scholarly journals Quantum Proof Encryption Technology

2021 ◽  
Vol 9 (11) ◽  
pp. 254-255
Author(s):  
Neelanjan Manna
Keyword(s):  
Quantum Computer ◽  
Research Paper ◽  
Data Sets ◽  
Sensitive Information ◽  
Bulk Data ◽  
Single Data ◽  
Audio Files ◽  
Multimedia File ◽  
Computer Proof

Abstract: Nowadays we use text passwords to encrypt a file. This research paper proposes to use multimedia files like images videos, audio files and even applications as the password key to encrypt sensitive information. This algorithm can encrypt bulk data as well as single data sets. Keywords: steganography, multimedia file as key, Quantum computer, cryptography, Quantum computer proof encryption.

Disparities Across Time: Exploring Absenteeism Patterns between Cohorts of Students with Disabilities

2020 ◽  
Vol 122 (11) ◽  
pp. 1-32
Author(s):  
Michael A. Gottfried ◽  
Vi-Nhuan Le ◽  
J. Jacob Kirksey
Keyword(s):  
Students With Disabilities ◽  
Social Needs ◽  
Data Sets ◽  
Chronic Absenteeism ◽  
Data Set ◽  
Full Day Kindergarten ◽  
Effective Interventions ◽  
Nationally Representative ◽  
Single Data ◽  
Over Time

Background It is of grave concern that kindergartners are missing more school than students in any other year of elementary school; therefore, documenting which students are absent and for how long is of upmost importance. Yet, doing so for students with disabilities (SWDs) has received little attention. This study addresses this gap by examining two cohorts of SWDs, separated by more than a decade, to document changes in attendance patterns. Research Questions First, for SWDs, has the number of school days missed or chronic absenteeism rates changed over time? Second, how are changes in the number of school days missed and chronic absenteeism rates related to changes in academic emphasis, presence of teacher aides, SWD-specific teacher training, and preschool participation? Subjects This study uses data from the Early Childhood Longitudinal Study (ECLS), a nationally representative data set of children in kindergarten. We rely on both ECLS data sets— the kindergarten classes of 1998–1999 and 2010–2011. Measures were identical in both data sets, making it feasible to compare children across the two cohorts. Given identical measures, we combined the data sets into a single data set with an indicator for being in the older cohort. Research Design This study examined two sets of outcomes: The first was number of days absent, and the second was likelihood of being chronically absent. These outcomes were regressed on a measure for being in the older cohort (our key measure for changes over time) and numerous control variables. The error term was clustered by classroom. Findings We found that SWDs are absent more often now than they were a decade earlier, and this growth in absenteeism was larger than what students without disabilities experienced. Absenteeism among SWDs was higher for those enrolled in full-day kindergarten, although having attended center-based care mitigates this disparity over time. Implications are discussed. Conclusions Our study calls for additional attention and supports to combat the increasing rates of absenteeism for SWDs over time. Understanding contextual shifts and trends in rates of absenteeism for SWDs in kindergarten is pertinent to crafting effective interventions and research geared toward supporting the academic and social needs of these students.

scholarly journals The role of artificial intelligence in cancer diagnostics - a review

2021 ◽  
Vol 11 (9) ◽  
pp. 113-122
Author(s):  
Paweł Stanicki ◽  
Katarzyna Nowakowska ◽  
Michał Piwoński ◽  
Klaudia Żak ◽  
Sylwiusz Niedobylski ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Standardized Test ◽  
Magnetic Resonance Images ◽  
Cancer Diagnostics ◽  
Data Sets ◽  
Sensitive Information ◽  
Biopsy Material ◽  
Computer Data ◽  
Tumor Diagnostics ◽  
Abnormal Cells

Introduction and purposeArtificial intelligence (AI) is more advanced than ever and finds more and more new applications. Attempts are being made to use computer data analysis in medicine. The aim of this study is to summarize the knowledge on the use of AI in the diagnosis of breast, prostate, skin and colorectal cancer with particular emphasis on the applications and effectiveness of AI in making diagnoses. A brief description of the state of knowledgeThe most frequently used form of artificial intelligence in diagnostics are algorithms that analyze databases and recognize patterns. They can capture the features of samples characteristic of tumors, such as abnormal cells in the biopsy material or the alarming size and color of the skin lesion. Additionally, AI is capable of analyzing magnetic resonance images, radiographs, and other standardized test results. In most cases, AI is more effective than clinicians, sometimes as effective as they are, and almost never less effective. As a rule, the most accurate and adequate diagnosis can be obtained by joining the forces of AI and medical specialists. Working with learning algorithms requires the use of very extensive data sets. Every effort should be made to protect sensitive information from patients' medical history. ConclusionsThe results of research on the effectiveness of AI in cancer diagnostics are very promising. Further research and development of information technology systems may positively affect the quality and effectiveness of tumor diagnostics.

scholarly journals Limitations of information extraction methods and techniques for heterogeneous unstructured big data

2019 ◽  
Vol 11 ◽  
pp. 184797901989077 ◽  
Author(s):  
Kiran Adnan ◽  
Rehan Akbar
Keyword(s):  
Big Data ◽  
Information Extraction ◽  
Extraction Methods ◽  
Unstructured Data ◽  
Future Research ◽  
Data Sets ◽  
Data Types ◽  
Efficiency And Effectiveness ◽  
Single Data ◽  
The Impact

During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews the existing IE techniques along with its subtasks, limitations, and challenges for the variety of unstructured data highlighting the impact of unstructured big data on IE techniques. To the best of our knowledge, there is no comprehensive study conducted to investigate the limitations of existing IE techniques for the variety of unstructured big data. The objective of the structured review presented in this article is twofold. First, it presents the overview of IE techniques from a variety of unstructured data such as text, image, audio, and video at one platform. Second, it investigates the limitations of these existing IE techniques due to the heterogeneity, dimensionality, and volume of unstructured big data. The review finds that advanced techniques for IE, particularly for multifaceted unstructured big data sets, are the utmost requirement of the organizations to manage big data and derive strategic information. Further, potential solutions are also presented to improve the unstructured big data IE systems for future research. These solutions will help to increase the efficiency and effectiveness of the data analytics process in terms of context-aware analytics systems, data-driven decision-making, and knowledge management.

Synthetic Data

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Trivellore E. Raghunathan
Keyword(s):  
Synthetic Data ◽  
Future Research ◽  
Annual Review ◽  
Publication Date ◽  
Data Sets ◽  
Sensitive Information ◽  
Inferential Justification ◽  
Widespread Access ◽  
Access To Data ◽  
Privacy And Confidentiality

Demand for access to data, especially data collected using public funds, is ever growing. At the same time, concerns about the disclosure of the identities of and sensitive information about the respondents providing the data are making the data collectors limit the access to data. Synthetic data sets, generated to emulate certain key information found in the actual data and provide the ability to draw valid statistical inferences, are an attractive framework to afford widespread access to data for analysis while mitigating privacy and confidentiality concerns. The goal of this article is to provide a review of various approaches for generating and analyzing synthetic data sets, inferential justification, limitations of the approaches, and directions for future research. Expected final online publication date for the Annual Review of Statistics, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models

2019 ◽  
Author(s):  
Chenling Xu ◽  
Romain Lopez ◽  
Edouard Mehlman ◽  
Jeffrey Regier ◽  
Michael I. Jordan ◽  
...  
Keyword(s):  
Single Cell ◽  
Probabilistic Approach ◽  
Cell Types ◽  
Generative Models ◽  
Marker Genes ◽  
Data Sets ◽  
Data Set ◽  
Cell State ◽  
Transcriptomics Data ◽  
Single Data

AbstractAs single-cell transcriptomics becomes a mainstream technology, the natural next step is to integrate the accumulating data in order to achieve a common ontology of cell types and states. However, owing to various nuisance factors of variation, it is not straightforward how to compare gene expression levels across data sets and how to automatically assign cell type labels in a new data set based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of cohorts of single-cell RNA-seq data sets, while accounting for uncertainty caused by biological and measurement noise. We also introduce single-cell ANnotation using Variational Inference (scANVI), a semi-supervised variant of scVI designed to leverage any available cell state annotations — for instance when only one data set in a cohort is annotated, or when only a few cells in a single data set can be labeled using marker genes. We demonstrate that scVI and scANVI compare favorably to the existing methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings such as a hierarchical structure of cell state labels. We further show that different from existing methods, scVI and scANVI represent the integrated datasets with a single generative model that can be directly used for any probabilistic decision making task, using differential expression as our case study. scVI and scANVI are available as open source software and can be readily used to facilitate cell state annotation and help ensure consistency and reproducibility across studies.

scholarly journals Data Privacy Preservation and Security Approaches for Sensitive Data in Big Data

2021 ◽  
Author(s):  
Rohit Ravindra Nikam ◽  
Rekha Shahapurkar
Keyword(s):  
Data Mining ◽  
Data Analytics ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Large Data ◽  
Research Area ◽  
Data Sets ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Data Mining Techniques

Data mining is a technique that explores the necessary data is extracted from large data sets. Privacy protection of data mining is about hiding the sensitive information or identity of breach security or without losing data usability. Sensitive data contains confidential information about individuals, businesses, and governments who must not agree upon before sharing or publishing his privacy data. Conserving data mining privacy has become a critical research area. Various evaluation metrics such as performance in terms of time efficiency, data utility, and degree of complexity or resistance to data mining techniques are used to estimate the privacy preservation of data mining techniques. Social media and smart phones produce tons of data every minute. To decision making, the voluminous data produced from the different sources can be processed and analyzed. But data analytics are vulnerable to breaches of privacy. One of the data analytics frameworks is recommendation systems commonly used by e-commerce sites such as Amazon, Flip Kart to recommend items to customers based on their purchasing habits that lead to characterized. This paper presents various techniques of privacy conservation, such as data anonymization, data randomization, generalization, data permutation, etc. such techniques which existing researchers use. We also analyze the gap between various processes and privacy preservation methods and illustrate how to overcome such issues with new innovative methods. Finally, our research describes the outcome summary of the entire literature.

Estimation across Data Sets: Two-Stage Auxiliary Instrumental Variables Estimation (2SAIV)

1989 ◽  
Vol 1 ◽  
pp. 1-23 ◽  
Author(s):  
Charles H. Franklin
Keyword(s):  
Instrumental Variables ◽  
Cross Sections ◽  
Data Sets ◽  
Important Research ◽  
Sample Design ◽  
The Past ◽  
Instrumental Variables Estimation ◽  
Research Questions ◽  
Single Data ◽  
The Relationship

Theories demand much of data, often more than a single data collection can provide. For example, many important research questions are set in the past and must rely on data collected at that time and for other purposes. As a result, we often find that the data lack crucial variables. Another common problem arises when we wish to estimate the relationship between variables that are measured in different data sets. A variation of this occurs with a split half sample design in which one or more important variables appear on the “wrong” half. Finally, we may need panel data but have only cross sections available. In each of these cases our ability to estimate the theoretically determined equation is limited by the data that are available.

Comparison of Bulk, Wet-Only, and Wet-plus-Dry Deposition Measurements at the Turkey Lakes Watershed

1988 ◽  
Vol 45 (S1) ◽  
pp. s26-s37 ◽  
Author(s):  
Robert J. Vet ◽  
Alain Sirois ◽  
Dean S. Jeffries ◽  
R. G. Semkin ◽  
N. W. Foster ◽  
...  
Keyword(s):  
Atmospheric Deposition ◽  
Dry Deposition ◽  
Measurement Methods ◽  
Bulk Deposition ◽  
Data Sets ◽  
Bulk Data ◽  
Different Types ◽  
Turkey Lakes Watershed ◽  
Seasonal Deposition ◽  
Nitrate Deposition

Four different types of atmospheric deposition measurements were made at the Turkey Lakes Watershed from 1981 to 1984. They included weekly and variable period bulk deposition measurements. The resulting annual and seasonal deposition estimates from the four methods were compared for numerical and statistical differences. Several results unexpected from the theory of the measurement methods appeared in the comparison: (1) one of the bulk deposition measurements produced lower deposition of acid-related ions than the two wet-only measurements and (2) the monthly wet-only measurements produced higher deposition of sulphate than the two bulk deposition data sets (by 6 and 19%). Several results were consistent with the theory of the measurements: (1) the daily wet-only measurements produced deposition values lower than the weekly bulk deposition measurements and (2) the wet-plus-dry deposition measurements produced higher estimates of sulphate and nitrate deposition than the two bulk data sets (15 – 35% higher). Laboratory biases appeared to be partially responsible for some of the differences found in the comparison.

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