Artificial Intelligence
The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.
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This code and related data is related to research work on quantified benchmarking of Reconfigurable Intelligent Surface (RIS). Related research article has been submitted to VTM titled "Reconfigurable Intelligent Surfaces: Tradeoff between Unit-Cell- and Surface-Level Design under Quantifiable Benchmarks". 'ReadMe.text' file in 'RIS_restricted_02.zip' explains how to use the code to generate RIS configurations for RIS of arbitrary size and unit cell design, which can accomodate restricting to certain size grouped control.
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Any work using this dataset should cite this paper as follows:
Nirmalya Thakur and Chia Y. Han, "Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions", Journal of Data, Volume 6, Issue 8, pp. 1-21, 2021.
Abstract
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The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established.
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Liver tumor segmentation.
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561 Views
<p>There is presently no solution to the problem of an autonomous bulldozer pushing mounds of material to desired goal locations in the presence of obstacles whilst obeying the kinematic constraints of the bulldozer. Past work has solved some aspects of this problem, but not all. This research presents the first complete, practical solution to the problem. It works by creating a fixed RRT in advance, and then during operation connecting pushing poses into this RRT using Bezier curves.
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We present an Arabic Twitter dataset for online extremism detection consisting of 89K tweets with associated metadata. The dataset was manually annotated by three experts and achieved a Gwet’s AC1 score of 0.6, indicating substantial inter-annotator agreement. We performed further analysis of the tweet metadata to identify important features. For the extremism dataset, there were 89,816 tweets in total published by 52,929 unique users.
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Industrial Internet of Things (IIoTs) are high-value cyber targets due to the nature of the devices and connectivity protocols they deploy. They are easy to compromise and, as they are connected on a large scale with high-value data content, the compromise of any single device can extend to the whole system and disrupt critical functions. There are various security solutions that detect and mitigate intrusions.
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Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular protocols for BCI is based on the extraction of the so-called P300 wave from EEG recordings. P300 wave is an event-related potential with a latency of 300 ms after the onset of a rare stimulus. In this paper, we used deep learning architectures, namely convolutional neural networks (CNNs), to improve P300-based BCIs.
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