1,721,030 research outputs found
Reporting guidelines for large language models in human-robot interaction
No full textThe comparatively recent advent of Large Language Models (LLMs) has resulted in a wide array of new capabilities and components relevant to Human-Robot Interaction (HRI) researchers. LLMs are being applied to vision, manipulation, planning, reasoning, learning, and HRI problems, frequently as "Scarecrows," in which LLMs serve as black-box modules integrated into robot architectures for the purpose of quickly enabling full-pipeline solutions. However, despite this explosion of applications, general questions remain about the best ways to incorporate LLMs into robot architectures, appropriate safety and guardrail considerations, and, critically, how HRI research that depends on LLMs should be conducted and reported. In this article, we explore the question of reporting guidelines for HRI researchers who utilize Scarecrows in robot architectures. We identify five key stakeholder groups in the HRI research process, discuss what information each group needs from HRI researchers, and identify appropriate mechanisms for conveying that information from HRI researchers to stakeholders either directly or indirectly. We end with a set of suggested guidelines as to what information should be included when researchers disseminate information about HRI research that uses LLMs
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Incrementally resolving references in order to identify visually present objects in a situated dialogue setting
Get PDFKennington C. Incrementally resolving references in order to identify visually present objects in a situated dialogue setting. Bielefeld: Universität Bielefeld; 2016.The primary concern of this thesis is to model the resolution of spoken referring expressions
made in order to identify objects; in particular, everyday objects that can be perceived visually
and distinctly from other objects. The practical goal of such a model is for it to be implemented
as a component for use in a live, interactive, autonomous spoken dialogue system. The requirement of interaction imposes an added complication; one that has been ignored in previous
models and approaches to automatic reference resolution: the model must attempt to resolve
the reference incrementally as it unfolds–not wait until the end of the referring expression to
begin the resolution process.
Beyond components in dialogue systems, reference has been a major player in the philosophy of meaning for longer than a century. For example, Gottlob Frege (1892) has distinguished
between Sinn (sense) and Bedeutung (reference), discussed how they are related and how they
relate to the meaning of words and expressions. It has furthermore been argued (e.g., Dahlgren
(1976)) that reference to entities in the actual world is not just a fundamental notion of semantic theory, but the fundamental notion; for an individual acquiring a language, understanding
the meaning of many words and concepts is done via the task of reference, beginning in early
childhood. In this thesis, we pursue an account of word meaning that is based on perception of
objects; for example, the meaning of the word red is based on visual features that are selected
as distinguishing red objects from non-red ones.
This thesis proposes two statistical models of incremental reference resolution. Given ex-
amples of referring expressions and visual aspects of the objects to which those expressions
referred, both model components learn a functional mapping between the words of the refer-
ring expressions and the visual aspects. A generative model, the simple incremental update
model, presented in Chapter 5, uses a mediating variable to learn the mapping, whereas a dis-
criminative model, the words-as-classifiers model, presented in Chapter 6, learns the mapping
directly and improves over the generative model. Both models have been evaluated in various
reference resolution tasks to objects in virtual scenes as well as real, tangible objects. This
thesis shows that both models work robustly and are able to resolve referring expressions made
in reference to visually present objects despite realistic, noisy conditions of speech and object
recognition. A theoretical and practical comparison is also provided.
Special emphasis is given to the discriminative model in this thesis because of its simplicity
and ability to represent word meanings. It is in the learning and application of this model that
gives credence to the above claim that reference is the fundamental notion for semantic theory
and that meanings of (visual) words is done through experiencing referring expressions made
to objects that are visually perceivable
Enriching Language Models with Visually-Grounded Word Vectors and the Lancaster Sensorimotor Norms
Get PDFLanguage models are trained only on text despite the fact that humans learn their first language in a highly interactive and multimodal environment where the first set of learned words are largely concrete, denoting physical entities and embodied states. To enrich language models with some of this missing experience, we leverage two sources of information: (1) the Lancaster Sensorimotor norms, which provide ratings (means and standard deviations) for over 40,000 English words along several dimensions of embodiment, and which capture the extent to which something is experienced across 11 different sensory modalities, and (2) vectors from coefficients of binary classifiers trained on images for the BERT vocabulary. We pre-trained the ELECTRA model and fine-tuned the RoBERTa model with these two sources of information then evaluate using the established GLUE benchmark and the Visual Dialog benchmark. We find that enriching language models with the Lancaster norms and image vectors improves results in both tasks, with some implications for robust language models that capture holistic linguistic meaning in a language learning context
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Word Concreteness and Word Originality: A Basque Case Study
Get PDFBasque is the oldest known spoken language of Western Europe that is not descended from Indo-European. Despite efforts to stop its use from external influences, the Basque people have shown resiliency throughout their history to keep the language alive. However, as is the case with all other languages, Basque has not been immune to influence from neighboring languages; indeed all Basque speakers are bilingual. The question explored here is whether a word’s concreteness using machine translation from Basque to English and English concreteness scores determines if an original Basque word is spoken today. We find that there is a significant difference of concreteness scores between old and contemporary Basque. We also compared old Basque words with common contemporary Basque words in a semantic vector space and found the old words to cluster more tightly than contemporary words suggesting that words with similar semantic vectors are able to retain usage through time
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