Developing a cross-age teaching programs: the benefits to the student ‘teachers’

Cross‐age teaching is a technique where an older student acts as ‘teacher’ and teaches concepts to younger students. This paper includes a literature review that discusses the benefits of cross‐age teaching to the older ‘teachers,’ and reviews a program developed by the researcher based on the benefits of good cross‐age teaching programs, where 11th grade honors chemistry students create, develop, and assess a science lesson to teach 5th grade science students. The process is detailed, and concludes with the students and teachers reflecting over the results and what changes could be made to improve such a program in the future. Finally, applications of cross‐age teaching programs are explored, and the research concludes the benefits of being the ‘teacher’ are advantageous to all levels of students. Such programs are in need of more collaboration and effort on the part of educators and researchers, but the benefits gained by all students make effort well worth it

Public attitudes to compulsory health programmes: generating questions from a focus group to support a willingness to pay study

Background The willingness to pay approach to valuing goods has been heavily criticised due to perceived biases in their resultant valuations. Recently, attempts have been made to use data relating to respondent attitudes to produce better informed preferences and remove "warm glow". This paper reports a study that uses rigorous methods by which salient attitudes can be identified and measured for use in a subsequent willingness to pay study. The topic area is that of compulsory health programmes (CHPs). Methods Six focus groups were undertaken among members of the public using a questioning route designed to highlight different attitudes between CHPs. Framework analysis was used, including thematic and contrast charting, to identify themes that described the issues raised by participants. The resultant coding framework was translated into a set of scales which were then used in a survey of 831 members of the general population. Factor analysis was applied to identify latent themes. Results Analysis of the focus group transcripts highlighted seven themes relating to the effects of policy, alternatives, the role of government, uncertainties, coherence of policy, rights and responsibilities, and other issues. These themes were translated into 48 statements that were used as attitude scales. The factor analysis of the general population survey identified 4 latent factors: "common sense", "government", "warm glow" and "rights and responsibilities". Conclusions The focus group work described in this paper shows that across individuals, coherent themes relating to public health and compulsion can be identified. It also demonstrates sophisticated thinking by participants about public health issues. This study shows that the work of Nunes (2002) and Pouta (2004) is potentially generalisable to other topic areas and that their methods can be improved upon. This work has been used in a subsequent analysis of WTP responses by using the attitudinal scales in an attempt to elicit better informed preferences and explain responses in terms of underlying attitudes and "warm glow"

Including patient choice in cost-effectiveness decision rules

There has been increasing discussion in the economic literature about the appropriateness of using general population values within technology appraisal. This paper proposes an alternative approach to incorporating patient values into the cost-effectiveness decision rule that lies at the heart of funding decisions. Whilst the current decision rule is constructed around a technical question, namely, "which treatment is the most cost-effective?", the key policy question is "which treatments should be offered to the patient?". A two-part decision rule is explored which gives the patient the choice of the most cost-effective treatment plus all cheaper options. Whilst the adoption of this patient-based cost-effectiveness rule may not alter many decisions compared to the current approach, it would represent a profound shift in the way that patient values and patient choice are incorporated into economic evaluation

Polyphonic music transcription using note onset and offset detection

In this paper, an approach for polyphonic music transcription based on joint multiple-F0 estimation and note onset/offset detection is proposed. For preprocessing, the resonator time-frequency image of the input music signal is extracted and noise suppression is performed. A pitch salience function is extracted for each frame along with tuning and inharmonicity parameters. For onset detection, late fusion is employed by combining a novel spectral flux-based feature which incorporates pitch tuning information and a novel salience function-based descriptor. For each segment defined by two onsets, an overlapping partial treatment procedure is used and a pitch set score function is proposed. A note offset detection procedure is also proposed using HMMs trained on MIDI data. The system was trained on piano chords and tested on classic and jazz recordings from the RWC database. Improved transcription results are reported compared to state-of-the-art approaches

A Corpus-based Study Of Rhythm Patterns

We present a corpus-based study of musical rhythm, based on a collection of 4.8 million bar-length drum patterns extracted from 48,176 pieces of symbolic music. Approaches to the analysis of rhythm in music information retrieval to date have focussed on low-level features for retrieval or on the detection of tempo, beats and drums in audio recordings. Musicological approaches are usually concerned with the description or implementation of manmade music theories. In this paper, we present a quantitative bottom-up approach to the study of rhythm that relies upon well-understood statistical methods from natural language processing. We adapt these methods to our corpus of music, based on the realisation that—unlike words—barlength drum patterns can be systematically decomposed into sub-patterns both in time and by instrument. We show that, in some respects, our rhythm corpus behaves like natural language corpora, particularly in the sparsity of vocabulary. The same methods that detect word collocations allow us to quantify and rank idiomatic combinations of drum patterns. In other respects, our corpus has properties absent from language corpora, in particular, the high amount of repetition and strong mutual information rates between drum instruments. Our findings may be of direct interest to musicians and musicologists, and can inform the design of ground truth corpora and computational models of musical rhythm. 1

Multiple-F0 estimation of piano sounds exploiting spectral structure and temporal evolution

This paper proposes a system for multiple fundamental frequency estimation of piano sounds using pitch candidate selection rules which employ spectral structure and temporal evolution. As a time-frequency representation, the Resonator Time-Frequency Image of the input signal is employed, a noise suppression model is used, and a spectral whitening procedure is performed. In addition, a spectral flux-based onset detector is employed in order to select the steady-state region of the produced sound. In the multiple-F0 estimation stage, tuning and inharmonicity parameters are extracted and a pitch salience function is proposed. Pitch presence tests are performed utilizing information from the spectral structure of pitch candidates, aiming to suppress errors occurring at multiples and sub-multiples of the true pitches. A novel feature for the estimation of harmonically related pitches is proposed, based on the common amplitude modulation assumption. Experiments are performed on the MAPS database using 8784 piano samples of classical, jazz, and random chords with polyphony levels between 1 and 6. The proposed system is computationally inexpensive, being able to perform multiple-F0 estimation experiments in realtime. Experimental results indicate that the proposed system outperforms state-of-the-art approaches for the aforementioned task in a statistically significant manner. Index Terms: multiple-F0 estimation, resonator timefrequency image, common amplitude modulatio

A temporally-constrained convolutive probabilistic model for pitch detection

A method for pitch detection which models the temporal evolution of musical sounds is presented in this paper. The proposed model is based on shift-invariant probabilistic latent component analysis, constrained by a hidden Markov model. The time-frequency representation of a produced musical note can be expressed by the model as a temporal sequence of spectral templates which can also be shifted over log-frequency. Thus, this approach can be effectively used for pitch detection in music signals that contain amplitude and frequency modulations. Experiments were performed using extracted sequences of spectral templates on monophonic music excerpts, where the proposed model outperforms a non-temporally constrained convolutive model for pitch detection. Finally, future directions are given for multipitch extensions of the proposed model

Multiple-instrument polyphonic music transcription using a convolutive probabilistic model

(Abstract to follow

DScent Final Report

DScent was a joint project between five UK universities combining research theories in the disciplines of computational inference, forensic psychology and expert decision-making in the area of counter-terrorism. This document discusses the work carried out by Leeds Metropolitan University which covers the research, design and development work of an investigator support system in the area of deception using artificial intelligence. For the purposes of data generation along with system and hypothesis testing the project team devised two closed world games, the Cutting Corners Board Game and the Location Based Game. DScentTrail presents the investigator with a ‘scent trail’ of a suspect’s behaviour over time, allowing the investigator to present multiple challenges to a suspect from which they may prove the suspect guilty outright or receive cognitive or emotional clues of deception (Ekman 2002; Ekman & Frank 1993; Ekman & Yuille 1989; Hocking & Leathers 1980; Knapp & Comadena 1979). A scent trail is a collection of ordered, relevant behavioural information over time for a suspect. There are links into a neural network, which attempts to identify deceptive behavioural patterns of individuals. Preliminary work was carried out on a behavioural based AI module which would work separately alongside the neural network, with both identifying deception before integrating their results to update DScentTrail. Unfortunately the data that was necessary to design such a system was not provided and therefore, this section of research only reached its preliminary stages. To date research has shown that there are no specific patterns of deceptive behaviour that are consistent in all people, across all situations (Zuckerman 1981). DScentTrail is a decision support system, incorporating artificial intelligence (AI), which is intended to be used by investigators and attempts to find ways around the problem stated by Zuckerman above

The impact of HIV and AIDS on Africa's economic development

The macroeconomic effects of HIV/AIDS in Africa are substantial, and policies fill. dealing with them may be controversial-one is whether expensive antiretroviral drugs Should be targeted at economically productive groups of people. The authors review the evidence and consider how economic theory can contribute to our response to the pandemic