*.csv
The data is collected in the form of csv file containing three attributes of X, Y, Z which represents the three coordinates of the graph x, y and z. The csv file is collected from the three signals generated by using a mobile app G sensor logger available publicly from google playstore. The data is generated for the first five Telugu language characters. The data is stored in the form of five folders where each folder represents the respective Telugu character. This dataset can be used for evaluating machine learning algorithms.
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The Surface Accelerations Reference is a catalog of all longitudinal and lateral accelerations experienced by SHRP2-NDS participants. The Strategic Highway Research Program Naturalistic Driving Study (SHRP2-NDS) is the largest naturalistic driving study in the world constituting of 34.5 million miles of recorded driving data. To create the surface accelerations reference, each and every acceleration event in SHRP2-NDS was detected, summarized, and recorded creating a database of more than 1.7 billion data points.
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The dataset contains information on the demand for milk in liters for a milk society over a certain period of time. The data is organized based on the week number and year number, providing a time-series view of the milk demand. The purpose of the dataset may be to analyze trends in milk demand over time, identify seasonal patterns, or inform production and distribution decisions for the milk society. Further analysis and exploration of the dataset can provide valuable insights into the milk industry and consumer behavior related to milk consumption
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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of Search Interests related to Online Learning Platforms from Different Countries since the Beginning of COVID-19” [Unpublished Paper - Paper submitted to HCI International 2023, Copenhagen, Denmark, 23-28 July 2023]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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The growth of the use of the Linux operating system in embedded systems projects brings to the spotlight essential questions about the capabilities of this operating system in real-time systems, in particular, soft real-time systems. In this context, the quantitative analysis of Linux-based embedded systems is the focus of this paper, which includes the evaluation of the latency time, jitter, and worst-case response time.
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Whole-program analysis is an essential technique that enables advanced compiler
optimizations. An important example of such a method is points-to analysis used
by ahead-of-time (AOT) compilers to discover program elements (classes, methods,
fields) that may be used on at least one program path during the run of the
program and hence need to be compiled. GraalVM Native Image uses a points-to
analysis to optimize Java applications, which is a time-consuming step of the
build. We explore how much the analysis time can be improved by replacing the
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This data provides price and tweet data for Bitcoin from February 21, 2021 to May 10, 2022.
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