One of the goals of artificial intelligence is to create a machine that can answer arbitrary questions about an image. This field is known as visual question answering (VQA). A Counting VQA is a subfield that can answ...
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Internet of things (IoT) has become a common place nowadays. It has revolutionized human lifestyle by reducing the gap between physical and digital worlds. IoT provides seamless integration of devices and services int...
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We demonstrate a method for visual creation of schema-backed federated queries that features schema summary visualizations and context-aware auto-completion of queries, based on schemas of multiple data *** method is ...
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The dual of a planar graph G is a planar graph G∗ that has a vertex for each face of G and an edge for each pair of adjacent faces of G. The profound relationship between a planar graph and its dual has been the algor...
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The generalization of edge coloring in hypergraphs, especially clustered ones, remains an open problem due to the intricate structure and organization of hyper-edges grouped in clusters. This study addresses the compu...
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In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Pent...
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Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to *** random walk hypothesis and efficient market hypothesis essentially state that it is not possible to sy...
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Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to *** random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically,reliably predict future stock prices or forecast changes in the stock market ***,machine learning(ML)techniques that use historical data have been applied to make such *** studies focused on a small number of stocks and claimed success with limited statistical *** this study,we construct feature vectors composed of multiple previous relative returns and apply the random forest(RF),support vector machine(SVM),and long short-term memory(LSTM)ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 *** apply this approach to all S&P 500 companies for the period *** assess performance using accuracy,precision,and recall and compare our results with a random choice *** observe that the LSTM classifier outperforms RF and SVM,and the data-driven ML methods outperform the random choice classifier(p=8.46e^(-17) for accuracy of LSTM).Thus,we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small.
In this paper,we propose a correlationaware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific *** core of ou...
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In this paper,we propose a correlationaware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific *** core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information,spatial location,and correlation distribution using Bayes’*** effectively preserves statistical properties without merging data blocks in different parallel computing nodes and repartitioning them,thus significantly reducing the computational ***,this enables reconstruction of the original data more accurately than existing *** demonstrate the effectiveness of our technique using six datasets,with the largest having one billion grid *** experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-art methods while providing a higher reconstruction accuracy at a lower computational cost.
This paper conducts a comprehensive study of the learning curves of kernel ridge regression (KRR) under minimal assumptions. Our contributions are three-fold: 1) we analyze the role of key properties of the kernel, su...
With the growing integration of chatbots, automated writing tools, game AI and similar applications into human society, there is a clear demand for artificially intelligent systems that can successfully collaborate wi...
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