Strasheela: Design and usage of a music composition environment based on the Oz programming model

Strasheela provides a means for the composer to create a symbolic score by formally describing it in a rule-based way. The environment defines a rich music representation for complex polyphonic scores. Strasheela enables the user to define expressive compositional rules and then to apply them to the score. Compositional rules can restrict many aspects of the music - including the rhythmic structure, the melodic structure and the harmonic structure - by constraining the parameters (e.g. duration or pitch) of musical events according to some numerical or logical relation. Strasheela combines this expressivity with efficient search strategies.

Towards an intelligent score following system: Handling of mistakes and jumps encountered during piano practicing

Score following has been an important area of research in AI and music since the mid 80's. Various systems were developed, but they were predominantly for providing automated accompaniment to live concert performances, dealing mostly with issues relating to pitch detection and identification of embellished melodies. They have a big potential in the area of education where student performers benefit in practice situations. Current accompaniment systems are not designed to deal with errors that may occur during practising. In this paper we present a system developed to provide accompaniment for students practising at home. First a survey of score following will be given. Then the capabilities of the system will be explained, and the results from the first experiments of the monophonic score following system will be presented.

Creating a spontaneous conversational speech corpus

Speech recognition and language analysis of spontaneous speech arising in naturally spoken conversations are becoming the subject of much research. However, there is a shortage of spontaneous speech corpora that are freely available for academics. We therefore undertook the building of a natural conversation speech database, recording over 200 hours of conversations in English by over 600 local university students. With few exceptions, the students used their own cell phones from their own rooms or homes to speak to one another, and they were permitted to speak on any topic they chose. Although they knew that they were being recorded and that they would receive a small payment, their conversations in the corpus are probably very close to being natural and spontaneous. This paper describes a detailed case study of the problems we faced and the methods we used to make the recordings and control the collection of these social science data on a limited budget.

Posting Incognito ... Males with Eating Problems: Online Emotional Expression and Support

Qualitative research in the area of eating disorders (eds) has predominantly focused on females,whilst the experiences of males’ remains poorly understood. due to the secretive nature of eating problems/eds it can be difficult to explore the experiences of males with these problems; however, online support groups/message boards, which are common and popular, provide a non-invasive
forum for researchers to conduct research. This study analyzed naturally occurring discussions on an internet message board dedicated to males and eating problems using content analysis. Two major overarching themes of emotional expression (sharing feelings of disturbed eating attitudes and emotions; being secretive) and support (informational and emotional) were identified. The message board provided a vital support system for this group, suggesting that online message boards may be an important avenue for health professionals to provide information, support, and advice.

Lightweight Risk Management in Agile projects

Risk management in software engineering has become a recognized project management practice but it seems that not all companies are systematically applying it. At the same time, agile methods have become popular, partly because proponents claim that agile methods implicitly reduce risks due
to, for example, more frequent and earlier feedback, shorter periods of development time and easier prediction of cost. Therefore, there is a need to investigate how risk management can be usable in iterative and evolutionary software development processes. This paper investigates the gathering of empirical data on risk management from the project environment and presents
a novel approach to manage risk in agile projects. Our approach is based on a prototype tool, Agile Risk Tool (ART). This tool reduces human effort in risk management by using software agents to identify, assess and monitor risk, based on input and data collected from the project environment and by applying
some designated rules. As validation, groups of student project data were used to provide evidence of the efficacy of this approach. We demonstrate the approach and the feasibility of using a lightweight risk management tool to alert, assess and monitor risk with reduced human effort.

Behavioral Model Generation from Use Cases Based on Ontology Mapping and GRASP Patterns

This paper contributes a new approach for developing UML software designs from Natural Language (NL), making use of a meta-domain oriented ontology, well established software design principles and Natural Language Processing (NLP) tools. In the approach described here, banks of grammatical rules are used to assign event flows from essential use cases. A domain specific ontology is also constructed, permitting semantic mapping between the NL input and the modeled domain. Rules based on the widely-used General Responsibility Assignment Software Principles (GRASP) are then applied to derive behavioral models.

Hierarchical demand response using dantzig-wolfe decomposition

Demand Side Management (DSM) programmes are designed to shift electrical loads from peak times. Demand Response (DR) algorithms automate this process for controllable loads. DR can be implemented explicitly in terms of Peak to Average Ratio Reduction (PARR), in which case the maximum peak load is minimised over a prediction horizon by manipulating the amount of energy given to controllable loads at different times. A hierarchical predictive PARR algorithm is presented here based on Dantzig-Wolfe decomposition. © 2013 IEEE.

Fair charging strategies for EVs connected to a low-voltage distribution network

The development of smart grid technologies and appropriate charging strategies are key to accommodating large numbers of Electric Vehicles (EV) charging on the grid. In this paper a general framework is presented for formulating the EV charging optimization problem and three different charging strategies are investigated and compared from the perspective of charging fairness while taking into account power system constraints. Two strategies are based on distributed algorithms, namely, Additive Increase and Multiplicative Decrease (AIMD), and Distributed Price-Feedback (DPF), while the third is an ideal centralized solution used to benchmark performance. The algorithms are evaluated using a simulation of a typical residential low voltage distribution network with 50% EV penetration. © 2013 IEEE.

A standing ovation for Nigel: An informal study

This article analyses a series of emails thanking Nigel for his stewardship of JASSS and the characteristics of their authors. It identifies a correlation between two measures of author activity in social simulation research, but no pattern between these activity measures and the email timing. Instead, the sequence suggests a classic standing ovation effect.

A spatial approach to network generation for three properties:Degree distribution, clustering coefficient and degree assortativity

Social networks generally display a positively skewed degree distribution and higher values for clustering coefficient and degree assortativity than would be expected from the degree sequence. For some types of simulation studies, these properties need to be varied in the artificial networks over which simulations are to be conducted. Various algorithms to generate networks have been described in the literature but their ability to control all three of these network properties is limited. We introduce a spatially constructed algorithm that generates networks with constrained but arbitrary degree distribution, clustering coefficient and assortativity. Both a general approach and specific implementation are presented. The specific implementation is validated and used to generate networks with a constrained but broad range of property values. © Copyright JASSS.