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