Are investigators’ access to trial data and rights to publish restricted and are potential trial participants informed about this? A comparison of trial protocols and informed consent materials

Objectives To determine to which degree industry partners in randomised clinical trials own the data and can constrain publication rights of academic investigators. Methods Cohort study of trial protocols, publication agreements and other documents obtained through Freedom of Information requests, for a sample of 42 trials with industry involvement approved by ethics committees in Denmark. The main outcome measures used were: proportion of trials where data was owned by the industry partner, where the investigators right to publish were constrained and if this was mentioned in informed consent documents, and where the industry partner could review data while the trial was ongoing and stop the trial early. Results The industry partner owned all data in 20 trials (48%) and in 16 trials (38%) it was unclear. Publication constraints were described for 30 trials (71%) and this was not communicated to trial participants in informed consent documents in any of the trials. In eight trials (19%) the industry partner could review data during the trial, for 20 trials (48%) it was unclear. The industry partner could stop the trial early without any specific reason in 23 trials (55%). Conclusions Publication constraints are common, and data is often owned by industry partners. This is rarely communicated to trial participants. Such constraints might contribute to problems with selective outcome reporting. Patients should be fully informed about these aspects of trial conduct.

Identifying Computer-Aided Design Action Types From Professional User Analytics Data

Inspired by popular personality type indicators, we develop a framework for classifying individuals by their computer-aided design (CAD) behaviours. We are motivated by the trend of modern CAD software towards cloud platforms and expanded collaborative features. Cloud-CAD platforms enable collaboration by increasing access, and reducing conflicts and barriers to file-sharing. In order to generate insight to support CAD collaboration, we analyze the real-world data from an industry partner’s product development project, consisting of eight professional designers working on a cloud-CAD platform. This data corresponds to more than 1,420,000 actions over a span of eight months. Via hierarchical clustering, we group 79 unique CAD activities into 14 categories of CAD action groups, such as Part Studio, Assembly, Comment, View/Scan and Export. Next, we identify the degree to which each of the eight designers performs activity in these CAD action groups. We demonstrate the usefulness of this framework by highlighting insights revealed by the CAD action group mapping, confirmed via discussion with the industry partner. This CAD-type behaviour framework provides a tool for assessment and reflection on the types of behavioural tendencies present or missing on a team of designers. It can assist CAD educators and trainees in understanding their comprehensive CAD learning trajectory. Future extensions of the framework could leverage artificial intelligence techniques to provide real-time feedback on designer roles.

Improving productivity in large scale testing at the compiler level by changing the intermediate language from C++ to Java

This paper is based on research results achieved by a collaboration between Ericsson Hungary Ltd. and the Large Scale Testing Research Lab of Eötvös Loránd University, Budapest. We present design issues and empirical observations on extending an existing industrial toolset with a new intermediate language1. Context: The industry partner’s toolset is using C/C++ as an intermediate language, providing good execution performance, but “somewhat long” build times, o ering a sub-optimal experience for users. Objective: In cooperation with our industry partner our task was to perform an experiment with Java as a different intermediate language and evaluate results, to see if this could improve build times. Method: We extended the mentioned toolset to use Java as an intermediate language. Results: Our measurements show that using Java as an intermediate language improves build times significantly. We also found that, while the runtime performance of C/C++ is better in some situations, Java, at least in our testing scenarios, can be a viable alternative to improve developer productivity. Our contribution is unique in the sense that both ways of building and execution can use the same source code as input, written in the same language, generate intermediate codes with the same high-level structure, compile into executables that are configured using the same files, run on the same machine, show the same behaviour and generate the same logs. Conclusions: We created an alternative build pipeline that might enhance the productivity of our industry partner’s test developers by reducing the length of builds during their daily work.

First Steps to Evaluate an NLP Tool’s Medication Extraction Accuracy from Discharge Letters

Introduction: The aim of this study is to evaluate the use of a natural language processing (NLP) software to extract medication statements from unstructured medical discharge letters. Methods: Ten randomly selected discharge letters were extracted from the data warehouse of the University Hospital Erlangen (UHE) and manually annotated to create a gold standard. The AHD NLP tool, provided by MIRACUM’s industry partner was used to annotate these discharge letters. Annotations by the NLP tool where then compared to the gold standard on two levels: phrase precision (whether or not the whole medication statement has been identified correctly) and token precision (whether or not the medication name has been identified correctly within correctly discovered medication phrases). Results: The NLP tool detected medication related phrases with an overall F-measure of 0.852. The medication name has been identified correctly with an overall F-measure of 0.936. Discussion: This proof-of-concept study is a first step towards an automated scalable evaluation system for MIRACUM’s industry partner’s NLP tool by using a gold standard. Medication phrases and names have been correctly identified in most cases by the NLP system. Future effort needs to be put into extending and validating the gold standard.

Assessment of Industry Partners: Inputs for Partnership Improvement and Selection of Host Training Establishments

Industry partners such as energy providers (in electric Power Systems for example) are critical in the successful implementation of the University’s internship program and attainment of objectives. Thus, it is important that the University adhere to the criteria and requirements outlined by the Commission on Higher Education for selection of reputable host training establishments. This study aims to provide additional guidelines for the Business Administration program in considering prospective industry partners for internship by incorporating the students’ assessment of industry partners. Using independent samples t-test, the researcher found that the profile of the industry partner has no significant effect on the level of implementation, the students’ feedback, attainment of objectives and the seriousness of problems encountered during internship. Through independent samples t-test and One-Way ANOVA, it was found that the students’ profile also has no effect on the perceived seriousness of problems encountered during the training. However, using Pearson correlation coefficient analysis, it was found that the level of implementation of the internship program within the industry partner has a relationship with the students’ feedback, level of attainment of internship objectives, and the seriousness of problems encountered. The level of implementation positively impacts the level of attainment of objectives and the seriousness of problems encountered. While the relationship with feedback was generally positive, it was found that the level of implementation negatively impacts the students’ feedback of industry partners in terms of decision making and empowerment.

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

Enhancing Cultural Intelligence and Digital Literacy in Accounting Education: Insights from a University’s Global Student Consulting Programme

Recent literature has advocated for the use of project based learning to engage students in active learning. This study examines how students’ learning is enhanced through an overseas project-based learning (PBL) programme at a Singapore University (UNIS), called the UNIS-XO pedagogy. Specifically, this study provides a framework through which students, faculty members, and industry partner can collaborate through consulting programs with the aim to provide feasible recommendations to the clients. Our findings suggest that an experiential PBL with an overseas client is an important learning experience through which students can strengthen their digital literacy as well as cross-cultural competency to make them more futureready for their work.