850,844 research outputs found
A Specification Language for the WIDE Workflow Model
This paper presents a workflow specification language developed in the WIDE project. The language provides a rich organisation model, an information model including presentation details, and a sophisticated process model. Workflow application developers should find the language a useful and compact means to capture and investigate design details. Workflow system developers would discover the language a good vehicle to study the interaction between different features as well as facilitate the development of more advanced features. Others would attain a better understanding of the workflow paradigm and could use the language ms a basis of evaluation for the functionality of workflow systems
Teacher guided reporting in a primary literacy context : the stepping stones of mode and interaction
In this paper I take Gibbons' notion of Teacher Guided Reporting (TGR) and explore
whether the features she identifies as being crucial in a science lesson with 9-10 year olds
also are found in a literacy lesson with 5-6 year olds. The findings support Gibbons'
claims for linguistic sequencing of tasks, degree of student initiations and role of teacherstudent
interaction. The analysis suggests a wider variety of mode continua which
underpin the linguistic sequencing of literacy tasks; and that these combine with the
developing focus of teacher feedback across the series of TGR interactions in three main
strands - language, content and process - which together promote the learners' language
development towards written academic registers appropriate for schooling in English
GATOR: Requirements capturing of telephony features
We are developing a natural language-based, requirements gathering system called GATOR (for the GATherer Of Requirements). GATOR assists in the development of more accurate and complete specifications of new telephony features. GATOR interacts with a feature designer who describes a new feature, set of features, or capability to be implemented. The system aids this individual in the specification process by asking for clarifications when potential ambiguities are present, by identifying potential conflicts with other existing features, and by presenting its understanding of the feature to the designer. Through user interaction with a model of the existing telephony feature set, GATOR constructs a formal representation of the new, 'to be implemented' feature. Ultimately GATOR will produce a requirements document and will maintain an internal representation of this feature to aid in future design and specification. This paper consists of three sections that describe (1) the structure of GATOR, (2) POND, GATOR's internal knowledge representation language, and (3) current research issues
Metaphor as categorisation: a connectionist implementation
A key issue for models of metaphor comprehension is to explain how in some metaphorical comparison , only some features of B are transferred to A. The features of B that are transferred to A depend both on A and on B. This is the central thrust of Black's well known interaction theory of metaphor comprehension (1979). However, this theory is somewhat abstract, and it is not obvious how it may be implemented in terms of mental representations and processes. In this paper we describe a simple computational model of on-line metaphor comprehension which combines Black's interaction theory with the idea that metaphor comprehension is a type of categorisation process (Glucksberg & Keysar, 1990, 1993). The model is based on a distributed connectionist network depicting semantic memory (McClelland & Rumelhart, 1986). The network learns feature-based information about various concepts. A metaphor is comprehended by applying a representation of the first term A to the network storing knowledge of the second term B, in an attempt to categorise it as an exemplar of B. The output of this network is a representation of A transformed by the knowledge of B. We explain how this process embodies an interaction of knowledge between the two terms of the metaphor, how it accords with the contemporary theory of metaphor stating that comprehension for literal and metaphorical comparisons is carried out by identical mechanisms (Gibbs, 1994), and how it accounts for both existing empirical evidence (Glucksberg, McGlone, & Manfredi, 1997) and generates new predictions. In this model, the distinction between literal and metaphorical language is one of degree, not of kind
AI Literature Review Suite
The process of conducting literature reviews is often time-consuming and
labor-intensive. To streamline this process, I present an AI Literature Review
Suite that integrates several functionalities to provide a comprehensive
literature review. This tool leverages the power of open access science, large
language models (LLMs) and natural language processing to enable the searching,
downloading, and organizing of PDF files, as well as extracting content from
articles. Semantic search queries are used for data retrieval, while text
embeddings and summarization using LLMs present succinct literature reviews.
Interaction with PDFs is enhanced through a user-friendly graphical user
interface (GUI). The suite also features integrated programs for bibliographic
organization, interaction and query, and literature review summaries. This tool
presents a robust solution to automate and optimize the process of literature
review in academic and industrial research.Comment: 7 Pages, 5 figures, Keywords: Literature Review, Artificial
Intelligence, Text Embeddings, Large Language Model
Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation
Referring video object segmentation (RVOS) aims to segment the target object
in a video sequence described by a language expression. Typical multimodal
Transformer based RVOS approaches process video sequence in a frame-independent
manner to reduce the high computational cost, which however restricts the
performance due to the lack of inter-frame interaction for temporal coherence
modeling and spatio-temporal representation learning of the referred object.
Besides, the absence of sufficient cross-modal interactions results in weak
correlation between the visual and linguistic features, which increases the
difficulty of decoding the target information and limits the performance of the
model. In this paper, we propose a bidirectional correlation-driven inter-frame
interaction Transformer, dubbed BIFIT, to address these issues in RVOS.
Specifically, we design a lightweight and plug-and-play inter-frame interaction
module in the Transformer decoder to efficiently learn the spatio-temporal
features of the referred object, so as to decode the object information in the
video sequence more precisely and generate more accurate segmentation results.
Moreover, a bidirectional vision-language interaction module is implemented
before the multimodal Transformer to enhance the correlation between the visual
and linguistic features, thus facilitating the language queries to decode more
precise object information from visual features and ultimately improving the
segmentation performance. Extensive experimental results on four benchmarks
validate the superiority of our BIFIT over state-of-the-art methods and the
effectiveness of our proposed modules
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