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Machine Learning

Social Network Datasets (SNDs) are structured data collected from social media platforms, online communities, or communication networks for the study of user behavior, information dissemination, community discovery, and so on. This kind of data usually contains nodes (users/entities) and edges (relationships/interactions), and is widely used in the fields of social network analysis (SNA), recommender systems, and public opinion monitoring.

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Securing smart grids relies in part on the reliable integration of blockchain technologies for the automation of energy transactions. However, the presence of vulnerabilities in smart contracts poses a direct threat to the integrity and resilience of these critical systems. This work presents a unique and structured dataset of real-world vulnerabilities observed in smart contracts, intended for cybersecurity research applied to smart energy infrastructures.

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We developed IIST BCI Dataset-9, a novel EEG-based Brain-Computer Interface (BCI)
dataset to improve wheelchair control systems using Malayalam dialect variations. BCI
systems help people with motor disabilities by allowing them to control devices using brain
signals. The limited number of BCI datasets in Indian languages makes it harder for native
speakers to use these systems. To address this, we created a dataset with 15 Malayalam
words related to basic wheelchair commands like Forward, Backward, Go, Stop, Reverse,

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Accurate and consistent monitoring of lake water levels is essential for understanding hydrological dynamics and climate-driven variability in remote and data-scarce regions. Satellite altimetry provides high-precision lake level observations, but its limited spatial and temporal coverage constrain large-scale monitoring. Combining Digital Elevation Model (DEM) and remote sensing imagery offers an alternative, but the accuracy of resultant water level is affected by the uncertainties of inherent elevation and image processing.

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ITDAV-25 (Indian Thermal Dataset for Autonomous Vehicles), a thermal image dataset specifically curated to advance research in Advanced Driver Assistance Systems (ADAS), particularly for environments characterized by low visibility, night-time conditions, and inclement weather. The dataset comprises of 13,688 raw thermal images, collected without any synthetic augmentation techniques.

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This dataset aims to collect and collate information about activity on the Bitcoin blockchain, with a focus directed at on-chain data which describes the state of the network at any given time. Some examples of these metrics include:

  • Datetime
  • Mempool Size
  • Transaction rate
  • market cap usd
  • Average block size
  • Market price usd
  • Exchange volume usd
  • Average confirmation time
  • Hash rate
  • Difficulty
  • Miners revenue
  • Total transaction fees
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This dataset was curated mainly to cater to mitigation strategies for the Human-Peafowl Conflict that exists in these regions. The absence of natural predators has contributed to a significant increase in the peafowl population, exacerbating challenges for farmers. Peafowls are sometimes considered agricultural pests due to their tendency to feed on and damage crops. The vocalizations are from the Indian Peafowl (Pavo cristatus), a species native to the Indian subcontinent and especially abundant in India and Sri Lanka.

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Hamstring Injuries (HSIs) are common among athletes and necessitate extended rehabilitation before Return to Sport (RTS). Post-injury, athletes undergo physical examinations, which often fall short in assessing injury severity or guiding rehabilitation. Therefore, imaging techniques such as Magnetic Resonance Imaging (MRI) are used to evaluate the injury more comprehensively, aiding in the assessment of optimal rehabilitation and RTS timelines. Given the significant impact of HSIs on athletic careers, early prediction is essential.

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The dataset is constructed using SUMO. It contains two road network datasets of different scales: a small-scale network (SR) and a larger regional network in Shenyang (SY). The dataset was constructed using the SUMO simulation platform, containing two road network datasets at different scales: a small-scale test network (SR) and a regional-level Shenyang network (SY). The SR network comprises 110 road segments, while the SY network contains 514 segments.

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