Both Software Engineering and Machine Learning have become recognized disciplines. In this article I analyse the combination of the two: engineering of machine learning applications. I believe the systematic way of working for machine learning applications is at certain points different from traditional (rule-based) software engineering. The question I set out to investigate is “How does software engineering change when we develop machine learning applications”?. This question is not an easy to answer and turns out to be a rather new, with few publications. This article collects what I have found until now.
LINK
We review over 10 years of research at Elsevier and various Dutch academic institutions on establishing a new format for the scientific research article. Our work rests on two main theoretical principles: the concept of modular documents, consisting of content elements that can exist and be published independently and are linked by meaningful relations, and the use of semantic data standards allowing access to heterogeneous data. We discuss the application of these concepts in five different projects: a modular format for physics articles, an XML encyclopedia in pharmacology, a semantic data integration project, a modular format for computer science proceedings papers, and our current work on research articles in cell biology.
BackgroundScientific software incorporates models that capture fundamental domain knowledge. This software is becoming increasingly more relevant as an instrument for food research. However, scientific software is currently hardly shared among and (re-)used by stakeholders in the food domain, which hampers effective dissemination of knowledge, i.e. knowledge transfer.Scope and approachThis paper reviews selected approaches, best practices, hurdles and limitations regarding knowledge transfer via software and the mathematical models embedded in it to provide points of reference for the food community.Key findings and conclusionsThe paper focusses on three aspects. Firstly, the publication of digital objects on the web, which offers valorisation software as a scientific asset. Secondly, building transferrable software as way to share knowledge through collaboration with experts and stakeholders. Thirdly, developing food engineers' modelling skills through the use of food models and software in education and training.
Genematics aims to help life science researchers and medical specialists to discover, interpret and communicate valuable patterns in biological data. Our software combines the recovery of data from public scientific resources with instant interpretation. It does so in such a way that the expert only needs a few seconds instead of hours or even days to retrieve answers from the available biological data. Use of our software should accelerate the research for new drugs, new treatments and other innovations in health-related research to build a better tomorrow.
Aanleiding: Automatisering kan leiden tot beter gebruik van materialen en afval reduceren. Dit brengt verbeteringen met zich mee voor 'people, planet and profit' (PPP) - mensen, het milieu en de winst. Een specifieke vorm van automatisering, de ontwikkeling van zelfrijdende auto's en vrachtauto's, gaat snel. Maar om zelfrijdende voertuigen beschikbaar te maken is er nog veel onderzoek en ontwikkeling nodig op verschillende gebieden. Er zijn nog veel vragen te beantwoorden op het gebied van onder andere truckontwerp, betrouwbare software, aansprakelijkheid, trajectplanning en logistiek. Doelstelling Het doel van het Intralog-project is om voor de maatschappij en de private sector een significante bijdrage te leveren aan de mogelijkheden van zelfrijdende voertuigen in de commerciële transportsector. Het Intralog-project onderzoekt de toegevoegde waarde voor PPP van 'automated guided trucks' (AGT's) aan logistieke operaties bij distributiecentra en interterminal/intermodal traffic hubs. Dit gebeurt in twee stappen: 1) het identificeren van het potentieel met betrekking tot de vraag vanuit de logistieke omgeving; 2. het ontwerpen, realiseren, testen en valideren van mogelijke strategieën voor het implementeren van AGT's in een logistiek scenario. Beoogde resultaten Het concrete resultaat van het project bestaat uit onderzoekstools en hardware- en softwaremodellen voor Intralog. Deze bieden een goede mogelijkheid om de opgedane kennis te verspreiden. De projectdeelnemers zullen bijdragen aan workshops, tentoonstellingen en in Nederland georganiseerde symposia. De onderzoeksresultaten verspreiden ze op conferenties en door middel van publicaties in technische vakbladen. De uiteindelijke Intralog-resultaten worden gepresenteerd op een afsluitend congres. De resultaten zullen worden samengevat in een boekje.
A huge amount of data are being generated, collected, analysed and distributed in a fast pace in our daily life. This data growth requires efficient techniques for analysing and processing high volumes of data, for which preserving privacy effectively is a crucial challenge and even a key necessity, considering the recently coming into effect privacy laws (e.g., the EU General Data Protection Regulation-GDPR). Companies and organisations in their real-world applications need scalable and usable privacy preserving techniques to support them in protecting personal data. This research focuses on efficient and usable privacy preserving techniques in data processing. The research will be conducted in different directions: - Exploring state of the art techniques. - Designing and applying experiments on existing tool-sets. - Evaluating the results of the experiments based on the real-life case studies. - Improving the techniques and/or the tool to meet the requirements of the companies. The proposal will provide results for: - Education: like offering courses, lectures, students projects, solutions for privacy preservation challenges within the educational institutes. - Companies: like providing tool evaluation insights based on case studies and giving proposals for enhancing current challenges. - Research centre (i.e., Creating 010): like expanding its expertise on privacy protection technologies and publishing technical reports and papers. This research will be sustained by pursuing following up projects actively.
