589 research outputs found
Senior Recital:Bill Roberts, Percussion
Kemp Recital Hall Saturday Afternoon March 20, 2004 3:00p.m
Cultural influences on word meanings revealed through large-scale semantic alignment
If the structure of language vocabularies mirrors the structure of natural divisions that are universally perceived, then the meanings of words in different languages should closely align. By contrast, if shared word meanings are a product of shared culture, history and geography, they may differ between languages in substantial but predictable ways. Here, we analysed the semantic neighbourhoods of 1,010 meanings in 41 languages. The most-aligned words were from semantic domains with high internal structure (number, quantity and kinship). Words denoting natural kinds, common actions and artefacts aligned much less well. Languages that are more geographically proximate, more historically related and/or spoken by more-similar cultures had more aligned word meanings. These results provide evidence that the meanings of common words vary in ways that reflect the culture, history and geography of their users
Can LLMs Grade Short-answer Reading Comprehension Questions : Foundational Literacy Assessment in LMICs
This paper presents emerging evidence of using generative large language
models (i.e., GPT-4) to reliably evaluate short-answer reading comprehension
questions. Specifically, we explore how various configurations of generative
(LLMs) are able to evaluate student responses from a new dataset, drawn from a
battery of reading assessments conducted with over 150 students in Ghana. As
this dataset is novel and hence not used in training runs of GPT, it offers an
opportunity to test for domain shift and evaluate the generalizability of
generative LLMs, which are predominantly designed and trained on data from
high-income North American countries. We found that GPT-4, with minimal prompt
engineering performed extremely well on evaluating the novel dataset (Quadratic
Weighted Kappa 0.923, F1 0.88), substantially outperforming transfer-learning
based approaches, and even exceeding expert human raters (Quadratic Weighted
Kappa 0.915, F1 0.87). To the best of our knowledge, our work is the first to
empirically evaluate the performance of generative LLMs on short-answer reading
comprehension questions, using real student data, and suggests that generative
LLMs have the potential to reliably evaluate foundational literacy. Currently
the assessment of formative literacy and numeracy is infrequent in many low and
middle-income countries (LMICs) due to the cost and operational complexities of
conducting them at scale. Automating the grading process for reading assessment
could enable wider usage, and in turn improve decision-making regarding
curricula, school management, and teaching practice at the classroom level.
Importantly, in contrast transfer learning based approaches, generative LLMs
generalize well and the technical barriers to their use are low, making them
more feasible to implement and scale in lower resource educational contexts
Composite Structure with Load Distribution Devices, and Method for Making Same
An improved composite structure and method for making same has been provided. The provided improved composite structure has locally strengthened areas within a reinforcement region. The locally strengthened areas within the reinforcement region have load distribution devices to redistribute load in order to (i) locally strengthen an area around damage induced by an initial momentary and direct transmitted load, and (ii) limit growth and propagation of damage induced by an initial momentary and direct transmitted load during a subsequent unbalance load. The improved composite structure reduces the impact of the fan blade out phenomenon in a weight efficient manner
Using State-of-the-Art Speech Models to Evaluate Oral Reading Fluency in Ghana
This paper reports on a set of three recent experiments utilizing large-scale
speech models to evaluate the oral reading fluency (ORF) of students in Ghana.
While ORF is a well-established measure of foundational literacy, assessing it
typically requires one-on-one sessions between a student and a trained
evaluator, a process that is time-consuming and costly. Automating the
evaluation of ORF could support better literacy instruction, particularly in
education contexts where formative assessment is uncommon due to large class
sizes and limited resources. To our knowledge, this research is among the first
to examine the use of the most recent versions of large-scale speech models
(Whisper V2 wav2vec2.0) for ORF assessment in the Global South.
We find that Whisper V2 produces transcriptions of Ghanaian students reading
aloud with a Word Error Rate of 13.5. This is close to the model's average WER
on adult speech (12.8) and would have been considered state-of-the-art for
children's speech transcription only a few years ago. We also find that when
these transcriptions are used to produce fully automated ORF scores, they
closely align with scores generated by expert human graders, with a correlation
coefficient of 0.96. Importantly, these results were achieved on a
representative dataset (i.e., students with regional accents, recordings taken
in actual classrooms), using a free and publicly available speech model out of
the box (i.e., no fine-tuning). This suggests that using large-scale speech
models to assess ORF may be feasible to implement and scale in lower-resource,
linguistically diverse educational contexts
The Wide Field Spectrograph (WiFeS): Performance and Data Reduction
This paper describes the on-telescope performance of the Wide Field
Spectrograph (WiFeS). The design characteristics of this instrument, at the
Research School of Astronomy and Astrophysics (RSAA) of the Australian National
University (ANU) and mounted on the ANU 2.3m telescope at the Siding Spring
Observatory has been already described in an earlier paper (Dopita et al.
2007). Here we describe the throughput, resolution and stability of the
instrument, and describe some minor issues which have been encountered. We also
give a description of the data reduction pipeline, and show some preliminary
results.Comment: Accepted for publication in Astrophysics & Space Science, 15pp, 11
figure
Crowdsourcing Computer Security Attack Trees
This paper describes an open-source project called RATCHET whose goal is to create software that can be used by large groups of people to construct attack trees. The value of an attack tree increases when the attack tree explores more scenarios. Crowdsourcing an attack tree reduces the possibility that some options might be overlooked. RATCHET has been tested in classroom settings with positive results. This paper gives an overview of RATCHET and describes some of the features that we plan to add
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