Structured ontology and information retrieval for email search and discovery

This paper discusses an document discovery tool based on formal concept analysis. The program allows users to navigate email using a visual lattice metaphor rather than a tree. It implements a virtual file structure over email where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in email discovery. The system described provides more flexibility in retrieving stored emails than what is normally available in email clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems.

Extending the theory of Owicki and Gries with asynchronous message passing

We describe an extension of the theory of Owicki and Gries (1976) to a programming language that supports asynchronous message passing based on unconditional send actions and conditional receive actions. The focus is on exploring the fitness of the extension for distributed program derivation. A number of experiments are reported, based on a running example problem, and with the aim of exploring design heuristics and of streamlining derivations and progress arguments.

Software and Services − Research and Education in Bulgaria

Доклад по покана, поместен в сборника на Националната конференция "Образованието в информационното общество", Пловдив, май, 2010 г.

An Analysis and Forecast of Software and Services Research in Bulgaria

In the last 40 years much has been achieved in Software Engineering research and still more is to be done. Although significant progress is being made on several fronts in Service-Oriented Architecture (SOA), there is still no set of clear, central themes to focus research activity on. A task within the EU FP7 Sister project aimed at defining research priorities for the Faculty of Mathematics and Informatics (Sofia University) in the area of Software and Services. A dedicated methodology was proposed and developed, based on various sources of information. The information accumulated was systematised and processed according to this methodology. The final results obtained are described and discussed here.

Prediction of subcellular localization using sequence-biased recurrent networks

Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging. Results: We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively.