Reviewerly/exHarmony huggingface dataset:
AUV 3D Path Planning Based on Improved Empire Competition Algorithm in Ocean Current Environment

Aiming at the global three-dimensional path planning of autonomous underwater vehicles (AUV) in ocean currents, a three-dimensional path planning method based on the improved imperial competition algorithm (ICA) is proposed. First, establish a three-dimensional environment model and a cone three-dimensional ocean current model, and derive a cost function that measures path length, energy consumption, and sailing time to establish a multi-objective performance evaluation model to achieve global comprehensive optimization of AUV path planning; secondly, in the algorithm The colonial revolution part added the differential evolution algorithm to ensure that the algorithm will not lose outstanding individuals while increasing the diversity of the population; finally, the algorithm proposed in this article and the other three algorithms are simulated and analyzed on three different scale maps. The simulation results show that the improved ICA has a strong global search capability, improves the accuracy of the optimization process, strengthens the global optimization, and effectively solves the problem that traditional ICA is easy to fall into local optimal solutions, and can make AUVs Obtain the global optimal path, and the comprehensive performance index of the path is better than other comparison algorithms.
ORGANISATIONAL AND PEDAGOGICAL CONDITIONS FOR TRAINING FUTURE PRIMARY SCHOOL TEACHERS TO USE GOOGLE SERVICES IN TEACHING ENGLISH TO PRIMARY SCHOOL PUPILS

The article is devoted to the issue of training primary school teachers to use Google services in teaching English to primary school pupils. The organizational and pedagogical conditions for this process are considered. In the article it is focused on a need to create a holistic system of knowledge, skills and abilities for the use of Google services by future primary school teachers in teaching English to junior pupils. It is determined that the purpose of teaching English in primary school is the formation of foreign language communicative competence, provided by language, speech and socio-cultural experience, consistent with the psycho-physiological and individual characteristics of younger students. The task of a foreign language primary school teacher is to develop lexical, grammatical and phonetic skills, as well as skills in four types of speaking activities: listening, speaking, reading and writing. The advantages in the use of cloud technologies in education as a kind of information and communication technologies are determined and theoretically substantiated. It is found out the possibilities and prospects of using cloud services from Google in teaching English to young students. In particular, we researched the following Google services: Google Presentation, YouTube, Hangouts, Gmail, Google Drive, etc. It is also determined that the effectiveness of the educational process can be improved by using them in English language teaching in primary school. The effectiveness of using Google services in teaching English is depicted in the ability of the primary school foreign language teacher to create a learning environment in English lessons, where the pupils can do a variety of learning tasks in grammar, vocabulary and phonetics, as well as develop their skills in four speech activities (listening, speaking, reading and writing). It is proved, that one of the tasks of professional training in pedagogical higher education institution should be the actualization of conscious mastery of information technology, intensification of pedagogical self-organization, self-improvement of knowledge in this field - forming the readiness of primary school teachers to use Google services in their English language teaching. Ways to successfully solve the task of training the future primary school teachers in their professional foreign language work are possible only in the appropriate organizational and pedagogical conditions that determine the effectiveness of educational and cognitive activities.
Bi-Directional Style Adaptation Network for Person Re-Identification

Large-scale surveillance camera system can provide multiple visual information. Nevertheless, the images exhibit diverse appearance features due to different parameters and installation positions of the cameras. This camera style variation deteriorates its benefit from capturing identity features in person re-identification (Re-Id). The existing methods for filtering shallow appearance information through the Instance Normalization (IN) layer are extremely unfavorable to supervised tasks. To mitigate this problem, a simplified and valid Bidirectional Style Adaptation Network (BSA-Net) is presented to incorporate a new branch containing IN layer to learn invariant features with changing appearance. For these two branches, the structure is completely independent and the parameters are partially shared. BAS-Net is able to focus on the extraction of identity information while preserving appearance features. Specially, this new branch is removed during the testing phase, which significantly facilitates performance without introducing new computation. The superiority of the model is confirmed in extensive experiments on widely used benchmarks.
Verifying Maze-Like Game Levels With Model Checker SPIN

This study presents a framework that procedurally generates maze-like levels and leverages an automated verification technique called model checking to verify and produce a winning action sequence for that level. By leveraging the counterexample generation feature of the SPIN model checker, one or more solutions to the level-in-test are found, and the solutions are animated using a video game description language, PyVGDL. The framework contains four behavioral templates developed to model the logic of maze-like puzzle games in the modeling language of SPIN. These models automatically are tailored according to the level-in-test. To show the proposed methodology&#x2019;s effectiveness, we conducted five different experiments. These experiments include performance comparisons in level-solving between the proposed and existing methodologies &#x2014;A* Search and Monte Carlo Tree Search&#x2014; and demonstrations of the use of the proposed approach to check a game level with respect to requirements. This study also proposes a pipeline to generate maze-like puzzle levels with two levels of cellular automata.
A Mathematical Model for COVID-19 Image Enhancement based on燤ittag-Leffler-Chebyshev Shift

The lungs CT scan is used to visualize the spread of the disease across the lungs to obtain better knowledge of the state of the COVID-19 infection. Accurately diagnosing of COVID-19 disease is a complex challenge that medical system face during the pandemic time. To address this problem, this paper proposes a COVID-19 image enhancement based on Mittag-Leffler-Chebyshev polynomial as pre-processing step for COVID-19 detection and segmentation. The proposed approach comprises the Mittag-Leffler sum convoluted with Chebyshev polynomial. The idea for using the proposed image enhancement model is that it improves images with low gray-level changes by estimating the probability of each pixel. The proposed image enhancement technique is tested on a variety of lungs computed tomography (CT) scan dataset of varying quality to demonstrate that it is robust and can resist significant quality fluctuations. The blind/referenceless image spatial quality evaluator (BRISQUE), and the natural image quality evaluator (NIQE) measures for CT scans were 38.78, and 7.43 respectively. According to the findings, the proposed image enhancement model produces the best image quality ratings. Overall, this model considerably enhances the details of the given datasets, and it may be able to assist medical professionals in the diagnosing process.
Categorical-ontological approach to information support of educational activities

An important basis for information support of educational activities is the effective presentation of knowledge and standardization of training tasks based on ontological models of learned subject areas. The competence approach to the organization of the educational process and the requirements for the training of specialists from the point view of enterprises and organizations, in which they will carry out their professional activities, requires the development of a comprehensive model of educational processes and objects that are participating in them, based on high-level abstractions. On the basis of the results obtained in this work, the methodology of informational support of educational activities was further developed on the basis of: organizing the educational process and standardization of education based on a categorical-ontological approach; construction and use of relevant on tologies and knowledge bases; automation of data processing by forming and executing queries to the content of ontological models. The use of the results of categorical-ontological modeling allowed to reconcile the interests of active agents of the organization-technical systems, the subjects of educational activity and the ability of students to acquire the necessary knowledge and skills.
Context-Aware Diagnosis in Smart Manufacturing: TAOISM, An Industry 4.0-Ready Visual Analytics Model

The integration of cyber-physical systems accelerates Industry 4.0. Smart factories become more and more complex, with novel connections, relationships, and dependencies. Consequently, complexity also rises with the vast amount of data. While acquiring data from all the involved systems and protocols remains challenging, the assessment and reasoning of information are complex for tasks like fault detection and diagnosis. Furthermore, through the risen complexity of smart manufacturing, the diagnosis process relies even more on the current situation, the context. Current Visual Analytics models prevail only a vague definition of context. This chapter presents an updated and extended version of the TAOISM Visual Analytics model based on our previous work. The model defines the context in smart manufacturing that enables context-aware diagnosis and analysis. Additionally, we extend our model in contrast to our previous work with context hierarchies, an applied use case on open-source data, transformation strategies, an algorithm to acquire context information automatically and present a concept of context-based information aggregation as well as a test of context-aware diagnosis with latest advances in neural networks. We fuse methodologies, algorithms, and specifications of both vital research fields, Visual Analytics and Smart Manufacturing, together with our previous findings to build a living Visual Analytics model open for future research.
Performance Evaluation of Machine Learning Algorithms to Predict Breast Cancer

Breast cancer is increasing in all over the world year by year [2, 13], and it is a dominant cancer in the worldwide. Due to the lack of medical facilities, these cases are not early diagnosed, and the early detection will help to lower the death rates. This paper proposes machine learning (ML) methods to foretell whether the person is suffering with disease or not. It compares results of various ML algorithms: decision tree (DT), logistic regression, random forest classifier (RF), LGBM classifier, support vector machine classifier (SVC), and K-nearest neighbor (KNN). The above-mentioned ML algorithms are applied on the data after filling the missing values, after removing outliers, after applying correlation, after applying the SMOTE technique and to predict the best model.
Towards Greener Applications: Enabling Sustainable-aware Cloud Native Applications Design

Data centers energy demand is increasing. While a great deal of effort has been made to reduce the amount of CO $$_2$$ generated by large cloud providers, too little has been done from the application perspective. We claim that application developers can impact the environmental footprint by enhancing the application design with additional features. Following the proposed Sustainable Application Design Process (SADP), the application design is enriched with information that can be leveraged by cloud providers to manage application execution in an energy-aware manner. This exploratory work aims to emphasize the awareness on the sustainability of applications by proposing a methodology for its evaluation. To this end, we first suggest possible actions to enrich the application design towards sustainability, and finally describe how this additional information can be leveraged in the application workflow. We discuss the feasibility of our methodology by referring to existing tools and technologies capable of supporting the design features proposed in a production environment.
Remote study for the humanities and social sciences: digitization and coaching

The article analyzes research on remote study in the areas of the humanities and social sciences, which strongly links to formation of key competences by means of digital technologies and coaching. The results of the survey are analysed and presented according to the feedback of students on the emotional and value attitude to remote study associated with the humanities and social sciences, which specify both the advantages and disadvantages of this form in education. The article suggests considering a number of initiatives for remote study in the mentioned connection and strongly encourages their implementation in higher education. The experience of structural distribution of remote study is described, in which the research findings reveal that digital technologies and communication with and between students and teachers in a coaching format enable classes to achieve a significant increase in the social and entrepreneurship competences. It is also proposed to look at feasibility and prospect development of a remote study strategy at Kryvyi Rih State Pedagogical University and the National University of Life and Environmental Sciences of Ukraine, in particular. The development plan includes three-factor support for active learning: assessment support, emotional support and information support. The recommendations accompany the development introduction of such digital and coaching competences for students.
TraffSign: Multilingual Traffic Signboard Text Detection and Recognition for Urdu and English

Scene-text detection and recognition methods have demonstrated remarkable performance on standard benchmark datasets. These methods can be utilized in human-driven/self-driving cars to perform navigation assistance through traffic signboard text detection and recognition. Existing datasets include scripts of numerous languages like English, Chinese, French, Arabic, German, etc. However, traffic navigation signboards in Pakistan and many states of India are written in Urdu along with the English translation to guide human drivers. To this end, we present Deep Learning Laboratory's Traffic Signboards Dataset (DLL-TraffSiD) to develop multi-lingual text detection and recognition methods for traffic signboards. In addition, we present a pipeline for multi-lingual text detection and recognition for an outdoor road environment. The results show that our presented system signified better applicability in text-detection and text recognition, and achieved 89% and 92.18% accuracy on the proposed dataset (The proposed dataset along with implementation is available at https://github.com/aatiibutt/TraffSign/ ).
Program Logic for Higher-Order Probabilistic Programs in Isabelle/HOL

The verification framework PPV (Probabilistic Program Verification) verifies functional probabilistic programs supporting higher-order functions, continuous distributions, and conditional inference. PPV is based on the theory of quasi-Borel spaces which is introduced to give a semantics of higher-order probabilistic programming languages with continuous distributions. In this paper, we formalize a theory of quasi-Borel spaces and a core part of PPV in Isabelle/HOL. We first construct a probability monad on quasi-Borel spaces based on the Giry monad in the Isabelle/HOL library. Our formalization of PPV is extended so that integrability of functions can be discussed formally. Finally, we prove integrability and convergence of the Monte Carlo approximation in our mechanized PPV.
Feasibility and Exploratory Study of Implementing the Blockchain Technology in the UAE Financial Markets

Blockchain technology is a digitized ledger system that allows decentralized access to transactional records. The system ensures that data shared cannot be altered or corrupted in any way. Some banking and securities systems have already adopted the Blockchain on their business. Also, financial markets in the United Arab Emirates (UAE) can benefit from using Blockchain technology by addressing data security issues, cyber fraud, tampering, and corruptibility of financial data. This research paper proposes a feasibility and exploratory study on adopting the Blockchain on UAE from all stockholders perspectives. This study has undertaken based on theoretical and empirical framework using survey analysis. The results received from all stockholders are very significant and are playing an important role on decision-making regarding adopting Blockchain technology in the UAE.
A Novel Defocus-Degree-Based Phase Unwrapping and Fusion Algorithm for High-Speed and Large-Depth-Range 3D Measurement

Binary defocusing technique is typically used in high-speed 3-D measurement. As the defocus degree varies with the measuring depth range, current defocusing technique has limitations while measuring large-depth-range (LDR) scene. Addressing this difficulty, this article proposes a novel defocus-degree-based phase unwrapping and phase fusion algorithm for LDR 3-D measurement. Firstly, we establish a mathematical model to present the relationship between defocus degree and phase, and present the defocus-degree-based phase unwrapping algorithm to directly obtain its accurate fringe order. Then, we derive a phase error model to estimate its phase error, and develop a phase fusion algorithm to obtain the lowest phase error by fusing two frequency phases based on the estimated error. With implementation of the proposed phase unwrapping and fusion algorithm, experiments are performed and the results demonstrate that our method can achieve a high-speed, high-precision and low-outlier 3-D measurement for LDR scene.
The Impact of Setting up a Cloud Computing Solution on Small and Medium Organization’s Management: A Qualitative Study

As we know, organizations are systems that affect their surrounding environment and get involved with their environment. Any changes can impact the outcome of any operation and decision in the organization, no matter how small it is or how irrelevant, from the most straightforward operation or process to the most complicated functions and strategic decisions. Migrating to cloud computing is one of the most important enterprise goals. This technology impacts the use of information and communication profoundly. Its main goal is to provide data storage and net computing services that are secure, rapid, and convenient. In this sense, the small and medium enterprise is the most interested since initial investments are avoided, and the technology allows gradual implementation. The present study aims to introduce the advantages of implementing cloud computing in small and medium enterprises. This paper studies cloud computing and its impact on the organization, which was carried out using a qualitative methodology to show the elements that may get impacted the most and the consequences of its adoption. By interviewing 13 managers and experts from four small and medium companies. Qualitative content analysis and open coding were used to analyze the data. Subsequently, five sub-themes were extracted from the central theme of the impact of cloud implementation on operational management in organizations. The sub-themes included information access, communication, collaborative work, and customer and supplier relationships. We found out that migrating into the cloud for organizations is primarily beneficial, with some exceptions, of course, any system or any technology has flaws, but in the case of the cloud technology, the benefits are more cover the flaws, especially if the implementation was well planned and well maintained, and we provide all the conditions needed for the good functioning of this technology.
Digital Competencies of Pre-service Teacher Students: Albanian Context

The chapter is dedicated to the digital competencies of pre-service teacher students in Albania. Therefore, the level of knowledge and skills of the future generation of teachers. The chapter is composed of two main parts; the first one is about the situation of digital competencies, and the second one is an empirical study focused on Moodle and Microsoft teams’ competencies of pre-service teacher students or future teachers. The chapter aims to explore the important policies and results of several policy documents of main Albanian and international institutions focusing on the digital competencies of pre-service teacher students. The chapter also aims to investigate Moodle and Microsoft Teams’ key competencies and academic progress through Moodle and Microsoft Team’s use. This chapter comprises five sections including (1) Introduction, (2) Theoretical framework and review of literature, (3) Methods, (4) Findings, and (5) Discussion. The introduction begins with the (a) Pre-University Education Strategy, and (b) Albania’s Curriculum and Assessment Frameworks. This is followed by (c) School autonomy, (d) Primary and secondary education, and (e) Tertiary education. The theoretical framework and literature review contains (a) Demand for quality teachers (Digital Agenda 2021–2025; Digital Skills in the field of education), (b) ICT use and e-Learning, (c) Digital skills in Albania, and (d) Digital skills during Covid-19 pandemic, (e) Literature review. The empirical study including (a) Methodology, (b) Results (Descriptive analysis, Inferential analysis), and (c) Discussion.
S<sup>2</sup>Net: Shadow Mask-Based Semantic-Aware Network for Single-Image Shadow Removal

Existing shadow removal methods often struggle with two problems: color inconsistencies in shadow areas and artifacts along shadow boundaries. To address these two problems, we propose a novel shadow mask-based semantic-aware network (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net) that uses shadow masks as guidance for shadow removal. The color inconsistency problem is solved in two steps. First, we use a series of semantic-guided dilated residual (SDR) blocks to transfer statistical information from non-shadow areas to shadow areas. The shadow mask-based semantic transformation (SST) operation in SDR enables the network to remove shadows while keeping non-shadow areas intact. Then, we design a refinement block by incorporating semantic knowledge of shadow masks and applying the learned modulated convolution kernels to get traceless and consistent output. To remove artifacts along shadow boundaries, we propose a newly designed boundary loss. The boundary loss encourages spatial coherence around shadow boundaries. By including the boundary loss as part of the loss function, a significant portion of artifacts along shadow boundaries can be removed. Extensive experiments on the ISTD, ISTD+, SRD and SBU datasets show our S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net outperforms existing shadow removal methods.