Information-Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory

EPSRC. Grant Number: EP/D038855/

Melody Generation using an Interactive Evolutionary Algorithm

Music generation with the aid of computers has been recently grabbed the attention of many scientists in the area of artificial intelligence. Deep learning techniques have evolved sequence production methods for this purpose. Yet, a challenging problem is how to evaluate generated music by a machine. In this paper, a methodology has been developed based upon an interactive evolutionary optimization method, with which the scoring of the generated melodies is primarily performed by human expertise, during the training. This music quality scoring is modeled using a Bi-LSTM recurrent neural network. Moreover, the innovative generated melody through a Genetic algorithm will then be evaluated using this Bi-LSTM network. The results of this mechanism clearly show that the proposed method is able to create pleasurable melodies with desired styles and pieces. This method is also quite fast, compared to the state-of-the-art data-oriented evolutionary systems.Comment: 5 pages, 4 images, submitted to MEDPRAI2019 conferenc

Negotiation in strategy making teams : group support systems and the process of cognitive change

This paper reports on the use of a Group Support System (GSS) to explore at a micro level some of the processes manifested when a group is negotiating strategy-processes of social and psychological negotiation. It is based on data from a series of interventions with senior management teams of three operating companies comprising a multi-national organization, and with a joint meeting subsequently involving all of the previous participants. The meetings were concerned with negotiating a new strategy for the global organization. The research involved the analysis of detailed time series data logs that exist as a result of using a GSS that is a reflection of cognitive theory

Music, computing and health: A roadmap for the current and future roles of music technology for health care and well-being.

Health and self-regulatio

Conflict of Interest Policies at Canadian Universities: Clarity and Content

[À l'origine dans / Was originally part of : ESPUM - Dép. médecine sociale et préventive - Travaux et publications]Abstract Discussions of conflict of interest (COI) in the university have tended to focus on financial interests in the context of medical research; much less attention has been given to COI in general or to the policies that seek to manage COI. Are university COI policies accessible and understandable? To whom are these policies addressed (faculty, staff, students)? Is COI clearly defined in these policies and are procedures laid out for avoiding or remedying such situations? To begin tackling these important ethical and governance questions, our study examines the COI policies at the Group of Thirteen (G13) leading Canadian research universities. Using automated readability analysis tools and an ethical content analysis, we begin the task of comparing the strengths and weaknesses of these documents, paying particular attention to their clarity, readability, and utility in explaining and managing COI.This study was supported by a grant from the Institute of Genetics of the Canadian Institutes of Health Researc

Accelerated Multi-Organization Conflict Resolution

In this paper, we discuss two situations where two organizations with different aims recognized the dysfunctionality of their relationship. In each of these cases, which were long running (6–8 months), the organizations had worked hard to resolve this dysfunctionality, and conflict, by organizing off-site meetings designed to resolve the conflict. These 1-day meetings failed. Subsequently Group Support System workshops were used for 1 day workshops and in each case the conflict was essentially resolved within 55 min. The research reported in this paper seeks to answer the question: what happened in these cases that led to a resolution of the conflict in such a short time period, given other attempts had failed? Specifically the paper explores the impact of the GSS used to facilitate two organizations seeking to resolve a conflictual situation

Evaluation of Musical Creativity and Musical Metacreation Systems

The field of computational creativity, including musical metacreation, strives to develop artificial systems that are capable of demonstrating creative behavior or producing creative artefacts. But the claim of creativity is often assessed, subjectively only on the part of the researcher and not objectively at all. This article provides theoretical motivation for more systematic evaluation of musical metacreation and computationally creative systems and presents an overview of current methods used to assess human and machine creativity that may be adapted for this purpose. In order to highlight the need for a varied set of evaluation tools, a distinction is drawn among three types of creative systems: those that are purely generative, those that contain internal or external feedback, and those that are capable of reflection and self-reflection. To address the evaluation of each of these aspects, concrete examples of methods and techniques are suggested to help researchers (1) evaluate their systems' creative process and generated artefacts, and test their impact on the perceptual, cognitive, and affective states of the audience, and (2) build mechanisms for reflection into the creative system, including models of human perception and cognition, to endow creative systems with internal evaluative mechanisms to drive self-reflective processes. The first type of evaluation can be considered external to the creative system and may be employed by the researcher to both better understand the efficacy of their system and its impact and to incorporate feedback into the system. Here we take the stance that understanding human creativity can lend insight to computational approaches, and knowledge of how humans perceive creative systems and their output can be incorporated into artificial agents as feedback to provide a sense of how a creation will impact the audience. The second type centers around internal evaluation, in which the system is able to reason about its own behavior and generated output. We argue that creative behavior cannot occur without feedback and reflection by the creative/metacreative system itself. More rigorous empirical testing will allow computational and metacreative systems to become more creative by definition and can be used to demonstrate the impact and novelty of particular approaches