This paper provides a novel algorithm for automatically extracting social hierarchy data from electronic communication behavior. The algorithm is based on data mining user behaviors to automatically analyze and catalog patterns of communications between entities in a email collection to extract social standing. The advantage to such automatic methods is that they extract relevancy between hierarchy levels and are dynamic over time.We illustrate the algorithms over real world data using the Enron corporation's email archive. The results show great promise when compared to the corporations work chart and judicial proceeding analyzing the major players.
We present our work on automatically extracting social hierarchies from electronic communication data. Data mining based on user behavior can be leveraged to analyze and catalog patterns of communications between entities to rank relationships. The advantage is that the analysis can be done in an automatic fashion and can adopt itself to organizational changes over time. We illustrate the algorithms over real world data using the Enron corporation's email archive. The results show great promise when compared to the corporations work chart and judicial proceeding analyzing the major players.
This paper proposes the CorpRank algorithm to extract social hierarchies from electronic communication data. The algorithm computes a ranking score for each user as a weighted combination of the number of emails, the number of responses, average response time, clique scores, and several degree and centrality measures. The algorithm uses principal component analysis to calculate the weights of the features. This score ranks users according to their importance, and its output is used to reconstruct an organization chart. We illustrate the algorithm over real-world data using the Enron corporation’s e-mail archive. Compared to the actual corporate work chart, compensation lists, judicial proceedings, and analyzing the major players involved, the results show promise.
For robots to operate in a three dimensional world and interact with humans, learning spatial relationships among objects in the surrounding is necessary. Reasoning about the state of the world requires inputs from many different sensory modalities including vision (V) and haptics (H). We examine the problem of desk organization: learning how humans spatially position different objects on a planar surface according to organizational "preference". We model this problem by examining how humans position objects given multiple features received from vision and haptic modalities. However, organizational habits vary greatly between people both in structure and adherence. To deal with user organizational preferences, we add an additional modality, "utility" (U), which informs on a particular human's perceived usefulness of a given object. Models were trained as generalized (over many different people) or tailored (per person). We use two types of models: random forests, which focus on precise multi-task classification, and Markov logic networks, which provide an easily interpretable insight into organizational habits. The models were applied to both synthetic data, which proved to be learnable when using fixed organizational constraints, and human-study data, on which the random forest achieved over 90% accuracy. Over all combinations of {H, U, V} modalities, UV and HUV were the most informative for organization. In a follow-up study, we gauged participants preference of desk organizations by a generalized random forest organization vs. by a random model. On average, participants rated the random forest models as 4.15 on a 5-point Likert scale compared to 1.84 for the random model.
Vitamin D binding protein (DBP) is the primary transport protein for the multiple forms of vitamin D in the body. Variations in the structure of DBP can affect the binding affinity with vitamin D, which can result in a vitamin D deficiency. Vitamin D deficiency is seen in various autoimmune disorders such as rheumatoid arthritis, systemic lupus erythematosus, and diabetes mellitus type 1 (DM1). The increasing prevalence of autoimmune disorders highlights the importance of identifying possible associations with deficient vitamin D serum levels. The objective of this research was to examine the relationship between the serum concentration of 25-hydroxyvitamin D and the concentration of the specific DBP isoforms in diabetic individuals. Vitamin D concentrations were measured using an EIA method, DBP concentrations were measured using an ELISA test, and the likely DBP isoform was determined using SNP TaqMan® analysis. Diabetic participants were compared to control participants. Allele frequencies were consistent with the standard European Ancestry reference population. A Mann Whitney U test revealed no significant difference among the DBP isoform values between the diabetic group and control population. Linear regression showed no correlation between DBP levels and vitamin D levels (R 2 =0.3402). There was no observed dosage effect in individuals having one or two copies of the mutant allele to the levels of DBP and vitamin D. DBP isoforms and concentrations of DBP had no effect on vitamin D concentrations in our DM1 testing population.
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