This study examines how Shenzhen, China's pioneering special economic zone, leverages intelligent technologies to drive new-quality productivity (NQP) through a distinctive digital leapfrog model. The research employs a mixed-methods approach combining panel data analysis of 1,200 enterprises (2018–2025), spatial econometric modeling, and qualitative case studies to investigate three core questions: how institutional innovation enables technological leapfrogging, what governance mechanisms resolve the centralization-vulnerability paradox, and how technology-society co-evolution generates sustainable productivity gains. Empirical findings reveal that Shenzhen achieves NQP advancement through three interconnected mechanisms: enterprise-level capability restructuring (82.7
The development of a practical, comprehensive, scientific, and rational evaluation index system for teachers’ ability is critically important for enhancing the quality and capabilities of educators in higher vocational colleges, as well as for fostering the high-quality development of higher vocational education. Therefore, this study first examined the suitability of the CIPP model for evaluating teachers’ ability and constructed a theoretical framework for such evaluations. This framework includes four dimensions: context evaluation, input evaluation, process evaluation, and product evaluation. Furthermore, the Delphi-AHP method was used to develop an evaluative index system that assesses teachers’ ability. This system comprises four primary indexes (fundamental ability, conditional ability, practical ability, and developmental ability), twenty secondary indexes, and fifty-six tertiary indexes. The aim is to provide a theoretical foundation for establishing a robust teachers’ ability evaluation system in higher vocational colleges.
This study examines whether health insurance enhances the subjective well-being (SWB) of China’s rural-to-urban migrant workers. Despite their key contributions to economic development, these workers remain marginalized in the social security system. As a crucial component of social protection, health insurance may improve healthcare access and financial security, thus boosting well-being. Using data from the 2018 China Family Panel Studies (CFPS) and applying an ordered logit model, this paper explores the effects of health insurance participation. The findings suggest that both coverage and participation depth significantly improve migrant workers’ SWB. Moreover, the effects vary across gender, education, and region. The study concludes with policy recommendations for government agencies, insurers, and migrant workers to enhance insurance effectiveness and support the well-being of this vulnerable population.
With the progress of ecological civilization, spreading ecological culture is key to raising public awareness. Yet traditional methods are inadequate. Artificial Intelligence (AI) offers new ways but faces challenges like low reliability, privacy concerns, and limited public engagement. This paper examines AI’s role in ecological culture dissemination through case studies, identifying current issues and proposing solutions, such as enhancing reliability, protecting privacy, and boosting participation. It concludes that AI has great potential to support ecological civilization by improving public awareness and spreading ecological values.
This paper explores the application of blockchain technology in enhancing the management and sharing of medical data. Traditional healthcare data management systems face challenges related to data security, privacy, and interoperability, often resulting in data fragmentation and potential security breaches. Blockchain technology offers a decentralized solution that enhances data security and privacy through immutability and the reduction of centralized risks. Our study evaluates the practicality of an RSA-Encrypted NFT framework for managing healthcare data, demonstrating its consistent performance across various data formats and its integration with the InterPlanetary File System (IPFS) for enhanced data accessibility and durability. Additionally, we conduct a comparative analysis of transaction fees on four EVM-compatible blockchain platforms to assess their economic efficiency and suitability for healthcare applications. Our findings highlight the importance of selecting blockchain solutions that balance cost with functionality to maximize value for healthcare data management.
Artificial intelligence, as the core driver of the new round of technological revolution and industrial transformation, has been elevated to the level of a national strategic priority. The Development Plan for the Next Generation of Artificial Intelligence clearly states that China will achieve the ambitious goal of leading the world in artificial intelligence technology by 2030. This strategic deployment not only injects strong impetus into the country's overall development, but also provides an important opportunity for the digital transformation of ethnic regions. In the context of building a “Digital China”, ethnic regions are actively integrating into this wave and promoting the all-round development of the economy and society through technological innovation. Take the Smart Education platform in the Guangdong-Hong Kong-Macao Greater Bay Area as an example. The platform makes full use of artificial intelligence technology to achieve efficient allocation and precise delivery of educational resources, significantly improving educational equity and teaching quality. This typical case vividly demonstrates the deep integration of technology and the building of the national community, and shows the great potential of artificial intelligence in promoting coordinated regional development. However, it is worth noting that there is still a significant gap in digital infrastructure construction between the East and the West. Problems such as limited 5G network coverage and uneven distribution of computing power resources have led to significant differences in regional development levels. This imbalance not only restricts the pace of digital transformation in ethnic minority areas, but also poses a serious challenge to the sharing of technological dividends. Therefore, how to narrow the digital divide between the east and the west through policy guidance and technological support has become an important issue that needs to be addressed urgently at present.
Supply Chain Management (SCM) plays a vital role in business operations, necessitating careful designing and planning. Emerging technologies, such as the Internet of Things (IoT), facilitate real-time, event-driven monitoring of supply chain performance. However, integrating IoT into SCM presents significant challenges for system architects, particularly in validating requirements due to the diversity of IoT technologies and communication frameworks. Model-Based Systems Engineering (MBSE) offers a structured approach to system design by providing models and views, along with tools that automate essential processes. In this paper, we propose an MBSE-based approach for requirements validation in event-driven supply chain management. Additionally, we introduce tool support to enhance usability. An architectural overview demonstrates how system designers can customize the tool to meet their specific requirements. Our approach is designed for system architects, supporting iterative modeling until the requirements are validated as illustrated through a sample case study.
Data exposures in software services consist of inadvertent or malicious leakages of confidential or sensitive information in software services. These leakages in software services are commonly caused by hardcoded secrets, misconfiguration in storage, insecure logging, improper transmission of data or unsafe deserialization. Existing approaches to detecting data exposures in software services rely on using regular expression with limited capabilities of detecting data exposure, specialized static analyses, deep learning technique, or a large language model. These approaches frequently yield high false-positive results or sometimes produce different results for the same input due to indeterminism of the large language model used in the approach. In this paper, an approach to detecting data exposures in software services using a large language model with low rank adapters and few-shot learning technique is presented. This approach can reduce false-positive results and avoid indeterminism of the large language model used in the approach.
A service robot is an Internet of Things (IoT) or a cyberphysical system comprising a robotic body integrated with one or more Cloud-based services. This architecture enables sophisticated human-machine interaction and expands the functional capabilities of conventional robotic systems. A service robot can take the form of anthropomorphic, zoomorphic, or even theomorphic design. Our research team is collaborating with the Canadian National Institute for the Blind (CNIB) to develop a prototype service dog robot. Due to this, this paper presents our current experiences in designing, developing, and evaluating a Do-It-Yourself (DIY) articulated robotic tail toolkit for zoomorphic robots at Ontario Tech University. We detail the design process that led to the development of a robotic tail mechanism, driven by microservices, which emphasizes reliability and expressive while utilizing readily accessible components. We range also conducted experiments and evaluations of the robotic tail during a hands-on workshop with students from diverse backgrounds, using a services computing approach. We continue to develop an effective DIY educational tool that promotes student engagement in Human-Robot Interaction (HRI) design for future service computing education.
This study aims to guide pre-service teachers in effectively utilizing Generative Artificial Intelligence (GAI) tools to enhance their Artificial Intelligence (AI) literacy. By ana-lyzing the impact of GAI on the AI literacy of pre-service teachers, this study proposes a “teacher-student-machine” triadic interactive teaching model based on self-organized learning and deep empowerment through GAI. Using an experimental class in a software engineering course as a case study, we construct an end-to-end experimental environment for the project-driven teaching lifecycle, covering requirement analysis, system design, development and implementation, as well as software testing and maintenance phases. GAI is used to support the completion of experimental tasks while fostering pre-service teachers' AI literacy in five core competencies: data literacy, digital communication and collaboration, critical thinking, computational thinking, and ethical literacy. The study demonstrates that this model effectively improves the AI literacy of pre-service teachers, provides a reference for innovating software engineering practice teaching, and offers a useful framework for the application of GAI in education.
[Context]: It is well known that understanding the evolution of technologies and its cause is essential for more discoveries and innovations. In the Artificial Intelligence (AI) domain, it has also been identified that scrutinising the development context and path of AI will be able to help both academia and industry better understand the current AI limitations, reveal future AI trends, and facilitate AI/digital transformations. [Objectives]: Given the dramatic boom of modern AI, this research aims to unearth the evolution pattern along the recent three AI waves (namely predictive AI, generative AI and agentic AI), and accordingly to guide AI research and development to focus on the most promising directions. [Method]: We employed analogical reasoning as the research method and referred to the existing software architectural styles to inspire our understanding of the architectural evolution of modern AI technologies. [Results]: We see a service-oriented trend in modern AI's working mechanisms, and the offering of AI power seems to be transiting from a heavyweight and monolithic paradigm to an organisational and collaborative paradigm with more and more specific separation of concerns. Following this service-oriented evolution trend, we borrow software architecture lessons and foresee opportunities to grow the current AI wave to a further height, e.g., standardising AI agent-friendly APIs and developing serverless AI agents. [Conclusions]: What is happening in the AI domain has happened before in the software engineering domain. It is worth reusing software architecture knowledge to evolve the architecture of AI technologies.
Currently, there are various scientific and technological resource retrieval databases in the world. The resources stored in these databases may be identified by different identification systems. How to determine whether scientific and technological resources identified by different identification systems are the same resource is an urgent problem to be solved. This paper proposes a software service that leverages BERT and large language models to perform semantic analysis and similarity matching of scientific and technological resource content, and then maps the resources to their respective identifiers. The service effectively solves the problem of how to quickly retrieve the same resource from a large number of scientific and technological resources with diverse identification types, and improves the efficiency and quality of the retrieval. Implemented as a Chrome plugin, the service facilitates seamless mapping heterogeneous scientific and technological resources to their identifiers. We conduct a series of experiments. Their results demonstrate the effectiveness, scalability and stability of the service. To the best of our knowledge, we are the first to propose the service integrating BERT with large language models to extract and identify the key content of scientific and technological resources from web pages.
This research explores strategies for using one-way anonymous Q&A software to enhance class participation and teaching effectiveness of undergraduate students. The study finds that undergraduate students, influenced by cultural factors, ed-ucational systems, and upbringing environments, tend to display introverted psychological traits, resulting in insufficient class participation. One-way anonymous Q&A software significantly improves student participation enthusiasm through mechanisms such as breaking psychological barriers, promoting deep thinking, enhancing classroom interaction, and providing diverse feedback. Case analysis shows that by introducing the proposed anonymous Q&A software to teaching activities, students asked questions more frequently, and the quality of questions also improved accordingly with a wider adoption of the software. Besides, it also shows that the learning interests of students have increased after using the software. The research suggests that future studies should deepen technological innovation, pro-mote teaching model reform, and strengthen interdisciplinary integration to better meet the needs of a modern educational environment in universities.
While microservices architectures have significantly enhanced software modularity and scalability, they introduce unique security challenges that traditional monolithic approaches do not face. The proliferation of microservices often leads to homogeneity within systems, where multiple instances share iden-tical container images and software versions, creating a systemic security vulnerability that attackers can exploit. In homogeneous microservice environments, a single vulnerability can be exploited to compromise multiple services simultaneously, facilitating rapid lateral movement attacks and significantly reducing system re-silience against cyber threats. While existing security research has focused primarily on preventive measures like authentication protocols, the potential of strategic deployment patterns as a reactive defense mechanism remains largely unexplored. In this paper, we base our analysis on information theory to demonstrate that microservice deployment schemes affect system heterogeneity, which in turn influences overall security. Additionally, we proposed the Heterogeneous Microservices Placement Problem HMPP and developed a heuristic-based deployment algorithm, H- HMPP, to effectively address it. Extensive experimental evaluations demonstrate that our approach yields high-quality solutions while significantly reducing execution times.
Application Understanding task aims to help users comprehend an application's capabilities by systematically analyzing its artifacts. Ideally, such summaries should align with how the application is used in practice, highlighting essential workflows and functional modules in a structured manner. However, existing automated approaches often fall short of this expectation. Lack of application-specific background and domain knowledge limits the system's ability to present functionalities meaningfully. To address these challenges, we propose a novel agentic approach leveraging multimodal LLMs that integrate code analysis, textual artifacts, and domain knowledge to identify key business flow entities-such as programs and tables-within a repository and infer application workflows. This work opens new avenues in LLM-guided software comprehension, bridging the gap between code-centric insights and high-level business process understanding.
In the era of transformative artificial intelligence (AI), professional Master's programs must navigate both technological breakthroughs and pedagogical shifts. This paper presents an innovative approach adopted by the College of Software and Microelectronics at Peking University to train Electronic Information Master's students through a multi-dimensional framework encompassing academic research, industry applications, and in-service capacity building. By transitioning from code-focused instruction to a higher-level AI toolchain paradigm, the curriculum leverages large-model technologies (e.g., GPT, Chat-GPT) to promote greater efficiency in problem-solving, project-based learning, and critical thinking. The New Engineering Experimental Class exemplifies how enterprises partner with academia to guide real-world project cycles under dual mentor-ship, thereby accelerating technology transfer and strengthening student competencies in both theory and practice. In parallel, a three- tiered engineering practice system-ranging from course-embedded labs to full-scale industrial internships-amplifies hands-on development and integrates rigorous assessments of engineering proficiency. Furthermore, by incorporating cross-disciplinary mentorships, AI-enhanced campus tools, and cutting-edge lectures on new engineering and data-driven domains, the program produces graduates who are well-positioned in algorithm development, system architecture, and technology management. These reforms demonstrate an effective means of aligning educational supply with market demand, empowering students to address the evolving challenges of AI -driven industries while championing ethical and sustainable practices. The paper concludes by discussing the program's adaptability for continued innovation amid AGI-frontier breakthroughs and digital transformation imperatives.
Incident prediction is critical in industrial settings to prevent disruptions and optimize operations by anticipating failures. To this end, equipment logs are commonly utilized and converted into an XES-like log format, wherein each event is associated with a single case object (i.e., equipment) reporting its status. However, the resulting event log overlooks other data sources, such as logs of pre- and post-incident processes. These logs report activities applied to the equipment's related objects (e.g., hardware, software) in non-structred way, presenting challenges when using the XES-like log format. This paper introduces a meta-model to integrate and represent these diverse data sources in formalized format. To this end, we propose a domain-specific object log tailored to the incident-monitoring domain to represent multiple equipment-related objects. This meta-model was validated with a real-life dataset, showing how it provides an effective and structured representation of incident-related data.
Building robust data pipelines often requires spe-cialized engineering skills, creating barriers for domain experts with limited coding expertise. We introduce Prompt2DAG, a modular prompting methodology that transforms natural language descriptions into executable Apache Airflow workflows by decomposing generation into three sequential stages: structured analysis, configuration generation, and code implementation. This approach aligns with established software engineering principles of separation of concerns and progressive refinement. Our evalu-ation across five different LLMs demonstrates that Prompt2DAG significantly outperforms conventional end-to-end generation, im-proving code quality (+78.4 %) and structural integrity (+43.2 %) of generated pipelines. Using a data enrichment case study, we show how this approach enables the development of high-quality workflows through natural language, effectively democratizing data pipeline development.
As the complexity of business and scenarios contin-ues to grow, the traditional, inefficient, and cumbersome domain modeling process can no longer adapt to the rapid iteration requirements. In recent years, technological breakthroughs in generative artificial intelligence (Generative AI), particularly in large language models (LLMs), present novel opportunities to enhance domain modeling efficiency. While LLMs demonstrate baseline capabilities in information extraction, their potential for constructing complex domain-specific models remains underexplored. This study investigates how LLMs can facilitate automated domain modeling tasks and support domain modeling education. Through systematic experimentation, we evaluate the impacts of LLM fine-tuning techniques and prompt engineering strategies on model outputs, while comparing two distinct generation modes: indirect model construction via domain element extraction versus direct domain model generation. Our empirical results demonstrate that LLMs exhibit significant potential in supporting high-efficiency domain modeling, with fine-tuning techniques and indirect generation modes yielding superior out-comes. Furthermore, we illustrate LLMs' utility as pedagogical tools for domain modeling education while identifying critical limitations and implementation risks that warrant consideration in both practical applications and future research.
