Reviewerly/exHarmony huggingface dataset:
Unconventional reservoir characterization using geochemical signatures: Examples from Paleozoic formations, Saudi Arabia

The soaring domestic energy consumption in the Kingdom of Saudi Arabia (KSA) continues to create a demand and supply gap. Paleozoic tight sand and shale reservoirs in KSA are considered potential sources of natural gas that could meet the growing domestic demand. To enhance the exploration and exploitation of these unconventional systems, research focusing on their reservoir quality has tremendously increased in the past few decades. A challenging aspect of these Paleozoic reservoirs is that they have similar lithologies and are poorly constrained biostratigraphically. Therefore, facies analysis, stratigraphic correlation, and geosteering are problematic. This paper describes how chemostratigraphic markers are used to identify and separate individual lithologies. Three hundred and twenty (320) data points were acquired by scanning both end caps of 165 core plug samples from continuously cored sections of three Paleozoic formations. Raw elemental spectral data were acquired using a high-resolution tabletop μXRF system. Multivariate statistical analysis including principal component analysis (PCA), pairwise correlation, and hierarchical clustering of principal components (HCPC) were used to characterize the acquired geochemical values. Eleven (11) chemofacies, within three lithofacies with clearly defined boundaries, were identified. Stratigraphic distribution patterns of the geochemical signatures (e.g., Si, Ca, Mn, Al, K, and Fe), such as sharp truncation at the upper and lower bounds, clearly defined each lithofacies. Thus, key marker elements for each lithofacies and chemozones were identified, even where the lithologies appeared homogeneous. The overall results indicate sediment deposition under varied depositional settings, from fluvial to shallow-marine environments. The results demonstrate that elemental compositional analysis can be used as a discriminating tool for resolving stratigraphic uncertainties. Such data can document subtle variations among samples that appear homogeneous using other techniques.
Efficient Designs for Practical Blockchain-IoT Integration

Analysis of existing work and studies lead to the observation that BIoT (Blockchain (BC) and Internet of Things (IoT) integration) faces various efficiency issues in IoT-to-BC adaption associated with (a) scalability, (b) energy efficiency, and (c) security. To solve these concerns, at the first step and via experimental analysis of multiple Distributed Applications (dApp), this paper and PhD thesis does identify key BIoT efficiency affecting metrics. In the second step, these metrics are used to design and evaluate an efficient BIoT architecture —termed as BIIT—. Extensive simulations conducted on IoT network proves that BIIT enhances the efficiency of IoT-to-BC communications even for a limiting IoT protocol such as LoRaWAN. As of the third, the design and evaluation of DLIT (Distributed Ledger (DL) for IoT daTa) is presented. DLIT offers a highly scalable and secure DL via a hybrid consensus mechanism, equipped with sharding, and IoT data modification possibilities.
A method for detecting pedestrian height and distance based on monocular vision technology

With the rapid development of smart cities, pedestrian detection information has expanded from human faces to human body features such as height due to the needs of urban security and urban management. In this paper, we propose a parametric method to detect the height and distance of pedestrians based on monocular cameras. First, we use target detection to detect the position of pedestrians in the figure. Then, the pedestrian's position in the frame is calibrated using a combination of an improved block differencing method and global contour detection to obtain the actual contour of the pedestrian with the complex background removed. After that, we use the perspective principle combined with the reference information to obtain the measurement height and distance formulas. Finally, we calculate the height of the pedestrian and the distance from the camera after checking the calibration. The experimental results show that the distance measurement accuracy is above 97%, and the height is above 95% a range of 25 m. The application of this method can evolve face detection to human body detection, extend the feature capture of the human face to the capture of human body height and other features, and improve the accuracy of face detection through the assistance of these features.
Investigating Factors Responsible for Teacher Burnout in English as Foreign Language Classes

Regarding the considerable role educators have within the area of studying language, their affections and sensations have currently been at the core of attention. Such affections and feelings can both elevate their career achievement or impede their achievement such as burnout. Indeed, they usually experience "burnout" because of their stressful job. As an issue, educator burnout should be considered because it has an influence both on learners and educators. To gain a setting with lower stress and prevent mental and physical burnout in students as well as the educators, the present article investigated the issue of burnout and the affective factors. This mini review aims to provide the definition, background, and the causes of burnout along with some implications for language stakeholders are presented.
Constructing an Enterprise Management System Using an Improved Clustering Algorithm

Cluster analysis is one of the most commonly used data analysis methods, and K-means is among the most popular and widely used clustering algorithms in recent years. Because of its performance in clustering huge amounts of enterprise data, the K-means algorithm is one of the most commonly used clustering methods in enterprises. Furthermore, in the context of the new era, enterprises are under growing pressure from both domestic and international sources, particularly market competition pressure. Moreover, the traditional K-means dispersed clustering algorithm has several issues when it comes to clustering big data in enterprises, such as instable clustering findings, poor clustering results, and low execution effectiveness. Based on the premise of the clustering algorithm in enterprises, this paper constructs an improved K-means clustering algorithm of the enterprise management system. It enables businesses to quickly address deficiencies, improve information in all areas, supplement adequate resources, and promote growth. The proposed algorithm uses the sample density, the distance between the clusters, and the cluster compact density, and defines the product of the three as different weight densities and the maximum difference as the initial cluster center to solve the problem. It finds the point of sampling with weight density, randomness, and low quality of early cluster center selection. The experimental results show that my planned algorithm has higher execution efficiency, accurateness, a lower error rate, and good stability.
Symmetric Diffeomorphic Image Registration with Multi-Label Segmentation Masks

Image registration aims to align two images through a spatial transformation. It plays a significant role in brain imaging analysis. In this research, we propose a symmetric diffeomorphic image registration model based on multi-label segmentation masks to solve the problems in brain MRI registration. We first introduce the similarity metric of the multi-label masks to the energy function, which improves the alignment of the brain region boundaries and the robustness to the noise. Next, we establish the model on the diffeomorphism group through the relaxation method and the inverse consistent constraint. The algorithm is designed through the local linearization and least-squares method. We then give spatially adaptive parameters to coordinate the descent of the energy function in different regions. The results show that our approach, compared with the mainstream methods, has better accuracy and noise resistance, and the transformations are more smooth and more reasonable.
Analysis of clustering algorithms for Speaker Diarization using LSTM

Speaker Diarization is a sub-field of speaker recognition whose task is to identify the duration and number of speakers in a conversation. In a multi-speaker environment, it becomes quite difficult to segregate speaker count. In various scenarios like meetings, conferences, dinner parties, and telephonic conversation diarization helps in knowing the solution of the prolonged question “who spoke when”. Instead, it had progressed remarkably well from the last decade after integrating with neural networks. With the advent of neural embeddings, the results achieved using d-vector are better than i-vectors & x-vectors. Clustering plays an important role in identifying the speaker's label without knowing the identity. In this paper, analysis of speaker diarization pipeline which uses LSTM based embeddings of d-vectors on various clustering techniques is done. This pipeline has been implemented on the recently introduced VoxConverse diarization corpus consisting of meetings, broadcasts, and telephonic conversation recordings. Spectral Clustering performed well on this dataset and resulted in the lowest Diarization Error Rate (DER) as compared to other clustering techniques that have been taken under this study.
Novel Block Chain Technique for Data Privacy and Access Anonymity in Smart Healthcare

The Internet of Things (IoT) and Cloud computing are gaining popularity due to their numerous advantages, including the efficient utilization of internet and computing resources.In recent years, many more IoT applications have been extensively used.For instance, Healthcare applications execute computations utilizing the user's private data stored on cloud servers.However, the main obstacles faced by the extensive acceptance and usage of these emerging technologies are security and privacy.Moreover, many healthcare data management system applications have emerged, offering solutions for distinct circumstances.But still, the existing system has issues with specific security issues, privacy-preserving rate, information loss, etc.Hence, the overall system performance is reduced significantly.A unique blockchain-based technique is proposed to improve anonymity in terms of data access and data privacy to overcome the above-mentioned issues.Initially, the registration phase is done for the device and the user.After that, the Geo-Location and IP Address values collected during registration are converted into Hash values using Adler 32 hashing algorithm, and the private and public keys are generated using the key generation centre.Then the authentication is performed through login.The user then submits a request to the blockchain server, which redirects the request to the associated IoT device in order to obtain the sensed IoT data.The detected data is anonymized in the device and stored in the cloud server using the Linear Scaling based Rider Optimization algorithm with integrated KL Anonymity (LSR-KLA) approach.After that, the Timestamp-based Public and Private Key Schnorr Signature (TSPP-SS) mechanism is used to permit the authorized user to access the data, and the blockchain server tracks the entire transaction.The experimental findings showed that the proposed LSR-KLA and TSPP-SS technique provides better performance in terms of higher privacy-preserving rate, lower information loss, execution time, and Central Processing Unit (CPU) usage than the existing techniques.Thus, the proposed method allows for better data privacy in the smart healthcare network.
Innovating aircraft control systems with the use of artificial intelligence and electronics

Abstract Despite all the research being done in an attempt to bridge the gap between control systems and artificial intelligence, there is still an immense risk of failure and instability that exists. One particular application that this research will look into and expand on is aircraft control mechanisms. This article will examine the existing uncertainties within these systems that could be suspected as the cause of failure in the artificial control operation of an aircraft. This study will act as a further extension of research on the feedback linearization of an aircraft's control architecture using adaptive neural networks to decrease the probability of an uncontrolled error resulting from the nonlinearity of the aircraft's dynamic characteristics. The stability of previously implemented mechanisms to control aircraft systems will also be investigated. This research will require a thorough approach and understanding of various possible areas of malfunction and instability caused by multiple factors, including, external interferences and inefficiencies that accumulate within the controller that can mislead or cause an undesirable effect on the system. Examining similar areas where this study may be used for further research, while also discussing opportunities to apply these procedures to relatable applications will be analyzed, as it is of key importance for the progression of this technology.
Deformable transformer for endoscopic video super-resolution

Video super-resolution aims to reconstruct a high-resolution video from a low-resolution video corresponding to a magnification scale. Video super-resolution, as a fundamental computer vision task, is widely used in various fields. Particularly, in the field of endoscopic, high-resolution endoscopic videos help doctors to observe more details of lesions and improve the accuracy and speed of diagnosis. A novel deformable Transformer network is proposed to solve the super-resolution problem of endoscopic video data. To address the problem that the Transformer's self-attention module cannot effectively capture local information, the self-attention module is improved by using convolution operations to increase the local feature capture capability of the self-attention module. In order to compensate for the deficiency of Transformer for continuous inter-frame alignment, a new bidirectional deformable convolutional network is designed as the feed-forward module of Transformer to achieve frame-to-frame feature alignment and feature propagation using deformable convolution. A high-resolution dataset for endoscopic video super-resolution is produced using endoscopic surgery videos. Our proposed deformable Transformer network is demonstrated to have the best performance with the competitive number of parameters in endoscopic imaging so far by comparing the performance of other existing video super-resolution methods in the endoscopic dataset through sufficient experiments. Our proposed deformable Transformer network improves the PSNR metric by 0.97 dB over the state-of-the-art method in the RGB channel, while reducing the number of network parameters by 0.39 million.
Educational Automatic Question Generation Improves Reading Comprehension in Non-native Speakers: A Learner-Centric Case Study

Asking learners manually authored questions about their readings improves their text comprehension. Yet, not all reading materials comprise sufficiently many questions and many informal reading materials do not contain any. Therefore, automatic question generation has great potential in education as it may alleviate the lack of questions. However, currently, there is insufficient evidence on whether or not those automatically generated questions are beneficial for learners' understanding in reading comprehension scenarios.We investigate the positive and negative effects of automatically generated short-answer questions on learning outcomes in a reading comprehension scenario.A learner-centric, in between-groups, quasi-experimental reading comprehension case study with 48 college students is conducted. We test two hypotheses concerning positive and negative effects on learning outcomes during the text comprehension of science texts and descriptively explore how the generated questions influenced learners.The results show a positive effect of the generated questions on the participants learning outcomes. However, we cannot entirely exclude question-induced adverse side effects on learning of non-questioned information. Interestingly, questions identified as computer-generated by learners nevertheless seemed to benefit their understanding.Automatic question generation positively impacts reading comprehension in the given scenario. In the reported case study, even questions recognized as computer-generated supported reading comprehension.
PHP: Vulnerabilities and Solutions

The vulnerabilities are found not only in the programming language syntax and core but also in the coding style of the programmer. It is necessary to welcome good coding practice with security in mind for every programmer and programming language student. In this work, a 5-steps model is proposed that can improve the coding style of the programmers and also help build secure web applications. This paper concentrates on practical implementation of server-side and client-side vulnerabilities and their solutions backed by two web applications developed using PHP. The first web application follows poor programming practice whereas the second web application follows 5-steps model with other recommendations. Both of the web applications were scanned using open source “man-in-the-middle” proxy tool and the outcome showed that following 5-steps model and using latest programming constructs and functions improve the coding practice dramatically and results in a secure web application.
An open-source software framework for reinforcement learning-based control of tracked robots in simulated indoor environments

AbstractA simulation framework based on the open-source robotic software Gazebo and the Robot Operating System is presented for articulated tracked robots, designed for reinforcement-learning-based (RL) control skill acquisition. In particular, it is destined to serve as a research tool in the development and evaluation of methods in the domain of mobility learning for articulated tracked robots, in 3D indoor environments. Its architecture allows to interchange between different RL libraries and algorithm implementations, while learning can be customized to endow specific properties within a control skill. To demonstrate its utility, we focus on the most demanding case of staircase ascent and descent using depth image data, while respecting safety via reward function shaping and incremental, domain randomization-based, end-to-end learning.GRAPHICAL ABSTRACTKeywords: Reinforcement learningrobot controlincremental learningdomain randomizationstaircase negotiation Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work is performed in the context of the project REACT, project VITAAL and is financed by Brest Metropole, the region of Brittany and the European Regional Development Fund.Notes on contributorsA. MitriakovAndrei Mitriakov is a PhD holder in computer science from école nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire. He received an engineer's degree from école nationale supérieure de techniques avancées Paris in 'Smart mobility and vehicle engineering'. Physics Master's and Bachelor's degrees were received from Novosibirsk State University. Currently, he is an R&D engineer. His fields of interest encompass autonomous robotics, reinforcement learning, and software development.P. PapadakisPanagiotis Papadakis is an Associate Professor in the Department of Informatics of IMT Atlantique, Bretagne/Pays de la Loire, campus of Brest, France and coleader of the team RAMBO of UMR CNRS 6285 Lab-STICC. He received the Informatics and Telecommunications Degree and the Doctorate degree in Information Technology from the National University of Athens, Greece, in 2005 and 2009, respectively. His main research interests lie in the fields of cognitive robotics, machine learning and computer vision with emphasis on applications related to robotic assistance technologies.S. GarlattiSerge Garlatti is an Emeritus Professor in the Department of Informatics of IMT Atlantique, Bretagne/Pays de la Loire, campus of Brest, France. He has worked for more than 30 years at the intersection of the fields of Artificial Intelligence (knowledge engineering) and TEL (Technology Enhanced Learning). His research activities focus on the fields of learning, lifelong learning and cultural heritage for formal and informal learning and the design of new computing environments to support researchers in History of Science and Technology - Digital Humanities.
Prototyping Opportunistic Learning in Resource Constrained Mobile Devices

With the increasing capabilities of pervasive computing devices, training machine learning models on-device has become feasible. At the same time, demands for increased user privacy and reduced communication overhead have brought decentralized machine learning to the forefront. In these paradigms, individual devices collaborate opportunistically to train models using locally available data. In this paper, we examine the practical feasibility of such opportunistic learning. In a basic opportunistic learning approach, when a device (the learner) encounters another device (the neighbor), it can request the neighbor to perform training on the learner’s behalf using the neighbor’s own local data. To realize an opportunistic learning in the real world, one must solve two challenges: (1) leveraging device-to-device communication to discover neighbors and exchange models and (2) training models on the device subject to resource and latency constraints. In this paper, we examine the feasibility of implementing opportunistic learning to learn a convolutional neural network (CNN) model for an image classification task using a small network testbed of diverse iOS devices. We demonstrate success in implementing a completely decentralized approach and characterize the challenges and opportunities that lie ahead.
An improved Firefly Algorithm based Task Scheduling in Cloud Computing for Effective Resource Utilization

Computing possessions may be dynamically scaled up and down at little cost in the cloud. In cloud computing, well-organized job scheduling is regarded the most important difficulties. For large tasks, task scheduling is a difficult problem since it is an NP-complete problem. If you're using a cloud computing platform, you may have to schedule many jobs on multiple virtual machines (VMs) to minimize the time it takes to complete each activity, as well as maximize the amount of resources utilized. This research proposes a multi-objective Firefly cyclic randomization (FCR) based job allocation method for dealing with the large number of cloud user requests. Randomization and the modified firefly algorithm are used to improve the task scheduling process of the suggested approach. FCR takes into account many objectives and discoveries the best technique of job allocation based on DI, time to completion and throughput. In order to assess FCR's performance, we compared its throughput, average response time, DI, and makespan metrics to those of many existing methods. The experiments' findings demonstrate that FCR outperforms other task allocation algorithms.