Artificial Intelligence
Abstract—Network slicing (NwS) is one of the main technologies
in the h-generation of mobile communication and
beyond (5G+). One of the important challenges in the NwS
is information uncertainty which mainly involves demand
and channel state information (CSI). Demand uncertainty is
divided into three types: number of users requests, amount
of bandwidth, and requested virtual network functions workloads.
Moreover, the CSI uncertainty is modeled by three
methods: worst-case, probabilistic, and hybrid. In this paper,
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These datasets are used for epidemilogical modeling using artifical neural network.
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# RSS data from smartwatch for Contact Tracing
This dataset was collected for the purpose to understand the proximity between any two smartwatches worn by human.
We used the Google's Wear OS based smartwatch, powered by a Qualcomm Snapdragon Wear 3100 processor, from Fossil sport to collect the data.
The smartwatch is powered by a Qualcomm Snapdragon Wear 3100 processor and has an internal memory of up to 1GB.
Two volunteers were required to wear the smartwatch on different hand and stand at a certain distance from each other.
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This dataset is the Cardiopulmonary Exercise Test(CPET) processed before using machine learning algorithms. The CPET cases went to a diverse feature engineering process that gives over 100 features and 4 labels. The labels are in binary and define if the patient has one of the following conditions, healthy, primary cardiac, pulmonary or other limitation.
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A Dataset Bundle for Building Automation and Control Systems
useful for Security Analysis and to study the normal operation of these systems
This document describes a dataset bundle with diverse types of attacks, and also a not poisoned dataset. The capture was obtained in a real house with a complete Building Automation and Control System (BACS). This document describes the several included datasets and how their data can be employed in security analysis of KNX based building Automation.
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Cheap MEMS sensors BMX055 were used to obtain the IMU data measurement. Three locations - thigh, shin, and foot were chosen strategically to capture good information about bipedal movement on a single leg. This is in contrast to previous studies, which often use highly accurate and thus expensive sensors. For each location, one BMX055 was used to capture 6DOF information of orthogonal accelerometer and gyroscope.
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Brainwave entrainment beats detection has become an important topic due to the ability of these beats to change human brain waves to decrease anxiety, help focus attention, improve memory, improve mood, enhance creativity, reduce pain, help with meditation, enhance mental flexibility, and enhance sleep quality. However, listening to it can cause unwanted side effects as it can increase feelings of depression, anxiety, anger, and confusion in some people.
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The MCNews dataset was obtained for this paper in May 2021 from major Internet news (Sina, Netease, Thepaper.cn, etc.) and official media (Xinhua, People’s Daily, China News, etc.) via the Internet.
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In this project, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the proposed testbed is organized into seven layers, including, Cloud Computing Layer, Network Functions Virtualization Layer, Blockchain Network Layer, Fog Computing Layer, Software-Defined Networking Layer, Edge Computing Layer, and IoT and IIoT Perception Layer.
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