This report investigates the perceptions of teaching staff on the prevalence of student cheating and the impact of Generative AI on academic integrity. Data was collected via an anonymous survey of teachers at the Department of Information Technology at Uppsala University and analyzed alongside institutional statistics on cheating investigations from 2004 to 2023. The results indicate that while teachers generally do not view cheating as highly prevalent, there is a strong belief that its incidence is increasing, potentially due to the accessibility of Generative AI. Most teachers do not equate AI usage with cheating but acknowledge its widespread use among students. Furthermore, teachers' perceptions align with objective data on cheating trends, highlighting their awareness of the evolving landscape of academic dishonesty.

Real-world software applications must constantly evolve to remain relevant. This evolution occurs when developing new applications or adapting existing ones to meet new requirements, make corrections, or incorporate future functionality. Traditional methods of software quality control involve software quality models and continuous code inspection tools. These measures focus on directly assessing the quality of the software. However, there is a strong correlation and causation between the quality of the development process and the resulting software product. Therefore, improving the development process indirectly improves the software product, too. To achieve this, effective learning from past processes is necessary, often embraced through post mortem organizational learning. While qualitative evaluation of large artifacts is common, smaller quantitative changes captured by application lifecycle management are often overlooked. In addition to software metrics, these smaller changes can reveal complex phenomena related to project culture and management. Leveraging these changes can help detect and address such complex issues. Software evolution was previously measured by the size of changes, but the lack of consensus on a reliable and versatile quantification method prevents its use as a dependable metric. Different size classifications fail to reliably describe the nature of evolution. While application lifecycle management data is rich, identifying which artifacts can model detrimental managerial practices remains uncertain. Approaches such as simulation modeling, discrete events simulation, or Bayesian networks have only limited ability to exploit continuous-time process models of such phenomena. Even worse, the accessibility and mechanistic insight into such gray- or black-box models are typically very low. To address these challenges, we suggest leveraging objectively [...]

This Friday the 13th of November at 5pm UTC, Talking Robotics are hosting an online talk with PhD student Natalia Calvo from Uppsala University in Sweden. Talking Robotics is a series of virtual seminars about Robotics and its interaction with other relevant fields, such as Artificial Intelligence, Machine Learning, Design Research, Human-Robot Interaction, among others. The aim is to promote reflections, dialogues, and a place to network. Talking Robotics happens virtually and bi-weekly, i.e., every other week, allocating 30 min for presentation and 30 min for Q&A and networking. Sessions have a roundtable format where everyone is welcome to share ideas.

Artificial intelligence (AI) may be an aid to interpreting ECG results, helping healthcare staff to diagnose diseases that affect the heart. Researchers at Uppsala University and heart specialists in Brazil have developed an AI that automatically diagnoses atrial fibrillation and five other common ECG abnormalities just as well as a cardiologist. The study has been published in Nature Communications. An electrocardiogram (ECG) is a simple test that can be used to check the heart's rhythm and electrical activity. The results are shown on a graph that can reveal various conditions that affect the heart.

You are welcome to apply no later than March 24, 2020.

The Pharmaceutical Bioinformatics research group focuses on mathematical and statistical modeling, informatics and quantitative analysis of pharmacological systems. We develop methods, algorithms and software to study and model pharmaceutical interactions, and a key focus in the group is how artificial intelligence (AI) and machine learning can aid the drug discovery process; e.g. in drug screening and when studying drug toxicity, metabolism and resistance. We combine in silico and in vitro experiments at the cellular level, and have access to a robotized high-content imaging lab connected to a modern IT-infrastructure to manage and analyze large-scale data. We are involved in several national and international consortia and have a tight connection to the pharmaceutical industry, Uppsala University Hospital, and Science for Life Laboratory. See the Projects page for more information on our ongoing research projects.

Common to all projects is support från Uppsala University Innovation and success in securing external funding to further enhance development opportunities. Proteins are the workers of the cell, and many proteins interact with each other. In order to understand the importance of these interactions, there is a need to measure both free and interacting proteins. Ola Söderberg, professor at the Department of Pharmaceutical Biosciences, has developed a method to label each protein with its own unique colour, making it possible to measure the proteins individually. At the same time, the proportion of proteins that bind to each other are labelled with a combination of the colours.

A. YONEZAWA and C. HEWITT 41 REPRESENTATIONS FOR ABSTRACT REASONING 4 A maximal method for set variables in automatic theorem-proving W. W. BLEDSOE

J. Do RAN 433 22 Chromosome classification and segmentation as exercises in knowing what to expect.

B.RANDELL 3 PROGRAM PROOF AND MANIPULATION 2 Some techniques for proving correctness of programs which alter data structures.