A workflow language for research e-infrastructures

Research e-infrastructures are “systems of systems,” patchworks of resources such as tools and services, which change over time to address the evolving needs of the scientific process. In such environments, researchers carry out their scientific process in terms of sequences of actions that mainly include invocation of web services, user interaction with web applications, user download and use of shared software libraries/tools. The resulting workflows are intended to generate new research products (articles, datasets, methods, etc.) out of existing ones. Sharing a digital and executable representation of such workflows with other scientists would enforce Open Science publishing principles of “reproducibility of science” and “transparent assessment of science.” This work presents HyWare, a language and execution platform capable of representing scientific processes in highly heterogeneous research e-infrastructures in terms of so-called hybrid workflows. Hybrid workflows can express sequences of “manually executable actions,” i.e., formal descriptions guiding users to repeat a reasoning, protocol or manual procedure, and “machine-executable actions,” i.e., encoding of the automated execution of one (or more) web services. An HyWare execution platform enables scientists to (i) create and share workflows out of a given action set (as defined by the users to match e-infrastructure needs) and (ii) execute hybrid workflows making sure input/output of the actions flow properly across manual and automated actions. The HyWare language and platform can be implemented as an extension of well-known workflow languages and platforms.

ARCH-COMP20 Repeatability Evaluation Report

This report presents the results of the repeatability evaluation for the 4th International Competition on Verifying Continuous and Hybrid Systems (ARCH-COMP’20). The competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2020, affiliated with the IFAC World Congress. In its fourth edition, twenty-eight tools submitted artifacts through a Git repository for the repeatability evaluation, applied to solve benchmark problems for seven competition categories. The majority of participants adhered to the requirements for this year’s repeatability evaluation, namely to submit scripts to automatically install and execute tools in containerized virtual environments (specifically Dockerfiles to execute within Docker), and several categories used performance evaluation information from a common execution platform. The repeatability results represent a snapshot of the current landscape of tools and the types of benchmarks for which they are particularly suited and for which others may repeat their analyses. Due to the diversity of problems in verification of continuous and hybrid systems, as well as basing on standard practice in repeatability evaluations, we evaluate the tools with pass and/or failing being repeatable.

Role of Digital Microfluidics in Enabling Access to Laboratory Automation and Making Biology Programmable

Digital microfluidics (DMF) is a liquid handling technique that has been demonstrated to automate biological experimentation in a low-cost, rapid, and programmable manner. This review discusses the role of DMF as a “digital bioconverter”—a tool to connect the digital aspects of the design–build–learn cycle with the physical execution of experiments. Several applications are reviewed to demonstrate the utility of DMF as a digital bioconverter, namely, genetic engineering, sample preparation for sequencing and mass spectrometry, and enzyme-, immuno-, and cell-based screening assays. These applications show that DMF has great potential in the role of a centralized execution platform in a fully integrated pipeline for the production of novel organisms and biomolecules. In this paper, we discuss how the function of a DMF device within such a pipeline is highly dependent on integration with different sensing techniques and methodologies from machine learning and big data. In addition to that, we examine how the capacity of DMF can in some cases be limited by known technical and operational challenges and how consolidated efforts in overcoming these challenges will be key to the development of DMF as a major enabling technology in the computer-aided biology framework.

Apply Iot Paradigm to Smart Communication Model and Experimentation using Beacons

Information and communication Technology has become a very significant means to support organization activities to reach its target. However, there is still not enough coordination and integration that makes the work proficient. This paper presents a model of classroom that makes several smart devices such as laptops, projectors connected to Bluetooth or WiFi within proximity area in order to establish communication between students and teachers within the smart environment. Also, the gateway manages classroom smart devices by automatic detection and connectivity and it serves as an application execution platform. The software that is used here is called Beacon with which we can train the students and teachers online. The earlier application of Beacon is only through the direct interaction with students and teachers which is same as the traditional way of teaching. But, here in this paper the trainer need not come into the classroom to guide students or to clear the doubts physically. The trainer communicates through the smart devices even though being so far from the classroom through WiFi. The authority can send circulars, alerts, notifications, and study materials to the students and the faculty can share the notes and case studies to the students whenever unavailable to meet the students directly. Whenever the student enters into the classroom, auto check-in happens and the attendance of the students is marked into the system and this list is centrally accessed by the faculty and authority. Thus, the time consumption of faculty is saved and the classroom becomes very smart with this implementation.

A model-driven approach to automate data visualization in big data analytics

In big data analytics, advanced analytic techniques operate on big datasets aimed at complementing the role of traditional OLAP for decision making. To enable companies to take benefit of these techniques despite the lack of in-house technical skills, the H2020 TOREADOR Project adopts a model-driven architecture for streamlining analysis processes, from data preparation to their visualization. In this article, we propose a new approach named SkyViz focused on the visualization area, in particular on (1) how to specify the user’s objectives and describe the dataset to be visualized, (2) how to translate this specification into a platform-independent visualization type, and (3) how to concretely implement this visualization type on the target execution platform. To support step (1), we define a visualization context based on seven prioritizable coordinates for assessing the user’s objectives and conceptually describing the data to be visualized. To automate step (2), we propose a skyline-based technique that translates a visualization context into a set of most suitable visualization types. Finally, to automate step (3), we propose a skyline-based technique that, with reference to a specific platform, finds the best bindings between the columns of the dataset and the graphical coordinates used by the visualization type chosen by the user. SkyViz can be transparently extended to include more visualization types on one hand, more visualization coordinates on the other. The article is completed by an evaluation of SkyViz based on a case study excerpted from the pilot applications of the TOREADOR Project.

On Rapid Development of Reactive Wireless Sensor Systems

We present a software platform for designing and testing wireless networks of sensors and actuators (WSNs). The platform consists of three components: an operating system for small-footprint microcontrollers (dubbed PicOS), a software development kit (SDK) amounting to a C-based, event-oriented (reactive) programming language, and a virtual execution platform (VUE2) capable of emulating complete deployment environments for WSNs and thus facilitating their rapid development.1 Its most recent incarnation introduced in the present paper is a component of the WSN lab being currently set up at Vistula in collaboration with Olsonet Communications Corporation.2 We highlight the platform’s most interesting features within the context of a production WSN installed at independent-living facilities.