Reviewerly/exHarmony huggingface dataset:
A Modified <i>δ</i>-Generalized Labeled Multi-Bernoulli Filtering for Multi-Source DOA Tracking With Coprime Array

For the target tracking problem where the number of targets fluctuates with time and the measurement is a point measurement, the random finite set (RFS) class filtering is an available solution. However, in direction of arrival (DOA) tracking, the array observation is a super-positional value, and the tracking performance can be severely impaired if the RFS-based filter approach is applied. As a result, a novel measurement association mapping (NMAP) approach has been presented to cope with the mapping problem between the array observations and sources. Nevertheless, the tracking performance is poor when the number of particles is small. In this paper, a modified delta-Generalized Labeled Multi-Bernoulli ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> -GLMB) DOA tracking particle filter is proposed in combination with the NMAP strategy, which can achieve the same tracking performance with a smaller number of particles by modifying the particles in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> -GLMB prediction step. Furthermore, the approach is extended to a coprime array and can achieve better DOA tracking performance than a uniform linear array. Simulation experiments validate the effectiveness of the proposed algorithm.
Digital Economy: The Economics of the Digital Economy

The digital economy is characterized by so-called infinite shelf space, zero marginal cost, increasing returns, and friction-free transactions. These and other factors have made crypto currencies an attractive alternative to fiat currency. This essay focuses on the larger implications of cryptocurrency and its role in the economy of the digital economy. While cryptocurrencies appear to be ideal for the digital era, they also have some serious limitations such as vulnerabilities to selfish mining and lack of consumer protections.
Opposition Learning Based Improved Bee Colony Optimization (OLIBCO) Algorithm for Data Clustering

Clustering of data in case of data mining has a major role in recent research as well as data engineers.It supports for classification and regression type of problems.It needs to obtain the optimized clusters for such application.The partitional clustering and meta-heuristic search techniques are two helpful tools for this task.However the convergence rate is one of the important factors at the time of optimization.In this paper, authors have taken a data clustering approach with improved bee colony algorithm and opposition based learning to improve the rate of convergence and quality of clustering.It introduces the opposite bees that are created using opposition based learning to achieve better exploration.These opposite bees occupy exactly the opposite position that of the mainstream bees in the solution space.Both the mainstream and opposite bees explore the solution space together with the help of Bee Colony Optimization based clustering algorithm.This boosts the explorative power of the algorithm and hence the convergence rate.The algorithm uses a steady state selection procedure as a tool for exploration.The crossover and mutation operation is used to get balanced exploitations.This enables the algorithm to avoid sticking in local optima.To justify the effectiveness of the algorithm it is verified with the open datasets from the UCI machine learning repository as the benchmark.The simulation result shows that it performs better than some benchmark as well as recently proposed algorithms in terms of convergence rate, clustering quality, and exploration and exploitation capability.
Multi-objective optimization of committee selection for hierarchicalByzantine fault tolerance-basedconsensus protocols

Decentralized platforms like blockchain have been attracting significant attention in recent years, especially in the contextof financial and payment systems. They are designed to provide a transparent, secure, and reliable environment for digital transactions without the need for a central authority. The core of a decentralized platform like blockchain is a consensus layer that allows all participants (called Workers), whoproperly operate and follow all network protocols and have access to the same stateof the distributed ledger, to coordinate their actions and arrive at the same decisions.However, some Workersmaybe temporarilyoffline at their own discretion, without any confirmation, or their work may be faulty due to technical circumstances, resulting in unpredictable behavior.The goal of this articleis to present an approach for multi-objective optimizing ofByzantine fault tolerance (BFT)-based consensus protocols,to reduce the impact on the network of faultyparticipants. Two criteria were considered –minimization of the number of sent service messages,and maximization of the mathematical expectation of the number of produced blocks.The result isa method to determine the optimal committee size and distribution of Workers,depending on their total number in the network and the expected proportionof Byzantine faultynodes.All protocol amendments presented in this work are tested with corresponding simulation models and have demonstrated notable enhancements in the performance of the system and decreased the load on network nodes. These improvementswill be implemented to the consensus protocol Gozalandia on the Waterfall platform,enhancingits overall reliability, performance, and security. In addition, the presented optimizing algorithm can be applied to a wide range of consensus protocols in blockchains,where blocks mustbe signed by randomly selectedcommitteesto confirmtheir validity.
Pengembangan Komik Digital Berbasis CTL Untuk Pemahaman Konsep IPA dan Motivasi Belajar Siswa Sekolah Dasar

This study is prompted by various discoveries, including the fact that teachers have not used learning media efficiently, resulting in students who do not comprehend the notion of learning and a lack of student interest in the learning process. The goal of this study was to create Contextual Teaching and Learning (CTL) based digital comic media and assess its practicality, efficacy, and students' reactions to it to improve students' conceptual comprehension and motivation in scientific learning. This is a sort of development study. The development model employed is a 4-D model that includes steps for defining, designing, creating, and deploying. The results revealed that digital comic validation was performed based on numerous factors, with the material aspect receiving an average of 3.6 with the Very Valid criterion. Using the Extremely Valid criterion, the aspects of the questions received an average of 3.6. Using the Valid criterion, Display Aspect received an average of 3.46. The final test results following the media application revealed that the average student learning outcomes in the experimental class were greater than those in the control class. The analysis of the findings of students filling out the motivation to learn questionnaire is likewise at a very high level.
Undergraduate Students’ Profiles of Cognitive Load in Augmented Reality–Assisted Science Learning and Their Relation to Science Learning Self-efficacy and Behavior Patterns

Research evidence indicated that a specific type of augmented reality-assisted (AR-assisted) science learning design or support might not suit or be effective for all students because students' cognitive load might differ according to their experiences and individual characteristics. Thus, this study aimed to identify undergraduate students' profiles of cognitive load in AR-assisted science learning and to examine the role of their distinct profiles in self-efficacy together with associated behavior patterns in science learning. After ensuring the validity and reliability of each measure, a latent profile analysis confirmed that 365 Chinese undergraduates carried diverse dimensions of cognitive load simultaneously. The latent profile analysis findings revealed four fundamental profiles: Low Engagement, Immersive, Dabbling, and Organized, characterized as carrying various respective cognitive loads. The multivariate analysis of variance findings revealed different levels of the six AR science learning self-efficacy dimensions across profiles. Low Engagement students displayed the lowest self-efficacy among all dimensions. Organized students recorded better conceptual understanding and higher-order cognitive skills than Dabbling ones. Students with the Immersive profile had the highest science learning self-efficacy. The lag sequential analysis results showed significant differences in behavior patterns among profiles. Among them, profiles with social interaction, test, and reviewing feedback behavior had a significantly higher score for self-efficacy than those patterns mainly based on test learning and resource visits. This finding provides a unified consideration of students' diverse profiles and can inform interventions for effective design of AR-assisted science learning to match appropriate strategies to facilitate the science learning effect.
Vardiational Bayesian Hybrid Multi-Bernoulli and CPHD Filters for Superpositional Sensors

This paper addresses the problem of multi-target tracking with superpositional sensors, while the covariance matrices of measurement noise are not known. The proposed method is based on the hybrid multi-Bernoulli cardinalized probability hypothesis density (HMB-CPHD) filter, which has been developed for superpositional sensors-based multi-target tracking with known measurement noises. Specifically, we firstly propose the Gaussian mixture (GM) implementation of the HMB-CPHD filter, and then the covariance matrices of measurement noises are augmented into the target state vector, resulting in the Gaussian and inverse Wishart mixture (GIWM) representation of the augmented state. Then the variational Bayesian (VB) method is exploited to approximate the posterior distribution so that it maintains the same form as the prior distribution. A remarkable feature of the proposed method is that it can jointly perform multi-target tracking and measurement noise covariance estimation. The performance of the proposed algorithm is demonstrated via simulations.
Automated object recognition in high-resolution optical remote sensing imagery

This paper reports the background and results of the Automated Object Recognition in Optical Remote Sensing Imagery, which is one of the tracks in 2022 International Algorithm Case Competition, as well as summarize the challenges, champion solutions, and future directions.
An Assessment on Renewable Power Sources Using the VIKOR Method

Renewable energy is Obtained from natural sources is energy, more than they consume are filled in proportion.For example, Like sunlight and wind Resources are constantly being replenished.Renewable energy sources There are plenty around us.Renewable energy, Within a relatively short period of time Generated by replenishable sources.Sunlight, Air, water, biomass and all Geothermal renewable energy proofs.Green energy, energy Just as renewable, more Having environmental benefits is a subset of sources. The VIKOR method is multi-criteria Decision Making (MCDM) or Multiple criteria The result is the analytical method. Contradictory and harmonious (different units) with scales To solve decision-making problems First by Seraphim Obrykovic Created, conciliation to resolve conflict Assuming it is acceptable The decision maker is highly idealisticWants a closer solution, too All installed According to criteria Evaluation of alternatives are done As a result, public recognition obtained first rank, At the same time employment generation Low grade.From the result it is seen that Public acceptance has got the first rank whereas is the Employment creation has the lowest rank.
Understanding Long Short-Term Memory LSTM Models in IBM SPSS Statistics

Long Short-Term Memory (LSTM) artificial neural network.Unlike conventional feedforward neural networks, LSTM has feedback connections.Considering that each of these models might discover enduring relationships between number of iterations in the input, LSTMs are frequently used to learn, analyse, and classify sequential data.Sentiment analysis, language modelling, natural language processing, and video analysis are examples of common LSTM applications.The long-term reliance or vanishing gradient problem of RNN is addressed by LSTM networks.Gradient vanishing is the term used to describe how information in some kind of a neural network is lost as interconnections repeat over extended periods of time.To put it simply, LSTM defeats gradient camouflage by ignoring irrelevant information as well as data in the network.LSTMs have several advantages over traditional RNNs.First, they are very good at dealing with long-term dependencies.This is due to the ability to remember for a long time with information.Second, LSTMs have a far lower vanishing slope problem sensitivity.Research significance: Both the intelligence and neurocomputing sectors have been changed by the Long Short-Term Memory (LSTM) Achine.Some internet sites claim that this methodology has significantly improved machine translations, Google Translate's speech recognition, and Amazon Alexa's answers.Facebook also employs this neural network, and as of 2017, it performs 4 billion LSTM-based translations every day.Surprisingly, recurrent neural networks were present.Up until LSTM was displayed, a distinguishing performance was displayed.a factor in success the burst/vanish gradient problem is an extremely challenging problem that this recurrent network excels at handling when it is trained repeatedly.In-depth neural networks.We give a thorough analysis of LSTM development and training, associated applications documented in the literature, and coding tools in this study.For illustration, a toy executes this model.Method: SPSS statistics is multivariate analytics, business intelligence, and criminal investigation data management, advanced analytics, developed by IBM for a statistical software package.A long time, spa inc.Was created by, IBM purchased it in 2009.The brand name for the most recent versions is IBM SPSS statistics.Evaluation parameters: Recurrent neural networks• Vanishing/exploding gradient • Long short-term memory and Deep learning.Result: The Cronbach's Alpha Reliability result.The overall Cronbach's Alpha value for the model is .860which indicates 86% reliability.From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.Conclusion: Emotional Intelligence the Cronbach's Alpha Reliability result.The overall Cronbach's Alpha value for the model is .860which indicates 86% reliability.From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.
A new federated learning-based wireless communication and client scheduling solution for combating COVID-19

Federated learning is a machine learning method that can break the data island. Its inherent privacy-preserving property has an important role in training medical image models. However, federated learning requires frequent communication, which incur high communication costs. Moreover, the data is heterogeneous due to different users' preferences, which may degrade the performance of models. To address the problem of statistical heterogeneity, we propose FedUC, an algorithm to control the uploaded updates for federated learning, where a client scheduling method is made on the basis of weight divergence, update increment, and loss. We also balance the local data of the clients by image augmentation to mitigate the impact of the non-independently identically distribution. The server assigns compression thresholds to the clients based on the weight divergence and update increment of the models for gradient compression to reduce the wireless communication costs. Finally, based on the weight divergence, update increment and accuracy, the server dynamically assigns weights to the model parameters for the aggregation. Simulation and analysis utilizing a publicly available chest disease dataset containing COVID-19 are compared with existing federated learning methods. Experimental results show that our proposed strategy has better training performance in improving model accuracy and reducing wireless communication costs.
Maximizing privacy and security of collaborative indoor positioning using zero-knowledge proofs

The increasing popularity of wearable-based Collaborative Indoor Positioning Systems (CIPSs) has led to the development of new methods for improving positioning accuracy. However, these systems often rely on protocols, such as iBeacon, that lack sufficient privacy protection. In addition, they depend on centralized entities for the authentication and verification processes. To address the limitations of existing protocols, this paper presents a groundbreaking contribution to the field of wearable-based CIPSs. We propose a decentralized Attribute-based Authentication (ABA) protocol that offers superior levels of privacy protection, untraceability, and unlinkability of user actions. Unlike existing protocols that rely on centralized entities, our approach leverages decentralized mechanisms for authentication and verification, ensuring the privacy of user location data exchange. Through extensive experimentation across multiple platforms, our results demonstrate the practicality and feasibility of the proposed protocol for real-world deployment. Overall, this work opens up new avenues for secure and privacy-preserving wearable-based CIPSs, with potential implications for the rapidly growing field of Internet of Things (IoT) applications.
THE CRISIS PLATFORM: A CROSS-BORDER PLATFORM FOR RISK ASSESSMENT AND MANAGEMENT

This paper aims to describe the CRISIS web-based platform (WBP) in all its parts and functionalities. The platform is the main result of the two-year EU-funded project CRISIS (Comprehensive RISk assessment of basic services and transport InfraStructure). It has been developed by EUCENTRE (European Centre for Training and Research in Earthquake Engineering) using the most up-to-date web programming frameworks and technologies. The CRISIS WBP is a user-friendly tool intended to support disaster and emergency management authorities in case of earthquakes and/or seismo-induced landslides in the cross-border region of Albania, North Macedonia, and Greece. It has been designed to collect, organise, and visualise for the project target area: i) the exposure data of educational facilities, health facilities, and bridges; ii) the seismic and landslide hazard data; iii) the earthquake damage scenarios (calculated both for selected historical events and in real-time); and iv) the landslide risk scenarios related to the considered exposure dataset. The tool also allows the identification of alternative routes to the nearest available safe facilities, if the main one cannot be used due to damage to the transport infrastructure following a seismic event. This feature can be particularly useful for rescuers who have to intervene promptly after damaging earthquakes. In addition to supporting emergency management, the CRISIS platform can also be used to identify the most vulnerable assets and prioritise actions to increase the resilience of the project target area. As a case study, two earthquakes that affected the cities of Ohrid and Valandovo in 1911 and 1931, respectively, have been simulated. The results of these simulations, also in terms of emergency management (e.g., how to get to the nearest hospitals in the cross-border areas), are presented in detail hereinafter.
ITER-SIS: Robust Unlimited Sampling Via Iterative Signal Sieving

Unlimited Sampling Framework (USF) is a digital acquisition protocol that recovers high dynamic range (HDR) input signals from their low dynamic range, modulo samples. Current USF theory and algorithms are predominantly focused on bandlimited signal classes that rely on a relatively high sampling rate. Recently, the "Fourier-Prony" algorithm was proposed and validated via hardware experiments with modulo ADCs. It was shown that this algorithm offers competitive performance in the presence of system noise and quantization, especially when periodic boundary conditions are satisfied.In practice, signals are often measured over a finite observation window and this implies leakage in the Fourier domain. Depending on the severity of spectral leakage, Fourier domain algorithms may fail to reconstruct. To overcome this bottleneck, in this paper, we propose an Iterative Signal Sieving Algorithm (ITER-SIS) that solely operates in the time domain. By utilizing a continuous-domain characterization of modulo samples, ITER-SIS achieves a robust, low-sampling-rate, FFT-free recovery of signals with a finite time observation window, even when there is considerable spectral leakage. Hardware experiments with the modulo ADC demonstrate the robustness of our method in a realistic, noisy and low-sampling rate settings, thus validating its high practical utility in a variety of applications.
Cloud vs Traditional Disaster Recovery Techniques : A Comparative Analysis

This study investigates the function of cloud computing in disaster recovery and contrasts on-premises disaster recovery strategies with cloud-based ones.Organizations must have a disaster recovery strategy in place since natural disasters, cyberattacks, and other disruptive events can have disastrous impacts on them.Traditional disaster recoveries techniques like hot and cold sites, clustering, and backup and recovery have drawbacks such as being costly, timeconsuming, and difficult to execute.Disaster recovery solutions based on the cloud and entail storing data and applications in a remote server provide numerous advantages such as expandability, reduced expenses, and adaptability.The report provides an overview of disaster recovery techniques and discusses the importance of disaster recovery for organizations.The report then compares traditional disaster recovery methods with cloud-based solutions and concludes with recommendations for organizations considering cloud-based disaster recovery solutions.
PI-Trans: Parallel-Convmlp and Implicit-Transformation Based Gan for Cross-View Image Translation

For semantic-guided cross-view image translation, it is crucial to learn where to sample pixels from the source view image and where to reallocate them guided by the target view semantic map, especially when there is little overlap or drastic view difference between the source and target images. Hence, one not only needs to encode the long- range dependencies among pixels in both the source view image and target view semantic map but also needs to translate these learned dependencies. To this end, we propose a novel generative adversarial network, PI-Trans, which mainly consists of a novel Parallel-ConvMLP module and an Implicit Transformation module at multiple semantic levels. Extensive experimental results show that PI-Trans achieves the best qualitative and quantitative performance by a large margin compared to the state-of-the-art methods on two challenging datasets. The source code is available at https://github.com/Amazingren/PI-Trans.