Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Mergers & acquisitions : post-merger financial performance in Singapore and Malaysia

This dissertation researches mergers and acquisitions (M and A) in the context of Singapore and Malaysia and attempts to emulate previous studies in the United States and United Kingdom to ascertain the pre and post-merger financial performance of Singapore and Malaysia M and As. It examines both book value (accounting data) performance (with and without market-adjustment) as well as market value (stock market) performance of acquiring and acquired firms.Master of Business Administration (Accountancy

Tomato Automation Cultivation System: Automatize Watering and Fertilizer Based On Sensory Information

This research is to build a tomato watering and fertilizing machine for household-based agriculture. The objective is to reduce the work of planting the tomato tree, keeping the tomato tree stays healthily, and increase the interest of the people on the innovative agriculture in the household. This project aims to increase the efficiency of planting tomato, by reducing the tomato growth period and promoting the innovative way of planting. The planting of tomato tree in the household environment has a high chance of suffering diseases such as black spot disease, mould leaf disease, and yellow leaf disease, due to reasons of poorly controlling of temperature, watering, and humanity. The outcome of this automated cultivation machine can prevent the tomato trees away from the above-mentioned diseases. In conclusion, the automated cultivation machine provides an eco-farming environment that closes to the natural environment and the tomato tree not only grow healthy but also speed up the growing process in the machine. Moreover, the size of the machine is suited to household and promotes the interest of the people in household-based agriculture