Unpacking capabilities underlying design (thinking) process

Engineering graduates must know how to frame and solve non-routine problems. While design classes explicitly teach problem framing and solving, it is lacking throughout much of the rest of the engineering curriculum and is often relegated to capstone classes at the end of the students’ educational experience. This paper explores problem framing and solving through the lens of experiential learning theory. It captures core problem framing and solving approaches from critical, design and systems thinking and concludes with a table of learning outcomes that might be drawn upon in designing an engineering curriculum that more fully develops the problem framing and solving capabilities of its students

Optimum Selection of DNN Model and Framework for Edge Inference

This paper describes a methodology to select the optimum combination of deep neuralnetwork and software framework for visual inference on embedded systems. As a first step, benchmarkingis required. In particular, we have benchmarked six popular network models running on four deep learningframeworks implemented on a low-cost embedded platform. Three key performance metrics have beenmeasured and compared with the resulting 24 combinations: accuracy, throughput, and power consumption.Then, application-level specifications come into play. We propose a figure of merit enabling the evaluationof each network/framework pair in terms of relative importance of the aforementioned metrics for a targetedapplication. We prove through numerical analysis and meaningful graphical representations that only areduced subset of the combinations must actually be considered for real deployment. Our approach can beextended to other networks, frameworks, and performance parameters, thus supporting system-level designdecisions in the ever-changing ecosystem of embedded deep learning technology.Ministerio de Economía y Competitividad (TEC2015-66878-C3-1-R)Junta de Andalucía (TIC 2338-2013)European Union Horizon 2020 (Grant 765866

CESEC Chair – Training Embedded System Architects for the Critical Systems Domain

Increasing complexity and interactions across scientific and tech- nological domains in the engineering of critical systems calls for new pedagogical approach. In this paper, we introduce the CESEC teaching chair. This chair aims at supporting new integrative ap- proach for the initial training of engineer and master curriculum to three engineering school in Toulouse: ISAE, INSA Toulouse and INP ENSEEIHT. It is supported by the EADS Corporate Foundation. In this paper, we highlight the rationale for this chair: need for sys- tem architect with strong foundations on technical domains appli- cable to the aerospace industry. We then introduce the ideal profile for this architect and the various pedagogical approaches imple- mented to reach this objective

Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier

As universities recognize the inherent value in the data they collect and hold, they encounter unforeseen challenges in stewarding those data in ways that balance accountability, transparency, and protection of privacy, academic freedom, and intellectual property. Two parallel developments in academic data collection are converging: (1) open access requirements, whereby researchers must provide access to their data as a condition of obtaining grant funding or publishing results in journals; and (2) the vast accumulation of 'grey data' about individuals in their daily activities of research, teaching, learning, services, and administration. The boundaries between research and grey data are blurring, making it more difficult to assess the risks and responsibilities associated with any data collection. Many sets of data, both research and grey, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities are exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities are besieging universities with requests for access to data or for partnerships to mine them. The privacy frontier facing research universities spans open access practices, uses and misuses of data, public records requests, cyber risk, and curating data for privacy protection. This paper explores the competing values inherent in data stewardship and makes recommendations for practice, drawing on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201

DIDET: Digital libraries for distributed, innovative design education and teamwork. Final project report

The central goal of the DIDET Project was to enhance student learning opportunities by enabling them to partake in global, team based design engineering projects, in which they directly experience different cultural contexts and access a variety of digital information sources via a range of appropriate technology. To achieve this overall project goal, the project delivered on the following objectives: 1. Teach engineering information retrieval, manipulation, and archiving skills to students studying on engineering degree programs. 2. Measure the use of those skills in design projects in all years of an undergraduate degree program. 3. Measure the learning performance in engineering design courses affected by the provision of access to information that would have been otherwise difficult to access. 4. Measure student learning performance in different cultural contexts that influence the use of alternative sources of information and varying forms of Information and Communications Technology. 5. Develop and provide workshops for staff development. 6. Use the measurement results to annually redesign course content and the digital libraries technology. The overall DIDET Project approach was to develop, implement, use and evaluate a testbed to improve the teaching and learning of students partaking in global team based design projects. The use of digital libraries and virtual design studios was used to fundamentally change the way design engineering is taught at the collaborating institutions. This was done by implementing a digital library at the partner institutions to improve learning in the field of Design Engineering and by developing a Global Team Design Project run as part of assessed classes at Strathclyde, Stanford and Olin. Evaluation was carried out on an ongoing basis and fed back into project development, both on the class teaching model and the LauLima system developed at Strathclyde to support teaching and learning. Major findings include the requirement to overcome technological, pedagogical and cultural issues for successful elearning implementations. A need for strong leadership has been identified, particularly to exploit the benefits of cross-discipline team working. One major project output still being developed is a DIDET Project Framework for Distributed Innovative Design, Education and Teamwork to encapsulate all project findings and outputs. The project achieved its goal of embedding major change to the teaching of Design Engineering and Strathclyde's new Global Design class has been both successful and popular with students

Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis

Even with impressive advances in automated formal methods, certain problems in system verification and synthesis remain challenging. Examples include the verification of quantitative properties of software involving constraints on timing and energy consumption, and the automatic synthesis of systems from specifications. The major challenges include environment modeling, incompleteness in specifications, and the complexity of underlying decision problems. This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general rules or concepts to conclusions about specific problem instances, includes techniques such as logical inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is used to guide the deductive engines. We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also discussed

Reinventing discovery learning: a field-wide research program

© 2017, Springer Science+Business Media B.V., part of Springer Nature. Whereas some educational designers believe that students should learn new concepts through explorative problem solving within dedicated environments that constrain key parameters of their search and then support their progressive appropriation of empowering disciplinary forms, others are critical of the ultimate efficacy of this discovery-based pedagogical philosophy, citing an inherent structural challenge of students constructing historically achieved conceptual structures from their ingenuous notions. This special issue presents six educational research projects that, while adhering to principles of discovery-based learning, are motivated by complementary philosophical stances and theoretical constructs. The editorial introduction frames the set of projects as collectively exemplifying the viability and breadth of discovery-based learning, even as these projects: (a) put to work a span of design heuristics, such as productive failure, surfacing implicit know-how, playing epistemic games, problem posing, or participatory simulation activities; (b) vary in their target content and skills, including building electric circuits, solving algebra problems, driving safely in traffic jams, and performing martial-arts maneuvers; and (c) employ different media, such as interactive computer-based modules for constructing models of scientific phenomena or mathematical problem situations, networked classroom collective “video games,” and intercorporeal master–student training practices. The authors of these papers consider the potential generativity of their design heuristics across domains and contexts