A novel application of deep learning with image cropping: a smart city use case for flood monitoring

© 2020, The Author(s). Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms

Explainable artificial intelligence for developing smart cities solutions

Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results

I will not go, I cannot go: cultural and social limitations of disaster preparedness in Asia, Africa, and Oceania

While much work has been invested in addressing the economic and technical basis of disaster preparedness, less effort has been directed towards understanding the cultural and social obstacles to and opportunities for disaster risk reduction. This paper presents local insights from five different national settings into the cultural and social contexts of disaster preparedness. In most cases, an early warning system was in place, but it failed to alert people to diverse environmental shocks. The research findings show that despite geographical and typological differences in these locations, the limitations of the systems were fairly similar. In Kenya, people received warnings, but from contradictory systems, whereas in the Philippines and on the island of Saipan, people did not understand the messages or take them seriously. In Bangladesh and Nepal, however, a deeper cultural and religious reasoning serves to explain disasters, and how to prevent them or find safety when they strike

Balancing end-to-end budgets of the Georges Bank ecosystem

Author Posting. © Elsevier, 2007. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in Progress In Oceanography 74 (2007): 423-448, doi:10.1016/j.pocean.2007.05.003.Oceanographic regimes on the continental shelf display a great range in the time scales of physical exchange, biochemical processes and trophic transfers. The close surface-to-seabed physical coupling at intermediate scales of weeks to months means that the open ocean simplification to a purely pelagic food web is inadequate. Top-down trophic depictions, starting from the fish populations, are insufficient to constrain a system involving extensive nutrient recycling at lower trophic levels and subject to physical forcing as well as fishing. These pelagic-benthic interactions are found on all continental shelves but are particularly important on the relatively shallow Georges Bank in the northwest Atlantic. We have generated budgets for the lower food web for three physical regimes (well mixed, transitional and stratified) and for three seasons (spring, summer and fall/winter). The calculations show that vertical mixing and lateral exchange between the three regimes are important for zooplankton production as well as for nutrient input. Benthic suspension feeders are an additional critical pathway for transfers to higher trophic levels. Estimates of production by mesozooplankton, benthic suspension feeders and deposit feeders, derived primarily from data collected during the GLOBEC years of 1995-1999, provide input to an upper food web. Diets of commercial fish populations are used to calculate food requirements in three fish categories, planktivores, benthivores and piscivores, for four decades, 1963-2002, between which there were major changes in the fish communities. Comparisons of inputs from the lower web with fish energetic requirements for plankton and benthos indicate that we obtained reasonable agreement for the last three decades, 1973 to 2002. However, for the first decade, the fish food requirements were significantly less than the inputs. This decade, 1963-1972, corresponds to a period characterized by a strong Labrador Current and lower nitrate levels at the shelf edge, demonstrating how strong bottom-up physical forcing may determine overall fish yields.The research was done under the aegis of the U.S.-GLOBEC Northwest Atlantic Georges Bank Study, a program sponsored jointly by the U.S. National Science Foundation and the U.S. National Oceanic and Atmospheric Administration. We acknowledge NOAA-CICOR award NA17RJ1233 (J.H. Steele), NSF awards OCE0217399 (D.J. Gifford), OCE0217122 (J.J. Bisagni) and OCE0217257 (M.E. Sieracki). W.T. Stockhausen was supported by the NOAA Sponsored Coastal Ocean Research Program

A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers

Breast cancer (BC) risk for BRCA1 and BRCA2 mutation carriers varies by genetic and familial factors. About 50 common variants have been shown to modify BC risk for mutation carriers. All but three, were identified in general population studies. Other mutation carrier-specific susceptibility variants may exist but studies of mutation carriers have so far been underpowered. We conduct a novel case-only genome-wide association study comparing genotype frequencies between 60,212 general population BC cases and 13,007 cases with BRCA1 or BRCA2 mutations. We identify robust novel associations for 2 variants with BC for BRCA1 and 3 for BRCA2 mutation carriers, P < 10−8, at 5 loci, which are not associated with risk in the general population. They include rs60882887 at 11p11.2 where MADD, SP11 and EIF1, genes previously implicated in BC biology, are predicted as potential targets. These findings will contribute towards customising BC polygenic risk scores for BRCA1 and BRCA2 mutation carriers

The ATLAS trigger system for LHC Run 3 and trigger performance in 2022

The ATLAS trigger system is a crucial component of the ATLAS experiment at the LHC. It is responsible for selecting events in line with the ATLAS physics programme. This paper presents an overview of the changes to the trigger and data acquisition system during the second long shutdown of the LHC, and shows the performance of the trigger system and its components in the proton-proton collisions during the 2022 commissioning period as well as its expected performance in proton-proton and heavy-ion collisions for the remainder of the third LHC data-taking period (2022–2025)