2021
Authors
Ludviga, I; Niezurawska, J; Duarte, N; Pereira, C; Sluka, I;
Publication
Academy of Management Proceedings
Abstract
2021
Authors
Grzywinska-Rapca, M; Duarte, N; Janusz, M;
Publication
Olsztyn Economic Journal
Abstract
2021
Authors
Kowalewska, G; Markowski, L; Wojarska, M; Duarte, N;
Publication
EUROPEAN RESEARCH STUDIES JOURNAL
Abstract
2021
Authors
Gao, J; Yue, XG; Hao, LL; Crabbe, MJC; Manta, O; Duarte, N;
Publication
INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING
Abstract
The rapid development of Internet technology and information technology is rapidly changing the way people think, recognize, live, work and learn. In the context of Internet + education, the emerging learning form of a cloud classroom has emerged. Cloud classroom refers to the process in which learners use the network as a way to obtain learning objectives and learning resources, communicate with teachers and other learners through the network, and build their own knowledge structure. Because it breaks the boundaries of time and space, it has the characteristics of freedom, high efficiency and extensiveness, and is quickly accepted by learners of different ages and occupations. The traditional cloud classroom teaching mode has no personalized recommendation module and cannot solve an information overload problem. Therefore, this paper proposes a cloud classroom online teaching system under the personalized recommendation system. The system adopts a collaborative filtering recommendation algorithm, which helps to mine the potential preferences of users and thus complete more accurate recommendations. It not only highlights the core position of personalized curriculum recommendation in the field of online education, but also makes the cloud classroom online teaching mode more intelligent and meets the needs of intelligent teaching.
2021
Authors
Chen, X; Ou, M; Liang, Y; Comite, U; Duarte, N; Yue, G;
Publication
ACM International Conference Proceeding Series
Abstract
Based on the perspective of information asymmetry theory and transaction cost theory, this paper discusses the effect of agricultural industrialization organizations on the quality of agricultural products. Through the survey of litchi growers in Guangdong Province and surrounding areas, it designs indicators suitable for measuring litchi quality from the two dimensions of safety and texture, uses factor analysis, correlation analysis, OLS model to test the hypothesis proposed herein. The results show that: the involvement of agricultural industrialization organizations has played an important role in improving litchi quality of growers. Enlightenment: unified management and unified standards through industrial organization forms such as enterprises, cooperatives, and associations are an important way to implement large-scale production of litchi, strengthen respective advantages, share risks, seek mutual benefit and win-win results. Government departments should play a leading and propaganda role, provide financial and technical support, improve the service system for the industrialization of the litchi industry, cultivate leading litchi enterprises, and accelerate the development of professional litchi cooperatives, associations and other intermediary organizations to make them become standardized and competitive main market players. © 2021 ACM.
2021
Authors
Sousa, M; Carneiro, D;
Publication
PROCEEDINGS OF 2021 16TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI'2021)
Abstract
Usually, Machine Learning systems are seen as something fully automatic. Recently, however, interactive systems in which human experts actively contribute towards the learning process have shown improved performance when compared to fully automated ones. This may be so in scenarios of Big Data, scenarios in which the input is a data stream, or when there is concept drift. In this paper, we present a system for supporting auditors in the task of financial fraud detection. The system is interactive in the sense that the auditors can provide feedback regarding the instances of the data they use, or even suggest new variables. This feedback is incorporated into newly trained Machine Learning models which improve over time.
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