Crowdsourced testing is increasingly dominant in mobile application (app) testing, but it is a great burden for app developers to inspect the incredible number of test reports. Many researches have been proposed to deal with test reports based only on texts or additionally simple image features. However, in mobile app testing, texts contained in test reports are condensed and the information is inadequate. Many screenshots are included as complements that contain much richer information beyond texts. This trend motivates us to prioritize crowdsourced test reports based on a deep screenshot understanding.In this paper, we present a novel crowdsourced test report prioritization approach, namely D e e p P r io r . We first represent the crowdsourced test reports with a novelly introduced feature, namely D e e p F e a t u r e , that includes all the widgets along with their texts, coordinates, types, and even intents based on the deep analysis of the app screenshots, and the textual descriptions in the crowdsourced test reports. D e e p F e a t u r e includes the Bug Feature, which directly describes the bugs, and the Context Feature, which depicts the thorough context of the bug. The similarity of the D e e p F e a t u r e is used to represent the test reports' similarity and prioritize the crowdsourced test reports. We formally define the similarity as D e e p Si m i l a r i t y . We also conduct an empirical experiment to evaluate the effectiveness of the proposed technique with a large dataset group. The results show that D e e p P r io r is promising, and it outperforms the stateof-the-art approach with less than half the overhead.
The best career goals are determined in accordance with their own careers by thoroughly analyzing and weighing their own interests, hobbies, abilities, and characteristics in light of their own career’s subjective and objective conditions, as well as by measuring, analyzing, and summarizing those conditions. To accomplish this, we follow the trend and develop sensible plans. This study investigates career planning and the development of creative and business-minded skills using well-respected neural network technology. According to the research, the neural network technique in this study has the highest accuracy of the three ways. The maximum accuracy rate can reach 78.3%, according to the trend in the figure. Data mining is next with the highest accuracy rate at 62.9%, and big data is last with the highest accuracy rate at just 46.7%. As can be seen, this approach is more suited for evaluating the correctness of theoretical knowledge. Shaanxi can optimize the benefits of higher education and improve students’ understanding of career planning and social competitiveness by incorporating the concept of career management into the practice of developing applied abilities.
Quorum sensing (QS) plays a critical role in the enhancement of biofilm performance at low temperatures. In this study, a uni-QS (Sphingomonas rubra) and three bisynergistic-QS [composed of S. rubra and one of the isolated QS aerobic denitrifying bacteria (QS-ADB) Pseudomonas mendocina QQQC, P. mendocina ABDF, and Enterobacter aerogenes] were added to biofilm reactors in the start-up phase at 8 °C. Results indicate that compared to the control reactor, bisynergistic-QS exhibited maximum TN removal efficiency (93.50 ± 5.44, 93.97 ± 3.93, and 92.35 ± 5.04%), largest functional genes amoA, narG, and norB abundance (15-fold, 15-fold, and 75-fold), and uprising C10-HSL and C14-HSL concentrations (22.32-to 39.28-fold and 3.87-to 8.66-fold). Network analysis revealed that the biofilm thickness which possessed strong relationships with its dominant bacteria might contribute to great performance in bisynergistic-QS reactors under low temperature and increased loading rate. Proteobacteria play an important role in the AHL-based QS system. The abundance of keystone taxa Raoultella, Micropruina, and Zoogloea deserved more attention utilizing exogenous aerobic denitrifier addition. This study provided a helpful guideline for the design of exogenously enhancing biofilm denitrification systems at low temperatures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.