Reviewerly/exHarmony huggingface dataset:
ROS Compatible Local Planner and Controller Based on Reinforcement Learning

The study’s main objective is to develop a ROS compatible local planner and controller for autonomous mobile robots based on reinforcement learning. Reinforcement learning based local planner and controller differs from classical linear or nonlinear deterministic control approaches using flexibility on newly encountered conditions and model free learning process. Two different reinforcement learning approaches are utilized in the study, namely Q-Learning and DQN, which are then compared with deterministic local planners such as TEB and DWA. Q-Learning agent is trained by positive reward on reaching goal point and negative reward on colliding obstacles or reaching the outer limits of the restricted movable area. The Q-Learning approach can reach an acceptable behaviour at around 70000 episodes, where the long training times are related to large state space that Q-Learning cannot handle well. The second employed DQN method can handle this large state space more easily, as an acceptable behaviour is reached around 7000 episodes, enabling the model to include the global path as a secondary measure for reward. Both models assume the map is fully or partially known and both models are supplied with a global plan that does not aware of the obstacle ahead. Both methods are expected to learn the required speed controls to be able to reach the goal point as soon as possible, avoiding the obstacles. Promising results from the study reflect the possibility of a more generic local planner that can consume in-between waypoints on the global path, even in dynamic environments, based on reinforcement learning.
Web-Based Intelligent Book Recommendation System Under Smart Campus Applications

Recommendation systems are essential as they help users to discover new books and resources and increase their engagement and satisfaction, thus improving the overall learning experience. This paper presents a web-based intelligent book recommendation system for smart campus applications at Izmir Bakircay University. The system is designed as an intelligent hybrid tool that combines collaborative and content-based filtering techniques to recommend books to users with methodological differences. It considers the user’s reading history and preferences and integrates with other smart campus applications to provide personalized recommendations. The system is important for the digital transformation of smart campuses as it helps to make education more personalized, efficient, and data-driven. Also, it allows for the effective use of public resources. The effectiveness of the system was evaluated through user feedbacks, 22 users evaluated 148 books and the results showed that users responded positively to about 70% of the recommended books thus, it provided accurate and personalized recommendations.
An Optimized Flexible Accelerator for Elliptic Curve Point Multiplication over NIST Binary Fields

This article proposes a flexible hardware accelerator optimized from a throughput and area point of view for the computationally intensive part of elliptic curve cryptography. The target binary fields, defined by the National Institute of Standards and Technology, are GF(2163), GF(2233), GF(2283), GF(2409), and GF(2571). For the optimization of throughput, the proposed accelerator employs a digit-parallel multiplier. The size of the digit is 41 bits. The proposed accelerator has reused the multiplication and squaring circuit for area optimization to compute modular inversions. Flexibility is included using three additional buffers on top of the proposed accelerator architecture to load different input parameters. Finally, a dedicated controller is used to optimize control signal handling. The architecture is modeled using Verilog and implemented up to the post-place-and-route level on a Xilinx Virtex-7 field-programmable gate array. The area utilization of our accelerator in slices is 1479, 1998, 2573, 3271, and 4469 for m=163 to 571. The time needed to perform one-point multiplication is 7.15, 10.60, 13.26, 20.96, and 30.42 μs. Similarly, the throughput over area figures for the same key lengths are 94.56, 47.21, 29.30, 14.58, and 7.35. Consequently, achieved results and a comprehensive performance comparison show the suitability of the proposed design for constrained environments that demand throughput/area-efficient implementations.
The Design of a Video Reflection Removal Method Based on Illumination Compensation and Image Completion Fusion

Our objective is to develop a video reflection removal algorithm that is both easy to compute and effective. Unlike previous methods that depend on machine learning, our approach proposes a local image reflection removal technique that combines image completion and lighting compensation. To achieve this, we utilized the MSER image region feature point matching method to reduce image processing time and the spatial area of layer separation regions. In order to improve the adaptability of our method, we implemented a local image reflection removal technique that utilizes image completion and lighting compensation to interpolate layers and update motion field data in real-time. Our approach is both simple and efficient, allowing us to quickly obtain reflection-free video sequences under a variety of lighting conditions. This enabled us to achieve real-time detection effects through video restoration. This experiment has confirmed the efficacy of our method and demonstrated its comparable performance to advanced methods.
Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise

The importance of monitoring earthquakes for disaster management, public safety, and scientific research can hardly be overstated. The emergence of low-cost seismic sensors offers potential for widespread deployment due to their affordability. Nevertheless, vehicular noise in low-cost seismic sensors presents as a significant challenge in urban environments where such sensors are often deployed. In order to address these challenges, this work proposes the use of an amalgamated deep neural network constituent of a DNN trained on earthquake signals from professional sensory equipment as well as a DNN trained on vehicular signals from low-cost sensors for the purpose of earthquake identification in signals from low-cost sensors contaminated with vehicular noise. To this end, we present low-cost seismic sensory equipment and three discrete datasets that—when the proposed methodology is applied—are shown to significantly outperform a generic stochastic differential model in terms of effectiveness and efficiency.
A Comparison Between ANNs and Ant Tracking Outputs for Fault and Fracture Detection

Abstract Knowing the fractures and faults in a field or reservoir leads to more precise exploration and makes development strategies less uncertain. To better identify these events, in this study, the outputs of multilayer perceptrons (MLP), and unsupervised vector quantizers (UVQ), were compared with the outputs of ant-tracking. Although the specific seismic attributes and ant-tracking allow for the depiction of faults and fractures, the combination of the shortlisted attributes in the ANNs, especially MLP, reveals small-scaled and, in some areas, vertical faults and fractures more continuously and precisely than previous studies that used only conventional ant-tracking. The chaos has some deficiencies in identifying the faulted and fractured zones. Ant-tracking has a much lower performance than the MLP output. The output of ant-tracking attributes shows fault detection with less accuracy than the MLP output or even that of UVQ. To be clearly described, for the given seismic survey data, finding small-scale and in some areas vertical fractures using the integration of seismic attributes were more reliable than the output of the ant-tracking.
Effect of Block-Based Python Programming Environment on Programming Learning

The advancement of computing technology has led to many changes in a variety of fields, and the importance of programming education has been emphasized in many countries worldwide. Despite the importance of programming education, the cognitive burden of text programming for beginners has not been reduced. The goal of this study was to implement an environment where a text programming language is used in a block-based programming environment and to determine at which school level this learning environment affects positive perceptions of programming. To achieve this goal, we conducted programming classes targeting 128 middle school, high school, and university students for 14 weeks and analyzed the effects of the factors of “understanding of programming instructions”, “usage confidence”, and “usefulness” on “positive perceptions of programming”. The results of the analysis by school level show that “usefulness” influenced positivity toward programming for middle school students, “usefulness” and “understanding of programming instruction” for high school students, and “understanding of programming instruction” and “usage confidence” for university students. Therefore, the significance of this study confirms the need to construct the learning environment differently depending on school level, even for beginners.
A Systematic Literature Review on Using the Encoder-Decoder Models for Image Captioning in English and Arabic Languages

With the explosion of visual content on the Internet, creating captions for images has become a necessary task and an exciting topic for many researchers. Furthermore, image captioning is becoming increasingly important as the number of people utilizing social media platforms grows. While there is extensive research on English image captioning (EIC), studies focusing on image captioning in other languages, especially Arabic, are limited. There has also yet to be an attempt to survey Arabic image captioning (AIC) systematically. This research aims to systematically survey encoder-decoder EIC while considering the following aspects: visual model, language model, loss functions, datasets, evaluation metrics, model comparison, and adaptability to the Arabic language. A systematic review of the literature on EIC and AIC approaches published in the past nine years (2015–2023) from well-known databases (Google Scholar, ScienceDirect, IEEE Xplore) is undertaken. We have identified 52 primary English and Arabic studies relevant to our objectives (The number of articles on Arabic captioning is 11, and the rest are for the English language). The literature review shows that applying the English-specific models to the Arabic language is possible, with the use of a high-quality Arabic database and following the appropriate preprocessing. Moreover, we discuss some limitations and ideas to solve them as a future direction.
Enhancing Drilling Operations and Management Through Digital Ecosystem

Abstract The digitalization of the drilling process has revolutionized the oil and gas industry by incorporating digital technologies and data analytics into drilling operations. This transformative shift aims to optimize operational efficiency, enhance safety measures, and enable prompt decision-making. In this paper, we will look into the various benefits of creating a digital ecosystem for drilling, focusing on improved operational efficiency, potential risk identification, advancements and application in smart equipment, enhanced communication and collaboration, and the reduction of operational expenses. We will also discuss a practical case study that demonstrates the effectiveness of digital ecosystem in reducing costs, minimizing downtime, and enhancing overall drilling performance. Furthermore, the integration of VR simulation with digital twin models in the digital ecosystem for drilling holds immense potential for operators. By providing a realistic and immersive training experience, operators can optimize their drilling operations, minimize risks, and ultimately save costs. Through this exploration, we aim to highlight the transformative power of digitalization in the drilling industry and showcase the benefits that can be achieved by embracing this technology-driven approach.
Improving Industrial Operations with Real-Time Information Using Augmented Reality

Abstract The objective and scope of this project are to leverage the latest technological advancements, such as Artificial Intelligence and Digital Transformation, to enhance operations. Specifically, this project will focus on the implementation of Augmented Reality to superimpose crucial digital information onto real-time views of various sections and elements of the ADNOC Gas Sites. This approach aims to improve accuracy, efficiency, and operations safety. Augmented Reality (AR) is a technology that overlays digital content onto the real world. The process for using AR in an industrial setting involves identifying equipment, gathering data, creating AR overlays, choosing an AR device, integrating device, data, training users, and monitoring and refining system. Data collection includes equipment specifications, design, P&amp;ID graphs, operational parameters, and related essential information. The AR device is then configured to recognize the equipment and display relevant overlays. User training is necessary, and the system must be monitored and updated to remain effective. The use of Augmented Reality (AR) technology in an industrial setting to identify equipment-related information can provide numerous benefits to various stakeholders. This includes improved accuracy, efficiency, and safety across the entire organization. The process of using AR in an industrial setting involves several steps as mentioned above. This process requires collaboration between various stakeholders, including AR experts, equipment engineers, IT specialists, and operational staff. The implementation of AR technology in ADNOC Gas sites can greatly enhance operations and improve overall performance, productivity, and safety. By utilizing AR overlays to display equipment-related information in real-time, operators can quickly and accurately identify issues and make necessary adjustments and actions. Engineers can use AR to visualize equipment designs and simulate potential changes or upgrades. Maintenance personnel can use AR overlays to identify equipment components and perform repairs more quickly and accurately. Management can use AR to receive real-time data on equipment performance to make data-driven decisions. Additionally, AR can be used in training programs to provide a more immersive and interactive experience for trainees. This paper will present novel information by showcasing a unique and transformative implementation of AR within the petroleum industry. Through a comprehensive review of existing literature and real-world examples, it will demonstrate how the strategic integration of AR overlays can significantly improve accuracy, efficiency, and safety in industrial operations. This valuable contribution to the existing body of knowledge can guide petroleum industry professionals in harnessing AR's potential to drive innovation and enhance productivity in their operations.
Enabling Contextual Natural Language Search on Oil and Gas Databases

Abstract The oil and gas (O&amp;G) industry generates a significant volume of data from various sources, such as seismic surveys, well logs, drilling reports and others, which are stored in either relational or non-relational databases. However, searching for pertinent data records requires the end-user to be conversant with database query syntaxes and schema definitions, which is challenging. In this paper, we introduce a novel framework to interact with O&amp;G databases using natural language. We trained a Text-to-Text Transfer Transformer (T5) (Raffel, 2020) in a multi-task setting incorporating natural language to structured query language (Text-to-SQL) conversion as a main task with query context classification, and passage context classification as auxiliary tasks. We also introduce a method for data augmentation with SQL to natural language task (SQL-to-Text). Additionally, we implemented database-aware query disambiguation for typos and spelling correction by incorporating string and phonetics similarity algorithms. We evaluated the performance of our trained language model (LM) on a test dataset consisting of 1711 natural language queries involving diverse O&amp;G entities, such as fields, wellbore, well logs, markers, etc. Our innovative multi-task approach resulted in a remarkable performance, achieving an exact set match accuracy (EM) of 88.5% on the Text-to-SQL task with impressive F1 scores of 96.3% for query context classification, and 87.7% for passage context classification tasks. We observed multi-task training of LM improved the EM accuracy of the SQL predictions. We also demonstrate the application of our approach to enable contextual natural language search on Open Subsurface Data Universe (OSDUTM) platform. Overall, our approach has potential to transform how end-users search and retrieve data from O&amp;G databases.
PECULIARITIES OF USING MOBILE TECHNOLOGIES FOR POSSESSING ENGLISH BY FUTURE BORDER GUARD OFFICERS

The article provides a theoretical substantiation and analysis of the use of individual mobile devices as a means of intensifying the educational process. It has been determined that mobile technologies in education are based mainly on the use of mobile phones, which, due to constant improvement, can be considered not only as a means of communication, but also as a means of learning. It has been established that high-quality professional training of future border guard officers requires continuous updating, expanding knowledge of English in the field of activity, which sets new tasks for the National Academy of the State Border Guard Service of Ukraine to train qualified personnel, requiring improvement of interpreting and translation skills. The article considers mobile technologies as a learning tool that helps to significantly diversify the process of learning English by future border guard officers in a higher military institution and has a number of advantages: introducing and processing new material; checking the lexical and grammatical material learned; conducting listening with the help of audio and video materials; compiling dictionaries for the relevant educational material, etc. Moreover, students can access course materials and participate in online classes from anywhere and at any time using their mobile devices. The article substantiates the practicality and expediency of their use in the process of mastering English by cadets of the National Academy of the State Border Guard Service of Ukraine. The practical significance of using mobile technologies in the learning of English by future border guard officers is considered, namely, the author’s own optional program of the distance learning course “English for Professional Communication of Border Guards”, as well as the applications Learn English Elementary, Wordshake, LinguaLeo, etc.
Possibilities of the XXI century music art in the spiritual formation of the youth audience

The article considers the problem of sovereignty of modern domestic culture, which has a great influence on the spiritual formation of young people. As a result, there is a growing need for contemporary art that can adequately respond to changes in public consciousness while retaining its individuality and independence. This has determined the purpose of the work – to show the new possibilities of modern musical art and its authors, whose activities are aimed at attracting and supporting greater attention and interest of young people to the spiritual and creative environment of the 21st century. The problem is solved both on the state level, due to the active role of public figures, and at the level of modern composers. As a result of their work, musical art has acquired new opportunities for the spiritual formation of a youth audience.
Observation-Based Filtering of State of a Nonlinear Dynamical System with Random Delays

We present a model of a stochastic observation system that allows for time delays between the received observation and the actual state of the observed object that formed these observations. Such delays can occur when observing the movement of an object in a water medium using acoustic sonars and have a significant impact on the accuracy of position tracking. We present equations to solve the optimal mean square filtering problem. Since the practical use of the optimal solution is barely feasible due to its computational complexity, we pay the main attention to an alternative, suboptimal but computationally efficient approach. Specifically, we adapted a conditional minimax nonlinear filter (CMNF) to the proposed model and formulated sufficient existence conditions for its estimate. We conducted a computational experiment on a model that is close to practical needs. The results of the experiment show the effectiveness of CMNF in the model considered. However, they also show a significant decrease in the quality of estimation compared to the model without random observation delays, which can be considered as a motivation for further research into the model and related problems.
Sequential Improvement Method in Probabilistic Criteria Optimization Problems for Linear-in-State Jump Diffusion Systems

Here we study the problems of probabilistic and quantile optimization of multidimensional controllable jump diffusion. As the main tool we use Chebyshev-type probability estimates. With them the problems under consideration are reduced to one auxiliary deterministic optimal control problem in terms of the moment characteristics of the process. To find its solution, we use Krotov’s global improvement method.
Method for Unmanned Vehicles Automatic Positioning Based on Signal Radially Symmetric Markers Recognition of Underwater Targets

The article describes a method for automatically recognizing the target points of the trajectories of unmanned vehicles moving underwater, such as autonomous submarines and flying underwater vehicles of aircraft-like structures. The coordinate of the center of an object with radial symmetry properties is considered a terminal control point. A method for constructing a multiscale weighted image model based on the developed fast radial symmetry transformation and the Hough method is proposed, which ensures noise stability and high speed of calculating the coordinates of the desired point. When the object of interest has a contour of a specific color, a model is based on our proposed chromatic and weight components. As an example of detection, we have given an algorithm for detecting a base underwater station with light markers as a signal luminous ring.
Data Fragment Classification of High Entropy Files Using Machine Learning

Accurate and effective file type classification of available evidentiary data in the absence of header and crucial file system information is one of the main challenges in digital forensics. Many approaches have been presented to tackle this issue, including byte frequency histograms, the Oscar method, and the keyword-based approach, but they were ineffective for high-entropy files. For fragments with high entropy data, our method is effective and very accurate. In which feature vectors are derived from normalized byte frequencies of a given fragment, using classification algorithms and forecast the kind of fragment that will be learned under supervision.