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Various studies have been conducted on applying intelligent recognition technology, especially speech recognition technology to improve English learning ability, mostly listening and speaking. However, few studies have touched on how image-to-text recognition technology can be used for writing. The present research was conducted to fill this gap by exploring the optimization of a deep-learning-based image captioning algorithm to facilitate English writing, so as to enable learners to break the time and space limitations and learn English writing (including sentence patterns, spelling, vocabulary, and grammar) anytime and anywhere by taking pictures. Therefore, this paper focused on image captioning based on CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory), using DenseNet201 or Vision Transformer trained on the ImageNet-1K image classification dataset as the image encoder and LSTM as the decoder. First, pre-training was performed on the Flickr8k dataset. After selecting the best-trained model as the pre-trained weight model for the COCO dataset, fine-tuning optimization was performed on the COCO dataset, and the attention mechanism was used to design the ablation experiment. The BLEU-4, CIDEr, METEOR, and ROUGE evaluation indexes of the optimized model on the test set were 0.3437,1.121,0.2750, and 0.5117, respectively. The study results showed that the convergence of the model was accelerated and had better performance. The model was used to automatically caption 12 images that had never been used during the training process. The descriptions generated by the optimized image captioning algorithm have lexical and syntactic accuracy, and matched what the images expressed, showing that this improved algorithm could be used as a learning tool to help English learners improve lexical and syntactic acquisition to promote writing through the generated descriptions of the pictures taken anytime and anywhere in real-life situations.

As a meta-heuristic algorithm, the Golden Jackal Optimization (GJO) algorithm has been widely used in traditional network intrusion detection due to its ease of use and high efficiency. This paper aims to extend its application to the emerging field of Software-Defined Networking (SDN), which is a new network architecture. To adapt the GJO for SDN intrusion detection, a modified Golden Jackal Optimization (mGJO) is proposed to enhance its performance with the use of two strategies. First, an Elite Dynamic Opposite Learning strategy operates during each iteration to find solutions opposite to the current global optimal solutions, which increases population diversity. Second, an updating strategy based on the Golden Sine II Algorithm is utilized in the exploitation phase to update the position information of the golden jackal pairs, which accelerates the search for the best feature subset indexes. To validate the feasibility of the mGJO algorithm, this paper first assesses its optimization capability using benchmark test functions. Then, four UCI datasets and the NSL-KDD dataset are used to test the classification capability of the mGJO algorithm and its application in traditional network intrusion detection. Furthermore, the InSDN dataset is used to validate the feasibility of the mGJO algorithm for SDN intrusion detection. The experimental results show that, when the mGJO algorithm is applied to SDN for intrusion detection, the various indexes of classification and the selection of feature subsets achieve better results. [ABSTRACT FROM AUTHOR]

The second multiplicative Zagreb eccentricity index E*2 (G) of a simple connected graph G is expressed as the product of the weights ε(a)εG(b) over all edges ab of G, where ε(a) stands for the eccentricity of the vertex a in G. In this paper, some extremal problems on the E*2 index over some special graph classes including trees, unicyclic graphs and bicyclic graphs are examined, and the corresponding extremal graphs are characterized. Besides, the relationships between this vertex-eccentricity-based graph invariant and some well-known parameters of graphs and existing graph invariants such as the number of vertices, number of edges, minimum vertex degree, maximum vertex degree, eccentric connectivity index, connective eccentricity index, first multiplicative Zagreb eccentricity index and second multiplicative Zagreb index are investigated. [ABSTRACT FROM AUTHOR]

In this paper, we calculate the expected values of the first and second Zagreb indices, denoted as E(M1) and E(M2) respectively, as well as the expected value of the forgotten index, E(F), for two models of random bipartite graphs. To evaluate our findings, we establish the growth rate by demonstrating that for a random bipartite graph G of order n in either model, the expected value of M1(G) is O(n³). Furthermore, we prove that the expected values of M2(G) and F(G) are both O(n4). [ABSTRACT FROM AUTHOR]

This work presents a longitudinal study of diversity among the Affective Computing research community members. We explore several dimensions of diversity, including gender, geography, institutional types of affiliations and selected combinations of dimensions. We cover the last 10 years of the IEEE Transactions on Affective Computing (TAFFC) journal and the International Conference on Affective Computing and Intelligent Interaction (ACII), the primary sources of publications in Affective Computing. We also present an analysis of diversity among the members of the Association for the Advancement of Affective Computing (AAAC). Our findings reveal a “leaky pipeline” in the field, with a low –albeit slowly increasing over the years– representation of women. They also show that academic institutions clearly dominate publications, ahead of industry and governmental centres. In terms of geography, most publications come from the USA, contributions from Latin America or Africa being almost non-existent. Lastly, we find that diversity in the characteristics of researchers (gender and geographic location) influences diversity in the topics. To conclude, we analyse initiatives that have been undertaken in other AI-related research communities to foster diversity, and recommend a set of initiatives that could be applied to the Affective Computing field to increase diversity in its different facets. The diversity data collected in this work are publicly available, ensuring strict personal data protection and governance rules. [ABSTRACT FROM AUTHOR]

AbstractIndexes are useful for summarizing multivariate information into single metrics for monitoring, communicating, and decision-making. While most work has focused on defining new indexes for specific purposes, more attention needs to be directed towards making it possible to understand index behavior in different data conditions, and to determine how their structure affects their values and the variability therein. Here we discuss a modular data pipeline recommendation to assemble indexes. It is universally applicable to index computation and allows investigation of index behavior as part of the development procedure. One can compute indexes with different parameter choices, adjust steps in the index definition by adding, removing, and swapping them to experiment with various index designs, calculate uncertainty measures, and assess indexes’ robustness. The paper presents three examples to illustrate the usage of the pipeline framework: comparison of two different indexes designed to monitor the spatio-temporal distribution of drought in Queensland, Australia; the effect of dimension reduction choices on the Global Gender Gap Index (GGGI) on countries’ ranking; and how to calculate bootstrap confidence intervals for the Standardized Precipitation Index (SPI). The methods are supported by a new R package, called tidyindex. Supplemental materials for the article are available online. [ABSTRACT FROM AUTHOR]

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Introduction: This chapter examines the impact of Financial Technology (Fintech) on Environmental, Social, and Governance (ESG) goals to promote a sustainable financial system. Digital payment platforms, blockchain applications, and AI-powered analytics have revolutionised the financial landscape in recent years. These advancements have made integrating ESG principles into investment decisions and business practices easier. Purpose: The main aim of this chapter is to analyse the connections and possibilities that Fintech offers to achieve ESG goals. Understanding how Fintech can facilitate sustainable finance practices is crucial for promoting investment in Fintech. Methodology: A series of indexes have been examined, including the Global FinTech Index (GFI) in Global and Regional Rank, the Global Sustainable Competitiveness Index, and performing the Green Growth Index, the Green Economic Opportunity Index, the Global Green Finance Index (GGFI), and the Financial Inclusion Index. Findings: Through comparative analysis, it can be concluded that the countries with the highest rankings are Sweden, Finland, Denmark, Switzerland, and Germany. Sweden ranks highly in the GFI. These results show that these countries rank highly in achieving ESG objectives. Balkan countries, specifically Albania, Bosnia and Herzegovina, and Montenegro, have the weakest results compared to other countries. Policymakers can benefit from the study's findings to design better regulations and frameworks that promote responsible fintech practices and foster sustainable finance. Practical Implications: Regulators and agencies responsible for measuring fintech and ESG should strive to align the indexes associated with these two domains as closely as possible. In addition, businesses can utilise the findings of this study to increase awareness about the diverse solutions that fintech offers to achieve the objectives of ESG. [ABSTRACT FROM AUTHOR]

This study aims to conduct an empirical comparative analysis of the performance of Shari'ah and conventional stock indexes during the period 2017–2023, which includes the COVID-19 pandemic. Additionally, it aims to investigate investors' preferences and analyse the long-term relationship of these indexes, as well as exploring the potential diversification benefits. The research methodology incorporates stochastic dominance analysis, the VARMAX procedure, and Johansen's co-integration approach. The data utilized consists of 31 conventional and 31 Islamic stock indexes, specifically from the FTSE, DJ, MSCI, and S&P series. The results show that there are no long-term co-integration links between 30 out of 31 pairs of Islamic and conventional indexes. While conventional indexes tend to outperform Islamic indexes, they also come with a higher risk. On the other hand, Islamic indexes are considered to be less risky, offering potential diversification opportunities that may be attractive for global portfolios, particularly during periods of financial distress. [ABSTRACT FROM AUTHOR]

Groundwater storage represents the ultimate source of drinking water in dry regions. Over-pumping, climate change, and diverse types of pollutants have all contributed to the deterioration of this precious resource. In order to protect human health and efficiently manage resources, it is crucial to conduct groundwater quality assessments for agricultural and domestic uses, especially drinking. In this study, two indices, the DWQI and NPI, are utilized to assess the fitness of the groundwater quality for drinking and to assess the magnitude of contamination by nitrate in Jerash region. Moreover, the fitness of the groundwater for irrigational purposes was assessed using the most commonly used indices, such as Kelly's index (KI), magnesium hazard index (MHI), sodium adsorption ratio (SAR), electrical conductivity (EC), and the sodium percentage (%Na). Hierarchical cluster analysis (HCA) and conventional hydrochemical methods were applied to evaluate the groundwater chemistry. Results showed that the groundwater in the studied area is basically of a Ca-Mg-HCO3 facies, hardvery hard water. Although 38% of the samples (dry season) and 35% of the samples (rainy season) possess NO3 - concentration above the maximum permissible limit (50 mg/L), the vast majority of the samples (96%) showed good to excellent water quality based on DWQI, authenticating suitability for drinking. On the other hand, the results of the NPI indicated that about 30% of the samples in both seasons present significant tovery significant levels of nitrate pollution with nitrate concentration surpassing 50 mg/L. In general, the NPI might be a better expression of water quality than the DWQI, which at low values, obscures or extremely masks important parameters such as nitrate, despite exceeding WHO guidelines. Thus, the DWQI should be used with high precaution, especially at low levels of the used hydrochemical parameters. Based on irrigational water quality indices, the groundwater in the studied area authenticates appropriateness for irrigation. [ABSTRACT FROM AUTHOR]

The number of spanning trees of a graph G is called the complexity of G. It is known that the complexity of the line graph of a given graph G can be computed as the sum over all spanning trees of G of contributions which depend on various types of products of degrees of vertices of G. We interpret the contributions in terms of three types of multiplicative Zagreb indices, obtaining simple and compact expressions for the complexity of line graphs of graphs with low cyclomatic numbers. As an application, we determine the unicyclic graphs whose line graphs have the smallest and the largest complexity. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the scholarly productivity of the University of Engineering and Technology (UET), Lahore, over 50 years, from 1973 to 2022. Data indexed in the Web of Science (WoS) database was used and included information such as document type, year-wise research output, leading authors, significant collaborative organizations, primary international author collaborations, key corresponding author affiliations by country, major funding agencies, and highly cited publications.For bibliometric visualization, the BiblioShiny and VOSviewer software tools were employed. A comprehensive analysis of 5502 publications across diverse categories, such as research articles, conference papers, abstracts, reviews, editorials and others, was conducted. The UET research productivity displayed a positive trend, with 2022 emerging as the most productive year. Research articles constituted the predominant publication type, showcasing their popularity. Collaboration emerged as a preferred approach among researchers, evidenced by the prevalence of co-authored publications. The majority of the corresponding co- authors were affiliated with Pakistani institutions. The University of the Punjab (PU), Pakistan, was a prominent collaborative partner among all institutes. China was identified as the primary international collaborator for the researchers. Furthermore, the Higher Education Commission (HEC) of Pakistan was seen to be the primary funding agency for researchers at UET. The research domain of "engineering" claimed the most productivity, and "performance" was the most frequently used keyword. This study helps understand UET's research performance in the domains of engineering, science and technology.

Spectral indices are of fundamental importance in providing insights into the distinctive characteristics of oil spills, making them indispensable tools for effective action planning. The normalized difference oil index (NDOI) is a reliable metric and suitable for the detection of coastal oil spills, effectively leveraging the visible and near-infrared (VNIR) spectral bands offered by commercial sensors. The present study explores the calculation of NDOI with a primary focus on leveraging remotely sensed imagery with rich spectral data. This undertaking necessitates a robust infrastructure to handle and process large datasets, thereby demanding significant memory resources and ensuring scalability. To overcome these challenges, a novel cloud-based approach is proposed in this study to conduct the distributed implementation of the NDOI calculation. This approach offers an accessible and intuitive solution, empowering developers to harness the benefits of cloud platforms. The evaluation of the proposal is conducted by assessing its performance using the scene acquired by the airborne visible infrared imaging spectrometer (AVIRIS) sensor during the 2010 oil rig disaster in the Gulf of Mexico. The catastrophic nature of the event and the subsequent challenges underscore the importance of remote sensing (RS) in facilitating decision-making processes. In this context, cloud-based approaches have emerged as a prominent technological advancement in the RS field. The experimental results demonstrate noteworthy performance by the proposed cloud-based approach and pave the path for future research for fast decision-making applications in scalable environments. [ABSTRACT FROM AUTHOR]

Phase unwrapping (PU) is a critical processing step for obtaining information on land surface deformation from interferometric synthetic aperture radar (InSAR) images. Traditional PU methods take the phase continuity assumption as the precondition. However, in actual applications, there are many factors that can cause phase discontinuities, resulting in large errors in the PU results. With the fast development of deep learning, the data-driven deep learning methods for PU may help address this issue. Therefore, this study proposes a coherence-guided InSAR PU method with cycle-consistent adversarial networks. In this method, PU was considered a pixel-level regression problem from the interferogram to the unwrapped phase; cycle-consistent adversarial networks as one-step PU model were imported with the interferogram as the input and the unwrapped phase as the output. Except for adversarial loss and cycle consistency loss, based on the PU theory and the coherence of SAR images, the $\mathcal {L}_{1}$ loss with the weight of the coherence coefficient was added to guide the training process of networks. In addition to the traditional mean square error index, the root mean square value of the phase loop closure was introduced to evaluate the quality of the generated unwrapped phase. The results show that the proposed method can not only achieve the accuracy of the traditional PU method in the region with good coherence but also obtain better unwrapping results in the region with poor coherence, where the traditional PU method is restricted. It is also superior to the existing PU method with a conditional adversarial network. [ABSTRACT FROM AUTHOR]

From geometric point of view, we introduced the Sombor-Wiener index of a graph and studied the basic properties of the new index. It was shown that the Sombor-Wiener index was useful in predicting the acentric factor of octane isomers. In addition, we proposed a degree-weighted Wiener index to generalize the Schultz index, the Gutman index, and the Sombor-Wiener index. Meanwhile, we gave the calculation formula of degree-weighted Wiener index for generalized Bethe trees. [ABSTRACT FROM AUTHOR]

How to drive safely in complex real-world traffic settings has long been a question and challenge for autonomous vehicles (AVs). Decision-making systems (DecSs) are the core of AVs, and their safety and rationality are crucial. Several decision-making techniques and algorithms have been applied to AVs; however, they are still subject to limitations and deficiencies, making it impossible to fully guarantee safety. Thus, it is necessary to conduct a safety assessment of the state performance of the DecSs of AVs to reduce driving risks and implement immediate safety measures. This article reviews relevant research on safety assessment methods of AVs’ DecSs. The insufficiency of the current DecSs in terms of safety is analyzed, and the importance and definition of decision-making safety assessment (DMSA) are established. On this basis, assessment indexes related to DMSA are analyzed. From the perspectives of methodology and industrialization development, the existing safety assessment methods are summarized categorically, their applicability and limitations are discussed, and corresponding constructive strategies for improvement are proposed. Additionally, the deficiencies of the existing DMSA methods and the areas that need improvement are analyzed. In this regard, the future challenges and development opportunities of DMSA are proposed. This article provides a useful reference for the perfect DMSA method. [ABSTRACT FROM AUTHOR]