The Difficulties in Measuring Individual Utilities of Product Attributes: A Choice Based Experiment

The study combines different theoretical approaches in the field of conjoint analysis to estimate the im-portance of product related attributes. This is of major importance in food marketing, where we still try to find a valid answer, in particular, how to measure the real willingness to pay (WTP) for specific product specifica-tions. Based on a comprehensive literature analysis, a common method was used to approximate the im-portance of several product attributes. As usually suggested in literature, we used discrete choice modeling and developed a choice based experimental design considering selected product attributes. The study object was frozen pizza, a convenience good frequently bought by most households.Up to this point, there is nothing special about the choice based experiment in comparison to direct measure-ment of the importance of product attributes. However, one of the core problems of discrete choice modeling – the approximation of individual utility functions – was then addressed by transforming the choices of con-sumers into scores. With these scores traditional conjoint measurement can be used to approximate individual utilities even in choice based experiments. The individual part-worth utilities will be compared with a usual but very complex approach to approximate individual part-worth utilities, the hierarchical Bayes method. Our ap-proach addresses methodological considerations concerning the restrictions of discrete choice modeling, namely the complexity of approximating individual utilities which is of huge importance in particular for market segmentation

Analyzing policy capturing data using structural equation modeling for within-subject experiments (SEMWISE)

We present the SEMWISE (structural equation modeling for within-subject experiments) approach for analyzing policy capturing data. Policy capturing entails estimating the weights (or utilities) of experimentally manipulated attributes in predicting a response variable of interest (e.g., the effect of experimentally manipulated market-technology combination characteristics on perceived entrepreneurial opportunity). In the SEMWISE approach, a factor model is specified in which latent weight factors capture individually varying effects of experimentally manipulated attributes on the response variable. We describe the core SEMWISE model and propose several extensions (how to incorporate nonbinary attributes and interactions, model multiple indicators of the response variable, relate the latent weight factors to antecedents and/or consequences, and simultaneously investigate several populations of respondents). The primary advantage of the SEMWISE approach is that it facilitates the integration of individually varying policy capturing weights into a broader nomological network while accounting for measurement error. We illustrate the approach with two empirical examples, compare and contrast the SEMWISE approach with multilevel modeling (MLM), discuss how researchers can choose between SEMWISE and MLM, and provide implementation guidelines

Digital forensics formats: seeking a digital preservation storage format for web archiving

In this paper we discuss archival storage formats from the point of view of digital curation and preservation. Considering established approaches to data management as our jumping off point, we selected seven format attributes which are core to the long term accessibility of digital materials. These we have labeled core preservation attributes. These attributes are then used as evaluation criteria to compare file formats belonging to five common categories: formats for archiving selected content (e.g. tar, WARC), disk image formats that capture data for recovery or installation (partimage, dd raw image), these two types combined with a selected compression algorithm (e.g. tar+gzip), formats that combine packing and compression (e.g. 7-zip), and forensic file formats for data analysis in criminal investigations (e.g. aff, Advanced Forensic File format). We present a general discussion of the file format landscape in terms of the attributes we discuss, and make a direct comparison between the three most promising archival formats: tar, WARC, and aff. We conclude by suggesting the next steps to take the research forward and to validate the observations we have made

Personal projects, affect, and need satisfaction : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in psychology, at Massey University

The present study investigated effects that patterns of purposeful human action, conceived as personal projects, have on positive and negative affect and need satisfaction. Replication was attempted of main effects reported in the literature for project attributes upon affective experience. More importantly, a more complex view of the effects of projects attributes was proposed whereby project attributes interact with each other and age and sex to influence affect. In addition, an investigation into the determinants of need satisfaction was conducted utilising both within- and between- subjects modes of analysis. Seventy respondents completed a questionnaire containing measures of positive and negative affect, a project elicitation list, and measures of the project attributes of need satisfaction, involvement, conflict, and time-frame. Regression analyses generally failed to replicate reported relationships between project attributes and positive or negative affect. In contrast, a number of significant interaction effects did emerge between project attributes and age and sex, although each of these related only to positive affect. These interactions were between involvement and age, conflict and sex, conflict and age. The determinants of need satisfaction were found to differ greatly in significance but not magnitude, according to the mode of analysis used. Need satisfaction was positively related to involvement, and engagement in long term projects, and negatively to interproject conflict. In addition to these main effects a hypothesized quadratic effect for project conflict was found and interaction between sex and conflict. The issues concerning which is the more appropriate level of analysis are discussed. It was concluded for the interaction analyses that, while project attributes may be considered as independent influences upon positive affect, they should not be considered independently of age and sex. It is concluded that projects did not adequately match expectations of relating to affect and need satisfaction and are limited in their seeming inability to account for negative affect

Learning to Generate Posters of Scientific Papers

Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 201

A general guide to applying machine learning to computer architecture

The resurgence of machine learning since the late 1990s has been enabled by significant advances in computing performance and the growth of big data. The ability of these algorithms to detect complex patterns in data which are extremely difficult to achieve manually, helps to produce effective predictive models. Whilst computer architects have been accelerating the performance of machine learning algorithms with GPUs and custom hardware, there have been few implementations leveraging these algorithms to improve the computer system performance. The work that has been conducted, however, has produced considerably promising results. The purpose of this paper is to serve as a foundational base and guide to future computer architecture research seeking to make use of machine learning models for improving system efficiency. We describe a method that highlights when, why, and how to utilize machine learning models for improving system performance and provide a relevant example showcasing the effectiveness of applying machine learning in computer architecture. We describe a process of data generation every execution quantum and parameter engineering. This is followed by a survey of a set of popular machine learning models. We discuss their strengths and weaknesses and provide an evaluation of implementations for the purpose of creating a workload performance predictor for different core types in an x86 processor. The predictions can then be exploited by a scheduler for heterogeneous processors to improve the system throughput. The algorithms of focus are stochastic gradient descent based linear regression, decision trees, random forests, artificial neural networks, and k-nearest neighbors.This work has been supported by the European Research Council (ERC) Advanced Grant RoMoL (Grant Agreemnt 321253) and by the Spanish Ministry of Science and Innovation (contract TIN 2015-65316P).Peer ReviewedPostprint (published version

Porting Decision Tree Algorithms to Multicore using FastFlow

The whole computer hardware industry embraced multicores. For these machines, the extreme optimisation of sequential algorithms is no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an approach for easy-yet-efficient porting of an implementation of the C4.5 algorithm on multicores. The parallel porting requires minimal changes to the original sequential code, and it is able to exploit up to 7X speedup on an Intel dual-quad core machine.Comment: 18 pages + cove

Adopting national vegetation guidelines and the National Vegetation Information System (NVIS) framework in the Northern Territory

Guidelines and core attributes for site-based vegetation surveying and mapping developed for the Northern Territory, are relevant to botanical research, forestry typing, rangeland monitoring and reporting on the extent and condition of native and non-native vegetated landscapes. These initiatives are consistent with national vegetation guidelines and the National Vegetation Information System (NVIS) framework. This paper provides a synopsis of vegetation site data collection, classification and mapping in the Northern Territory, and discusses the benefits of consistency between the guidelines, core attributes and the NVIS framework; both of which has an emphasis on the NVIS hierarchical classification system for describing structural and floristic attributes of vegetation. The long-term aim of the NVIS framework is that national attributes are adopted at regional levels to enable comparability of vegetation information within survey and jurisdictional boundaries in the Northern Territory and across Australia. The guidelines and core attributes are incorporated in current and future vegetation survey and mapping programs in the Northern Territory

Current practice and challenges towards handling uncertainty for effective outcomes in maintenance

The combination of viable heuristic attributes with statistical measurements presents significant challenges in industrial maintenance for complex assets under through-life service contracts. Techniques to obtain and process heuristic attributes raise numerous uncertainties which often go undefined and unmitigated. A holistic view of these uncertainties may improve decision-making capabilities and reduce maintenance costs and turnaround time. It is therefore necessary to identify and rank factors that influence uncertainties originating from challenges in the above context. This, along with an identification of who contributes to such challenges and current practice to handle them, sets the focus for this study. The influence of 32 categorised factors on uncertainty is assessed through a questionnaire completed by nine experienced maintenance managers from a leading defence company. The pedigree approach is applied to score validity of respondents’ answers according to their experience and job role to normalise scores. Results are discussed in interviews with respondents along with current practice in and ways to improve uncertainty assessment. Scores are weighted through the Analytical Hierarchy Process (AHP) in order to identify the most influential factors on uncertainty in maintenance. The analysis revealed that these include: intellectual property rights (IPR), maintainer performance, quality of information, resistance to change, stakeholder communication and technology integration. These are verified with 40 practitioners from various industrial backgrounds. From the interviews, it is deemed that a holistic view of heuristic and statistical attributes ultimately allows for more accomplished decision-making but requires trade-offs between quality and cost over the asset’s life cycle