This is a test

A random sample of households were invited to participate in this survey.

In the dataset, you will find the respondent level data in each row with the questions in each column. The numbers represent a scale option from the survey, such as 1=Excellent, 2=Good, 3=Fair, 4=Poor. The question stem, response option, and scale information for each field can be found in the var "variable labels" and "value labels" sheets.

VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

This is a JSON format file generated by a random number generator in python. The range is 0 to 1000, and numbers are float number.This data will be used by a python script for further transformation.

* Repository author: Tommaso Zanotti
* email: tommaso.zanotti@unimore.it or francescomaria.puglisi@unimore.it
* Version v1.0

This repository includes MATLAB files and datasets related to the IEEE IIRW 2023 conference proceeding:
T. Zanotti et al., "Reliability Analysis of Random Telegraph Noisebased True Random Number Generators," 2023 IEEE International Integrated Reliability Workshop (IIRW), South Lake Tahoe, CA, USA, 2023, pp. 1-6, doi: 10.1109/IIRW59383.2023.10477697

The repository includes:

• The data of the bitmaps reported in Fig. 4, i.e., the results of the simulation of the ideal RTN-based TRNG circuit for different reseeding strategies.
  To load and plot the data use the "plot_bitmaps.mat" file.
• The result of the circuit simulations considering the EvolvingRTN from the HfO2 device shown in Fig. 7, for two Rgain values. Specifically, the data is contained in the following csv files:
  • "Sim_TRNG_Circuit_HfO2_3_20s_Vth_210m_no_Noise_Ibias_11n.csv" (lower Rgain)
  • "Sim_TRNG_Circuit_HfO2_3_20s_Vth_210m_no_Noise_Ibias_4_8n.csv" (higher Rgain)
• The result of the circuit simulations considering the temporary RTN from the SiO2 device shown in Fig. 8. Specifically, the data is contained in the following csv files:
  • "Sim_TRNG_Circuit_SiO2_1c_300s_Vth_180m_Noise_Ibias_1.5n.csv" (ref. Rgain)
  • "Sim_TRNG_Circuit_SiO2_1c_100s_200s_Vth_180m_Noise_Ibias_1.575n.csv" (lower Rgain)
  • "Sim_TRNG_Circuit_SiO2_1c_100s_200s_Vth_180m_Noise_Ibias_1.425n.csv" (higher Rgain)

Dataset

This dataset was created by Adam

Contents

ibragim-bad/random-data-0 dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Muhammad Faheem Naeem

Contents

This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health. In this dataset, four 18650 Li-ion batteries (Identified as RW9, RW10, RW11 and RW12) were continuously operated using a sequence of charging and discharging currents between -4.5A and 4.5A. This type of charging and discharging operation is referred to here as random walk (RW) operation. Each of the loading periods lasted 5 minutes, and after 1500 periods (about 5 days) a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.

Implement a function y = 3x + 6 on a dataset, which is a list of 1000 random numbers ranged from 1 to 100

This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health. In this dataset, four 18650 Li-ion batteries (Identified as RW3, RW4, RW5 and RW6) were continuously operated by repeatedly charging them to 4.2V and then discharging them to 3.2V using a randomized sequence of discharging currents between 0.5A and 4A. This type of discharging profile is referred to here as random walk (RW) discharging. After every fifty RW cycles a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.

This dataset comprises two resources. The first resource contains a list of random people with their date and place of birth. This can be used for demographics and hypothetical scenario testing. The second resource includes user behavior data on various device models, detailing app usage, screen time, and other metrics, which is beneficial for analyzing mobile usage patterns.

Random Things

## Overview

Random Things is a dataset for object detection tasks - it contains Spoons annotations for 268 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by ANINDYA GHOSAL

Contents

Consider a scenario in which the data owner has some private or sensitive data and wants a data miner to access them for studying important patterns without revealing the sensitive information. Privacy-preserving data mining aims to solve this problem by randomly transforming the data prior to their release to the data miners. Previous works only considered the case of linear data perturbations - additive, multiplicative, or a combination of both - for studying the usefulness of the perturbed output. In this paper, we discuss nonlinear data distortion using potentially nonlinear random data transformation and show how it can be useful for privacy-preserving anomaly detection from sensitive data sets. We develop bounds on the expected accuracy of the nonlinear distortion and also quantify privacy by using standard definitions. The highlight of this approach is to allow a user to control the amount of privacy by varying the degree of nonlinearity. We show how our general transformation can be used for anomaly detection in practice for two specific problem instances: a linear model and a popular nonlinear model using the sigmoid function. We also analyze the proposed nonlinear transformation in full generality and then show that, for specific cases, it is distance preserving. A main contribution of this paper is the discussion between the invertibility of a transformation and privacy preservation and the application of these techniques to outlier detection. The experiments conducted on real-life data sets demonstrate the effectiveness of the approach.

Zhang et al. (https://link.springer.com/article/10.1140/epjb/e2017-80122-8) suggest a temporal random network with changing dynamics that follow a Markov process, allowing for a continuous-time network history moving from a static definition of a random graph with a fixed number of nodes n and edge probability p to a temporal one. Defining lambda = probability per time granule of a new edge to appear and mu = probability per time granule of an existing edge to disappear, Zhang et al. show that the equilibrium probability of an edge is p=lambda/(lambda+mu) Our implementation, a Python package that we refer to as RandomDynamicGraph https://github.com/ScanLab-ossi/DynamicRandomGraphs, generates large-scale dynamic random graphs according to the defined density. The package focuses on massive data generation; it uses efficient math calculations, writes to file instead of in-memory when datasets are too large, and supports multi-processing. Please note the datetime is arbitrary.

This dataset provides information about the number of properties, residents, and average property values for Random Drive cross streets in Greensboro, NC.

Consider a scenario in which the data owner has some private/sensitive data and wants a data miner to access it for studying important patterns without revealing the sensitive information. Privacy preserving data mining aims to solve this problem by randomly transforming the data prior to its release to data miners. Previous work only considered the case of linear data perturbations — additive, multiplicative or a combination of both for studying the usefulness of the perturbed output. In this paper, we discuss nonlinear data distortion using potentially nonlinear random data transformation and show how it can be useful for privacy preserving anomaly detection from sensitive datasets. We develop bounds on the expected accuracy of the nonlinear distortion and also quantify privacy by using standard definitions. The highlight of this approach is to allow a user to control the amount of privacy by varying the degree of nonlinearity. We show how our general transformation can be used for anomaly detection in practice for two specific problem instances: a linear model and a popular nonlinear model using the sigmoid function. We also analyze the proposed nonlinear transformation in full generality and then show that for specific cases it is distance preserving. A main contribution of this paper is the discussion between the invertibility of a transformation and privacy preservation and the application of these techniques to outlier detection. Experiments conducted on real-life datasets demonstrate the effectiveness of the approach.

Dataset

This dataset was created by SakshiRahangdale

Released under Apache 2.0

Contents

generate 1000 random numbers ranged from 0 to 100

Market Overview:

The global quantum random number generator RNG sales market is expected to grow at a CAGR of 7.5% during the forecast period from 2022 to 2030. The growth of the quantum RNG market can be attributed to the increasing demand for secure communication and data security. In addition, the growing adoption of Quantum Cryptography in various applications. However, a lack of awareness about quantum cryptography among end users may restrain the growth of this market during the forecast period.

Product Definition:

Quantum Random Number Generator Sales is the process of selling quantum random number generators. These are devices that generate random numbers using the principles of quantum mechanics. They are used for security applications, such as generating cryptographic keys, and in other settings where true randomness is important.

PCIe Type:

PCIe is a high-speed I/O interconnect standard for external cards. It is used in computers, servers, storage devices, and other electronic devices. PCIe provides better performance over PCI and also uses less power; making it an ideal choice for high-end systems that require more than basic functions such as graphics adapters.

USB Type:

USB Type is a specification for a type of connector used on portable devices, such as personal computers. USB Connectors are typically rectangular with a protrusion in one corner that fits into the corresponding receptacle on the device. It has three major interfaces, namely USB Mass Storage (MS), Universal Serial Bus (USB) Power Delivery, and USB Host Control.

Application Insights:

Quantum communication is expected to be the fastest-growing application segment over the forecast period. Quantum communication offers enhanced security and privacy as compared to classical communication systems due to characteristics of quantum mechanics such as uncertainty principle, non-locality, and entanglement. Traditional Information Security is expected to be the second-fastest growing application segment over the forecast period. Traditional Information Security applications use classical security methods such as passwords, firewalls, and intrusion detection systems to protect information.

Cryptography is expected to be the third-fastest growing application segment over the forecast period. Cryptography uses mathematical algorithms to secure data and communication. The betting industry is expected to be the fourth-fastest growing application segment over the forecast period. The betting industry uses cryptography for security purposes such as preventing fraud and ensuring fairness in gambling transactions. Other is expected to be the slowest growing application segment over the forecast period. Other includes applications that are not classified into any other category

Regional Analysis:

North America dominated the global market in terms of revenue share in 2019. The region is expected to continue its dominance over the forecast period owing to the high demand for secure and private communication channels among enterprises and government agencies. Moreover, the growing adoption of PCIe-type RNGs by several key companies for their critical applications is also likely to drive the regional growth over the forecast period. Europe is expected to witness modest growth over the forecast period owing to the increasing demand for quantum-safe cryptography and other applications in the region. The Asia Pacific is expected to grow at a faster pace than other regions due to the growing adoption of blockchain technology and increased investment in RNGs by key companies in this region. The Middle East & Africa is expected to account for a small share of the global market over the forecast period, as there are limited opportunities for quantum-safe cryptography and other key applications in this region.

Growth Factors:

• Increasing demand for secure data transmission and storage.
• A growing number of cyber-attacks and data breaches.
• The proliferation of IoT devices and big data analytics.
• Development of quantum computing technology.
• Rising awareness about the benefits of using quantum random number generators.

Report Scope