The media equation and team formation: Further evidence for experience as a moderator

This study extends previous media equation research, which showed that interdependence but not identity leads to team affiliation effects with computers. The current study used an identity manipulation that more closely replicated the manipulation used in traditional team and group formation research than the original media equation research in this area. The study also sought further evidence for the relationship between experience with computers and behaviour reflecting a media equation pattern of results. Sixty students from the University of Queensland voluntarily participated in the study. Participants were assigned to one of three conditions: control, human team (a team made of only humans) or human-computer team (a team made of computers and humans). Questionnaire measures assessing participants’ affective experience, attitudes and opinions were taken. Participants of high experience, but not low experience, when assigned to either of the team conditions enjoyed the tasks completed on the computer more than participants who worked on their own. When assigned to a team that involved a computer, participants of high experience, but not low experience, reacted negatively towards the computer (in comparison to high experience participants working on their own or on a team without a computer as a team member) – rating the information provided by the computer lower, rating themselves as less influenced by the computer and changing their own ratings and rankings to be less like those of the computer. These results are interpreted in light of the ‘Black Sheep’ literature and recognized as a media equation pattern of results

How Many Topics? Stability Analysis for Topic Models

Topic modeling refers to the task of discovering the underlying thematic structure in a text corpus, where the output is commonly presented as a report of the top terms appearing in each topic. Despite the diversity of topic modeling algorithms that have been proposed, a common challenge in successfully applying these techniques is the selection of an appropriate number of topics for a given corpus. Choosing too few topics will produce results that are overly broad, while choosing too many will result in the "over-clustering" of a corpus into many small, highly-similar topics. In this paper, we propose a term-centric stability analysis strategy to address this issue, the idea being that a model with an appropriate number of topics will be more robust to perturbations in the data. Using a topic modeling approach based on matrix factorization, evaluations performed on a range of corpora show that this strategy can successfully guide the model selection process.Comment: Improve readability of plots. Add minor clarification

Do performance measures of donors' aid allocation underperform?

Indices of donor performance abound. Their recent popularity has occurred within the context of pessimism over aid's impact and optimism over the effect of changes in donor behaviour. Rankings of donor allocative performance aim to change donor behaviour, either through direct pressure on governments or indirectly through public engagement. The indices themselves rely on descriptive measures, and typically claim methodological superiority over positive alternatives due to their simplicity. However, there are two problems. First, measures do not seem robust to simple variations in methodology. Second, correlation amongst competing indices is low, leading to a host of contradictory judgements. This offers neither clear technical guidance nor consistent political pressure. The advantages and disadvantages of the approach are discussed, building upon the more general critique of aggregate indices. I suggest a graphical solution that embraces the advantages of the descriptive approach (including ease of public communication) while avoiding some of its major weaknesses (which typically stem from aggregation)

Stability and aggregation of ranked gene lists

Ranked gene lists are highly instable in the sense that similar measures of differential gene expression may yield very different rankings, and that a small change of the data set usually affects the obtained gene list considerably. Stability issues have long been under-considered in the literature, but they have grown to a hot topic in the last few years, perhaps as a consequence of the increasing skepticism on the reproducibility and clinical applicability of molecular research findings. In this article, we review existing approaches for the assessment of stability of ranked gene lists and the related problem of aggregation, give some practical recommendations, and warn against potential misuse of these methods. This overview is illustrated through an application to a recent leukemia data set using the freely available Bioconductor package GeneSelector

Lower Bounds for Complementation of omega-Automata Via the Full Automata Technique

In this paper, we first introduce a lower bound technique for the state complexity of transformations of automata. Namely we suggest first considering the class of full automata in lower bound analysis, and later reducing the size of the large alphabet via alphabet substitutions. Then we apply such technique to the complementation of nondeterministic \omega-automata, and obtain several lower bound results. Particularly, we prove an \omega((0.76n)^n) lower bound for B\"uchi complementation, which also holds for almost every complementation or determinization transformation of nondeterministic omega-automata, and prove an optimal (\omega(nk))^n lower bound for the complementation of generalized B\"uchi automata, which holds for Streett automata as well