Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy

Expert finding is an information retrieval task concerned with the search for the most knowledgeable people, in some topic, with basis on documents describing peoples activities. The task involves taking a user query as input and returning a list of people sorted by their level of expertise regarding the user query. This paper introduces a novel approach for combining multiple estimators of expertise based on a multisensor data fusion framework together with the Dempster-Shafer theory of evidence and Shannon's entropy. More specifically, we defined three sensors which detect heterogeneous information derived from the textual contents, from the graph structure of the citation patterns for the community of experts, and from profile information about the academic experts. Given the evidences collected, each sensor may define different candidates as experts and consequently do not agree in a final ranking decision. To deal with these conflicts, we applied the Dempster-Shafer theory of evidence combined with Shannon's Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list. Experiments made over two datasets of academic publications from the Computer Science domain attest for the adequacy of the proposed approach over the traditional state of the art approaches. We also made experiments against representative supervised state of the art algorithms. Results revealed that the proposed method achieved a similar performance when compared to these supervised techniques, confirming the capabilities of the proposed framework

Keyword based profile creation using latent dirichlet allocation, domain dictionary and domain ontology / Nor Adzlan Jamaludin

Expert Finding is a field in information retrieval that focuses on finding an expert based on several criteria. Some of the methods that have been applied for expert finding include statistical, machine learning and ontology-based methods. Profile creation is one of the steps or tasks that are required in expert finding, which is the process of capturing and representing the details of experts and users which later can be used for retrieval. An issue that is faced for profile creation in expert finding is that the profiles being created are focused on the details of the experts but not on the users who are searching for these experts. This research explores a profile creation model that creates domain specific keyword-based profiles of users using Latent Dirichlet Allocation, domain dictionary and domain ontology from bookmarks. The domain of agriculture is selected as the case study for this research. The model is implemented in a form of a prototype and is evaluated by comparing how similar the prototype created profiles with manually built ones. From the results and analysis of the research, it is concluded that the method can successfully create domain specific profiles. The significances and contributions of the research include the application of LDA in user profiling, the proposed model, model prototype and the results and findings of the experiments conducted throughout the research

Controlling the Precision-Recall Tradeoff in Differential Dependency Network Analysis

Graphical models have gained a lot of attention recently as a tool for learning and representing dependencies among variables in multivariate data. Often, domain scientists are looking specifically for differences among the dependency networks of different conditions or populations (e.g. differences between regulatory networks of different species, or differences between dependency networks of diseased versus healthy populations). The standard method for finding these differences is to learn the dependency networks for each condition independently and compare them. We show that this approach is prone to high false discovery rates (low precision) that can render the analysis useless. We then show that by imposing a bias towards learning similar dependency networks for each condition the false discovery rates can be reduced to acceptable levels, at the cost of finding a reduced number of differences. Algorithms developed in the transfer learning literature can be used to vary the strength of the imposed similarity bias and provide a natural mechanism to smoothly adjust this differential precision-recall tradeoff to cater to the requirements of the analysis conducted. We present real case studies (oncological and neurological) where domain experts use the proposed technique to extract useful differential networks that shed light on the biological processes involved in cancer and brain function

An Economic Analysis of the Diet, Growth, and Health of Young Children in the United States

The purpose of this paper is to investigate the extent to which family income and education are obstacles to the provision of adequate diets for young children in the United States. An examination of the Health and Nutrition Examination Survey reveals the following: 1. Average nutrient intakes of young children are well above recommended dietary standards, with the exception of iron. 2. Average nutrient intakes for children in households of lower economic status are very similar to intakes of children in households of higher economic status. Rates of children's growth are also similar in these households. 3. Family income and education of the household head have statistically significant but very small positive effects on the nutrient intake levels of young children. 4. There are substantial effects of protein intakes on children's height and head growth, even though protein is consumed in excess of dietary standards. This finding and the apparent correlation between children's growth and their intellectual development brings to question the adequacy of present protein standards. Could American mothers, who provide very high protein diets for their children in households at all levels of socioeconomic status know more about what constitutes an adequate diet for their children than the experts do?

Interpreting maps through the eyes of expert and novice users

The experiments described in this article combine response time measurements and eye movement data to gain insight into the users' cognitive processes while working with dynamic and interactive maps. Experts and novices participated in a user study with a 'between user' design. Twenty screen maps were presented in a random order to each participant, on which he had to execute a visual search. The combined information of the button actions and eye tracker reveals that both user groups showed a similar pattern in the time intervals needed to locate the subsequent names. From this pattern, information about the users' cognitive load could be derived: use of working memory, learning effect and so on. Moreover, the response times also showed that experts were significantly faster in finding the names in the map image. This is further explained by the eye movement metrics: experts had significantly shorter fixations and more fixations per second meaning that they could interpret a larger part of the map in the same amount of time. As a consequence, they could locate objects in the map image more efficiently and thus faster

The role of star performers in software design teams

Purpose – This study seeks to extend previous research on experts with mainly ad-hoc groups from laboratory research to a field setting. Specifically, this study aims to investigate experts’ relative importance in team performance. Expertise is differentiated into two categories (task functions and team functions) and the paper aims to investigate whether experts in task and team functions predict team performance over and above the team’s average expertise level. Design/methodology/approach – Longitudinal, multi-source data from 96 professional software design engineers were used by means of hierarchical regression analyses. Findings – The results show that both expert members in task functions (i.e. behavior that aids directly in the completion of work-related activities) and the experts in team functions (i.e. facilitation of interpersonal interaction necessary to work together as a team) positively predicted team performance 12 months later over and above the team’s average expertise level. Research limitations/implications – Samples from other industry types are needed to examine the generalizability of the study findings to other occupational groups. Practical implications – For staffing, the findings suggest that experts are particularly important for the prediction of team performance. Organizations should invest effort into finding “star performers” in task and team functions in order to create effective teams. Originality/value – This paper focuses on the relationship between experts (in task functions and team functions) and team performance. It extends prior research on team composition and complements expertise research: similar to cognitive ability and personality, it is important to take into account member expertise when examining how to manage the people mix within teams. Benefits of expertise are not restricted to laboratory research but are broadened to real-world team settings