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Mini Dragon Group (ages 6-7)

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Abstract:Oil and Gas organizations are dependent on their IT infrastructure, which is a small part of their industrial automation infrastructure, to function effectively. The oil and gas (O&G) organizations industrial automation infrastructure landscape is complex. To perform focused and effective studies, Industrial systems infrastructure is divided into functional levels by The Instrumentation, Systems and Automation Society (ISA) Standard ANSI/ISA-95:2005. This research focuses on the ISA-95:2005 level-4 IT infrastructure to address network anomaly detection problem for ensuring the security and reliability of Oil and Gas resource planning, process planning and operations management. Anomaly detectors try to recognize patterns of anomalous behaviors from network traffic and their performance is heavily dependent on extraction time and quality of network traffic features or representations used to train the detector. Creating efficient representations from large volumes of network traffic to develop anomaly detection models is a time and resource intensive task. In this study we propose, implement and evaluate use of Deep learning to learn effective Network data representations from raw network traffic to develop data driven anomaly detection systems. Proposed methodology provides an automated and cost effective replacement of feature extraction which is otherwise a time and resource intensive task for developing data driven anomaly detectors. The ISCX-2012 dataset is used to represent ISA-95 level-4 network traffic because the O&G network traffic at this level is not much different than normal internet traffic. We trained four representation learning models using popular deep neural network architectures to extract deep representations from ISCX 2012 traffic flows. A total of sixty anomaly detectors were trained by authors using twelve conventional Machine Learning algorithms to compare the performance of aforementioned deep representations with that of a human-engineered handcrafted network data representation. The comparisons were performed using well known model evaluation parameters. Results showed that deep representations are a promising feature in engineering replacement to develop anomaly detection models for IT infrastructure security. In our future research, we intend to investigate the effectiveness of deep representations, extracted using ISA-95:2005 Level 2-3 traffic comprising of SCADA systems, for anomaly detection in critical O&G systems.Keywords: autoencoders; ANSI/ISO-95; convolutional neural networks; IT infrastructures; data driven security; deep learning; information security; intrusion detection; network anomaly detection; oil and gas IT infrastructure; representation learning

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Naseer, S.; Faizan Ali, R.; Dominic, P.D.D.; Saleem, Y. Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures. Symmetry 2020, 12, 1882.

Naseer, Sheraz, Rao Faizan Ali, P.D.D Dominic, and Yasir Saleem. 2020. "Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures" Symmetry 12, no. 11: 1882.

In the period investigated, the suicide rate decreased in all age groups, the most considerable decrease happened for the oldest old. In the beginning of the 90s, the pattern of suicide by age showed a modest bimodal distribution for males, while the newer data show rather a suicide mortality increasing in a monotonic manner with age (Fig 2). Apart from the elderly, the increased suicide risk for males in economically active age is characteristic of Eastern European countries, which is supposed to be linked to alcohol consumption and economic traumas [1, 37].

High-fidelity CAD Translation Unreal Studio offers final conformation and translation of many popular CAD formats. Entire scenes are converted with high fidelity into Unreal Engine, automating many of the time-intensive processes involved with data prep.

Data Prep with Scripting Unreal Studio enables you to perform regular expression search on assemblies and meta data to manipulate objects, substitute materials and more, either with Blueprints or Python scripting.

- Epic Games Launcher Portable.exe = main program and data file (the only executable included: it's REQUIRED to run anything!)- twinmotion20195.svm = Twinmotion application pack- engine423.svm = Unreal Engine application default pack, which includes: Core Components (11.7GB), Starter Content (870MB), Template and Feature Packs (670MB), Engine Source (150MB), also Unreal Datasmith and Substance in UE included)- additional Platform packs for UE4: Add these directly from Launcher.

HOW TO GET UE4: Download Launcher + UE .svm pack in the same folder. Run the Launcher, login with your Epic account, and in the 'Library' tab you will be able to run UE according to the version included in the .svm pack you downloaded.HOW TO GET TWINMOTION: Download Launcher + Twinmotion .svm pack in the same folder. Run the Launcher, login with your Epic account, and in the 'Twinmotion' tab you will be able to run it according to the version included in the .svm pack you downloaded.

- Note: You can also have both .svm files and have both programs available to use in Epic launcher.- Note: more info about Twinmotion can be found in its own posts here. ALL download links are the same.- Note: Launcher v10.5.4 forces update at start and you can't run it. Either run it offline and skip login, or update to v10.7+ (where I disabled auto-update), or download this little launcher to run it with update disabled.

This is portable = no installation! Just download launcher pack (.rar file), extract with winrar and execute it.If you want UE app you can either download v4.23 up here in this post (.svm file) or directly download latest version (now 4.24) from the launcher app you extracted

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Mocha Pro 2019 features GPU-accelerated planar tracking and object removal, advanced rotoscoping tools, stabilization, 3D camera solver, stereo 360/VR support, and more. Available now on macOS, Windows, and Linux, and as integrated plug-in for Adobe, Avid & OFX hosts such as Nuke, Fusion, HitFilm, and VEGAS Pro. You can also download Boris RED 5.6 Windows and macOS.

Epigenetic regulation of gene expression via DNA methylation, histone acetylation, or microRNAs plays an important role during cardiomyocyte development [237, 238] and can influence PSC-CM differentiation and maturation [239]. DNA methylation is dynamically regulated throughout cardiomyocyte development and maturation, with increased gene expression of cardiac genes occurring with demethylation during early development and methylation occurring later to silence fetal genes [237]. Interestingly, maternal diets rich in methyl group donors (such as folate) can affect the epigenetic state of the offspring via altered DNA methylation status [240]. Population-based data have associated abnormal maternal DNA methylation with congenital heart disease in offspring, although a causal association between diet and cardiac defects has not been established [240, 241]. Epigenetic priming of differentiating PSC-CMs during the mesoderm stage promotes histone H3 lysine 9 acetylation (H3K9ac) of Notch-related genes, accelerating PSC-CM maturation [242]. Increased expression of DNA methyltransferase and increased global methylation during postnatal rat heart development suggests that decreased chromatin accessibility accompanies cardiomyocyte maturation [243]. For instance, reducing histone H3 acetylation and increasing H3 lysine 9 trimethylation (H3K9me3) downregulates expression of the fetal isoform of troponin I (ssTNI) [238]. As discussed above, microRNAs can also regulate the epigenetic state by post-transcriptional regulation of gene expression [122]. MiRNAs are involved in cardiomyocyte differentiation and maturation [244, 245]. For example, miR-1 can promote cardiomyocyte differentiation [124], while overexpression of miR-200c impairs differentiation and maturation of PSC-CMs [246]. Mitochondrial miRNAs (mitomiRs) can also regulate metabolism, energy status, and mitochondrial dynamics [247, 248].

The crawl archive for December 2017 is now available! The archive is located in the commoncrawl bucket at crawl-data/CC-MAIN-2017-51/. It contains 2.9 billion web pages and over 240 TiB of uncompressed content.

To assist with exploring and using the dataset, we provide gzipped files which list all segments, WARC, WAT and WET files.By simply adding either s3://commoncrawl/ or to each line, you end up with the S3 and HTTP paths respectively. 041b061a72

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