Cross-timescale experience evaluation framework for productive teaming

This paper presents the initial concept for an evaluation framework to systematically evaluate productive teaming (PT). We consider PT as adaptive human-machine interactions between human users and augmented technical production systems. Also, human-to-human communication as part of a hybrid team with multiple human actors is considered, as well as human-human and human-machine communication for remote and mixed remote- and co-located teams. The evaluation comprises objective, performance-related success indicators, behavioral metadata, and measures of human experience. In particular, it considers affective, attentional and intentional states of human team members, their influence on interaction dynamics in the team, and researches appropriate strategies to satisfyingly adjust dysfunctional dynamics, using concepts of companion technology. The timescales under consideration span from seconds to several minutes, with selected studies targeting hour-long interactions and longer-term effects such as effort and fatigue. Two example PT scenarios will be discussed in more detail. To enable generalization and a systematic evaluation, the scenarios’ use cases will be decomposed into more general modules of interaction

Beyond Static Datasets: A Deep Interaction Approach to LLM Evaluation

Large Language Models (LLMs) have made progress in various real-world tasks, which stimulates requirements for the evaluation of LLMs. Existing LLM evaluation methods are mainly supervised signal-based which depends on static datasets and cannot evaluate the ability of LLMs in dynamic real-world scenarios where deep interaction widely exists. Other LLM evaluation methods are human-based which are costly and time-consuming and are incapable of large-scale evaluation of LLMs. To address the issues above, we propose a novel Deep Interaction-based LLM-evaluation framework. In our proposed framework, LLMs' performances in real-world domains can be evaluated from their deep interaction with other LLMs in elaborately designed evaluation tasks. Furthermore, our proposed framework is a general evaluation method that can be applied to a host of real-world tasks such as machine translation and code generation. We demonstrate the effectiveness of our proposed method through extensive experiments on four elaborately designed evaluation tasks

Leveraging Human-Machine Interactions for Computer Vision Dataset Quality Enhancement

Large-scale datasets for single-label multi-class classification, such as \emph{ImageNet-1k}, have been instrumental in advancing deep learning and computer vision. However, a critical and often understudied aspect is the comprehensive quality assessment of these datasets, especially regarding potential multi-label annotation errors. In this paper, we introduce a lightweight, user-friendly, and scalable framework that synergizes human and machine intelligence for efficient dataset validation and quality enhancement. We term this novel framework \emph{Multilabelfy}. Central to Multilabelfy is an adaptable web-based platform that systematically guides annotators through the re-evaluation process, effectively leveraging human-machine interactions to enhance dataset quality. By using Multilabelfy on the ImageNetV2 dataset, we found that approximately $47.88\%$ of the images contained at least two labels, underscoring the need for more rigorous assessments of such influential datasets. Furthermore, our analysis showed a negative correlation between the number of potential labels per image and model top-1 accuracy, illuminating a crucial factor in model evaluation and selection. Our open-source framework, Multilabelfy, offers a convenient, lightweight solution for dataset enhancement, emphasizing multi-label proportions. This study tackles major challenges in dataset integrity and provides key insights into model performance evaluation. Moreover, it underscores the advantages of integrating human expertise with machine capabilities to produce more robust models and trustworthy data development. The source code for Multilabelfy will be available at https://github.com/esla/Multilabelfy. \keywords{Computer Vision \and Dataset Quality Enhancement \and Dataset Validation \and Human-Computer Interaction \and Multi-label Annotation.

Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web

Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun

Human-robot coexistence and interaction in open industrial cells

Recent research results on human\u2013robot interaction and collaborative robotics are leaving behind the traditional paradigm of robots living in a separated space inside safety cages, allowing humans and robot to work together for completing an increasing number of complex industrial tasks. In this context, safety of the human operator is a main concern. In this paper, we present a framework for ensuring human safety in a robotic cell that allows human\u2013robot coexistence and dependable interaction. The framework is based on a layered control architecture that exploits an effective algorithm for online monitoring of relative human\u2013robot distance using depth sensors. This method allows to modify in real time the robot behavior depending on the user position, without limiting the operative robot workspace in a too conservative way. In order to guarantee redundancy and diversity at the safety level, additional certified laser scanners monitor human\u2013robot proximity in the cell and safe communication protocols and logical units are used for the smooth integration with an industrial software for safe low-level robot control. The implemented concept includes a smart human-machine interface to support in-process collaborative activities and for a contactless interaction with gesture recognition of operator commands. Coexistence and interaction are illustrated and tested in an industrial cell, in which a robot moves a tool that measures the quality of a polished metallic part while the operator performs a close evaluation of the same workpiece

Towards industry 5.0 through metaverse

[EN] The digital transformation of the industry allows the optimization of resources through enabling technology that virtualizes the behavior of Cyber-Physical Systems (CPS) along the entire value chain. However, these virtual environments characterized by machine-to-machine interactions lacked the presence of humans who are at the center of the next defined industrial revolution, Industry 5.0. The goal is for humans to be actively integrated into these virtual environments called metaverses where interactions with environmental digital assets are possible. To achieve this human-centered industrial metaverse perspective, it is necessary to provide humans with technologies that allow them to reach a more immersive and realistic conception of the production processes. For this purpose, we present in this paper, a framework based on hyperconnectivity where several enabling technologies (e.g., Digital Twins, Virtual Reality, Industrial Internet of Things (IIoT)) are integrated in order to converge towards the industrial human-centered metaverse. To validate our framework, a demonstrator has been developed enabling the evaluation of the behavior of humans in virtual environments when facing collaborative tasks that require human-to-human interaction. Within the evaluation of this demonstrator, an experiment based on an assembly that requires interaction with an autonomous vehicle has been carried out both in reality and in the virtual world. The results obtained indicate that the avatars’ metaverse performance is closer to reality when individuals have previous experience with VR goggles, even proving, in this case, the effectiveness of metaverse for industrial operators’ training. In addition, the performance of the application has been evaluated with technical parameters and the perception of the users has been analyzed by conducting a survey receiving very positive feedback and results. Therefore, the industrial metaverse, blending cutting-edge tech with a human-centric approach for Industry 5.0, is now a reality.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL