Constructing an Indigenous Model of the Self to Address Cultural and Mental Health Issues in the Canadian Subarctic

Since the last decades, academic research has paid much attention to the phenomenon of revitalizing indigenous cultures and, more precisely, the use of traditional indigenous healing methods both to deal with individuals' mental health problems and with broader cultural issues. The re-evaluation of traditional indigenous healing practices as a mode of psychotherapeutic treatment has been perhaps one of the most interesting sociocultural processes in the postmodern era. In this regard, incorporating indigenous forms of healing in a contemporary framework of indigenous mental health treatment should be interpreted not simply as an alternative therapeutic response to the clinical context of Western psychiatry, but also constitutes a political response on the part of ethno-cultural groups that have been stereotyped as socially inferior and culturally backward. As a result, a postmodern form of "traditional healing" developed with various forms of knowledge, rites and the social uses of medicinal plants, has been set in motion on many Canadian indigenous reserves over the last two decades.

Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation

Staff detection and removal is one of the most important issues in optical music recognition (OMR) tasks since common approaches for symbol detection and classification are based on this process. Due to its complexity, staff detection and removal is often inaccurate, leading to a great number of errors in posterior stages. For this reason, a new approach that avoids this stage is proposed in this paper, which is expected to overcome these drawbacks. Our approach is put into practice in a case of study focused on scores written in white mensural notation. Symbol detection is performed by using the vertical projection of the staves. The cross-correlation operator for template matching is used at the classification stage. The goodness of our proposal is shown in an experiment in which our proposal attains an extraction rate of 96 % and a classification rate of 92 %, on average. The results found have reinforced the idea of pursuing a new research line in OMR systems without the need of the removal of staff lines.