Temporal construal in advertising : the moderating role of temporal orientation and attribute importance in consumer evaluations

In two experiments, we study how the temporal orientation of consumers (i.e., future-oriented or present-oriented), temporal construal (distant future, near future), and product attribute importance (primary, secondary) influence advertisement evaluations. Data suggest that future-oriented consumers react most favorably to ads that feature a product to be released in the distant future and that highlight primary product attributes. In contrast, present-oriented consumers prefer near-future ads that highlight secondary product attributes. Study 2 shows that consumer attitudes are mediated by perceptions of attribute diagnosticity (i.e., the perceived usefulness of the attribute information). Together, these experiments shed light on how individual differences, such as temporal orientation, offer valuable insights into temporal construal effects in advertising.

Graduate Attribute Mapping With the Extended CDIO Framework

The CDIO (Conceive-Design-Implement-Operate) Initiative has been globally recognised as an enabler for engineering education reform. With the CDIO process, the CDIO Standards and the CDIO Syllabus, many scholarly contributions have been made around cultural change, curriculum reform and learning environments. In the Australasian region, reform is gaining significant momentum within the engineering education community, the profession, and higher education institutions. This paper presents the CDIO Syllabus cast into the Australian context by mapping it to the Engineers Australia Graduate Attributes, the Washington Accord Graduate Attributes and the Queensland University of Technology Graduate Capabilities. Furthermore, in recognition that many secondary schools and technical training institutions offer introductory engineering technology subjects, this paper presents an extended self-rating framework suited for recognising developing levels of proficiency at a preparatory level. A demonstrator mapping tool has been created to demonstrate the application of this extended graduate attribute mapping framework as a precursor to an integrated curriculum information model.

Attribute-based authenticated key exchange

We introduce the concept of attribute-based authenticated key exchange (AB-AKE) within the framework of ciphertext policy attribute-based systems. A notion of AKE-security for AB-AKE is presented based on the security models for group key exchange protocols and also taking into account the security requirements generally considered in the ciphertext policy attribute-based setting. We also extend the paradigm of hybrid encryption to the ciphertext policy attribute-based encryption schemes. A new primitive called encapsulation policy attribute-based key encapsulation mechanism (EP-AB-KEM) is introduced and a notion of chosen ciphertext security is de�ned for EP-AB-KEMs. We propose an EP-AB-KEM from an existing attribute-based encryption scheme and show that it achieves chosen ciphertext security in the generic group and random oracle models. We present a generic one-round AB-AKE protocol that satis�es our AKE-security notion. The protocol is generically constructed from any EP-AB-KEM that satis�es chosen ciphertext security. Instantiating the generic AB-AKE protocol with our EP-AB-KEM will result in a concrete one-round AB-AKE protocol also secure in the generic group and random oracle models.

X.509v3 Certificates for Secure Shell authentication

X.509 public key certificates use a signature by a trusted certification authority to bind a given public key to a given digital identity. This document specifies how to use X.509 version 3 public key certificates in public key algorithms in the Secure Shell protocol.

Attribute exclusion strategies in airline choice : accounting for exogenous information on decision maker processing strategies in models of discrete choice

We examine the impact of individual-specific information processing strategies (IPSs) on the inclusion/exclusion of attributes on the parameter estimates and behavioural outputs of models of discrete choice. Current practice assumes that individuals employ a homogenous IPS with regards to how they process attributes of stated choice (SC) experiments. We show how information collected exogenous of the SC experiment on whether respondents either ignored or considered each attribute may be used in the estimation process, and how such information provides outputs that are IPS segment specific. We contend that accounting the inclusion/exclusion of attributes will result in behaviourally richer population parameter estimates.

Development of a data mining-based analysis framework for multi-attribute construction project information

Data mining techniques extract repeated and useful patterns from a large data set that in turn are utilized to predict the outcome of future events. The main purpose of the research presented in this paper is to investigate data mining strategies and develop an efficient framework for multi-attribute project information analysis to predict the performance of construction projects. The research team first reviewed existing data mining algorithms, applied them to systematically analyze a large project data set collected by the survey, and finally proposed a data-mining-based decision support framework for project performance prediction. To evaluate the potential of the framework, a case study was conducted using data collected from 139 capital projects and analyzed the relationship between use of information technology and project cost performance. The study results showed that the proposed framework has potential to promote fast, easy to use, interpretable, and accurate project data analysis.

Attribute-based functional encryption on lattices

We introduce a broad lattice manipulation technique for expressive cryptography, and use it to realize functional encryption for access structures from post-quantum hardness assumptions. Specifically, we build an efficient key-policy attribute-based encryption scheme, and prove its security in the selective sense from learning-with-errors intractability in the standard model.

Strategies and solutions for housing sustainability : building information files and performance certificates

The overall aim of this research project was to provide a broader range of value propositions (beyond upfront traditional construction costs) that could transform both the demand side and supply side of the housing industry. The project involved gathering information about how building information is created, used and communicated and classifying building information, leading to the formation of an Information Flow Chart and Stakeholder Relationship Map. These were then tested via broad housing industry focus groups and surveys. The project revealed four key relationships that appear to operate in isolation to the whole housing sector and may have significant impact on the sustainability outcomes and life cycle costs of dwellings over their life cycle. It also found that although a lot of information about individual dwellings does already exist, this information is not coordinated or inventoried in any systematic manner and that national building information files of building passports would present value to a wide range of stakeholders.

Bayesian imputation of non-chosen attribute values in revealed preference surveys

Obtaining attribute values of non-chosen alternatives in a revealed preference context is challenging because non-chosen alternative attributes are unobserved by choosers, chooser perceptions of attribute values may not reflect reality, existing methods for imputing these values suffer from shortcomings, and obtaining non-chosen attribute values is resource intensive. This paper presents a unique Bayesian (multiple) Imputation Multinomial Logit model that imputes unobserved travel times and distances of non-chosen travel modes based on random draws from the conditional posterior distribution of missing values. The calibrated Bayesian (multiple) Imputation Multinomial Logit model imputes non-chosen time and distance values that convincingly replicate observed choice behavior. Although network skims were used for calibration, more realistic data such as supplemental geographically referenced surveys or stated preference data may be preferred. The model is ideally suited for imputing variation in intrazonal non-chosen mode attributes and for assessing the marginal impacts of travel policies, programs, or prices within traffic analysis zones.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

Cortical organization of environmental sounds by attribute

Semantic knowledge is supported by a widely distributed neuronal network, with differential patterns of activation depending upon experimental stimulus or task demands. Despite a wide body of knowledge on semantic object processing from the visual modality, the response of this semantic network to environmental sounds remains relatively unknown. Here, we used fMRI to investigate how access to different conceptual attributes from environmental sound input modulates this semantic network. Using a range of living and manmade sounds, we scanned participants whilst they carried out an object attribute verification task. Specifically, we tested visual perceptual, encyclopedic, and categorical attributes about living and manmade objects relative to a high-level auditory perceptual baseline to investigate the differential patterns of response to these contrasting types of object-related attributes, whilst keeping stimulus input constant across conditions. Within the bilateral distributed network engaged for processing environmental sounds across all conditions, we report here a highly significant dissociation within the left hemisphere between the processing of visual perceptual and encyclopedic attributes of objects.

Secondary attribute retrieval using tree data structures

Several techniques are known for searching an ordered collection of data. The techniques and analyses of retrieval methods based on primary attributes are straightforward. Retrieval using secondary attributes depends on several factors. For secondary attribute retrieval, the linear structures—inverted lists, multilists, doubly linked lists—and the recently proposed nonlinear tree structures—multiple attribute tree (MAT), K-d tree (kdT)—have their individual merits. It is shown in this paper that, of the two tree structures, MAT possesses several features of a systematic data structure for external file organisation which make it superior to kdT. Analytic estimates for the complexity of node searchers, in MAT and kdT for several types of queries, are developed and compared.