Reviewerly/exHarmony huggingface dataset:
Machine Learning Assisted Petrophysical Logs Quality Control, Editing and Reconstruction

Abstract Mature field operators collect log data for tens of years. Collection of log dataset include various generation and multiple vintages of logging tool from multiple vendors. Standard approach is to correct the logs for various artefacts and normalize the logs over a field scale. Manually conducting this routine is time consuming and subjective. The objective of the study was to create a machine learning (ML) assisted tool for logs in a giant Lower Cretaceous Carbonate Onshore field in Abu Dhabi, UAE to automatically perform data QC, bad data identification and log reconstruction (correcting for borehole effects, filling gaps, cleaning spikes, etc.) of Quad Combo well logs. The study targets Quad Combo logs acquired since mid-60's. Machine learning algorithm was trained on 50 vertical wells, spread throughout the structure of the field. The workflow solution consists of several advanced algorithms guided by domain knowledge and physics based well logs correlation, all embedded in an ML-data-driven environment. The methodology consists of the following steps: oOutliers detection and complete data clustering.oSupervised ML to map outliers to clusters.oRandom Forest based ML training by clusters, by logs combination on complete data.oSaved models are applied back to the whole data including outliers and sections with one or several logs missing.oValidation and Blind test of results.oModels can be stored and re-used for prediction on new data. The ML tool demonstrated its effectiveness while correcting logs for outliers’ like Depth Offsets between logs, identifying Erroneous readings, logs prediction for absent data and Synthetic logs corrections. The tool has a tendency to harmonize logs. First test demonstrated robustness of the selected algorithm for outliers’ detection. It cleaned data from most of contamination, while keeping good but statistically underrepresented logs readings. Clustering algorithm was enhanced to supplement cluster assignment by extraction of the corresponding probabilities that were used as a cut-off value and utilized for a mixture of different ML models results. This application made results more realistic in the intervals where clustering was problematic and at the transition between different clusters. Several intervals of bad and depth shifted logs corrections were noticed. Outliers’ corrections for these logs was performed the way that at Neutron-Density or Neutron-Sonic cross-plots points were moved towards expected lithology lines. Algorithm could pick-up hidden outliers (such as synthetic logs) and edited the logs to make it look intuitively natural to a human analyst. The work successfully demonstrated effectiveness of ML tool for log editing in a complex environment working on a big dataset that was subject of manual editing and has number of hidden outliers. This strong log quality assurance further assisted in building Rock Typing based Static Model in complex and diagenetically altered Carbonates.
Spectral-spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification

Early detection of cancerous tissue is crucial for long-term patient survival. In the head and neck region, a typical diagnostic procedure is an endoscopic intervention where a medical expert manually assesses tissue using RGB camera images. While healthy and tumor regions are generally easier to distinguish, differentiating benign and malignant tumors is very challenging. This requires an invasive biopsy, followed by histological evaluation for diagnosis. Also, during tumor resection, tumor margins need to be verified by histological analysis. To avoid unnecessary tissue resection, a non-invasive, image-based diagnostic tool would be very valuable. Recently, hyperspectral imaging paired with deep learning has been proposed for this task, demonstrating promising results on ex-vivo specimens. In this work, we demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. We analyze the value of using multiple hyperspectral bands compared to conventional RGB images and we study several machine learning models’ ability to make use of the additional spectral information. Based on our insights, we address spectral and spatial processing using recurrent-convolutional models for effective spectral aggregating and spatial feature learning. Our best model achieves an AUC of \({76.3\,\mathrm{\%}}\), significantly outperforming previous conventional and deep learning methods.
Bridging Classical and Quantum with SDP initialized warm-starts for QAOA

We study the Quantum Approximate Optimization Algorithm ( QAOA ) in the context of the Max-Cut problem. Noisy quantum devices are only able to accurately execute QAOA at low circuit depths, while classically-challenging problem instances may call for a relatively high circuit-depth. This is due to the need to build correlations between reachable pairs of vertices in potentially large graphs [ 16 ]. To enhance the solving power of low-depth QAOA, we introduce a classical pre-processing step that initializes QAOA with a biased superposition of possible cuts in the graph, referred to as a warm-start . In particular, we initialize QAOA with a solution to a low-rank semidefinite programming relaxation of the Max-Cut problem. Our experimental results show that this variant of QAOA , called QAOA-warm , is able to outperform standard QAOA on lower circuit depths in solution quality and training time. While this improvement is partly due to the classical warm-start, we find strong evidence of further improvement using QAOA circuit at small depth. We provide experimental evidence of improved performance as well as theoretical properties of the proposed framework.
Problematising reality: the promises and perils of synthetic media

This commentary article focuses on the emergence of synthetic media-computer-generated content that is created by employing Artificial Intelligence (AI) technologies. It discusses three of the most notable current forms of this emerging form of content: deepfakes, virtual influencers, and augmented and virtual reality (collectively known as extended reality). Their key features are introduced, and the main challenges and opportunities associated with the technologies are analysed. In all cases, a crucial change is underway: reality (or, at least, the perception thereof) is seen as increasingly less stable, and potential for manipulation is on the rise. In fact, it transpires that personalisation of (perceived) reality is the likely outcome, with increasing societal fragmentation as a result. Mediatisation is used as a broad-ranging metatheory that explains the permeation by media of everyday affairs to explain the degree of impact that synthetic media have on the society. In this context, it is suggested that we search for new and alternative criteria for reality that would be capable of accounting for the changing nature of agency and impact in today's world.
AI-Biometric-Driven Smartphone App for Strict Post-COVID Home Quarantine Management

COVID-19 has been announced as a Global Communal Health Extremity by WHO on January 2020.Meaningful preventive solutions have been taken with smartphone selfie/geofencing apps toward managing mandatory home quarantine and physical distancing.In the post-COVID world, fast screening and strict quarantine can play a crucial role in bringing back normality.Quarantine being offered at home can be a comfortable solution for both government and patients.On the other hand, it can be hazardous if not strictly followed and adequately realized.However, the existing geofencing/face selfie apps take static photographs and location data at certain time intervals that can allow patients to violate the rules between those periods, thus failing to ensure active user identity.To realize unbreached home quarantine policies, this article introduces a CUBA-HQM smartphone app that performs continuous user biometric authentication (CUBA) augmented with geofencing using AI technology.The purpose of continuous tracking is to strictly control the spread of infectious diseases in society by monitoring the individual move in/out in the quarantine zone.
Attention-based Bidirectional Long Short-Term Memory Networks for Relation Classification Using Knowledge Distillation from BERT

Relation classification is an important task in the field of natural language processing. Today the best-performing models often use huge, transformer-based neural architectures like BERT and XLNet and have hundreds of millions of network parameters. These large neural networks have led to the belief that the shallow neural networks of the previous generation for relation classification are obsolete. However, due to large network size and low inference speed, these models may be impractical in on-line real-time systems or resource-restricted systems. To address this issue, we try to accelerate these well-performing language models by compressing them. Specifically, we distill knowledge for relation classification from a huge, transformer-based language model, BERT, into an Attention-Based Bidirectional Long Short-Term Memory Network. We run our model on the SemEval-2010 relation classification task. According to the experiment results, the performance of our model exceeds that of other LSTM-based methods and almost catches up that of BERT. For model inference time, our model has 157 times fewer network parameters, and as a result, it uses about 229 times less inference time than BERT.
Blockchain Technology and Facilitation of International Trade: An Empirical Analysis

Blockchain technology, a distributed and decentralized ledger, has gained significant prominence in the sphere of information technology for introducing new innovations in international trade. It ensures safety and can reduce trade costs by removing the middleman from the trade process. Studies examining the effects of blockchain on international trade are scarce. This research aims to fill this research gap. By using time series world data for the period 2009–2018, this study empirically examines the link between blockchain and international trade. It uses a cointegration test and a generalized linear model (GLM) test to analyse the data. The robust findings of this research reveal that blockchain has positive effects on international trade. The findings further displays that blockchain accelerates and facilitates international trade and that there exists a unidirectional causality from blockchain to international trade. The research findings are of great significance for policymakers in developing policies to foster the use of blockchain applications as a facilitator of international trade.
Bangla Document Classification using Character Level Deep Learning

Last few decades, the availability and accessibility of the Bangla document and its content have rapidly increased due to the rapid technological advancement. Intense research needs to be performed on various Bangla documents due to the diversity of the language and associated sentiment. Document classification is one of the fundamental problems of Natural Language Processing. To handle miss-classification and convenient indexing and searching of Bangla documents on the web, researchers nowadays exploring different fields of computer science to classify Bangla documents. In this paper, Deep Learning based approaches are implemented to classify Bangla text documents. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) is used here for the classification task. Here we have implemented an advanced technique that encoded the documents at their character level. Documents from three different data sources are used to validate and test of the working models. The highest classification accuracy is 95.42% that is achieved on the Prothom Alo data set using LSTM. Furthermore, we presented a comparison between two models and explained how well the classification task can be carried out using our character level approach with higher accuracy.
Organizing recurrent network dynamics by task-computation to enable continual learning

Biological systems face dynamic environments that require continual learning. It is not well understood how these systems balance the tension between flexibility for learning and robustness for memory of previous behaviors. Continual learning without catastrophic interference also remains a challenging problem in machine learning. Here, we develop a novel learning rule designed to minimize interference between sequentially learned tasks in recurrent networks. Our learning rule preserves network dynamics within activity-defined subspaces used for previously learned tasks. It encourages dynamics associated with new tasks that might otherwise interfere to instead explore orthogonal subspaces, and it allows for reuse of previously established dynamical motifs where possible. Employing a set of tasks used in neuroscience, we demonstrate that our approach successfully eliminates catastrophic interference and offers a substantial improvement over previous continual learning algorithms. Using dynamical systems analysis, we show that networks trained using our approach can reuse similar dynamical structures across similar tasks. This possibility for shared computation allows for faster learning during sequential training. Finally, we identify organizational differences that emerge when training tasks sequentially versus simultaneously.
Image Stitching for Chest Digital Radiography Using the SIFT and SURF Feature Extraction by RANSAC Algorithm

Abstract Image stitching is one of the branches of computer vision. It combines two or more images for a scene to acquire a high-resolution panoramic image. An invariant local function often uses to stitch two images together. Since the flat plate of a digital radiography (DR) system does not cover all parts of the body, the whole bone structure image cannot seize in a single scan. To solved this problem, image stitching is broadly utilized by medical systems to stitch DR images, which can be helpful for scoliosis or lower extremity deformities in the diagnosis, and pre-operative planning are of great importance. In this paper, the stitching and retrieval of medical images planned. To conquer the background noise in medical images, and improve the recovery of quality and stitching rapidity of medical images, a random sample consensus (RANSAC) algorithm is useful to stitching the images of Chest digital radiography by scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) feature extraction. Down-sampling utilizes to lessen the size of the images and reduction the measure of calculation. In the interim, the phase correlation is engaged to discover the overlapping region. After feature matching and perspective transformation, the stitched image is gotten dependent on the homography. At last, experimentation has finished showing the presentation.
QaSD: A Quality-aware Strategic Dashboard for supporting decision makers in Agile Software Development

Software and data analytics solutions support improving development processes and the quality of the software produced in Agile Software Development (ASD). However, decision makers in software teams (e.g., product owner, project manager) are demanding powerful tools providing evidence data that support their strategic decision-making processes. In this paper, we present and provide access to QaSD, a Quality-aware Strategic Dashboard supporting decision makers in ASD. The dashboard allows decision makers to define high-level strategic indicators (e.g., customer satisfaction, process performance) related to software quality and to measure, explore, simulate and forecast the values of those indicators in order to explain and justify their decisions. Moreover, we also provide the results of a conducted evaluation of the dashboard quality in a real environment that evaluated the QaSD as usable, easy to use, with good navigation, and reliable.
Development of experience in the application of technologies in the field of alternative energy: World experience, Russian practice

The development of alternative energy is considered as one of the ways to reduce the environmental pollution and carbon dioxide emissions. In Russia, the current level of development of alternative energy is not high. Russia requires the regulatory mechanisms that would contribute to the development of this energy sector. The objective of the article is to analyze and determine the directions for improving state influence by creating regulatory mechanisms for the development of alternative energy. In this work, the following scientific research methods were used: data analysis, study of statistical data, and analysis of literary sources. The role of alternative energy has been determined since it helps to reduce greenhouse gas emissions and prevent climate change. In the world, this determines the development, considering international agreements and the implementation of the national policies of many states in this regard. Alternative energy has not been sufficiently supported in Russia, many researchers have doubts about the effectiveness of supporting such projects. Prospects for further research are related to further study of the dynamics of the alternative energy development, as well as the study of government decisions in this area.
AI and Blockchain Integrated Billing Architecture for Charging the Roaming Electric Vehicles

Due to the proliferation of extended travel range electric vehicles (EVs), these will travel through different networks that might be served by different utility companies. Therefore, we propose an architecture capable of offering a charging service to roaming vehicles. Furthermore, although the energy internet supports both the flow of energy and information, it does not support seamless EV roaming service, because it is based on a centralized architecture. The blockchain technology that is based on a decentralized system has the potential to support a secure billing platform for charging the EVs roaming through different electrical jurisdictions. Furthermore, the integration of artificial intelligence (AI) ensures that the participating players get a fair portion of the revenue. Thus, the objective of this paper is to develop an AI and blockchain integrated billing architecture that would offer a charging service to the “roaming” EVs and present a fair and unified billing solution.
Analysis of the Trusted‐Device Scenario in Continuous‐Variable Quantum Key Distribution

The assumption that detection and/or state-preparation devices used for continuous-variable quantum key distribution (CV-QKD) are beyond influence of potential eavesdroppers leads to a significant performance enhancement in terms of achievable key rate and transmission distance. We provide a detailed and comprehensible derivation of the Holevo bound in this so-called trusted-device scenario. Modelling an entangling-cloner attack and using some basic algebraic matrix transformations, we show that the computation of the Holevo bound can be reduced to the solution of a quadratic equation. As an advantage of our derivation, the mathematical complexity of our solution does not increase with the number of trusted-noise sources. Finally, we provide a numerical evaluation of our results, illustrating the counter-intuitive fact that an appropriate amount of trusted receiver loss and noise can even be beneficial for the key rate.
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms

The information-theoretic framework of Russo and J. Zou (2016) and Xu and Raginsky (2017) provides bounds on the generalization error of a learning algorithm in terms of the mutual information between the algorithm's output and the training sample. In this work, we study the proposal, by Steinke and Zakynthinou (2020), to reason about the generalization error of a learning algorithm by introducing a super that contains the training as a random subset and computing mutual information conditional on the super sample. We first show that these new bounds based on the conditional mutual information are tighter than those based on the unconditional mutual information. We then introduce yet tighter bounds, building on the individual sample idea of Bu, S. Zou, and Veeravalli (2019) and the data dependent ideas of Negrea et al. (2019), using disintegrated mutual information. Finally, we apply these bounds to the study of Langevin dynamics algorithm, showing that conditioning on the super allows us to exploit information in the optimization trajectory to obtain tighter bounds based on hypothesis tests.
Adaptive characterization of spatially inhomogeneous fields and errors in qubit registers

Abstract New quantum computing architectures consider integrating qubits as sensors to provide actionable information useful for calibration or decoherence mitigation on neighboring data qubits, but little work has addressed how such schemes may be efficiently implemented in order to maximize information utilization. Techniques from classical estimation and dynamic control, suitably adapted to the strictures of quantum measurement, provide an opportunity to extract augmented hardware performance through automation of low-level characterization and control. In this work, we present an adaptive learning framework, Noise Mapping for Quantum Architectures (NMQA), for scheduling of sensor–qubit measurements and efficient spatial noise mapping (prior to actuation) across device architectures. Via a two-layer particle filter, NMQA receives binary measurements and determines regions within the architecture that share common noise processes; an adaptive controller then schedules future measurements to reduce map uncertainty. Numerical analysis and experiments on an array of trapped ytterbium ions demonstrate that NMQA outperforms brute-force mapping by up to 20× (3×) in simulations (experiments), calculated as a reduction in the number of measurements required to map a spatially inhomogeneous magnetic field with a target error metric. As an early adaptation of robotic control to quantum devices, this work opens up exciting new avenues in quantum computer science.
Message Security Through AES and LSB Embedding in Edge Detected Pixels of 3D Images

This paper proposes an advanced scheme of message security in 3D cover images using multiple layers of security. Cryptography using AES-256 is implemented in the first layer. In the second layer, edge detection is applied. Finally, LSB steganography is executed in the third layer. The efficiency of the proposed scheme is measured using a number of performance metrics. For instance, mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), mean absolute error (MAE) and entropy.