Insight Centre for Data Analytics - Journal Articles
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Item Detecting Halyomorpha halys using a low-power edge-based monitoring system(Elsevier, 2024-04-25) Kargar, Amin; Zorbas, Dimitrios; Tedesco, Salvatore; Gaffney, Michael; O'Flynn, Brendan; European Regional Development Fund; Department of Agriculture, Food and the Marine, Ireland; Teagasc; Science Foundation IrelandSmart monitoring systems in orchards can automate agriculture monitoring processes and provide useful information about the presence of insects, such as the Brown Marmorated Stink Bug (BMSB), that threaten the production quantity and quality of fruit such as pears. Unlike other approaches in the literature, we propose a low-cost image monitoring system which exhibits a very low power consumption without compromising much of the accuracy that existing expensive systems incorporating significant computing and processing capability can achieve in such applications. The proposed system relies on a microcontroller unit and a camera which can take pictures of a double-sided sticky insect trap which, with the help of novel machine learning algorithms, can report on the presence of BMSB via a long-range communication link. The Internet of Things data capture and analysis system has recently been deployed in a real orchard in Italy which is subject to BMSB infestation and the first images have been analysed. This paper presents how the system works, the image processing, detection and classification algorithms, as well as a demonstration of the memory and energy consumption associated with the processing algorithms. The system achieves an accuracy of over 90% with multiple times less memory and energy consumption compared to other similar approaches in the literature.Item IoT-enabled water distribution systems - a comparative technological review(IEEE, 2022-09-20) Velayudhan, N. K.; Pradeep, Preeja; Rao, S. N.; Devidas, A. R.; Ramesh, M. V.; Department of Science and Technology, Ministry of Science and Technology, IndiaWater distribution systems are one of the critical infrastructures and major assets of the water utility in a nation. The infrastructure of the distribution systems consists of resources, treatment plants, reservoirs, distribution lines, and consumers. A sustainable water distribution network management has to take care of accessibility, quality, quantity, and reliability of water. As water is becoming a depleting resource for the coming decades, the regulation and accounting of the water in terms of the above four parameters is a critical task. There have been many efforts towards the establishment of a monitoring and controlling framework, capable of automating various stages of the water distribution processes. The current trending technologies such as Information and Communication Technologies (ICT), Internet of Things (IoT), and Artificial Intelligence (AI) have the potential to track this spatially varying network to collect, process, and analyze the water distribution network attributes and events. In this work, we investigate the role and scope of the IoT technologies in different stages of the water distribution systems. Our survey covers the state-of-the-art monitoring and control systems for the water distribution networks, and the status of IoT architectures for water distribution networks. We explore the existing water distribution systems, providing the necessary background information on the current status. This work also presents an IoT Architecture for Intelligent Water Networks - IoTA4IWNet, for real-time monitoring and control of water distribution networks. We believe that to build a robust water distribution network, these components need to be designed and implemented effectively.Item Citizenship attitudes and social inequality among Moroccan university students(Taylor & Francis, 2022-07-11) Idrissi, Hajar; Takky, Salma; Idrissi, Hind; Science Foundation IrelandDrawing on social identity approach, comprising of social identity theory and self-categorisation theory, this article compares the ways in which public and private university students in Morocco approach the controversial relationship between citizenship and identity. By revealing students’ self-identification and the role socio-economic factors have in this process, we seek to gain knowledge about the extent to which citizenship is perceived as a legal status as opposed to membership in a political community and how the transformation inherent in global market capitalism and the distribution of resources affect the youth’s behaviours and attitudes towards social action. The sample represented the public and private dichotomy divide through 150 participants from four differently located Moroccan universities, namely Sidi Mohamed Ben Abdellah University, Mohammed V University, Al-Akhawayn University and International University of Rabat. Data were collected by means of a self-administered questionnaire and a semi-structured interview and were analysed using a mixed method approach to triangulate findings and ensure trustworthiness.Item Regular pattern-free coloring(Elsevier, 2022-11-15) Escamocher, Guillaume; O'Sullivan, Barry; Science Foundation Ireland; European Regional Development FundWe study the graph coloring problem under two kinds of simultaneous restrictions. First we forbid some patterns to appear in the graph, where a pattern is a small subgraph. Second we only consider regular graphs, meaning that all nodes have the same degree. Having both types of constraints at once leads us to the discovery of new tractable classes for graph coloring. However, we also show that some classes of pattern-free graphs remain NP-Complete even after enforcing regularity. Based on the latter results, we provide several complementary ways to generate difficult graph coloring instances, relying on balancing the degree of the nodes and avoiding a particular subgraph. Our constructions are parameterizable, so characteristics of the instances like size (number of nodes) and density (number of edges) can be set to any value.Item Analyzing and improving stability of matrix factorization for recommender systems(Springer, 2022-01-27) D'Amico, Edoardo; Gabbolini, Giovanni; Bernardis, Cesare; Cremonesi, Paolo; Science Foundation Ireland; European Regional Development FundThanks to their flexibility and scalability, collaborative embedding-based models are widely employed for the top-N recommendation task. Their goal is to jointly represent users and items in a common low-dimensional embedding space where users are represented close to items for which they expressed a positive preference. The training procedure of these techniques is influenced by several sources of randomness, that can have a strong impact on the embeddings learned by the models. In this paper we analyze this impact on Matrix Factorization (MF). In particular, we focus on the effects of training the same model on the same data, but with different initial values for the latent representations of users and items. We perform several experiments employing three well known MF implementations over five datasets. We show that different random initializations lead the same MF technique to generate very different latent representations and recommendation lists. We refer to these inconsistencies as instability of representations and instability of recommendations, respectively. We report that stability of item representations is positively correlated to the accuracy of the model. We show that the stability issues affect also the items for which the recommender correctly predicts positive preferences. Moreover, we highlight that the effect is stronger for less popular items. To overcome these drawbacks, we present a generalization of MF called Nearest Neighbors Matrix Factorization (NNMF). The new framework learns the embedding of each user and item as a weighted linear combination of the representations of the respective nearest neighbors. This strategy has the effect to propagate the information about items and users also to their neighbors and allows the embeddings of users and items with few interactions to be supported by a higher amount of information. To empirically demonstrate the advantages of the new framework, we provide a detailed description of the NNMF variants of three common MF techniques. We show that NNMF models, compared to their MF counterparts, largely improve the stability of both representations and recommendations, obtain a higher and more stable accuracy performance, especially on long-tail items, and reach convergence in a fraction of epochs.