IS-impact utility and intuitiveness : a rigorous approach to relevance

The study proposes to test the ‘IS-Impact’ index as Analytic Theory (AT). To (a) methodically evaluate the ‘relevance’ qualities of IS-Impact; namely, Utility & Intuitiveness. In so doing, to (b) document an exemplar of ‘a rigorous approach to relevance’, while (c) treating the overarching study as a higher-order case study having AT as the unit-of-analysis, and assessing adequacy of the 6 AT qualities, both for IS-Impact and for similar taxonomies. Also to (d) look beyond IS-Impact to other forms of Design Science, considering the generality of the AT qualities; and (e) further validating IS-Impact in new system organisation contexts taking account of contemporary understandings of construct theorisation, operationalization and validation.

Selection of solar energy for green building using superiority and inferiority multi-criteria ranking (SIR) method

Green energy is one of the key factors, driving down electricity bill and zero carbon emission generating electricity to green building. However, the climate change and environmental policies are accelerating people to use renewable energy instead of coal-fired (convention type) energy for green building that energy is not environmental friendly. Therefore, solar energy is one of the clean energy solving environmental impact and paying less in electricity fee. The method of solar energy is collecting sun from solar array and saves in battery from which provides necessary electricity to whole house with zero carbon emission. However, in the market a lot of solar arrays suppliers, the aims of this paper attempted to use superiority and inferiority multi-criteria ranking (SIR) method with 13 constraints establishing I-flows and S-flows matrices to evaluate four alternatives solar energies and determining which alternative is the best, providing power to sustainable building. Furthermore, SIR is well-known structured approach of multi-criteria decision support tools and gradually used in construction and building. The outcome of this paper significantly gives an indication to user selecting solar energy.

An examination of monotony and hypovigilance, independent of fatigue : relevance to road safety

Driving is a vigilance task, requiring sustained attention to maintain performance and avoid crashes. Hypovigilance (i.e., marked reduction in vigilance) while driving manifests as poor driving performance and is commonly attributed to fatigue (Dinges, 1995). However, poor driving performance has been found to be more frequent when driving in monotonous road environments, suggesting that monotony plays a role in generating hypovigilance (Thiffault & Bergeron, 2003b). Research to date has tended to conceptualise monotony as a uni-dimensional task characteristic, typically used over a prolonged period of time to facilitate other factors under investigation, most notably fatigue. However, more often than not, more than one exogenous factor relating to the task or operating environment is manipulated to vary or generate monotony (Mascord & Heath, 1992). Here we aimed to explore whether monotony is a multi-dimensional construct that is determined by characteristics of both the task proper and the task environment. The general assumption that monotony is a task characteristic used solely to elicit hypovigilance or poor performance related to fatigue appears to have led to there being little rigorous investigation into the exact nature of the relationship. While the two concepts are undoubtedly linked, the independent effect of monotony on hypovigilance remains largely ignored. Notwithstanding, there is evidence that monotony effects can emerge very early in vigilance tasks and are not necessarily accompanied by fatigue (see Meuter, Rakotonirainy, Johns, & Wagner, 2005). This phenomenon raises a largely untested, empirical question explored in two studies: Can hypovigilance emerge as a consequence of task and/or environmental monotony, independent of time on task and fatigue? In Study 1, using a short computerised vigilance task requiring responses to be withheld to infrequent targets, we explored the differential impacts of stimuli and task demand manipulations on the development of a monotonous context and the associated effects on vigilance performance (as indexed by respone errors and response times), independent of fatigue and time on task. The role of individual differences (sensation seeking, extroversion and cognitive failures) in moderating monotony effects was also considered. The results indicate that monotony affects sustained attention, with hypovigilance and associated performance worse in monotonous than in non-monotonous contexts. Critically, performance decrements emerged early in the task (within 4.3 minutes) and remained consistent over the course of the experiment (21.5 minutes), suggesting that monotony effects can operate independent of time on task and fatigue. A combination of low task demands and low stimulus variability form a monotonous context characterised by hypovigilance and poor task performance. Variations to task demand and stimulus variability were also found to independently affect performance, suggesting that monotony is a multi-dimensional construct relating to both task monotony (associated with the task itself) and environmental monotony (related to characteristics of the stimulus). Consequently, it can be concluded that monotony is multi-dimensional and is characterised by low variability in stimuli and/or task demands. The proposition that individual differences emerge under conditions of varying monotony with high sensation seekers and/or extroverts performing worse in monotonous contexts was only partially supported. Using a driving simulator, the findings of Study 1 were extended to a driving context to identify the behavioural and psychophysiological indices of monotony-related hypovigilance associated with variations to road design and road side scenery (Study 2). Supporting the proposition that monotony is a multi-dimensional construct, road design variability emerged as a key moderating characteristic of environmental monotony, resulting in poor driving performance indexed by decrements in steering wheel measures (mean lateral position). Sensation seeking also emerged as a moderating factor, where participants high in sensation seeking tendencies displayed worse driving behaviour in monotonous conditions. Importantly, impaired driving performance was observed within 8 minutes of commencing the driving task characterised by environmental monotony (low variability in road design) and was not accompanied by a decline in psychophysiological arousal. In addition, no subjective declines in alertness were reported. With fatigue effects associated with prolonged driving (van der Hulst, Meijman, & Rothengatter, 2001) and indexed by drowsiness, this pattern of results indicates that monotony can affect driver vigilance, independent of time on task and fatigue. Perceptual load theory (Lavie, 1995, 2005) and mindlessness theory (Robertson, Manly, Andrade, Baddley, & Yiend, 1997) provide useful theoretical frameworks for explaining and predicting monotony effects by positing that the low load (of task and/or stimuli) associated with a monotonous task results in spare attentional capacity which spills over involuntarily, resulting in the processing of task-irrelevant stimuli or task unrelated thoughts. That is, individuals – even when not fatigued - become easily distracted when performing a highly monotonous task, resulting in hypovigilance and impaired performance. The implications for road safety, including the likely effectiveness of fatigue countermeasures to mitigate monotony-related driver hypovigilance are discussed.

Observing the relevance of an information systems success model

This paper contributes to the rigor vs. relevance debate in the Information Systems (IS) discipline. Using the Action Research methodology, this study evaluates the relevance of a rigorously validated IS evaluation model in practice. The study captures observations of operational end-users employing a market leading Enterprise System application for procurement and order fulfillment in an organization. The analysis of the observations demonstrates the broad relevance of the measurement instrument. More importantly, the study identifies several improvements and possible confusions in applying the instrument in the practice.

Me and my body : the relevance of the difference for the distinction between withdrawing life support and euthanasia

In this paper I discuss David Shaw’s claim that the body of a terminally ill person can be conceived as a kind of life-support, akin to an artificial ventilator. I claim that this position rests upon an untenable dualism between the mind and the body. Given that dualism continues to be attractive to some thinkers, I attempt to diagnose the reasons why it continues to be attractive, as well as to demonstrate its incoherence, drawing on some recent work in the philosophy of psychology. I conclude that, if my criticisms are sound, Shaw’s attempt to deny the distinction between withdrawal and euthanasia fails.

Tensor query expansion : a cognitively motivated relevance model

In information retrieval, a user's query is often not a complete representation of their real information need. The user's information need is a cognitive construction, however the use of cognitive models to perform query expansion have had little study. In this paper, we present a cognitively motivated query expansion technique that uses semantic features for use in ad hoc retrieval. This model is evaluated against a state-of-the-art query expansion technique. The results show our approach provides significant improvements in retrieval effectiveness for the TREC data sets tested.

Relevance feature discovery for text analysis

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term- based ones in describing user preferences, but many experiments do not support this hypothesis. This research presents a promising method, Relevance Feature Discovery (RFD), for solving this challenging issue. It discovers both positive and negative patterns in text documents as high-level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the high-level features. The thesis also introduces an adaptive model (called ARFD) to enhance the exibility of using RFD in adaptive environment. ARFD automatically updates the system's knowledge based on a sliding window over new incoming feedback documents. It can efficiently decide which incoming documents can bring in new knowledge into the system. Substantial experiments using the proposed models on Reuters Corpus Volume 1 and TREC topics show that the proposed models significantly outperform both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and other pattern-based methods.

Automata modeling for cognitive interference in users' relevance judgement

Quantum theory has recently been employed to further advance the theory of information retrieval (IR). A challenging research topic is to investigate the so called quantum-like interference in users’ relevance judgement process, where users are involved to judge the relevance degree of each document with respect to a given query. In this process, users’ relevance judgement for the current document is often interfered by the judgement for previous documents, due to the interference on users’ cognitive status. Research from cognitive science has demonstrated some initial evidence of quantum-like cognitive interference in human decision making, which underpins the user’s relevance judgement process. This motivates us to model such cognitive interference in the relevance judgement process, which in our belief will lead to a better modeling and explanation of user behaviors in relevance judgement process for IR and eventually lead to more user-centric IR models. In this paper, we propose to use probabilistic automaton(PA) and quantum finite automaton (QFA), which are suitable to represent the transition of user judgement states, to dynamically model the cognitive interference when the user is judging a list of documents.

Text mining in negative relevance feedback

It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.

Concentration and oxidative potential of on-road particle emissions and their relationship with traffic composition : Relevance to exposure assessment

Particles emitted by vehicles are known to cause detrimental health effects, with their size and oxidative potential among the main factors responsible. Therefore, understanding the relationship between traffic composition and both the physical characteristics and oxidative potential of particles is critical. To contribute to the limited knowledge base in this area, we investigated this relationship in a 4.5 km road tunnel in Brisbane, Australia. On-road concentrations of ultrafine particles (<100 nm, UFPs), fine particles (PM2.5), CO, CO2 and particle associated reactive oxygen species (ROS) were measured using vehicle-based mobile sampling. UFPs were measured using a condensation particle counter and PM2.5 with a DustTrak aerosol photometer. A new profluorescent nitroxide probe, BPEAnit, was used to determine ROS levels. Comparative measurements were also performed on an above-ground road to assess the role of emission dilution on the parameters measured. The profile of UFP and PM2.5 concentration with distance through the tunnel was determined, and demonstrated relationships with both road gradient and tunnel ventilation. ROS levels in the tunnel were found to be high compared to an open road with similar traffic characteristics, which was attributed to the substantial difference in estimated emission dilution ratios on the two roadways. Principal component analysis (PCA) revealed that the levels of pollutants and ROS were generally better correlated with total traffic count, rather than the traffic composition (i.e. diesel and gasoline-powered vehicles). A possible reason for the lack of correlation with HDV, which has previously been shown to be strongly associated with UFPs especially, was the low absolute numbers encountered during the sampling. This may have made their contribution to in-tunnel pollution largely indistinguishable from the total vehicle volume. For ROS, the stronger association observed with HDV and gasoline vehicles when combined (total traffic count) compared to when considered individually may signal a role for the interaction of their emissions as a determinant of on-road ROS in this pilot study. If further validated, this should not be overlooked in studies of on- or near-road particle exposure and its potential health effects.

Is the unigram relevance model term independent? Classifying term dependencies in query expansion

This paper develops a framework for classifying term dependencies in query expansion with respect to the role terms play in structural linguistic associations. The framework is used to classify and compare the query expansion terms produced by the unigram and positional relevance models. As the unigram relevance model does not explicitly model term dependencies in its estimation process it is often thought to ignore dependencies that exist between words in natural language. The framework presented in this paper is underpinned by two types of linguistic association, namely syntagmatic and paradigmatic associations. It was found that syntagmatic associations were a more prevalent form of linguistic association used in query expansion. Paradoxically, it was the unigram model that exhibited this association more than the positional relevance model. This surprising finding has two potential implications for information retrieval models: (1) if linguistic associations underpin query expansion, then a probabilistic term dependence assumption based on position is inadequate for capturing them; (2) the unigram relevance model captures more term dependency information than its underlying theoretical model suggests, so its normative position as a baseline that ignores term dependencies should perhaps be reviewed.

Integration of medical images to the teaching of systematic pathology : an evaluation of relevance

A sound knowledge of pathological disease processes is required for professional practice within health professions. The project described in this paper reviewed the resources currently available for the delivery of systematic pathology tutorials. Additional complementary resources were developed and the inclusion of these additional learning resources in practical tutorial sessions was evaluated for their impact on student learning. Student evaluation of the learning resources was undertaken across one semester with two different cohorts of health profession students using questionnaires and focus group discussion. Both cohorts reported an enhancement to their understanding of pathological disease processes through the use of the additional resources. Results indicate student perception of the value of the resources correlates with staff perception and is independent of prior experiences.