Abstract

Abstract
This paper describes the open-source software toolchain developed by the Underwater Systems and Technology Laboratory (LSTS) for supporting networked heterogeneous air and ocean vehicle systems. The toolchain supports the deployment of air and ocean vehicles interacting over limited acoustic and wireless networks combined with disruption-tolerant networking protocols. We present the different components of the toolchain and how they can be deployed and extended for different scenarios. We conclude with descriptions of recent applications to onboard deliberative planning and integration of low-cost micro UAVs into the toolchain.

Abstract
The Message Passing Interface (MPI) is the de facto standard message-passing infrastructure for developing parallel applications. Two decades after the first version of the library specification, MPI-based applications are nowadays routinely deployed on super and cluster computers. These applications, written in C or Fortran, exhibit intricate message passing behaviours, making it hard to statically verify important properties such as the absence of deadlocks. Our work builds on session types, a theory for describing protocols that provides for correct-by-construction guarantees in this regard. We annotate MPI primitives and C code with session type contracts, written in the language of a software verifier for C. Annotated code is then checked for correctness with the software verifier. We present preliminary results and discuss the challenges that lie ahead for verifying realistic MPI program compliance against session types.

Abstract

Abstract
Scientific publications are the main vehicle to disseminate information in the field of biotechnology for wastewater treatment. Indeed, the new research paradigms and the application of high-throughput technologies have increased the rate of publication considerably. The problem is that manual curation becomes harder, prone-to-errors and time-consuming, leading to a probable loss of information and inefficient knowledge acquisition. As a result, research outputs are hardly reaching engineers, hampering the calibration of mathematical models used to optimize the stability and performance of biotechnological systems. In this context, we have developed a data curation workflow, based on text mining techniques, to extract numerical parameters from scientific literature, and applied it to the biotechnology domain. A workflow was built to process wastewater-related articles with the main goal of identifying physico-chemical parameters mentioned in the text. This work describes the implementation of the workflow, identifies achievements and current limitations in the overall process, and presents the results obtained for a corpus of 50 full-text documents.

Abstract
Monitoring and managing traffic are vital elements to the operation of a network. Traffic prediction is an essential tool that captures the underlying behavior of a network and can be used, for example, to detect anomalies by defining acceptable data traffic thresholds. In this context, most current solutions are heavily based on historical time data, which makes it difficult to employ them in a dynamic environment such as cloud computing. We propose a traffic prediction approach based on a statistical model where observations are weighted with a Poisson distribution inside a sliding window. The evaluation of the proposed method is performed by assessing the Normalized Mean Square Error of predicted values over observed values from a real cloud computing dataset, collected by monitoring the utilization of Dropbox. Compared with other predictors, our solution exhibits the strongest correlation level and shows a close match with real observations.