This paper investigates the application of Reinforcement Learning (RL) to optimise call routing in call centres to minimise client waiting time and staff idle time. Two methods are compared: a model-based approach using Value Iteration (VI) under known system dynamics, and a model-free approach using Proximal Policy Optimisation (PPO) that learns from experience. For the model-based approach, a theoretical model is used, while a simulation model combining Discrete Event Simulation (DES) with the OpenAI Gym environment is developed for model-free learning. Both models frame the problem as a Markov Decision Process (MDP) within a Skills-Based Routing (SBR) framework, with Poisson client arrivals and exponentially distributed service and abandonment times. For policy evaluation, random, VI, and PPO policies are evaluated using the simulation model. After 1,000 test episodes, PPO consistently achives the highest rewards, along with the lowest client waiting time and staff idle time, despite requiring longer training time.

The effects of personnel scheduling on the quality of care and working conditions for healthcare personnel have been thoroughly documented. However, the ever-present demand and large variation of constraints make healthcare scheduling particularly challenging. This problem has been studied for decades, with limited research aimed at applying Satisfiability Modulo Theories (SMT). SMT has gained momentum within the formal verification community in the last decades, leading to the advancement of SMT solvers that have been shown to outperform standard mathematical programming techniques. In this work, we propose generic constraint formulations that can model a wide range of real-world scheduling constraints. Then, the generic constraints are formulated as SMT and MILP problems and used to compare the respective state-of-the-art solvers, Z3 and Gurobi, on academic and real-world inspired rostering problems. Experimental results show how each solver excels for certain types of problems; the MILP solver generally performs better when the problem is highly constrained or infeasible, while the SMT solver performs better otherwise. On real-world inspired problems containing a more varied set of shifts and personnel, the SMT solver excels. Additionally, it was noted during experimentation that the SMT solver was more sensitive to the way the generic constraints were formulated, requiring careful consideration and experimentation to achieve better performance. We conclude that SMT-based methods present a promising avenue for future research within the domain of personnel scheduling.

Understanding the meaning of data is crucial for performing data analysis, yet for the users to gain insight into the content and structure of their database, a tedious data exploration process is often required [2, 16]. A common industry practice taken on by specialists such as Knowledge Engineers is to explicitly construct an intermediate layer between the database and the user -- a semantic layer -- abstracting away certain details of the database schema in favor of clearer data semantics [3, 10]. In the era of Large Language Models (LLMs), industry practitioners and researchers attempt to circumvent this costly process using LLM-powered Natural Language Interfaces [4, 6, 12, 18, 19, 22]. The promise of such Text-to-SQL solutions is to allow users without technical expertise to seamlessly interact with databases. For example, a new company employee could effectively issue queries in natural language without programming expertise or even explicit knowledge of the database structure, e.g., knowing the names of entities or properties, the exact location of data sources, etc.

In the era of AI-driven language technologies, there is a growing demand for the participation and leadership of deaf communities in sign language technology development, often framed as co-creation. This paper, developed through collaborative and iterative dialogue between the authors with data from informal participant observations, examines the involvement of the European Union of the Deaf in two EU Horizon 2020 projects, EASIER and SignON. These projects aimed to develop mobile translation applications between signed and spoken languages, bringing together predominantly hearing, non-signing technology experts with predominantly hearing sign language academics and organizations representing deaf end users in large multi-partner consortia. While co-creation is sometimes presented as the best or required way to do research or even as emancipatory, it frequently masks systemic issues of power imbalances and tokenism. Drawing from EUD's experiences of these projects, we highlight several inconvenient truths of co-creation, and propose seven lessons for future initiatives: recognizing deaf partners' invisible labour as work, managing expectations about technologies, cripping co-creation processes, exploring alternative methods to mitigate co-creation fatigue, seeking intersectional feedback, ensuring co-creation is not just virtue signalling, and fostering deaf leadership in AI sign language research. We argue for co-creation as a transformative activity that fundamentally alters the status quo and levels the playing field. This necessitates increasing the number of deaf researchers and enhancing AI literacy among deaf communities. Without these critical transformative actions, co-creation risks merely paying lip service to deaf communities.

The Canadian women's soccer team was dealt a heavy blow Saturday after FIFA announced the women's national team would be deducted six points from the standings in the Paris Olympics after staffers were caught using drones to spy on New Zealand during closed-door training sessions. Following its investigation, the FIFA Appeal Committee announced the Canadian Soccer Association was responsible for failing to ensure its staff members were in compliance with Olympic rules. "CSA was found responsible for failing to respect the applicable FIFA regulations in connection with its failure to ensure the compliance of its participating officials of the Games of the XXXIII Olympiad Paris 2024 Final Competition (OFT) with the prohibition on flying drones over any training sites," the statement said. "The officials were each found responsible for offensive behavior and violation of the principles of fair play in connection with the CSA's Women's representative team's drones usage in the scope of the OFT." Head coach Bev Priestman was removed from her position Thursday night after two staff members were sent home from Paris when an investigation found that analyst Joseph Lombardi had allegedly used a drone to spy on New Zealand's practice sessions.

If you like the sound of earning money while playing games, IKEA has the opportunity for you. The Swedish furniture giant will start paying people up to 13.15 an hour to become staff members in a virtual version of its stores. The'fully remote' role will include helping customers choose their furniture and serving up meatballs in a digital recreation of its iconic bistro. Anyone interested will have to fill out an application form and submit a CV – although IKEA says there's only 10 positions available. The company is taking applications for the game on a dedicated webpage from now until June 16, before the game launches on June 24.

A Baltimore, Maryland school district has launched an investigation after a high school principal was allegedly recorded making racist comments to students and staff. In a Wednesday email to parents, Baltimore County Schools superintendent Myriam Rogers said that while the statements were "deeply disturbing," the district could not "confirm the veracity of this recording at this time." "I understand how upsetting this recording is for many members of the Team BCPS community," Rogers said, according to a report in WMAR2 News. "We will not tolerate disparaging remarks about any member of the Team BCPS community." Things became more perplexing after The Council of Administrative & Supervising Employees (CASE), the union representing Pikesville High School Principal Eric Eiswert, claimed the recording was fraudulent and generated using artificial intelligence (AI).

Previous studies have used prescriptive process monitoring to find actionable policies in business processes and conducted case studies in similar domains, such as the loan application process and the traffic fine process. However, care processes tend to be more dynamic and complex. For example, at any stage of a care process, a multitude of actions is possible. In this paper, we follow the reinforcement approach and train a Markov decision process using event data from a care process. The goal was to find optimal policies for staff members when clients are displaying any type of aggressive behavior. We used the reinforcement learning algorithms Q-learning and SARSA to find optimal policies. Results showed that the policies derived from these algorithms are similar to the most frequent actions currently used but provide the staff members with a few more options in certain situations.

Patient monitoring in intensive care units, although assisted by biosensors, needs continuous supervision of staff. To reduce the burden on staff members, IT infrastructures are built to record monitoring data and develop clinical decision support systems. These systems, however, are vulnerable to artifacts (e.g. muscle movement due to ongoing treatment), which are often indistinguishable from real and potentially dangerous signals. Video recordings could facilitate the reliable classification of biosignals using object detection (OD) methods to find sources of unwanted artifacts. Due to privacy restrictions, only blurred videos can be stored, which severely impairs the possibility to detect clinically relevant events such as interventions or changes in patient status with standard OD methods. Hence, new kinds of approaches are necessary that exploit every kind of available information due to the reduced information content of blurred footage and that are at the same time easily implementable within the IT infrastructure of a normal hospital. In this paper, we propose a new method for exploiting information in the temporal succession of video frames. To be efficiently implementable using off-the-shelf object detectors that comply with given hardware constraints, we repurpose the image color channels to account for temporal consistency, leading to an improved detection rate of the object classes. Our method outperforms a standard YOLOv5 baseline model by +1.7% mAP@.5 while also training over ten times faster on our proprietary dataset. We conclude that this approach has shown effectiveness in the preliminary experiments and holds potential for more general video OD in the future.

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