A comparative study of multiple-criteria decision-making methods under stochastic inputs

This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative

A psychometric modeling approach to fuzzy rating data

Modeling fuzziness and imprecision in human rating data is a crucial problem in many research areas, including applied statistics, behavioral, social, and health sciences. Because of the interplay between cognitive, affective, and contextual factors, the process of answering survey questions is a complex task, which can barely be captured by standard (crisp) rating responses. Fuzzy rating scales have progressively been adopted to overcome some of the limitations of standard rating scales, including their inability to disentangle decision uncertainty from individual responses. The aim of this article is to provide a novel fuzzy scaling procedure which uses Item Response Theory trees (IRTrees) as a psychometric model for the stage-wise latent response process. In so doing, fuzziness of rating data is modeled using the overall rater's pattern of responses instead of being computed using a single-item based approach. This offers a consistent system for interpreting fuzziness in terms of individual-based decision uncertainty. A simulation study and two empirical applications are adopted to assess the characteristics of the proposed model and provide converging results about its effectiveness in modeling fuzziness and imprecision in rating data

Leadership Challenges In Today’s Academia

Starting from the anecdotic hypothesis that “leading academics is like trying to herd cats”, the paper reviews the main challenges and barriers to present academic leadership. The context is that of the on-going Bologna transformation of the university, and of the renewed quest for competitiveness. The method employed is that of the individual case-study, with a single-embedded design. The case study is exploratory, as we don’t know from sure which the effects of leadership in the university are, and to what degree are they alike, across sub-units of study. The case study is also intrinsic, as its main outcome is not theory-building, but understanding the particularities of a phenomenon strongly tied to its context. Our unit of study is the largest business university in the country, with its faculties and departments. The main data sources are short structured interviews with members of the academic staff. The analysis implies both explanation-building and cross-case synthesis. The results of the study give insights on the context of leadership, enablers and barriers, as well as on the content of leadership, in the particular setting of the academia. Conclusions connect our research with similar endeavours, outlining the particularities and patterns of educational transition in a transition country.academic leadership, structural equation model of academic leadership, Romanian academia

Learning Representations of Emotional Speech with Deep Convolutional Generative Adversarial Networks

Automatically assessing emotional valence in human speech has historically been a difficult task for machine learning algorithms. The subtle changes in the voice of the speaker that are indicative of positive or negative emotional states are often "overshadowed" by voice characteristics relating to emotional intensity or emotional activation. In this work we explore a representation learning approach that automatically derives discriminative representations of emotional speech. In particular, we investigate two machine learning strategies to improve classifier performance: (1) utilization of unlabeled data using a deep convolutional generative adversarial network (DCGAN), and (2) multitask learning. Within our extensive experiments we leverage a multitask annotated emotional corpus as well as a large unlabeled meeting corpus (around 100 hours). Our speaker-independent classification experiments show that in particular the use of unlabeled data in our investigations improves performance of the classifiers and both fully supervised baseline approaches are outperformed considerably. We improve the classification of emotional valence on a discrete 5-point scale to 43.88% and on a 3-point scale to 49.80%, which is competitive to state-of-the-art performance

The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City

When providing directions to a place, web and mobile mapping services are all able to suggest the shortest route. The goal of this work is to automatically suggest routes that are not only short but also emotionally pleasant. To quantify the extent to which urban locations are pleasant, we use data from a crowd-sourcing platform that shows two street scenes in London (out of hundreds), and a user votes on which one looks more beautiful, quiet, and happy. We consider votes from more than 3.3K individuals and translate them into quantitative measures of location perceptions. We arrange those locations into a graph upon which we learn pleasant routes. Based on a quantitative validation, we find that, compared to the shortest routes, the recommended ones add just a few extra walking minutes and are indeed perceived to be more beautiful, quiet, and happy. To test the generality of our approach, we consider Flickr metadata of more than 3.7M pictures in London and 1.3M in Boston, compute proxies for the crowdsourced beauty dimension (the one for which we have collected the most votes), and evaluate those proxies with 30 participants in London and 54 in Boston. These participants have not only rated our recommendations but have also carefully motivated their choices, providing insights for future work.Comment: 11 pages, 7 figures, Proceedings of ACM Hypertext 201

How Jurors Evaluate Fingerprint Evidence: The Relative Importance of Match Language, Method Information, and Error Acknowledgment

Fingerprint examiners use a variety of terms and phrases to describe a finding of a match between a defendant\u27s fingerprints and fingerprint impressions collected from a crime scene. Despite the importance and ubiquity of fingerprint evidence in criminal cases, no prior studies examine how jurors evaluate such evidence. We present two studies examining the impact of different match phrases, method descriptions, and statements about possible examiner error on the weight given to fingerprint identification evidence by laypersons. In both studies, the particular phrase chosen to describe the finding of a match-whether simple and imprecise or detailed and claiming near certainty-had little effect on participants\u27 judgments about the guilt of a suspect. In contrast, the examiner admitting the possibility of error reduced the weight given to the fingerprint evidence-regardless of whether the admission was made during direct or cross-examination. In addition, the examiner providing information about the method used to make fingerprint comparisons reduced the impact of admitting the possibility of error. We found few individual differences in reactions to the fingerprint evidence across a wide range of participant variables, and we found widespread agreement regarding the uniqueness of fingerprints and the reliability of fingerprint identifications. Our results suggest that information about the reliability of fingerprint identifications will have a greater impact on lay interpretations of fingerprint evidence than the specific qualitative or quantitative terms chosen to describe a fingerprint match

Evaluation of live human-computer music-making: Quantitative and qualitative approaches

NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Human-Computer Studies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Human-Computer Studies, [VOL 67,ISS 11(2009)] DOI: 10.1016/j.ijhcs.2009.05.00

Landscapes of Danger: A Geospatial Analysis of Perceived and Realistic Risk in Bryce Canyon National Park

The quantification of risk has inspired a wide breath of literature from the physical sciences, social sciences, and interdisciplinary disciplines like geography. Many attempts to estimate risk via natural hazards either focus on quantifying realistic risk or perceived risk of lay persons, with very little overlap between these paradigms. Due to this, a considerable knowledge gap exists within perceived risk and natural hazards research. This study aims to provide a comprehensive, risk estimation and assessment strategy through a multi-hazard risk assessment of Bryce Canyon National Park (BRCA). This case study analyzed knowledge of risk among visitors with perception surveys and Likert-based scales, in addition to identifying high risk areas of the park through Geographic Information Systems (gis). With a sample size of 254, a systematic stratified sampling method was implemented at specific sites in the park chosen for their distinctive viewsheds, accessibility, and popularity. To identify risky areas, two fuzzy logic models were built: one to identify areas susceptible to rockfall and another to identify areas susceptible to landslides/slumps. Overall, respondents reported feeling largely unconcerned when ranking their perception of various risks within the park (µ = 2.1, σ = .78), however, perception gaps and demographic influences were revealed on individual event types. When asked to identify dangerous areas of the park, participants tended to select locations in the main amphitheater – the most highly trafficked area of the park – even though the fuzzy logic models showed a wider range of locations were susceptible to mass wasting events