Reviewerly/exHarmony huggingface dataset:
CaldanAugmenty – Augmented Reality and Serious Game App for Urban Cultural Heritage Learning

This research presents CaldanAugmenty, a GPS-enabled geolocalised augmented reality (AR) application. Applied to the historic village of Caldana, the application uses serious gaming elements to create an interactive treasure hunt that offers users an enriching journey through the local history and architecture of the region. CaldanAugmenty features a unique collection of 3D animated historical characters that bring the village’s heritage to life. In the treasure hunt, players must find these characters by exploring the physical landscape of Caldana; these characters appear within the AR interface at various points of interest. These characters delve into the local history and architectural heritage of the village and challenge users with intriguing questions that provide elements for localization of the treasures. CaldanAugmenty incorporates the village’s hidden treasures into the virtual gameplay. These treasures are located in four real underground passages that are physically inaccessible for security reasons. The application incorporates these dungeons into a ‘virtual tour’ function, complete with immersive photo spheres. As users discover treasures during their virtual exploration, these are manifested in AR at the entrances to the real dungeons. The effectiveness of the application as an interactive educational tool was evaluated using a comprehensive questionnaire administered to 79 middle school students. The questionnaire was designed to assess the dimensions of intrinsic motivation, ease of use and intention to use the application in the future. Preliminary results suggest that CaldanAugmenty is a highly engaging tool to facilitate learning about local history and heritage, with the majority of students expressing a high intention to continue using the application.
Stegano-Purge: An integer wavelet transformation based adaptive universal image sterilizer for steganography removal

Sterilization is the method of annihilating steganographic data hidden in a cover media. This paper proposes a visual quality preserving image sterilization method that effectively modifies stego-data embedded by various steganography algorithms on random greyscale images. The method applies one-level Integer Wavelet Transform (IWT) on a suspected stego image to produce HH, HL, LH and LL sub-bands. The value of a sub-band coefficient is modified according to its agitation quotient. The coefficient agitation is computed using the proposed metric Unnormalized Hellinger Distance (UHD) between a specific sub-band coefficient and its neighbours. To mitigate the issue of variable noise tolerance of IWT sub-bands, the proposed method performs parameter-controlled adaptive modification. Purification of up to 3 bits is done in the most noise-resilient HH channel and up to 2 bits in moderately noise-sensitive HL and LH channels. The highly noise-susceptible LL channel is sterilized by adaptive bit-flipping. A parameter control algorithm balances the amount of channel-wise bit modification to optimize the visual quality. The stego images for evaluation have been prepared by applying eleven state-of-the-art steganography methods including UNIWARD and HUGO on benchmark datasets of USC-SIPI and BossBase 1.01. The proposed method achieves improved performance in comparison with the state-of-the-art sterilization techniques in terms of the visual quality of sterilized image, embedding scheme-wise percentage of sterilization and the sterilization time.
Chaotic time series prediction of nonlinear systems based on various neural network models

This paper discusses the chaos prediction of nonlinear systems using various neural networks based on the modified substructure data-driven modeling architecture. In the modeling step, we construct two-coefficient loss functions according to the linear multi-step method to improve the prediction accuracy of neural networks. Then, the predicted response data of the system is given by the forward Euler method and neural networks. Under such architecture, chaos forecasting is carried out on a five-degree-of-freedom duffing oscillator system via the feedforward neural network (FNN), long short-term memory (LSTM) network and LSTM encoder-decoder (LSTM ED). The numerical simulation results show that the model can predict chaotic time series even if a small amount of information and samples are known, and the prediction window is twice that of the observation window. Among these models, LSTM ED exhibits the highest accuracy in both short-term and long-term chaos prediction. Furthermore, the prediction results mainly involve three evaluation indicators: absolute error, mean absolute error, normalized root mean square error. Through error analysis and noise processing, LSTM ED shows superior stability, robustness and extrapolation ability. Its prediction error is about half of FNN and the maximum increase in accuracy is 71.3 %.
Federated Learning With Lossy Distributed Source Coding: Analysis and Optimization

Recently, federated learning (FL), which replaces data sharing with model sharing, has emerged as an efficient and privacy-friendly machine learning (ML) paradigm. One of the main challenges in FL is the huge communication cost for model aggregation. Many compression/quantization schemes have been proposed to reduce the communication cost for model aggregation. However, the following question remains unanswered: What is the fundamental trade-off between the communication cost and the FL convergence performance? In this paper, we manage to answer this question. Specifically, we first put forth a general framework for model aggregation performance analysis based on the rate-distortion theory. Under the proposed analysis framework, we derive an inner bound of the rate-distortion region of model aggregation. We then conduct an FL convergence analysis to connect the aggregation distortion and the FL convergence performance. We formulate an aggregation distortion minimization problem to improve the FL convergence performance. Two algorithms are developed to solve the above problem. Numerical results on aggregation distortion, convergence performance, and communication cost demonstrate that the baseline model aggregation schemes still have great potential for further improvement.
Sustainable Financial Fraud Detection Using Garra Rufa Fish Optimization Algorithm with Ensemble Deep Learning

Sustainable financial fraud detection (FD) comprises the use of sustainable and ethical practices in the detection of fraudulent activities in the financial sector. Credit card fraud (CCF) has dramatically increased with the advances in communication technology and e-commerce systems. Recently, deep learning (DL) and machine learning (ML) algorithms have been employed in CCF detection due to their features’ capability of building a powerful tool to find fraudulent transactions. With this motivation, this article focuses on designing an intelligent credit card fraud detection and classification system using the Garra Rufa Fish optimization algorithm with an ensemble-learning (CCFDC-GRFOEL) model. The CCFDC-GRFOEL model determines the presence of fraudulent and non-fraudulent credit card transactions via feature subset selection and an ensemble-learning process. To achieve this, the presented CCFDC-GRFOEL method derives a new GRFO-based feature subset selection (GRFO-FSS) approach for selecting a set of features. An ensemble-learning process, comprising an extreme learning machine (ELM), bidirectional long short-term memory (BiLSTM), and autoencoder (AE), is used for the detection of fraud transactions. Finally, the pelican optimization algorithm (POA) is used for parameter tuning of the three classifiers. The design of the GRFO-based feature selection and POA-based hyperparameter tuning of the ensemble models demonstrates the novelty of the work. The simulation results of the CCFDC-GRFOEL technique are tested on the credit card transaction dataset from the Kaggle repository and the results demonstrate the superiority of the CCFDC-GRFOEL technique over other existing approaches.
Va.Si.Li-Lab as a collaborative multi-user annotation tool in virtual reality and its potential fields of application

During the last thirty years a variety of hypertext approaches and virtual environments -- some virtual hypertext environments -- have been developed and discussed. Although the development of virtual and augmented reality technologies is rapid and improving, and many technologies can be used at affordable conditions, their usability for hypertext systems has not yet been explored. At the same time, even for virtual three-dimensional virtual and augmented environments, there is no generally accepted concept that is similar or nearly as elegant as hypertext. This gap will have to be filled in the next years and a good concept should be developed; in this article we aim to contribute in this direction and also introduce a prototype for a possible implementation of criteria for virtual hypertext simulations.
BERT-CNN-BiLSTM: A Hybrid Deep Learning Model for Accurate Sentiment Analysis

Comments can influence people's choices. It is important to accurately determine the sentiment polarity of comments. Around this problem, this paper proposed a sentiment analysis method based on BERT-CNN-BiLSTM model. It first used Bidirectional Encoder Representations from Transformers (BERT) to transform the words in the input sequence into a vector representation. Then, Convolutional Neural Network (CNN) was used to extract features, i.e., the output vector of BERT was convolved along the dimension of sequence length to further extract the features in the sequence. Next, Bidirectional Long Short-Term Memory (BiLSTM) was used to further encode the features and capture the long-term dependencies in the sequence. Finally, the output vector of the BiLSTM was fed into a fully connected layer to make classification predictions. Accuracy was used as an evaluation metric to monitor the performance of the model. Compared with BERT, BiLSTM, CNN, and BERT-BiLSTM models, the accuracy of this model has been improved by 6.98%, 21.05%, 27.78%, and 3.37%, respectively.
Internal leakage rate prediction and unilateral and bilateral internal leakage identification of ball valves in the gas pipeline based on pressure detection

In the petrochemical industry, the ball valve internal leakage has been identified as a significant cause of production safety accidents, leading to mass casualties and economic losses. Depending on the flowing direction of the fluid, the form of ball valve internal leakage can be separated into unilateral and bilateral internal leakage. If a ball valve has a unilateral internal leakage, it is capable to play a sealing role, which can reduce gas venting and economic losses during natural gas fire operations. Therefore, this study aimed to develop a method for predicting the internal leakage rate and recognizing the unilateral and bilateral internal leakage used in gas transmission pipelines. For predicting the ball valve internal leakage rate, an Adaboost Integrated Circular Search Algorithm Optimised Support Vector Regression (CSA-SVR-Adaboost) model was employed and compared with other models. The CSA-SVR-Adaboost model demonstrated superior performance, displaying the lowest errors and highest correlation coefficients compared to the other models considered. Furthermore, this study aimed to identify unilateral and bilateral internal leakage of ball valves. To achieve this, a Dung Beetle Optimization Algorithm for Optimizing Kernel Extreme Learning Machine (DBO-KELM) model was adopted and evaluated based on its recognition performance. The recognition accuracy of DBO-KELM was 85.19%, surpassing the recognition accuracy of the Extreme Learning Machine (ELM) and Kernel Extreme Learning Machine (KELM) algorithms by 9.5% and 14.9%, respectively.
Harnessing the Power of Blockchain Technology in Modern China

This chapter looks beyond cryptocurrencies to examine the wider usage and ramifications of blockchain technology in China. After introducing blockchain concepts, it examines China's regulatory environment and its efforts to promote confidence in digital exchanges. The chapter then delves into the specifics of China's technical implementations, dissecting enabling tools, new architectural frameworks, and emergent movements like the internet of things and big data. It examines the effects on businesses, communities, and individuals, paying special attention to the ethical and regulatory settings in which these shifts occur. The dialogue is backed up by real-world case studies that show how blockchain can revolutionize many different industries. This chapter is an effort to educate interested parties about the blockchain scene in China and its worldwide implications.
Distributed AI and Blockchain for 6G-Assisted Terrestrial and Non-Terrestrial Networks: Challenges and Future Directions

The integration of Terrestrial and Non-Terrestrial Networks (TNTNs) with the sixth-generation (6G) wireless ecosystem will revolutionize the futuristic communication networks by enabling comprehensive interconnection and quality of service. However, such an integrated network will raise serious security and privacy issues due to the insecure communication among untrusted participating entities over an open unsecured public channel. As a result, the entire ecosystem can be accessed by both legitimate users and adversaries. Distributed AI when integrated with blockchain has a great potential to provide a feasible alternative to solve the security and privacy issues of 6G-assisted TNTNs. As a case study, a distributed AI is first adopted to cooperatively participate in the training process of a global model directly on devices. This approach ensures privacy and security of user data, and also confirms that only valid data is used by smart contracts to execute consensus mechanism. The multiple parallel blockchain are employed to securely and efficiently manage, and share data at each layer of 6G-assisted TNTNs. The efficiency of the proposed framework is demonstrated by numerical findings. Finally, prospective open research issues in employing distributed AI and blockchain for 6G-assisted TNTNs are highlighted.
Neutrosophic Monte Carlo Simulation Approach for Decision Making In Medical Diagnostic Process Under Uncertain Environment

This work emphasis on the basic notions regarding the Neutrosophic Fuzzy Sets (NFSs) with operations and their applicability in medical diagnostic process. In this manuscript, we developed neutrosophic fuzzy set-based Monte Carlo simulation technique for the decision making in medical diagnostic processin fuzzy environment. In this work, we managed the waiting time and idle time of the doctor during the treatment process of the patients. The various parameters are stated as linguistic variable in the form of NFSs. The developed neutrosophic Monte Carlo simulation technique (NMCST) is extended in the planning strategy of a doctor to treat the patient in a neutrosophic fuzzy environment. For the validation and authentication of the efficiency of the proposed NMCST, numerical computations are carried out with the examples of medical problems.
Discrete and Balanced Spectral Clustering With Scalability

Spectral Clustering (SC) has been the main subject of intensive research due to its remarkable clustering performance. Despite its successes, most existing SC methods suffer from several critical issues. First, they typically involve two independent stages, i.e., learning the continuous relaxation matrix followed by the discretization of the cluster indicator matrix. This two-stage approach can result in suboptimal solutions that negatively impact the clustering performance. Second, these methods are hard to maintain the balance property of clusters inherent in many real-world data, which restricts their practical applicability. Finally, these methods are computationally expensive and hence unable to handle large-scale datasets. In light of these limitations, we present a novel Discrete and Balanced Spectral Clustering with Scalability (DBSC) model that integrates the learning the continuous relaxation matrix and the discrete cluster indicator matrix into a single step. Moreover, the proposed model also maintains the size of each cluster approximately equal, thereby achieving soft-balanced clustering. What's more, the DBSC model incorporates an anchor-based strategy to improve its scalability to large-scale datasets. The experimental results demonstrate that our proposed model outperforms existing methods in terms of both clustering performance and balance performance. Specifically, the clustering accuracy of DBSC on CMUPIE data achieved a 17.93% improvement compared with that of the SOTA methods (LABIN, EBSC, etc.).
A Reinforcement Learning List Recommendation Model Fused with Graph Neural Networks

Existing list recommendation methods present a list consisting of multiple items for feedback recommendation to user requests, which has the advantages of high flexibility and direct user feedback. However, the structured representation of state data limits the embedding of users and items, making them isolated from each other, missing some useful infomation for recommendation. In addition, the traditional non-end-to-end learning series takes a long time and accumulates errors. During the model training process, the results of each task can easily affect the next calculation, thus affecting the entire training effect. Aiming at the above problems, this paper proposes a Reinforcement Learning List Recommendation Model Fused with a Graph Neural Network, GNLR. The goal of this model is to maximize the recommendation effect while ensuring that the list recommendation system accurately analyzes user preferences to improve user experience. To this end, firstly, we use an user–item bipartite graph and Graph Neural Network to aggregate neighborhood information for users and items to generate graph structured representation; secondly, we adopt an attention mechanism to assign corresponding weights to neighborhood information to reduce the influence of noise nodes in heterogeneous information networks; finally, we alleviate the problems of traditional non-end-to-end methods through end-to-end training methods. The experimental results show that the method proposed in this paper can alleviate the above problems, and the recommendation hit rate and accuracy rate increase by about 10%.
LET-Net: locally enhanced transformer network for medical image segmentation

Abstract Medical image segmentation has attracted increasing attention due to its practical clinical requirements. However, the prevalence of small targets still poses great challenges for accurate segmentation. In this paper, we propose a novel locally enhanced transformer network (LET-Net) that combines the strengths of transformer and convolution to address this issue. LET-Net utilizes a pyramid vision transformer as its encoder and is further equipped with two novel modules to learn more powerful feature representation. Specifically, we design a feature-aligned local enhancement module, which encourages discriminative local feature learning on the condition of adjacent-level feature alignment. Moreover, to effectively recover high-resolution spatial information, we apply a newly designed progressive local-induced decoder. This decoder contains three cascaded local reconstruction and refinement modules that dynamically guide the upsampling of high-level features by their adaptive reconstruction kernels and further enhance feature representation through a split-attention mechanism. Additionally, to address the severe pixel imbalance for small targets, we design a mutual information loss that maximizes task-relevant information while eliminating task-irrelevant noises. Experimental results demonstrate that our LET-Net provides more effective support for small target segmentation and achieves state-of-the-art performance in polyp and breast lesion segmentation tasks.
Fraud Detection in Healthcare Insurance Claims Using Machine Learning

Healthcare fraud is intentionally submitting false claims or producing misinterpretation of facts to obtain entitlement payments. Thus, it wastes healthcare financial resources and increases healthcare costs. Subsequently, fraud poses a substantial financial challenge. Therefore, supervised machine and deep learning analytics such as random forest, logistic regression, and artificial neural networks are successfully used to detect healthcare insurance fraud. This study aims to develop a health model that automatically detects fraud from health insurance claims in Saudi Arabia. The model indicates the greatest contributing factor to fraud with optimal accuracy. The labeled imbalanced dataset used three supervised deep and machine learning methods. The dataset was obtained from three healthcare providers in Saudi Arabia. The applied models were random forest, logistic regression, and artificial neural networks. The SMOT technique was used to balance the dataset. Boruta object feature selection was applied to exclude insignificant features. Validation metrics were accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC). Random forest classifiers indicated policy type, education, and age as the most significant features with an accuracy of 98.21%, 98.08% precision, 100% recall, an F1 score of 99.03%, specificity of 80%, and an AUC of 90.00%. Logistic regression resulted in an accuracy of 80.36%, 97.62% precision, 80.39% recall, an F1 score of 88.17%, specificity of 80%, and an AUC of 80.20%. ANN revealed an accuracy of 94.64%, 98.00% precision, 96.08% recall, an F1 score of 97.03%, a specificity of 80%, and an AUC of 88.04%. This predictive analytics study applied three successful models, each of which yielded acceptable accuracy and validation metrics; however, further research on a larger dataset is advised.
Enhancing Financial Fraud Detection through Addressing Class Imbalance Using Hybrid SMOTE-GAN Techniques

The class imbalance problem in finance fraud datasets often leads to biased prediction towards the nonfraud class, resulting in poor performance in the fraud class. This study explores the effects of utilizing the Synthetic Minority Oversampling TEchnique (SMOTE), a Generative Adversarial Network (GAN), and their combinations to address the class imbalance issue. Their effectiveness was evaluated using a Feed-forward Neural Network (FNN), Convolutional Neural Network (CNN), and their hybrid (FNN+CNN). This study found that regardless of the data generation techniques applied, the classifier’s hyperparameters can affect classification performance. The comparisons of various data generation techniques demonstrated the effectiveness of the hybrid SMOTE and GAN, including SMOTified-GAN, SMOTE+GAN, and GANified-SMOTE, compared with SMOTE and GAN. The SMOTified-GAN and the proposed GANified-SMOTE were able to perform equally well across different amounts of generated fraud samples.
Immersing in Mesopotamia

Immersive technologies and 3D modeling hold the potential for improving how we teach students about archaeological topics, especially the spatial and visual aspects of the past. This article presents a rapid exploratory pilot experiment that deployed virtual reality (VR) devices for remote group tours of sites within an introductory Mesopotamian archaeology university course. Creating the 3D models of the sites took the most time, so it is hoped that future publication norms will encourage the direct sharing of models for reuse. Through student interviews, the authors found that students could remember and explain the spatial layouts of the sites presented in VR better than those presented by traditional means. Students also informed about challenges with using the technology and their enjoyment of interacting with the sites and other students in this new way. Future archaeological teaching with VR will build upon the practical knowledge gained from this initial pilot.