—Tele-echography has emerged as a promising and effective solution, leveraging the expertise of sonographers and the autonomy of robots to perform ultrasound scanning for patients residing in remote areas, without the need for in-person visits by the sonographer. Designing effective and natural human-robot interfaces for tele-echography remains challenging, with patient safety being a critical concern. In this article, we develop a teleoperation system for robot-assisted sonography with two different interfaces, a haptic device-based interface and a low-cost 3D Mouse-based interface, which can achieve continuous and intuitive telemanipulation by a leader device with a small workspace. To achieve compliant interaction with patients, we design impedance controllers in Cartesian space to track the desired position and orientation for these two teleoperation interfaces. We also propose comprehensive evaluation metrics of robot-assisted sonography, including subjective and objective evaluation, to evaluate tele-echography interfaces and control performance. We evaluate the ergonomic performance based on the estimated muscle fatigue and the acquired ultrasound image quality. We conduct user studies based on the NASA Task Load Index to evaluate the performance of these two human-robot interfaces. The tracking performance and the quantitative comparison of these two teleoperation interfaces are conducted by the Franka Emika Panda robot. The results and findings provide guidance on human-robot collaboration design and implementation for robot-assisted sonography. Note to Practitioners —Robot-assisted sonography has demonstrated efficacy in medical diagnosis during clinical trials. However, deploying fully autonomous robots for ultrasound scanning remains challenging due to various constraints in practice, such as patient safety, dynamic tasks, and environmental uncertainties. Semi-autonomous or teleoperation-based robot sonography

Design and Quantitative Assessment of Teleoperation-Based Human–Robot Collaboration Method for Robot-Assisted Sonography

AbstractThis work presents a model for improving transparency during robot learning tasks in Human-Robot Interaction scenarios. Our model puts the human in the learning loop by using two categories of robot’s emotional/behavioural reactions, one associated with the learning process of the robot and another elicited as a response to the feedback provided by the user. Preliminary results from a between-subjects study show that people empathized more with a robot expressing its emotions in both the above categories. We noticed a slight increase in the transparency of the robot while it expressed emotions during the learning process and as a response to the user. These findings highlight the importance of emotional behaviours for improving the transparency in the learning systems, which are fundamental for social learning scenarios in future humanoid robotic applications.KeywordsInteractive reinforcement learningHuman-robot interactionEmotional behaviourTransparency 

Transparent Interactive Reinforcement Learning Using Emotional Behaviours

Competition and cooperation among agents in multiagent systems can be effectively modeled as differential games. One of the typical tasks is capturing the flag, in which the agents can be divided into attackers’ alliance and defenders’ alliance with two-phase competitive behaviors. The attackers’ alliance aims to capture flags in the first phase and then return to the safe region in the second phase, while the defenders’ alliance aims to protect the flags and apprehend as many attackers as possible. Throughout the interaction, agents are actively involved in perception, cognitive learning, and the formulation of optimal decisions rooted in their acquired knowledge. Consequently, this article delves into the central challenges posed by capture-the-flag differential games, particularly in terms of task allocation and coordinated apprehension strategies among defenders. First, we use the Apollonius circle to transform the multiplayer capture-the-flag problems into one-defense-one or two-defense-one scenarios. By analyzing the advantages of cooperation between defenders, a two-stage joint optimal strategy is obtained. Moreover, we propose an approximation algorithm that achieves optimal task assignment, significantly reducing computational complexity. The performance and effectiveness of the proposed algorithm are demonstrated through numerical simulations.

Matching-Based Capture-the-Flag Games for Multi-Agent Systems

While analyzing scanned documents, handwritten text can overlap with printed text. This overlap causes difficulties during the optical character recognition (OCR) and digitization process of documents, and subsequently, hurts downstream NLP tasks. Prior research either focuses solely on the binary classification of handwritten text or performs a three-class segmentation of the document, i.e., recognition of handwritten, printed, and background pixels. This approach results in the assignment of overlapping handwritten and printed pixels to only one of the classes, and thus, they are not accounted for in the other class. Thus, in this research, we develop novel approaches to address the challenges of handwritten and printed text segmentation. Our objective is to recover text from different classes in their entirety, especially enhancing the segmentation performance on overlapping sections. To support this task, we introduce a new dataset, SignaTR6K, collected from real legal documents, as well as a new model architecture for the handwritten and printed text segmentation task. Our best configuration outperforms prior work on two different datasets by 17.9% and 7.3% on IoU scores. The SignaTR6K dataset is accessible for download via the following link: https://forms.office.com/r/2a5RDg7cAY. 

Handwritten and Printed Text Segmentation: A Signature Case Study

The Strategic Research and Innovation Agenda (SRIA) of the European Commission identifies Knowledge Graphs (KGs) as one of the most important technologies for building an interoperability framework and enabling data exchange among users across countries, sectors, and disciplines [1]. KG is a graph-structured knowledge base containing a terminology (vocabulary or ontology) and data entities interrelated via the terminology [2]. KGs are based on semantic web technologies (RDF, SPARQL, etc.) and often used for agile data integration. KGs also play an essential role within Germany as a vehicle to connect research data and research-related entities and make those accessible – examples include the GESIS Knowledge Graph Infrastructure, TIB Open Research Knowledge Graph, and GND.network. Furthermore, the Wikidata knowledge graph, maintained by Wikimedia Germany, contains a large number of research-related entities and is widely used in scientific knowledge management in addition to being an important advocacy tool for open data [3]. Extending domain-specific ontology-supported KGs with the multidisciplinary, crowdsourced knowledge in Wikidata KG would enable significant applications. The linking between expert knowledge systems and world knowledge empowers lay persons to benefit from high-quality research data and ultimately contributes to increasing confidence in scientific research in society.

The Case for a Common, Reusable Knowledge Graph Infrastructure for NFDI

Historical archival records present many challenges for OCR systems to correctly encode their content, due to visual complexity, e.g. mixed printed text and handwritten annotations, paper degradation and faded ink. This paper addresses the problem of automatic identification and separation of handwritten and printed text in historical archival documents, including the creation of an artificial pixel-level annotated dataset and the presentation of a new FCN-based model trained on historical data. Initial test results indicate 18% IoU performance improvement on recognition of printed pixels and 10% IoU performance improvement on recognition of handwritten pixels in synthesised data when compared to the state-of-the-art trained on modern documents. Furthermore, an extrinsic OCR-based evaluation on the printed layer extracted from real historical documents shows 26% performance increase.

/pdf/handwritten-and-printed-text-identification-in-historical-n59wa76g.pdf

Handwritten and Printed Text Identification in Historical Archival Documents

Preserving historical city architectures and making them (publicly) available has emerged as an important field of the cultural heritage and digital humanities research domain. In this context, the TRANSRAZ project is creating an interactive 3D environment of the historical city of Nuremberg which spans over different periods of time. Next to the exploration of the city’s historical architecture, TRANSRAZ is also integrating information about its inhabitants, organizations, and important events, which are extracted from historical documents semi-automatically. Knowledge Graphs have proven useful and valuable to integrate and enrich these heterogeneous data. However, this task also comes with versatile data modeling challenges. This paper contributes the TRANSRAZ data model, which integrates agents, architectural objects, events, and historical documents into the 3D research environment by means of ontologies. Goal is to explore Nuremberg’s multifaceted past in different time layers in the context of its architectural, social, economical, and cultural developments. 

TRANSRAZ Data Model: Towards a Geosocial Representation of Historical Cities

Terminology sources, such as controlled vocabularies, thesauri and classification systems, play a key role in digitizing cultural heritage. However, Information Retrieval (IR) systems that allow to query and explore these lexical resources often lack an adequate representation of the semantics behind the user's search, which can be conveyed through multiple expression modalities (e.g., images, keywords or textual descriptions). This paper presents the implementation of a new search engine for one of the most widely used iconography classification system, Iconclass. The novelty of this system is the use of a pre-trained vision-language model, namely CLIP, to retrieve and explore Iconclass concepts using visual or textual queries.

https://export.arxiv.org/pdf/2306.16529v1.pdf

Multimodal Search on Iconclass using Vision-Language Pre-Trained Models

Knowledge Extraction (KE) techniques are used to convert unstructured information present in texts to Knowledge Graphs (KGs) which can be queried and explored. Despite their potential for cultural heritage domains, such as Art History, these techniques often encounter limitations if applied to domain-specific data. In this paper we present the main challenges that KE has to face on art-historical texts, by using as case study Giorgio Vasari’s The Lives of The Artists . This paper discusses the following NLP tasks for art-historical texts, namely entity recognition and linking, coreference resolution, time extraction, motif extraction and artwork extraction. Several strategies to annotate art-historical data for these tasks and evaluate NLP models are also proposed.

Knowledge Extraction for Art History: the Case of Vasari's The Lives of The Artists

. NFDI4Culture is establishing a knowledge graph-based infrastructure for research data on material and immaterial cultural heritage in the context of the German National Research Data Infrastructure (NFDI) in compliance with the FAIR principles. The NFDI4Culture Knowledge Graph is developed and integrated with the Culture Information Portal to aggregate heterogeneous and isolated data from the culture research landscape and thereby increase their discov-erability, interoperability and reusability. This paper presents the research data management strategy in the long-term project NFDI4Culture, which combines a CMS and a knowledge graph-based infrastructure to enable an intuitive and meaningful interaction with research resources in the cultural heritage domain.

From Floppy Disks to 5-Star LOD: FAIR Research Infrastructure for NFDI4Culture

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