Abstract Music is the art of ‘language of emotions’. Recently, music mood recognition is an emerging task. An efficient supervised framework for music mood recognition using autoencoder‐based optimised support vector regression (SVR) model is developed for the music emotion recognition. Our main intention is to increase the accuracy of emotion classification of music by considering text‐dependent and non‐text‐dependent features. For the high level feature representation, stacked autoencoder is used with two hidden layers. Modified K‐Medoid‐based brain storm optimisation‐based support vector regression (SVR_KMBSO) model is utilised for the emotion classification. Using the K‐Medoid‐based brain storm algorithm, the optimal parameters of the SVR are selected. The proposed framework utilises ISMIR2012 dataset and NJU_V1 dataset for English and for Hindi; online songs are also gathered and used for the music mood recognition. All the three datasets include songs based on four emotions like happy, angry, relax and sad. The experimental results are evaluated and compared with the existing classifiers including SVR, deep belief network (DBN) and Recurrent neural network (RNN). The proposed method SVR_KMBSO achieved high accuracy using three different datasets.

SUMMARY: Glycoinformatics plays a major role in glycobiology research, and the development of a comprehensive glycoinformatics knowledgebase is critical. This application note describes the GlyGen data model, processing workflow and the data access interfaces featuring programmatic use case example queries based on specific biological questions. The GlyGen project is a data integration, harmonization and dissemination project for carbohydrate and glycoconjugate-related data retrieved from multiple international data sources including UniProtKB, GlyTouCan, UniCarbKB and other key resources. AVAILABILITY AND IMPLEMENTATION: GlyGen web portal is freely available to access at https://glygen.org. The data portal, web services, SPARQL endpoint and GitHub repository are also freely available at https://data.glygen.org, https://api.glygen.org, https://sparql.glygen.org and https://github.com/glygener, respectively. All code is released under license GNU General Public License version 3 (GNU GPLv3) and is available on GitHub https://github.com/glygener. The datasets are made available under Creative Commons Attribution 4.0 International (CC BY 4.0) license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Chickpea (Cicer arietinum) is a widely grown legume crop in tropical, sub-tropical and temperate regions. Molecular breeding approaches seem to be essential for enhancing crop productivity in chickpea. Until recently, limited numbers of molecular markers were available in the case of chickpea for use in molecular breeding. However, the recent advances in genomics facilitated the development of large scale markers especially SSRs (simple sequence repeats), the markers of choice in any breeding program. Availability of genome sequence very recently opens new avenues for accelerating molecular breeding approaches for chickpea improvement. DESCRIPTION: In order to assist genetic studies and breeding applications, we have developed a user friendly relational database named the Chickpea Microsatellite Database (CicArMiSatDB http://cicarmisatdb.icrisat.org). This database provides detailed information on SSRs along with their features in the genome. SSRs have been classified and made accessible through an easy-to-use web interface. CONCLUSIONS: This database is expected to help chickpea community in particular and legume community in general, to select SSRs of particular type or from a specific region in the genome to advance both basic genomics research as well as applied aspects of crop improvement.

The third generation CMT (chip multithreaded) microprocessor from Sun Microsystems has 16 cores and is optimized for high throughput without compromising high single thread performance. This paper describes the unique challenges faced in DFX of this complex CMT processor and the DFX solutions deployed. Some of the notable new DFX features include a highly configurable scan architecture, a memory test network that leverages functional access paths, BIST of special memories, and a test mode for running functional tests in the presence of non-deterministic serdes interfaces. Identification of chips with partially good cores and caches is supported in manufacturing for yield and in the field for availability.

We propose a fast video transcoding technique based on region-of-interest (ROI) determination. The ROIs are identified using the properties of the human visual system (HVS), applied in the compressed domain. We use the edge, motion and spatial frequency content of the video frame as the parameters in identifying perceptually important regions in the compressed domain (Agarwal, G. et al., 2003). The TM5 rate-control of the video transcoder is modified to assign relatively more bits to the ROIs, thereby providing better quality to the areas that are likely to attract the viewer's attention. The ROI determination in the compressed domain has been modified to give more robust results. Our algorithm achieves a speed-up of 15-20 times as compared to similar algorithms in the pixel domain, while giving comparable results. This computation is only around 8% of the total transcoder complexity.

We propose a fast, yet simple algorithm to find the region of interest (ROI) from a compressed MPEG video bitstream, with partial decoding. We have used the properties of the human visual system (HVS), applied in the DCT domain, to extract the ROIs. Though a lot of work has been done to obtain ROIs in images based on the HVS, all these systems work in the pixel domain. However, finding the ROIs in the compressed domain has wide applications such as emphasizing perceptually important regions while transrating, defining descriptors for encoded video (MPEG-7), changing image size to adapt to heterogeneous client displays, etc.

This paper discusses the implementation of a very low bit rate video codec (coder-decoder) which achieves a bit rate of as low as about 25 Kbps for test sequences having frame rate of 7 fps and frame size of 256×256 pixels. We have employed techniques like Comer matching and tracking for motion prediction that is instrumental in reducing the bitrate and the optimized rainfalling watershed algorithm results in better and faster image segmentation. We also discuss the results of incorporating Smart (yet simple and flexible) Error Resiliency features like Reversible Variable Length Coding and Resynchronization Markers into out very low bit-rate video codec.