Abstract
Bug localization (BL) from the bug report is the strategic activity of the software maintaining process. Because BL is a costly and tedious activity, BL techniques information retrieval-based and machine learning-based could aid software engineers. We propose a method for BUg Localization with word embeddings and Network Regularization (BULNER). The preliminary results suggest that BULNER has better performance than two state-of-the-art methods.

Abstract
Consumer High Dynamic Range (HDR) displays are appearing on the market. Capable of generating a peak luminance of up to 2,000 nits, the improved dynamic range they provide can only be perceived when viewed in a dark environment. In this paper, we present a display architecture that is capable of generating a peak luminance of 10,000 nits. We demonstrate, with a subjective evaluation, that the increased peak luminance is required to perceive a high dynamic range in bright ambient environments. Furthermore, we show that by fitting a surface through the data, we can predict the dynamic range that can be perceived from the luminance and illuminance with low error. We can also invert the prediction to estimate the required peak luminance for a particular combination of dynamic range and ambient lighting.

Abstract
Managing large volumes of digital documents along with the information they contain, or are associated with, can be challenging. As systems become more intelligent, it increasingly makes sense to power retrieval through all available data, where every lead makes it easier to reach relevant documents or entities. Modern search is heavily powered by structured knowledge, but users still query using keywords or, at the very best, telegraphic natural language. As search becomes increasingly dependent on the integration of text and knowledge, novel approaches for a unified representation of combined data present the opportunity to unlock new ranking strategies. We tackle entity-oriented search using graph-based approaches for representation and retrieval. In particular, we propose the graph-of-entity, a novel approach for indexing combined data, where terms, entities and their relations are jointly represented. We compare the graph-of-entity with the graph-of-word, a text-only model, verifying that, overall, it does not yet achieve a better performance, despite obtaining a higher precision. Our assessment was based on a small subset of the INEX 2009 Wikipedia Collection, created from a sample of 10 topics and respectively judged documents. The offline evaluation we do here is complementary to its counterpart from TREC 2017 OpenSearch track, where, during our participation, we had assessed graph-of-entity in an online setting, through team-draft interleaving.

Abstract
Purpose: E-learning has been revealed as an a useful tool among continuing education within health professionals, namely for radiographers or radiologic technologists. Therefore like traditional learning, this teaching approach needs continuous evaluation in order to validate its effectiveness and impact. Kirkpatrick's model has been widely used for this purpose by health information management instructors. Our aim was to assess an E-learning Course on Breast Imaging for radiographers based on the first three levels of Kirkpatrick's framework: reaction, learning and behaviour. Methods and materials: An E-learning course was developed for radiographers in order to provide an easy-to-understand, succinct and current overview in breast imaging, namely mammography technique and image interpretation. The program structure were built based on the guidelines proposed by the European Society of Breast Cancer Specialists (EUSOMA). Learner's satisfaction was assessed through a questionnaire and Knowledge gain was assessed using pre- and post-testing. After 6 months of complying the course, the learners were contacted through a questionnaire in order to give feedback on whether their behaviour changed in workplace. Results: Two editions of the breast imaging course were performed by 64 learners. In general, 97% of the learners stated that the program content was very good and excellent, all learners considered the content was delivered in a very good or excellent way. High percentages of learners stated to be satisfied with the distribution of the content among each module (94%) and 86% of learners stated that your level of dedication was high or very high. Concerning improvement of knowledge, we found an evolution of 4 percentual points between pre and post-tests (p = 0,001). The learners have identified two main changes on their practice, the first one related with patient care, improving communications and positioning skills and the second one related with image interpretation, improving the image processing and analyses. Conclusion: These global results show that e-learning can provide statistically relevant knowledge gains in Radiographers. This course is an important contribution to the improvement of mammography education, impacting on the development of students' and radiographers' skills.

Abstract
A big concern in seeking to understand the importance of knowledge management initiatives within organizations is shown by the wide range of existing literature. This paper aims to present the relevant information on how to implement a knowledge academy within industrial organizations, the main goal being to reflect on what are the best practices on knowledge capture and transfer. First, a framework on knowledge management to support the problem contextualization and objectives regarding knowledge initiatives is presented. Next, a knowledge academy will be implemented in an industrial company following some good practices reported in the literature. Finally, and based on the experience of implementing this project, a knowledge academy methodology to capture and transfer critical knowledge, detailing the best practices in each implementation phase is proposed and detailed. The results revealed the importance of having a structured plan and guidelines aligned with the overall organizational strategy. In fact, this alignment is the key to the efficiency and effectiveness of knowledge management initiatives.

Abstract
Software Operational Profile (SOP) is a software specification based on how users use the software. This specification corresponds to a quantitative representation of software that identifies the most used software parts. As software reliability depends on the context in which users operate the software, ones use SOP in software reliability engineering. However, there are evidence of a misalignment between the software tested parts and SOP. Therefore, this paper investigates a possible misalignment between SOP and the tested software parts to obtain, based on experimental data, more evidence of this misalignment. We performed an exploratory study composed of three activities to verify: a) whether there are significant variations in how users operate the software; b) whether there is a misalignment between SOP and the tested software parts; c) if failures occur in untested SOP parts in case of misalignment. To perform these verifications, we defined the term”test profile” and presented it in this paper. We instrumented three software to collect data from them while the users were operating this software. Posteriorly, we analyzed these collected data in an attempt to reach the goals of this paper. To evaluate the originality of this research, we performed a Literature Systematic Review (SLR) and presented its conclusions. The obtained results evidence that there are significant variations in how users operate the software and also that there was a misalignment between SOP and the tested software parts when we evaluated the three software mentioned above. There were also indications of the occurrence of failures in the untested SOP parts. These results indicate that SOP becomes relevant not only to software reliability engineering but also to contribute to testing activities, regardless of the adopted strategy.