Journal of Computing Science and Engineering
From the webpage:
Journal of Computing Science and Engineering (JCSE) is a peer-reviewed quarterly journal that publishes high-quality papers on all aspects of computing science and engineering. The primary objective of JCSE is to be an authoritative international forum for delivering both theoretical and innovative applied researches in the field. JCSE publishes original research contributions, surveys, and experimental studies with scientific advances.
The scope of JCSE covers all topics related to computing science and engineering, with a special emphasis on the following areas: embedded computing, ubiquitous computing, convergence computing, green computing, smart and intelligent computing, and human computing.
I got here from following a sponsor link at a bioinformatics conference.
Then just picking at random from the current issue I see:
A Fast Algorithm for Korean Text Extraction and Segmentation from Subway Signboard Images Utilizing Smartphone Sensors by Igor Milevskiy, Jin-Young Ha.
Abstract:
We present a fast algorithm for Korean text extraction and segmentation from subway signboards using smart phone sensors in order to minimize computational time and memory usage. The algorithm can be used as preprocessing steps for optical character recognition (OCR): binarization, text location, and segmentation. An image of a signboard captured by smart phone camera while holding smart phone by an arbitrary angle is rotated by the detected angle, as if the image was taken by holding a smart phone horizontally. Binarization is only performed once on the subset of connected components instead of the whole image area, resulting in a large reduction in computational time. Text location is guided by user’s marker-line placed over the region of interest in binarized image via smart phone touch screen. Then, text segmentation utilizes the data of connected components received in the binarization step, and cuts the string into individual images for designated characters. The resulting data could be used as OCR input, hence solving the most difficult part of OCR on text area included in natural scene images. The experimental results showed that the binarization algorithm of our method is 3.5 and 3.7 times faster than Niblack and Sauvola adaptive-thresholding algorithms, respectively. In addition, our method achieved better quality than other methods.
Secure Blocking + Secure Matching = Secure Record Linkage by Alexandros Karakasidis, Vassilios S. Verykios.
Abstract:
Performing approximate data matching has always been an intriguing problem for both industry and academia. This task becomes even more challenging when the requirement of data privacy rises. In this paper, we propose a novel technique to address the problem of efficient privacy-preserving approximate record linkage. The secure framework we propose consists of two basic components. First, we utilize a secure blocking component based on phonetic algorithms statistically enhanced to improve security. Second, we use a secure matching component where actual approximate matching is performed using a novel private approach of the Levenshtein Distance algorithm. Our goal is to combine the speed of private blocking with the increased accuracy of approximate secure matching.
A Survey of Transfer and Multitask Learning in Bioinformatics by Qian Xu, Qiang Yang.
Abstract:
Machine learning and data mining have found many applications in biological domains, where we look to build predictive models based on labeled training data. However, in practice, high quality labeled data is scarce, and to label new data incurs high costs. Transfer and multitask learning offer an attractive alternative, by allowing useful knowledge to be extracted and transferred from data in auxiliary domains helps counter the lack of data problem in the target domain. In this article, we survey recent advances in transfer and multitask learning for bioinformatics applications. In particular, we survey several key bioinformatics application areas, including sequence classification, gene expression data analysis, biological network reconstruction and biomedical applications.
And the ones I didn’t list from the current issue are just as interesting and relevant to identity/mapping issues.
This journal is a good example of people who have deliberately reached further across disciplinary boundaries than most.
About the only excuse for not doing so left is the discomfort of being the newbie in a field not your own.
Is that a good enough reason to miss possible opportunities to make critical advances in your home field? (Only you can answer that for yourself. No one can answer it for you.)