Provenance analysis for instagram photos

As a feasible device fingerprint, sensor pattern noise (SPN) has been proven to be effective in the provenance analysis of digital images. However, with the rise of social media, millions of images are being uploaded to and shared through social media sites every day. An image downloaded from social networks may have gone through a series of unknown image manipulations. Consequently, the trustworthiness of SPN has been challenged in the provenance analysis of the images downloaded from social media platforms. In this paper, we intend to investigate the effects of the pre-defined Instagram images filters on the SPN-based image provenance analysis. We identify two groups of filters that affect the SPN in quite different ways, with Group I consisting of the filters that severely attenuate the SPN and Group II consisting of the filters that well preserve the SPN in the images. We further propose a CNN-based classifier to perform filter-oriented image categorization, aiming to exclude the images manipulated by the filters in Group I and thus improve the reliability of the SPN-based provenance analysis. The results on about 20, 000 images and 18 filters are very promising, with an accuracy higher than 96% in differentiating the filters in Group I and Group II

Genomic Expansion of Magnetotactic Bacteria Reveals an Early Common Origin of Magnetotaxis with Lineage-specific Evolution

The origin and evolution of magnetoreception, which in diverse prokaryotes and protozoa is known as magnetotaxis and enables these microorganisms to detect Earth’s magnetic field for orientation and navigation, is not well understood in evolutionary biology. The only known prokaryotes capable of sensing the geomagnetic field are magnetotactic bacteria (MTB), motile microorganisms that biomineralize intracellular, membrane-bounded magnetic single-domain crystals of either magnetite (Fe3O4) or greigite (Fe3S4) called magnetosomes. Magnetosomes are responsible for magnetotaxis in MTB. Here we report the first large-scale metagenomic survey of MTB from both northern and southern hemispheres combined with 28 genomes from uncultivated MTB. These genomes expand greatly the coverage of MTB in the Proteobacteria, Nitrospirae, and Omnitrophica phyla, and provide the first genomic evidence of MTB belonging to the Zetaproteobacteria and “Candidatus Lambdaproteobacteria” classes. The gene content and organization of magnetosome gene clusters, which are physically grouped genes that encode proteins for magnetosome biosynthesis and organization, are more conserved within phylogenetically similar groups than between different taxonomic lineages. Moreover, the phylogenies of core magnetosome proteins form monophyletic clades. Together, these results suggest a common ancient origin of iron-based (Fe3O4 and Fe3S4) magnetotaxis in the domain Bacteria that underwent lineage-specific evolution, shedding new light on the origin and evolution of biomineralization and magnetotaxis, and expanding significantly the phylogenomic representation of MTB

Kinesthesia in a sustained-attention driving task

This study investigated the effects of kinesthetic stimuli on brain activities during a sustained-attention task in an immersive driving simulator. Tonic and phasic brain responses on multiple timescales were analyzed using time-frequency analysis of electroencephalographic (EEG) sources identified by independent component analysis (ICA). Sorting EEG spectra with respect to reaction times (RT) to randomly introduced lane-departure events revealed distinct effects of kinesthetic stimuli on the brain under different performance levels. Experimental results indicated that EEG spectral dynamics highly correlated with performance lapses when driving involved kinesthetic feedback. Furthermore, in the realistic environment involving both visual and kinesthetic feedback, a transitive relationship of power spectra between optimal-, suboptimal-, and poor-performance groups was found predominately across most of the independent components. In contrast to the static environment with visual input only, kinesthetic feedback reduced theta-power augmentation in the central and frontal components when preparing for action and error monitoring, while strengthening alpha suppression in the central component while steering the wheel. In terms of behavior, subjects tended to have a short response time to process unexpected events with the assistance of kinesthesia, yet only when their performance was optimal. Decrease in attentional demand, facilitated by kinesthetic feedback, eventually significantly increased the reaction time in the suboptimal-performance state. Neurophysiological evidence of mutual relationships between behavioral performance and neurocognition in complex task paradigms and experimental environments, presented in this study, might elucidate our understanding of distributed brain dynamics, supporting natural human cognition and complex coordinated, multi-joint naturalistic behavior, and lead to improved understanding of brain-behavior relations in operating environments. © 2014 Elsevier Inc

Evaluating extreme rainfall changes over Taiwan using a standardized index

The annual daily maximum precipitation (rx1day) is widely used to represent extreme events and is an important parameter in climate change studies. However, the climate variability in rx1day is sensitive to outliers and has difficulty representing the characteristics of large areas. We propose to use the probability index (PI), based on the cumulative density function (CDF) of a generalized extreme value (GEV) distribution to fit and standardize the rx1day to represent extreme event records in this study. A good correlation between the area-averaged PIs of the observed stations and those of the gridded dataset can be found over Taiwan. From the past PI records, there is no distinct trend in western Taiwan before the end of the 20th century, but a climate regime change happened during 2002 - 2003. The dual change effects from both the variance and linear trend of extreme events are identified over the northeastern and southern parts of Taiwan, along with the island's central and southern regions, showing different abrupt changing trends and intensity. The PI can also be calculated using climate projection data to represent the characteristics of future extreme changes. The climate variability of PIs on the present (ALL) and future (RCP4.5 and RCP8.5) scenarios were evaluated using the 16 Couple Model Intercomparison Projects Phase-5 models (CMIP5). The simulated present fluctuations in PIs are smaller than those of actual observations. In the 21st century, the RCP8.5 scenario shows that the PI significantly increases by 10% during the first half of the century, and 14% by the end of the century.1112Ysciescopu

Samlande och visualiserande av verksamhetsstyrande data för en digital marknadsföringsbyrå

I dagens läge, med stor konkurrens mellan företag i samma bransch är det ytterst viktigt för företag att veta hur de når sina målgrupper. I detta examensarbete diskuteras vikten av verksamhetsstyrande data, hur det samlats och visualiserats tidigare jämfört med idag, och hur företag använder sig av den för att göra beslut och ändringar i sina strategier. Arbetet hänvisar till flera artiklar och intervjuer med experter inom visualisering av data, samt datadriven beslutsfattning inom företag. Målet med arbetet är att ha en fungerande nätverksapplikation, skriven i Node.js, som automatiskt sköter samlande av data från olika molntjänster ett företag använder. Utöver samlandet skall applikationen kunna utföra räkningar på data och skicka den vidare till en tjänst som visualiserar den i olika widgets på en dashboard.With increasing competition between companies within the same area, it is becoming more important than ever for companies to know how to reach their target groups. This thesis focus on the importance of performance related data, how it has been gathered and visualized earlier compared to today, and how companies use it to make data-based decisions for their strategies. Articles and interviews with experts in the fields of data visualization and data driven management are reviewed in the thesis. The goal is to have a working network application that gathers data from several online services in use by the company. In addition to gathering, the application will also perform calculations and format the data in such a way that it is easy to visualize using widgets on dashboards

An EEG-based perceptual function integration network for application to drowsy driving

© 2015 Elsevier B.V. All rights reserved. Drowsy driving is among the most critical causes of fatal crashes. Thus, the development of an effective algorithm for detecting a driver's cognitive state demands immediate attention. For decades, studies have observed clear evidence using electroencephalography that the brain's rhythmic activities fluctuate from alertness to drowsiness. Recognition of this physiological signal is the major consideration of neural engineering for designing a feasible countermeasure. This study proposed a perceptual function integration system which used spectral features from multiple independent brain sources for application to recognize the driver's vigilance state. The analysis of brain spectral dynamics demonstrated physiological evidenced that the activities of the multiple cortical sources were highly related to the changes of the vigilance state. The system performances showed a robust and improved accuracy as much as 88% higher than any of results performed by a single-source approach

Inactivation of myosin binding protein C homolog in zebrafish as a model for human cardiac hypertrophy and diastolic dysfunction

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A quantitative link between microplastic instability and macroscopic deformation behaviors in metallic glasses

2009-2010 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Hard X-ray standing-wave photoemission insights into the structure of an epitaxial Fe/MgO multilayer magnetic tunnel junction

The Fe/MgO magnetic tunnel junction is a classic spintronic system, with current importance technologically and interest for future innovation. The key magnetic properties are linked directly to the structure of hard-to-access buried interfaces, and the Fe and MgO components near the surface are unstable when exposed to air, making a deeper probing, nondestructive, in-situ measurement ideal for this system. We have thus applied hard X-ray photoemission spectroscopy (HXPS) and standing-wave (SW) HXPS in the few kilo-electron-volt energy range to probe the structure of an epitaxially grown MgO/Fe superlattice. The superlattice consists of 9 repeats of MgO grown on Fe by magnetron sputtering on an MgO(001) substrate, with a protective Al2O3 capping layer. We determine through SW-HXPS that 8 of the 9 repeats are similar and ordered, with a period of 33 ± 4 Å, with the minor presence of FeO at the interfaces and a significantly distorted top bilayer with ca. 3 times the oxidation of the lower layers at the top MgO/Fe interface. There is evidence of asymmetrical oxidation on the top and bottom of the Fe layers. We find agreement with dark-field scanning transmission electron microscope (STEM) and X-ray reflectivity measurements. Through the STEM measurements, we confirm an overall epitaxial stack with dislocations and warping at the interfaces of ca. 5 Å. We also note a distinct difference in the top bilayer, especially MgO, with possible Fe inclusions. We thus demonstrate that SW-HXPS can be used to probe deep buried interfaces of novel magnetic devices with few-angstrom precision

Single channel wireless EEG device for real-time fatigue level detection

© 2015 IEEE. Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver's drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real-time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver's fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth-enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments