Reviewerly/exHarmony huggingface dataset:
Aerial Image Denoising Using a Best-So-Far ABC-based Adaptive Filter Method

Nowadays, digital images play an increasingly important role in helping to explain phenomena and to attract people’s attention through various types of media rather than the use of text. However, the quality of digital images may be degraded due to noise that has occurred either during their recording or their transmission via a network. Therefore, removal of image noise, which is known as “image denoising”, is one of the primary required tasks in digital image processing. Various methods in earlier studies have been developed and proposed to remove the noise found in images. For example, the use of metric filters to eliminate noise has received much attention from researchers in recent literature. However, the convergence speed when searching for the optimal filter coefficient of these proposed algorithms is quite low. Previous research in the past few years has found that biologically inspired approaches are among the more promising metaheuristic methods used to find optimal solutions. In this work, an image denoising approach based on the best-so-far (BSF) ABC algorithm combined with an adaptive filter is proposed to enhance the performance of searching for the optimal filter coefficient in the denoising process. Experimental results indicate that the denoising of images employing the proposed BSF ABC technique yields good quality and the ability to remove noise while preventing the features of the image from being lost in the denoising process. The denoised image quality obtained by the proposed method achieves a 20% increase compared with other recently developed techniques in the field of biologically inspired approaches.
Simulating the Hamiltonian of dimer atomic spin model of one-dimensional optical lattice on quantum computers

The one-dimensional Ising model with its connections to several physical concepts plays a vital role in comprehension of several principles, phenomena and numerical methods. The Hamiltonian of a coupled one-dimensional dissipative spin system in the presence of magnetic field can be obtained from the Ising model. We simulate the above Hamiltonian by designing a quantum circuit with precise gate measurement and execute with the IBMQ experience platform through different [Formula: see text] states with controlled energy separation where we can check quantum synchronization in a dissipative lattice system. Our result shows the relation between various entangled states, the relation between the different energy separation ([Formula: see text]) with the spin–spin coupling ([Formula: see text]) in the lattice, along with fidelity calculations for several iterations of the model used. We also estimate the ground and first excited energy states of Ising-Hamiltonian using VQE algorithm and investigate the lowest energy values varying the number of layers of ansatz.
Iterative dual regression network for blind image super-resolution

Previous single-image super-resolution methods assume that the blur kernel is known (e.g., bicubic) when degrading from high-resolution (HR) images to low-resolution (LR) images. They use a single degradation to train a model to restore HR images. However, the actual degradation in the real world is often unknown. It is difficult to deal with LR images caused by different degradations. To cope with the above situation, previous methods attempt to restore SR images using a blur kernel estimation structure that combines with a non-blind SR network. There are two problems that should be earnestly considered: (1) For accurate blur kernel estimation, insufficient correlation of consecutive kernels lead to an unsatisfied reconstruction result. (2) For ill-posed issue of image reconstruction, a more efficient constraint condition is worth trying. To solve the two problems, we propose an iterative dual regression network for an adaptive and precision blur kernel estimation, which improves the speed of kernel estimation by learning a dual mapping. Specifically, we design a Predictor-Generator structure: the Predictor, through several iterations, searching for accurate kernels through intermediate kernels and generated SR images; the Generator, generating final SR images with the help of the predicted kernels. More importantly, the elaborately designed dual learning strategy can not only provide additional constraints for accurate kernel estimation but also reduce the domain gap between SR images and HR images. Experiments on synthetic degraded images and real-world images show that our network is competitive in performance and superior in visual results.
Computational Assessment Model for Blind Medical Image Watermarking with Deep Learning

Blind watermarking is identified as one of the effective method of data hiding in image processing; however, existing literatures shows an adoption of sophisticated technique considering specific attacks. However, a potential gap is found where there is no report of resiliency of using blind watermarking towards resisting lethal threats. Therefore, this manuscript contributes towards offering a computational assessment model by constructing a lethal blind watermarking attacker model where a discrete orthogonal moments are extracted followed by dithering. The model is assessed on multiple modalities of standard medical image dataset as well as deep learning models. The outcome shows presented model accomplishes more than 45% of performance degradation from accuracy perspective. This outcome will offer clear guidelines of using deep learning models considering different medical image modalities to achieve better watermarking performance.
Approach of decision making system modeling

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Dmitriy Bystrov, Dilfuza Yakubova, Azamat Kurbonov, Ulugbek Yokubov; Approach of decision making system modeling. AIP Conf. Proc. 5 January 2023; 2552 (1): 060014. https://doi.org/10.1063/5.0116771 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Automatic Number Extraction from Fixed Imaging Distance

Developed countries are facing many challenges to convert large areas of existing services to electronic modes, reflecting the current nature of workflow and the equipment utilized for achieving such services. For instance, electricity bill collection still tend to be based on traditional approaches (paper-based and relying on human interaction) making them comparatively time-consuming and prone to human error. This research aims to recognize numbers in mechanical electricity meters and convert them to digital figures utilizing Optical Character Recognition (OCR) in Matlab. The research utilized the location of red region in color electricity meters image to determine the crop region that contain the meters numbers, then extracts this numbers region and convert it into binary image and extract the numbers as a text using OCR technique. A camera for the Iphone 6 (8-megapixel) is used to take a snapshot of the meter screen. The red box in the meter is used to calculate the window coordinates (vertical and horizontal length) that contain the numbers in the original image. The results show a high level of accuracy, reaching 100% due to the effort done on pre-processing the digital images before feeding the part that contains the numbers into the OCR engine. Compared to the maximum accuracy obtained in other previous research of less than 100% in most of related works, the suggested method provide better approach to obtain the optimum results . Despite the strong results, some challenges still need to be investigated further to find the best solutions, for example the issue of scratched or unclear meter screen, also the meter type 2 (the type that do not have the red box)
Case Study on the Development and Implementation of STEM Projects: “DIY Traffic Lights”

A popular education mode in today’s era of knowledge economy is STEM education, whose fundamental goal is to cultivate innovative scientific and technological talents, as well as talents with interdisciplinary knowledge literacy and the ability to solve real problems. This article combs the connotation, educational value and application mode of STEM, and discusses students’ learning process in the STEM project of “DIY traffic lights”.
Data hiding in the optimal keyframes using circular shifting and mutation operations for improvement in imperceptibility

Video steganography hides the information in the cover video and helps in a secure communication over the unsecured network. The existing steganography methods are lacking in terms of efficient keyframe selection algorithm and also show less imperceptibility. The proposed method used the Kullback Leibler divergence (KLD) and edge features to extract the keyframes from any type of video dataset whereas circular shifting and mutation operations are used to improve the imperceptibility. The circular shifting operation helps in searching for the optimal direction of secret data, whereas the mutation operation was deployed to adjust the pixel values to reduce the variability more. To validate the proposed algorithm, the computations were performed on the standard videos and image datasets. The computational results validate that the proposed algorithm has achieved better imperceptibility in a fewer number of iterations. Furthermore, the keyframes extraction technique helps to increase the security of the proposed system for any type of video dataset.
TKIFRPM: A Novel Approach for Topmost-K Identical Frequent Regular Patterns Mining from Incremental Datasets

The regular frequent pattern mining (RFPM) approaches are aimed to discover the itemsets with significant frequency and regular occurrence behavior in a dataset. However, these approaches mainly suffer from the following two issues: (1) setting the frequency threshold parameter for the discovery of regular frequent patterns technique is not an easy task because of its dependency on the characteristics of a dataset, and (2) RFPM approaches are designed to mine patterns from the static datasets and are not able to mine dynamic datasets. This paper aims to solve these two issues by proposing a novel top-K identical frequent regular patterns mining (TKIFRPM) approach to function on online datasets. The TKIFRPM maintains a novel synopsis data structure with item support index tables (ISI-tables) to keep summarized information about online committed transactions and dataset updates. The mining operation can discover top-K regular frequent patterns from online data stored in the ISI-tables. The TKIFRPM explores the search space in recursive depth-first order and applies a novel progressive node’s sub-tree pruning strategy to rapidly eliminate a complete infrequent sub-tree from the search space. The TKIFRPM is compared with the MTKPP approach, and it found that it outperforms its counterpart in terms of runtime and memory usage to produce designated topmost-K frequent regular pattern mining on the datasets following incremental updates.
Penerapan Model Pembelajaran AIR Berbasis Edutaiment di SD terhadap Kemampuan Pemahaman Konsep Matematika

This study aims to determine the application of the edutaiment-based water learning model at SD Negeri 1 Sukamaju to the ability to understand mathematical concepts of fifth grade students. This study used a Quasi-Experimental Research type. The results showed that the use of the AIR learning model could improve the ability to understand mathematical concepts of class V-A students at SD Negeri 1 Sukamaju in the 2022 academic year, this was demonstrated by an increase in the test results for understanding the ability of concepts from pretest to posttest. The average ability to understand mathematical concepts in the experimental class was 76.68, after being analyzed using the two average similarity test formula (t test) it showed that the value was &gt; (2.4677 &gt; -1.67722). In conclusion, H0 is rejected and Ha is accepted, so the hypothesis can be concluded that the average ability to understand mathematical concepts of elementary school students increases.&#x0D; &#x0D; Keywords: AIR learning model, understanding of mathematical concepts
The length of the initial longest increasing sequence in a permutation

Let Sn be the set of all permutations of {1, 2, …, n} represented in cycle notation. Define an, m to be the number of π ∈ Sn such that the length of the initial longest increasing sequence (ILIS) in π is at most m. For fixed m, we find the exponential generating function for the sequence an, m, and give an asymptotic formula for an, m when n → ∞. Moreover, we show that the expected value of the total ILIS in all cycles of π over all permutations of Sn is asymptotic to e ∑j=1 n 1/j.
Application of ant colony optimization algorithm to determine optimal value in choosing tourist attractions in Bangkalan – Madura

The impact of COVID-19 leaves a deep sadness, especially in the tourism sector. Many tourist places are not open to prevent the transmission of this virus. Madura is one of the islands located in the province of East Java which has many tourist attractions. Bangkalan is one of the districts in Madura which has 21 tourism spots. There are many tourist attractions scattered in Bangkalan district, but there is a problem arisen in determining the shortest path to get to these tourist attractions. There are several route options available in each area. Optimal value search can be used to obtain the highest and lowest values of a problem. One of the popular problems that can be solved by optimization algorithms is the Traveling Salesman Problem (TSP) to determine the closest route using the Ant Colony Optimization algorithm. The ant algorithm is an algorithm adopted from the behaviour of the ant colony. An ant colony can find the shortest route between the nest and a food source based on footprints on the trajectory it has traversed. The more ants that pass through a track, the clearer the footprints will be. Ant Colony algorithm is very appropriate to be applied in solving optimization problems, one of which is to determine the shortest path. The final result of this discussion is the algorithm used is able to determine the shortest path to find tourist destinations as an alternative route. The accuracy results obtained are 100% with a rho value of 0.5, an alpha value of 1, and a beta value of 1.
Analysis average waiting time search performance in the queue process on CPU scheduling using the Round Robin, shortest job first and first in first out algorithm

In its activities, the CPU has a pattern of processes that coincide or precede each other. Where when one activity enters, another activity will come, and so on which is multiprogramming. Round Robin (RR), Shortest Job First (SJF), and First In First Out (FIFO) methods are CPU scheduling algorithms that are popularly used in multiprogramming systems. These three algorithms are used to find Average Waiting Time (AWT) with different process sequences. In the Round Robin algorithm, there is Qu that affects the number of process sequences. The AWT value is obtained from the number of waiting for processes divided by the number of existing processes. The makespan value in each algorithm must be the same even though the process sequence is different depending on the arrival time. This study describes and analyzes how the RR, SJF, and FIFO algorithms work in the queuing process on the CPU (Central Processing Unit). The result of this research is an analysis that is easy to understand in the form of an interactive programming application. In the resulting application, it can be seen that the queuing process in AWT runs in stages according to the principles of the three algorithms.
Clustering tourism places in Madura based facilities using fuzzy C-means

Madura Island consists of four districts namely Bangkalan, Sampang, Pamekasan, and Sumenep. Each district has many choices of interesting tourist attractions. There are various types of tourism ranging from natural tourism, cultural tourism, historical tourism, and artificial tourism. With the diversity of these tourist attractions, it is enough to invite many tourists to come on vacation to Madura Island. Each tourist attraction has a different number of visitors, the number of public facilities, and ticket prices. All the criteria possessed by each tourist attraction have their own assessment for potential tourists. The local Tourism Office must know the developments in each tourist attraction, so that it can maintain the quality of the tourist attraction. Improvement of infrastructure can be through public facilities provided at tourist objects is very necessary. The purpose of this study was to determine how well the Fuzzy C-Means method in grouping tourism objects in Madura. Fuzzy C-Means is a grouping method which the development of the k-means cluster is not a hierarchical method that allocates data into each group by utilizing fuzzy set theory. From the trials that have been carried out, the best grouping results are found in cluster 10 with a silhouette coefficient value of 0.825 which is included in the strong category.
Fusion of Machine Learning and Blockchain Techniques in IoT-based Smart Healthcare Systems

Smart healthcare is one of the rising areas of research in collaboration with smart techniques like sensors, the Internet of Things (IoT), and various information analytics techniques to convey effective healthcare services to the patients at a lesser amount of cost. The healthcare system faces lots of problems in dealing with a large volume of data generated day by day, and processing and exploring useful information from it. Such a large volume of data is generated through various IoT-based medical gadgets, electronic health records, wearable sensors, telemedicine, and mobile health services that may demand effective, fast, and secure data analysis techniques to offer high-quality services to patients. To provide such types of services, machine learning and blockchain techniques are highly appreciated in various domains. This study mainly discusses the categorization of powerful machine learning and blockchain techniques highly effective to process such a vast amount of data. As the healthcare system encompasses highly sensitive data regarding patient health as well as personal information which must require appropriate security services, blockchain technology can be used as an effective solution for offering security services to highly sensitive data. Machine learning 246and blockchain technology together provide lots of opportunities for the healthcare system to attain all its goals like reduced healthcare cost, effective diagnosis, timely treatment, offering transparency in regulatory reporting, and effective health data management. The main aim of this chapter is to provide a detailed description of the relationship between blockchain and machine learning techniques to empower an IoT-based healthcare system. Along with that, this chapter also discusses the major issues and challenges faced while implementing IoT-based smart healthcare systems by collaborating machine learning and blockchain techniques.
A Critical Analysis of the Role of Teachers in Providing Professional Guidance to Students in Government Secondary Schools

Professional guidance is not well organized in Pakistan. As we all know that before planting a tree, we should give a strong base like giving nutrition for proper growth and development of plants, so that it would be able to give us the result we are expecting from it. But in Pakistan the backdrop is, that our teachers themselves are aware of the real factors of guidance, but don’t have availability to give professional guidance due to lack of resources and don’t have this subject as curriculum so that they can guide the students. Therefore, students can’t be properly and fully guided by their institutor. This research found that that teachers have knowledge related to professional guidance, it gives positive effect to the students and can bring improvement in their lives and students are mentally able to understand principles of professional guidance. Teachers counsel students individually but can’t be able to conduct class related to professional guidance. Ultimately it was found that teachers need more education for professional guidance before guiding the students. One of the major reasons for this lack was that schools do not get fund to spend on their expenses so can’t make use of technologies related to professional career.
Bottom-Up Trust Registry in Self Sovereign Identity

Self sovereign identity is a form of decentralised credential management. During credential verification, data exchange only happens between the data owner and the verifier without passing through any third parties. While this approach offers a privacy-centric solution, it poses a challenge. How do verifiers trust that the credential is vouched by a trusted source? More specifically, how do verifiers know that the issuer has the reputation or is authorised to issue the credential? In this paper, we propose a trust registry design that handles the aspect of human trust in self sovereign identity. We also introduce an incentivisation mechanism for the trust registry in order to motivate each stakeholder to participate actively and honestly.
Empirical Investigation on the Effect of the Number of Resamplings on the Distribution of Bootstrap Standard Error Using Response Time Data

Aims: To investigate how the number of bootstrapping B affects the values returned by the bootstrap standard error of the arithmetic mean and the α-trimmed mean of response data using bootstrap confidence intervals (CI) at 95% level; carried out to fill up observed gap for study on standard error, the tool generally employed in assessing the long run accuracy of a given statistical estimator of θ.&#x0D; Study Design: This was a parametric, empirical bootstrap simulation study.&#x0D; Place and Duration of the Study: Departments of Computer Science and Statistics, Federal Polytechnic Oko, 2020/2021 session.&#x0D; Methodology: Response time data were generated with student customers of mobile telephone network (mtn) Nigeria and stored in SPSS. A sample n = 51 responses was selected using “Select Cases” command to increase precision and minimize bias. Bootstrap simulation study was carried out using R programming language. Four approaches for estimating bootstrap confidence intervals were used. The interval coverage and the interval lengths were determined and compared for B = 20, 50, 100, 500, 1000, 5000, and 10000.&#x0D; Results: The 95% CI for 0.2266338 (the estimated sample standard error of 10% trimmed mean) returned the best interval for our skewed data set; when B = 20; the CI for returned (0.2051, 0.2343) for the normal approach, (0.2082, 0.2371) for the basic, (0.2071, 0.2360) for the percentile and (0.2071, 0.2360) for the BCa method. As B increased to 5000, it returned (0.2259, 0.2277) for the normal approach, (0.2259, 0.2277) for the basic, (0.2260, 0.2277) for the percentile and (0.2261, 0.2279) for the BCa showing a shorter interval yet covering the estimate.&#x0D; Conclusion: Thus for our response data study, increasing B in estimating standard error increases the chances of more precise and shorter confidence intervals rather than the chances for coverage.