Articles

Artificial Intelligence (AI) systems, particularly deep learning models, have revolutionized numerous sectors with their unprecedented performance capabilities. However, the intricate structures of these models often result in a "black-box" characterization, making their decisions difficult to understand and trust. Explainable AI (XAI) emerges as a solution, aiming to unveil the inner workings of complex AI systems. This paper embarks on a comprehensive exploration of prominent XAI techniques, evaluating their effectiveness, comprehensibility, and robustness across diverse datasets. Our findings highlight that while certain techniques excel in offering transparent explanations, others provide a cohesive understanding across varied models. The study accentuates the importance of crafting AI systems that seamlessly marry performance with interpretability, fostering trust and facilitating broader AI adoption in decision-critical domains.

The integration of Artificial Intelligence (AI) in cloud computing heralds a transformative phase for the tech industry. As cloud infrastructures become more sophisticated, the potential of optimizing these services using AI has captured significant attention. This study aimed to explore how cloud providers can leverage AI to optimize resource allocation, enhance scalability, and offer innovative AI-as-a-Service (AIaaS) solutions. Through a mixed-method approach, insights were gleaned from companies that have adopted AI in their cloud architectures. The findings elucidate that AI-driven methods have led to substantial operational savings and a reduction in downtimes. Moreover, the proliferation of AIaaS models is particularly beneficial for mid-level enterprises and startups. However, concerns around data privacy, potential biases, and integration costs emerge as significant challenges. Future work in this domain promises to delve deeper into these challenges, aiming for a harmonious synergy between AI and cloud computing.

Computer vision, an interdisciplinary field bridging artificial intelligence and image processing, seeks to bestow machines with the capability to interpret and make decisions based on visual data. As the digital age propels forward, the ubiquity of visual content underscores the importance of efficient and effective automated interpretation. This paper delves deeply into the modern advancements and methodologies of computer vision, emphasizing its transformative role in various applications ranging from medical imaging to autonomous driving. With the increasing complexity of visual data, challenges arise pertaining to real-time processing, scalability, and the ethical implications of automated decision-making. Through an exhaustive literature review and novel experimentation, this research demystifies the multifaceted domain of computer vision, elucidating its potential and constraints. The study culminates in a visionary outlook, highlighting future avenues for research, including the fusion of augmented reality with computer vision, novel deep learning architectures, and ensuring ethical AI practices in visual interpretation.

Vibroseis seismic acquisition technology is a method of acquiring seismic data utilizing correlation technology. Specifically, the seismic single shot record is obtained through correlation between the reference signal and the mother record. Specifically, the seismic single shot record is obtained through correlation between the reference signal and the mother record. Specifically, the seismic single shot record is obtained through correlation between the reference signal and the mother record. In line with pertinent technical regulations, greater correlation between the two signals equates to superior correlation outcomes. The reference signal is transmitted to the surface via the vibration of the vibrating plate. This is achieved using the vibroseis machine. However, due to the coupling relationship between the vibrating plate and the surface, the vibration signal output by the former is not equivalent to the vibration signal received by the latter. Consequently, the correlation between the reference signal and parent record fails to procure the best correlation result. In this paper, a technique is presented for establishing a correlation between the surface vibration signals captured by the geophone in proximity to the vibroseis and the reference record. This approach improves the quality of the correlated single shot, and holds significant potential for broad dissemination.

Natural Language Processing (NLP), a branch of artificial intelligence, is gaining traction as a potent tool for business analytics. With the proliferation of unstructured textual data, businesses are actively seeking methodologies to distill valuable insights from vast textual repositories. The introduction of NLP in the realm of business analytics offers a transformative approach, automating traditional manual processes and fostering real-time, data-driven decision-making. From sentiment analysis to text summarization, NLP is facilitating businesses in deciphering consumer feedback, predicting market trends, and breaking down linguistic barriers in the age of globalization. This paper sheds light on the evolution of NLP techniques in business analytics, their applications, and the inherent challenges and opportunities they present.

The intersection of artificial intelligence (AI) and software engineering marks a transformative phase in the technology industry. This paper delves into AI-driven software engineering, exploring its methodologies, implications, challenges, and benefits. Drawing from data sources such as GitHub and Bitbucket and insights from industry experts, the study offers a comprehensive view of the current landscape. While the results indicate a promising uptrend in the integration of AI techniques in software development, challenges like model interpretability, ethical concerns, and integration complexities emerge as significant. Nevertheless, the transformative potential of AI within software engineering is profound, ushering in new paradigms of efficiency, innovation, and user experience. The study concludes by emphasizing the need for further research, better tooling, ethical guidelines, and education to fully harness the potential of AI-driven software engineering.

In the context of the widespread application of artificial intelligence technology, international Chinese language education is taking on new characteristics. The next generation of artificial intelligence is profoundly impacting the global education landscape and transforming the methods of knowledge production, driving the digitization of international Chinese language education and innovating all elements of education. It is systematically constructing a new ecosystem for the future of international Chinese language education, attracting extensive attention and lively discussions in the education field, including the domain of international Chinese language education. This paper, through a review of the application of computer technology in the field of Chinese language teaching and a discussion of the challenges and opportunities it currently presents, analyzes the strengths and weaknesses of artificial intelligence technology in the context of international Chinese language education. It offers strategies for Chinese language teachers to effectively utilize artificial intelligence technology, employ flexible teaching methods to address challenges, and enhance teaching effectiveness.

As Artificial Intelligence (AI) systems increasingly weave into the fabric of diverse sectors, their intricate and often opaque decision-making processes pose challenges to users and stakeholders alike. The 'black box' nature of AI, especially deep learning models, highlights a pressing need for transparency and interpretability. This paper delves into the significance of making AI decisions transparent and provides a comprehensive exploration of methods aimed at demystifying AI processes. Through the lens of Explainable AI (XAI) and advanced visualization tools, we underscore the importance of bridging the chasm between sophisticated AI operations and human-centric understanding. By fostering transparency, it is anticipated that AI systems can not only enhance efficacy but also fortify trust, ensuring that decisions are both informed and explicable.

With the escalating complexity of IT networks and the surge in cyber threats, the need for advanced, real-time security solutions has never been more paramount. Machine learning (ML) and deep learning (DL) present promising avenues for enhancing the detection, analysis, and mitigation of threats in these intricate networks. The paper delves into the confluence of ML and DL techniques in the realm of cybersecurity, focusing on their application for real-time threat detection within IT infrastructures. Drawing from recent research and developments, the study underscores the potential of these techniques in outmaneuvering conventional security models, while also shedding light on the inherent challenges and areas for future exploration.

Recently, makeup transfer task has been widely explored with the development of deep learning. However, existing methods have shortcomings in more complex lighting situations in the real world because they do not consider the interference of lighting factors on facial features. To solve the above problem, we propose a local highlight and shadow adaptively repairing GAN for illumination-robust makeup transfer. We first map the 2D face images to UV representations and perform makeup transfer in the UV texture space, which explicitly removes the spatial misalignment to achieve pose and expression invariant makeup transfer. Furthermore, we take advantage of the face symmetry in the UV texture space to design an illumination repair module. It can adaptively repair the features affected by asymmetric local highlight and shadow based on a process of flipping and multi-layer attention fusion. In addition, the multi-layer attention maps are obtained by a pre-trained illumination classification network and hence have the ability to indicate local highlight and shadow areas. Comprehensive experiment results demonstrate the consistent effectiveness and clear advantages of our method, which significantly improve the robustness against local light effects and generate natural transfer results.

Platelet-rich plasma (PRP) has gained widespread usage in the treatment of various chronic wounds due to its ease of preparation and high safety profile. But not every treatment can achieve satisfactory results. The quality of PRP is based on individual biological characteristics. Autologous PRP technology is greatly affected by the patient himself. Individual responses to PRP treatment are different and will produce different therapeutic effects. Especially in patients with uncontrollable factors such as aging, diabetes, and coronary heart disease, the use of antiplatelet drugs may potentially reduce the quality of autologous platelet-rich plasma (PRP). When treating related diseases, it is important to consider the impact of factors such as age, gender, and the specific disease. Currently, many researchers are focusing on the development of allogeneic PRP technology to avoid the inconvenience of collecting autologous blood and reduce potential negative effects that may exist in the patient's disease itself. In this review, we explore the factors generally recognized in current research which influence the efficacy of PRP.

Given the pressure on land in the Central Region due to economic development and the search for opportunities, the best land for agriculture needs to be identified, so as to preserve it from urbanization, the expansion of mining areas, infrastructure and other occupations. It is in this context that the issue of mapping to identify agricultural zones and assess their accessibility was raised. This is an opportune moment to feed reflections on planning, agricultural production and natural resource management in the Center Region of Cameroon. GIS-based multi-criteria spatial analysis of data on land use, hydrographic network, slopes, soils and their suitability for cultivation provided precise, geolocalized information on land potentially suitable for agriculture in general. Following the analyses, we were able to establish and highlight the areas suitable for agriculture and accessible, which amounted to 8%, or 5662.42 km2 for high-potential and accessible areas. This was followed by 56%, or 37144.95 km2 for medium-potential and accessible zones, and finally 6%, or 4238.93 km2 for low-potential and accessible zones. Once the accessible areas had been removed, 5% or 3028.27 km2 of high-potential inaccessible areas remained, 19%, that is 14639.46 km2 of medium-potential inaccessible areas and 2% that is 967.12km2 of low-potential inaccessible areas.

NLP models have demonstrated susceptibility to adversarial attacks, thereby compromising their robustness. Even slight modifications to input text possess the capacity to deceive NLP models, leading to inaccurate text classifications. In the present investigation, we introduce Lexi-Guard: an innovative method for Adversarial Text Generation. This approach facilitates the rapid and efficient generation of adversarial texts when supplied with initial input text. To illustrate, when targeting a sentiment classification model, the utilization of product categories as attributes is employed, ensuring that the sentiment of reviews remains unaltered. Empirical assessments were conducted on real-world NLP datasets to showcase the efficacy of our technique in producing adversarial texts that are both more semantically meaningful and exhibit greater diversity, surpassing the capabilities of numerous existing adversarial text generation methodologies. Furthermore, we leverage the generated adversarial instances to enhance models through adversarial training, demonstrating the heightened resilience of our generated attacks against model retraining endeavors and diverse model architectures.

This study aims to explore the key factors influencing Chinese higher vocational students' reluctance to pursue higher education. By combining two analytical methods, Random Forest and Structural Equation Modeling, the study targeted students enrolled in Guangdong Finance and Trade Vocational College, designed a questionnaire containing various aspects such as study habits, self-perception, and internship experience, and collected 107 samples for analysis. The study found that students' personal intention to pursue higher education was the most critical factor influencing whether they chose to continue their studies, followed by the evaluation of their own learning ability and economic considerations. In addition, family cultural resources and learning environment also influence students' attitudes towards further education to a certain extent. The study also reveals the effects of the positive interaction between personal factors and family cultural resources support, and the negative interaction between personal factors and economic factors on the intention to pursue higher education. Based on these findings, the article presents recommendations for the school, family, and social levels to promote higher education students' willingness to pursue higher education and educational equity.

MIMO technology was proposed as early as 1908 to cope with wireless channel fading. In 1995, Bell Labs was the first to discover the great potential of MIMO system in channel capacity, and in 1996, Foshini of Bell Labs first proposed a space-time coding scheme, i.e., the diagonal-Bell Labs hierarchical space-time model, which can obtain very high spectrum utilization, but due to the complexity of its structure, it is difficult to be applied in practice, and is now rarely investigated. 1998, P.W. Wolniansky et al. gave a simple and practical space-time coding scheme on this basis, i.e., the vertical-Bell Labs layered space-time model. In 1998, P.W. Wolniansky et al. gave a simple and practical space-time coding scheme on this basis, i.e., Vertical Bell Labs Layered Space-Time (V-BLAST, Vertical Bell Labs Layered Space-Time) model, which can obtain very high spectrum utilisation and is easy to implement, and therefore has received wide attention once it was proposed. In this paper, we focus on airtime layered codes as well as the ZF detection algorithm and the MMSE detection algorithm in VBLAST systems and improve them to further enhance the performance of the two detection algorithms through sequential serial interference cancellation.

This research paper critically examines China’s role as the world’s largest emitter of carbon dioxide and its pivotal position in global climate change mitigation efforts. The analysis encompasses China’s environmental policies initiated since 1979, formally approved by the legislative body, the NPC, in 1989. Though significant economic developments were made since the country’s reform and opening in 1979, it is acknowledged that this progress has been accompanied by substantial environmental degradation. In response, the government amended environmental laws in 2014, reflecting a commitment to address these challenges. However, this paper contends that substantial efforts are still required to achieve meaningful environmental improvement. The research further delves into the anticipated impacts of Chinese policies on crucial aspects, namely Climate Change, Resource Management, and the well-being of Future Generations, providing comprehensive insights into the multifaceted implications of China’s environmental trajectory.

Blockchain technology, primarily acclaimed for its instrumental role in underpinning cryptocurrencies, has seen rising prominence in a multitude of applications outside the digital currency realm. Its decentralized infrastructure coupled with cryptographic integrity offers solutions to longstanding challenges across various industries, including supply chain, healthcare, and finance. This research endeavor delves into these multifarious applications, providing insights into the potential benefits and the existing impediments in the broader adoption of blockchain technology.

The rapid expansion of comprehensive sports datasets and the successful application of data mining techniques in various domains have given rise to the emergence of sports data prediction techniques. These techniques enable the extraction of hidden knowledge that can significantly impact the sports industry, as more and more clubs are using Machine Learning (ML) and Deep Learning (DL) methods to manage athletes and training. In this research, the focusing and intriguing aspects is predicting the outcomes of a specific basketball athletes, which has garnered significant attention for research. The paper was motivated by a dual interest in college and NBA basketball matches, alongside a keen observation of the evolving strategies employed by coaches in athlete management. Additionally, the interest was further reinforced by firsthand observations of such evolving methods during a baseball game at City Field in New York. These factors collectively underpin the relevance and significance of this research endeavor, highlighting the intersection of personal interest and the evolving landscape of sports management as compelling reasons for its pursuit. In the process of data selection, we acquired data from previously published essays as well as from Kaggle, a reputable online platform. Following this, we proceeded to evaluate several prominent machine learning models, namely Linear Regression, KNN, Gradient Boosting, Elastic Net, and Lasso, to ascertain their effectiveness in predicting the performance of specific players. Through rigorous analysis and comparison, we concluded that Linear Regression and Gradient Boosting exhibited superior predictive capabilities compared to the other models considered. These two models demonstrated a higher degree of accuracy and reliability in forecasting player performance, thus establishing them as the most suitable choices for our predictive modeling purposes. This meticulous selection process, involving both data acquisition and model evaluation, forms the foundation of our research methodology and underscores the rigor and precision with which our conclusions are drawn.

The formulation of a calcined clay-based hydraulic binder is an innovative solution for reducing the carbon footprint in the construction industry. This binder is made from calcined clay, a natural and abundant material that does not require high-temperature firing in the 400°C to 950°C range. In our study, we worked respectively under the following temperatures: 550°C, 650°C, 750°C and 850°C, unlike traditional binders whose clinker is fired at a temperature of around 1450°C. The blaine tests showed that the binder had been well ground, resulting in a much finer binder. We then obtained various blaines varying between 5000 and 8000 Cm2/g, unlike the blaine of the traditional binder, which is 3800 Cm2/g; the residue rate is very high compared with that of the traditional binder, due to the grinding carried out in a laboratory ball mill. The results of resistance tests at 30% gave 34.8, 48.8, 36.5 and 46 respectively; those at 40% gave 42.7, 45.4, 44.4 and 44.8, while the resistance of traditional binder gave 42.5. In other words, we obtained strengths comparable to those of the traditional binder. In addition, it was found that the manufacture of calcined clay-based hydraulic binders reduces CO2 emissions compared with traditional binders, which emit very high levels of CO2, making the production of calcined clay-based binders a more environmentally-friendly option for the cement industry. The financial assessment carried out at the end of this study shows that the production of a calcined clay-based binder is more economical than that of a traditional binder (Portland cement).

Digital media technology is playing an increasingly important role in curriculum teaching, which provides new possibilities for the development and reform of education. This paper discusses the application of digital media technology in curriculum teaching, and points out its advantages in improving teaching quality, enhancing teacher-student interaction, promoting personalized learning, enhancing learning experience and improving teaching efficiency. However, the application of digital media technology also faces some challenges and problems, such as information overload, privacy protection, and high technical threshold, which need to be further studied and discussed. At the same time, the application of digital media technology needs to be closely combined with the teaching content of the course, and according to different course characteristics and student needs, the appropriate technology and way can be selected to achieve the best teaching effect. In the future, with the continuous development and progress of digital media technology, its application in curriculum teaching will be more extensive and in-depth. Digital media technology will be closely integrated with education, creating a more intelligent, personalized and efficient teaching environment for us, and promoting the development and progress of education. It can be seen that digital media technology has broad application prospects and potential in curriculum teaching, and it is worth our further exploration and research to make greater contributions to the development and reform of education.