Reviewerly/exHarmony huggingface dataset:
SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

The competent software architecture plays a crucial role in the difficult task of big data processing for SQL and NoSQL databases. SQL databases were created to organize data and allow for horizontal expansion. NoSQL databases, on the other hand, support horizontal scalability and can efficiently process large amounts of unstructured data. Organizational needs determine which paradigm is appropriate, yet selecting the best option is not always easy. Differences in database design are what set SQL and NoSQL databases apart. Each NoSQL database type also consistently employs a mixed-model approach. Therefore, it is challenging for cloud users to transfer their data among different cloud storage services (CSPs). There are several different paradigms being monitored by the various cloud platforms (IaaS, PaaS, SaaS, and DBaaS). The purpose of this SLR is to examine the articles that address cloud data portability and interoperability, as well as the software architectures of SQL and NoSQL databases. Numerous studies comparing the capabilities of SQL and NoSQL of databases, particularly Oracle RDBMS and NoSQL Document Database (MongoDB), in terms of scale, performance, availability, consistency, and sharding, were presented as part of the state of the art. Research indicates that NoSQL databases, with their specifically tailored structures, may be the best option for big data analytics, while SQL databases are best suited for online transaction processing (OLTP) purposes.
AI explainability framework for environmental management research

Deep learning networks powered by AI are essential predictive tools relying on image data availability and processing hardware advancements. However, little attention has been paid to explainable AI (XAI) in application fields, including environmental management. This study develops an explainability framework with a triadic structure to focus on input, AI model and output. The framework provides three main contributions. (1) A context-based augmentation of input data to maximize generalizability and minimize overfitting. (2) A direct monitoring of AI model layers and parameters to use leaner (lighter) networks suitable for edge device deployment, (3) An output explanation procedure focusing on interpretability and robustness of predictive decisions by AI networks. These contributions significantly advance state of the art in XAI for environmental management research, offering implications for improved understanding and utilization of AI networks in this field.
A federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation for rolling bearing fault diagnosis

Intelligent mechanical fault diagnosis techniques have been extensively developed in recent years. Owing to the advantage of data privacy protection, federated learning has recently received increasing attention; this approach can utilize monitoring data from multiple local clients to train an optimal global diagnosis model. However, low-quality data are often present for some clients, including mislabeled data and incomplete data that lack some health states. Furthermore, the data distributions are usually different across different clients owing to variations in machine operating conditions. Therefore, the performance of the federated diagnostic model may be limited by low-quality data and data distribution discrepancies. To address this issue, a federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation is proposed. First, a dynamic filter is designed by filtering out low label probabilities predicted by low-quality source domain models to construct high-confidence pseudo-labels for the target domain data. Then, the batch normalized maximum mean discrepancy (BN-MMD) distance metric is introduced into the training loss function to reduce the data distribution discrepancy between the source clients and the target client without private data leakage. While building the global model in each training round, a dynamic model aggregation algorithm is proposed to mitigate the influence of low-quality clients. This algorithm evaluates the weight of each client according to its contribution to the total consensus diagnostic knowledge and then aggregates the local models with adaptive weights. Consequently, it can overcome the drawback of the classical federated averaging (FedAvg) algorithm, where all local clients are assigned the same weight when constructing the global model. Experiments are conducted on three bearing datasets under various loads and speeds. Compared with some existing diagnostic methods, the proposed federated transfer learning method can reduce the impact of low-quality data and achieve higher diagnostic accuracy while preserving data privacy.
A Mathematical Investigation of Hallucination and Creativity in GPT Models

In this paper, we present a comprehensive mathematical analysis of the hallucination phenomenon in generative pretrained transformer (GPT) models. We rigorously define and measure hallucination and creativity using concepts from probability theory and information theory. By introducing a parametric family of GPT models, we characterize the trade-off between hallucination and creativity and identify an optimal balance that maximizes model performance across various tasks. Our work offers a novel mathematical framework for understanding the origins and implications of hallucination in GPT models and paves the way for future research and development in the field of large language models (LLMs).
Impact of Big Data Analytics and ChatGPT on Cybersecurity

Every day, network assaults and cyber threats get more sophisticated, and the worldwide number of attacks increases, as do the approaches and methods used to infiltrate business systems and personal devices. The options for pursuing assaults are gradually expanding, and recently such attacks have resulted in broad, severe effects and results. This study examines how Bigdata analytics and Artificial Intelligence (AI) technologies or platforms such as ChatGPT may be utilized to mitigate cybersecurity risks. When it comes to cyber hazards, ChatGPT has both a beneficial and a bad influence. It can be utilized at times to provide preventive measures, but it can also be used as a catalyst to assist complex cyber-attacks with no coding solutions. The study also looks at existing AI and data analytics technologies and how they may improve cybersecurity. The incoming AI application in ChatGPT shortcomings is also discussed in this research focusing on its pros and cons in managing cyber threats. The article focuses a heavy emphasis on security systems in order to respond to greater preventive and predictive reactions and monitoring. Finally, this study examines the barriers to such adoptions, proposing potential approaches to address them.
Cross-View Recurrence-Based Self-Supervised Super-Resolution of Light Field

Compared with external-supervised learning-based (ESLB) methods, self-supervised learning-based (SSLB) methods can overcome the domain gap problem caused by different light field (LF) acquisition conditions, which results in the performance degradation of light field super-resolution on unseen test datasets. Current SSLB methods exploit the cross-scale recurrence feature in the single view image for super-resolution, ignoring the correlation information among views. Different from previous works, we propose a cross-view recurrence-based self-supervised mapping framework to correlate complementary information among views in the down-scaled input LF. Specifically, the cross-view recurrence information consists of geometry structure features and similar structure features. The former is to provide sub-pixel information according to disparity correlations among adjacent views, and the latter is to acquire similar color and contour information among arbitrary views, which can compensate for error disparity guidance of geometry structure features in sharp variance areas. Moreover, instead of the widely used “All-to-All” strategy, we propose a “Part-to-Part” mapping strategy, which is better competent for SSLB approaches with limited training examples solely extracted from input LF. Finally, considering that self-supervised methods need to retrain from the beginning toward each test image, based on the proposed “part-to-part” strategy, an efficient end-to-end network is designed to extract these cross-view features for superior SASR performance with less training time. Experiment results demonstrate that our method outperforms other state-of-the-art ESLB methods on both large and small domain gap cases. Compared with the only SSLB method (LFZSSR [1]), our approach achieves better performance with 524 times less training time.
Federated transfer learning in fault diagnosis under data privacy with target self-adaptation

The past decades have witnessed great developments and applications of the data-driven machinery fault diagnosis methods. Due to the difficulties and significant expenses in collecting labeled data, multiple industrial users generally would like to collaborate for training powerful models. However, for data privacy concerns, the local data generally cannot be shared with others for centralized training. Moreover, the data at different users are usually obtained under different conditions. The model generalization problem is quite challenging since no simultaneous data access is permitted. To address these issues, a federated transfer learning method is proposed. Fake data of additional classes are generated to suppress the decision boundaries, and only the models are communicated across different clients. A prediction alignment scheme is proposed at the target client for self-adaptation. A model and instance-level consensus scheme is introduced to enhance model performance. The proposed method achieves promising knowledge transfer effects with only communications of models, and the challenging partial transfer learning problems can be also well addressed. Experiments on real-world machinery datasets validate the proposed method, which is promising for applications in the real industries.
Unveiling the Metaverse: Exploring Emerging Trends, Multifaceted Perspectives, and Future Challenges

The metaverse is an expanding network of digital spaces that are gaining popularity in the realm of virtual environments.By utilizing digital representations known as avatars, users can interact and engage with each other in an immersive and interconnected space.This paradigm shift in computing represents a merging of the physical and digital worlds, offering users an unprecedented level of interactivity and experience.Driven by advancements in technologies such as virtual and augmented reality, artificial intelligence, and other technologies, the metaverse is unlocking new avenues for human interaction and innovation.The metaverse presents a wide range of possibilities for the development of interactive experiences, including virtual social spaces, remote work, and novel forms of entertainment.However, it also faces significant challenges, including privacy and security concerns, issues of equitable access, and ethical and legal considerations.Establishing a robust legal and ethical framework is crucial to ensure that the metaverse is utilized for the greater good, such as promoting social connection and fostering creativity.The development and evolution of the metaverse are poised to shape the future of digital life and experience, offering new and exciting opportunities for human engagement and creativity.This review paper provides a comprehensive analysis of the foundational principles, safety measures, and confidentiality of the virtual world, including its overall design, characteristics, and potential security and privacy hazards.Our discussion aims to provide perspective on the challenges and opportunities presented by this emerging technology, and how we can leverage its potential while addressing any associated risks.
Attention-Aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services

Metaverse encapsulates our expectations of the next-generation Internet, while bringing new key performance indicators (KPIs). Although conventional ultra-reliable and low-latency communications (URLLC) can satisfy objective KPIs, it is difficult to provide a personalized immersive experience that is a distinctive feature of the Metaverse. Since the quality of experience (QoE) can be regarded as a comprehensive KPI, the URLLC is evolved towards the next generation URLLC (xURLLC) with a personalized resource allocation scheme to achieve higher QoE. To deploy Metaverse xURLLC services, we study the interaction between the Metaverse service provider (MSP) and the network infrastructure provider (InP), and provide an optimal contract design framework. Specifically, the utility of the MSP, defined as a function of Metaverse users’ QoE, is to be maximized, while ensuring the incentives of the InP. To model the QoE mathematically, we propose a novel metric named Meta-Immersion that incorporates both the objective KPIs and subjective feelings of Metaverse users. Furthermore, we develop an attention-aware rendering capacity allocation scheme to improve QoE in xURLLC. Using a user-object-attention level dataset, we validate that the xURLLC can achieve an average of 20.1% QoE improvement compared to the conventional URLLC with a uniform resource allocation scheme. The code for this paper is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HongyangDu/AttentionQoE</uri> .
Meerkat optimization algorithm: A new meta-heuristic optimization algorithm for solving constrained engineering problems

In this paper, we proposed a meerkat optimization algorithm(MOA) by simulating the behavior pattern of meerkats in nature. MOA is mainly inspired by the survival strategies of meerkat populations, whose sentinel mechanism controls meerkats to switch between different behavior patterns. Some mathematical properties of meerkat optimization algorithm are proved, and the advantages of MOA are verified with classical optimization test functions. MOA is applied to solve real-world engineering problems with constraints, which proves the effectiveness and superiority of MOA in solving such problems.
Evolutionary Game Analysis of Artificial Intelligence Such as the Generative Pre-Trained Transformer in Future Education

As an emerging research area since generative artificial intelligence (represented by Chat Generative Pre-trained Transformer (ChatGPT)) has been accessible to the public, especially in education, appropriate AI application could bring numerous benefits to education; however, its abuse has the potential to be harmful. In this paper, we aimed to explore the potential of AI in the future of education with the analytical method of evolutionary game analysis (EGA). By studying the behavior of two agents, the school and the students, EGA can be used to identify strategies that can be used to improve the effectiveness of the education model in the context of the AI era. A stable evolutionary strategy for the school and students was devised under a variety of scenarios. Additionally, we conducted a numerical analysis to further explore the impact of several key factors on the stable strategy. The results indicated that schools should adopt positive supervision to standardize the use of AI in education, and students should be more active in becoming involved in AI technology. Based on this study, we believe that the school has the ability to provide effective suggestions and practical guidelines to help students succeed academically and embrace future trends in AI education.
Hierarchical graph-based text classification framework with contextual node embedding and BERT-based dynamic fusion

We propose a novel hierarchical graph-based text classification framework that leverages the power of contextual node embedding and BERT-based dynamic fusion to capture the complex relationships between the nodes in the hierarchical graph and generate a more accurate classification of text. The framework consists of seven stages: Linguistic Feature Extraction, Hierarchical Node Construction with Domain-Specific Knowledge, Contextual Node Embedding, Multi-Level Graph Learning, Dynamic Text Sequential Feature Interaction, Attention-Based Graph Learning, and Dynamic Fusion with BERT. The first stage, Linguistic Feature Extraction, extracts the linguistic features of the text, including part-of-speech tags, dependency parsing, and named entities. The second stage constructs a hierarchical graph based on the domain-specific knowledge, which is used to capture the relationships between nodes in the graph. The third stage, Contextual Node Embedding, generates a vector representation for each node in the hierarchical graph, which captures its local context information, linguistic features, and domain-specific knowledge. The fourth stage, Multi-Level Graph Learning, uses a graph convolutional neural network to learn the hierarchical structure of the graph and extract the features of the nodes in the graph. The fifth stage, Dynamic Text Sequential Feature Interaction, captures the sequential information of the text and generates dynamic features for each node. The sixth stage, Attention-Based Graph earning, uses an attention mechanism to capture the important features of the nodes in the graph. Finally, the seventh stage, Dynamic Fusion with BERT, combines the output from the previous stages with the output from a pre-trained BERT model to obtain the final integrated vector representation of the text. This approach leverages the strengths of both the proposed framework and BERT, allowing for better performance on the classification task. The proposed framework was evaluated on several benchmark datasets and compared to state-of-the-art methods, demonstrating significant improvements in classification accuracy.
Development of Approaches and Schemes for Proactive Information Protection in Computer Networks

This paper proposes approaches to protecting information from the standpoint of the paradigm of their proactive security and a method for proactively protecting information from malicious code based on expert assessments. The architecture of proactive information protection based on scripts and regular expressions and the scheme of the decision-making module against malicious code for the implementation of more complex algorithms for proactive information protection will be constructed.
ReX-Net: A reflectance-guided underwater image enhancement network for extreme scenarios

Due to the complex underwater environment, underwater images exhibit different degradation characteristics, severely affecting their practical applications. Although underwater image enhancement networks with physical priors exist, the statistical priors are not applicable in extreme underwater scenes. Therefore, we propose ReX-Net, a reflectance-guided underwater image enhancement network for extreme scenarios. ReX-Net leverages the complementary information of reflectance and the underwater image obtained through the original encoder and reflectance encoder to minimize the impact of different scene environments. As underwater images contain object information at different scales, the encoder includes a TriFuse reflected-image object extractor module (TRIOE), which employs Tri-scale convolutions to capture features at different scales and utilize attention mechanisms to enhance channel and spatial information. In the decoder, we design a context-sensitive multi-level integration module (CSMLI) to fuse feature vectors at different resolutions, thereby improving the expressiveness and robustness of features while avoiding artifacts and ensuring pixel accuracy. Experiments on multiple datasets demonstrate that ReX-Net outperforms existing methods. Furthermore, application experiments show the practicality of ReX-Net in other visualization tasks.
Employing large language models in survey research

This article discusses the promising potential of employing large language models (LLMs) for survey research, including generating responses to survey items. LLMs can address some of the challenges associated with survey research regarding question-wording and response bias. They can address issues relating to a lack of clarity and understanding but cannot yet correct for sampling or nonresponse bias challenges. While LLMs can assist with some of the challenges with survey research, at present, LLMs need to be used in conjunction with other methods and approaches. With thoughtful and nuanced approaches to development, LLMs can be used responsibly and beneficially while minimizing the associated risks.
A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching

Image stitching is a technique that is often employed in image processing and computer vision applications. The feature points in an image provide a significant amount of key information. Image stitching requires accurate extraction of these features since it may decrease misalignment flaws in the final stitched image. In recent years, a variety of feature detectors and descriptors that may be utilized for image stitching have been presented. However, the computational cost and correctness of feature matching restrict the utilization of these techniques. To date, no work compared feature detectors and descriptors for image stitching applications, i.e., no one has considered the effect of detectors and descriptors on the generated final stitched image. This paper presents a detailed comparative analysis of commonly used feature detectors and descriptors proposed previously. This study gives various contributions to the development of a general comparison of feature detectors and descriptors for image stitching applications. These detectors and descriptors are compared in terms of number of matched points, time taken and quality of stitched image. After analyzing the obtained results, it was observed that the combination of AKAZE with AKAZE can be preferable almost in all possible situations.
Orbital-optimized pair-correlated electron simulations on trapped-ion quantum computers

Abstract Variational quantum eigensolvers (VQE) are among the most promising approaches for solving electronic structure problems on near-term quantum computers. A critical challenge for VQE in practice is that one needs to strike a balance between the expressivity of the VQE ansatz versus the number of quantum gates required to implement the ansatz, given the reality of noisy quantum operations on near-term quantum computers. In this work, we consider an orbital-optimized pair-correlated approximation to the unitary coupled cluster with singles and doubles (uCCSD) ansatz and report a highly efficient quantum circuit implementation for trapped-ion architectures. We show that orbital optimization can recover significant additional electron correlation energy without sacrificing efficiency through measurements of low-order reduced density matrices (RDMs). In the dissociation of small molecules, the method gives qualitatively accurate predictions in the strongly-correlated regime when running on noise-free quantum simulators. On IonQ’s Harmony and Aria trapped-ion quantum computers, we run end-to-end VQE algorithms with up to 12 qubits and 72 variational parameters—the largest full VQE simulation with a correlated wave function on quantum hardware. We find that even without error mitigation techniques, the predicted relative energies across different molecular geometries are in excellent agreement with noise-free simulators.