Reviewerly/exHarmony huggingface dataset:
Modification of Water Rescue Tool for Saving the Drowned People at the Swimming Pool

This research aims to determine the feasibility of a modified a water rescue tool. This research was conducted in a swimming pool. The number of respondents are for about 35 people (5 lifeguards and 3 pool attendants, 12 trainers in the swimming pool, 10 students, and 5 swimming pool visitors). The results of this study are expected to be one of the contributions of researchers to realize a modification tool for water rescue that is simple, safe, and feasible to use. The stages in this research were using the ADDIE research model in which there were 5 stages: analysis, design, development, implementation, and evaluation. The research method used the quantitative and qualitative approach with the type of research of development research. The data collection technique used the observation, interviews, questionnaires, and documentation. The data analysis was conducted through the recapitulation of expert and respondent validation data in the form of percentage scores. The results of the assessment of 35 respondents from the buoyancy aspect obtain a score of 95% (in the "Very decent" category), the target accuracy aspect gains a score of 95% (in the very feasible category), the aspect of the use of the tool gains a score of 96% (in the " Very decent" category), the effectiveness aspect obtains a score of 95% (in the " Very decent" category), the durability aspect obtains a score of 92% (in the "Very decent" category). the physical aspect gains a score of 90% (in the " Very decent" category), and the design aspect gains a score of 96% (in the "Very decent" category), while the average total score of the five aspects is at 96.% (in the "Very decent" category). The conclusion is that the feasibility of the Water rescue modification tool is included in the " Very decent" category.
Suhail: A Deep Learning-Based System for Identifying Missing People

Many people become missing in Saudi Arabia every day, including children, young people, and the mentally ill as well as the elderly with Alzheimer&amp;rsquo;s. There are many missing people cases that are still unsolved. In Saudi Arabia, people use social media platforms such as Twitter to report missing people cases. The application of deep learning has been successful in a wide range of fields including computer vision and machine vision. In particular, face recognition techniques are effective in saving time and effort, especially when searching for missing individuals. Hence, the goal of this research is to solve this issue by developing a deep learning-based system for identifying missing individuals. This paper introduces a new system called Suhail. The system has been implemented and developed using Android Studio and open-source libraries such as TensorFlow. First, users or governments can report missing persons by uploading photos. Updates and information will then be shared with the rest of the system&amp;rsquo;s users (volunteers). Once a volunteer discovers a suspect, they scan their face using camera. Then, our application uses face recognition techniques to compare the suspect&amp;#39;s photo with photos from the repository. Finally, once a comparison is found, our application contacts the suspect&amp;rsquo;s family, informs them of his location and then notifies the police that a missing person has been located. By using our application and face recognition systems, we help families and police locate and reach a missing person which saves time and effort. In this study, 759 participants were enrolled to evaluate the performance of the Suhail system. Engagement, aesthetics, and functionality are used to evaluate the user experience. The results of the experiment show that users enjoy the new features of the application and that the system is simple to use. Moreover, the system would help governments and individuals identify missing people faster.
The Disappearance-Appearance Phenomenon (DAP) A Natural Law of the Electronic Transitions in the Atoms

In this research paper we predicted the existence of a quantum phenomenon never discovered since the beginning of quantum mechanics, we called it the Disappearance-Appearance Phenomenon (DAP), it is a natural law of the electronic transitions in atoms without quantum jumps.
The Mathematics of the Harp: Modeling the Classical Instrument and Designing Futuristic Ones

We analyze and model the neck of the classical harp based on the length of the strings, their tension and density.We then use the results to design new and innovative harp shapes by adjusting the parameters of the model.
Analysis of Cross Platform Application Development Over Multiple Devices using Flutter &amp; Dart

With the looming of new mobile applications, the organizations, and industries of all types whether it is IT Consultancy, Human Resources, Logistics and Supply Chain Management etc. or to be more specific any industry or organization that is thriving for their development in their sectors are relying on application development now a days. Through which app development industry is quickly advancing day by day. Embracing of various Operating Systems like Android, iOS, Symbian OS etc. in mobile development industry only Android OS is most loved and used by the people now a days and this Android OS is gaining popularity in upcoming generations also. Secondly talking about iOS by Apple it is also gaining popularity but not up to that extant as Android OS has gained and stabilizes itself in the industry. Although the iOS is very famous for its security reasons. Symbian OS now is a desisted OS or in simple terms extinct from the industry was owned by NOKIA used up C++ as the programming language to provide a simple yet implicit User Interface to their user for a seamless experience in mobile applications. This thesis/Research paper aims to present the benefits of using Cross Platform Application Development using Flutter as while Java or Kotlin is basically used in Application Development but the only drawback of it was that for individual Operating System we have to code individually and for another Operating System we have to code individually. So, to overcome this obstacle we are using Dart Programming Language using Flutter as a Platform upon the Android Studio or the Visual Studio Code for Developing our App.
Penggunaan Media Pembelajaran Sway pada Pembelajaran Membaca Permulaan di Sekolah Dasar

The use of image and SWAY media can improve students' reading skills in the Indonesian language at State Elementary School 32 Tumpumpua VI. This type of research uses the classroom action research method, which consists of two cycles. The research procedure includes planning, implementation, observation, and reflection. The research subjects are grade 1 students, a total of 28 students, 13 male and 15 female. The research results show an improvement in students' learning achievements in the skill of reading beginners in the second cycle, from a success rate of 57% completed with an average score of 70.86 in the first cycle to 100% completed with an average score of 83.82 in the second cycle. SWAY learning media can improve students' reading beginner skills in various ways, such as attractive visualization, enjoyable learning, flexible, interactive, and personalized. Therefore, the use of SWAY learning media needs to be applied and implemented in elementary school learning to improve students' learning achievements.
steganography in colored images based on biometrics

Due to the great significance of the concealment and transfer of confidential data and the many and varied algorithms presented in this field, as well as the big and rapid development of the use of biometrics in the issues of reliability and verification of the identity of people in support of information security. Therefore this research has provided a suggested algorithm to hide confidential data in colorful images based on the features of engineering dimensions of the human hand as one of the types of biometrics; we extracted a set of these characteristics and processed them to build a matrix that specifies the map of the distribution of confidential data in the cover image. The proposed method was applied to a set of images to hide a set of confidential messages and the visual quality of the cover image was not affected after the concealment. The practical results explain the efficiency of this method was measured in terms of comparison between the original image before and after concealment. The performance of the algorithm and the method of its application were measured using measures of efficiency of the work, whereas the Peak Signal Noise Ratio was used, the highest ratio obtained was 81.3417, and Mean Square Error was approximately 0.0008 as a maximum limit, while the error rate was the restored confidential message after hiding Bit Error Rate = 0 in all cases
Pacing-based Procedural Dungeon Level Generation: Alternating Level Creation to Meet Designer’s Expectations

Dungeon Crawler games are getting a greater prominent rise in attention in the present day.Therefore, managing the pace in dungeon games is necessary.In this paper, we generate dungeon levels with the game pacing as intended by the game designer by integrating the concept of the Mission and Space framework and Game Design Patterns to generate dungeon game levels with the intended pacing.The Mission and Space framework handles the generation of tasks and in-game geometry spaces.We also present Pacing Patterns as patterns of the pacing on a game level to drive the level generations.We use a genetic algorithm to generate the potential level candidates.Those processes are encapsulated in a mixed-initiative tool, Pacing-based Dungeon Generator, to generate meaningful dungeon levels for players based on game designer preferences of game pacing.The proposed approach can minimize both time and expenses used to create game levels and effectively provide a more formal approach for game designers.Moreover, it keeps the players' progression and experience as what the designer expected.
Decision Making System based on Collaborative Agents

In this paper are presented the results of research done in the system projecting and research for making informed decisions based on the collaborative Agents. The mathematical model used in this research had the goal to find an optimal solution in a multi-objective space by using methods inspired by nature, especially evolutional calculus algorithms. The calculus system’s architecture consists of two sets of Agents: agents that deliver data and information, and Agents that consume it. The interconnection process between Agents is a dynamic one which evolves in time and it determines the topology of the calculus system.
Multi-Objective Optimal Solution Search based on Genetic Algorithms

The paper presents the results of research carried out to solve complex problems aimed at the efficient use of natural and energy resources. The objectives of the paper are achieved by identifying the control process based on a Multi-Agent system with distributed data processing that implements a Multi-objective optimal solution search model based on the application of a Genetic Algorithm with Collective Computation. The set of Agents presents a computational architecture that forms a structured network topology based on a P-Systems model presented in the form of a Venn diagram. The Object diagram and the Venn diagram of the P-Systems model are presented in the paper. The correctness of the developed models was verified on the basis of a control system of the artificial lighting process that provides for the minimization of energy consumption, while ensuring the quality of the lighting process.
A comparative study of metaheuristics algorithms based on their performance of complex benchmark problems

Metaheuristic approaches with extremely important improvements are very promising in the solution of intractable optimization problems. The objective of the present study is to test the capability of applications and compare the performance of the four selected algorithms from “classical” (simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE)) and “new generation” (firefly algorithm (FFA), krill herd (KH), grey wolf optimization (GWO), and symbiotic organism search (SOS)) each by solving selected benchmark problems that are used in the literature for algorithm testing purpose. The selected test problems had very complex objective functions and associated constraints with multiple local optima. Among all selected algorithms, the “new generation” SOS and KH algorithm successfully solved most of all the selected benchmark problems and achieved the best solution for most of them. Among four “classical” algorithms, DE, and PSO effectively attained the optimal solution which was very close to the best one. However, the “new generation” algorithm performed much better than the “classical” one. Therefore, no firm conclusion can be done about the universally best algorithm and their performance may be varied for different benchmark problems. However, in this study for the seven selected test problems, SOS and KH exhibited the most promising result and great potential with respect to execution time also. This study gives some insights to use SOS and KH as the best-performing algorithms to the novice user who can easily get lost in the plethora of large optimization algorithms.
Feature Selection based on Genetic Algorithm for Classification of Mammogram Using K-means, k-NN and Euclidean Distance

There have been several supervised classification attempts for mammograms in the recent times, but very few research works have focused on unsupervised classification to explore its potentialities and weaknesses. I have in this paper attempted to utilize unsupervised clusters to classify malignant, and benign mammograms samples. MiniMIAS database has total 322 mammogram images out which 64 are benign and 51 are malignant. I used 115 images for my experimentation i.e. 64 benign and 51 malignant. Out of these 115, 60% were used for training and 40% for testing. Therefore from 64 benign cases 39 images were used for training and rest for testing, and out of 51 malignant cases 31 images were used for training and rest for testing., the classifications was done on the bases of the features selected using genetic algorithm. Attempts have also been made to study the performance of each feature selected by Genetic Algorithm (GA) in classification. The initially identified clusters using K-means are used to classify 60 unknown samples using k-NN. The proposed work got reasonably good results with 96.23% accuracy for malignant samples, 95.37% for benign. The proposed work can help the radiologists and oncologist as second opinion during screening sessions for early detection.
Automated Computer Vision System for Urine Color Detection

Urine color analysis is one of the most helpful indicators of health status, and any changes in urine color might be a symptom of serious disease, dehydration of the body, or caused by drugs. To get better assistance for urine color detection in the proposed system, a urine color automatic identification has been developed based on computer vision. The proposed system uses a web camera to capture an image in real-time, analyze it, and then classify the color of urine by using the random forest (RF) algorithm and show the result via the Graphical User Interface (GUI). In addition, the proposed system can send the results to the mobile phone of the patient or care provider by using an Arduino microcontroller and GSM module. Moreover, sending a voice message about the color of urine is related to pathological conditions. The results showed that the proposed system has high accuracy (approximately about 97%) in detecting urine color under different light conditions, with low cost, short time, and easy implementation. In the comparison with the current methods the proposed system has maximum accuracy and minimum error rate. This methodology can pave the way for an additional case study in medical applications, particularly in diagnosis, and patient health monitoring.
Copula-based Risk Aggregation with Trapped Ion Quantum Computers

Abstract Copulas are mathematical tools for modeling joint probability distributions. In the past 60 years they have become an essential analysis tool on classical computers in various fields. The recent finding that copulas can be expressed as maximally entangled quantum states has revealed a promising approach to practical quantum advantages: performing tasks faster, requiring less memory, or, as we show, yielding better predictions. Studying the scalability of this quantum approach as both the precision and the number of modeled variables increase is crucial for its adoption in real-world applications. In this paper, we successfully apply a Quantum Circuit Born Machine (QCBM) based approach to modeling 3-and 4-variable copulas on trapped ion quantum computers. We study the training of QCBMs with different levels of precision and circuit design on a simulator and a state-of-the-art trapped ion quantum computer. We observe decreased training efficacy due to the increased complexity in parameter optimization as the models scale up. To address this challenge, we introduce an annealing-inspired strategy that dramatically improves the training results. In our end-to-end tests, various configurations of the quantum models make a comparable or better prediction in risk aggregation tasks than the standard classical models.
Model predictive control for constrained robot manipulator visual servoing tuned by reinforcement learning

&lt;abstract&gt;&lt;p&gt;For constrained image-based visual servoing (IBVS) of robot manipulators, a model predictive control (MPC) strategy tuned by reinforcement learning (RL) is proposed in this study. First, model predictive control is used to transform the image-based visual servo task into a nonlinear optimization problem while taking system constraints into consideration. In the design of the model predictive controller, a depth-independent visual servo model is presented as the predictive model. Next, a suitable model predictive control objective function weight matrix is trained and obtained by a deep-deterministic-policy-gradient-based (DDPG) RL algorithm. Then, the proposed controller gives the sequential joint signals, so that the robot manipulator can respond to the desired state quickly. Finally, appropriate comparative simulation experiments are developed to illustrate the efficacy and stability of the suggested strategy.&lt;/p&gt;&lt;/abstract&gt;
Hybrid Algorithms Use Monomials in Encryption

In this study we used Diffie-Hellman (D-H) method with algorithms used monomials to get on hybrid algorithms to find a new key which can be used in clear text encryption
CB-FL: Cluster-Based Federated Learning applied to Quality of Experience modelling

The Federated Learning (FL) approach can be exploited to build a solution to data sparsity and privacy protection issues (e.g., utilization of user-sensitive data) in Quality of Experience (QoE) modelling. In this paper, we investigate whether it is possible to obtain improvements in FL-based inference by grouping data sources to build separate inference systems. To this, we adopted an experimental based approach: firstly, we identified different clusters of users, from a public QoE dataset, based on user-related QoE influence factors and the distributions of the quality rating scores provided by the users; secondly, we developed a Cluster-Based FL QoE pre-dictor and conducted experimental tests to compare the QoE prediction performance with that obtained by a centralised learning approach and a standard FL approach. The obtained results show that the proposed approach achieved the best QoE prediction performance (in terms of accuracy, precision, recall, and F1-Score), followed respectively by the standard FL and the centralised approach.
Knowledge-driven perceptual organization reshapes information sampling via eye movements.

Humans constantly move their eyes to explore the environment. However, how image-computable features and object representations contribute to eye-movement control is an ongoing debate. Recent developments in object perception indicate a complex relationship between features and object representations, where image-independent object knowledge generates objecthood by reconfiguring how feature space is carved up. Here, we adopt this emerging perspective, asking whether object-oriented eye movements result from gaze being guided by image-computable features, or by the fact that these features are bound into an object representation. We recorded eye movements in response to stimuli that initially appear as meaningless patches but are experienced as coherent objects once relevant object knowledge has been acquired. We demonstrate that fixations on identical images are more object-centered, less dispersed, and more consistent across observers once these images are organized into objects. Gaze guidance also showed a shift from exploratory information sampling to exploitation of object-related image areas. These effects were evident from the first fixations onwards. Importantly, eye movements were not fully determined by knowledge-dependent object representations but were best explained by the integration of these representations with image-computable features. Overall, the results show how information sampling via eye movements is guided by a dynamic interaction between image-computable features and knowledge-driven perceptual organization. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Principle of Psychocentrism in Designing Curriculum for Adult᾽s Learning and Education

This article addresses the issue of curriculum for adults' learning and education in a formal and nonformal terms from a psychological perspective.In this sense, learning and education of adults are strongly focused on psychocentric approaches, which must be relevantly reflected in the curriculum.Namely, the curriculum has as object of reflection the relationship between the individual, the society and the educational (learning) institution.The psychological approach of the curriculum for adults' learning and education is focused on the following conceptual approaches: the process of changing/developing the curriculum in relation to psychological factors; organizing and conducting adult education through a curriculum that creates contexts for active learning and respects personal and professional needs.At the same time, the concept of curriculum is approached from the perspective of metacognitive competences (conscious awareness, metacognitive judgment, metacognitive decision), the peculiarities of mental processes in adults (perception, memory, thinking, attention), but also motivational factors (psychological, social, andragogic).The motivational framework is achieved in the educational curriculum through the following components: the conceptual component, the outcomes component, the contents component, the procedural component.The express study showed what reflections teachers have on the curriculum taught in psychocentric vision.