scholarly journals Risk assessment model of compromising personal data on mobile devices

2021 ◽  
Vol 270 ◽  
pp. 01013
Author(s):  
Dmitry Izergin ◽  
Michael Eremeev
Keyword(s):  
Mobile Devices ◽  
Public Information ◽  
Personal Data ◽  
Large Data ◽  
Assessment Model ◽  
Data Sets ◽  
Types Of Information ◽  
Intelligent Data Processing ◽  
Modern Services

Development of the information space to an avalanche-like increase in the volume of mobile data on the Internet. The generated digital portraits of users are becoming one of the main products for sale. The high quality of user digital portraits and their number is achieved through the use of intelligent data processing methods and the presence of large data sets. The volume of data processed by mobile devices and the number of modern services that collect various types of information make the issue of ensuring the confidentiality of user information the most important. Existing security mechanisms for mobile operating systems, as a rule, are aimed at neutralizing harmful effects and do not ensure the safety of personal data from legitimate services. The article proposes a model for assessing the risks of compromising personal data on mobile devices based on the correlation analysis of public information about service developers in order to detect the possibility of aggregating data from various sources.

The applying of machine learning methods to improve the quality of well casing

2020 ◽  
pp. 81-93
Author(s):  
D. V. Shalyapin ◽  
D. L. Bakirov ◽  
M. M. Fattakhov ◽  
A. D. Shalyapina ◽  
A. V. Melekhov ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
New Approach ◽  
Geological Conditions ◽  
Expert Assessments ◽  
The Impact ◽  
The Relationship

The article is devoted to the quality of well casing at the Pyakyakhinskoye oil and gas condensate field. The issue of improving the quality of well casing is associated with many problems, for example, a large amount of work on finding the relationship between laboratory studies and actual data from the field; the difficulty of finding logically determined relationships between the parameters and the final quality of well casing. The text gives valuable information on a new approach to assessing the impact of various parameters, based on a mathematical apparatus that excludes subjective expert assessments, which in the future will allow applying this method to deposits with different rock and geological conditions. We propose using the principles of mathematical processing of large data sets applying neural networks trained to predict the characteristics of the quality of well casing (continuity of contact of cement with the rock and with the casing). Taking into account the previously identified factors, we developed solutions to improve the tightness of the well casing and the adhesion of cement to the limiting surfaces.

Web Tools for Molecular Biological Data Analysis

2005 ◽  
pp. 3068-3073
Author(s):  
Denise Fukumi Tsunoda ◽  
Heitor Silvério Lopes ◽  
Ana Tereza Vasconcelos
Keyword(s):  
Computer Science ◽  
Protein Structures ◽  
Large Data ◽  
Biological Data ◽  
Amino Acid Sequences ◽  
Data Sets ◽  
Efficient Access ◽  
Biological Data Analysis ◽  
Types Of Information ◽  
New Algorithms

Bioinformatics means solving problems arising from biology using methods from computer science. The National Center for Biotechnology Information (www.ncbi.nih.gov) defines bioinformatics as: “…the field of science in which biology, computer science, and information technology merge into a single discipline...There are three important sub-disciplines within bioinformatics: the development of new algorithms and statistics with which to access relationships among members of large data sets; the analysis and interpretation of various types of data including nucleotide and amino acid sequences, protein domains, and protein structures; and the development and implementation of tools that enable efficient access and management of different types of information.”

Digitization and Smart Tourism

2021 ◽  
pp. 402-413
Author(s):  
Bindi Varghese
Keyword(s):  
Data Exchange ◽  
Large Data ◽  
Local Area ◽  
Service Systems ◽  
Social Media Analytics ◽  
Data Sets ◽  
Tourism Destinations ◽  
Technological Platform ◽  
Smart Tourism

Commitment to technology, sustainability, innovation, and accessibility has not only improved the quality of life but has also created a niche for luxury tourism with smart tourism eco-space. Smart tourism destinations (STD) need vigorous, well-connected stakeholders with the help of a technological platform for data exchange. Instant data exchange creates extremely large data sets known as big data. This chapter aims to contribute to the understanding on how smart tourism destinations could potentially enhance luxury tourism that is more personalized to meet visitors' unique needs and preferences. Developing smart tourism destinations can be an effective mode to engage all the stakeholders and tourists. In improving destination performance, smart tourism ecosystem applies social media analytics and smart tourism technologies. This chapter aims to relate local area as smart tourism local service systems (S-TLSS) and luxury tourism.

scholarly journals Comparison of marker selection methods for high throughput scRNA-seq data

2019 ◽  
Author(s):  
Anna C. Gilbert ◽  
Alexander Vargo
Keyword(s):  
Performance Measures ◽  
Synthetic Data ◽  
Large Data ◽  
Ground Truth ◽  
Selection Method ◽  
Large Data Sets ◽  
Data Sets ◽  
Selection Methods ◽  
Marker Selection

AbstractHere, we evaluate the performance of a variety of marker selection methods on scRNA-seq UMI counts data. We test on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells. In addition, we propose several performance measures for evaluating the quality of a set of markers when there is no known ground truth. According to these metrics, most existing marker selection methods show similar performance on experimental scRNA-seq data; thus, the speed of the algorithm is the most important consid-eration for large data sets. With this in mind, we introduce RANKCORR, a fast marker selection method with strong mathematical underpinnings that takes a step towards sensible multi-class marker selection.

scholarly journals Bezpieczeństwo informacyjne w jednostkach samorządu terytorialnego

2020 ◽  
pp. 89-106
Author(s):  
Jacek Gerwatowski
Keyword(s):  
Local Government ◽  
Information Security ◽  
Public Administration ◽  
New Technologies ◽  
Public Information ◽  
Personal Data ◽  
Security Threats ◽  
Cyber Terrorism ◽  
Types Of Information

This article analyses issues in the feld of information and IT securityand attempts to explain what is meant by information security and presentspotential opportunities and threats in this feld. The article discusses various types of information security threats, from those with a “traditional” character, such as espionage, through to threats resulting from the developmentof new technologies, e.g. cyber-terrorism and threats resulting from the activities of natural forces. The article presents the tasks of public administration and local government units in the feld of information security resulting,inter alia, from the provisions of the Act on access to public information orregulations on the protection of personal data.

Location-Based Collaborative Filtering for Web Service Recommendation

2019 ◽  
Vol 12 (1) ◽  
pp. 34-40
Author(s):  
Mareeswari Venkatachalaappaswamy ◽  
Vijayan Ramaraj ◽  
Saranya Ravichandran
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Information Filtering ◽  
Service Selection ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Service Recommendation ◽  
Location Aware

Background: In many modern applications, information filtering is now used that exposes users to a collection of data. In such systems, the users are provided with recommended items’ list they might prefer or predict the rate that they might prefer for the items. So that, the users might be select the items that are preferred in that list. Objective: In web service recommendation based on Quality of Service (QoS), predicting QoS value will greatly help people to select the appropriate web service and discover new services. Methods: The effective method or technique for this would be Collaborative Filtering (CF). CF will greatly help in service selection and web service recommendation. It is the more general way of information filtering among the large data sets. In the narrower sense, it is the method of making predictions about a user’s interest by collecting taste information from many users. Results: It is easy to build and also much more effective for recommendations by predicting missing QoS values for the users. It also addresses the scalability problem since the recommendations are based on like-minded users using PCC or in clusters using KNN rather than in large data sources. Conclusion: In this paper, location-aware collaborative filtering is used to recommend the services. The proposed system compares the prediction outcomes and execution time with existing algorithms.

scholarly journals RECENT RESULTS OF THE FIREBIRD MISSION

2017 ◽  
Vol XLII-3/W2 ◽  
pp. 105-111
Author(s):  
E. Lorenz ◽  
W. Halle ◽  
C. Fischer ◽  
N. Mettig ◽  
D. Klein
Keyword(s):  
Dynamic Range ◽  
Large Data ◽  
Data Sets ◽  
Online Information ◽  
Processing Unit ◽  
Application Field ◽  
Small Satellites ◽  
German Aerospace ◽  
Infrared Instruments

Two years ago the German Aerospace Center (DLR) reported on the ISRSE36 in Berlin about the FireBird Mission (Lorenz, 2015). FireBird is a constellation of two small satellites equipped with a unique Bi- Spectral Infrared Instrument. Whereas this instrumentation is mainly dedicated to the investigation of high temperature events a much wider application field was meanwhile examined. The first of these satellites &amp;ndash; TET-1 &amp;ndash; was launched on July 22<sup>nd</sup> 2012. On the ISRSE36 could be presented the results of two years of operation. The second satellite &amp;ndash; BIROS &amp;ndash; was launched on June 22<sup>nd</sup> 2016. The outstanding feature of the Infrared Instruments is their higher ground sampling resolution and dynamic range compared to systems such as MODIS. This allows the detection of smaller fire events and improves the quality of the quantitative analysis. The detailed analysis of the large number of data sets acquired by TET in the last two years caused significant methodically improvements in the data processing. Whereas BIROS has the same instrumentation as TET a number of additional technological features implemented in the satellite bus expand the application field of the instruments remarkably. New High-Torque-Wheels will allow new scanning modes and generating with this new data products to be discussed. A Giga Bit Laser downlink terminal will enable near real time downlinks of large data volumes reducing the response time to disaster events. With an advanced on board processing unit, it is possible to reduce the data stream to a dedicated list of desired parameters to be sent to users by an OrbCom modem. This technology can serve such Online Information Portals like the Advanced Fire Information System (<i>AFIS</i>) in South Africa. The paper will focus on these new items of the FireBird mission.

scholarly journals Small Data and Its Visualization for Diabetes Self-Management: Qualitative Study

JMIR Diabetes ◽  
10.2196/10324 ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. e10324 ◽  
Author(s):  
Sally Jane Burford ◽  
Sora Park ◽  
Paresh Dawda
Keyword(s):  
Mobile Devices ◽  
Information Seeking ◽  
Self Care ◽  
Personal Data ◽  
The Self ◽  
Self Management ◽  
Small Data ◽  
Data Sets ◽  
Online Information ◽  
The Impact

Background As digital healthcare expands to include the use of mobile devices, there are opportunities to integrate these technologies into the self-management of chronic disease. Purpose built apps for diabetes self-management are plentiful and vary in functionality; they offer capability for individuals to record, manage, display, and interpret their own data. The optimal incorporation of mobile tablets into diabetes self-care is little explored in research, and guidelines for use are scant. Objective The purpose of this study was to examine an individual’s use of mobile devices and apps in the self-management of type 2 diabetes to establish the potential and value of this ubiquitous technology for chronic healthcare. Methods In a 9-month intervention, 28 patients at a large multidisciplinary healthcare center were gifted internet connected Apple iPads with preinstalled apps and given digital support to use them. They were invited to take up predefined activities, which included recording their own biometrics, monitoring their diet, and traditional online information seeking. Four online surveys captured the participants’ perceptions and health outcomes throughout the study. This article reports on the qualitative analysis of the open-ended responses in all four surveys. Results Using apps, participants self-curated small data sets that included their blood glucose level, blood pressure, weight, and dietary intake. The dynamic visualizations of the data in the form of charts and diagrams were created using apps and participants were able to interpret the impact of their choices and behaviors from the diagrammatic form of their small personal data sets. Findings are presented in four themes: (1) recording personal data; (2) modelling and visualizing the data; (3) interpreting the data; and (4) empowering and improving health. Conclusions The modelling capability of apps using small personal data sets, collected and curated by individuals, and the resultant graphical information that can be displayed on tablet screens proves a valuable asset for diabetes self-care. Informed by their own data, individuals are well-positioned to make changes in their daily lives that will improve their health.

scholarly journals The personal data is political

2018 ◽  
Author(s):  
Bastian Greshake Tzovaras ◽  
Athina Tzovara
Keyword(s):  
Personalized Medicine ◽  
Data Sharing ◽  
Human Population ◽  
Personal Data ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets

The success of personalized medicine does not only rely on methodological advances but also on the availability of data to learn from. While the generation and sharing of large data sets is becoming increasingly easier, there is a remarkable lack of diversity within shared datasets, rendering any novel scientific findings directly applicable only to a small portion of the human population. Here, we are investigating two fields that have been majorly impacted by data sharing initiatives, neuroscience and genetics. Exploring the limitations that are a result of a lack of participant diversity, we propose that data sharing in itself is not enough to enable a global personalized medicine.

scholarly journals Exploring the effects of non-monetary reimbursement for participants in HCI research

2017 ◽  
Vol 4 (1) ◽  
pp. 1-24
Author(s):  
Sarah Wiseman ◽  
Anna L. Cox ◽  
Sandy J. J. Gould ◽  
Duncan P. Brumby
Keyword(s):  
Data Collection ◽  
Citizen Science ◽  
Environmental Science ◽  
Personal Data ◽  
Large Data ◽  
Monetary Reward ◽  
Data Sets ◽  
Great Success ◽  
Science Projects ◽  
To Come

When running experiments within the field of Human Computer Interaction (HCI) it is common practice to ask participants to come to a specified lab location, and reimburse them monetarily for their time and travel costs. This, however, is not the only means by which to encourage participation in scientific study. Citizen science projects, which encourage the public to become involved in scientific research, have had great success in getting people to act as sensors to collect data or to volunteer their idling computer or brain power to classify large data sets across a broad range of fields including biology, cosmology and physical and environmental science. This is often done without the expectation of payment. Additionally, data collection need not be done on behalf of an external researcher; the Quantified Self (QS) movement allows people to reflect on data they have collected about themselves. This too, then, is a form of non-reimbursed data collection. Here we investigate whether citizen HCI scientists and those interested in personal data produce reliable results compared to participants in more traditional lab-based studies. Through six studies, we explore how participation rates and data quality are affected by recruiting participants without monetary reimbursement: either by providing participants with data about themselves as reward (a QS approach), or by simply requesting help with no extrinsic reward (as in citizen science projects). We show that people are indeed willing to take part in online HCI research in the absence of extrinsic monetary reward, and that the data generated by participants who take part for selfless reasons, rather than for monetary reward, can be as high quality as data gathered in the lab and in addition may be of higher quality than data generated by participants given monetary reimbursement online. This suggests that large HCI experiments could be run online in the future, without having to incur the equally large reimbursement costs alongside the possibility of running experiments in environments outside of the lab.

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