Standards Research Data

The Army Cyber Institute (ACI) Internet of Things (IoT) Network Traffic Dataset 2023 (ACI-IoT-2023) is a novel dataset tailored for machine learning (ML) applications in the realm of IoT network security. This effort focuses on delivering a distinctive and realistic dataset designed to train and evaluate ML models for IoT network environments. By addressing a gap in existing resources, this dataset aims to propel advancements in ML-based solutions, ultimately fortifying the security of IoT operations.

This paper proposes a novel low-bitrate animation codec leveraging pose-guided human video generation with on-the-fly training. On the encoder side, the whole sequence is divided into key and non-key frames. Instead of compressing the whole sequences, only the keyframes and pose information are compressed. On the decoder side, the non-key frames are generated using a novel pose-guided human video generation model. The model is trained on-the-fly using keyframes to learn the mapping from pose to full frames.

The provided dataset appears to contain weather-related information for New Delhi Safdarjung, India, spanning from January 1, 2023, to July 21, 2023. The dataset includes the following columns: Station ID, Station Name, Date, Precipitation (PRCP), Average Temperature (TAVG), Maximum Temperature (TMThe dataset includes daily observations with information on precipitation and temperature. It seems that some values are missing (NULL values), and there are variations in the units used for precipitation AX), and Minimum Temperature (TMIN).

This dataset is primarily intended for the submission of simulation files and code associated with the letter that has been submitted to IEEE-AWPL. The letter is titled 'Flatness Scanning and Superdirectivity in Planar Antenna Arrays: An Analysis and Design Approach using IDMBSA.'

For further details about the simulation, please contact via email: cschen1997@gmail.com.

10.000 Smart contract addresses and its source codes. We previously had addresses whose codes were uploaded to Ethereum. The codes for these addresses were get from Ethereum using Python web3 libraries.

The data set contains addresses in the ScAddresses.txt file. The source code of each address is also included in numbered text files.

Other functions available in the API provided by Ethereum can be used for code-verified smart contracts.

Python file functions were used to process these files.

Compared with traditional finance, digital finance introduces digital technology for financial innovation, which largely reduces financial exclusion and discrimination, but improved financial services, such as mobile payment, online lending, virtual currency, and investment and wealth management, also involve potential risks. Hence, we propose a sentiment analysis model, GABP-News, to study the predictive ability of the information contained in news texts on digital financial development in China.

Please cite the following paper when using this dataset:

N. Thakur, S. Cui, K. A. Patel, N. Azizi, V. Knieling, C. Han, A. Poon, and R. Shah, “Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior,” Journal of Computation, Vol. 11, Issue. 11, Article. 234, Nov. 2023, DOI: http://dx.doi.org/10.3390/computation11110234

Abstract

This DATASET includes the raw data set and the statistical analysis of a family of experiment to empirically study the factors that affect the effectiveness of MDD based on the measurement of accuracy.

This paper presents a bi-directional Long ShortTerm Memory (LSTM) model for the detection of landslides. Previous uses of machine learning in this setting have demonstrated its general potential, which necessitates the implementation of a suitable algorithm. Landslides are natural disasters that can cause significant destruction and disruption in the affected areas. Early detection is the key to minimizing the impact of landslides, so it is important to develop accurate and efficient models.

The Numerical Latin Letters (DNLL) dataset consists of Latin numeric letters organized into 26 distinct letter classes, corresponding to the Latin alphabet. Each class within this dataset encompasses multiple letter forms, resulting in a diverse and extensive collection. These letters vary in color, size, writing style, thickness, background, orientation, luminosity, and other attributes, making the dataset highly comprehensive and rich.