Reviewerly/exHarmony huggingface dataset:
Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

Radial Basis Function Neural Network (RBFNN) ensembles have long suffered from non-efficient training, where incorrect parameter settings can be computationally disastrous. This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network (SRBFNN) through the behavior’s integration of satisfiability programming. Inspired by evolutionary algorithms, which can iteratively find the near-optimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN-2SAT). The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language. With the use of SRBFNN-2SAT, a training method based on these algorithms has been presented, then training has been compared among algorithms, which were applied in Microsoft Visual C++ software using multiple metrics of performance, including Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), Systematic Error (SD), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). Based on the results, the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms. It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight, accompanied by the slightest iteration error, which minimizes the objective function of SRBFNN-2SAT.
Hybrid Models for Daily Global Solar Radiation Assessment

Daily solar radiation forecasting has recently become critical in developing solar energy and its integration into grid systems. Despite the huge number of proposed forecasting techniques, an accurate estimation remains a significant challenge because of the non-stationary variation of solar radiation components due to the continuously changing climatic conditions. Usually, several input data predictors are used for the forecasting process, which can cause redundancy and correlation between data features. This work assesses a set of feature selection techniques to check their ability to select the relevant predictors and reduce redundant and irrelevant information. An Artificial Neural Network is used to fit the measured solar radiation based on the selected features. The developed model is evaluated through various objective evaluation metrics using historical data of three years measured at the Ghardaiaregion inAlgeria. Results show the effectiveness of the proposed method, where values of 0,0189, 0.0286, 5.4387, and 98.28% have been found as MABE, RMSE, nRMSE and r, respectively.
Percepciones de los estudiantes de ciencias de la salud sobre el aprendizaje cooperativo en la modalidad virtual

Objective: To identify the perceptions of implementing online cooperative learning during the 2021 II academic semesterby first-year students of Universidad Nacional Mayor de San Marcos (UNMSM) School of Medicine. Materials and methods: A descriptive and cross-sectional research design was used in this study. The population consisted of 592 students from the professional schools of Human Medicine, Nursing, Obstetrics, Nutrition and Medical Technology. A random probability sampling was carried out and a sample of 429 students was obtained. The Cooperative Learning Questionnaire with an 8-point Likert scale response format developed by McLeish was used as an instrument for data collection. It is a reliable and valid instrument that was administered online to the sample of students at the end of the course using Google Forms. The quantitative data were analyzed descriptively using IBM SPSS Statistics V22. The descriptive statistics included frequencies, averages and standard deviation. Results: Out of the students in the sample, 65.9 % were females and 34.1 % were males. Most participants had favorableperceptions of online cooperative learning, with averages between 3.77 and 4.53. Favorable perceptions of this methodologywere mainly identified in the dimensions quality of the group interaction process, teaching of interpersonal skills in smallgroups and positive interdependence. Conclusions: The students had favorable perceptions of online cooperative learning because it helped them to improve their social skills, favored their attitude toward group work, allowed them to participate in the sessions with enthusiasm,helped them to improve peer interaction, and promoted the development of their creativity and interpersonal skills. Therefore, its implementation and analysis of achievements in online environments are recommended.
Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images

Aiming at answering questions based on the content of remotely sensed images, visual question answering for remote sensing data (RSVQA) has attracted much attention nowadays. However, previous works in RSVQA have focused little on the robustness of RSVQA. As we aim to enhance the reliability of RSVQA models, how to learn robust representations against new words and different question templates with the same meaning is the key challenge. With the proposed augmented dataset, we are able to obtain more questions in addition to the original ones with the same meaning. To make better use of this information, in this study, we propose a contrastive learning strategy for training robust RSVQA models against diverse question templates and words. Experimental results demonstrate that the proposed augmented dataset is effective in improving the robustness of the RSVQA model. In addition, the contrastive learning strategy performs well on the low resolution (LR) dataset.
Automated Rice Crop Yield Prediction using Sine Cosine Algorithm with Weighted Regularized Extreme Learning Machine

Crop yield prediction is a not a simple task in precision agriculture. To be specific, paddy is one of the globally significant cereal crops and extremely important for decision-making and crop management. Although there exist more crop yield prediction methods, but lacks a better outcome in paddy yield prediction. Attaining optimal crop yield was always a difficult task for the farmer because of changeable ecological conditions. The main factor for uncertainty of crop yield are changing nature of weather, land types, and availability of resources. Hence, the scientists around the world making continuous efforts to find methods, which can precisely and efficiently predict the crop, yield in much advance such that the agronomists can make best course of action to encounter the future challenges. Rice yield prediction includes estimation of the amount of rice that would be produced in a specific area, generally a region or a farm, related to several factors that could affect the crop yield. Therefore, this article introduces an Automated Rice Crop Yield Prediction using Sine Cosine Algorithm with Weighted Regularized Extreme Learning Machine (SCA-WRELM). The proposed SCA-WRELM technique aims to forecast the rice crop productivity effectually. To do this, the SCA-WRELM technique performs min-max data normalization process to scale the data into uniform format. For yield prediction, the SCA-WRELM technique uses WRELM model to forecast the rice productivity. Finally, the SCA is exploited for the optimal parameter tuning of the WRELM model and it results in improved predictive results. The simulation values of the SCA-WRELM approach is tested on rice yield dataset and the outcomes demonstrate the improved outcomes of the SCA-WRELM approach over other existing methods.
Joint Reconstruction-Segmentation on Graphs

Practical image segmentation tasks concern images which must be reconstructed from noisy, distorted, and/or incomplete observations. A recent approach for solving such tasks is to perform this reconstruction jointly with the segmentation, using each to guide the other. However, this work has so far employed relatively simple segmentation methods, such as the Chan–Vese algorithm. In this paper, we present a method for joint reconstruction-segmentation using graph-based segmentation methods, which have been seeing increasing recent interest. Complications arise due to the large size of the matrices involved, and we show how these complications can be managed. We then analyze the convergence properties of our scheme. Finally, we apply this scheme to distorted versions of “two cows” images familiar from previous graph-based segmentation literature, first to a highly noised version and second to a blurred version, achieving highly accurate segmentations in both cases. We compare these results to those obtained by sequential reconstruction-segmentation approaches, finding that our method competes with, or even outperforms, those approaches in terms of reconstruction and segmentation accuracy.
Analysis of the Effectiveness of Metaheuristic Methods on Bayesian Optimization in the Classification of Visual Field Defects

Bayesian optimization (BO) is commonly used to optimize the hyperparameters of transfer learning models to improve the model's performance significantly. In BO, the acquisition functions direct the hyperparameter space exploration during the optimization. However, the computational cost of evaluating the acquisition function and updating the surrogate model can become prohibitively expensive due to increasing dimensionality, making it more challenging to achieve the global optimum, particularly in image classification tasks. Therefore, this study investigates and analyses the effect of incorporating metaheuristic methods into BO to improve the performance of acquisition functions in transfer learning. By incorporating four different metaheuristic methods, namely Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) Optimization, Harris Hawks Optimization, and Sailfish Optimization (SFO), the performance of acquisition function, Expected Improvement (EI), was observed in the VGGNet models for visual field defect multi-class classification. Other than EI, comparative observations were also conducted using different acquisition functions, such as Probability Improvement (PI), Upper Confidence Bound (UCB), and Lower Confidence Bound (LCB). The analysis demonstrates that SFO significantly enhanced BO optimization by increasing mean accuracy by 9.6% for VGG-16 and 27.54% for VGG-19. As a result, the best validation accuracy obtained for VGG-16 and VGG-19 is 98.6% and 98.34%, respectively.
Text-Based Sentiment Analysis

The explosion of data has sparked a surge in interest in sentiment analysis in recent years. We may use it in various industries, including marketing, psychology, human-computer interface, and e-Learning. Sentiment analysis may take numerous forms, including facial expressions, speech recognition, and text classification. Sentiment analysis from text is helpful in various industries. Still, it is particularly significant in e-Learning for assessing students’ emotional states and, as a result, putting in place the required interactions to drive students to engage in and complete their courses. Because sentiment analysis from text is a relatively new topic that still needs more work and study, this article focuses on it. In this article, we discuss many approaches for extracting sentiment from the text in the literature, starting with selecting features or text representation and ending with the training of the prediction model using either supervised or unsupervised learning algorithms. There is still work that can be done to improve performance. We must first review the current methods and approaches in this field, discuss improvements in specific techniques or even propose new ones.
Separability and Its Approximations in Ontology-based Data Management

Given two datasets, i.e., two sets of tuples of constants, representing positive and negative examples, logical separability is the reasoning task of finding a formula in a certain target query language that separates them. As already pointed out in previous works, this task turns out to be relevant in several application scenarios such as concept learning and generating referring expressions. Besides, if we think of the input datasets of positive and negative examples as composed of tuples of constants classified, respectively, positively and negatively by a black-box model, then the separating formula can be used to provide global post-hoc explanations of such a model. In this paper, we study the separability task in the context of Ontology-based Data Management (OBDM), in which a domain ontology provides a high-level, logic-based specification of a domain of interest, semantically linked through suitable mapping assertions to the data source layer of an information system. Since a formula that properly separates (proper separation) two input datasets does not always exist, our first contribution is to propose (best) approximations of the proper separation, called (minimally) complete and (maximally) sound separations. We do this by presenting a general framework for separability in OBDM. Then, in a scenario that uses by far the most popular languages for the OBDM paradigm, our second contribution is a comprehensive study of three natural computational problems associated with the framework, namely Verification (check whether a given formula is a proper, complete, or sound separation of two given datasets), Existence (check whether a proper, or best approximated separation of two given datasets exists at all), and Computation (compute any proper, or any best approximated separation of two given datasets).
Privacy-Preserving Distributed Machine Learning Made Faster

With the development of machine learning, it is difficult for a single server to process all the data. So machine learning tasks need to be spread across multiple servers, turning the centralized machine learning into a distributed one. Multi-key homomorphic encryption is one of the suitable candidates to solve the problem. However, the most recent result of the Multi-key homomorphic encryption scheme (MKTFHE) only supports the NAND gate. Although it is Turing complete, it requires efficient encapsulation of the NAND gate to further support mathematical calculation. This paper designs and implements a series of operations on positive and negative integers accurately. First, we design basic bootstrapped gates, the efficiency of which is times that the number of using NAND to build. Second, we construct practical k-bit complement mathematical operators based on our basic binary bootstrapped gates. The constructed created can perform addition, subtraction, multiplication, and division on both positive and negative integers. Finally, we demonstrated the generality of the designed operators by achieving a distributed privacy-preserving machine learning algorithm, i.e. linear regression with two different solutions. Experiments show that the consumption time of the operators built with our gate is about 50 ∼ 70% shorter than built directly with NAND gate and the iteration time of linear regression with our gates is 66.7% shorter than with NAND gate directly.
Using Feedforward to Reveal Interaction Possibilities in Virtual Reality

In virtual reality (VR), interactions may fail when users encounter new, unknown, or unexpected objects. We propose using feedforward in VR to help users interact with objects by revealing how such objects work. Feedforward lets users know what to do and how to do it by showing the available actions and outcomes before an interaction. In this article, we first chart the design space of feedforward in VR and illustrate how to design feedforward for specific VR interactions. We discuss starting the feedforward, previewing actions and outcomes, and returning the virtual world to its state before the feedforward. Second, we implement three real-world VR applications to show how feedforward can be applied to multistep interactions, perceived interactivity, and discoverability. Third, we conduct an evaluation of the design space with 14 VR experts to understand its usefulness. Finally, we summarize the findings of our work on VR feedforward in 15 guidelines.
Unbiased Scene Graph Generation via Two-stage Causal Modeling

Despite the impressive performance of recent unbiased Scene Graph Generation (SGG) methods, the current debiasing literature mainly focuses on the long-tailed distribution problem, whereas it overlooks another source of bias, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , semantic confusion, which makes the SGG model prone to yield false predictions for similar relationships. In this paper, we explore a debiasing procedure for the SGG task leveraging causal inference. Our central insight is that the Sparse Mechanism Shift (SMS) in causality allows independent intervention on multiple biases, thereby potentially preserving head category performance while pursuing the prediction of high-informative tail relationships. However, the noisy datasets lead to unobserved confounders for the SGG task, and thus the constructed causal models are always causal-insufficient to benefit from SMS. To remedy this, we propose Two-stage Causal Modeling (TsCM) for the SGG task, which takes the long-tailed distribution and semantic confusion as confounders to the Structural Causal Model (SCM) and then decouples the causal intervention into two stages. The first stage is causal representation learning, where we use a novel Population Loss (P-Loss) to intervene in the semantic confusion confounder. The second stage introduces the Adaptive Logit Adjustment (AL-Adjustment) to eliminate the long-tailed distribution confounder to complete causal calibration learning. These two stages are model agnostic and thus can be used in any SGG model that seeks unbiased predictions. Comprehensive experiments conducted on the popular SGG backbones and benchmarks show that our TsCM can achieve state-of-the-art performance in terms of mean recall rate. Furthermore, TsCM can maintain a higher recall rate than other debiasing methods, which indicates that our method can achieve a better tradeoff between head and tail relationships.
An approximate representation of objects underlies physical reasoning.

People make fast and reasonable predictions about the physical behavior of everyday objects. To do so, people may use principled mental shortcuts, such as object simplification, similar to models developed by engineers for real-time physical simulations. We hypothesize that people use simplified object approximations for tracking and action (the body representation), as opposed to fine-grained forms for visual recognition (the shape representation). We used three classic psychophysical tasks (causality perception, time-to-collision, and change detection) in novel settings that dissociate body and shape. People's behavior across tasks indicates that they rely on coarse bodies for physical reasoning, which lies between convex hulls and fine-grained shapes. Our empirical and computational findings shed light on basic representations people use to understand everyday dynamics, and how these representations differ from those used for recognition. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
An Exploration into Human–Computer Interaction: Hand Gesture Recognition Management in a Challenging Environment

Abstract Scientists are developing hand gesture recognition systems to improve authentic, efficient, and effortless human–computer interactions without additional gadgets, particularly for the speech-impaired community, which relies on hand gestures as their only mode of communication. Unfortunately, the speech-impaired community has been underrepresented in the majority of human–computer interaction research, such as natural language processing and other automation fields, which makes it more difficult for them to interact with systems and people through these advanced systems. This system’s algorithm is in two phases. The first step is the Region of Interest Segmentation, based on the color space segmentation technique, with a pre-set color range that will remove pixels (hand) of the region of interest from the background (pixels not in the desired area of interest). The system’s second phase is inputting the segmented images into a Convolutional Neural Network (CNN) model for image categorization. For image training, we utilized the Python Keras package. The system proved the need for image segmentation in hand gesture recognition. The performance of the optimal model is 58 percent which is about 10 percent higher than the accuracy obtained without image segmentation.
ATM Theft Detection Using Artificial Intelligence

Most often, users use ATMs to withdraw cash from automated electronic machines. When used, a client doesn't need assistance from a bank employee to handle monetary changes. In the current set-up, many people who take cash from an ATM are vulnerable to robberies and theft since there aren’t enough protections in place. ATM banks are increasingly installing surveillance cameras, but police forces' ability to keep tabs on the footage has lagged behind. Attempts may be made to steal the money from an ATM by breaking into the machine or by damaging it in a defamatory manner. As a preventative measure, we will equip the ATM with a camera module permanently installed in the room to conduct continual video monitoring. Humans and the activities they take inside the ATM while attempting to break in are identifiable to the camera. Consequently, this software is concerned with the programme created to automate video monitoring and identify any fraudulent actions occurring at ATMs. Such algorithms may be used to identify people in a photo, locate, and follow the camera as it moves and ultimately determine what steps should be taken to avoid illegal behaviour. As a result, we determined whether or not the assault was indeed taking place and notified the police. The ability to foresee an impending assault is a significant advantage.