Artificial Intelligence
The India Weather Forecast built a state-level standard rainfall forecast system using a multi-model ensemble approach with model outputs from five prominent worldwide NWP centers. Pre-assigned grid point weights based on anomalous correlations (CC) between values observed and predicted are established for each element model using two seasonal datasets, and multi provision of appropriate predictions are created in real-time similar resolution. Then, forecasts are created for each state node lying within a given district by averaging the ensemble prediction fields' values.
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Human activity recognition, which involves recognizing human activities from sensor data, has drawn a lot of interest from researchers and practitioners as a result of the advent of smart homes, smart cities, and smart systems. Existing studies on activity recognition mostly concentrate on coarse-grained activities like walking and jumping, while fine-grained activities like eating and drinking are understudied because it is more difficult to recognize fine-grained activities than coarse-grained ones.
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This dataset contains approx. 5000 labeled images of marker on Smith Chart of Keysight VNA 9914a. Since smith chart contains "infinity" (although compensated in the device mathematically), the data from Smith Chart window pane is not apt for tuning the all-pole MW filters.
This dataset can be used to track the marker position while tuning the circuitary when considering Image Processing based Filter Tuning. Images are taken from various angles to ensure the robust behaviour and high detection accuracy.
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A dataset comprising 500 data points was gathered by collecting answers to 250 computer science problems assigned in classes and quizzes from students. To generate this dataset, a response was selected from a random student for each question. The same questions were then asked to ChatGPT 3.0, and the answers were recorded. Based on the source of the response (either student or GPT), the dataset was labeled accordingly. The resulting labeled dataset includes the list of assignment and quiz questions, along with the corresponding answers from students and ChatGPT.
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Accurate recognition of targets in the orchard environment is the key to vision perception for picking robots. Factors such as small, densely growing plum fruit targets and high occlusion lead to unsatisfactory recognition of plum fruit by vision algorithms. Therefore, this paper proposes an improved YOLOv5s model to detect highly occluded and dense plums in orchards. First, the backbone network of YOLOv5s is improved in this paper.
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CAPTCHA (Completely Automated Public Turing Tests to Tell Computers and Humans Apart). Only humans can successfully complete this test; current computer systems cannot. It is utilized in several applications for both human and machine identification. Text-based CAPTCHAs are the most typical type used on websites. Most of the letters in this protected CAPTCHA script are in English, it is challenging for rural residents who only speak their native tongues to pass the test.
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Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
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Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
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Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19”, Proceedings of the 25th International Conference on Human-Computer Interaction (HCII 2023), Copenhagen, Denmark, July 23-28, 2023 (Accepted for Publication)
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
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Please cite the following paper when using this dataset:
N. Thakur, K. Khanna, S. Cui, N. Azizi, and Z. Liu, “Mining and Analysis of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]
Brief Description of Dataset file - Interest_Dataset.csv:
Attribute Name: Week
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