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.
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The pace of introduction of new technology and thus continuous change in skill needs at workplaces, especially for the engineers, has increased. While digitization induced changes in manufacturing, construction and supply chain sectors may not be felt the same in every sector, this will be hard to escape. Both young and experienced engineers will experience the change, and the need to continuously assess and close the skills gap will arise. How will we, the continuing engineering educators and administrators will respond to it? Prepared for engineering educators and administrators, this workshop will shed light on the future of continuing engineering education as we go through exponentially shortened time frames of technological revolution and in very recent time, in an unprecedented COVID-19 pandemic. S. Chakrabarti, P. Caratozzolo, E. Sjoer and B. Norgaard.
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In the fall of 1999, we started, the Integrated Product Development- Collaborative Engineering ( IPD-CE) project as a first pilot. We experimented with modern communication technology in order to find useful tools for facilitating the cooperative work and the contacts of all the participants. Teams have been formed with engineering students from Lehigh University in the US, the Fontys University in Eindhoven, The Netherlands and from the Otto-von-Guericke University in Magdeburg, Germany. In the fall of 2000 we continued and also cooperated with the Finnish Oulu Polytechnic. It turned out that group cohesion stayed low (students did not meet in real life), and that Internet is not mature enough yet for desktop video conferencing. Chatting and email were in these projects by far the most important communication media. We also found out that the use of a Computer Support for Cooperative Work (CSCW) server is a possibility for information interchange. The server can also be used as an electronic project archive. Points to optimise are: 1. We didn't fully match the complete assignments of the groups; 2. We allowed the groups to divide the work in such parts that those were developed and prototyped almost locally; 3. We haven't guided the fall 2000 teams strong enough along our learning curve and experiences from previous groups. 4. We didn't stick strong enough to the, by the groups developed, protocols for email and chat sessions. 5. We should facilitate video conferencing via V-span during the project to enhance the group performance and commitment.
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The current set of research methods on ictresearchmethods.nl contains only one research method that refers to machine learning: the “Data analytics” method in the “Lab” strategy. This does not reflect the way of working in ML projects, where Data Analytics is not a method to answer one question but the main goal of the project. For ML projects, the Data Analytics method should be divided in several smaller steps, each becoming a method of its own. In other words, we should treat the Data Analytics (or more appropriate ML engineering) process in the same way the software engineering process is treated in the framework. In the remainder of this post I will briefly discuss each of the existing research methods and how they apply to ML projects. The methods are organized by strategy. In the discussion I will give pointers to relevant tools or literature for ML projects.
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The importance of teaching engineering students innovation development is commonly clearly understood. It is essential to achieve products which are attractive and profitable in the market. To achieve this, an institute of engineering education has to provide students with needed knowledge, skills and attitudes including both technical and business orientation. This is important especially for SME’s. Traditionally, education of engineering provides students with basic understanding how to solve common technical problems. However companies need wider view to achieve new products. Universities of applied Sciences in Oulu and Eindhoven want to research what is the today’s educational situation for this aim, to find criteria to improve the content of the educational system, and to improve the educational system. Important stakeholders are teachers and students within the institute but also key-persons in companies. The research is realized by questionnaires and interviews from which a current situation can be found. The research will also include the opinion of management who give possibilities to change the curriculum. By this research more insight will be presented about how to re-design a current curriculum. The research will act as basis for this discussion in SEFI-conference about formulating a curriculum that includes elements for wide-ranging knowledge and skills to achieve innovations especially in SME’s.
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From the article: This paper describes the external IT security analysis of an international corporate organization, containing a technical and a social perspective, resulting in a proposed repeatable approach and lessons learned for applying this approach. Part of the security analysis was the utilization of a social engineering experiment, as this could be used to discover employee related risks. This approach was based on multiple signals that indicated a low IT security awareness level among employees as well as the results of a preliminary technical analysis. To carry out the social engineering experiment, two techniques were used. The first technique was to send phishing emails to both the system administrators and other employees of the company. The second technique comprised the infiltration of the office itself to test the physical security, after which two probes were left behind. The social engineering experiment proved that general IT security awareness among employees was very low. The results allowed the research team to infiltrate the network and have the possibility to disable or hamper crucial processes. Social engineering experiments can play an important role in conducting security analyses, by showing security vulnerabilities and raising awareness within a company. Therefore, further research should focus on the standardization of social engineering experiments to be used in security analyses and further development of the approach itself. This paper provides a detailed description of the used methods and the reasoning behind them as a stepping stone for future research on this subject. van Liempd, D., Sjouw, A., Smakman, M., & Smit, K. (2019). Social Engineering As An Approach For Probing Organizations To Improve It Security: A Case Study At A Large International Firm In The Transport Industry. 119-126. https://doi.org/10.33965/es2019_201904l015
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In the fall of 1999, an international integrated product development pilot project based on collaborative engineering was started with team members in two international teams from the United States, The Netherlands and Germany. Team members interacted using various Internet capabilities, including, but not limited to, ICQ (means: I SEEK YOU, an internet feature which immediately detects when somebody comes "on line"), web phones, file servers, chat rooms and Email along with video conferencing. For this study a control group with all members located in the USA only also worked on the same project.
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This paper describes a model for education in innovative engineering. The kernel of this model is, that students from different departments of the faculty of Applied Science and Technology are placed in industry for a period of eighteen months after two-and-a-half year of theoretical studies. During this period students work in multi-disciplinary projects on different themes. Students will grow to fully equal employees in industry. Therefore it is important that besides students, teachers and company employees will participate in the projects. Also the involvement of other level students (University and high school) is recommended. The most important characteristics of the model can be summarized in innovative, interdisciplinary and international orientation.
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The past two years I have conducted an extensive literature and tool review to answer the question: “What should software engineers learn about building production-ready machine learning systems?”. During my research I noted that because the discipline of building production-ready machine learning systems is so new, it is not so easy to get the terminology straight. People write about it from different perspectives and backgrounds and have not yet found each other to join forces. At the same time the field is moving fast and far from mature. My focus on material that is ready to be used with our bachelor level students (applied software engineers, profession-oriented education), helped me to consolidate everything I have found into a body of knowledge for building production-ready machine learning (ML) systems. In this post I will first define the discipline and introduce the terminology for AI engineering and MLOps.
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This paper is a case report of why and how CDIO became a shared framework for Community Service Engineering (CSE) education. CSE can be defined as the engineering of products, product-service combinations or services that fulfill well-being and health needs in the social domain, specifically for vulnerable groups in society. The vulnerable groups in society are growing, while fewer people work in health care. Finding technical, interdisciplinary solutions for their unmet needs is the territory of the Community Service Engineer. These unmet needs arise in local niche markets as well as in the global community, which makes it an interesting area for innovation and collaboration in an international setting. Therefore, five universities from Belgium, Portugal, the Netherlands, and Sweden decided to work together as hubs in local innovation networks to create international innovation power. The aim of the project is to develop education on undergraduate, graduate and post-graduate levels. The partners are not aiming at a joined degree or diploma, but offer a shared short track blended course (3EC), which each partner can supplement with their own courses or projects (up to 30EC). The blended curriculum in CSE is based on design thinking principles. Resources are shared and collaboration between students and staff is organized at different levels. CDIO was chosen as the common framework and the syllabus 2.0 was used as a blueprint for the CSE learning goals in each university. CSE projects are characterized by an interdisciplinary, human centered approach leading to inter-faculty collaboration. At the university of Porto, EUR-ACE was already used as the engineering education framework, so a translation table was used to facilitate common development. Even though Thomas More and KU Leuven are no CDIO partner, their choice for design thinking as the leading method in the post-Masters pilot course insured a good fit with the CDIO syllabus. At this point University West is applying for CDIO and they are yet to discover what the adaptation means for their programs and their emerging CSE initiatives. CDIO proved to fit well to in the authentic open innovation network context in which engineering students actively do CSE projects. CDIO became the common language and means to continuously improve the quality of the CSE curriculum.
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