Artificial Intelligence
This is version 2 of the LogiQA dataset, first released as a multi-choice reading comprehension dataset. The dataset is collected from the Chinese Civil Service Entrance Examination. The dataset is both in Chinese and English (by translation)
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The increasing complexity of intelligent systems in the Internet of Things (IoT) domain makes it essential to explain their behavior and decision-making processes to users. However, selecting an appropriate explanation method for a particular intelligent system in this domain can be challenging, given the diverse range of available XAI (eXplainable Artificial Intelligence) methods and the heterogeneity of IoT applications. This dataset is a case base elicited from an exhaustive literature review on existing explanation solutions of AIoT (Artificial Intelligence of the Things) systems.
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The "Noisy Imperfect Partial Symbolic Sketches" (NIPSS) dataset provides symbolic sketches generated from the texture extraction method described in [1] when the inputs are associated with, either Imagenet animals [2] that have been preprocessed by [3], or the CelebAMask-HQ person faces described in [4].
Any sketch is an image containing a multichannel (artificial color) binary sequence of information, where artificial colors have consisted in concatenating the results of different regularizers from [1].
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Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.
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Description
Prognostics and health management is an important topic in industry for predicting state of assets to avoid downtime and failures. This data set is the Kaggle version of the very well known public data set for asset degradation modeling from NASA. It includes Run-to-Failure simulated data from turbo fan jet engines.
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To promote intelligent water services and accelerate the water industry's modernization process, accurately predicting regional residents' water demand and reducing energy consumption for secondary water supply is a major challenge for scientific scheduling and efficient management of urban water supply. This paper proposes a deep learning-based approach for demand forecasting in residential communities. The approach first identifies and corrects outliers in raw water supply data, and incorporates additional features such as epidemics and meteorological information.
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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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