Machine Learning
Insecurity is a problem that affects all cities around the world to a greater or lesser extent, and some of them make use of video surveillance to combat it, setting up monitoring centres with hundreds of cameras. For the most part, these centres are staffed by personnel responsible for observation and incident response. The advancement of technology in the market offers the possibility to optimise and add value to these processes.
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In the digital era of the Industrial Internet of Things (IIoT), the conventional Critical Infrastructures (CIs) are transformed into smart environments with multiple benefits, such as pervasive control, self-monitoring and self-healing. However, this evolution is characterised by several cyberthreats due to the necessary presence of insecure technologies. DNP3 is an industrial communication protocol which is widely adopted in the CIs of the US. In particular, DNP3 allows the remote communication between Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA).
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This dataset is the outcome of an observation on Hyssop germination under Lead (Pb) tension and Gixberlin Acid based hormonal priming. Pb tension levels are 0, 25, 50,75, and 100 mg/L, respectively in this study. Gixberlin acid priming is done under 0, 50,100, 150, and 200 mg/L and each scenario is repeated four times during this study. Mean Germination Time (MGT), Root Length( RL) and Shoot Length (SL) have been measured. Moreover, different enzyme levels including superoxide dismutase, catalase, ascorbate peroxidase, and Guaiacol Peroxidase.
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The greenhouse remote sensing image dataset we produced contains 2101 tiles and 23914 greenhouses. And in the data set, 37.9% of dense scenes were added, so that the model trained through this data set could better adapt to the dense scene detection task.
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The Paddy Doctor dataset contains 16,225 labeled paddy leaf images across 13 classes (12 different paddy diseases and healthy leaves). It is the largest expert-annotated visual image dataset to experiment with and benchmark computer vision algorithms. The paddy leaf images were collected from real paddy fields using a high-resolution (1,080 x 1,440 pixels) smartphone camera. The collected images were carefully cleaned and annotated with the help of an agronomist.
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Recently, unmanned aerial vehicles (UAVs) have been receiving significant attention due to the wide range of potential application areas. To support UAV use cases with beyond visual line of sight (BVLOS) and autonomous flights, cellular networks can provide connectivity points to UAVs and provide remote control and payload communications. However, there are limited datasets to study the coverage of cellular technologies for UAV flights at different altitudes.
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The problem of effective disposal of the trash generated by people has rightfully attracted major interest from various sections of society in recent times. Recently, deep learning solutions have been proposed to design automated mechanisms to segregate waste. However, most datasets used for this purpose are not adequate. In this paper, we introduce a new dataset, TrashBox, containing 17,785 images across seven different classes, including medical and e-waste classes which are not included in any other existing dataset.
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