The driver concept for the DLR Lightweight Robot III

In this paper we present the synchronization and driver architecture of the DLR LWR-III, which supplies an easy to use interface for applications. For our purpose we abstracted the robot hardware entirely from the control algorithms using the common device driver concept of modern operating systems. The software architecture is split into two modular parts. On the one side, there are device drivers that communicate with the hardware components. On the other side, there are realtime ap- plications realized as Simulink Models, which provide advanced control algorithms. This ensures a clean separation between the two modules and provides a communication over a common and approved interface. Furthermore we investigated how we can ensure synchronization to the hardware over the device driver interfaces and how we can ensure that it meets hard realtime requirements. The main result of this paper is to realize a synchronization between LWR-III hardware and Simulink control applications while targeting small latencies with respect to hard realtime requirements. The design is implemented and verified on WindRiverTM VxWorksTM

Workforce issues in nursing in Queensland: 2001 and 2004

[Abstract]: Aims and objectives: The aim of the study was to identify the factors impacting upon nursing work and to use the results to inform strategic planning of the Queensland Nurses Union. Background: In 2001 and 2004, a study was undertaken to gather data on the level of satisfaction of nurses with their working life. This paper reports the 2004 results on workload, skill mix, remuneration and morale. Where applicable, the results are compared to 2001 data. Methods: A questionnaire was mailed to 3000 Assistants-in-Nursing, Enrolled and Registered Nurses in October 2004. All participants were members of the Queensland Nurses Union. The results are reported in three sectors – public, private and aged care. A total of 1349 nurses responded to the survey, a response rate of 45%. Results: Nurses in the 2004 study believed: their workload was heavy; their skills and experience poorly rewarded; work stress was high; morale was perceived to be poor and, similar to 2001, deteriorating; the skill mix was often inadequate; and the majority of nurses are unable to complete their work in the time available. Nursing morale was found to be associated with autonomy, workplace equipment, workplace safety, teamwork, work stress, the physical demand of nursing work, workload, rewards for skills and experience, career prospects, status of nursing, and remuneration. Conclusion: Overall the findings of the study are consistent with those determined by the 2001 survey. Relevance to clinical practice. The findings of this study indicate the importance of factors such as workplace autonomy, teamwork, the levels of workplace stress, workload and remuneration on nursing morale. The data also indicate that workplace safety and workplace morale are linked. These findings provide information for policy makers and nurse managers on areas that need to be addressed to retain nurses within aged care, acute hospital and community nursing

Nurses worth listening to

[Executive Summary]: In 2001 the University of Southern Queensland (USQ) in conjunction with the Queensland Nurses’ Union (QNU) undertook a study of enrolled and registered nurse and assistant-in-nursing members. In Queensland, registered nurses (RNs) and enrolled nurses (ENs) are qualified to practice nursing and are licensed by the Queensland Nursing Council (QNC), an independent body responsible for the setting and maintaining of nursing standards in the State. Although not licensed by the QNC Assistants in Nursing (AINs) work within a nursing model of care. These workers may also have other titles such as Personal Care Assistants or Carers. Regardless of their title, they work under the direct or indirect supervision of a RN. The study was confined to nurses employed in the public sector (acute hospitals, community health), the private sector (acute hospitals and domicillary nursing) and the aged care sector (government and non-government). In 2004 a similar study was conducted. The major findings of the 2004 study were that nurses believed: • nursing is emotionally challenging and physically demanding • their workload is heavy and that their skills and experience as a professional nurse are poorly rewarded (remunerated or recognised) • work stress is high and morale is perceived to be poor and, similar to 2001, deteriorating • there are insufficient staff in their workplace and that the skill mix is inadequate • the majority of nurses are unable to complete their work to their level of professional satisfaction in the time available. While there were some changes between 2001 and 2004 (some could be seen as improvements, others deteriorations), the overwhelming impression one has, especially from the qualitative data, is of a workforce frustrated and unable to provide safe and quality care to their patients/clients within the time allocated

Donkii: Can Annotation Error Detection Methods Find Errors in Instruction-Tuning Datasets?

Instruction-tuning has become an integral part of training pipelines for Large Language Models (LLMs) and has been shown to yield strong performance gains. In an orthogonal line of research, Annotation Error Detection (AED) has emerged as a tool for detecting quality issues of gold-standard labels. But so far, the application of AED methods is limited to discriminative settings. It is an open question how well AED methods generalize to generative settings which are becoming widespread via generative LLMs. In this work, we present a first and new benchmark for AED on instruction-tuning data: Donkii. It encompasses three instruction-tuning datasets enriched with annotations by experts and semi-automatic methods. We find that all three datasets contain clear-cut errors that sometimes directly propagate into instruction-tuned LLMs. We propose four AED baselines for the generative setting and evaluate them comprehensively on the newly introduced dataset. Our results demonstrate that choosing the right AED method and model size is indeed crucial, thereby deriving practical recommendations. To gain insights, we provide a first case-study to examine how the quality of the instruction-tuning datasets influences downstream performance

Establishing Trustworthiness: Rethinking Tasks and Model Evaluation

Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades. Traditionally, facets of linguistic intelligence have been compartmentalized into tasks with specialized model architectures and corresponding evaluation protocols. With the advent of large language models (LLMs) the community has witnessed a dramatic shift towards general purpose, task-agnostic approaches powered by generative models. As a consequence, the traditional compartmentalized notion of language tasks is breaking down, followed by an increasing challenge for evaluation and analysis. At the same time, LLMs are being deployed in more real-world scenarios, including previously unforeseen zero-shot setups, increasing the need for trustworthy and reliable systems. Therefore, we argue that it is time to rethink what constitutes tasks and model evaluation in NLP, and pursue a more holistic view on language, placing trustworthiness at the center. Towards this goal, we review existing compartmentalized approaches for understanding the origins of a model's functional capacity, and provide recommendations for more multi-faceted evaluation protocols.Comment: Accepted at EMNLP 2023 (Main Conference), camera-read

An online authoring and publishing platform for field guides and identification tools

Various implementation approaches are available for digital field guides and identification tools that are created for the web and mobile devices. The architecture of the “biowikifarm” publishing platform and some technical and social advantages of a document- and author-centric approach based on the MediaWiki open source software over custom-developed, database driven software are presented

GRASS GIS, Star Trek and old Video Tape

This paper discusses the need for the preservation of audiovisual content in the OSGeo communities beyond the established software repositories. Audiovisual content related to OSGeo projects such as training videos can be preserved by multimedia archiving and retrieval services which are currently developed by the library community. This is demonstrated by the reference case of a newly discovered version of the GRASS GIS 1987 promotional video which is being included into the AV-portal of the German National Library of Science and Technology (TIB). Access to the video will be provided upon the release of the web-based portal, allowing for extended search capabilities based on enhanced metadata derived by automated video analysis. This is a reference case for future preservation activities regarding semanticenhanced Web2.0 content from OSGeo project