On the Completeness of Reconstructed Data for Database Forensics

2013 ◽  
pp. 220-238 ◽  
Author(s):  
Oluwasola Mary Adedayo ◽  
Martin S. Olivier
Keyword(s):  
Database Forensics ◽  
Reconstructed Data

A Decryption Process for Android Database Forensics

2019 ◽  
Vol 7 (3) ◽  
pp. 23-26
Author(s):  
Nibedita Chakraborty ◽  
Krishna Punwar
Keyword(s):  
Database Forensics

scholarly journals EXPRESS: Exploration of Principal Component Analysis: Deriving PCA Visually Using Spectra

2021 ◽  
pp. 000370282098784
Author(s):  
James Renwick Beattie ◽  
Francis Esmonde-White
Keyword(s):  
Principal Components ◽  
Principal Component ◽  
Original Data ◽  
Specific Chemical ◽  
Successive Refinement ◽  
Minimal Loss ◽  
Components Analysis ◽  
The Mathematical Model ◽  
Application Specific ◽  
Reconstructed Data

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal Components Analysis (PCA) is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning PCA is not well understood by many applied analytical scientists and spectroscopists who use PCA. The meaning of features identified through PCA are often unclear. This manuscript traces the journey of the spectra themselves through the operations behind PCA, with each step illustrated by simulated spectra. PCA relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of PCA, such the scores representing ‘concentration’ or ‘weights’. The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a PCA model shows how to interpret application specific chemical meaning of the PCA loadings and how to analyze scores. A critical benefit of PCA is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

scholarly journals Maximizing Image Information Using Multi-Chimera Transform Applied on Face Biometric Modality

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 115
Author(s):  
Ahmad Saeed Mohammad ◽  
Dhafer Zaghar ◽  
Walaa Khalaf
Keyword(s):  
Mobile Technology ◽  
Data Reduction ◽  
Research Field ◽  
File Size ◽  
Facial Image ◽  
Information Preservation ◽  
Media Data ◽  
Reconstructed Data ◽  
Face Biometric ◽  
High Information

With the development of mobile technology, the usage of media data has increased dramatically. Therefore, data reduction represents a research field to maintain valuable information. In this paper, a new scheme called Multi Chimera Transform (MCT) based on data reduction with high information preservation, which aims to improve the reconstructed data by producing three parameters from each 16×16 block of data, is proposed. MCT is a 2D transform that depends on constructing a codebook of 256 picked blocks from some selected images which have a low similarity. The proposed transformation was applied on solid and soft biometric modalities of AR database, giving high information preservation with small resulted file size. The proposed method produced outstanding performance compared with KLT and WT in terms of SSIM and PSNR. The highest SSIM was 0.87 for the proposed scheme MCT of the full image of AR database, while the existed method KLT and WT had 0.81 and 0.68, respectively. In addition, the highest PSNR was 27.23 dB for the proposed scheme on warp facial image of AR database, while the existed methods KLT and WT had 24.70 dB and 21.79 dB, respectively.

scholarly journals Towards Consistent Variational Auto-Encoding (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13869-13870
Author(s):  
Yijing Liu ◽  
Shuyu Lin ◽  
Ronald Clark
Keyword(s):  
Original Data ◽  
Generative Models ◽  
Generative Model ◽  
Approximate Inference ◽  
Inference Model ◽  
Approach To Learning ◽  
Successful Approach ◽  
Data Point ◽  
Reconstructed Data

Variational autoencoders (VAEs) have been a successful approach to learning meaningful representations of data in an unsupervised manner. However, suboptimal representations are often learned because the approximate inference model fails to match the true posterior of the generative model, i.e. an inconsistency exists between the learnt inference and generative models. In this paper, we introduce a novel consistency loss that directly requires the encoding of the reconstructed data point to match the encoding of the original data, leading to better representations. Through experiments on MNIST and Fashion MNIST, we demonstrate the existence of the inconsistency in VAE learning and that our method can effectively reduce such inconsistency.

scholarly journals InnoDB Database Forensics: Reconstructing Data Manipulation Queries from Redo Logs

2012 ◽  
Author(s):  
Peter Fruhwirt ◽  
Peter Kieseberg ◽  
Sebastian Schrittwieser ◽  
Markus Huber ◽  
Edgar Weippl
Keyword(s):  
Data Manipulation ◽  
Database Forensics

scholarly journals Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information

2018 ◽  
Vol 10 (7) ◽  
pp. 1112 ◽  
Author(s):  
Jian Kang ◽  
Junlei Tan ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Total Error ◽  
Predictor Variables ◽  
Multiple Time ◽  
Modeling Error ◽  
Multi Temporal ◽  
Reconstructed Data

Land surface temperature (LST) products derived from the moderate resolution imaging spectroradiometer (MODIS) sensor are one of the most important data sources used to research land surface energy and water balance at regional and global scales. However, MODIS data are severely contaminated by cloud cover, which limits the applications of LST products. In this paper, based on the spatio-temporal autocorrelation of land surface variables, a reconstruction algorithm depending on the correlations between spatial pixels in multiple time phases from available MODIS LST data is developed to reconstruct clear-sky LST values for missing pixels. Considering the impacts of correlation and bias between predictors and reconstructed data on the modeling error, the known data in the reconstructed time phase are combined with the data temporally nearest to them as predictor variables to establish their temporal relationships with the reconstructed data. The reconstructed results are validated by a series of evaluation indices. The average correlation coefficient between the reconstructed results and ground-based observations is 0.87, showing high temporal change accuracy. The difference in Moran’s I, representing spatial structure characteristics between the known and reconstructed data, is 0.03 on average, indicating a slight loss of spatial accuracy. The average reconstruction rate is approximately 87.0%. The modeling error, as part of the reconstruction error, is only 1.40 K on average and accounts for 5.0% of the total error. If the product and modeling errors are removed, the residual error represents approximately 3.5 K and 5.6 K of the annual mean difference between the cloudy and cloudless LST at night and during the day, respectively. In addition, different reconstruction cases are demonstrated using various predictor data, including many combinations of multi-temporal MODIS LST data, the microwave brightness temperature, and the combination of the normalized difference vegetation index and terrain data. Comparisons among cases show that the known MODIS LST data are more reliable as predictor variables and that the data combination advocated in this paper is optimal.

Ten years of critical review on database forensics research

2019 ◽  
Vol 29 ◽  
pp. 180-197 ◽  
Author(s):  
Rupali Chopade ◽  
V.K. Pachghare
Keyword(s):  
Critical Review ◽  
Database Forensics

scholarly journals An extension of Phase Linearity Measurement for revealing cross frequency coupling among brain areas

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Pierpaolo Sorrentino ◽  
Michele Ambrosanio ◽  
Rosaria Rucco ◽  
Fabio Baselice
Keyword(s):  
A Priori ◽  
Behavioral Responses ◽  
Real Data ◽  
Frequency Synchronization ◽  
Brain Areas ◽  
Neural Ensembles ◽  
Frequency Coupling ◽  
Cross Frequency Coupling ◽  
Reconstructed Data

Abstract Background Brain areas need to coordinate their activity in order to enable complex behavioral responses. Synchronization is one of the mechanisms neural ensembles use to communicate. While synchronization between signals operating at similar frequencies is fairly straightforward, the estimation of synchronization occurring between different frequencies of oscillations has proven harder to capture. One specifically hard challenge is to estimate cross-frequency synchronization between broadband signals when no a priori hypothesis is available about the frequencies involved in the synchronization. Methods In the present manuscript, we expand upon the phase linearity measurement, an iso-frequency synchronization metrics previously developed by our group, in order to provide a conceptually similar approach able to detect the presence of cross-frequency synchronization between any components of the analyzed broadband signals. Results The methodology has been tested on both synthetic and real data. We first exploited Gaussian process realizations in order to explore the properties of our new metrics in a synthetic case study. Subsequently, we analyze real source-reconstructed data acquired by a magnetoencephalographic system from healthy controls in a clinical setting to study the performance of our metrics in a realistic environment. Conclusions In the present paper we provide an evolution of the PLM methodology able to reveal the presence of cross-frequency synchronization between broadband data.

scholarly journals Reconstructed data of landings for the artisanal beach seine fishery in the marine-coastal area of Taganga, Colombian Caribbean Sea

Data in Brief ◽  
2020 ◽  
Vol 30 ◽  
pp. 105604
Author(s):  
Jairo Altamar ◽  
Jesús Correa-Helbrum ◽  
Diego Restrepo-Leal ◽  
Carlos Robles-Algarín
Keyword(s):  
Coastal Area ◽  
Caribbean Sea ◽  
Beach Seine ◽  
Reconstructed Data
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