The integration of collaborative robots (cobots) in industrial settings raises concerns about worker well-being, particularly due to reduced social interactions. Avatars - designed to facilitate worker interactions and engagement - are promising solutions to enhance the human-robot collaboration (HRC) experience. However, real-world perspectives on avatar-supported HRC remain unexplored. To address this gap, we conducted a focus group study with employees from a German manufacturing company that uses cobots. Before the discussion, participants engaged with a scripted, industry-like HRC demo in a lab setting. This qualitative approach provided valuable insights into the avatar's potential roles, improvements to its behavior, and practical considerations for deploying them in industrial workcells. Our findings also emphasize the importance of personalized communication and task assistance. Although our study's limitations restrict its generalizability, it serves as an initial step in recognizing the potential of adaptive, context-aware avatar interactions in real-world industrial environments.

One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models excessive dependence on the low-level attributes used to train them, which reduces the models portability. To solve this issue, the use of high level attributes with more semantic meaning, such as ontologies, may be very useful. Along this line, we propose the utilization of an ontology that uses a taxonomy of actions that summarises students interactions with the Moodle learning management system. We compare the results of this proposed approach against our previous results when we used low-level raw attributes obtained directly from Moodle logs. The results indicate that the use of the proposed ontology improves the portability of the models in terms of predictive accuracy. The main contribution of this paper is to show that the ontological models obtained in one source course can be applied to other different target courses with similar usage levels without losing prediction accuracy.

There is a growing desire to create computer systems that can communicate effectively to collaborate with humans on complex, open-ended activities. Assessing these systems presents significant challenges. We describe a framework for evaluating systems engaged in open-ended complex scenarios where evaluators do not have the luxury of comparing performance to a single right answer. This framework has been used to evaluate human-machine creative collaborations across story and music generation, interactive block building, and exploration of molecular mechanisms in cancer. These activities are fundamentally different from the more constrained tasks performed by most contemporary personal assistants as they are generally open-ended, with no single correct solution, and often no obvious completion criteria. We identified the Key Properties that must be exhibited by successful systems. From there we identified "Hallmarks" of success -- capabilities and features that evaluators can observe that would be indicative of progress toward achieving a Key Property. In addition to being a framework for assessment, the Key Properties and Hallmarks are intended to serve as goals in guiding research direction.

When you visited a hotel few years back, what was the scenario? A front-desk receptionist will greet you, ask you some of your personal details and the timing of your check-out. But time is changing and so does the technology involved and utilized in the hotels! Just think, won't it be a jaw-dropping experience if a talking robot welcomes you at the front-desk instead of a human being! Front-desk robots, Internet of Things, and voice & chat-based solutions are the next-big-things in the hospitality industry and few countries like China has already started implementing it.

You've created a model to predict which sales leads are likely to turn into convert into paying customers. The model is a true work of algorithmic beauty -- its your Mona Lisa, but better since it actually does something useful. You have tuned the parameters and cross-validated the results, which means the only thing left to do is present your work to the company's executives. "No problem" you think to yourself, you'll just talk them through a Confusion Matrix and ROC curve and wait for them to lavish you in riches and praise. Anyone who has tried to walk business stakeholders through predictive model performance knows that you are more likely to get strained expressions and blank stares than a standing ovation.

We communicate a lot with our eyes; a glance can mean one thing, a stare something completely different. That's the promise of a new, peripheral-free eye tracking software called IrisGo that was introduced at CES 2018, the consumer technology show that ran Jan. 9-12, 2018, in Las Vegas. According to its developer, San Sebastián, Spain-based Irisbond, the software measures and responds to the motion of the human eye and allows users to navigate devices and control screens with a gaze. Because it's not tethered to additional hardware, it also enables users to communicate on any device with an embedded camera. IrisGo has been used to control collaborative robots at ABB Spain; improve the fidelity of neuromarketing data collected by Lumen Research; and further the public conversation by being tested in smartphones by Twitter.

Natural language instructions are often underspecified and imprecise which makes them hard to understand for an artificial agent. In this article we present a system of connected knowledge representations that is used to control a robot through instructions. As actions are a key component of instructions and the robot's behavior the representation of action is central in our approach. First, the system consists of a conceptual schema representation which provides a parameter interface for action. Second, we present an intermediate representation of the temporal structure of action and show how this generic action structure can be mapped to detailed action controllers as well as language.

Ohio State University) focused on the use of case-based reasoning for both planning and world modeling. Nicola Muscettola (NASA Ames) focused on reactive behaviors. Laboratory) described an approach Within this general theme, to planning with multiagent the aim was to bring together researchers execution. The presentation ecent developments in multiagent shown promising results in the robotics, intelligent autonomous of Thomas Wagner (University of modeling of autonomous, collaborative vehicles). The common denominator Brement), Christoph Schlieder (University behavior between agents in different that these groups share is the pragmatic of Bamberg), and Ubbo Visser environments.