MuseCoco: Generating Symbolic Music from Text

Generating music from text descriptions is a user-friendly mode since the text is a relatively easy interface for user engagement. While some approaches utilize texts to control music audio generation, editing musical elements in generated audio is challenging for users. In contrast, symbolic music offers ease of editing, making it more accessible for users to manipulate specific musical elements. In this paper, we propose MuseCoco, which generates symbolic music from text descriptions with musical attributes as the bridge to break down the task into text-to-attribute understanding and attribute-to-music generation stages. MuseCoCo stands for Music Composition Copilot that empowers musicians to generate music directly from given text descriptions, offering a significant improvement in efficiency compared to creating music entirely from scratch. The system has two main advantages: Firstly, it is data efficient. In the attribute-to-music generation stage, the attributes can be directly extracted from music sequences, making the model training self-supervised. In the text-to-attribute understanding stage, the text is synthesized and refined by ChatGPT based on the defined attribute templates. Secondly, the system can achieve precise control with specific attributes in text descriptions and offers multiple control options through attribute-conditioned or text-conditioned approaches. MuseCoco outperforms baseline systems in terms of musicality, controllability, and overall score by at least 1.27, 1.08, and 1.32 respectively. Besides, there is a notable enhancement of about 20% in objective control accuracy. In addition, we have developed a robust large-scale model with 1.2 billion parameters, showcasing exceptional controllability and musicality

Improving feature location using structural similarity and iterative graph mapping

Locating program element(s) relevant to a particular feature is an important step in efficient maintenance of a software system. The existing feature location techniques analyze each feature independently and perform a one-time analysis after being provided an initial input. As a result, these techniques are sensitive to the quality of the input. In this paper, we propose to address the above issues in feature location using an iterative context-aware approach. The underlying intuition is that features are not independent of each other, and the structure of source code resembles the structure of features. The distinguishing characteristics of the proposed approach are: (1) it takes into account the structural similarity between a feature and a program element to determine feature-element relevance; (2) it employs an iterative process to propagate the relevance of the established mappings between a feature and a program element to the neighboring features and program elements. We evaluate our approach using two different systems, DirectBank, a small-scale industry financial system, and Linux kernel, a large-scale open-source operating system. Our evaluation suggests that the proposed approach is more robust and can significantly increase the recall of feature location with only a minor decrease of precision

Mediators for sustainable livelihoods : Promoting sustainable livelihoods in vocational and adult education through university curricula and programs

This preliminary study was accomplished during November 2021-March 2022, as part of “global innovation networks in teaching and learning”-initiative of the Finnish Ministry of Education and Culture, carried out by a team of senior and junior researchers from Finland, China, Tanzania, Kenya and Uganda. The ambition of the study was to clarify conceptual and methodological framework for future co-creative collaboration between key university and non-university actors, towards development and implementation strategies and practices of research-based curricula and programs, to shape expertise for sustainable livelihoods in vocational and adult education. Sustainable development has become a universal aim in national and supranational economic, social, and educational agencies and belongs to the repertoire of industries, businesses and civil society organisations. The study assumes that social metabolism - the material and energy flows by social organisations of different scales - is potentially the foremost concept for sustainable development. The combat against unsustainable social metabolism happens in local and planetary organisation of work, industries, social and political life. For humans, sustainable social metabolism means livelihood in collectives or assemblies of humans and nonhumans. Although directly targeting this, vocational and adult education have remained marginal in policies and discourse of sustainable development. While universities are prime institutions shaping agendas and expertise for political, economic and social development, we ask why they are ignoring vocational and adult education, despite their critical function for sustainable livelihoods. We hypothesise the impact of the established, taken-for-granted principles and practices in curriculum and program development and implementation, which overlook interaction and collaboration with non-university actors. From our experiences, we find this critical for analysing, understanding and shaping research-based expertise for sustainable livelihoods in vocational and adult education

Metatranscriptomics and Pyrosequencing Facilitate Discovery of Potential Viral Natural Enemies of the Invasive Caribbean Crazy Ant, Nylanderia pubens

BACKGROUND: Nylanderia pubens (Forel) is an invasive ant species that in recent years has developed into a serious nuisance problem in the Caribbean and United States. A rapidly expanding range, explosive localized population growth, and control difficulties have elevated this ant to pest status. Professional entomologists and the pest control industry in the United States are urgently trying to understand its biology and develop effective control methods. Currently, no known biological-based control agents are available for use in controlling N. pubens. METHODOLOGY AND PRINCIPAL FINDINGS: Metagenomics and pyrosequencing techniques were employed to examine the transcriptome of field-collected N. pubens colonies in an effort to identify virus infections with potential to serve as control agents against this pest ant. Pyrosequencing (454-platform) of a non-normalized N. pubens expression library generated 1,306,177 raw sequence reads comprising 450 Mbp. Assembly resulted in generation of 59,017 non-redundant sequences, including 27,348 contigs and 31,669 singlets. BLAST analysis of these non-redundant sequences identified 51 of potential viral origin. Additional analyses winnowed this list of potential viruses to three that appear to replicate in N. pubens. CONCLUSIONS: Pyrosequencing the transcriptome of field-collected samples of N. pubens has identified at least three sequences that are likely of viral origin and, in which, N. pubens serves as host. In addition, the N. pubens transcriptome provides a genetic resource for the scientific community which is especially important at this early stage of developing a knowledgebase for this new pest