Github is a very popular collaborative software-development platform that provides typical source-code management and issue tracking features augmented by strong social-networking features such as following developers and watching projects. These features help "spread the word" about individuals and projects, building the reputation of the former and increasing the popularity of the latter. In this paper, we investigate the relation between project popularity and regular, consistent documentation updates. We found strong indicators that consistently popular projects exhibited consistent documentation effort and that this effort tended to attract more documentation collaborators. We also found that frameworks required more documentation effort than libraries to achieve similar adoption success, especially in the initial phase.

In previous work by Alipour et al., a methodology was proposed for detecting duplicate bug reports by comparing the textual content of bug reports to subject-specific contextual material, namely lists of software-engineering terms, such as non-functional requirements and architecture keywords. When a bug report contains a word in these word-list contexts, the bug report is considered to be associated with that context and this information tends to improve bug-deduplication methods.In this paper, we propose a method to partially automate the extraction of contextual word lists from software-engineering literature. Evaluating this software-literature context method on real-world bug reports produces useful results that indicate this semi-automated method has the potential to substantially decrease the manual effort used in contextual bug deduplication while suffering only a minor loss in accuracy.Index Terms-duplicate bug reports; information retrieval; software engineering textbooks; machine learning; software literature; documentation.978-1-4799-8469-5/15/$31.00 c 2015 IEEE SANER 2015, Montréal, Canada

Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments. In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management. While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together. We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model. Our implementation can be found at https://github. com/kaggarwal/ClinicalNotesICU.

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One of the key challenges in predictive maintenance is to predict the impending downtime of an equipment with a reasonable prediction horizon so that countermeasures can be put in place. Classically, this problem has been posed in two different ways which are typically solved independently: (1) Remaining useful life (RUL) estimation as a long-term prediction task to estimate how much time is left in the useful life of the equipment and (2) Failure prediction (FP) as a short-term prediction task to assess the probability of a failure within a prespecified time window. As these two tasks are related, performing them separately is sub-optimal and might results in inconsistent predictions for the same equipment. In order to alleviate these issues, we propose two methods: Deep Weibull model (DW-RNN) and multi-task learning (MTL-RNN). DW-RNN is able to learn the underlying failure dynamics by fitting Weibull distribution parameters using a deep neural network, learned with a survival likelihood, without training directly on each task. While DW-RNN makes an explicit assumption on the data distribution, MTL-RNN exploits the implicit relationship between the longterm RUL and short-term FP tasks to learn the underlying distribution. Additionally, both our methods can leverage the non-failed equipment data for RUL estimation. We demonstrate that our methods consistently outperform baseline RUL methods that can be used for FP while producing consistent results for RUL and FP. We also show that our methods perform at par with baselines trained on the objectives optimized for either of the two tasks.

Psoralen + ultraviolet A (PUVA) therapy is an established modality for psoriasis. As India is a tropical country that has good availability of natural sunlight psoralen + sunlight (PUVAsol) may be a more convenient option. To compare the efficacy and cost-effectiveness of PUVA versus PUVAsol in chronic plaque psoriasis. Cases of chronic plaque psoriasis with body surface area ≥10% or Psoriasis Area and Severity Index (PASI) ≥10, excluding erythrodermic or pustular psoriasis, were randomized to receive either PUVA or PUVAsol, with endpoint being the achievement of PASI 90 or completion of 12 weeks treatment, whichever is earlier. Cost analysis was also undertaken. Thirty-six cases (16 in PUVA and 20 in PUVAsol group) completed treatment. In the PUVA group, 15 cases (93.75%) responded to therapy while in the PUVAsol group, 15 (75%) responded (P = 0.29). Mean baseline PASI in the PUVA and PUVAsol groups was 16 and 14.4, respectively, and at endpoint was 1.62 and 3.77. There was a significantly greater reduction in PASI in the PUVA group at 2 and 4 weeks but at 8 and 12 weeks and endpoint, it was comparable. Treatment failure occurred in 6.25% and 25% of cases respectively (P = 0.29). Side effects were higher with PUVA. Total cost of therapy was significantly higher in the PUVA group (P = 0.002). Cost-effectiveness ratio was US$0.72 with PUVA and US$0.37 with PUVAsol. Both PUVA and PUVAsol were equally efficacious, with PUVAsol being twice as cost effective. Hence, PUVAsol may be recommended as treatment for psoriasis in a developing economy such as India.

Time-series forecasting is a significant discipline of data modeling where past observations of the same variable are analyzed to predict the future values of the time series. Its prominence lies in different use cases where it is required, including economic, weather, stock price, business development, and other use cases. In this work, a review was conducted on the methods of analyzing time series starting from the traditional linear modeling techniques until the automated machine learning (AutoML) frameworks, including deep learning models. The objective of this review article is to support identifying the time-series forecasting challenge and the different techniques to meet the challenge. This work can be additionally an assist and a reference for researchers and industries demanding to use AutoML to solve the problem of forecasting. It identifies the gaps of the previous works and techniques used to solve the problem of forecasting time series.

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