Decentralized creation of academic documents using a Network Attached Storage (NAS) server

Scholarly document creation continues to face various obstacles. Scholarly text production requires more complex word processors than other forms of texts because of the complex structures of citations, formulas and figures. The need for peer review, often single-blind or double-blind, creates needs for document management that other texts do not require. Additionally, the need for collaborative editing, security and strict document access rules means that many existing word processors are imperfect solutions for academics. Nevertheless, most papers continue to be written using Microsoft Word (Sadeghi et al. 2017). We here analyze some of the problems with existing academic solutions and then present an argument why we believe that running an open source academic writing solution for academic purposes, such as Fidus Writer, on a Network Attached Storage (NAS) server could be a viable alternative.Comment: 15 pages, paper presented at the Enabling Decentralised Scholarly Communication workshop co-located with the Extended Semantic Web Conference (ESWC 2017

Balanced Knowledge Distribution among Software Development Teams -- Observations from Open-Source and Closed-Source Software Development

In software development teams, developer turnover is among the primary reasons for project failures as it leads to a great void of knowledge and strain for the newcomers. Unfortunately, no established methods exist to measure how knowledge is distributed among development teams. Knowing how this knowledge evolves and is owned by key developers in a project helps managers reduce risks caused by turnover. To this end, this paper introduces a novel, realistic representation of domain knowledge distribution: the ConceptRealm. To construct the ConceptRealm, we employ a latent Dirichlet allocation model to represent textual features obtained from 300k issues and 1.3M comments from 518 open-source projects. We analyze whether the newly emerged issues and developers share similar concepts or how aligned the developers' concepts are with the team over time. We also investigate the impact of leaving members on the frequency of concepts. Finally, we evaluate the soundness of our approach to closed-source software, thus allowing the validation of the results from a practical standpoint. We find out that the ConceptRealm can represent the high-level domain knowledge within a team and can be utilized to predict the alignment of developers with issues. We also observe that projects exhibit many keepers independent of project maturity and that abruptly leaving keepers harm the team's concept familiarity.Comment: 33 pages, 17 figures, journal submissio

Ein neuer Algorithmus zur Zeitsynchronisierung von Ereignis-basierten Zeitreihendaten als Alternative zur Kreuzkorrelation

Mit der Verwendung von Sensordaten aus mehreren Quellen entsteht oft die Notwendigkeit einer Synchronisierung der entstandenen Messreihen. Ein Standardverfahren dazu ist die Kreuzkorrelation, die jedoch übereinstimmende Zeitstempel voraussetzt und empfindlich gegenüber Ausreißern reagiert. In diesem Paper wird daher ein alternativer Algorithmus für die Synchronisierung von Ereignis-basierten Zeitreihendaten vorgestellt.With the use of sensor data from multiple sources, the need for synchronization of the resulting measurement series often arises. A standard method for this is cross-correlation, but this requires matching timestamps and is sensitive to outliers. This paper therefore presents an alternative algorithm for the synchronization of event-based time series data

Glycodelin A is a prognostic marker to predict poor outcome in advanced stage ovarian cancer patients

Background Glycodelin is a cell surface glycoprotein offering a unique gender specific carbohydrate configuration. Sialylated carbohydrate structures, which are unusual for mammals, characterize Glycodelin isolated from amniotic fluid (Glycodelin A, GdA). Glycodelin in general exerts multiple, partly opposing functions ranging from immunosuppression to cell differentiation. As these markedly influence tumorigenesis, this study aimed to clarify whether expression of different Glycodelin isoforms is related to clinicopathological characteristics and prognosis of ovarian cancer patients. Further the use of Glycodelin as a serum marker in benign and malignant ovarian diseases was evaluated. Methods Ovarian cancer specimens (n = 152) were stained for Glycodelin with carbohydrate and peptide specific antibodies. Associations between Glycodelin expression and histological grading, FIGO stage as well as patient’s prognosis were examined. Glycodelin was correlated to expression of gonadotropin receptors and mucin-1, which are discussed as ovarian cancer tissue markers. In addition, Glycodelin serum concentrations were analyzed in patients suffering from benign (n = 73) or malignant (n = 38) ovarian neoplasias. Results Glycodelin A was found to be an independent prognostic marker for poor prognosis in advanced ovarian cancer patients. GdA staining correlated with gonadotropin receptor (FSHR and LHCGR) and with hCG expression. Gd expression showed a positive correlation with a tumour-associated epitope of mucin 1 (TA-MUC1). Further, compared to ovarian cancer, serum Gd was increased in patients with benign ovarian tumors. Conclusion Glycodelin A might be related to tumor aggressiveness and poor clinical outcome in advanced epithelial ovarian cancer. Glycodelin serum levels found in patients suffering from benign ovarian tumors, might contribute to a more global attenuation during progression of these precursor lesions