Reviewerly/exHarmony huggingface dataset:
Success Stories and Lessons Learned from Pilot Implementation of Unmanned Air Vehicle and Computer Vision to Improve Transmission &amp; Distribution Reliability in Complex Oilfield

Abstract Electrical power supply reliability is a key enabler in supporting massive and aggressive exploration and exploitation campaigns in the upstream sector. For a certain complex oilfield asset, a total of more than 3,000 km of power transmission and distribution lines are operated and maintained to support its massive operation. All these power lines shall be monitored and inspected regularly to ensure it is free of operational threat that could reduce their reliability. From historical data, there are two main threats in power line operations, vegetation risk (trees and animals) and broken insulators. The current method of manual inspection and monitoring through Operator Routine Duties Check (ORDC) is not very effective since it took a considerably long period to complete and can only cover a limited area for each inspection activity session whereas the area to be inspected is vast. As a result, the inspection and monitoring program was sub-optimal to detecting the operational threat earlier. The advances in digital technology, particularly computer vision, cloud computing, and artificial intelligence, enable every device with a camera and internet connection to become additional "eyes" that monitor, inspect and analyze everything in sight. One of the optical devices with high potential for utilization in power system inspection is the Unmanned Aerial Vehicle (UAV) or best known as the drone. Drone enhanced with computer vision will have the optimal capability for inspection and surveillance of vegetation risk and broken insulators for larger areas in each inspection round. Hence, we could automate the inspection and surveillance activities and even improve their effectiveness and efficiency compared with the manual method. In this paper, we will discuss the successful pilot implementation of drones, computer vision, and artificial intelligence technology in power system operations that have improved the effectiveness of the surveillance program at a lower cost. The pilot implementation has been proven to reduce the number of power outages caused by vegetation risk and broken insulators by 50% and bring verified financial benefit of USD 7,619 per month from avoiding loss of production opportunities due to power outages related to vegetation risk and broken insulators. The financial benefit can pay off the implementation cost in 4 months of continuous operation. As a path forward, the pilot implementation will be expanded further to several other areas with a high risk of vegetation threat and broken insulators to assess its applicability in other locations before full-scale implementation.
Real-time profile measurement method for a large-scale satellite antenna

To improve the detection capability of satellite-based synthetic aperture radar, a large antenna array with a length scale of 100 meters is urgently needed. However, the structural deformation of the large antenna leads to phase errors, which will significantly reduce the antenna gain; hence, real-time and high-precision profile measurements of the antenna are essential for active compensation of the phase and thus improving the antenna gain. Nevertheless, the conditions of antenna in-orbit measurements are rather severe because of limited installation locations of measurement instruments, large areas, and long distance to be measured, and unstable measurement environments. To deal with the issues, we propose a three-dimensional displacement measurement method for the antenna plate based on laser distance measuring and digital image correlation (DIC). The proposed method uses the DIC method to retrieve the in-plane displacement information in combination with a laser range finder to provide depth information. A Scheimpflug camera is used to overcome the limitation of the depth of field of traditional cameras and enable clear imaging of the full field. Moreover, a vibration compensation scheme is proposed to eliminate the measurement error of the target displacement caused by the random vibration (within 0.01°) of the camera support rod. The results of the experiment in a laboratory setting show that the proposed method can effectively eliminate the measurement error caused by camera vibration (50 mm) and reduce the displacement measurement error to within 1 mm with a measurement range of 60 m, which can meet the measurement requirements of next-generation large satellite antennas.
The impact of gas corrosion testing on the gray scale value of the security camera

The security camera is widely used. With the long-term aging effect of the environment and the increase in service time, its imaging quality will inevitably decrease. However, there are few research on the impact of environmental aging tests on its imaging quality. This article focuses on the gray scale level of image quality. Considering the complexity of the use environment of the camera, the imaging quality of the typical optical imaging system before and after the gas corrosion testing is analyzed. At the same time, the impact of different light source conditions is also considered here. Studies have found that after environmental testing, the gray scale resolution ability of the imaging system decreases. In addition, light source conditions have a greater impact on its gray scale resolution ability. In the case of optical compensation, the gray scale value is significantly reduced, and the camera cannot perform gray scale resolution. In this paper, two test methods, transmitted standard board method and reflective standard board method, are used to analyze the gray scale value. Studies have found that the results of the two methods are self-consistent.
General characterization of qubit-preserving impairments on two-qubit Bell nonlocality

A general technique for experimentally characterizing the effect of qubit-preserving impairments on the Clauser-Horne-Shimony-Holt parameter is introduced. This technique is independent of the underlying qubit encoding and is theoretically demonstrated for specific example impairments in polarization-encoded quantum-optical qubits. Included in this analysis is how spectrotemporal impairments can be incorporated into this technique.
Research on interactive teaching platform based on VR technology

Under the background of information technology, virtual reality technology has gradually been applied to the field of education and training. Immersive VR can promote the progress of university education, realize the transformation of university education from two-dimensional flat teaching to three-dimensional three-dimensional teaching, and enhance the situational and contextual nature of classroom teaching. Interactivity. The application of VR technology in education can effectively improve the learning effect and enthusiasm of students. Students can independently choose their own learning time, learning location, and learning style, with stronger learning motivation and higher autonomy. This article is based on the establishment of an immersive interactive teaching platform and a teaching resource library so that users can better enter the learning atmosphere.
Sign Language Detection Application Using CNN

Deaf or mute persons frequently use sign language to communicate, but it takes a lot of practice to learn. Principal mode of communication of the Hard-of-Hearing and Deaf community is sign language. Autism, Apraxia of speech, Cerebral Palsy, and Down syndrome are just a few of the disorders that may benefit from sign language. We're utilizing ASL (American Sign Language) for this project. Although ASL uses the same alphabet as English, it is not a dialect of English. American Sign Language is a separate language with its own linguistic framework. Signs are not expressed in the same order as words are in English. This is due to sign language's distinct grammar and visual aspect. In the United States, around half a million people use ASL. We will develop and implement a mobile application that will serve as a translation system to enable people communicate more efficiently using signs in this project. We will demonstrate a real-time sign language recognition system that uses existing datasets to transform a video of a user's signs into text.
Deep Learning Approach to New Age Cinematic Video Editing

This paper gives a new technique for video editing by introducing modules that are trained to create realistic and dramatic sky backgrounds in videos. This project is different from what is being used in the video editing ecosystem as it does not require any static photos or inertial measurements. The module can be simply used on any device without having any prerequisites in the device. This is a game- changer when it comes to capturing cinematic sky videos. This project is further branched into three different modules to segregate the different tasks including sky routing, flow reckoner, and image emulsifier. These methods will run in real- time and are user friendly. This project can generate high fidelity videos with different lighting and dramatics in outdoor environments. Adding further we can also easily synthesize different weather conditions. This editing technique is much simpler and easier giving a more aesthetic image for cinematic shots.
Security in Cloud Technologies: A Brief Overview

Cloud computing, a next-generation computing technique, enables extremely flexible, scalable, and cost-effective computing that is available on demand. Organizations can expect increased agility, improved business interaction, and continuity as a result of cloud technologies. However, with increased demand, the need for data confidentiality, integrity, and availability has become one of the most crucial challenges in cloud computing. Various cryptographic approaches are employed in the cloud to guarantee that these needs are met, such as safeguarding against data breaches and malicious attacks. This paper outlines cloud infrastructure, cloud security requirements, and their challenges. We then present an overview of various cryptographic techniques such as AES, DES, RSA, etc. that have been used to ensure cloud security. We also reviewed hybrid algorithms proposed and implemented by various authors and mentioned their advantages and shortcomings.
Quality-guided mask cut method for phase unwrapping

The phase unwrapping method is the important step of phase retrieval in fringe projection profilometry. Although the mask cut (MC) algorithm has been successfully applied in multiple fields, it also has inherent flaws. In order to overcome the shortcomings of MC algorithm, and synthesize the advantages of MC and quality-guided (QG) algorithm, a quality-guided mask-cutting (QG - MC) algorithm for phase unwrapping is proposed. The basic idea of QG - MC algorithm is to reduce the effects of noise on phase unwrapping at first., Then, the process of phase unwrapping is guided by the phase quality map from the point with the highest quality value to the point around that point. Take the point with the highest quality as the seed point, put its adjacent points into the queue, sort by quality value, and the new highest quality point is used as a seed point. Repeats the process until the queue is empty and the unwrapped phase will be obtained. To verify the feasibility and reliability of QG - MC algorithm, computer simulations and real experiments are carried out. The results show that the algorithm improves the efficiency of phase unwrapping.
Effect of the salt mist testing on imaging resolution of the optical imaging system

The standard evaluation methods of the imaging quality of optical imaging systems are divided into evaluation methods based on electrical signals and evaluation methods based on images, but the impact of environmental testing on the imaging quality is rarely studied. This paper aims to analyze the imaging quality of a typical optical imaging system before and after the salt mist testing. By studying its imaging resolution and setting up different optical environments, the influence of salt mist testing on its imaging resolution under different optical environments is discussed here. It is found that different illumination conditions have great influence on imaging resolution, and good illumination conditions can effectively improve the resolution of camera products. In the other hand, salt mist aging testing has a great influence on the imaging resolution of the camera products, and longtime exposure to sodium chloride solution has an irreversible effect on the imaging performance of the camera. The tolerance of cameras to salt mist aging test shows two-level distribution. Some products are completely invalid, and some products have good tolerance and no obvious failure.
Research on method of avoiding phase unwrapping error in 3D measurement of gray code

In the field of industrial automation, accurate acquisition of object three-dimensional information is beneficial to better product design. Structured light three-dimensional measurement technique has been widely used in industrial inspection and reverse engineering because of its advantages of non-contact and high measurement accuracy. The structured light three-dimensional measurement technique combined with gray code and phase shift method has high efficiency and strong robustness. However, in the process of extract the phase map of the measured objects, there will be misalignment between the wrapped phase and the fringe order. This will lead to the jump error of wrapping phase edge in phase unwrapping process, which will reduce the measurement accuracy. To solve this problem, the fast tripartite phase unwrapping method is proposed in this paper, which can effectively eliminate the phase unwrapping error and significantly improve the computational efficiency. Firstly, the mask image is obtained by threshold segmentation of full-bright images using OTSU method. Subsequently, phase unwrapping is carried out under the guidance of gray code coding sequence, and the absolute phase value of the target is obtained. In the process of phase unwrapping, the mask image is used as the guide to improve the phase decoding efficiency, and tripartite phase unwrapping method is used to eliminate the phase unwrapping error. Experimental results show that this algorithm eliminates the edge jump error and improves the efficiency of normal gray code phase unwrapping method.
Machine-vision-based automatic levels compensation error verification

Algorithms for locating the center position of the crosshair image are studied, and a method based on machine vision to verify the compensation error of automatic levels is proposed. An illumination device is added behind the reticle of the automatic level. The reticle image is focused to infinity, imaged on the focal plane of the lens of the collimator, and images are captured using a CCD camera. Moreover, the necessary processing is performed to locate the center position of the crosshair image by the gray squared center of gravity method and the iterative weighted fitting method to obtain the subpixel level position coordinates. The metrological verification of the compensation error is completed after calibrating the angular grid values of the CCD in the x and y directions, the error angles of the x and y axes, and the parameters of the collimator coordinate system and attitude. The experimental results show that the machine vision-based method meets the requirements of the metrological verification of the compensation error of automatic levels, and the accuracy of the measurement results is higher than the traditional optical method.
Ambientes virtuales de aprendizaje en proyectos de comunicación y medios en la escuela de suboficiales y nivel ejecutivo “Gonzalo Jiménez de Quesada”

One of the great challenges for education in the 21st century is Information and Communication Technology (ICT) with new modes of expression, based on a concept of education with the aim of identifying teaching-learning processes.The elaboration of the Virtual Environments project strengthens communication skills in the students of the Police technician program. The purpose of the project is to create a communication and media strategy using virtual learning environments, the implemented methodology is qualitative in that the description of everyday situations is used.
Not all #complaints are equally contagious: an Instagram experiment

Instagram has been perceived as the least appropriate platform to express negativity online, yet the number of posts tagged under #complain, #complaint, #complaints, and #complaining are increasing. We conducted a controlled web-based experiment to examine the extent to which exposure to other's quotes of complaints contributed to increased similarity in emotions for the audience (i.e., digital emotion contagion). Participants (n = 591 Instagram users in Indonesia; 82.23% females; Mage = 28.06, SD = 6.39) were randomly assigned to exposure of complaint quotes containing seven basic emotions. We found that exposure to three of the five complaint quotes (i.e., anger, disgust, and sadness) induced similar emotions in the participants while the two other complaint quotes (i.e., fear and anxiety) induced overlapping emotions, but a non-complaint quote (i.e., desire and satisfaction) induced alternative emotions. Taken together, digital emotion contagion was likely produced by exposure to complaint quotes, while exposure to non-complaint quotes induced different, possibly complementary-like, emotions. Although these findings should be considered as a snapshot of the complex emotional dynamics online, it highlights that exposure to simple Instagram quotes has the potential to go beyond mere contagion.
Weather Image Classification Using Convolution Neural Network

A real-world weather prediction system that detects and describes weather condition in image data is becoming prominent subject in machine vision . These systems are designed to address the challenge of weather classification using machine vision. Advances in the fields of Artificial Intelligence and Machine Learning enables applications to take on the image recognition capabilities to identify the input image . Deep learning is a vast field and narrow focusing a bit and takes up the challenge of solving an Image Classification process. Proposed deep learning algorithms by tensorflow or keras by classifying the image of weather reports by convolution neural network. CNN is an artificial neural network which inspires animal neural cortex and built upon it . The images are passed through the neural network which consists of multiple layers and filters and then identified and classified according to the weather type. The algorithm is inspired by the brain and so named as Convolution neural network.
A Novel K Means Biclustering Fusion Based Collaborative Recommender System

Collaborative Filtering (CF) examines a consumer's interests and delivers automatic and personalized suggestions for purchasing a variety of products. Sparsity, on the other hand, is one of the approach's primary flaws. This difficulty is inherent in the system due to an ever-increasing quantity of people and things. To address the problem of sparsity, many existing techniques have been given. The user-item rating matrix can only provide minimal information to estimate unknown evaluations in both user-based and item-based instances. In scarce data contexts, they are ineffective. In sparse data situations, many clustering-based methods are useless. In sparse situations, traditional similarity measurements like cosine, pearson correlation, and jaccard similarity are ineffective. Although the system is able to analyze similarity in this scenario, there is a chance that the similarity is unreliable due to insufficient information processed. As the accuracy of prediction drops, this has an impact on the performance of a recommender system. As a result, neighbourhood formation is an important phase in collaborative filtering. As a result, a neighbourhood approach that can perform well in a sparse environment is required. A K Means biclustering fusion based technique is proposed to mitigate the sparsity problem by fusing item-based CF with user-based CF. A Mean absolute difference measure is used to find a neighbouring bicluster that has a significant partial similarity with the active user's preferences, which supports the algorithm in locating quality neighbours. The quality of the neighbours improves the accuracy of your recommendations.
Optimizing Information Leakage in Multi Cloud Storage Services

Many new strategies for storing data across different clouds have lately been proposed. Because a single point of attack can’t leak all the information, distributing data among multiple cloud storage providers (CSP) automatically gives users some control over information leaking. However, if data chunks are dispersed in an unstructured way over different clouds, there will be a lot of information leakage. To prevent data loss due to hacking or server failure, the data is uploaded to different servers. To access owners' data, secure files, and prevent data leakage, we employ advanced techniques such as Advanced Encryption Standard (AES) and Fully Homomorphic Encryption (FHE).
Novel Nutritional Recipe Recommendation

Food is essential for living and is the foremost important energy source, making us do all the work. Nowadays, the variability in these food items is increasing. To find out about any new dish or recipe, we mainly depend upon people around us or by trial-and-error method, but neither method tells us about its nutritional content. Since the web has begun to grow, the advent of food recommender systems has made food suggestions easier but these systems work only on the feedback provided by the customer. Hence, here comes a requirement for a nutritional-based recommender system that considers ratings and nutrition and provides the user with an absolute best recommendation so that the users’ taste preferences and well-being are given equal priority. This study intends to use graph embedding approaches to develop a food recipe recommender system, which uses the ingredients’ nutritional value alongside the recipe’s taste and customer feedback. These food recommender systems can impact people’s dietary practices, as their suggestions are both healthy and relevant. People can now eat healthily without being compromised on taste.
Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification

Abstract Real-world data of multi-class classification tasks often show complex data characteristics that lead to a reduced classification performance. Major analytical challenges are a high degree of multi-class imbalance within data and a heterogeneous feature space, which increases the number and complexity of class patterns. Existing solutions to classification or data pre-processing only address one of these two challenges in isolation. We propose a novel classification approach that explicitly addresses both challenges of multi-class imbalance and heterogeneous feature space together. As main contribution, this approach exploits domain knowledge in terms of a taxonomy to systematically prepare the training data. Based on an experimental evaluation on both real-world data and several synthetically generated data sets, we show that our approach outperforms any other classification technique in terms of accuracy. Furthermore, it entails considerable practical benefits in real-world use cases, e.g., it reduces rework required in the area of product quality control.