A microtonal wind controller building on Yamaha’s technology to facilitate the performance of music based on the “19-EDO” scale

We describe a project in which several collaborators adapted an existing instrument to make it capable of playing expressively in music based on the microtonal scale characterised by equal divsion of the octave into 19 tones (“19-EDO”). Our objective was not just to build this instrument, however, but also to produce a well-formed piece of music which would exploit it idiomatically, in a performance which would provide listeners with a pleasurable and satisfying musical experience. Hence, consideration of the extent and limits of the playing-techniques of the resulting instrument (a “Wind-Controller”) and of appropriate approaches to the composition of music for it were an integral part of the project from the start. Moreover, the intention was also that the piece, though grounded in the musical characteristics of the 19-EDO scale, would nevertheless have a recognisable relationship with what Dimitri Tymoczko (2010) has called the “Extended Common Practice” of the last millennium. So the article goes on to consider these matters, and to present a score of the resulting new piece, annotated with comments documenting some of the performance issues which it raises. Thus, bringing the project to fruition involved elements of composition, performance, engineering and computing, and the article describes how such an inter-disciplinary, multi-disciplinary and cross-disciplinary collaboration was co-ordinated in a unified manner to achieve the envisaged outcome. Finally, we consider why the building of microtonal instruments is such a problematic issue in a contemporary (“high-tech”) society like ours

Enhancing Undergraduate AI Courses through Machine Learning Projects

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class

Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning

We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of various complexities and analysed the alignment results quantitatively. We show the model has very good performance (AUC $\sim 1$ for simple data sets and AUC $\sim 0.85$ for very complex data sets). Further, our model easily outperforms existing algorithms on complex data sets. Compared with existing methods, ChromAlignNet is very easy to use as it requires no user input of reference chromatograms and parameters. This method can easily be adapted to other similar data such as those from liquid chromatography. The source code is written in Python and available online

Quality of Neonatal Health Care: Learning From Health Workers’ Experiences in Critical Care in Kilimanjaro Region, Northeast Tanzania

Neonatal deaths are generally attributed to suboptimal standards of health care. Health care worker motivation and adherence to existing guidelines are rarely studied. To assess the performance of health workers for neonatal health care in the hospitals of Kilimanjaro region. A descriptive study using a semi-structured interview for health care workers at a tertiary referral hospital and peripheral health facilities (regional referral, district hospitals and health centres).was used. Health Care Workers (HCW) were asked to recall a scenario of a critically ill neonate admitted in the wards and the treatment that was provided. The WHO Emergency Triage Assessment and Treatment (ETAT) guidelines were used as a standard reference for knowledge of critical care. Birth asphyxia was the most recalled health problem requiring critical care, reported by 27.5% of 120 HCW at both peripheral hospitals and by 46.4% of 28 health workers in tertiary referral centres. Half of the HCW commented on their own performance (47.5%, n=140). HCW presented with low to moderate levels of knowledge for critical care were at 92%. Supplementary training was associated with a higher levels of knowledge of neonatal critical care (p value = 0.05). HCW in peripheral hospital had lower levels of knowledge (only 44.7% at peripheral hospitals had sufficient ratings compared to 82.1% at the referral centre). [Pearson χ2 (2) = 12.10, p value = 0.002]. Guided Practical-Competence Diagnostic Specific neonatal health care training is highly needed in the peripheral facilities of rural Kilimanjaro region

Social Bots for Online Public Health Interventions

According to the Center for Disease Control and Prevention, in the United States hundreds of thousands initiate smoking each year, and millions live with smoking-related dis- eases. Many tobacco users discuss their habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that leverages machine learning to identify users posting pro-tobacco tweets and select individualized interventions to address their interest in tobacco use. We searched the Twitter feed for tobacco-related keywords and phrases, and trained a convolutional neural network using over 4,000 tweets dichotomously manually labeled as either pro- tobacco or not pro-tobacco. This model achieves a 90% recall rate on the training set and 74% on test data. Users posting pro- tobacco tweets are matched with former smokers with similar interests who posted anti-tobacco tweets. Algorithmic matching, based on the power of peer influence, allows for the systematic delivery of personalized interventions based on real anti-tobacco tweets from former smokers. Experimental evaluation suggests that our system would perform well if deployed. This research offers opportunities for public health researchers to increase health awareness at scale. Future work entails deploying the fully operational Notobot system in a controlled experiment within a public health campaign

Review of Western Australian drug driving laws

In 2007, the Western Australian Road Traffic Act 1974 was amended to allow for new police enforcement practices designed to reduce the incidence of drug driving. The Road Traffic Amendment (Drugs) Act 2007 made provision for two new offences: driving with the presence of a prescribed illicit drug in oral fluid or blood, and driving while impaired by a drug. The prescribed drugs were methamphetamine, methylenedioxymethamphetamine (MDMA or ecstasy) and delta-9-tetrahydrocannabinol (THC, the psychoactive compound in cannabis). As part of the new laws, statute 72A was inserted into the Act requiring that the Western Australian State Government undertake a review of the amended legislation after 12 months of operation. This report provides a review of the amended legislation and the associated drug driving law enforcement. It includes a process review of the roadside oral fluid testing and drug impaired driving enforcement programs; an analysis of testing, offence detection and legal penalty data pertaining to the first year of operation of the new drug enforcement measures; and a report on consultations with various stakeholders. These form the basis for recommendations on possible improvements to the processes and legislation related to the deterrence of driving after drug use among Western Australian drivers.J.E. Woolley and M.R.J. Baldoc