Detecting Large Concept Extensions for Conceptual Analysis

When performing a conceptual analysis of a concept, philosophers are interested in all forms of expression of a concept in a text---be it direct or indirect, explicit or implicit. In this paper, we experiment with topic-based methods of automating the detection of concept expressions in order to facilitate philosophical conceptual analysis. We propose six methods based on LDA, and evaluate them on a new corpus of court decision that we had annotated by experts and non-experts. Our results indicate that these methods can yield important improvements over the keyword heuristic, which is often used as a concept detection heuristic in many contexts. While more work remains to be done, this indicates that detecting concepts through topics can serve as a general-purpose method for at least some forms of concept expression that are not captured using naive keyword approaches

Disease-modifying therapies alter gut microbial composition in MS.

Objective:To determine the effects of the disease-modifying therapies, glatiramer acetate (GA) and dimethyl fumarate (DMF), on the gut microbiota in patients with MS. Methods:Participants with relapsing MS who were either treatment-naive or treated with GA or DMF were recruited. Peripheral blood mononuclear cells were immunophenotyped. Bacterial DNA was extracted from stool, and amplicons targeting the V4 region of the bacterial/archaeal 16S rRNA gene were sequenced (Illumina MiSeq). Raw reads were clustered into Operational Taxonomic Units using the GreenGenes database. Differential abundance analysis was performed using linear discriminant analysis effect size. Phylogenetic investigation of communities by reconstruction of unobserved states was used to investigate changes to functional pathways resulting from differential taxon abundance. Results:One hundred sixty-eight participants were included (treatment-naive n = 75, DMF n = 33, and GA n = 60). Disease-modifying therapies were associated with changes in the fecal microbiota composition. Both therapies were associated with decreased relative abundance of the Lachnospiraceae and Veillonellaceae families. In addition, DMF was associated with decreased relative abundance of the phyla Firmicutes and Fusobacteria and the order Clostridiales and an increase in the phylum Bacteroidetes. Despite the different changes in bacterial taxa, there was an overlap between functional pathways affected by both therapies. Interpretation:Administration of GA or DMF is associated with differences in gut microbial composition in patients with MS. Because those changes affect critical metabolic pathways, we hypothesize that our findings may highlight mechanisms of pathophysiology and potential therapeutic intervention requiring further investigation

Automatic Recall of Lessons Learned for Software Project Managers

Lessons learned (LL) records constitute a software organization’s memory of successes and failures. LL are recorded within the organization repository for future reference to optimize planning, gain experience, and elevate market competitiveness. However, manually searching this repository is a daunting task, so it is often overlooked. This can lead to the repetition of previous mistakes and missing potential opportunities, which, in turn, can negatively affect the organization’s profitability and competitiveness. In this thesis, we present a novel solution that provides an automatic process to recall relevant LL and to push them to project managers. This substantially reduces the amount of time and effort required to manually search the unstructured LL repositories, and therefore, it encourages the utilization of LL. In this study, we exploit existing project artifacts to build the LL search queries on-the-fly, in order to bypass the tedious manual search process. While most of the current LL recall studies rely on case-based reasoning, they have some limitations including the need to reformat the LL repository, which is impractical, and the need for tight user involvement. This makes us the first to employ information retrieval (IR) to address the LL recall. An empirical study has been conducted to build the automatic LL recall solution and evaluate its effectiveness. In our study, we employ three of the most popular IR models to construct a solution that considers multiple classifier configurations. In addition, we have extended this study by examining the impact of the hybridization of LL classifiers on the classifiers’ performance. Furthermore, a real-world dataset of 212 LL records from 30 different software projects has been used for validation. Top-k and MAP, well-known accuracy metrics, have been used as well. The study results confirm the effectiveness of the automatic LL recall solution by a discerning accuracy of about 70%, which was increased to 74% in the case of hybridization. This eliminates the effort needed to manually search the LL repository, which positively encourages project managers to reuse the available LL knowledge – which in turn avoids old pitfalls and unleash hidden business opportunities