445,078 research outputs found
Multimodality And The Sociality Of Literacies: Shaping First-Year Writing Students’ Literacies Through Multimodal Approaches
The research presented here focuses on approaches to developing multimodal literacies through social semiotics, digital modes of communication, and multiliteracies. Intentionally developing these literacies opens the door for first-year writing students to build upon social discourses in which they already engage and develop new modes of meaning making outside of solely alphabetic literacy. Composition textbooks today, both traditional and Open Educational Resources (OER), become more effective in developing post-process and collaborative pedagogy writing standards when they focus on multimodal literacies and practices as outlined in this research. My research addresses both the historical precedent for multimodality in the Composition classroom as well as scholarship on how and why it is used in Composition classrooms today. I conclude by comparing and contrasting two first-year writing textbooks, one a traditional class text and the other an OER text, in order to assess their capacity for and applicability of multimodal approaches. Specific focus is given to both textbooks in terms of competency in adaptability, sociality, and digital contexts as they relate to student literacies
From Method Fragments to Method Services
In Method Engineering (ME) science, the key issue is the consideration of
information system development methods as fragments. Numerous ME approaches
have produced several definitions of method parts. Different in nature, these
fragments have nevertheless some common disadvantages: lack of implementation
tools, insufficient standardization effort, and so on. On the whole, the
observed drawbacks are related to the shortage of usage orientation. We have
proceeded to an in-depth analysis of existing method fragments within a
comparison framework in order to identify their drawbacks. We suggest
overcoming them by an improvement of the ?method service? concept. In this
paper, the method service is defined through the service paradigm applied to a
specific method fragment ? chunk. A discussion on the possibility to develop a
unique representation of method fragment completes our contribution
Weak n-categories: comparing opetopic foundations
We define the category of tidy symmetric multicategories. We construct for
each tidy symmetric multicategory Q a cartesian monad (E_Q,T_Q) and extend this
assignation to a functor. We exhibit a relationship between the slice
construction on symmetric multicategories, and the `free operad' monad
construction on suitable monads. We use this to give an explicit description of
the relationship between Baez-Dolan and Leinster opetopes.Comment: 31 page
Cue Phrase Classification Using Machine Learning
Cue phrases may be used in a discourse sense to explicitly signal discourse
structure, but also in a sentential sense to convey semantic rather than
structural information. Correctly classifying cue phrases as discourse or
sentential is critical in natural language processing systems that exploit
discourse structure, e.g., for performing tasks such as anaphora resolution and
plan recognition. This paper explores the use of machine learning for
classifying cue phrases as discourse or sentential. Two machine learning
programs (Cgrendel and C4.5) are used to induce classification models from sets
of pre-classified cue phrases and their features in text and speech. Machine
learning is shown to be an effective technique for not only automating the
generation of classification models, but also for improving upon previous
results. When compared to manually derived classification models already in the
literature, the learned models often perform with higher accuracy and contain
new linguistic insights into the data. In addition, the ability to
automatically construct classification models makes it easier to comparatively
analyze the utility of alternative feature representations of the data.
Finally, the ease of retraining makes the learning approach more scalable and
flexible than manual methods.Comment: 42 pages, uses jair.sty, theapa.bst, theapa.st
Cross-Platform Comparison of Untargeted and Targeted Lipidomics Approaches on Aging Mouse Plasma.
Lipidomics - the global assessment of lipids - can be performed using a variety of mass spectrometry (MS)-based approaches. However, choosing the optimal approach in terms of lipid coverage, robustness and throughput can be a challenging task. Here, we compare a novel targeted quantitative lipidomics platform known as the Lipidyzer to a conventional untargeted liquid chromatography (LC)-MS approach. We find that both platforms are efficient in profiling more than 300 lipids across 11 lipid classes in mouse plasma with precision and accuracy below 20% for most lipids. While the untargeted and targeted platforms detect similar numbers of lipids, the former identifies a broader range of lipid classes and can unambiguously identify all three fatty acids in triacylglycerols (TAG). Quantitative measurements from both approaches exhibit a median correlation coefficient (r) of 0.99 using a dilution series of deuterated internal standards and 0.71 using endogenous plasma lipids in the context of aging. Application of both platforms to plasma from aging mouse reveals similar changes in total lipid levels across all major lipid classes and in specific lipid species. Interestingly, TAG is the lipid class that exhibits the most changes with age, suggesting that TAG metabolism is particularly sensitive to the aging process in mice. Collectively, our data show that the Lipidyzer platform provides comprehensive profiling of the most prevalent lipids in plasma in a simple and automated manner
Semantic-driven matchmaking of web services using case-based reasoning
With the rapid proliferation of Web services as the medium of choice to securely publish application services beyond the firewall, the importance of accurate, yet flexible matchmaking of similar services gains importance both for the human user and for dynamic composition engines. In this paper, we present a novel approach that utilizes the case based reasoning methodology for modelling dynamic Web service discovery and matchmaking. Our framework considers Web services execution experiences in the decision making process and is highly adaptable to the service requester constraints. The framework also utilises OWL semantic descriptions extensively for implementing both the components of the CBR engine and the matchmaking profile of the Web services
Contracts as specifications for dynamical systems in driving variable form
This paper introduces assume/guarantee contracts on continuous-time control
systems, hereby extending contract theories for discrete systems to certain new
model classes and specifications. Contracts are regarded as formal
characterizations of control specifications, providing an alternative to
specifications in terms of dissipativity properties or set-invariance. The
framework has the potential to capture a richer class of specifications more
suitable for complex engineering systems. The proposed contracts are supported
by results that enable the verification of contract implementation and the
comparison of contracts. These results are illustrated by an example of a
vehicle following system.Comment: 8 pages, 2 figures; minor changes in the final version, as accepted
for publication in the Proceedings of the 2019 European Control Conference,
Naples, Ital
A Study of Metrics of Distance and Correlation Between Ranked Lists for Compositionality Detection
Compositionality in language refers to how much the meaning of some phrase
can be decomposed into the meaning of its constituents and the way these
constituents are combined. Based on the premise that substitution by synonyms
is meaning-preserving, compositionality can be approximated as the semantic
similarity between a phrase and a version of that phrase where words have been
replaced by their synonyms. Different ways of representing such phrases exist
(e.g., vectors [1] or language models [2]), and the choice of representation
affects the measurement of semantic similarity.
We propose a new compositionality detection method that represents phrases as
ranked lists of term weights. Our method approximates the semantic similarity
between two ranked list representations using a range of well-known distance
and correlation metrics. In contrast to most state-of-the-art approaches in
compositionality detection, our method is completely unsupervised. Experiments
with a publicly available dataset of 1048 human-annotated phrases shows that,
compared to strong supervised baselines, our approach provides superior
measurement of compositionality using any of the distance and correlation
metrics considered
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