| 1. |
- Maska, Martin, et al.
(författare)
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A benchmark for comparison of cell tracking algorithms
- 2014
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 30:11, s. 1609-1617
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. Results: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately.
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| 2. |
- Pastor-Escuredo, David, et al.
(författare)
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An Ethical Framework for Artificial Intelligence and Sustainable Cities
- 2022
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Ingår i: AI. - : MDPI AG. - 2673-2688. ; 3:4, s. 961-974
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The digital revolution has brought ethical crossroads of technology and behavior, especially in the realm of sustainable cities. The need for a comprehensive and constructive ethical framework is emerging as digital platforms encounter trouble to articulate the transformations required to accomplish the sustainable development goal (SDG) 11 (on sustainable cities), and the remainder of the related SDGs. The unequal structure of the global system leads to dynamic and systemic problems, which have a more significant impact on those that are most vulnerable. Ethical frameworks based only on the individual level are no longer sufficient as they lack the necessary articulation to provide solutions to the new systemic challenges. A new ethical vision of digitalization must comprise the understanding of the scales and complex interconnections among SDGs and the ongoing socioeconomic and industrial revolutions. Many of the current social systems are internally fragile and very sensitive to external factors and threats, which lead to unethical situations. Furthermore, the multilayered net-like social tissue generates clusters of influence and leadership that prevent communities from a proper development. Digital technology has also had an impact at the individual level, posing several risks including a more homogeneous and predictable humankind. To preserve the core of humanity, we propose an ethical framework to empower individuals centered on the cities and interconnected with the socioeconomic ecosystem and the environment through the complex relationships of the SDGs. Only by combining human-centered and collectiveness-oriented digital development will it be possible to construct new social models and interactions that are ethical. Thus, it is necessary to combine ethical principles with the digital innovation undergoing in all the dimensions of sustainability.
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| 3. |
- Rosenberg, Emelie, et al.
(författare)
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Sentiment analysis on Twitter data towards climate action
- 2023
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Ingår i: Results in Engineering (RINENG). - : Elsevier BV. - 2590-1230. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the progress of the Sustainable Development Goals (SDGs) proposed by the United Nations (UN) is important, but difficult. In particular, policymakers would need to understand the sentiment within the public regarding challenges associated with climate change. With this in mind and the rise of social media, this work focuses on the task of uncovering the sentiment of Twitter users concerning climate-related issues. This is done by applying modern natural-language-processing (NLP) methods, i.e. VADER, TextBlob, and BERT, to estimate the sentiment of a gathered dataset based on climate-change keywords. A transfer-learning-based model applied to a pre-trained BERT model for embedding and tokenizing with logistic regression for sentiment classification outperformed the rule-based methods VADER and TextBlob; based on our analysis, the proposed approach led to the highest accuracy: 69%. The collected data contained significant noise, especially from the keyword 'energy'. Consequently, using more specific keywords would improve the results. The use of other methods, like BERTweet, would also increase the accuracy of the model. The overall sentiment in the analyzed data was positive. The distribution of the positive, neutral, and negative sentiments was very similar in the different SDGs.
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