Can you teach all of the people some of the time? The development and evaluation of information literacy programs at the University of Queensland Library

Information skills and computer literacy are now seen by many within the academic community as essential. They are key graduate attributes required by students for lifelong learning and leadership roles in business and industry, government and society. Trends in the higher education sector bringing a renewed focus to teaching and preparing students for a global knowledge economy are outlined.

This paper focuses upon the power of a teaching and learning policy framework which supports the integration of information literacy into the curriculum. The increasing ease with which collaborative partnerships are formed between academic planners, course coordinators and librarians is highlighted by case studies of successful programs. Challenges are identified and change strategies described.

Building genetic networks using relatedness information : a novel approach for the estimation of dispersal and characterization of group structure in social animals

Natal dispersal is an important life history trait driving variation in individual fitness, and therefore, a proper understanding of the factors underlying dispersal behaviour is critical to many fields including population dynamics, behavioural ecology and conservation biology. However, individual dispersal patterns remain difficult to quantify despite many years of research using direct and indirect methods. Here, we quantify dispersal in a single intensively studied population of the cooperatively breeding chestnut-crowned babbler (Pomatostomus ruficeps) using genetic networks created from the combination of pairwise relatedness data and social networking methods and compare this to dispersal estimates from re-sighting data. This novel approach not only identifies movements between social groups within our study sites but also provides an estimation of immigration rates of individuals originating outside the study site. Both genetic and re-sighting data indicated that dispersal was strongly female biased, but the magnitude of dispersal estimates was much greater using genetic data. This suggests that many previous studies relying on mark–recapture data may have significantly underestimated dispersal. An analysis of spatial genetic structure within the sampled population also supports the idea that females are more dispersive, with females having no structure beyond the bounds of their own social group, while male genetic structure expands for 750 m from their social group. Although the genetic network approach we have used is an excellent tool for visualizing the social and genetic microstructure of social animals and identifying dispersers, our results also indicate the importance of applying them in parallel with behavioural and life history data.

The impact of ITIL (information technology infrastructure library) recommended practices on the IT outsourcing relationship

Over the last two decades, the outsourcing of IT services has become a popular topic for many IS researchers. Furthermore, managing IT services (both internally and externally provided) has become an emerging area for academic research, given the criticality of IT services in modern organizations. One of the better known IT service management frameworks is the Information Technology Infrastructure Library (ITIL) framework. While there are many claims made about the relationship between ITIL and IT outsourcing, these claims still need further empirical research. Using data gathered from a preliminary focus group, this study investigates how ITIL impacts recommended practices on the success of IT outsourcing arrangement.

Exploring the global and local networks of national information system integration initiatives : a case study of national cultural collection integration from an actor-network perspective

Australian Museums Online (AMOL) was the earliest attempt to make Australia’s distributed cultural collections accessible from a single online resource. Despite early successes, significant achievements and the considerable value it offered certain groups, the project ran into operational difficulties and was eventually discontinued. By using Actor-Network Theory and analysing the global and local actor-networks, it is revealed that although the project originated from large, state museums, buy-in was restricted to individuals, rather than institutions and the most significant value was for smaller, regional institutions. Furthermore, although the global networks that governed the project could translate their visions through the local production networks, because the network’s underlying weaknesses were never addressed, over time this destablised the global networks. This case study offers advice for projects attempting to consolidate data sources from disparate sources, and highlights the importance of individual actors in championing the project.

The role of health literacy and social networks in arthritis patients' health information-seeking behavior : a qualitative study

Background. Patients engage in health information-seeking behaviour to maintain their wellbeing and to manage chronic diseases such as arthritis. Health literacy allows patients to understand available treatments and to critically appraise information they obtain from a wide range of sources.

Aims. To explore how arthritis patients' health literacy affects engagement in arthritis-focused health information-seeking behaviour and the selection of sources of health information available through their informal social network.

Methods. An exploratory, qualitative study consisting of one-on-one semi-structured interviews. Twenty participants with arthritis were recruited from community organizations. The interviews were designed to elicit participants' understanding about their arthritis and arthritis medication and to determine how the participants' health literacy informed selection of where they found information about their arthritis and pain medication.

Results. Participants with low health literacy were less likely to be engaged with health information-seeking behaviour. Participants with intermediate health literacy were more likely to source arthritis-focused health information from newspapers, television, and within their informal social network. Those with high health literacy sourced information from the internet and specialist health sources and were providers of information within their informal social network.

Conclusion. Health professionals need to be aware that levels of engagement in health information-seeking behaviour and sources of arthritis-focused health information may be related to their patients' health literacy.

DDoS attack detection at local area networks using information theoretical metrics

DDoS attacks are one of the major threats to Internet services. Sophisticated hackers are mimicking the features of legitimate network events, such as flash crowds, to fly under the radar. This poses great challenges to detect DDoS attacks. In this paper, we propose an attack feature independent DDoS flooding attack detection method at local area networks. We employ flow entropy on local area network routers to supervise the network traffic and raise potential DDoS flooding attack alarms when the flow entropy drops significantly in a short period of time. Furthermore, information distance is employed to differentiate DDoS attacks from flash crowds. In general, the attack traffic of one DDoS flooding attack session is generated by many bots from one botnet, and all of these bots are executing the same attack program. As a result, the similarity among attack traffic should higher than that among flash crowds, which are generated by many random users. Mathematical models have been established for the proposed detection strategies. Analysis based on the models indicates that the proposed methods can raise the alarm for potential DDoS flooding attacks and can differentiate DDoS flooding attacks from flash crowds with conditions. The extensive experiments and simulations confirmed the effectiveness of our proposed detection strategies.

An adaptive approach to information discovery in multi-dimensional wireless sensor networks

Multidimensional WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multidimensional data). Such networks present unique challenges to data dissemination, data storage and in-network query processing (information discovery). In this paper, we investigate efficient strategies for information discovery in large-scale multidimensional WSNs and propose the Adaptive MultiDimensional Multi-Resolution Architecture (A-MDMRA) that efficiently combines “push” and “pull” strategies for information discovery and adapts to variations in the frequencies of events and queries in the network to construct optimal routing structures. We present simulation results showing the optimal routing structure depends on the frequency of events and query occurrence in the network. It also balances push and pull operations in large scale networks enabling significant QoS improvements and energy savings.

Are the popular users always important for information dissemination in online social networks?

This article verifies the importance of popular users in OSNs. The results are counter-intuitive. First, for dissemination speed, a large amount of users can swiftly distribute information to the masses, but they are not highly-connected users. Second, for dissemination scale, many powerful forwarders in OSNs cannot be identified by the degree measure. Furthermore, to control dissemination, popular users cannot capture most bridges of social communities.

Information discovery in multi-dimensional autonomous wireless sensor networks

 The thesis proposed four novel algorithms of information discovery for Multidimensional Autonomous Wireless Sensor Networks (WSNs) that can significantly increase network lifetime and minimize query processing latency, resulting in quality of service improvements that are of immense benefit to Multidimensional Autonomous WSNs are deployed in complex environments (e.g., mission-critical applications).

A sword with two edges: propagation studies on both positive and negative information in online social networks

Online social networks (OSN) have become one of the major platforms for people to exchange information. Both positive information (e.g., ideas, news and opinions) and negative information (e.g., rumors and gossips) spreading in social media can greatly influence our lives. Previously, researchers have proposed models to understand their propagation dynamics. However, those were merely simulations in nature and only focused on the spread of one type of information. Due to the human-related factors involved, simultaneous spread of negative and positive information cannot be thought of the superposition of two independent propagations. In order to fix these deficiencies, we propose an analytical model which is built stochastically from a node level up. It can present the temporal dynamics of spread such as the time people check newly arrived messages or forward them. Moreover, it is capable of capturing people's behavioral differences in preferring what to believe or disbelieve. We studied the social parameters impact on propagation using this model. We found that some factors such as people's preference and the injection time of the opposing information are critical to the propagation but some others such as the hearsay forwarding intention have little impact on it. The extensive simulations conducted on the real topologies confirm the high accuracy of our model.

Novel approach for information discovery in autonomous wireless sensor networks

Autonomous Wireless sensor networks(WSNs) have sensors that are usually deployed randomly to monitor one or more phenomena. They are attractive for information discovery in large-scale data rich environments and can add value to mission–critical applications such as battlefield surveillance and emergency response systems. However, in order to fully exploit these networks for such applications, energy efficient, load balanced and scalable solutions for information discovery are essential. Multi-dimensional autonomous WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). Such networks present unique challenges to data dissemination, data storage of in-network information discovery. In this paper, we propose a novel method for information discovery for multi-dimensional autonomous WSNs which sensors are deployed randomly that can significantly increase network lifetime and minimize query processing latency, resulting in quality of service (QoS) improvements that are of immense benefit to mission–critical applications. We present simulation results to show that the proposed approach to information discovery offers significant improvements on query resolution latency compared with current approaches.

Complex network theoretical analysis on information dissemination over vehicular networks

How to enhance the communication efficiency and quality on vehicular networks is one critical important issue. While with the larger and larger scale of vehicular networks in dense cities, the real-world datasets show that the vehicular networks essentially belong to the complex network model. Meanwhile, the extensive research on complex networks has shown that the complex network theory can both provide an accurate network illustration model and further make great contributions to the network design, optimization and management. In this paper, we start with analyzing characteristics of a taxi GPS dataset and then establishing the vehicular-to-infrastructure, vehicle-to-vehicle and the hybrid communication model, respectively. Moreover, we propose a clustering algorithm for station selection, a traffic allocation optimization model and an information source selection model based on the communication performances and complex network theory.

Two studies on Twitter networks and tweet content in relation to Amyotrophic Lateral Sclerosis (ALS): conversation, information, and ‘Diary of a Daily Life’

To date, there is no research examining how adults with Amyotrophic Lateral Sclerosis (ALS) or Motor Neurone Disease (MND) and severe communication disability use Twitter, nor the use of Twitter in relation to ALS/MND beyond its use for fundraising and raising awareness. In this paper we (a) outline a rationale for the use of Twitter as a method of communication and information exchange for adults with ALS/MND, (b) detail multiple qualitative and quantitative methods used to analyse Twitter networks and tweet content in the our studies, and (c) present the results of two studies designed to provide insights on the use of Twitter by an adult with ALS/MND and by #ALS and #MND hashtag communities in Twitter. We will also discuss findings across the studies, implications for health service providers in Twitter, and directions for future Twitter research in relation to ALS/MND.