultant design is a lightweight, modular four-wheeled differential steer vehicle incorporating custom twin in-hub electric drives with emergency brakes. The vehicle is designed for a balance between low soil impact, stability, energy efficiency and traction. The paper includes modelling of the robot’s dynamics during an emergency brake in order to determine the potential for tipping. The vehicle is powered by a selection of energy sources including rechargeable lithium batteries and petrol-electric generators.

Building personas of students accessing a peer-facilitated support for learning program

The modern student represents a change from the traditional learner. More than ever before, additional resources are available online and yet personalised learning and peer-assistance programs are becoming an essential part of tertiary education delivery. This paper presents the first stage in a user-centred design approach to the analysis of the completeness and efficacy of such a personalised, peer-based support for learning program. This approach used an iterative design methodology based on contextual interview, workshops and focus groups to develop personas representing students visiting the program. Initial uses of these developed personas have included training of new personnel as well as the evaluation of the program. Overall the use of this user-centred approach and iterative persona development methodology has yielded an invaluable resource for the design of support for learning programs across the higher education industry within Australia and beyond.

Algorithm of medication review in frail older people: Focus on minimizing the use of high-risk medications

Aim Frail older people typically suffer several chronic diseases, receive multiple medications and are more likely to be institutionalized in residential aged care facilities. In such patients, optimizing prescribing and avoiding use of high-risk medications might prevent adverse events. The present study aimed to develop a pragmatic, easily applied algorithm for medication review to help clinicians identify and discontinue potentially inappropriate high-risk medications. Methods The literature was searched for robust evidence of the association of adverse effects related to potentially inappropriate medications in older patients to identify high-risk medications. Prior research into the cessation of potentially inappropriate medications in older patients in different settings was synthesized into a four-step algorithm for incorporation into clinical assessment protocols for patients, particularly those in residential aged care facilities. Results The algorithm comprises several steps leading to individualized prescribing recommendations: (i) identify a high-risk medication; (ii) ascertain the current indications for the medication and assess their validity; (iii) assess if the drug is providing ongoing symptomatic benefit; and (iv) consider withdrawing, altering or continuing medications. Decision support resources were developed to complement the algorithm in ensuring a systematic and patient-centered approach to medication discontinuation. These include a comprehensive list of high-risk medications and the reasons for inappropriateness, lists of alternative treatments, and suggested medication withdrawal protocols. Conclusions The algorithm captures a range of different clinical scenarios in relation to potentially inappropriate medications, and offers an evidence-based approach to identifying and, if appropriate, discontinuing such medications. Studies are required to evaluate algorithm effects on prescribing decisions and patient outcomes.