Reviewerly/exHarmony huggingface dataset:
A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps

Algorithms that automatically search for routes have various applications, such as games, navigation, and robots, and are used to create optimal paths in the space of real and virtual worlds. This paper introduces an indoor navigation algorithm allowing rescuers to create a topology map of floor plans, given that the amount of data provided by conventional mapping constructs is limited. Traditional pathfinding algorithms include the A* algorithm, which has demonstrated excellent performance; however, its applicability is limited in the case of high-resolution images, as the number of nodes increases significantly. Here, we use the corner node algorithm (CNA) to improve pathfinding performance by preprocessing the data to create a topology map for the pathfinding algorithm. The algorithm can reduce the number of nodes required for the pathfinding process by generating topology maps in advance through two preprocessing processes: node creation and node connection. This effectively reduces the number of nodes to be processed, as well as the error rate when creating more accurate routes. Furthermore, a generated topology map can be reused by the algorithm unless its structure changes, which enhances efficiency. For the user, this means that the sought-after mapping information of the floor plan is quickly made available for navigation. With this algorithm, we created an augmented reality navigation solution to display virtual objects for rescuers, to demonstrate the enhanced performance of our approach.
A Delphi Study on Metaverse Campus Social Perspectives

Metaverse is a new learning spectrum for Malaysians where it proffers a parallel virtual universe to academics and students. The metaverse campus (MC) is an idea of converting learning from physical classes into virtual worlds. The initiative of MC allows students worldwide to partake in classes and events in the same virtual world. A study was conducted among 25 Malaysian academicians from local and private tertiary instructions using the fuzzy Delphi method (FDM). This study is aimed to get consensus from the academicians to discover the social perspectives towards MC from accessibility, diversity, equality, and humanity perspective.
Analysis of Metaverse Technology

Humanity has entered a new digitalization formation with the invention of the computer. The formation of digitalization has passed through many phases, but even today it shows its effect to a great extent. The establishment of network connections and provision of the internet for the use of humanity, wireless network connections, cloud computing, and cryptocurrencies comes at the very beginning of these phases. Today, the formation of digitalization has taken on a completely different concept from the metaverse and has taken the whole world under its influence. This chapter would provide detailed information about the metaverse universe with an overview on its advantages and disadvantages. Additionally, the possibility of taking the role of the real world will be evaluated by comparing the metaverse universe with the real world.
Exploring the Incredible Potential and Opportunity of the Metaverse World

Technology is playing a very important role in transforming the industry. Currently, the advancement has been to Web3 technology, where we are using mutual interaction online in order to communicate with each other, or to organize the meetings or any social gathering with the help of internet. In the evolution of technology from radio to internet, metaverse is the current and upcoming technology supporting web3. Metaverse is expanding exponentially, the implementation of new technology is necessary for the progression to deliver the best experience using AR (augmented reality), VR (virtual reality), XR (extended reality). This chapter discusses the incredible potentials in which one can achieve outstanding results for implementing Metaverse technology, or to establish connection and connect with people in more live manner (in virtual spaces) and to achieve real-time experience.
Wonders of the World

This chapter overviewed several existing works being conducted on the metaverse in immersive education. Most of the reviews have clearly stated that the metaverse would be a promising asset and tool in education besides the conventional method. The metaverse gives space where real-world specifications can be replicated via digital twin technology. This enables smarter problem-solving in industries like construction, architecture, healthcare, life sciences, and more. Reviewed studies on the effectiveness of those technologies for education found moderate evidence for the metaverse to assist students in their daily learning. The application of the metaverse to the education sector has been well studied and widely accepted as an engaging and effective method of delivering learning experiences. Hence, the metaverse might be a promising tool to provide immersive learning experiences to users nowadays. An application named wonders of the world (WoW) has been developed using XR technology to provide immersive learning experiences to the users.
The Fourth Illusion

This chapter aims to analyze the business models' opportunity represented by the Metaverse (Foundation, n.d.), proposing a framework composed of four interdependent blocks; the “illusions” (Slater, 2003). The word Metaverse has been used to describe a sort of thing, mainly related to virtual reality (VR) (Lanier, 1992) and the non-fungible tokens (NFTs). The immersive experience of virtual reality is characterized by three illusions: the illusion of place, the illusion of embodiment, and the illusion of plausibility (Slater et al., 2009). This chapter describes the use of blockchain technology, namely the NFTs, as the origin of a fourth illusion.
The nature and composition of hydrothermal fluids related to gold mineralization in parts of Akobo greenstone belt, southwestern Ethiopia

Gold mineralization in the Akobo greenstone belt of Southwestern Ethiopia is confined to metamorphosed volcano-sedimentary rocks of greenschist-amphibolite facies and is closely associated with shear/fault zones. Gold is found in quartz veins of the Gindibab-Wolleta area, while altered mafic-ultramafic rocks are the hosts for Chamo-Segele prospects. Microstructures found in vein quartz indicate intra-crystalline ductile deformation followed by fracturing during later brittle deformation episode. The assemblage of fluid inclusions in mineralized quartz veins in Gindibab-Wolleta area has been grouped into three types and inferred to be compositionally and texturally modified during the formation-deformation of quartz veins. Aqueous biphase inclusions are the only fluid inclusions found in the vein quartz of the Chamo-Segele area and retain the primary nature of fluid entrapment. Microthermometric analyses of inclusions indicate a striking difference in the composition of hydrothermal fluids responsible for the quartz vein formation in Gindibab-Wolleta and Chamo-Segele areas. The ore-forming fluids related to vein formation and gold mineralization in the Gindibab-Wolleta area were low saline aqueous carbonic. In contrast, in the case of the Chamo-Segele area, the quartz veins were formed by low to moderate saline aqueous fluid. The results of the fluid inclusion in the Akobo Greenstone Belt indicate an orogenic type of lode gold mineralization in the Gindibab-Wolleta area of the Gambella region, Ethiopia.
Denoising image by matrix factorization in U-shaped convolutional neural network

Image denoising requires both spatial details and global contextualized information to recover a clean version from the deteriorative one. Previous deep convolution networks usually focus on modeling the local feature and stacked convolution blocks to expand the receptive field, which can catch the long-distance dependencies. However, contrary to the expectation, the extracted local feature incapacity recovers the global details by traditional convolution while the stacked blocks hinder the information flow. To tackle these issues, we introduce the Matrix Factorization Denoising Module (MD) to model the interrelationship between the global context aggregating process and the reconstructed process to attain the context details. Besides, we redesign a new basic block to ease the information flow and maintain the network performance. In addition, we conceive the Feature Fusion Module (FFU) to fuse the information from the different sources. Inspired by the multi-stage progressive restoration architecture, we adopt two-stage convolution branches progressively reconstructing the denoised image. In this paper, we propose an original and efficient neural convolution network dubbed MFU. Experimental results on various image denoising datasets: SIDD, DND, and synthetic Gaussian noise datasets show that our MFU can produce comparable visual quality and accuracy results with state-of-the-art methods.
Sum-of-Max partition under a Knapsack constraint

Sequence partition problems arise in many fields, such as sequential data analysis, information transmission, and parallel computing. In this paper, we study the following partition problem variant: given a sequence of n items 1,…,n, where each item i is associated with weight wi and another parameter si, partition the sequence into several consecutive subsequences, so that the total weight of each subsequence is no more than a threshold w0, and the sum of the largest si in each subsequence is minimized. This problem admits a straightforward solution based on dynamic programming, which costs O(n2) time and can be improved to O(nlogn) time easily. Our contribution is an O(n) time algorithm, which is nontrivial yet easy to implement. We also study the corresponding tree partition problem. We prove that the problem on the tree is NP-complete and we present an O(w0n2) time (O(w02n2) time, respectively) algorithm for the unit weight (integer weight, respectively) case.
Application of Syntactic Analysis in Children's Fiction Essays by Slow Learner Students in Primary School: Focus on Sentence Building

This study focuses on the application of syntactic analysis in the aspect of sentence building in children's fiction essays by slow learner students in primary school in Selangor and Putrajaya, Malaysia. The objectives of this study are: to analyze the structure of syntactic elements produced in children's fiction essays by a selected sample; to associate the structure analysis of the syntactic elements with the ability of the study sample in the writing of children's fiction essays from a language point of view; as well as to recommend teaching and learning (PdP) strategies in the aspect of writing for slow learner students.Â The syntactic aspect selected for the analysis of this study is the build-up of the sentence, which includes a single sentence, a combined compound sentence, a plural sentence of spikes and a mixed compound sentence. The construction aspect of a sentence is a basic aspect of syntax that needs to be mastered in order to convey information. The syntactic analysis model used is a model highlighted by Awang (2015), which is an adaptation of the Short and Leech Models (1981, 2007) in the fictional prose analysis method. From the analysis, it was found that the sample of this study consists of slow learner students who are able to apply syntactic analysis to produce sentences in syntactic hierarchies, from single sentences (the simplest sentences) to more complex sentences (combined compound sentences and spike compound sentences).
Cloud Segmentation and Motion Tracking in Sky Images

Cloud cover significantly affects the solar irradiance incident on a photovoltaic (PV) module, so identifying and predicting cloud motion is useful for PV applications, such as informing tracker movements and predicting short-term power. This work presents two algorithms to aid in real-time weather predictions, i.e., a convolutional autoencoder (CAE) to identify clouds and a particle tracker to predict cloud movement. The CAE model integrates information from multiple cloud segmentation approaches, and then utilizes transfer learning on these unreliable, automatically generated masks to bootstrap model performance. The presented model improves upon the state-of-the art metrics with a resultant pixelwise accuracy greater than 90% while remaining lightweight in number of samples used. For tracking and prediction of cloud movements, particle tracking is useful in areas where cloud coverage is transient and clouds move in smaller fragments. By combining neural networks and more classical image processing techniques, the system becomes more robust and explainable than image processing or pure neural network technologies alone, while also demonstrating the power of transfer learning techniques in application.
A low-complexity AMP detection algorithm with deep neural network for massive mimo systems

Signal detection plays an essential role in massive Multiple-Input Multiple-Output (MIMO) systems. However, existing detection methods have not yet made a good tradeoff between Bit Error Rate (BER) and computational complexity, resulting in slow convergence or high complexity. To address this issue, a low-complexity Approximate Message Passing (AMP) detection algorithm with Deep Neural Network (DNN) (denoted as AMP-DNN) is investigated in this paper. Firstly, an efficient AMP detection algorithm is derived by scalarizing the simplification of Belief Propagation (BP) algorithm. Secondly, by unfolding the obtained AMP detection algorithm, a DNN is specifically designed for the optimal performance gain. For the proposed AMP-DNN, the number of trainable parameters is only related to that of layers, regardless of modulation scheme, antenna number and matrix calculation, thus facilitating fast and stable training of the network. In addition, the AMP-DNN can detect different channels under the same distribution with only one training. The superior performance of the AMP-DNN is also verified by theoretical analysis and experiments. It is found that the proposed algorithm enables the reduction of BER without signal prior information, especially in the spatially correlated channel, and has a lower computational complexity compared with existing state-of-the-art methods.
PedVis-VGG-16: A Fine-tuned deep convolutional neural network for pedestrian image classifications

Recently, pedestrian detection has attracted a lot of attention in recent years. It is known as a computer vision research hotspot, widely used in different fields. Despite the impressive progress of its approaches, their performance remains unsatisfactory. This paper proposes PedVis-Vgg-16a deep learning network for automatically detecting pedestrians presence in visible images. The suggested architecture is based on the fine-tuned VGG-16 architecture with modifications to the last block of the model. Different improvement components including data augmentation, parameter optimization, and parameter adaption, were taken to enhance the architecture performance. The newly designed architecture is validated on the publicly available dataset INRIA, which contains 4001 images and the results provided are satisfactory.
Massive Data Visualization Techniques for use in Virtual Reality Devices

Scientific simulations executed on supercomputers produce massive amounts of data. Visualizing this data is essential to discovery and dissemination, but methods for transforming and displaying such large data visualizations for use in Extended Reality (XR) devices are not commonly supported. We investigated the viability of existing XR applications (i.e., ParaView VR, SummitVR, and Omniverse XR) to display large data visualizations. Our investigations led us to create a proof-of-concept Virtual Reality (VR) application with Unity using Universal Scene Description (USD) files exported from Houdini to display and interact with large time-varying scientific data visualizations. We present our investigations as a basis for future work to display and interact with scientific data visualizations in XR.
Smart contract for cloud SLA using Service Oriented Architecture and Blockchain

The traditional cloud Service-Level Agreement (C-SLA) ensures that cloud providers meet certain business-level requirements and provide customers with a set of clearly defined services as clauses in a simple IT contract. These agreed terms create a foundation of trust on which a customer agrees to use the services of a cloud provider. They also reflect the provider's commitments to Quality of Service (QoS) and cloud infrastructure. Suppliers must also have KPIs that they can measure the reality is essential to have a dashboard and monitoring for the governance of these agreements such contracts require dedicated administration teams with financial, operational and time-consuming compliance. Our objective in this paper is to propose a new service-oriented smart contract model of smart cloud SALs to cloud providers is a microservices approach that also allows to create several independent services that then interact in a secure and very useful way. During the execution of these microservices, all the validation steps are recorded in the blockchain, which makes it possible to secure and control the effectiveness of their actions using predefined performance indicators.
Atmospheric Visibility Image-Based System for Instrument Meteorological Conditions Estimation: A Deep Learning Approach

Visibility degradation is the cause of many road accidents and air crashes around the globe, it is caused and controlled by multiple key factors that hinder the observer from having a clear vision of what's ahead, playing a crucial role in aviation safety. Alas, researchers and engineers created many tools to measure or restore images suffering from those degradations. This paper compares different image-based deep learning architectures for Atmospheric Visibility Range Classification. A Vision Transformer trained from scratch and three pre-trained Convolution Neural Network (CNN) models were examined and contrasted in terms of accuracy, obtaining a validation accuracy of more than 95 %. Our experiment shows that the different models trained on the Federal Aviation Administration (FAA) initial dataset can be used towards building an efficient tool to aid in Flight Control for long-range visibility estimation. Results showed that DenseNet121 was the best performing model with 99 % training accuracy and 98 % validation accuracy and shows early convergence, while the Vision Transformer kept on showing signs of improving but falling behind only by a bit behind DenseNet121 by the end of the experiment.
DeepSR: A deep learning tool for image super resolution

An open source tool is introduced that provides a versatile environment to meet the needs of researchers in developing deep learning (DL) algorithms for single image super-resolution reconstruction (SISR). The processes of SISR were carefully studied, unified and integrated to create software that can be used by the community for any type of imaging method such as aerial, medical, optical, etc. DeepSR allows easy implementation of SISR application with rapidly prototyped DL models, and detailed reporting and recording of the results. The entire experiment can be done with simple command line scripts. It can be easily extended by user-defined metrics, augmentations, callbacks, etc.
Hate Sentiment Recognition System For Persian Language

People’s lives in societies are tied to social networks and these networks face problems such as the existence of hateful speech. Most social networks try to identify and prevent the spread of this phenomenon by using natural language processing (NLP) methods. On the internet, hate speech causes arguments between different groups in society. Given that anyone is able to put any content on social media in the form of a short text, this leads to the uncontrollable spread of hatred on social networks and can cause harm to individuals and various groups in society. This is necessary to have control over users’ content on social media. In this study, a method for identifying hateful content in short texts is proposed. First, cosine similarities of word-based and character-based n-grams (features) and sentences (samples) are calculated. Then, employing calibrated support vector machine, the probability of each feature related to hatred is calculated. Finally, another SVM is applied for the final classification. The proposed method is compared to the state-of-the-art methods such as pars-bert and a multi-view SVM approach using Instagram comments in terms of various performance metrics. Results show that the proposed method outperforms the previous studies.
Nuclei Segmentation in Colon Histology Images by Using the Deep CNNs: A U-Net Based Multi-class Segmentation Analysis

As is known, pathologists visually examine the tissue distributions by using microscopes traditionally. The rise in digital image processing and machine learning also allows high-performance computerized analysis of histology images taken with modern imaging systems. In general, histological image segmentation is the first step in the quantitative analysis of histology images. Therefore, a high-accuracy segmentation is essential for histology image analysis in most cases. In this paper, we performed a deep Convolutional Neural Networks (CNNs) based nuclei segmentation study on colon histology images. By using the U-Net biomedical image segmentation model, it is aimed to classify each pixel in colon histology images into one of the following 6 types of nucleus: epithelial, lymphocyte, plasma, eosinophil, neutrophil, connective tissue or classify it as the image background. In comprehensive experimental tests performed on Colon Nuclei Identification and Counting (CoNIC) Challenge dataset, commonly used segmentation and classification metrics were measured, and promising segmentation performances were achieved.
Deep Feature Synthesis for Accurate Breast Cancer Prediction

With 2.3 million women diagnosed with breast cancer and 685.000 deaths globally in 2020, breast cancer is the most prevalent form of cancer worldwide. However, 7.8 million women are alive after being diagnosed with breast cancer in the past five years, indicating that breast cancer is survivable if diagnosed early. One in eight women will have invasive breast cancer during her lifetime. Therefore, it is vital that regular screenings are conducted for early detection and, thus, survival. Previous studies have been completed on the Breast Cancer Coimbra Data Set from the UCI repository to identify biomarkers with reliable confidence intervals for further investigation with less emphasis on a perfect model due to lack of enough data. Here, Deep Feature Synthesis and Conditional Generative Adversarial Networks (CTGANs) are implemented for data generation and to increase the performance of the models in terms of specificity, accuracy, and sensitivity. Logistic Regression outperforms the other classifiers with 100% specificity, accuracy, and sensitivity when only Deep Feature Synthesis is used. Performance decrease of models, when introduced to data CTGANs-generated data, indicates that CTGANs do not generate similar enough data. However, the large range of variability in the model's performance shows that BMI, Resistin, Age, and Glucose are not trustable biomarkers for early-stage breast cancer.
Automated Service Configuration Management in IP/MPLS Networks

The increasing number and diversity of telecommunication services cause a strong need for automatic configuration management tools for service providers. Combining configuration flexibility with high configuration artifact reusability in a single solution is a challenging task. This paper addresses this issue and describes a goal-driven approach for automated configuration management in IP/MPLS networks. Our solution is based on automated planning techniques that allow automatically generating sequences of configuration steps to bring the service from its current state to the desired state. We describe the prototype which allows validating our approach and present how it deals with several examples of typical service configuration problems.