Reviewerly/exHarmony huggingface dataset:
Image Steganography using Encoder - Decoder Architectures

Network security and data protection lie at the very core of everything technology - from messaging apps to IOT devices. It is fundamental that all users send out and receive information intended only for them with cybersecurity best practices guarding their information at all times. A substantial aspect of cybersecurity is data protection which is brought to life via encryption. Encryption protects private information and sensitive data and is essential to everyday communication on the Internet. Steganography and Cryptography are important methods that are used to enhance the process of encryption. While effective, encryption is dependent on keys which if compromised can lead to data getting in unauthorised hands and potentially getting misused. Additionally, with the advent of quantum technologies there is a conversation around the possibility of quantum algorithms being able to break through the highest forms of encryption. This has inspired the research on using neural networks to implement data protection which can effectively protect data without a dependency on keys. This paper aims to research and compare various techniques of performing data protection using image steganography through encoder-decoder architectures which are a special type of neural networks and to identify aspects from reviewed papers that will form basis for implementation of the same.
Double embedding-transfer-based multi-view spectral clustering

It is crucial for multi-view clustering to mine the consistency among multiple views; this is useful for improving the performance of multi-view clustering, especially with noisy data. In this paper, a novel clustering model is proposed for learning consistency using multi-view data, namely double embedding-transfer-based multi-view spectral clustering (DETMSC). In contrast to many existing methods, DETMSC can simultaneously learn consistency embedding and feature embedding in a unified framework. This is due to the power of the implemented knowledge transfer between consistency embedding and feature embedding among multiple views, which is achieved through bipartite graph co-clustering. In this way, DETMSC can simultaneously consider both the consistency of multiple views and the diversity of the feature embedding for each view. Optimal consistency embedding may be approached using the diversity of feature embedding, which can further result in a good performance for multi-view clustering. Better consistency embedding can in turn help with learning the diversity of feature embedding. The DETMSC method is also robust to the noise within each view because it uses the sparse constraint in feature embedding. Extensive experimental results on several real-world benchmark datasets show that the proposed model outperforms other state-of-the-art multi-view clustering algorithms. • A multi-view clustering model is proposed to mine the consistency of multi-view data. • This jointly learns consistency and feature embedding in a unified framework. • Bipartite graph co-clustering achieves knowledge transfer between the two embeddings.
Automated fault surfaces extraction from 3D fault imaging volume

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyAutomated fault surfaces extraction from 3D fault imaging volumeAuthors: Nam NguyenAlejandro JaramilloNam Nguyen Halliburton LandmarkSearch for more papers by this author and Alejandro Jaramillo Halliburton LandmarkSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3747476.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract A new method leverages network analysis data science technique to automatically extract fault surfaces from 3D fault imaging volume by applying a three-step process: (1) determine fault samples with primary attributes of depth, amplitude, and vertical thickness for each trace location from an input volume; (2) compute additional attributes dip and azimuth for each fault sample; and (3) extract fault surfaces by applying network analysis to connect nearby fault samples with similar fault attributes. The extracted fault surfaces can be integrated into a geological model for identification of hydrocarbon bearing formations, improving structural trapping definition, and preventing drilling hazards. Keywords: fault imaging, fault extraction, automation, fault interpretation, network analysisPermalink: https://doi.org/10.1190/image2022-3747476.1FiguresReferencesRelatedDetailsCited byInterpreting Subtle Faults and Multiple Horizons Layers Using Machine Learning and Data Driven Approach31 October 2022 Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Nam Nguyen and Alejandro Jaramillo, (2022), "Automated fault surfaces extraction from 3D fault imaging volume," SEG Technical Program Expanded Abstracts : 1232-1236. https://doi.org/10.1190/image2022-3747476.1 Plain-Language Summary Keywordsfault imagingfault extractionautomationfault interpretationnetwork analysisPDF DownloadLoading ...
A paradigm for safe adaptation of collaborating robots

The dynamic forces that transit back and forth traditional boundaries of system development have led to the emergence of digital ecosystems. Within these, business gains are achieved through the development of intelligent control that requires a continuous design and runtime co-engineering process endangered by malicious attacks. The possibility of inserting specially crafted faults capable to exploit the nature of unknown evolving intelligent behavior raises the necessity of malicious behavior detection at runtime.
Emergent web server

Contemporary deployment environments are volatile, with conditions that are often hard to predict in advance, demanding solutions that are able to learn how best to design a system at runtime from a set of available alternatives. While the self-adaptive systems community has devoted significant attention to online learning, there is less research specifically directed towards learning for open-ended architectural adaptation - where individual components represent alternatives that can be added and removed dynamically. In this paper we present the Emergent Web Server (EWS), an architecture-based adaptive web server with 42 unique compositions of alternative components that present different utility when subjected to different workload patterns. This artefact allows the exploration of online learning techniques that are specifically able to consider the composition of logic that comprises a given system, and how each piece of logic contributes to overall utility. It also allows the user to add new components at runtime (and so produce new composition options), and to remove existing components; both are likely to occur in systems where developers (or automated code generators) deploy new code on a continuous basis and identify code which has never performed well. Our exemplar bundles together a fully-functional web server, a number of pre-packaged online learning approaches, and utilities to integrate, evaluate, and compare new online learning approaches.
Devops for digital business

Software is playing an increasingly crucial role for any modern enterprise. Due to the high volatility of software and its environment, management of software change has become a crucial ability of a digital enterprise. Indeed, the ever-changing technical aspects of cloud-based software might influence its economic aspects at the same rate. This PhD work proposes an autonomic management framework for economic-driven adaptive resource allocation, during which performance and economics are considered simultaneously. The framework consists of a hybrid performance-economic model for cloud software systems and a self-adaptive system for resource management at run time. The proposed framework will be developed and evaluated on use cases from the domain of financial services. The objective in these use cases is to improve productivity of data scientists when they train and deploy analytical and ML models, while maintaining a low resource consumption. Considering productivity and cost as the economic factors, and latency and resource consumption as the technical ones, we will use the proposed hybrid model to guide initial deployment, and the self-adaptive system to adjust the deployment at run time.
Evaluating machine learning techniques to define the factors related to boar taint

• A real-life scenario of detecting the factors associated with boar taint leveraging novel machine learning/deep learning (ML/DL) techniques coupled with eXplainable AI (XAI) approaches and statistical methods. • A highly imbalanced dataset was leveraged, including measurements from various farms and slaughterhouses. This highlights the challenges that a real-life scenario may pose to researchers and practitioners, especially with data handling and preprocessing. • A comparison of various machine learning models, deep learning, and statistical methods was conducted. The experiments, enhanced with XAI methods, were based on an imbalanced dataset. • Usage of a custom genetic algorithm as an undersampling/oversampling technique. • Based on this study, boar taint seems related to feed and ventilation types, pharmaceutical treatment, and waiting time in lairage. Several industries and sectors such as health care, agriculture, and finance exploit the added value of data to produce valuable insights for decision-making. The case of so-called ’boar taint’, the unwanted taste and odor that can be present in meat of entire male pigs, is one real-life scenario that showcases the added value of utilizing collected data. This information may yield insights for pig farmers about how they could adjust their management to reduce boar taint. This study examines multiple predictive data-driven approaches coupled with eXplainable AI (XAI) methods, evaluating them against various explainable metrics while trying to generate actionable insights and recommendations. Specifically, in this approach, the examined use case was modeled as a binary classification task resulting in a highly imbalanced dataset. This yielded some functional attributes regarding the farm/stable and slaughterhouse conditions, such as the type of feed, type of ventilation system, pharmaceutical treatment, floor type, and the duration of waiting in lairage.
Distributed graph pattern matching via bounded dual simulation

Graph Pattern Matching (GPM) finds subgraphs of a large data graph that are similar to an input query graph. It has many applications, such as pattern recognition, detecting plagiarism, and finding communities in social networks. Current real-world applications generate massive amounts of data that cannot be stored on the memory of a single machine, which raises the need for distributed storage and processing. Recent relaxed GPM models, although of polynomial time complexity, are nevertheless not distributed by nature. Moreover, the existing relaxed GPM algorithms are limited in terms of scalability. In this paper, we propose Bounded Dual Simulation (BDSim) as a new relaxed model for a scalable evaluation of GPM in massive graphs. BDSim captures more semantic similarities compared to graph simulation, dual simulation, and even strong simulation. It preserves the vertices’ proximity by eliminating cycles of unbounded length from the resulting match graph. Furthermore, we propose distributed vertex-centric algorithms to evaluate BDSim. We prove their effectiveness and efficiency through detailed theoretical validation and extensive experiments conducted on real-world and synthetic datasets. To the best of our knowledge, BDSim is the first relaxed GPM model that captures the cyclic structure of the query graph while being feasible in cubic time.
Deteksi Dini Varian Covid-19 Dengan Metode CBR-AHP Dan Sorgenfrei

The case fatality rate caused by Covid-19 in Indonesia is around 2.9%. The death rate by age group is from all Covid-19 patients who died, including 0.5% aged 0-5 years, 0.5% aged 6-18 years, 2.8% aged 19-30 years, 12.7 % were aged 31–45 years, 36.8% were aged 46–59 years, and 46.7% were aged 60 years and over. With the large number of cases that occurred in Indonesia, it turned out to be due to the lack of proper handling of the early symptoms of the Covid-19 virus. To overcome this, it is needed as a tool for early detection of the Covid-19 variant by using the CBR-AHP expert system with the Sorgenfrei algorithm. The results of the consultation for the early detection of the Covid-19 variant were obtained from the highest similarity value of Sorgenfrei. Of all the variants found above, the highest similarity value of Sorgenfrei is Omicron with a similarity of 1,000. Omicron has the highest value because it has a value of a, which is the same symptom between old cases and large new cases.
Making ProB Compatible with SWI-Prolog

Abstract Even though the core of the Prolog programming language has been standardized by ISO since 1995, it remains difficult to write complex Prolog programs that can run unmodified on multiple Prolog implementations. Indeed, implementations sometimes deviate from the ISO standard and the standard itself fails to cover many features that are essential in practice. Most Prolog applications thus have to rely on nonstandard features, often making them dependent on one particular Prolog implementation and incompatible with others. We examine one such Prolog application: ProB, which has been developed for over 20 years in SICStus Prolog. The article describes how we managed to refactor the codebase of ProB to also support SWI-Prolog, with the goal of verifying ProB’s results using two independent toolchains. This required a multitude of adjustments, ranging from extending the SICStus emulation in SWI-Prolog on to better modularizing the monolithic ProB codebase. We also describe notable compatibility issues and other differences that we encountered in the process, and how we were able to deal with them with few major code changes.
SmartTensors: Unsupervised and physics-informed machine learning framework for the geoscience applications

SmartTensors (https://github.com/SmartTensors) is a novel framework for unsupervised and physics-informed machine learning for geoscience applications. The methods in SmartTensors AI platform are developed using advanced matrix/tensor factorization constrained by penalties enforcing robustness and interpretability (e.g., nonnegativity, sparsity, physics, and mathematical constraints; etc.). This framework has been applied to analyze diverse datasets related to a wide range of problems: from COVID-19 to wildfires and climate. Here, we will focus on the analysis of geothermal prospectivity of the Great Basin, U.S. The basin covers a vast area that is yet to be thoroughly explored to discover new geothermal resources. The available regional geochemical data are expected to provide critical information about the geothermal reservoir properties in the basin, including temperature, fluid/heat flow, boundary conditions, and spatial extent. The geochemical data may also include hidden (latent) information that is a proxy for geothermal prospectivity. We processed the sparse geochemical dataset of 18 geochemical attributes observed at 14,341 locations. The data are analyzed using our GeoThermalCloud toolbox for geothermal exploration (https://github.com/SmartTensors/GeoThermalCloud.jl) whichis also a part of the SmartTensors framework. An unsupervised machine learning using non-negative matrix factorization with customized k-means clustering (NMFk) as implemented in SmartTensors identified three hidden geothermal signatures representing low-, medium-, and high-temperature reservoirs, respectively (Fig). NMFk also evaluated the probability of occurrence of these types of resources through the studied region. NMFk also reconstructed attributes from sparse into continuous over the study domain. Future work will add in the ML analyses other regional- and site-scale datasets including geological, geophysical, and geothermal attributes.
A smart merge workflow for dual-azimuth images

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyA smart merge workflow for dual-azimuth imagesAuthors: Xu LiDiwi SnyderDez ChuInmaculada Dura-GomezXu LiExxonMobilSearch for more papers by this author, Diwi SnyderExxonMobilSearch for more papers by this author, Dez ChuExxonMobilSearch for more papers by this author, and Inmaculada Dura-GomezExxonMobilSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3751642.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractIn this paper, a smart merge flow for dual azimuth images is proposed. It is implemented by three major steps. First, an attribute volume of image quality is derived for each survey, based on the local image coherence and magnitude. Then the merging weights are assigned based on this attribute: higher image quality will get a larger merging weight. Second, a relative shift controlled by weight is performed to achieve the best alignment dynamically, and third, a final summation with weight is applied. Some common pitfalls during application are discussed and possible solutions are proposed. This flow was successfully applied to a Brazil dual azimuth project. The merged volume was confirmed to achieve a better image quality with enhanced continuity and magnitude over two individual azimuth images.Keywords: Brazil, DAZ, smart merge, image qualityPermalink: https://doi.org/10.1190/image2022-3751642.1FiguresReferencesRelatedDetailsCited byVelocity model building and imaging driven by FWI with dual-azimuth data in a complex salt environment: Brazil case studyXu Li, Christine Gans, Young Ho Cha, Diwi Snyder, Gopal Palacharla, Vijay Gottumukkula, Yaofeng He, Hongchuan Sun, Tetyana Vdovina, Xueyi Jia, Valeriy Brytik, and Inmaculada Dura-Gomez15 August 2022 Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Xu Li, Diwi Snyder, Dez Chu, and Inmaculada Dura-Gomez, (2022), "A smart merge workflow for dual-azimuth images," SEG Technical Program Expanded Abstracts : 2656-2660. https://doi.org/10.1190/image2022-3751642.1 Plain-Language Summary KeywordsBrazilDAZsmart mergeimage qualityPDF DownloadLoading ...
Causal discoveries for high dimensional mixed data

Causal relationships are of crucial importance for biological and medical research. Algorithms have been proposed for causal structure learning with graphical visualizations. While much of the literature focuses on biological studies where data often follow the same distribution, for example, the normal distribution for all variables, challenges emerge from epidemiological and clinical studies where data are often mixed with continuous, binary, and ordinal variables. We propose to use a mixed latent Gaussian copula model to estimate the underlying correlation structure via the rank correlation for mixed data. This correlation structure is then incorporated into a popular causal discovery algorithm, the PC algorithm, to identify causal structures. The proposed algorithm, called the latent-PC algorithm, is able to discover the true causal structure consistently under mild conditions in high dimensional settings. From simulation studies, the latent-PC algorithm delivers a competitive performance in terms of a similar or higher true positive rate and a similar or lower false positive rate, compared with other variants of the PC algorithm. In the high dimensional settings where the number of variables is more than the number of observations, the causal graphs identified by the latent-PC algorithm are closer to the true causal structures, compared to other competing algorithms. Further, we demonstrate the utility of the latent-PC algorithm in a real dataset for hepatocellular carcinoma. Causal structures for patient survival are visualized and connected with clinical interpretations in the literature.
Copying safety and liveness properties of computational artefacts

Abstract This paper shows how safety and liveness properties are not necessarily preserved by different kinds of copies of computational artefacts and proposes procedures to preserve them, which are consistent with ethical analyses on software property rights infringement. Safety and liveness are second-order properties that are crucial in the definition of the formal ontology of computational artefacts. Software copies are analysed at the level of their formal models as exact, inexact and approximate copies, according to the taxonomy in [3]. First, it is explained how exact copies are the only kind of copies that preserve safety and liveness properties, and how inexact and approximate copies do not necessarily preserve them. Secondly, two model checking algorithms are proposed to verify whether inexact and approximate copies actually preserve safety and liveness properties. Essential properties of termination, correctness and complexity are proved for these algorithms. Finally, contraction and expansion algorithmic operations are defined, allowing for the automatic design of safety- and liveness-preserving approximate copies. As a conclusion, the relevance of the present logical analysis for the ongoing debates in miscomputation and computer ethics is highlighted.
Pre-correlation and post-correlation RF fingerprinting methods for GNSS spoofer identification with real-field measurement data

Radio Frequency fingerprinting (RFF) methods are gaining popularity as physical-layer identification or authentication methods in various navigation and communication applications. Traditionally, RFF has been used in terrestrial communications to identify the genuine transmitters from spoofers and jammers. In recent literature, RFF has gained attention also in the context of satellite navigation and Low Earth Orbit (LEO) satellite communications, though this research area is still in an incipient phase. RFF studies in the context of satellite transmitters (or transceivers) are typically hindered by the challenges in acquiring high-quality raw measurement data. In this paper, we analyze via RFF methods, in both pre-correlation and post-correlation domain, the raw GNSS data collected at three locations: Tampere (Finland), Nottingham (UK), and Nuremberg (Germany). The datasets for the first two scenarios have been collected by the authors, while the third dataset is available in open access. We show that we are able to reach average classification probabilities of spoofer versus GNSS up to 99.99&#x0025; (i.e., Nuremberg measurements) with pre-correlation data and up to 87.72 &#x0025; (i.e., Nottingham measurements) with post-correlation data. We also discuss the challenges and limitations of RFF in the context of GNSS.