ScotMap : Participatory mapping of inshore fishing activity to inform marine spatial planning in Scotland

Acknowledgements The authors would like to thank all the fishermen interviewed who gave freely of their time, fisheries compliance staff, government scientists, contractors and fishing industry representatives for their heroic efforts conducting interviews. Furthermore, the authors would like to thank all the staff in Marine Scotland who staffed the data verification workshops. Lastly, the authors would also like to thank colleagues Gareth Jones, Robert Watret, and Liam Mason for their advice and support during the project. Marine Scotland has financially supported the data collection and conduct of research, as well as preparation and publishing of this article. The writing of this manuscript was also supported by the “Marine Collaboration Research Forum” writing retreat co-funded by Marine Scotland and the University of Aberdeen which took place in November 2015.Peer reviewedPublisher PD

Arithmetic--some drill aspects

Thesis (M.A.)--Boston Universit

Applications and Challenges of Task Mining: A Literature Review

Task mining is a technological innovation that combines current developments in process mining and data mining. Using task mining, the interactions of workers with their workstations can be recorded, processed, and linked with the business data of the organization. The approach can provide a holistic picture of the business processes and related tasks. Currently, there is no overview of application scenarios and the challenges of task mining. In our work, we reflect application scenarios as well as technological, legal, and organizational challenges of task mining using a structured literature review. The application areas include discovery of automation potentials, monitoring, as well as optimization of business processes. The challenges include the cleansing, collection, data protection, explainability, merging, organization, processing, and segmentation of task mining data

Accessibility in User Reviews for Mobile Apps: An Automated Detection Approach

In recent years, mobile accessibility has become an important trend with the goal of allowing all users the possibility of using any app without many restrictions. Recent work demonstrated that user reviews include insights that are useful for app evolution. However, with the increase in the amount of received reviews, manually analyzing them is tedious and time-consuming, especially when searching for accessibility reviews. The goal of this thesis is to support the automated identification of accessibility in user reviews, to help practitioners in prioritizing their handling, and thus, creating more inclusive apps. Particularly, we design a model that takes as input accessibility user reviews, learns their keyword-based features, in order to make a binary decision, for a given review, on whether it is about accessibility or not. The model is evaluated using a total of 5326 mobile app reviews. The findings show that (1) our approach can accurately identify accessibility reviews, outperforming two baselines, namely keyword-based detector and a random classifier; (2) our model achieves F1-measure of 90.7\% with relatively small training dataset; however, F1-measure value improves as we add to the training dataset

The Estimation of Product Standard Time by Artificial Neural Networks in the Molding Industry

Determination of exact standard time with direct measurement procedures is particularly difficult in companies which do not have an adequate environment suitable for time measurement studies or which produce goods requiring complex production schedules. For these companies new and special measurement procedures need to be developed. In this study, a new time estimation method based on different robust algorithms of artificial neural networks (ANNs) is developed. For the proposed method, the products that have similar production processes were chosen from among the whole product range within the cleansing department of a molding company. While using ANNs, to train the network, some of the chosen products' standard time that had been previously measured is used to estimate the standard time of the remaining products. The different ANN algorithms are trained and four of them, which are converged the data, are stated and compared in different architectures. In this way, it is concluded that this estimation method could be applied accurately in many similar processes using the relevant algorithms

Automating Data Science: Prospects and Challenges

Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process. Key insights: * Automation in data science aims to facilitate and transform the work of data scientists, not to replace them. * Important parts of data science are already being automated, especially in the modeling stages, where techniques such as automated machine learning (AutoML) are gaining traction. * Other aspects are harder to automate, not only because of technological challenges, but because open-ended and context-dependent tasks require human interaction.Comment: 19 pages, 3 figures. v1 accepted for publication (April 2021) in Communications of the AC

No stain, no pain – A multidisciplinary review of factors underlying domestic laundering

Today\u27s washing appliances are much more efficient than those of a decade ago, but the environmental benefits of this efficiency are counteracted by shifts in consumer behavior. Initiatives to reverse these shifts have often proven futile, indicating a basic lack of clarity on why we clean our clothes. This article is an explorative review with the aim of identifying dominant factors that shape how we do our laundry. The results can be used both as an introduction to laundry research in general, as well as a baseline for future interdisciplinary research. Three guiding principles are presented that describe the most influential factors underlying laundering: (1) technology changes conventions, while social context dictates technology acceptance; (2) technological solutions are often suggested to influence consumers, but individual concerns seem to override the effect of such interventions; (3) consumers are guided by social conventions, rooted in underlying psychological dynamics (e.g. moral dimensions of cleanliness). Looking at these principles it is understandable why interventions for sustainability are failing. Many interventions address only a part of a principle while disregarding other parts. For example, consumers are often informed of the importance of sustainability (e.g. “washing at lower temperature is good for the environment”), while questions of social belonging are left out (e.g. “many of your neighbors and friends wash at lower temperature”). To increase the possibility of a lasting change, it would be beneficial if instead all of the three principles could be addressed given the specific consumer group of interest