Secure and Decentralized Federated Learning Framework with Non-IID Data based on Blockchain

Abstract: Federated learning enables the collaborative training of machine learning models across multiple organizations, eliminating the need for sharing sensitive data. Nevertheless, in practice, the data distributions among these organizations are often non-independent and identically distributed (non-IID), which poses significant challenges for traditional federated learning. To tackle this challenge, we present a hierarchical federated learning framework based on blockchain technology, which is designed to enhance the training of non-IID data., protect data privacy and security, and improve federated learning performance. The framework builds a global shared pool by constructing a blockchain system to reduce the non-IID degree of local data and improve model accuracy. In addition, we use smart contracts to distribute and collect models and design a main blockchain to store local models for federated aggregation, achieving decentralized federated learning. We train the MLP model on the MNIST dataset and the CNN model on the Fashion-MNIST and CIFAR-10 datasets to verify its feasibility and effectiveness. The experimental results show that the proposed strategy significantly improves the accuracy of decentralized federated learning on three tasks with non-IID data.


Video generalized semantic segmentation via Non-Salient Feature Reasoning and Consistency

Abstract: Video semantic segmentation is beneficial for dynamic scene processing in real-world environments, and achieves superior performance on independent and identically distributed data. However, it suffers from performance degradation in environments with various domain styles, which is known as the distribution shift problem. Although some previous studies on image generalized semantic segmentation considered the distribution shift problem, temporal-frame information could not be used to obtain more accurate prediction. Thus, in this study, we explore a new task, known as the video generalized semantic segmentation (VGSS) task, which establishes a connection between continuous frames and domain generalization. We propose a novel method named Non-Salient Feature Reasoning and Consistency (NSFRC) for this task. Specifically, we first define the class-wise non-salient feature, which describes the features of the class-wise non-salient region that carry more generalized information. We then propose a class-wise non-salient feature reasoning strategy to select and enhance generalized channels adaptively. This strategy adopts a new form to use domain-invariant features by treating the domain-invariant features as prior information to assist domain-invariant model learning. Finally, we propose a non-salient centroid alignment loss to alleviate the temporally inconsistent and negative transfer problems in the VGSS task. We also extend our video-based framework to the image generalized semantic segmentation (IGSS) task. Experiments demonstrate that our NSFRC framework yields significant improvements in both the VGSS and IGSS tasks. To explain the idea of this research in a clear and attractive way, we provide the visual abstract shown in Fig. 1.


Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review

Abstract: Sentiment analysis is a method within natural language processing that evaluates and identifies the emotional tone or mood conveyed in textual data. Scrutinizing words and phrases categorizes them into positive, negative, or neutral sentiments. The significance of sentiment analysis lies in its capacity to derive valuable insights from extensive textual data, empowering businesses to grasp customer sentiments, make informed choices, and enhance their offerings. For the further advancement of sentiment analysis, gaining a deep understanding of its algorithms, applications, current performance, and challenges is imperative. Therefore, in this extensive survey, we began exploring the vast array of application domains for sentiment analysis, scrutinizing them within the context of existing research. We then delved into prevalent pre-processing techniques, datasets, and evaluation metrics to enhance comprehension. We also explored Machine Learning, Deep Learning, Large Language Models and Pre-trained models in sentiment analysis, providing insights into their advantages and drawbacks. Subsequently, we precisely reviewed the experimental results and limitations of recent state-of-the-art articles. Finally, we discussed the diverse challenges encountered in sentiment analysis and proposed future research directions to mitigate these concerns. This extensive review provides a complete understanding of sentiment analysis, covering its models, application domains, results analysis, challenges, and research directions.


Couplings and matchings combinatorial notes on Strassen’s theorem

Abstract: Some mathematical theorems represent ideas that are discovered again and again in different forms. One such theorem is Hall’s marriage theorem. This theorem is equivalent to several other theorems in combinatorics and optimization theory, in the sense that these results can easily be derived from each other. Remarkably, this equivalence extends to Strassen’s theorem, a celebrated result on couplings of probability measures. In this paper the equivalence between Strassen’s theorem and Hall’s theorem is investigated from a combinatorial perspective. A novel combinatorial lemma will be introduced that can be used to deduce both Hall’s theorem and a finite version of Strassen’s theorem, providing a simple proof of their equivalence.


Development of an intelligent system for early forecasting and modelling of flood situation on the example of the Republic of Bashkortostan using a proprietary machine and deep learning library

Abstract: Reliable early flood forecasting models and systems are essential for minimizing and preventing the impacts of floods worldwide. However, the predictive accuracy of such models and systems is often unacceptable for long-term forecasting, creating uncertainty in flood management decision-making. To address this problem, we propose an intelligent «Flood 2.0» system based on a proprietary machine learning and deep learning library and a flood zone visualisation module. The development of a new version of the system is preceded by a previous study «A system based on an artificial neural network of the second generation for decision support in especially significant situations». The machine learning and deep learning library developed within «Flood 2.0» consists of two parts: preprocessing and prediction. Initially, the study considers the method of data preprocessing. The essence of this method is the development and application of an impulse neural network for generation of information filtering rules, which makes it possible to generate a high-quality initial set of retrospective data in an automated mode, cutting off unnecessary digital noise. Next, a predictive part (method) for water level prediction based on a dataset generated by a data preprocessing method is discussed. This water level prediction method consists of: a) a machine learning model represented as a polynomial regression; b) a deep learning model implemented as a modified recurrent neural network (RNN), a classical neural network with long-term short-term memory (LSTM) and a pulse neural network (PNN); c) a flood zone visualisation module, which is implemented on the basis of the Leaflet library and allows modelling flood zones without the use of digital elevation models, i.e. only on the basis of predicted water levels. To verify the effectiveness of the developed «Flood 2.0» system in general and the machine learning and deep learning library in particular, many 10-day-ahead prediction experiments have been conducted. The empirical results show that the developed recurrent neural network model predicts more accurately under the proposed intelligent system “Flood 2.0″. Moreover, the difference compared to other models ranges from 49.22% to 78.98%, which proves the effectiveness of the development of the Flood 2.0 intelligent system in general and mathematical/technical modifications in particular. Thus, an early and accurate forecast, as well as visualised flood zones will give special services the necessary time to carry out flood control measures to prepare for the protection of technical facilities of enterprises and evacuation of the population. Additional research has also shown that the Flood 2.0 system can be used to predict water levels and model flood zones in other regions.


Existence of the Map <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:msup><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:msup></mml:math>

Abstract: In this paper we show the existence of a nontrivial linear map detS3:Vd⊗(3d3)→k with the property that detS3(⊗1≤i<j<k≤3d(vi,j,k))=0 if there exists 1≤x<y<z<t≤3d such that vx,y,z=vx,y,t=vx,z,t=vy,z,t. This gives a partial answer to a conjecture form [10]. As an application, we use the map detS3 to study those d-partitions of the complete hypergraph K3d3 that have zero Betti numbers. We also discuss algebraic and combinatorial properties of a map detSr:Vd⊗(rdr)→k which generalizes the determinant map, the map detS2 from [9], and detS3.


User-defined weight based multi objective task scheduling in cloud using whale optimisation algorithm

Abstract: Cloud computing has revolutionized the IT landscape, providing scalable, on-demand computing resource. For efficiency in cloud environments, it is essential for modern organizations, where objectives often include cost reduction, resource consumption, operational efficiency and load balancing etc, to implement multi objective solutions. Single-objective systems can fail in handling dynamic and diverse workloads. This study introduces the Multi-Objective Whale Optimization-Based Scheduler (WOA-Scheduler) for efficient task scheduling in cloud computing environments. Leveraging the Whale Optimization Algorithm (WOA), the scheduler optimizes multiple objectives simultaneously, including cost, time, and load balancing. A key feature of the WOA-Scheduler is its flexibility in accommodating user-defined weights for different objectives, allowing organizations to prioritize optimization goals based on their specific requirements. Comparative analysis across various cloud environments demonstrates the superiority of the WOA-Scheduler over traditional single-objective approaches. By achieving a better balance between cost, time, and resource utilization, the scheduler enhances overall performance. Moreover, its multi-objective optimization capabilities enable dynamic adjustment of task assignments in response to changing workload conditions, ensuring efficient resource utilization and workload distribution. Overall, the WOA-Scheduler offers a customizable and adaptable solution for addressing the complexities of modern cloud services, ultimately improving performance and efficiency.


Between money and speculative asset: The role of financial literacy on the perception towards Bitcoin in Italy

Abstract: With Bitcoin at the forefront, cryptocurrencies are gaining traction as an alternative asset investment, particularly among young investors. Although most of the empirical evidence has shown that it could not be defined as a currency, some Bitcoin users argue the opposite. This paper analyzes the factors influencing the perception of Bitcoin, i.e., whether it is a currency or an asset, with a focus on financial literacy among a subject pool of university students in Italy. The results show that, after controlling for several individual characteristics such as behavioral biases, personal attitudes, psychological traits, and socio-demographic information, this cryptocurrency is considered more than just an asset, and thus it could replace currency, among subjects with lower financial literacy, higher knowledge of Bitcoin, and those who do not trust the banking system. In contrast, Bitcoin is considered a speculative asset among those individuals with higher financial literacy. In line with the recent evidence that cryptocurrencies are mostly owned by young investors, results indicate the importance of increasing the level of financial education among them.


L2-BiTCN-CNN: Spatio-temporal features fusion-based multi-classification model for various internet applications identification

Abstract: Identifying various types of Internet applications is a key issue for network management. Traffic classification is one of the important technologies for identifying Internet applications traffic. However, some secure technologies such as protocol encryption, information hiding, and traffic obfuscation pose new challenges to traffic identification and classification, especially in feature extraction. In addition, multi-classification has always been a difficult problem in traffic classification research. This paper proposes a spatio-temporal features fusion-based multi-classification model, named as L2-BiTCN-CNN. Our approach combines bidirectional temporal convolutional network (TCN) and convolutional neural network (CNN) to create an enhanced model. The L2-BiTCN model utilizes a bidirectional approach to learn both forward and backward sequence features of traffic, with a focus on the hierarchical structure of network traffic known as “byte-packet-session flow”. To fully extract timing characteristics between adjacent bytes and packets, this model incorporates attention mechanisms for both “byte hierarchy” and “packet hierarchy”. Filters of various sizes are utilizes in the CNN stage to capture packet features of different ranges. The spatial features of the packets are then obtained through two convolution and average pooling processes. The results of the experiments provide evidence of the effectiveness and superiority of our approach. Our model achieved an impressive overall accuracy of 99.839% and 97.186% in two public available datasets, namely USTC-TFC2016 and ISCX VPN-nonVPN2016, respectively. It outperforms the state-of-the-art models in terms of classification performance, which suggests that the model has the potential to be a valuable tool in various applications that require high accuracy.


A randomized algorithm for clustering discrete sequences

Abstract: Cluster analysis is one of the most important research issues in data mining and machine learning. To date, numerous clustering algorithms have been proposed to tackle the fixed-length vector data. In many real applications, we need to detect clusters from a set of discrete sequences in which each sequence is an ordered list of items. Due to the sequential and discrete nature, the discrete sequence clustering problem is more challenging and most of existing vector data clustering algorithms cannot be directly employed. In this paper, we present a stochastic algorithm for clustering discrete sequences. Our method first quickly generates a set of random partitions over the sequential data set and then merges these random clustering results via weighted graph construction and partition. We perform extensive empirical comparisons on real data sets to show that our method is comparable to those state-of-the-art clustering algorithms with respect to both accuracy and efficiency.


Blockchain for secure and decentralized artificial intelligence in cybersecurity: A comprehensive review

Abstract: As the usage of artificial intelligence (AI) grows within the field of cybersecurity, so too does the demand for secure and decentralized AI systems to protect against potential cyber threats. Blockchain technology (BT) has emerged as the ideal approach for increasing both the security and privacy of AI systems since it provides decentralized and immutable data storage. This systematic literature review focuses on the integration of BT with decentralized AI within cybersecurity. It provides a comprehensive taxonomy of BT and decentralized AI for cybersecurity serving as the starting point for the study. This paper begins with an overview of BT and its possible uses in cybersecurity and also analyzes its challenges and opportunities. Decentralized AI is also covered in the study, along with its potential advantages and difficulties in decentralized AI cybersecurity. Building on that foundation, this study provides convincing findings highlighting the beneficial relationships between BT and decentralized AI for cybersecurity, contributing to a nuanced comprehension of their integration. Moreover, it offers real-world uses of blockchain-enabled decentralized AI to discuss its usefulness in tackling cybersecurity concerns. Finally, this paper discusses future research directions for blockchain-enabled decentralized AI in cybersecurity, including potential applications and implications of this technology in the field, emphasizing the potential of blockchain-enabled decentralized AI in cybersecurity to enhance security, privacy, and trust in AI systems.


An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graph

Abstract: Extrapolating future events based on historical information in temporal knowledge graphs (TKGs) holds significant research value and practical applications. In this field, the methods currently utilized can be classified as either embedding-based or logical rule-based. Embedding-based methods depend on learned entity and relation embeddings for prediction, but they suffer from the lack of interpretability due to the opaque reasoning process. On the other hand, logical rule-based methods face scalability challenges as they heavily rely on predefined logical rules. To overcome these limitations, we propose a hybrid model that combines embedding-based and logical rule-based methods to capture deep causal logic. Our model, called the Inductive Reasoning Model based on Interpretable Logical Rule (ILR-IR), aims to provide interpretable insights while effectively predicting future events in TKGs. ILR-IR delves into historical information, extracting valuable insights from logical rules embedded within relations and interaction preferences between entities. By considering both logical rules and interaction preferences, ILR-IR offers a comprehensive perspective for predicting future events. In addition, we propose the incorporation of a one-class augmented matching loss during optimization, which serves to enhance performance of the model during training. We evaluate ILR-IR on multiple datasets, including ICEWS14, ICEWS0515, and ICEWS18. Experimental results demonstrate that ILR-IR outperforms state-of-the-art baselines, showcasing its superior performance in TKG extrapolation reasoning. Moreover, ILR-IR demonstrates remarkable generalization capabilities, even when applied to related datasets that share a common relation vocabulary. This suggests that our proposed model exhibits robust zero-shot reasoning abilities. For interested parties, we have made our code publicly available at https://github.com/mxadorable/ILR-IR.


Blockchain-based CP-ABE data sharing and privacy-preserving scheme using distributed KMS and zero-knowledge proof

Abstract: Nowadays, the integration of blockchain technology with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has drawn the researcher attention because it can provide key security auditing and transaction traceability in the context of data sharing. However, in a majority of existing blockchain-based CP-ABE schemes, private keys were still issued by one central authority that would lead to heavy computation, higher transaction costs, and restricted scalability within the decentralized system. To address these challenges, we present an enhancement approach towards utilizing distributed key management service (KMS) and zero-knowledge paradigms. In our improved novel blockchain system model, we define two types of blockchain nodes for the CP-ABE scheme through staking mechanism. Firstly, the proxy re-encryption nodes are introduced to offer secure multi-party management and distribution of the CP-ABE's master secret key, eliminating dependence on a central authority and producing proofs of re-encryption correctness. Secondly, the operator nodes can collect all transactional information in blockchain-based CP-ABE scheme and then send the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) proofs to verify the batch’s integrity via smart contract. Subsequently, we employ the staking economic incentive model with reward determination and slashing in the decentralized blockchain system to ensure network security. Finally, simulation results validate the effectiveness of our proposed scheme in achieving secure and efficient data sharing. Even amidst the pressure of 100 simultaneous transactions, the average response time for a single node remains at an approximate 28 s. Additionally, there is a notable decrease in on-chain gas consumption, with a gas reduction exceeding 61%. Comparative analyses further indicate that our blockchain-based CP-ABE scheme, in conjunction with a decentralized KMS, offers a superior balance between computational efficiency and functional capability.


Exploring explainable artificial intelligence techniques for evaluating cervical intraepithelial neoplasia (CIN) diagnosis using colposcopy images

Abstract: Although artificial intelligence techniques have performed well in analysing medical images, it is essential to consider the degree of understanding or interpretation in evaluations. This is especially important when diagnostic support is involved, as the trustworthiness of machine learning predictions needs to be clarified and evaluated. For deep learning algorithms to be translated into clinical practice, they must be made less opaque. To examine the classification of cervical dysplasia-diagnosed patients, we will explore explainable artificial intelligence (XAI) approaches, by implementing seven different interpretation techniques (Saliency, XRAI, Integrated Gradients, Smooth Gradients, Smooth Integrated Gradients, Grad-CAM, Smoothgrad-CAM). These XAI techniques shall be explored in order to assess the performance of four Convolutional Neural Network models (AlexNet, VGG16, MobileNet, and InceptionNet). Our results show that saliency qualities most closely match expert annotations, and there is a moderate to strong association between a model's sensitivity and the agreement between humans and computers. We also discovered a relevant relationship between expert insights and computational learning. This highlights the potential role of human expertise in influencing effective computer learning.


Introducing teacher students to digital fabrication to support children’s mathematical learning

Abstract: While digital fabrication has been closely linked to narratives of science, technology, engineering, and mathematics (STEM) education and design thinking, it can provide a gateway to reimagining mathematical teaching and deepening the understanding of both mathematical content and pedagogy. In this paper, we present a workshop series that engages teacher students in the creation of their own mathematical manipulatives using digital fabrication tools. Manipulatives are tangible objects that can be used to support children’s learning of mathematical concepts, such as numbers, fractions, or geometry. The workshops included activities for finding, adapting, creating, and sharing manipulatives (FACS) using 2D and 3D modeling and fabrication techniques. Our in-depth analysis of video recordings presents how teacher students successfully acquire digital fabrication skills and reflect on the use of customized manipulatives to support children’s mathematical learning. The findings suggest that introducing digital fabrication in teacher education programs can shift the focus from consuming ready-made manipulatives to creating customized materials that better suit the teaching context. The authors propose FACS as a specific approach for introducing digital fabrication to teacher students.


Application of cluster analysis to identify different reader groups through their engagement with a digital reading supplement

Abstract: The focus of this study is the identification of reader profiles that differ in performance and progression in an educational literacy app. A total of 19,830 students in Grade 2 from 347 Elementary schools located in 30 different districts in the United States played the app from 2020 to 2021. Our aim was to identify unique groups of readers using an unsupervised statistical learning technique - cluster analysis. Six indicators generated from the students’ log files were included to provide insights into engagement and learning across four different reading-related skills: phonological awareness, early decoding, vocabulary, and comprehension processes. A key aim was to evaluate the implementation and performance of Gaussian mixture models, k-means, k-medoids, clustering large applications and hierarchical clustering, alongside provision of detailed guidance that can benefit researchers in the field. K-means algorithm performed the best and identified nine groups of readers. Children with low initial reading ability showed greater engagement with code-related games (phonological awareness, early decoding) and took longer to master these games, whereas children with higher initial ability showed more engagement with meaning-related games (vocabulary, comprehension processes). Our findings can inform further research that aims to understand individual differences in learning behaviour within digital environments both over time and across various cohorts of children.


Batch Active Learning of Reward Functions from Human Preferences

Abstract: Data generation and labeling are often expensive in robot learning. Preference-based learning is a concept that enables reliable labeling by querying users with preference questions. Active querying methods are commonly employed in preference-based learning to generate more informative data at the expense of parallelization and computation time. In this paper, we develop a set of novel algorithms, batch active preference-based learning methods, that enable efficient learning of reward functions using as few data samples as possible while still having short query generation times and also retaining parallelizability. We introduce a method based on determinantal point processes (DPP) for active batch generation and several heuristic-based alternatives. Finally, we present our experimental results for a variety of robotics tasks in simulation. Our results suggest that our batch active learning algorithm requires only a few queries that are computed in a short amount of time. We showcase one of our algorithms in a study to learn human users’ preferences.


MHGCN+: Multiplex Heterogeneous Graph Convolutional Network

Abstract: Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous graph data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex networks between multi-typed nodes and the different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a M ultiplex H eterogeneous G raph C onvolutional N etwork (MHGCN+) for multiplex heterogeneous network embedding. Our MHGCN+ can automatically learn the useful heterogeneous meta-path interactions of different lengths with different importance in multiplex heterogeneous networks through multi-layer convolution aggregation. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments on seven real-world datasets with various network analytical tasks demonstrate the significant superiority of MHGCN+ against state-of-the-art embedding baselines in terms of all evaluation metrics. The source code of our method is available at: https://github.com/FuChF/MHGCN-plus.


Can using a pre-trained deep learning model as the feature extractor in the bag-of-deep-visual-words model always improve image classification accuracy?

Abstract: This article investigates whether higher classification accuracy can always be achieved by utilizing a pre-trained deep learning model as the feature extractor in the Bag-of-Deep-Visual-Words (BoDVW) classification model, as opposed to directly using the new classification layer of the pre-trained model for classification. Considering the multiple factors related to the feature extractor -such as model architecture, fine-tuning strategy, number of training samples, feature extraction method, and feature encoding method-we investigate these factors through experiments and then provide detailed answers to the question. In our experiments, we use five feature encoding methods: hard-voting, soft-voting, locally constrained linear coding, super vector coding, and fisher vector (FV). We also employ two popular feature extraction methods: one (denoted as Ext-DFs(CP)) uses a convolutional or non-global pooling layer, and another (denoted as Ext-DFs(FC)) uses a fully-connected or global pooling layer. Three pre-trained models-VGGNet-16, ResNext-50(32×4d), and Swin-B-are utilized as feature extractors. Experimental results on six datasets (15-Scenes, TF-Flowers, MIT Indoor-67, COVID-19 CXR, NWPU-RESISC45, and Caltech-101) reveal that compared to using the pre-trained model with only the new classification layer re-trained for classification, employing it as the feature extractor in the BoDVW model improves the accuracy in 35 out of 36 experiments when using FV. With Ext-DFs(CP), the accuracy increases by 0.13% to 8.43% (averaged at 3.11%), and with Ext-DFs(FC), it increases by 1.06% to 14.63% (averaged at 5.66%). Furthermore, when all layers of the pre-trained model are fine-tuned and used as the feature extractor, the results vary depending on the methods used. If FV and Ext-DFs(FC) are used, the accuracy increases by 0.21% to 5.65% (averaged at 1.58%) in 14 out of 18 experiments. Our results suggest that while using a pre-trained deep learning model as the feature extractor does not always improve classification accuracy, it holds great potential as an accuracy improvement technique.


Base-extension semantics for modal logic

Abstract: Abstract In proof-theoretic semantics, meaning is based on inference. It may seen as the mathematical expression of the inferentialist interpretation of logic. Much recent work has focused on base-extension semantics, in which the validity of formulas is given by an inductive definition generated by provability in a ‘base’ of atomic rules. Base-extension semantics for classical and intuitionistic propositional logic have been explored by several authors. In this paper, we develop base-extension semantics for the classical propositional modal systems $K$, $KT$, $K4$ and $S4$, with $\square $ as the primary modal operator. We establish appropriate soundness and completeness theorems and establish the duality between $\square $ and a natural presentation of $\lozenge $. We also show that our semantics is in its current form not complete with respect to euclidean modal logics. Our formulation makes essential use of relational structures on bases.


An NLP Technique on Sentiment Analysis

Abstract: We have to structure the data which was given to us from the Twitter social media for accurate analysis and make something outof it. We will be finding the sentiment behind the given comment by a user on twitter so that we can sort out the meaning of the text. To get the negative emotions of the text, we will be using different algorithms to find the intention behind it. Fathom this kind of issue, estimation investigation and profound learning methods are two combining methods. We are using Naive Bayes algorithms, SVM (Support Vector Machine) and otherclassification algorithms to get our required output.These are known deep learning /Machine Learning ways to extract the feelings in sentences. At the end of the result we will get the desired output and we will check the accuracy of our output accordingly.


The Formal Analysis on Negative Information Selections for Privacy Protection in Data Publishing

Abstract: Negative information selection is an approach to protect the privacy by using negative information to replace original information. In this paper, we prove some bounds for negative information selection. Those bounds reveal the privacy protection strength of quantitative probability analysis. We also analyzed the reconstruction probability of original information from available negative information. The formal analysis can specify the bound on the strength of security and utility for negative information selection. Besides, we simulate brute force attacks under different data leakage ratios. Specifically, we calculate the attacker’s guess times before and after the data leakage. Experimental results indicate that the data leakage of over 30% can put the original information in a dangerous situation. Furthermore, we found that the leakage possibility has little relevance to the number of elements in the full set, but it is influenced by the ratio of the leaked information.


Semiparametric constant false alarm rate method for radar and sonar images

Abstract: Abstract This work proposed a novel constant false alarm rate detection method based on Gaussian mixture model (GMM). The main contribution contains two aspects. First, the Gabor wavelet is used to determine the GMM component number. Second, the expression of probability of false alarm is derived based on the GMM parameter.


Periodization of the sociocultural genesis of Russian private school education (the 11th century – the 20s of the 21st century)

Abstract: The study aims to present the periodization of the sociocultural genesis of private school education in Russia as an integrated process, combining historical and modern periods. The article addresses the comprehensive sociocultural study of the processes of historical genesis and modern development of the Russian non-state school as a pedagogical phenomenon to propose the periodization of its evolution. The scientific novelty lies in: the systematization of the historical and modern processes of private school education development, presented in the form of periodization, with the key sociocultural factor of influence, taken as its criterion; creating a holistic representation of the Russian private school genesis by combining in the proposed periodization all the sociocultural stages of its formation and development, starting from its inception in the 11th century and including the current period of its development; identifying the pedagogical specifics of the modern period of private schools development based on a theoretical analysis of the latest scientific and pedagogical publications devoted to their educational activities; introducing new information into the scientific discourse of pedagogical research, reflecting the analytics and statistics of modern private school education. As a result of the study, according to the major factor of sociocultural influence specified in the research, five periods of historical and pedagogical development of the Russian private school were defined, with all the stages of its historical genesis and modern development covered. The author has concluded that the complex socio-cultural influence of the society and state is the major factor determining the private school evolution and its pedagogical specifics in the modern period.


Artificial intelligence as a tool to optimize the work of a higher school teacher

Abstract: The aim of the article is to justify the possibility of using artificial intelligence technologies – neural networks – to optimize the activities of a higher education teacher. The article discusses the educational capabilities of artificial intelligence for use in the higher education process. Procedural characteristics of artificial intelligence are presented in order to identify and describe its functions for use in higher education. The scientific novelty of the research is in identifying the potential of artificial intelligence technologies – neural networks – in relation to the educational process and optimizing the work of a higher education teacher. The results of the study include the following indicators: 1) artificial intelligence has a wide range of applications in the educational process; 2) neural networks have demonstrated the potential of their capabilities to optimize the work of a higher education teacher.


Identification and segregation of genes with improved recurrent neural network trained with optimal gene level and mutation level features

Abstract: Even though many different approaches have been employed to address the complex mutational heterogeneity of cancer, finding driver genes is still problematic since other genomic factors cannot be fully integrated for combined analyses. This research paper presents a novel gene identification and segregation model with five key processes (a) pre-processing, (b) treatment of class imbalances, (c) feature extraction, (d) feature selection, and (e) gene classification. To increase the data quality, the gathered initial information is first pre-processed utilizing data cleaning and data normalization. This turns the raw data into something that is both useful and effective. In actuality, the sample is skewed against drivers because passenger mutation markers appear in proportionally less instances than drivers do. To address the Class Imbalance Problem, improved K-Means + SMOTE are applied to the preprocessed data. The most crucial characteristics, including those at the gene and mutation levels, are then extracted from the balanced dataset. To lessen the computational load in terms of time, the best features from the retrieved features are selected using Forensic interpretation tailored hunger food search optimization (FIHFSO). The ideal features are used to train the deep learning classifier that conducts the separation procedure. In this research, an Improved Recurrent Neural Network (I-RNN) is used to make a final decision about genes. At 90% of learning percentage, the accuracy of the proposed method achieves 0.98% of 0.83, 0.81, 0.65, 0.80, 0.92 and 0.63% which is compared to the other methods like HGS, FBIO, AOA, AO, GOA and PRO respectively.


Creative workshop as a modern form of professional development for teachers in Russian folk dance and its regional characteristics

Abstract: The research objective is substantiation of the initiation of a creative workshop as a modern form of enhancing the professional skills of teachers in Russian folk dance and its regional peculiarities. The article argues for the need to ensure the process of preservation, study, and popularization of Russia's national heritage – Russian folk dance and its regional features. Russian folk dance allows forming in the younger generation a sense of love for their homeland, respect for its history, folk traditions, customs, and cultural diversity. The article emphasizes the importance of proper education on the specifics of Russian folk dance and the professional development of dance teachers. The leading forms of professional development for teachers and leaders of amateur dance groups include studying scientific and educational literature, grant activities, competitions and festivals, qualification courses, and masterclasses. The article places special emphasis on exploring the creative projects of the autonomous non-profit organization "Creative Union of Generations 'Transformation'." The modern form of enhancing the professional skills of dance teachers is presented as the cultural-educational project "All-Russian Creative Workshop on Russian Folk Dance and its Regional Features." The author provides an original definition of the essence of this new creative-pedagogical phenomenon, detailing the educational course program (from 2022 to 2024).


A Study on the Relationship Between Deep Learning and Statistical Models

Abstract: Recently, deep learning has become a pervasive tool in prediction problems for structured and/or unstructured big data in various areas including science and engineering. In particular, deep neural network models (i.e. a basic core model of deep learning) can be viewed as an extension of statistical models by going through the incorporation of hidden layers. In this paper, we study the relationship between both models in terms of model structures and model learning. For this purpose, we also compare the predictive performances of both models, with two practical examples.


Cost-benefit Analysis of Adopting Blockchain Technology in the Radiology Unit of a District Hospital

Abstract: Background: Blockchain technology has better security and immutable features, which are beneficial for healthcare data management. Decentralisation data management allows better sharing of medical images between units and departments. Objectives: The objectives of this study were to determine the cost of adopting the blockchain-based system for medical images and the cost-benefit analysis for five years of adoption of this technology at a district hospital in Malaysia. Methodology: Data regarding the yearly costs and workload for 2016 until 2018 were collected from a district hospital in Malaysia. The cost of blockchain implementation was obtained from a company that developed this technology. The cost-benefit analysis was done using the capital budgeting method for five years of this technology adoption. Sensitivity analysis was done to assess uncertainty conditions during this adoption. Results: The total yearly cost of Radiology Unit for 2018 was MYR490,168.96. The cost for adopting blockchain technology was MYR80,000 for the software, MYR115,000 for upgrading the assets and yearly maintenance cost was MYR16,000. The total present value of net benefits for five years of technology adoption was MYR865,295.01. Sensitivity analysis shows that consumables cost-saving is the most sensitive variable. Most pessimistic situation analyses showed positive net benefits. Conclusion: The adoption of blockchain technology in managing medical images offers numerous benefits to public hospitals in Malaysia. Investment in this technology can provide a good return to the organisation.


Enhancing learning motivation and academic achievement in nursing students through metaverse‐based learning: A randomized controlled study

Abstract: Generation Z students do not get much benefit from traditional distance education methods. Instead, they need new platforms and methods that will motivate them and increase their success.A randomized controlled study was performed on 42 students. The experimental group students were presented with metaverse, and the control group students were taught the distance education course.In this study, in the students' Instructional Materials Motivation Survey, the mean scores of both the experimental and control groups exhibited no significant difference before the intervention. Regarding the effect of metaverse-based learning on students' Instructional Materials Motivation Survey, the mean score of the control group was 82.55 ± 13.18, and the mean score of the experimental group was 96.52 ± 9.54 and there was a significant difference between groups after the intervention. When the academic achievement levels of the groups were examined, it was determined that the experimental group scored 76.57 ± 13.33 and the control group received 64.40 ± 16.42 points. It was determined there was a significant difference between the groups in terms of learning motivation and academic achievement.The study results show that the participants accepted the important role of metaverse as an effective learning method compared to traditional distance education methods. It is recommended to expand the use of metaverse in various fields of nursing education.


Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

Abstract: Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.


Protecting FPGA-Based Cryptohardware Implementations from Fault Attacks Using ADCs

Abstract: The majority of data exchanged between connected devices are confidential and must be protected against unauthorized access. To ensure data protection, so-called cryptographic algorithms are used. These algorithms have proven to be mathematically secure against brute force due to the key length, but their physical implementations are vulnerable against physical attacks. The physical implementation of these algorithms can result in the disclosure of information that can be used to access confidential data. Some of the most powerful hardware attacks presented in the literature are called fault injection attacks. These attacks involve introducing a malfunction into the normal operation of the device and then analyzing the data obtained by comparing them with the expected behavior. Some of the most common methods for injecting faults are the variation of the supply voltage and temperature or the injection of electromagnetic pulses. In this paper, a hardware design methodology using analog-to-digital converters (ADCs) is presented to detect attacks on cryptocircuits and prevent information leakage during fault injection attacks. To assess the effectiveness of the proposed design approach, FPGA-based ADC modules were designed that detect changes in temperature and supply voltage. Two setups were implemented to test the scheme against voltage and temperature variations and injections of electromagnetic pulses. The results obtained demonstrate that, in 100% of the cases, when the correct operating voltage and temperature range were established, the detectors could activate an alarm signal when the cryptographic module was attacked, thus avoiding confidential information leakage and protecting data from being exploited.


Chiral resolution based on non-adiabatic holonomic quantum control via a transmon qutrit

Abstract: Once the concept of chiral molecules was proposed, the task of chiral resolution has accompanied around in the related area. Naturally, scientists need fast and accurate methods to distinguish different chiralities. Here, we show a feasible method based on non-adiabatic holonomic quantum computation (NHQC), which can achieve over 99% discrimination of different chiralities. Based on the original scheme, we studied the modified version, NHQC+. The experimental data proved that it has better robustness than the original protocol, indicating a promising development in the relevant fields.


Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

Abstract: To address the recognition challenges faced by arrester pointer instruments’ dial scales in various scenarios, this paper introduces a deep learning-based recognition method for pointer instrument scales. An attention module is integrated into the YOLOv5 network architecture, enhancing the accuracy and robustness of the model. After correcting the dial, dial recognition is conducted with OpenCV to achieve precise identification of the instrument scales. The proposed method was tested using images of arrester pointer instruments against diverse backgrounds. The experimental results demonstrate that the method processes instrument data images in an average time of 0.662 s and achieves a successful recognition rate of 96% with an average error of 0.923%. This method provides a rapid and efficient approach for recognizing instrument scales and offers a novel solution for identifying similar types of instruments.


MgMViT: Multi-Granularity and Multi-Scale Vision Transformer for Efficient Action Recognition

Abstract: Nowadays, the field of video-based action recognition is rapidly developing. Although Vision Transformers (ViT) have made great progress in static image processing, they are not yet fully optimized for dynamic video applications. Convolutional Neural Networks (CNN) and related models perform exceptionally well in video action recognition. However, there are still some issues that cannot be ignored, such as high computational costs and large memory consumption. In the face of these issues, current research focuses on finding effective methods to improve model performance and overcome current limits. Therefore, we present a unique Vision Transformer model based on multi-granularity and multi-scale fusion to accomplish efficient action recognition, which is designed for action recognition in videos to effectively reduce computational costs and memory usage. Firstly, we devise a multi-scale, multi-granularity module that integrates with Transformer blocks. Secondly, a hierarchical structure is utilized to manage information at various scales, and we introduce multi-granularity on top of multi-scale, which allows for a selective choice of the number of tokens to enter the next computational step, thereby reducing redundant tokens. Thirdly, a coarse-fine granularity fusion layer is introduced to reduce the sequence length of tokens with lower information content. The above two mechanisms are combined to optimize the allocation of resources in the model, further emphasizing critical information and reducing redundancy, thereby minimizing computational costs. To assess our proposed approach, comprehensive experiments are conducted by using benchmark datasets in the action recognition domain. The experimental results demonstrate that our method has achieved state-of-the-art performance in terms of accuracy and efficiency.


Cooperative Coverage Path Planning for Multi-Mobile Robots Based on Improved K-Means Clustering and Deep Reinforcement Learning

Abstract: With the increasing complexity of patrol tasks, the use of deep reinforcement learning for collaborative coverage path planning (CPP) of multi-mobile robots has become a new hotspot. Taking into account the complexity of environmental factors and operational limitations, such as terrain obstacles and the scope of the task area, in order to complete the CPP task better, this paper proposes an improved K-Means clustering algorithm to divide the multi-robot task area. The improved K-Means clustering algorithm improves the selection of the first initial clustering point, which makes the clustering process more reasonable and helps to distribute tasks more evenly. Simultaneously, it introduces deep reinforcement learning with a dueling network structure to better deal with terrain obstacles and improves the reward function to guide the coverage process. The simulation experiments have confirmed the advantages of this method in terms of balanced task assignment, improvement in strategy quality, and enhancement of coverage efficiency. It can reduce path duplication and omission while ensuring coverage quality.


Spam Detection in Social Networks Using Machine Learning

Abstract: Many social media platforms have emerged as a result of the online social network's (OSN) rapid expansion. They have become important in day-to-day life, and spammers have turned their attention to them. Spam detection is done in two different ways, such as machine learning (ML) and expert-based detection. The expert-based detection technique’s accuracy depends on expert knowledge, and the manual process is a time-consuming task. Thus, ML-based spam detection is preferred in OSN. Spam identification on social networks is a difficult operation involving a variety of factors, and spam and ham have resulted in an imbalanced data distribution, which gives an advantage to spammers for corrupting our devices. Spam detection based on ML algorithms like Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Trees (DT), Random Forest (RF), Support Vector Machine (SVM), and XGB, Voting Classifier (VC), and many other algorithms are used to design the address balance and to attain high assessment accuracy. There is a non-balance issue. Text is vectorized by vectorizers and all the relative results are stored. The experimental result shows that, as compared to KN, NB, ETC, RF, SVC, LR, XGB, and DT, the proposed VC provides a higher classification accuracy rate of 97.96%. The proposed methods are effective in identifying balanced and imbalanced datasets, as evidenced by the validation results. The website was created to detect messages as spam or not.


Powerlifting total score prediction based on an improved random forest regression algorithm

Abstract: This paper discusses the use of an improved random forest regression algorithm (RFRA) to predict the total score of powerlifters. The paper collected the age, weight, and total score of multiple powerlifters, and then used an improved RFRA to build a predictive model. The parameters of this model are optimized by a differential squirrel search algorithm. The experimental results show that our proposed method can effectively predict the total score of powerlifters with an error of less than 10%, which can provide a reference for experts and athletes before training or competition.


Chaotic fuzzy iterated function systems

Abstract: It is observed that IFSs are defined based on the concept that the iterates take only an integer number of times. This work studies the dynamics of functions, where a function can iterate r times for every r ∈ R. Utilizing concepts from fuzzy sets, r-times iterates of a function are defined for r ∈ R. The study demonstrates that the chaotic property can be generalized to this new iterative concept. The chaotic behavior of a function is then extended using this iterative concept.


Open Science and the impact of Open Access, Open Data, and FAIR publishing principles on data-driven academic research: Towards ever more transparent, accessible, and reproducible academic output?

Abstract: Contemporary evidence-informed policy-making (EIPM) and societies require openly accessible high-quality knowledge as input into transparent and accountable decision-making and informed societal action. Open Science1 supports this requirement. As both enablers and logical consequences of the paradigm of Open Science, the ideas of Open Access, Open Data, and FAIR publishing principles revolutionise how academic research needs to be conceptualised, conducted, disseminated, published, and used. This ‘academic openness quartet’ is especially relevant for the ways in which research data are created, annotated, curated, managed, shared, reproduced, (re-)used, and further developed in academia. Greater accessibility of scientific output and scholarly data also aims at increasing the transparency and reproducibility of research results and the quality of research itself. In the applied ‘academic openness quartet’ perspective, they also function as remedies for academic malaises, like missing replicability of results or secrecy around research data. Against this backdrop, the present article offers a conceptual discussion on the four academic openness paradigms, their meanings, interrelations, as well as potential benefits and challenges arising from their application in data-driven research.


A decentralized framework for enhancing security in power systems through blockchain technology and trading system

Abstract: The primary objective of implementing secure smart power networks is to reduce the risk of data privacy breaches, including adversarial data poisoning and inference attacks. This study presents a novel approach, namely the Blockchain-based Power System Security Model (BC-PSSM), to augment security measures within power systems. The proposed method utilizes blockchain technology to enhance the security of data storage and verification and the mechanisms for data signing and authentication. It employs decentralized detection to improve the effectiveness and precision of intrusion detection with a Support Vector Machine (SVM). The BC-PSSM method exhibits promising outcomes in multiple facets of power system security detection, as evidenced by thorough simulations and evaluations. The simulation results demonstrate a significant decrease in computational burden and energy consumption by maintaining a high energy output level. The BC-PSSM algorithm performs remarkably in detection and false positive rates, resulting in heightened security and enhanced efficiency. The results also illustrate the positive impact of BC-PSSM in enhancing the security of power systems, offering robust protection against cybersecurity threats. BC-PSSM aims to enhance the security of power systems, thereby promoting a more robust and dependable energy infrastructure. This initiative plays a crucial role in protecting essential assets and mitigating the potential threats posed by cyber-attacks. The BC-PSSM algorithm enhanced efficiency to 92.15% and boosted overall security to 96.34%. It achieved this by achieving an amazing identification rate of 96.75% and an extraordinarily low false positive rate of 1.82%.


Broadband Diffractive Neural Networks Enabling Classification of Visible Wavelengths

Abstract: Diffractive neural networks (DNNs) are emerging as a new machine learning hardware based on optical diffraction with parallel and high‐throughput information processing. The optical inputs to DNNs are spatially modulated by propagating through passive diffractive layers that work in succession to achieve an inference. Herein, visible wavelength classification using single‐ and two‐layer DNNs fabricated using direct laser writing is demonstrated. The proposed DNN approach accepts the point spread function of two different wavelengths modeled after a microscope objective as the input and modulates the input field toward the target detector for classification. Of the three models trained to classify different wavelength pairs, the highest performance observed is for the classification of 561 and 785 nm, achieving over 90% accuracy. This work demonstrates the potential of all‐optical artificial neural networks for applications requiring visible wavelengths, from visible light beam shaping to spectral analysis and optical imaging.


FPHO: Fractional Pelican Hawks optimization based container consolidation in CaaS cloud

Abstract: Abstract Containers in cloud computing provide a logical packaging technique for applications to be isolated from the computing environment in which they actually execute, allowing for efficient sharing of memory, processor, storage, and network resources at the Operating System (OS) level. Since they are so compact, container‐based clouds have recently gained significant popularity. In order to maximize resource usage and minimize energy consumption, the container consolidation technique is widely employed in the cloud environment. This work introduces container consolidation in cloud computing that exploits Fractional Pelican Hawks Optimization (FPHO). In Container as a Service (CaaS) model, containers are placed in the Virtual Machines (VMs), and virtual machines are hosted in Physical Machines (PMs) or servers. The proposed method for container consolidation consists of two modules, namely, the host status module and the consolidation module. In the host status module, the PM's load is predicted using Long Short Term Memory (LSTM) and checked whether the PM is overloaded or underloaded using a threshold. If it is overloaded, the container selection algorithm is performed, and the migration list is also generated. In the consolidation module, the created migration list which is employed for the destination list to be created by an overloaded destination selector. In the same way, the underloaded list is also generated by the underloaded destination selector. Finally, the container and VM migration is carried out by considering the multi‐objectives such as predicted load, migration cost, resource utilization, energy consumption, network, and bandwidth which are optimally selected by the proposed FHPO. Here, FHPO is the combination of Fractional Pelican Optimization (FPO) and Fire Hawk Optimizer (FHO). The designed model achieved the measures with minimum energy consumption, resource utilization, Service Level Agreement (SLA), and Makespan as 0.066, 0.019, 0.054, and 0.066, respectively for setup one.


CCSFLF: Cloud‐edge‐terminal collaborative self‐adaptive federated learning framework

Abstract: Summary This article addresses the issue of ensuring model accuracy and training efficiency in a constrained federated learning environment. In an actual federated learning environment, each device's software, hardware, and network conditions are heterogeneous. Some terminal devices may not be able to undertake the work assigned by the server, resulting in poor model accuracy and slower convergence speed. However, existing research cannot ensure that each terminal device participating in training can handle the workload allocated by the system without collecting too much equipment information. This article proposes the cloud‐edge‐terminal collaborative self‐adaptive federated learning framework (CCSFLF) to solve this problem. This framework combines the advantages of federated learning and edge computing, reduces the probability that devices cannot handle the workload of system allocation, solves the system heterogeneity, and improves the efficiency of federated learning. CCSFLF can adaptively adjust the number of training tasks for terminal devices and select valuable training participants using a terminal device selection strategy. Multiple edge servers can simultaneously aggregate local models. Cloud servers are responsible for the aggregation and task distribution of global models. The above strategy enables the framework to have a faster convergence rate and higher model accuracy. The experimental results confirm that this framework can reduce the dropout rate of terminal devices by more than 5% in heterogeneous federated learning systems, improve the model accuracy by about 2%, and reduce the training time by 1/3 compared with similar methods, with better performance and applicability.


<scp>GMM‐LIME</scp> explainable machine learning model for interpreting sensor‐based human gait

Abstract: Abstract Machine learning (ML) has been used in human gait data for appropriate assistive device prediction. However, their uptake in the medical setup still remains low due to their black box nature which restricts clinicians from understanding how they operate. This has led to the exploration of explainable ML. Studies have recommended local interpretable model‐agnostic explanation (LIME) because it builds sparse linear models around an individual prediction in its local vicinity hence fast and also because it could be used on any ML model. LIME is however, is not always stable. The research aimed to enhance LIME to attain stability by avoid the sampling step through combining Gaussian mixture model (GMM) sampling. To test performance of the GMM‐LIME, supervised ML were recommended because study revealed that their accuracy was above 90% when used on human gait. Neural networks were adopted for GaitRec dataset and Random Forest (RF) was adopted and applied on HugaDB datasets. Maximum accuracies attained were multilayer perceptron (95%) and RF (99%). Graphical results on stability and Jaccard similarity scores were presented for both original LIME and GMM‐LIME. Unlike original LIME, GMM‐LIME produced not only more accurate and reliable but also consistently stable explanations.


Uncertainty in Artificial Neural Network Models: Monte-Carlo Simulations Beyond the GUM Boundaries

Abstract: In this study, the accuracy of the artificial neural network (ANN) was assessed considering the uncertainties associated with the randomness of the data and the lack of learning. The Monte-Carlo algorithm was applied to simulate the randomness of the input variables and evaluate the output distribution. It has been shown that under certain conditions, the GUM framework for uncertainty evaluation may completely fail. The ANN modeling technique can be used as an alternative method for estimating the expectation value and evaluating the associated uncertainty. Furthermore, unlike the GUM and Monte-Carlo frameworks, the ANN models do not require mathematical expressions between the input and output variables. On the other hand, owing to the uncertainty associated with the lack of learning, the ANN model may produce unrealistic results, even if a global minimum is approached. This behavior is explained by Bayesian theory which assumes that the output values generated by various runs are normally distributed at each target. This may lead to an unrealistic output when the overall distribution of the target values has a different distribution than that presumed by Bayesian theory. To minimize this drawback, the ANN model output should be calculated from sufficiently large repeated runs with new starting values of the weights and biases.


Consumers’ Brand Experiences With Robotic Service Failures: Modeling Appraisal, Attribution, and Psychological Reactance

Abstract: Due to the stretched capacities of human staff, consumers are increasingly placed in situations where they are “required” to use technology amidst their travel experiences, despite potential service failures in robotic technologies. Yet, research into how robotic service failures could potentially spill over to consumers’ brand experiences, robot experiences, and adoption intention remains unexplored. Drawing on appraisal, attribution, and psychological reactance theories, an interconnected research model of service failure, attribution, and (in)voluntary robot adoption was tested via two experiments. A mixed-design quasi-experiment (Study 1) found a significant interaction effect of service failure and attribution on brand experience in the hotel concierge context. Study 2 provided further evidence for the causal effects in Study 1 by employing a between-subject quasi-experiment in the hotel front desk context with additional measures. Collectively, this research contributes to the literature by highlighting how temporal, situational, and contextual factors in HRI may impact evaluations of brand and robot experiences.


Self-Adaptive Waste Management System: Utilizing Convolutional Neural Networks for Real-Time Classification

Abstract: This research presents a novel Self-Adaptive Waste Management System (SAWMS) that integrates advanced technology to address the pressing challenges of waste sorting and classification. SAWMS leverages Convolutional Neural Networks (CNNs) in conjunction with conveyor belt technology to achieve real-time object classification and self-training capabilities. The system utilizes sensors for object detection and a camera for image capture, enabling an accurate initial classification of waste objects into predefined categories such as food waste, metal, and plastic bottles. Notably, our proposed system sets itself apart by its unique ability to adapt and self-train based on classification errors, ensuring ongoing accuracy even in the face of changing waste compositions. Through dynamic adjustments of the conveyor belt’s destination, it efficiently directs waste objects to their appropriate bins for disposal or recycling. This research demonstrates the potential of SAWMS to revolutionize waste management practices, offering an agile and sustainable solution to the evolving challenges of waste sorting and disposal.


Techniques for Accelerometer Reading Processing on Railway Transport Using Wavelet Transform

Abstract: Introduction. Safety issues of railway transport are inevitably connected with the condition of railway tracks and railway wheels. Various defects, such as irregularities of the railway track, may lead to emergencies and incidents. Therefore, it is important to measure and calculate short and impulse irregularities. A joint analysis of vibration acceleration signals is needed in order to study the types and size of railway track irregularities. Aim. Development of an algorithm for irregularity search based on accelerometer readings with the vertical measurement axis. Materials and methods. The research encompassed wavelet transformation and wavelet-based signal processing including discrete-time signal processing and continuous wavelet processing. In addition, time-frequency analysis based on Fourier transform and continuous wavelet scalogram was used. These methods provide for time-frequency localization for irregularity detection and measurement. Results. Algorithms for vibrational signal processing using the continuous and discrete-time wavelet transform are proposed. The results show that the discrete wavelet transform is effective for multiresolution and multiband analysis, and continuous wavelet transform and wavelet scalogram allows extraction of irregularities and determination of their parameters. The relative error for irregularity depth was improved by 18 %, and the absolute error for irregularity length determinations was reduced by 7 times. Conclusion. Application of the discrete Fourier transform and Fourier spectrogram provides for fine resolution in the frequency domain. However, separation of signal components in the time-frequency domain is impeded. The continuous-time wavelet transform ensures sufficient resolution in the low-frequency domain for component localization and visualization of irregularities.


Chronoclasm: Real-Time Data Animation

Abstract: Chronoclasms are crises of time that occur when practices and concepts of time come into contradiction. The clash between the sovereign authority of the clock that is the material heartbeat of every computer (and every network) and the multidimensional flow of subatomic interactions enables another time to emerge, the time we call history. Temporal concepts and practices emerge into this third, historical form of time in forms that are themselves historically mutable. Close attention to techniques of animation, specifically solar, surveillance, and financial visualizations and their MPEG codec and generative adversarial network infrastructures, can give privileged entry to analysis of chronoclasm as crisis and as the underpinning structure of time under capital.


Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism

Abstract: The inductive relation prediction of knowledge graphs, as an important research topic, aims at predicting the missing relation between unknown entities with many real-world applications. Existing approaches toward this problem mostly use enclosing subgraphs to extract the features of target nodes to make predictions; however, there is a tendency to ignore the neighboring relations outside the enclosing subgraph, thus leading to inaccurate predictions. In addition, they also neglect the rich commonsense information that can help filter out less convincing results. In order to address the above issues, this paper proposes a commonsense-guided inductive relation prediction method with a dual attention mechanism called CNIA. Specifically, in addition to the enclosing subgraph, we added the multi-hop neighboring relations of target nodes, thereby forming a neighbor-enriched subgraph where the initial embeddings are generated. Next, we obtained the subgraph representations with a dual attention (i.e., edge-aware and relation-aware) mechanism, as well as the neighboring relational path embeddings. Then, we concatenated the two embeddings before feeding them into the supervised learning model. A commonsense re-ranking mechanism was introduced to filter the results that conformed to commonsense. Extensive experiments on WN18RR, FB15k-237, and NELL995 showed that CNIA achieves better prediction results when compared to the state-of-the-art models. The results suggested that our proposed model can be considered as an effective and state-of-the-art solution for inductive relation prediction.


Special Issue on Information Security and Cryptography: The Role of Advanced Digital Technology

Abstract: Information security has become a potential prospect that ensures information cannot be breached throughout the process of delivery while being exchanged over the Internet [...]


A Deep Learning-Based Preprocessing Method for Single Interferometric Fringe Patterns

Abstract: A novel preprocessing method based on a modified U-NET is proposed for single interference fringes. The framework is constructed by introducing spatial attention and channel attention modules to optimize performance. In this process, interferometric fringe maps with an added background intensity, fringe amplitude, and ambient noise are used as the input to the network, which outputs fringe maps in an ideal state. Simulated and experimental results demonstrated that this technique can preprocess single interference fringes in ~1 microsecond. The quality of the results was further evaluated using the root mean square error, peak signal-to-noise ratio, structural similarity, and equivalent number of views. The proposed method outperformed U-NET, U-NET++, and other conventional algorithms as measured by each of these metrics. In addition, the model produced high-quality normalized fringes by combining objective data with visual effects, significantly improving the accuracy of the phase solutions for single interference fringes.


Comparative Analysis of Machine Learning Models for Prediction of Acute Liver Injury in Sepsis Patients

Abstract: Abstract Introduction: Acute liver injury (ALI) is a common complication of sepsis and is associated with adverse clinical outcomes. We aimed to develop a model to predict the risk of ALI in patients with sepsis after hospitalization. Methods: Medical records of 3196 septic patients treated at the Lishui Central Hospital in Zhejiang Province from January 2015 to May 2023 were selected. Cohort 1 was divided into ALI and non-ALI groups for model training and internal validation. The initial laboratory test results of the study subjects were used as features for machine learning (ML), and models built using nine different ML algorithms were compared to select the best algorithm and model. The predictive performance of model stacking methods was then explored. The best model was externally validated in Cohort 2. Results: In Cohort 1, LightGBM demonstrated good stability and predictive performance with an area under the curve (AUC) of 0.841. The top five most important variables in the model were diabetes, congestive heart failure, prothrombin time, heart rate, and platelet count. The LightGBM model showed stable and good ALI risk prediction ability in the external validation of Cohort 2 with an AUC of 0.815. Furthermore, an online prediction website was developed to assist healthcare professionals in applying this model more effectively. Conclusions: The Light GBM model can predict the risk of ALI in patients with sepsis after hospitalization.


Virtual Worlds for Learning in Metaverse: A Narrative Review

Abstract: As digital technologies continue to evolve, they offer unprecedented opportunities to transform traditional educational paradigms. Virtual worlds offer a dynamic and immersive platform for fostering sustainability education, bridging the gap between theoretical knowledge and practical application. In these interactive environments, students can engage with complex ecological systems and sustainability challenges in a risk-free setting, allowing for experimentation and exploration that would be impractical or impossible in the real world. This study aims to investigate the application of various types of virtual worlds in educational settings, examine their characteristics and potential, and explore how they foster critical 21st-century skills like critical thinking, creativity, communication, and collaboration. This paper comprehensively explores various types of virtual worlds—Adventure World, Simulation World, Creative World, Role-Playing World, and Collaborative World—assessing their impact on educational processes and outcomes. Adventure Worlds, with narrative-driven quests, engage students in exploratory learning within a story context. Simulation Worlds replicate real-world environments, allowing students to practice and hone practical skills in a risk-free setting. Creative Worlds provide open-ended, sandbox-like environments where innovation and imagination are paramount. Role-Playing Worlds facilitate empathy and perspective-taking through character-driven scenarios, while Collaborative Worlds emphasize teamwork and problem-solving in group projects. The narrative review methodology was adopted for the comprehensive analysis and synthesis of the literature to assess the impact and integration of virtual worlds in education, focusing on identifying trends, challenges, and opportunities within this domain. The evaluation methodology used in this study incorporates a mix of the Theory of Inventive Problem Solving (TRIZ), Concept-Knowledge (C-K) theory, Structure-behavior-function (SBF) modeling, the Framework for 21st Century Learning (P21), and Universal Design for Learning (UDL) to evaluate the characteristics and educational potential of different virtual world types. Findings indicate that virtual worlds effectively support critical thinking, creativity, communication, and collaboration skills, presenting a comprehensive analysis of how these environments can support, supplement, or transform traditional educational models. The main outcome of the study is the comprehensive exploration of various types of virtual worlds—Adventure World, Simulation World, Creative World, Role-Playing World, and Collaborative World—in education, demonstrating their significant potential to enhance learning experiences and outcomes through immersive, interactive environments that foster critical thinking, creativity, communication, and collaboration skills.


Robust Chinese Short Text Entity Disambiguation Method Based on Feature Fusion and Contrastive Learning

Abstract: To address the limitations of existing methods of short-text entity disambiguation, specifically in terms of their insufficient feature extraction and reliance on massive training samples, we propose an entity disambiguation model called COLBERT, which fuses LDA-based topic features and BERT-based semantic features, as well as using contrastive learning, to enhance the disambiguation process. Experiments on a publicly available Chinese short-text entity disambiguation dataset show that the proposed model achieves an F1-score of 84.0%, which outperforms the benchmark method by 0.6%. Moreover, our model achieves an F1-score of 74.5% with a limited number of training samples, which is 2.8% higher than the benchmark method. These results demonstrate that our model achieves better effectiveness and robustness and can reduce the burden of data annotation as well as training costs.


Translation Strategy Through Pragmatic Lens: Speech Act Analysis on The Campaign Media

Abstract: Communication plays an essential role in transporting messages, feelings, ideas, or even an influence. They are transmitted both verbally and non-verbally. This study elaborates on the use of speech acts in campaign advertising, specifically on billboards and posters as a means of conveying a candidate’s message through written communication in non-English languages in Tasikmalaya City. The capacity to use language in an assortment of settings while reflecting the relationship between the speakers and social cultural context of the situation is known as pragmatic competence. This research is a descriptive qualitative study; data analysis was conducted in two steps. firstly, it was carried out through a translation process which was done by English Education Department students in varied study grades; furthermore, the translation result was analyzed by the researcher to seek Chesterman's translation strategy through the lens of Pragmatics. The writer applied Miles and Huberman's data reduction, data display, and conclusion drawing to analyse the data. The data reduction categorized the message into several categories of illocutionary acts using the translations that have been organized, then it was continued by classifying them according to Searle’s theory of speech act. This study shows that coherence change at 42.3%, trans editing at 23.1%, explicit change at 19.2%, cultural filtering at 11.5%, and illocutionary change at 3.8%; those reflect the translation strategy usage. Furthermore, the illocutionary act shows assertive 58.3%, directive 25%, and commissive 16.7%. Moreover, the pedagogical implications of the analysis are leading students to the awareness that translation can be done using various strategies, enhancing students’ understanding of translation strategies, and sharpening students’ critical thinking.


Security Threats and Promising Solutions Arising from the Intersection of AI and IoT: A Study of IoMT and IoET Applications

Abstract: The hype of the Internet of Things as an enabler for intelligent applications and related promise for ushering accessibility, efficiency, and quality of service is met with hindering security and data privacy concerns. It follows that such IoT systems, which are empowered by artificial intelligence, need to be investigated with cognisance of security threats and mitigation schemes that are tailored to their specific constraints and requirements. In this work, we present a comprehensive review of security threats in IoT and emerging countermeasures with a particular focus on malware and man-in-the-middle attacks. Next, we elaborate on two use cases: the Internet of Energy Things and the Internet of Medical Things. Innovative artificial intelligence methods for automating energy theft detection and stress levels are first detailed, followed by an examination of contextual security threats and privacy breach concerns. An artificial immune system is employed to mitigate the risk of malware attacks, differential privacy is proposed for data protection, and federated learning is harnessed to reduce data exposure.


Edge-Enhanced TempoFuseNet: A Two-Stream Framework for Intelligent Multiclass Video Anomaly Recognition in 5G and IoT Environments

Abstract: Surveillance video analytics encounters unprecedented challenges in 5G and IoT environments, including complex intra-class variations, short-term and long-term temporal dynamics, and variable video quality. This study introduces Edge-Enhanced TempoFuseNet, a cutting-edge framework that strategically reduces spatial resolution to allow the processing of low-resolution images. A dual upscaling methodology based on bicubic interpolation and an encoder–bank–decoder configuration is used for anomaly classification. The two-stream architecture combines the power of a pre-trained Convolutional Neural Network (CNN) for spatial feature extraction from RGB imagery in the spatial stream, while the temporal stream focuses on learning short-term temporal characteristics, reducing the computational burden of optical flow. To analyze long-term temporal patterns, the extracted features from both streams are combined and routed through a Gated Recurrent Unit (GRU) layer. The proposed framework (TempoFuseNet) outperforms the encoder–bank–decoder model in terms of performance metrics, achieving a multiclass macro average accuracy of 92.28%, an F1-score of 69.29%, and a false positive rate of 4.41%. This study presents a significant advancement in the field of video anomaly recognition and provides a comprehensive solution to the complex challenges posed by real-world surveillance scenarios in the context of 5G and IoT.


Exploring Public Data Vulnerabilities in Semi-Supervised Learning Models through Gray-box Adversarial Attack

Abstract: Semi-supervised learning (SSL) models, integrating labeled and unlabeled data, have gained prominence in vision-based tasks, yet their susceptibility to adversarial attacks remains underexplored. This paper unveils the vulnerability of SSL models to gray-box adversarial attacks—a scenario where the attacker has partial knowledge of the model. We introduce an efficient attack method, Gray-box Adversarial Attack on Semi-supervised learning (GAAS), which exploits the dependency of SSL models on publicly available labeled data. Our analysis demonstrates that even with limited knowledge, GAAS can significantly undermine the integrity of SSL models across various tasks, including image classification, object detection, and semantic segmentation, with minimal access to labeled data. Through extensive experiments, we exhibit the effectiveness of GAAS, comparing it to white-box attack scenarios and underscoring the critical need for robust defense mechanisms. Our findings highlight the potential risks of relying on public datasets for SSL model training and advocate for the integration of adversarial training and other defense strategies to safeguard against such vulnerabilities.


Uncertainty Quantification of Machine Learning Model Performance via Anomaly-Based Dataset Dissimilarity Measures

Abstract: The use of Machine Learning (ML) models as predictive tools has increased dramatically in recent years. However, data-driven systems (such as ML models) exhibit a degree of uncertainty in their predictions. In other words, they could produce unexpectedly erroneous predictions if the uncertainty stemming from the data, choice of model and model parameters is not taken into account. In this paper, we introduce a novel method for quantifying the uncertainty of the performance levels attained by ML classifiers. In particular, we investigate and characterize the uncertainty of model accuracy when classifying out-of-distribution data that are statistically dissimilar from the data employed during training. A main element of this novel Uncertainty Quantification (UQ) method is a measure of the dissimilarity between two datasets. We introduce an innovative family of data dissimilarity measures based on anomaly detection algorithms, namely the Anomaly-based Dataset Dissimilarity (ADD) measures. These dissimilarity measures process feature representations that are derived from the activation values of neural networks when supplied with dataset items. The proposed UQ method for classification performance employs these dissimilarity measures to estimate the classifier accuracy for unseen, out-of-distribution datasets, and to give an uncertainty band for those estimates. A numerical analysis of the efficacy of the UQ method is conducted using standard Artificial Neural Network (ANN) classifiers and public domain datasets. The results obtained generally demonstrate that the amplitude of the uncertainty band associated with the estimated accuracy values tends to increase as the data dissimilarity measure increases. Overall, this research contributes to the verification and run-time performance prediction of systems composed of ML-based elements.


Analysing lexical semantic changes in chinese educational texts by integrating machine learning methods

Abstract: Analyzing Lexical Semantic Changes (LSC) in Educational Texts (ET) refers to examining how the meanings of words, terms, or phrases used in ET have evolved. It involves learning shifts in the semantic content, connotations, and language associations within educational resources such as textbooks, research articles, and instructional content. The analysis can reveal how educational models, pedagogical methods, and terminology have transformed in response to technological innovations, societal changes, and pedagogical developments. This analysis provides visions into the dynamic nature of educational discourse, helping researchers, educators, and policymakers understand how language has adapted to reflect changes in educational paradigms and the broader educational context. This research investigates the semantic analysis and classification performance within ET, employing the innovative Decision Tree + Feed Forward Neural Networks (DT + FFNNs) framework. This research shows the dynamic semantic relationships inherent in educational terminology by diverse semantic similarity measures and contextualized embeddings. It looks at how educational language changes to reflect changes in society, technology, and pedagogy. The study uses a DT + FFNN framework for semantic analysis and classification. The study uses several embeddings and semantic similarity metrics, and Spearman’s Correlation Coefficient (SCC) is employed to evaluate their effectiveness. This study highlights the DT + FFNN framework’s capacity to capture complex semantics in an educational setting and offers insights into the adaptive nature of educational discourse. SCC serves as a guiding metric, offering insights into the efficiency of several embeddings and measures. The findings show the pivotal role of fine-tuning in significantly enhancing the accuracy of DT + FFNNs across measures, revealing its remarkable potential in capturing semantics within an educational context.


Research on discourse role recognition in task-oriented collaborative dialogue

Abstract: Task-oriented collaborative dialogues have become an indispensable form of communication in our daily work and learning, in which participants exchange ideas and share information to advance goals. It is crucial to automatically analyze participants’ contributions and understand these dialogues relative to individuals with limited attention spans. In this paper, seven Discourse Role (DR) labels are designed to describe discourse’s different roles in collaborative dialogues for goal achievement. We collected about 11K discourses from a publicly available dialogue corpus and annotated them with DR tags to construct a dataset named MRDR (Meeting Recorder Discourse Role). In addition, this paper proposes a novel hierarchical model, STTAHM (Speaker Turn and Topic-Aware Hierarchical Model), for Discourse Role classification. The model is equipped to perceive speaker turn and dialogue topic and can effectively capture the discourse’s local and global semantic information. Experimental results show that our proposed method is effective on the constructed dataset, and the accuracy of Discourse Role classification reaches 86.99%.


Optimized multiscale deep bidirectional gated recurrent neural network fostered practical teaching of university music course

Abstract: Music education has a rich historical background. Nevertheless, the introduction of modern teaching methods is relatively delayed. In recent years, there has been a remarkable acceleration in the advancement of music education. A promising tool that has emerged to revolutionize education as a whole is Virtual Reality (VR) technology, which offers immersive and interactive experiences across various disciplines. At the university level, integrating VR technology into music education opens up exciting opportunities to enhance practical teaching methods and provide students with enriched musical experiences. Virtual Reality together with Internet of Things (IoT) demonstrates its capabilities in various tasks, but its widespread availability in online learning remainders a pressing challenge that needs to be addressed. In pre-processing, it removes noise data using Dynamic Context-Sensitive Filtering (DCSF). VR technology creates an unparalleled learning environment, it transporting students to virtual concert halls, recording studios, or historical music venues. Hence the Multiscale deep bidirectional gated recurrent neural Network (MDBGNN) improves the practical teaching of music course concept, like Music theory, harmony, and rhythm can be visualized and experienced in VR. Finally, Dung Beetle Optimization Algorithm (DBOA) is employed to optimize the weight parameters of MDBGNN. The proposed MDBGNN-DBO-UMC-VRT is implemented in Python. The proposed method is analysed with the help of performance metrics, like precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time and RoC. The proposed MDBGNN-DBO-UMC-VRT method attains 13.11%, 18.12% and 18.73% high specificity, 11.13%, 11.04% and 19.51% lower computation Time, 15.29%, 15.365% and 14.551% higher ROC and 13.65%, 15.98%, and 17.15% higher Accuracy compared with existing methods, such as Enhancing Vocal Music Teaching through the Fusion of Artificial Intelligence Algorithms and VR Technology (CNN-UMC-VRT), Exploring the Efficacy of VR Technology in Augmenting Music Art Teaching (BPNN-UMC-VRT) and Implementing an Interactive Music-Assisted Teaching System Using VR Technology (DNN-UMC-VRT) respectively.


TSRNet: A Trans-Scale and Refined Low-Light Image Enhancement Network

Abstract: Retinex-based deep learning methods show good low-light enhancement performance and are mainstream approaches in this field. However, the current methods for enhancing low-light images are insufficient in accurately separating illumination and comprehensively restoring degraded information, especially in images with uneven or extremely low illumination levels. This situation often leads to the over-enhancement of bright regions, a loss of detail, and color distortion in the final images. To address these issues, we improved three subnetworks in the classic KinD network, and proposed a trans-scale and refined low-light image enhancement network. Compared with KinD, our method shows more precise image decomposition performance, enhancing the expressiveness of the reflection and illumination components in order to better depict image details, colors, and lighting information. For reflectance restoration, we use a U-shaped network for cross-scale denoising, incorporating attention mechanisms and a color saturation loss to restore image textures and colors. For light adjustment, we apply fine-grained light adjustment approaches to simultaneously enhance brightness in dark areas and prevent excessive enhancement in bright areas. The experimental results demonstrate that with the LOL dataset, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of TSRNet are improved by 2–31% and 5–34%, respectively, when compared with the mainstream methods.


Using iterative 2δ‐technique and 4δ‐outlier test for evaluating the geochemical background concentrations of trace elements in topsoil at Cao Bang province, Vietnam

Abstract: Abstract This study investigated the geochemical background values for seven trace elements in topsoil samples collected from highland areas within Cao Bang province, Vietnam. Total concentrations of these elements were determined, revealing the following order of mean concentration: Zn > Cr > Pb > Cu > As > Ni > Co. The background values were calculated using two different methods including iterative 2δ‐technique and 4δ‐outlier test. The 4δ‐outlier test yielded the following background concentrations (dry weight) for the seven elements: As (24.41 ± 12.42 mg kg −1 ), Co (15.64 ± 10.57 mg kg −1 ), Cr (49.31 ± 33.41 mg kg −1 ), Cu (29.77 ± 27.14 mg kg −1 ), Ni (22.52 ± 13.33 mg kg −1 ), Pb (38.06 ± 31.48 mg kg −1 ), Zn (71.79 61.09 mg kg −1 ). The 2δ‐technique, on the other hand, resulted in the following background concentrations (dry weight) for the same elements: As (8.41 ± 8.57 mg kg −1 ), Co (5.06 ± 4.31 mg kg −1 ), Cr (26.72 ± 26.20 mg kg −1 ), Cu (25.31 ± 24.81 mg kg −1 ), Ni (8.02 ± 7.83 mg kg −1 ), Pb (28.12 ± 27.58 mg kg −1 ), Zn (51.91 ± 49.44 mg kg −1 ). These results provide valuable baseline data for future environmental assessments and monitoring programs in the region. They also highlight the potential impact of human activities, such as mining, on the geochemical landscape of highland areas.


Revolutionizing Supply Chain With Machine Learning and Blockchain Integration

Abstract: Efficient supply chain management has emerged as a crucial determinant of organizational performance in the contemporary dynamic corporate environment. The incorporation of nascent technology, such as machine learning and blockchain, is revolutionizing how enterprises manage their supply chain operations. By examining extensive datasets, machine learning algorithms can predict future demand, optimize inventory levels, and improve the planning of routes. By discerning regularities and deviations within datasets, these algorithms facilitate enterprises in making well-informed choices and managing potential hazards. Additionally, the utilization of machine learning facilitates the automation of monotonous jobs, hence mitigating the occurrence of human fallibility and augmenting the overall efficacy of supply chain operations. The utilization of blockchain technology, renowned for its decentralized and unalterable ledger system, effectively tackles several critical issues encountered in the realm of supply chain management.


The Financial Dynamics of AI-Enhanced Supply Chain Management

Abstract: This chapter explores the evolving landscape of AI-enhanced supply chain management, emphasizing the pivotal role of artificial intelligence (AI) in optimizing supply chain operations. It commences with an introduction that defines AI-enhanced supply chain management and underscores the importance of AI in this context. Subsequently, the chapter delves into the multifaceted role of AI in supply chain management, elucidating its diverse benefits. These advantages encompass enhanced supply chain efficiency, streamlined inventory and transportation management, and improved revenue generation through superior customer service and data-driven demand forecasting.