How might healthcare workers (HCWs) leverage augmented reality head-mounted displays (AR-HMDs) to enhance teamwork? Although AR-HMDs have shown immense promise in supporting teamwork in healthcare settings, design for Emergency Department (ER) teams has received little attention. The ER presents unique challenges, including procedural recall, medical errors, and communication gaps. To address this gap, we engaged in a participatory design study with healthcare workers to gain a deep understanding of the potential for AR-HMDs to facilitate teamwork during ER procedures. Our results reveal that AR-HMDs can be used as an information-sharing and information-retrieval system to bridge knowledge gaps, and concerns about integrating AR-HMDs in ER workflows. We contribute design recommendations for seven role-based AR-HMD application scenarios involving HCWs with various expertise, working across multiple medical tasks. We hope our research inspires designers to embark on the development of new AR-HMD applications for high-stakes, team environments.

In the field of automated programming, large language models (LLMs) have demonstrated foundational generative capabilities when given detailed task descriptions. However, their current functionalities are primarily limited to function-level development, restricting their effectiveness in complex project environments and specific application scenarios, such as complicated image-processing tasks. This paper presents a multi-agent framework that utilises a hybrid set of LLMs, including GPT-4o and locally deployed open-source models, which collaboratively complete auto-programming tasks. Each agent plays a distinct role in the software development cycle, collectively forming a virtual organisation that works together to produce software products. By establishing a tree-structured thought distribution and development mechanism across project, module, and function levels, this framework offers a cost-effective and efficient solution for code generation. We evaluated our approach using benchmark datasets, and the experimental results demonstrate that VisionCoder significantly outperforms existing methods in image processing auto-programming tasks.

Businesses and software platforms are increasingly turning to Large Language Models (LLMs) such as GPT-3.5, GPT-4, GLM-3, and LLaMa-2 for chat assistance with file access or as reasoning agents for customer service. However, current LLM-based customer service models have limited integration with customer profiles and lack the operational capabilities necessary for effective service. Moreover, existing API integrations emphasize diversity over the precision and error avoidance essential in real-world customer service scenarios. To address these issues, we propose an LLM agent named CHOPS (CHat with custOmer Profile in existing System), designed to: (1) efficiently utilize existing databases or systems for accessing user information or interacting with these systems following existing guidelines; (2) provide accurate and reasonable responses or carry out required operations in the system while avoiding harmful operations; and (3) leverage a combination of small and large LLMs to achieve satisfying performance at a reasonable inference cost. We introduce a practical dataset, the CPHOS-dataset, which includes a database, guiding files, and QA pairs collected from CPHOS, an online platform that facilitates the organization of simulated Physics Olympiads for high school teachers and students. We have conducted extensive experiments to validate the performance of our proposed CHOPS architecture using the CPHOS-dataset, with the aim of demonstrating how LLMs can enhance or serve as alternatives to human customer service. Code for our proposed architecture and dataset can be found at {https://github.com/JingzheShi/CHOPS}.

Software issues contain units of work to fix, improve, or create new threads during the development and facilitate communication among the team members. Assigning an issue to the most relevant team member and determining a category of an issue is a tedious and challenging task. Wrong classifications cause delays and rework in the project and trouble among the team members. This paper proposes a set of carefully curated linguistic features for shallow machine learning methods and compares the performance of shallow and ensemble methods with deep language models. Unlike the state-of-the-art, we assign issues to four roles (designer, developer, tester, and leader) rather than to specific individuals or teams to contribute to the generality of our solution. We also consider the level of experience of the developers to reflect the industrial practices in our solution formulation. We collect and annotate five industrial data sets from one of the top three global television producers to evaluate our proposal and compare it with deep language models. Our data sets contain 5324 issues in total. We show that an ensemble classifier of shallow techniques achieves 0.92 for issue assignment in accuracy which is statistically comparable to the state-of-the-art deep language models. The contributions include the public sharing of five annotated industrial issue data sets, the development of a clear and comprehensive feature set, the introduction of a novel label set, and the validation of the efficacy of an ensemble classifier of shallow machine learning techniques.

Business processes that involve AI-powered automation have been gaining importance and market share in recent years. These business processes combine the characteristics of classical business process management, goal-driven chatbots, conversational recommendation systems, and robotic process automation. In the new context, prescriptive process monitoring demands innovative approaches. Unfortunately, data logs from these new processes are still not available in the public domain. We describe the main challenges in this new domain and introduce a synthesized dataset that is based on an actual use case of intelligent process automation with chatbot orchestration. Using this dataset, we demonstrate crowd-wisdom and goal-driven approaches to prescriptive process monitoring.

We are Docplanner Tech, a group of people working in Engineering, BI, & Product teams across the globe, driven by the Docplanner Groups' mission to make the healthcare experience more human for our patients, doctors, and clinics; and we do it well. We are a leader in 13 countries (and growing!), creating a SaaS tool for over 130k active doctors, who are directly connected to our marketplace which is trusted by over 80m patients, monthly. We keep it simple and focus on what matters most. Patients easily book a visit and experience healthcare as it should be: humane. And doctors grow their office and dedicate time to help their patients, all whilst we cover the administration, so they don't have to!

You are passionate about AI and Deep Learning and want to apply this technology to solve real life problems. Your solid scientific background in these topics give you the required know-how and possibility to (a) keep up with rapidly moving state-of-the-art, (b) quickly prune scientific literature and (c) select promising methodologies for your problem at hand. You are business oriented and pragmatic and understand that in a business context solution must be conceived, build and tested within a limited time frame. You are hands-on and fluent with modern Deep Learning tools. You have a programming background in Python, Matlab and C/C .

Recently introduced Petri net-based formalisms advocate the importance of proper representation and management of case objects as well as their co-evolution. In this work we build on top of one of such formalisms and introduce the notion of soundness for it. We demonstrate that for nets with non-deterministic synchronization between case objects, the soundness problem is decidable.

You can see the results from the rest of the teams in this discipline here, or watch the recorded livestreams of both days on their website. We had the pleasure to interview Christian Bermes, team leader of the HSR Enhanced team in both 2016 and 2020 editions. After CYBATHLON 2020, he handed over the team leads, as he moved from OST to be Professor for Mobile Robotics at the University of Applied Sciences of the Grisons. D. C. Z.: What does it mean for your team to have won in your CYBATHLON category? C.B.: It is a huge confirmation that our first win in 2016 was not just a coïncidence, but again the result of human-centered innovation together with our pilot Florian Hauser, meticulous engineering, proper prior planning, hard training and of course a next-level pilot performance.

This post was published on April 1st, 2020, and should not be taken too seriously. You use new formulas, you gather insights, you vote and you have action points. Sometimes you start to regret being just a human. What if you could process incoming requests in parallel? And provide always most accurate responses?