A Study on Identity Construction of First Person Pronouns in Academic Papers from the Perspective of Evidentiality

The first person pronoun plays an important role in identity construction, however, there is few study on it from the perspective of evidentiality. This paper took the first person pronouns as evidentials, and conducted a comparable analysis on the frequency of them and the identities they constructed in academic papers between soft and hard sciences, aiming to find the differences between different discourse communities and explore their preferences for academic identity construction. The results showed that both fields prefer to use plural and subjective cases of first person pronouns, and they both prefer to construct the authorial identity of “researcher”, but scarcely construct the authorial identity of “responsible person”. Researchers in hard science use less evidentials than researchers in soft science, and they prefer to use evidentials “we” and “statement”, which weaken the authorial identity. Evidentials that embody authorial identity, including singular first person pronouns and “participation” evidentials, account for higher proportion in soft science than those in hard science

Collaborative driving mode of sustainable marketing and supply chain management supported by metaverse technology

In this article, we aim to explore the relationship between sustainable marketing and supply chain management (SCM) under the background of metaverse technology to realize the sustainable development of enterprises. First, this study deeply studies the influence of metaverse technology on sustainable marketing strategy from the theoretical level. Second, it deeply discusses the integration of digital transformation and sustainable development in SCM. Finally, this study implements a collaborative driving model of sustainable marketing and SCM supported by metaverse. By designing and analyzing the questionnaire on the sustainable performance of enterprises, it is found that SCM, cooperation with customers, investment recovery, sustainable marketing, R&D and design, production, and manufacturing have a significant positive influence on the sustainable performance of enterprises (p<0.01). In addition, the distribution and retail in sustainable marketing negatively impact the sustainable performance of enterprises, and the standardization coefficient is −0.225 (p<0.05). These research results emphasize the importance of sustainable marketing and SCM, which jointly promote enterprises to achieve sustainable performance, and ultimately provide valuable practical guidance for building a sustainable digital economy and contribute to collaborative optimization in enterprise engineering

Sustainable digital marketing under big data: an AI random forest model approach

Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms and channels using the internet and electronic devices in a digital environment. Its aim is to attract and engage target audiences through various strategies and methods, driving brand promotion and sales growth. The primary objective of this scholarly study is to seamlessly integrate advanced big data analytics and artificial intelligence (AI) technology into the realm of digital marketing, thereby fostering the progression and optimization of sustainable digital marketing practices. First, the characteristics and applications of big data involving vast, diverse, and complex datasets are analyzed. Understanding their attributes and scope of application is essential. Subsequently, a comprehensive investigation into AI-driven learning mechanisms is conducted, culminating in the development of an AI random forest model (RFM) tailored for sustainable digital marketing. Subsequent to this, leveraging a real-world case study involving enterprise X, fundamental customer data is collected and subjected to meticulous analysis. The RFM model, ingeniously crafted in this study, is then deployed to prognosticate the anticipated count of prospective customers for said enterprise. The empirical findings spotlight a pronounced prevalence of university-affiliated individuals across diverse age cohorts. In terms of occupational distribution within the customer base, the categories of workers and educators emerge as dominant, constituting 41% and 31% of the demographic, respectively. Furthermore, the price distribution of patrons exhibits a skewed pattern, whereby the price bracket of 0–150 encompasses 17% of the population, whereas the range of 150–300 captures a notable 52%. These delineated price bands collectively constitute a substantial proportion, whereas the range exceeding 450 embodies a minority, accounting for less than 20%. Notably, the RFM model devised in this scholarly endeavor demonstrates a remarkable proficiency in accurately projecting forthcoming passenger volumes over a seven-day horizon, significantly surpassing the predictive capability of logistic regression. Evidently, the AI-driven RFM model proffered herein excels in the precise anticipation of target customer counts, thereby furnishing a pragmatic foundation for the intelligent evolution of sustainable digital marketing strategies

Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement

Underwater images suffer from complex and diverse degradation, which inevitably affects the performance of underwater visual tasks. However, most existing learning-based Underwater image enhancement (UIE) methods mainly restore such degradations in the spatial domain, and rarely pay attention to the fourier frequency information. In this paper, we develop a novel UIE framework based on spatial-frequency interaction and gradient maps, namely SFGNet, which consists of two stages. Specifically, in the first stage, we propose a dense spatial-frequency fusion network (DSFFNet), mainly including our designed dense fourier fusion block and dense spatial fusion block, achieving sufficient spatial-frequency interaction by cross connections between these two blocks. In the second stage, we propose a gradient-aware corrector (GAC) to further enhance perceptual details and geometric structures of images by gradient map. Experimental results on two real-world underwater image datasets show that our approach can successfully enhance underwater images, and achieves competitive performance in visual quality improvement

Artificial intelligence-based human–computer interaction technology applied in consumer behavior analysis and experiential education

In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce (E-commerce) has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial intelligence (AI) image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human–computer interaction (HCI) in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network (DNN) is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce

Joint Inference on Truth/Rumor and Their Sources in Social Networks

In the contemporary era of information explosion, we are often faced with the mixture of massive \emph{truth} (true information) and \emph{rumor} (false information) flooded over social networks. Under such circumstances, it is very essential to infer whether each claim (e.g., news, messages) is a truth or a rumor, and identify their \emph{sources}, i.e., the users who initially spread those claims. While most prior arts have been dedicated to the two tasks respectively, this paper aims to offer the joint inference on truth/rumor and their sources. Our insight is that a joint inference can enhance the mutual performance on both sides. To this end, we propose a framework named SourceCR, which alternates between two modules, i.e., \emph{credibility-reliability training} for truth/rumor inference and \emph{division-querying} for source detection, in an iterative manner. To elaborate, the former module performs a simultaneous estimation of claim credibility and user reliability by virtue of an Expectation Maximization algorithm, which takes the source reliability outputted from the latter module as the initial input. Meanwhile, the latter module divides the network into two different subnetworks labeled via the claim credibility, and in each subnetwork launches source detection by applying querying of theoretical budget guarantee to the users selected via the estimated reliability from the former module. The proposed SourceCR is provably convergent, and algorithmic implementable with reasonable computational complexity. We empirically validate the effectiveness of the proposed framework in both synthetic and real datasets, where the joint inference leads to an up to 35\% accuracy of credibility gain and 29\% source detection rate gain compared with the separate counterparts

Game-based Platforms for Artificial Intelligence Research

Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios. Learning and optimisation, decision making in dynamic and uncertain environments, game theory, planning and scheduling, design and education are common research areas shared between games and real-world problems. Numerous open-sourced games or game-based environments have been implemented for studying artificial intelligence. In addition to single- or multi-player, collaborative or adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing artificial intelligence ideas and techniques. This paper reviews the game-based platforms for artificial intelligence research, discusses the research trend induced by the evolution of those platforms, and gives an outlook

Closed-Loop Magnetic Manipulation for Robotic Transesophageal Echocardiography

This paper presents a closed-loop magnetic manipulation framework for robotic transesophageal echocardiography (TEE) acquisitions. Different from previous work on intracorporeal robotic ultrasound acquisitions that focus on continuum robot control, we first investigate the use of magnetic control methods for more direct, intuitive, and accurate manipulation of the distal tip of the probe. We modify a standard TEE probe by attaching a permanent magnet and an inertial measurement unit sensor to the probe tip and replacing the flexible gastroscope with a soft tether containing only wires for transmitting ultrasound signals, and show that 6-DOF localization and 5-DOF closed-loop control of the probe can be achieved with an external permanent magnet based on the fusion of internal inertial measurement and external magnetic field sensing data. The proposed method does not require complex structures or motions of the actuator and the probe compared with existing magnetic manipulation methods. We have conducted extensive experiments to validate the effectiveness of the framework in terms of localization accuracy, update rate, workspace size, and tracking accuracy. In addition, our results obtained on a realistic cardiac tissue-mimicking phantom show that the proposed framework is applicable in real conditions and can generally meet the requirements for tele-operated TEE acquisitions.Comment: Accepted by IEEE Transactions on Robotics. Copyright may be transferred without notice, after which this version may no longer be accessibl

Twist-3 Generalized Parton Distribution for the Proton from Basis Light-Front Quantization

We investigate the twist-3 generalized parton distributions (GPDs) for the valence quarks of the proton within the basis light-front quantization (BLFQ) framework. We first solve for the mass spectra and light-front waved functions (LFWFs) in the leading Fock sector using an effective Hamiltonian. Using the LFWFs we then calculate the twist-3 GPDs via the overlap representation. By taking the forward limit, we also get the twist-3 parton distribution functions (PDFs), and discuss their properties. Our prediction for the twist-3 scalar PDF agrees well with the CLAS experimental extractions