Reviewerly/exHarmony huggingface dataset:
Image and Video Processing-Based Traffic Analysis Using OpenCV

Traffic a significant problem in a lot of cities at different places due to increase in number of vehicles. For traffic related problems, computer vision approaches are preferable. System during any installation will not trouble traffic and are easily changed. We proposed an advance vehicle detection, vehicle classification and vehicle counting system for traffic analysis. Input either image or a video. First detection and classification of vehicles takes place followed by vehicle count. In this project, we detect and classify heavy motor and light motor vehicles like car, motorbike, truck, bus on the roads and also count the number of vehicles travel through the roads. YOLOv4 model, Configuration file and Weight file of YOLOv4, COCO dataset, Non Max Suppression and OpenCV are used to implement the system. Counted data is stored in file to analyze different vehicles that are traveling through the roads which is used for analysis and diversion.
Top Five Machine Learning Libraries in Python: A Comparative Analysis

Nowadays machine learning (ML) is used in all sorts of fields like health care, retail, travel, finance, social media, etc. ML system is used to learn from input data to construct a suitable model by continuously estimating, optimizing, and tuning parameters of the model. To attain the stated, Python programming language is one of the most flexible languages, and it does contain special libraries for ML applications, namely SciKit-Learn, TensorFlow, PyTorch, Keras, Theano, etc., which is great for linear algebra and getting to know kernel methods of machine learning. The Python programming language is great to use when working with ML algorithms and has easy syntax relatively. When taking the deep-dive into ML, choosing a framework can be daunting. The most common concern is to understand which of these libraries has the most momentum in ML system modeling and development. The major objective of this paper is to provide extensive knowledge on various Python libraries and different ML algorithms in comparison with meet multiple application requirements. This paper also reviewed various ML algorithms and application domains.
Review on Load Balancing Techniques, Resource Scheduling, Sidechannel Attacks in Cloud Environment

Cloud computing is an inventive innovation that has acquired a progressive changes in computing administrations. It has empowered to drive the concentration from neighborhood/individual calculation to datacenter-driven algorithm by giving resources powerfully in a virtualized way by means of Internet. The aim of load adjusting is to clearly comprehend the client prerequisites, the information and data which can be sent and obtained without taking additional time. In this paper, we have focused on cloud computing, load balancing techniques, cloud resource scheduling process. Review on load balancing techniques and resource scheduling, sidechannel attack is also presented in this paper.
Deep Learning and Blockchain for Electronic Health Record in Healthcare System

The emerging technologies like Artificial intelligence and block chain technology has wide range of applications in the field of healthcare system. Deep neural networks or deep learning technology in Artificial intelligence which works similar to human brain is coupled with blockchain technology provides effective tracking and personalized collection of data in the medical field. Integration of these two technologies together allows data security and transparency in the medical care system with a high accuracy. Review on several research paper by using deep learning and blockchain technology illustrated security and efficiency advancement for the prediction and decision making process in biomedical applications. Blockchain technology stores cryptographic data which artificial intelligence requires. Real life data increases the accuracy of regression or classification problems in deep neural network. Block chain technology ensures the safety of data exchanging and analysis among data suppliers. Comparison studies between deep learning technology and blockchain technology in medical field aims to give a brief informations and process flow about their integration process in medical electronic health care sector. Integration of artificial intelligence and Block chain technology provides time consuming and more accurate result with all safety towards data exchanged.
Raising awareness without disclosing truth

Agents use their own vocabularies to reason and talk about the world. Public signature awareness is satisfied if agents are aware of the vocabularies, or signatures, used by all agents they may, eventually, interact with. Multi-agent modal logics and in particular Dynamic Epistemic Logic rely on public signature awareness for modeling information flow in multi-agent systems. However, this assumption is not desirable for dynamic and open multi-agent systems because (1) it prevents agents to use unique signatures other agents are unaware of, (2) it prevents agents to openly extend their signatures when encountering new information, and (3) it requires that all future knowledge and beliefs of agents are bounded by the current state. We propose a new semantics for awareness that enables us to drop public signature awareness. This semantics is based on partial valuation functions and weakly reflexive relations. Dynamics for raising public and private awareness are then defined in such a way as to differentiate between becoming aware of a proposition and learning its truth value. With this, we show that knowledge and beliefs are not affected through the raising operations.
Tool-Based Prediction of SQL Injection Vulnerabilities and Attacks on Web Applications

In today’s present time, SQL injection has become a significant security threat over the web for diverse dynamic web applications and websites. SQL Injection may be a sort of associate injection attack that produces it doable to execute malicious SQL statements into an online application consisting of SQL information. Attackers use these SQL Injection Queries or Statements specified if an Internet site or an application hosted on web contain SQL vulnerabilities to bypass application security measures. The Attacker will even go around authentication associated with authorization of an online page or Internet application and might bypass these methods and retrieve the content of the whole SQL information of an online application. The purpose of the proposed system is to predict the occurrence of a SQL injection attack on a particular server where an application is deployed from a given supply at a particular point in time. This predictive experiment is managed using the JMeter tool. From network logs, you can now pre-measure, exclude choices, analyze, and feed machine learning models to predict SQLIA.
Optimizing Deep Neural Network for Viewpoint Detection in 360-Degree Images

360° image enables the user to interact with the view and explore the whole environment around the camera. As there can be infinite number of viewports in a 360° image, the work of viewer becomes cumbersome and confusing. This work aims to study 360° images and to classify those into 10 different categories based on places and then to predict the viewpoint using deep CNN architectures. This study explores the advantages of transfer learning and uses the same to create a classifier to classify the 360° images into different categories. Further, two approaches are proposed to predict the viewpoint in a 360° image to recommend the viewer best viewport.
Research on Interference-Free Monitoring of Driver's Steering Behavior

The study of driver's driving behavior is not only an important part of traffic safety, but also of great significance to promoting the development of autonomous vehicles. In this paper, a Triboelectric Nanogenerators (AK-TENG) with an aluminum (AI)-Kapton friction layer structure is developed for driving behavior monitoring of drivers. AK-TENG has high sensitivity and certain flexibility. Compared with ordinary Triboelectric Nanogenerators, the structure is stronger and more stable, and less damaged. It is used to obtain the driving behavior data of the driver on the steering wheel. This paper uses AK-TENG to obtain driving behavior information data to monitor the driver's steering behavior, and also provides a new idea for the design of high-sensitivity self-powered sensors in intelligent transportation systems.
Predictive Analysis for Prognostication of Breast Cancer

Nowadays, most of the women affected by one of the type of cancer is Breast Cancer (BC). The main objective is to identify and prognosis of the Breast Cancer at its earlier stages using supervised machine learning (ML) techniques like Naive Bayes (NB) classifier, support vector machine (SVM), random forest classifier (RFC), K-nearest neighbor (KNN) and decision tree classifier (DT). The data are collected from the Wisconsin Diagnostic Breast Cancer dataset then malignant and benign tumors are classified based on the dataset and using various ML techniques to predict the Breast Cancer in advance manner. Various parameters like accuracy, precision, sensitivity (recall), F-measure (specificity), regression score, variance measure, maximum error and balanced accuracy are measured in each algorithms. Using Python experimental tool PyCharm to execute ML algorithms for predicts the Breast Cancer. Our final result indicates that support vector machine is the best one for predictive Breast Cancer efficiently with accuracy of 99%.
Dynamic cognitive maps for robot route planning in complex workplaces represented by abstract mazes

Abstract Adaptive path planning and optimization for robots are a persistent challenge due to the lack of a cognitive map of the complex and dynamic environments. This paper presents a cognitive‐map‐based method for dynamic robot path planning by a formal maze model for workplace layouts representation. The maze model and global path trees provide a cognitive map for the robot to aware the environment. A theory of dynamic Paths Finding and Optimization (PFO) is developed. Powered by the PFO algorithm, a robot is able to autonomously explore the optimal path in a complex workplace by a single sight‐read of its layout. The maze based PFO methodology provides robot for an efficient real time path cognition and optimization algorithm for dealing with dynamic workplaces. A set of experiments demonstrates the efficiency of the method, which extends traditional stepwise robot vision technologies to cognitive‐map‐driven global path optimization.
Carbon Footprint Management Using Blockchain

Abstract There is a broad consensus that greenhouse gas emissions management requires coordinated efforts and collaboration in all sectors and at all levels of an organization or within the value chain. However, potential conflicts of interest and lack of trust between stakeholders make this collaboration extremely challenging. Blockchain has opened the door for a series of innovative applications that can propose an efficient carbon footprint traceability and management solution. Blockchain is a distributed secure database, called a ledger, among different parties, used to hold and verify tamper-proof records or transactions without the need to trust any participant of this process except the mechanism. In the race to net zero, the need for an advanced information and communication technology has become vital to global climate change management with increased digitalization, decarbonization, security, and decentralization challenges. Blockchain is proposed as an integrated platform for various applications, such as carbon traceability, carbon trading, certification, and value chain management. This work presents an overview of blockchain technology and its working principles. It describes blockchain's novelty and innovation to the industry and climate action. Also, the paper investigates blockchain's potential for carbon footprint traceability and management. It explores the latest use cases and the current challenges. It concludes that enabling innovation for climate action requires digging further into evolving disruptive technologies such as blockchain.
Synthetic Data Generation for Machine Learning Applications in the Energy Industry

Abstract With the ubiquity of machine learning technologies, their adoption in the energy industry is bound to become mainstream. However, a major setback of developing innovative modern machine learning technologies is their significant reliance on large labeled datasets during model training and development. Acquiring and labeling such large datasets can be prohibitively expensive as you need to manually capture and label massive amounts of data points depending on the application. Additionally, when dealing with images in the energy industry, it can be hard to capture images due to elevated safety and security standards as well as sensitive restricted areas. Therefore, an emerging technique that is being utilized is by generating synthetic datasets as an alternative to actual field images for training machine learning models. These modern methods of using synthetic datasets are not exclusive to visual data as we can also simulate acoustic and time-series data, among others. However, in this study we will limit our scope to focus on computer vision related applications and generating synthetic images. We present a discussion of the 3D rendering software that could be used to generate synthetic visual data, such as Blender and Unity, by outlining their respective strengths and weaknesses. Furthermore, a detailed account of common image augmentations is presented to showcase their effects on model accuracy. In order to ensure that models trained on synthetic data perform well when deployed, we validate the model with a relatively small subset of real labeled images. The difference in accuracy between real and synthetic data will be largely determined by how well the synthetic images represent real images. As such, we show that the simulated images look realistic enough and we expect better transference when predicting real images. To validate our methodology, Convolutional Neural Networks (CNNs) are used to train multiple models for visual inspection using synthetic data generated by the aforementioned 3D rendering software for the following applications: Flange integrityClassifying lever positions (open/closed) As visual inspections via robotic technologies such as drones increase in facilities, the need for labeled data for machine learning model training increases so as to enable automated asset integrity. We propose a procedure to streamline the model training process by utilizing synthetic images. This will present cost and time reduction as well as the ability to train models for features with sparse real data.
Implementing a Hybrid Digital Twin Approach in a Crude Unit Operation

Abstract Over the past decade, Machine Learning or, more generally, Artificial Intelligence, has made a stelar entry into the O&amp;G world and is today routinely used from exploration all the way up to retail. At the same time, physics-based tools are still being seen as key for value generation and the O&amp;G community is increasingly looking at combining "traditional" with "new" technologies in what has come to be known as "hybrid" tools. In this paper we explain how we Baker Hughes, C3 and KBC are combining Physics and Machine Learning to create Hybrid Digital Twins that help operators improve their margins, reduce their emissions and, in general, position themselves for sustainable growth. Before they are in operation, plants (and oilfields alike) are typically simulated using physics-based tools. In practice, therefore, physics-based simulation models pre-date any plant data. Once plant data starts being produced, however, it becomes clear they do not match exactly the theoretical models that were used before operation. It also becomes clear that plants are operated in a very narrow range, due to quality, production and HSE requirements. In this paper we document a hybrid approach for a digital twin used to generate optimized targets to a Crude Unit operation: synthetic data has been generated to overcome the limitations of available plant data. These synthetic data have then been used as additional training input for ML models, complementing instrumentation data, while at the same time, instrumentation data is used for calibration of the physics-based model. This hybrid approach has been applied to a Crude Unit, in an optimization use case, with remarkable results. The final goal was to make energy optimization targets to operations through its machine learning algorithms which are based on two years of historical data optimized by a rigorous process simulator. The targets are manually downloaded to the unit multivariable controller by the operators. The hybrid approach has been found to combine the benefits of each type of technique: First Principles provides a sound bases for (limited) extrapolation, while Machine Learning ensures the First Principles models remain tuned to the reality of the process and complements the physics in areas where it is insufficient to model the reality (as is the case of equipment performance degradation/failure prediction). The range of what-if studies (and, correspondingly, optimization options) is thus radically extended. This paper shows incremental benefits in optimization cases illustrated by a Crude Unit use case, resulting from the application of a hybrid digital twin using both physics-based and machine-learning models. The hybrid approach allows optimizers to evaluate scenarios beyond the historical operating envelope and opens the door to incorporating equipment condition considerations.
Unleash the Potential of Upstream Data Using Search, AI and Computer Vision

Data is at the heart of Digital Transformation whether it is generated by legacy systems, a word processor, or state-of-the-art sensors. This makes it harder for Upstream professionals to find the right information for operational needs and making decisions. In this work, we describe an approach that utilizes Enterprise Search, AI and Computer Vision to construct a single efficient search layer, called USEARCH, over multiple data repositories. This enables Upstream professionals to perform searches using simple business language and get the information they need wherever it resides.Our approach to build USEARCH consists of five steps. First, establishing a search infrastructure. Second, indexing content and metadata of documents to make them easily searchable from a single layer regardless of the source repository. Third, utilizing Artificial Intelligence to intelligently tag information within data and documents such as wells, reservoirs and business process labels. Fourth, applying Computer Vision techniques to extract tabular information from documents. Fifth, developing an intuitive user interface to simplify finding data. It can intelligently set the business context based on user domain such as Exploration, Drilling or Petroleum Engineering.We applied our approach on over 3 million documents of different types such as drilling reports, reservoir studies, well test analysis, biostratigraphy and geological maps and stored on multiple repositories. This results in a massive improvement. Upstream professionals can now find data through a single layer and thus eliminate the overhead of switching searches between repositories. In addition, multiple search queries that used to take minutes have now been replaces by a single query that takes a few seconds and even milliseconds in some cases. Furthermore, search results are displayed in a business context with direct links to data at the source repositories. The application of Computer Vision allows any tabular data within documents to be exported to databases for fast analysis.Our approach connects all different data repositories and provides user with an intuitive user interface to find data. This unleashes the potential of Upstream data and empowers Upstream analysts to spend more time using data than finding it.
An Integrated Management of Change Program (MOC) for Oil and Gas Industry through Blockchain Technology and Smart Contracts.

Abstract New Technologies (blockchain, autonomous cars, artificial intelligence, and the internet of things) are revolutionizing industries such as finance, banking and transportation by improving efficiency and effectiveness in these businesses around the world. However, not much work is done on applying these technologies in the oil and gas industry. So, a tremendous opportunity exists to leverage these new technologies for possible applications in oil and gas. This paper explores the application of blockchain and smart contracts in managing an effective Management of Change (MOC) program and integrating it with operations to process real-time changes leading to faster and safer decision making. The paper is divided into two sections; the first provides an overview of the technology, and the second focuses on integrating smart contracts and blockchain technology to leverage the MOC program. This paper could be used to roll out new and improve existing MOC programs. The application of emerging technologies such as distributed ledger and smart contracts are still at the rudimentary level for oil and gas with few applications around trading and logistics. This paper unlocks the possible application in managing changes in the oil and gas, which will provide a solid platform for future innovations. The oil and gas industry are moving at a faster pace to achieve the next level of efficiency and effectiveness for sustainable growth, the key will remain in the early adoption of these emerging technologies for safer operation. The application mentioned in this paper will provide a company with an additional competitive advantage as they could effectively manage their data to make faster decisions; and achieve higher efficiency and safer operations simultaneously.
Applying Blockchain Technology to Process Safety with Focus on Integrity Management for Oil and Gas Industry

Abstract Disruptive innovation has extensively changed human interactions, market trends and other aspects such as transportation and communication. Over last decade many new technologies (artificial intelligence, block chain technology, Internet-Of-Things) has changed the way world do business. These new technologies are playing an important role in increasing efficiency and effectiveness in businesses around the world. This paper focuses on application of blockchain technology and use of distributed ledgers to leverage process safety and integrity management program. The focus of safety management system is to evaluate hazards and decrease risks with an assurance that the identified safeguards are effective. All the elements of process safety management program are interlinked so failure or gap in one element will adversely affect the efficacy of the overall process safety management program. The effectiveness of a process safety management depends on many factors such as management support, personnel participation, effective and tamper-proof data management and reporting. So, it takes considerable number of resources to set up and manage these systems. This paper explores use of blockchain and distributed ledgers to effectively manage elements of process safety management program with major focus around integrity management for oil and gas industry. The paper focuses on core 6 elements however the discussion could be applied for the other elements of the process safety management program. This paper provides an overview application of blockchain to different elements of process safety management program discussing the pros and cons. The paper provides a detailed scoping of application of blockchain and distributed ledgers for integrity management program.