The role of information avoidance in everyday-life information behaviors

This paper presents the results from a study of information behaviors in the context of people's everyday lives as part of a larger study of information behaviors (IB). 34 participants from across 6 countries maintained a daily information journal or diary – mainly through a secure web log – for two weeks, to an aggregate of 468 participant days over five months. The text-rich diary data was analyzed using Grounded Theory analysis. The findings indicate that information avoidance is a common phenomenon in everyday life and consisted of both passive avoidance and active avoidance. This has implications for several aspects of peoples' lives including health, finance, and personal relationships.

Towards developing an integrated model of information behaviour

This paper presents the results from a study of information behaviors in the context of people's everyday lives undertaken in order to develop an integrated model of information behavior (IB). 34 participants from across 6 countries maintained a daily information journal or diary – mainly through a secure web log – for two weeks, to an aggregate of 468 participant days over five months. The text-rich diary data was analyzed using a multi-method qualitative-quantitative analysis in the following order: Grounded Theory analysis with manual coding, automated concept analysis using thesaurus-based visualization, and finally a statistical analysis of the coding data. The findings indicate that people engage in several information behaviors simultaneously throughout their everyday lives (including home and work life) and that sense-making is entangled in all aspects of them. Participants engaged in many of the information behaviors in a parallel, distributed, and concurrent fashion: many information behaviors for one information problem, one information behavior across many information problems, and many information behaviors concurrently across many information problems. Findings indicate also that information avoidance – both active and passive avoidance – is a common phenomenon and that information organizing behaviors or the lack thereof caused the most problems for participants. An integrated model of information behaviors is presented based on the findings.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

Barriers to and facilitators of older adult’s adherence to health recommendations: Towards an EngAging two-way health communication

Non-adherence to health recommendations (e.g. medical prescriptions) presents potential costs for healthcare, which could be prevented or mitigated. This is often attributed to a person’s rational choice, to not adhere. However, this may also be determined by individual and contextual factors implied in the recommendations communication process. In accordance, this chapter focuses specifically on barriers to and facilitators of adherence to recommendations and engagement with the healthcare process, particularly concerning the communication between health professionals and patients. For this, the authors present examples of engagement increment through different degrees of participation, from a one-way/directive towards a two-way/engaging communication process. This focuses specifically on a vulnerable population group with increasing healthcare needs: older adults. Future possibilities for two-way engaging communications are discussed, aimed at promoting increased adherence to health recommendations and people’s self-regulation of their own health.

Avoidance of advertising on social networking sites : the teenage perspective

Technology has provided consumers with the means to control and edit the information that they receive and share effectively, especially in the online environment. Although previous studies have investigated advertising avoidance in traditional media and on the Internet, there has been little investigation of advertising on social networking sites. This exploratory study examines the antecedents of advertising avoidance on online social networking sites, leading to the development of a model. The model suggests that advertising in the online social networking environment is more likely to be avoided if the user has expectations of a negative experience, the advertising is not relevant to the user, the user is skeptical toward the advertising message, or the consumer is skeptical toward the advertising medium.

A crash avoidance framework for heavy equipment control systems using 3D imaging sensors

This paper presents a preliminary crash avoidance framework for heavy equipment control systems. Safe equipment operation is a major concern on construction sites since fatal on-site injuries are an industry-wide problem. The proposed framework has potential for effecting active safety for equipment operation. The framework contains algorithms for spatial modeling, object tracking, and path planning. Beyond generating spatial models in fractions of seconds, these algorithms can successfully track objects in an environment and produce a collision-free 3D motion trajectory for equipment.

On the moderators of trait avoidance motivation in predicting cognitive biases and adjustment

The present research focused on motivational and personality traits measuring individual differences in the experience of negative affect, in reactivity to negative events, and in the tendency to avoid threats. In this thesis, such traits (i.e., neuroticism and dispositional avoidance motivation) are jointly referred to as trait avoidance motivation. The seven studies presented here examined the moderators of such traits in predicting risk judgments, negatively biased processing, and adjustment. Given that trait avoidance motivation encompasses reactivity to negative events and tendency to avoid threats, it can be considered surprising that this trait does not seem to be related to risk judgments and that it seems to be inconsistently related to negatively biased information processing. Previous work thus suggests that some variable(s) moderate these relations. Furthermore, recent research has suggested that despite the close connection between trait avoidance motivation and (mal)adjustment, measures of cognitive performance may moderate this connection. However, it is unclear whether this moderation is due to different response processes between individuals with different cognitive tendencies or abilities, or to the genuinely buffering effect of high cognitive ability against the negative consequences of high trait avoidance motivation. Studies 1-3 showed that there is a modest direct relation between trait avoidance motivation and risk judgments, but studies 2-3 demonstrated that state motivation moderates this relation. In particular, individuals in an avoidance state made high risk judgments regardless of their level of trait avoidance motivation. This result explained the disparity between the theoretical conceptualization of avoidance motivation and the results of previous studies suggesting that the relation between trait avoidance motivation and risk judgments is weak or nonexistent. Studies 5-6 examined threat identification tendency as a moderator for the relationship between trait avoidance motivation and negatively biased processing. However, no evidence for such moderation was found. Furthermore, in line with previous work, the results of studies 5-6 suggested that trait avoidance motivation is inconsistently related to negatively biased processing, implying that theories concerning traits and information processing may need refining. Study 7 examined cognitive ability as a moderator for the relation between trait avoidance motivation and adjustment, and demonstrated that cognitive ability moderates the relation between trait avoidance motivation and indicators of both self-reported and objectively measured adjustment. Thus, the results of Study 7 supported the buffer explanation for the moderating influence of cognitive performance. To summarize, the results showed that it is possible to find factors that consistently moderate the relations between traits and important outcomes (e.g. adjustment). Identifying such factors and studying their interplay with traits is one of the most important goals of current personality research. The present thesis contributed to this line of work in relation to trait avoidance motivation.

More than naming and shaming needed to stop corporate tax avoidance

The report of the Senate Economics References Committee inquiry into corporate tax avoidance comes with the subtitle – “You cannot tax what you cannot see”, with a strong focus on increased transparency. The majority of the 17 recommendations in the interim report relate to improved transparency of the tax affairs of corporate taxpayers. This is a significant step in the right direction. Recent experiences in the war on corporate tax avoidance both in Australia and overseas confirm that “information is power”. Most notably, we have seen increased transparency changing the behaviour of multinational enterprises as well as inducing governments to act.

Evolving Philosophies on Autonomous Obstacle/Collision Avoidance of Unmanned Aerial Vehicles

Much of the benefits of deploying unmanned aerial vehicles can be derived from autonomous missions. For such missions, however, sense-and-avoid capability (i.e., the ability to detect potential collisions and avoid them) is a critical requirement. Collision avoidance can be broadly classified into global and local path-planning algorithms, both of which need to be addressed in a successful mission. Whereas global path planning (which is mainly done offline) broadly lays out a path that reaches the goal point, local collision-avoidance algorithms, which are usually fast, reactive, and carried out online, ensure safety of the vehicle from unexpected and unforeseen obstacles/collisions. Even though many techniques for both global and local collision avoidance have been proposed in the recent literature, there is a great interest around the globe to solve this important problem comprehensively and efficiently and such techniques are still evolving. This paper presents a brief overview of a few promising and evolving ideas on collision avoidance for unmanned aerial vehicles, with a preferential bias toward local collision avoidance.

Decoding an olfactory mechanism of kin recognition and inbreeding avoidance in a primate.

BACKGROUND: Like other vertebrates, primates recognize their relatives, primarily to minimize inbreeding, but also to facilitate nepotism. Although associative, social learning is typically credited for discrimination of familiar kin, discrimination of unfamiliar kin remains unexplained. As sex-biased dispersal in long-lived species cannot consistently prevent encounters between unfamiliar kin, inbreeding remains a threat and mechanisms to avoid it beg explanation. Using a molecular approach that combined analyses of biochemical and microsatellite markers in 17 female and 19 male ring-tailed lemurs (Lemur catta), we describe odor-gene covariance to establish the feasibility of olfactory-mediated kin recognition. RESULTS: Despite derivation from different genital glands, labial and scrotal secretions shared about 170 of their respective 338 and 203 semiochemicals. In addition, these semiochemicals encoded information about genetic relatedness within and between the sexes. Although the sexes showed opposite seasonal patterns in signal complexity, the odor profiles of related individuals (whether same-sex or mixed-sex dyads) converged most strongly in the competitive breeding season. Thus, a strong, mutual olfactory signal of genetic relatedness appeared specifically when such information would be crucial for preventing inbreeding. That weaker signals of genetic relatedness might exist year round could provide a mechanism to explain nepotism between unfamiliar kin. CONCLUSION: We suggest that signal convergence between the sexes may reflect strong selective pressures on kin recognition, whereas signal convergence within the sexes may arise as its by-product or function independently to prevent competition between unfamiliar relatives. The link between an individual's genome and its olfactory signals could be mediated by biosynthetic pathways producing polymorphic semiochemicals or by carrier proteins modifying the individual bouquet of olfactory cues. In conclusion, we unveil a possible olfactory mechanism of kin recognition that has specific relevance to understanding inbreeding avoidance and nepotistic behavior observed in free-ranging primates, and broader relevance to understanding the mechanisms of vertebrate olfactory communication.

A downlink power control scheme for interference avoidance in femtocells

Femtocells being small low powered base stations provide sufficient increase in system capacity along with better indoor coverage. However, the dense deployment of femtocells face the main challenge of co channel interference with macrocell users. In this paper, this interference problem is addressed by proposing a novel downlink power control algorithm for femtocells. The proposed algorithm gradually reduces the downlink transmit power of femtocells when they are informed about a nearby macrocell user under interference. This information is given to the femtocells by the macrocell base station through a unidirectional downlink broadcast channel. Simulation results show that the algorithm causes the macrocell to accommodate large number of femtocells within its area, whereas at the same time protecting the macrocell users from any harmful interference.

Can Rachman’s indirect pathways be used to un-learn fear? A prospective paradigm to test whether children’s fears can be reduced using positive information and modelling a non-anxious response

This study investigated whether children’s fears could be un-learned using Rachman’s indirect pathways for learning fear. We hypothesised that positive information and modelling a non-anxious response are effective methods of un-learning fears acquired through verbal information. One hundred and seven children aged 6–8 years received negative information about one animal and no information about another. Fear beliefs and behavioural avoidance were measured. Children were randomised to receive positive verbal information, modelling, or a control task. Fear beliefs and behavioural avoidance were measured again. Positive information and modelling led to lower fear beliefs and behavioural avoidance than the control condition. Positive information was more effective than modelling in reducing fear beliefs and both methods significantly reduced behavioural avoidance. The results support Rachman’s indirect pathways as viable fear un-learning pathways and supports associative learning theories.