Hidden Correlations: A Self-Exciting Tale from the FX World

2018 ◽  
Author(s):  
Laura Ballotta ◽  
Alessandro Morico
Keyword(s):  
Hidden Correlations

Big Data Analytics for Intrusion Detection

2020 ◽  
pp. 292-316 ◽  
Author(s):  
Luis Filipe Dias ◽  
Miguel Correia
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Semantic Gap ◽  
Behavioral Analysis ◽  
Event Management ◽  
Security Monitoring ◽  
Hidden Correlations ◽  
Security Information

Intrusion detection has become a problem of big data, with a semantic gap between vast security data sources and real knowledge about threats. The use of machine learning (ML) algorithms on big data has already been successfully applied in other domains. Hence, this approach is promising for dealing with cyber security's big data problem. Rather than relying on human analysts to create signatures or classify huge volumes of data, ML can be used. ML allows the implementation of advanced algorithms to extract information from data using behavioral analysis or to find hidden correlations. However, the adversarial setting and the dynamism of the cyber threat landscape stand as difficult challenges when applying ML. The next generation security information and event management (SIEM) systems should provide security monitoring with the means for automation, orchestration and real-time contextual threat awareness. However, recent research shows that further work is needed to fulfill these requirements. This chapter presents a survey on recent work on big data analytics for intrusion detection.

scholarly journals Hidden correlations in quantum optics and quantum information

2018 ◽  
Vol 1071 ◽  
pp. 012015 ◽  
Author(s):  
Margarita A Man’ko
Keyword(s):  
Quantum Information ◽  
Quantum Optics ◽  
Hidden Correlations

scholarly journals A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 516
Author(s):  
Jesús Peral ◽  
David Gil ◽  
Sayna Rotbei ◽  
Sandra Amador ◽  
Marga Guerrero ◽  
...  
Keyword(s):  
Input Data ◽  
Developmental Disorder ◽  
Distributed Processing ◽  
Repetitive Behavior ◽  
Developed Countries ◽  
Cognitive Disorder ◽  
Hidden Correlations ◽  
Restricted And Repetitive Behavior ◽  
Diverse Data

About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results.

Hidden Correlations and Information-Entropic Inequalities in Systems of Qudits†

2019 ◽  
Vol 40 (4) ◽  
pp. 293-312 ◽  
Author(s):  
Igor Ya. Doskoch ◽  
Margarita A. Man’ko
Keyword(s):  
Entropic Inequalities ◽  
Hidden Correlations

scholarly journals Nested Intellectual Data Grouping and Clusterization for the Interactive Visual Explorer

2020 ◽  
Vol 226 ◽  
pp. 03011
Author(s):  
Maria Grigorieva ◽  
Mikhail Titov ◽  
Timofei Galkin ◽  
Igal Milman
Keyword(s):  
Data Analysis ◽  
Visual Analytics ◽  
Large Data ◽  
Integral Approach ◽  
Flexible Grouping ◽  
Hidden Correlations ◽  
Level Of Details ◽  
Data Grouping ◽  
Interactive 3D ◽  
3D Scene

The Interactive Visual Explorer (InVEx) application is designed as a visual analytics tool for Big Data analysis. Visual analytics is an integral approach to data analysis, combining methods of intellectual data analysis with advanced interactive visualization. One of the main objectives of InVExis to process large data samples by decreasing their level of detail (LoD).The proposed approach includes clustering as well as flexible grouping by different parameters, providing the exploration of data from the lowest to the highest level of details. The results of grouping and clusterization arevisualized using interactive 3D scene and parallel coordinates, allowing the user to gain insight into data, to explore hidden correlations and trends of parameters.

Chemometric Approaches In Analysis Of Vegetable Oils As A Way To Improve An Efficiency Of Forensic Examination

2019 ◽  
Vol 14 (4) ◽  
pp. 192-199
Author(s):  
V. Rudniev ◽  
E. Simakova-Yefremian ◽  
V. Khosha ◽  
V. Ostropilets
Keyword(s):  
Vegetable Oils ◽  
Methyl Esters ◽  
Principal Component ◽  
Detailed Examination ◽  
Chemometric Methods ◽  
Split Ratio ◽  
Forensic Examination ◽  
Hidden Correlations ◽  
Identification Research

The approach to forensic examination performance through accelerated classification and identification research of vegetable oils is demonstrated. It includes derivatization of the original objects, analysis of obtained methyl esters mixture using GC-MS technique and applying of chemometric tools for gathering preliminary data. Subsequent processing of obtained chromatograms using principal component analysis for grouping of objects simplifies further detailed examination. An analysis of hidden correlations between variables and influence of the initial data on the first to third major components formation is provided. Using values of content of only 5 most widespread fat acids leads to satisfied visual pattern for prior recognition of oil samples. Applying of various split ratios is recommended at different stages of gas-chromatographic analysis. Split ratio 1 : 50 is recommended for gathering of data treated by chemometric methods and 1 : 2 is useful for determination of minor components presence as specific features.

scholarly journals Privacy preserving association rule hiding using border based approach

2021 ◽  
Vol 23 (2) ◽  
pp. 1137
Author(s):  
Suma B. ◽  
Shobha G.
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Sensitive Information ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Large Databases ◽  
Hidden Correlations ◽  
Rule Set

<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

scholarly journals Computational target fishing by mining transcriptional data using a novel Siamese spectral-based graph convolutional network

2020 ◽  
Author(s):  
Feisheng Zhong ◽  
Xiaolong Wu ◽  
Xutong Li ◽  
Dingyan Wang ◽  
Zunyun Fu ◽  
...  
Keyword(s):  
Small Molecules ◽  
Chemical Space ◽  
Chemical Compounds ◽  
Gene Signature ◽  
Connectivity Map ◽  
Convolutional Network ◽  
Protein Targets ◽  
Hidden Correlations ◽  
Cellular Context ◽  
Target Fishing

AbstractComputational target fishing aims to investigate the mechanism of action or the side effects of bioactive small molecules. Unfortunately, conventional ligand-based computational methods only explore a confined chemical space, and structure-based methods are limited by the availability of crystal structures. Moreover, these methods cannot describe cellular context-dependent effects and are thus not useful for exploring the targets of drugs in specific cells. To address these challenges, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. Using a benchmark set, the model achieved impressive target inference results compared with previous methods such as Connectivity Map and ProTINA. More importantly, the powerful generalization ability of the model observed with the external LINCS phase II dataset suggests that the model is an efficient target fishing or repositioning tool for bioactive compounds.

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