Reviewerly/exHarmony huggingface dataset:
Lifelong CycleGAN for continual multi-task image restoration

Recent years have witnessed the great success of deep learning in the applications of image restoration. However, there are still some challenging problems. First, most deep learning methods rely heavily on paired training images which are difficult to capture in the real world. Second, most existing methods are generally designed for a specific task of image restoration and they suffer from catastrophic forgetting when learn multiple tasks continually. Third, for most multi-task image restoration methods, they learn an individual network for each task and require to know the type of distortion for both training and test, which costs a lot of computational time and memory. To address the above issues, we propose a new lifelong learning framework based on CycleGAN for continual multi-task image restoration, called Lifelong CycleGAN (LCGAN), which can enhance low-light images, deblur and denoise simultaneously. The model utilizes knowledge distillation and memory replay to transfer knowledge and replay information that learned previously to alleviate forgetting. In addition, to regularize the unpaired training, we introduce the local discriminator and feature consistency constraint to preserve the color, edge and texture of input images. The proposed method can continually learn the three tasks using one network model and doesn’t need to prejudge the type of distortion, which has low time and memory requirements. Experimental results demonstrate that LCGAN can achieve better visual and numerical results across the three tasks.
THE INEVITABILITY OF THE UNITY OF FUNDAMENTAL PHYSICS AND METAPHYSICS AT THE TRANSITIONAL STAGE OF DEVELOPMENT OF PHYSICS

The article shows that at the present time in physics the conditions are ripe for a decisive revision of the prevailing ideas about physical reality. To solve this problem, metaphysical principles must be considered. Their manifestations in modern physics are shown in the presence of three paradigms. It has been demonstrated that this is most fully implemented within the framework of the relational paradigm, which has long been in the shadows. The results already obtained within its framework are shown and it is noted that the development of this direction most closely corresponds to materialist philosophy.
Detecting Real-Time Fall of Elderly People Using Machine Learning

Abstract: Fall is a significant national health issue for the elderly people, generally resulting in severe injuries when the person lies down on the floor over an extended period without any aid after experiencing a great fall. Thus, elders need to be cared very attentively. A supervised-machine learning based fall detection approach with accelerometer, gyroscope is devised. The system can detect falls by grouping different actions as fall or non-fall events and the care taker is alerted immediately as soon as the person falls. The public dataset SisFall with efficient class of features is used to identify fall. The Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms are employed to detect falls with lesser false alarms. The SVM algorithm obtain a highest accuracy of 99.23% than RF algorithm. Keywords: Fall detection, Machine learning, Supervised classification, Sisfall, Activities of daily living, Wearable sensors, Random Forest, Support Vector Machine
Using the <i>Cataloguing Code of Ethics</i> Principles for a Retrospective Project Analysis

This study uses the recently released Cataloguing Code of Ethics to evaluate a project which explored how to ethically, efficiently, and accurately add demographic terms for African-American authors to catalog records. By reviewing the project through the lens of these principles the authors were able to examine how their practice was ethical in some ways but could have been improved in others. This helped them identify areas of potential improvement in their current and future research and practice and explore ethical difficulties in cataloging resources with records that are used globally, especially in a linked data environment.
Discrete Spike-and-Slab Priors: Models and Computational Aspects

A large body of research has been devoted to variable selection in recent years. Bayesian methods have been successful in applications, particularly in settings where the amount of measured variables can be much greater than the number of observations. This chapter reviews mixture priors that employ a point mass distribution at zero for variable selection in regression settings. The popular stochastic search MCMC algorithm with add-delete-swap moves is described and posterior inference and prediction via Bayesian model averaging are briefly discussed. Regression models for non Gaussian data, including binary, multinomial, survival and compositional count data are also addressed. Prior constructions that take into account specific structures in the covariates are described. These constructions have been particularly successful in applications as they allow the integration of different sources of biological information into the analysis. A discussion of computational strategies, in particular variational algorithms for scalable inference, concludes the chapter. Throughout the chapter, some emphasis is given to the author's contribution.
TEMPORAL INTERPRETATION OF MONADIC INTUITIONISTIC QUANTIFIERS

Abstract We show that monadic intuitionistic quantifiers admit the following temporal interpretation: “always in the future” (for $\forall $ ) and “sometime in the past” (for $\exists $ ). It is well known that Prior’s intuitionistic modal logic ${\sf MIPC}$ axiomatizes the monadic fragment of the intuitionistic predicate logic, and that ${\sf MIPC}$ is translated fully and faithfully into the monadic fragment ${\sf MS4}$ of the predicate ${\sf S4}$ via the Gödel translation. To realize the temporal interpretation mentioned above, we introduce a new tense extension ${\sf TS4}$ of ${\sf S4}$ and provide a full and faithful translation of ${\sf MIPC}$ into ${\sf TS4}$ . We compare this new translation of ${\sf MIPC}$ with the Gödel translation by showing that both ${\sf TS4}$ and ${\sf MS4}$ can be translated fully and faithfully into a tense extension of ${\sf MS4}$ , which we denote by ${\sf MS4.t}$ . This is done by utilizing the relational semantics for these logics. As a result, we arrive at the diagram of full and faithful translations shown in Figure 1 which is commutative up to logical equivalence. We prove the finite model property (fmp) for ${\sf MS4.t}$ using algebraic semantics, and show that the fmp for the other logics involved can be derived as a consequence of the fullness and faithfulness of the translations considered.
PRM Path Smoothening by Circular Arc Fillet Method for Mobile Robot Navigation

The problem of motion planning and navigation for mobile robots in complex environments has been a central issue in robotics. Navigating these environments requires sophisticated algorithms that handle obstacles and provide smooth, efficient paths. The Probabilistic Roadmap (PRM) method is a widespread technique in robotics for constructing paths for mobile robot navigation. In this study, we propose a novel path-smoothing method using arc fillets for path planning, building on PRM's foundation in the presence of obstacles. Our method operates in two primary stages to improve path efficiency and quality. The first stage generates the shortest path between the initial and goal states in an obstacle-rich environment using PRM, constructing a straight-line, collision-free route. The second stage smooths corners caused by nodes with arc fillets, ensuring smooth turns and minimizing abrupt changes in direction, resulting in more natural and efficient robot motion. We conducted simulations and tests using various PRM features to evaluate the proposed method. The results indicate that the built route offers a smooth turning motion and quicker, more compact movement while evading obstacles. This study contributes to mobile robot navigation by offering a practical approach to improving pathway quality in challenging environments.
Heterogeneous Graph Neural Networks

AbstractHeterogeneous graphs (HGs) also called heterogeneous information networks (HINs) have become ubiquitous in real-world scenarios. Recently, employing graph neural networks (GNNs) to heterogeneous graphs, known as heterogeneous graph neural networks (HGNNs) which aim to learn embedding in low-dimensional space while preserving heterogeneous structure and semantic for downstream tasks, has drawn considerable attention. This chapter will first give a brief review of the recent development on HG embedding, then introduce typical methods from the perspective of shallow and deep models, especially HGNNs. Finally, it will point out future research directions for HGNNs.
Minimizing of the quadratic functional on Hopfield networks

In this paper, we consider the continuous Hopfield model with a weak interaction of network neurons. This model is described by a system of differential equations with linear boundary conditions. Also, we consider the questions of finding necessary and sufficient conditions of solvability and constructive construction of solutions of the given problem, which turn into solutions of the linear generating problem, as the parameter $\varepsilon$ tends to zero. An iterative algorithm for finding solutions has been constructed. The problem of finding the extremum of the target functions on the given problem solution is considered. To minimize a functional, an accelerated method of conjugate gradients is used. Results are illustrated with examples for the case of three neurons.
Impact of COVID-19 on the Nature of Academic Work in Colleges of Education in Ghana

This study investigated the impact of COVID-19 on the nature of academic work in Colleges of Education in Ghana. The study adopted the descriptive survey design. A sample of 346 students was selected for the study. The participants chosen from the Evangelical Presbyterian College of Education, Peki College of Education and the St. Theresah College of Education in the Volta Region of Ghana through purposive, convenience and snowball sampling procedures. Data were gathered using an online questionnaire. Data were received from 270 students resulting in a 78% return rate. The data were analysed using frequencies and percentages as well as means and standard deviations. The results showed that tutors in Colleges of Education mostly taught during the COVID-19 period by sending audio recordings, texts or slide presentations to students. This was however not done in real time. Also, the tutors gave assignments and quizzes and gave feedback on the performance of the students. Finally, it was found that most students’ services were not provided satisfactorily when school came to an abrupt end because of the COVID-19 Pandemic. Specifically, student counselling services, student affairs services, organisation of tutorials/seminars and practical classes as well as technical support or IT services and supervisions and mentorships were not satisfactorily provided. It was therefore recommended that authorities in Colleges of Education should liaise with officers in charge of specific services like counselling, student affairs and technical or IT support so that they can effectively and satisfactorily assist students even when they are not on campus.
Roosters Algorithm: A Novel Nature-Inspired Optimization Algorithm

Some species of females, e.g., chicken, bird, fish etc., might mate with more than one males.In the mating of these polygamous creatures, there is competition between males as well as among their offspring.Thus, male reproductive success depends on both male competition and sperm rivalry.Inspired by this type of sexual life of roosters with chickens, a novel nature-inspired optimization algorithm called Roosters Algorithm (RA) is proposed.The algorithm was modelled and implemented based on the sexual behavior of roosters.13 well-known benchmark optimization functions and 10 IEEE CEC 2018 test functions are utilized to compare the performance of RA with the performance of well-known algorithms; Standard Genetic Algorithm (SGA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Grey Wolf Optimizer (GWO).Also, non-parametric statistical tests, Friedman and Wilcoxon Signed Rank Tests, were performed to demonstrate the significance of the results.In 20 of the 23 functions that were tested, RA either offered the best results or offered similar results to other compared algorithms.Thus, in this paper, we not only present a novel nature-inspired algorithm, but also offer an alternative method to the well-known algorithms commonly used in the literature, at least as effective as them.
Automatic Credit Fraud Detection Using Ensemble Model

As dependency on credit cards and online transactions is increasing, the scope for unauthorized payments or fraudulent transactions is on the rise. Huge financial losses are borne by banks every year due to fraudulent transactions. The process of identifying such transactions among millions of transaction records can be perfected using machine learning models, even more so by ensembling them. This study takes a real-time financial dataset and analyzes the efficiency of various machine learning models in predicting fraudulent transactions. Individual ML models are also analysed, ensembled and tested on the dataset. Upon extensive analysis and research, it is concluded that an ensembled model of LightGBM, decision tree classifier and XGBoost gave the highest AuC score of 87%. Further, a user-interactive tool is also created, which predicts fraudulent transactions from real-time transactions. The tool makes use of the model with highest prediction efficiency and presents it in an interactive manner. This study and practical demonstration show advancement in prediction of fraudulent transactions when compared to existing ML models and its user-friendly software incorporation.
Graph Neural Networks for Node Classification

AbstractGraph Neural Networks are neural architectures specifically designed for graph-structured data, which have been receiving increasing attention recently and applied to different domains and applications. In this chapter, we focus on a fundamental task on graphs: node classification.We will give a detailed definition of node classification and also introduce some classical approaches such as label propagation. Afterwards, we will introduce a few representative architectures of graph neural networks for node classification. We will further point out the main difficulty— the oversmoothing problem—of training deep graph neural networks and present some latest advancement along this direction such as continuous graph neural networks.
Fast Synthesis of Algebraic Heuristic Functions for Video-game Pathfinding

Heuristic search is widely used in games for pathfinding and general planning. High-quality heuristic functions are key to finding a low-cost solution quickly. Commonly used heuristic functions for video-game pathfinding are either manually designed and generic or pre-computed for a specific map. The former fail to take advantage of pathfinding specifics while the latter tend to have a large memory footprint, may require substantial pre-computation and are not portable to other maps or easily presentable to humans. In this work we attempt to combine the best of both approaches by automatically synthesizing well performing pathfinding-specific yet compact and human-readable heuristics. We do so by defining a space of algebraic formulae expressing heuristic functions and then conducting an automated search of the space. To make the synthesis tractable we employ a multi-tier evaluation which allows us to quickly filter out low-quality heuristics while saving time to more thoroughly evaluate better ones. Such triage of candidate heuristics enables us to synthesize compact heuristics that outperform the standard baseline on video-game pathfinding benchmarks. By then adding the synthesized heuristics back to the synthesis space we show that synthesis on new maps can be substantially sped up to merely few minutes per map.
A Big Data Experiment to Evaluate the Effectiveness of Traditional Machine Learning Techniques Against LSTM Neural Networks in the Hotels Clients Opinion Mining

Context: Nowadays, client reviews on social networks can be a great source of knowledge extraction for strategic marketing planning. In the tourism area, opinions given by hotel clients in tourism social networks can drive improvements in service. In this context, traditional text mining techniques and new deep learning technologies should be tried out to select the best options for classifying opinions. Objective: Evaluate the performance and quality of the LDA (Latent Dirichlet Allocation), Naives Bayes (NB), Logistic Regression, SVM (Support Vector Machine) and LSTM - Long Short-Term Memory Units algorithms in the task of opinion mining of hotel reviews published on the TripAdvisor hotel booking website. Method: An In Vivo Controlled Experiment (Case Study) to compare the performance of the classifiers by means of accuracy, precision, recall, F1-measure and average training and classification times. Results: The LSTM model presented the best results regarding quality metrics. However, it did not present satisfactory results regarding processing time. Conclusion: The LSTM classifier had clearly superior performance when compared to the other evaluated ones. On the other hand, its average training and classification times greater than the others classifiers considered.
Using Machine Learning to Correlate Twitter Data and Weather Patterns

In the modern era, social media constitutes a large portion of societal interaction. These social media platforms now offer vast amounts of data to perform analyses that correlate online data with real-world phenomena. These correlations have potential applications in not only many parallel aspects of data science but also in social sciences that study human behavior online. This paper conducts a comparative analysis amongst various machine learning algorithms by correlating Twitter text data and weather. These algorithms process tweet data to infer the type of weather. This paper details the implementations, conceptual structures, and advantages and disadvantages of these algorithms. It was determined that the multilayer perceptrons and the naive Bayes classifier were unable to find any significant correlations, and achieved accuracies similar to guessing. On the contrary, both the convolutional and recurrent neural networks reached categorization accuracies well above the baseline test, demonstrating the benefit of considering context and groups of tweets at a time and indicating that there exists some correlation between weather and tweet text data. Ultimately, the recurrent network performed best with a maximum validation accuracy of 65.7%.
The Impact of Customer Behavior on E-business during of the COVID-19 Crisis in Jordan

Aims: This study was conducted to study the impact of customer behavior on e-business in Jordan in light of the increasing spread of the virus.&#x0D; Study Design: A quantitative research.&#x0D; Place and Duration of Study: Jordan, between January - September 2020.&#x0D; Methodology: The Statistical Analysis Software (SPSS) software is used by the researcher. The survey questionnaires framed by the researcher are helping the researcher in representing the data appropriately&#x0D; Results: The results showed that online purchase was acceptable and high during the Corona pandemic period despite the complete paralysis of movement Before the customer so that there is an alternative in emergency and necessary cases such as the Corona pandemic and the need to take into account the terms and behavior of the consumer when advertising, selling or promoting the commodity on electronic sales platforms.&#x0D; Conclusion: Through the foregoing according to the data analysis carried out above shows the negative impact on e-business in Jordan due to the Coronavirus (COVID-19) the negative impact created affects sales as well as the purchasing behavior of customers. The business environment in Jordan is seen as weak due to COVID-19. The challenges that retailers in the e-commerce business face is declining customer buying behavior. Also, lower buying behavior reduces bids and shipping as per the surveys carried out this will lead to the deterioration of the e-business as well as huge losses that the e-business will incur. Research questions as well as research objectives set by the researcher a greater impact has been made on the current e-business in the Jordanian market. Buy food products from food stock for this reason. And the hypothesis of the research papers he developed lies a change in food contamination. For this reason, human-to-human contact is restricted which leads to the deterioration of e-commerce sales. Thus the presented analysis supports for this reason a greater impact has been created on the existing e-business in the Jordanian market since there is a concern of the buyer that with Delivery of food products through the electronic company.
Voice-Enabled Virtual Assistant

Frameworks are being learned step by step nowadays and will assist people in their daily lives. Moreover, artificial intelligence [AI] techniques are now available in a broad number of sectors, ranging from ventures in manufacturing to medical innovation. As a result, within the completed framework, this research work has created a virtual assistant to solve college-related queries. The resulting framework is essentially a virtual assistant, who is meticulously college organized and resolve all college-related questions for students, teachers, and administration. Students confront a lack of critical information about college difficulties for a variety of reasons. The reason might be a result of system flaws. Such causes include a communication gap between the student and the college administration, a lack of student engagement, a lack of suitable guidance, and ignorance on the part of the student and/or the college administration. The student may be unaware of the class schedule, event timings, event location, vacations, examination schedule, permissions, and placement details. Here, the virtual assistant plays the key role in providing the necessary information. The advancements like natural language processing are utilized with the help of GTTS, Google API to change over from text to speech and the reverse way around. Here, the whole input is taken by the user and driven via graphical user interface (GUI). This application reduces the time and manual work of the user.
Removing Noise During the Filtering Images

In the problems of image recognition, various approaches used when the image is noisy and there is a small sample of observations. The article discusses the issue of noise filtering in image processing. The lack of a priori information complicates the processing of data, as a result of which it is necessary to rely on some statistical models of signals and noise. The use of known filters does not always give the desired result. A Gaussian filter can be used for additive noise, a modified Kalman filter eliminates a wider range of noise.