Faculty development serves as a critical foundation for ensuring the quality of higher education.To meet the needs of cultivating specialized software talents and promoting teaching reform,it is particularly crucial t...Faculty development serves as a critical foundation for ensuring the quality of higher education.To meet the needs of cultivating specialized software talents and promoting teaching reform,it is particularly crucial to build a faculty team with knowledge in industry application fields and experience in domestic software development.This paper first analyzes the new requirements for the faculty imposed by the cultivation of specialized software talents and the existing problems in the current faculty.Then,in response to these issues,it introduces the reforms and explorations carried out by the School of Software Engineering at Beijing Jiaotong University in the construction of the faculty for cultivating specialized software talents.The aim is to build a high-caliber and diversified faculty that boasts strong political qualities,interdisciplinary integration,complementary advantages between full-time and part-time faculty,and in-depth integration of industry and education.展开更多
The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education....The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education.To do so,there is a need to understand the impact of LLMs on software engineering education.In this paper,we conducted a preliminary case study on three software requirements engineering classes where students are allowed to use LLMs to assist in their projects.Based on the students’experience,performance,and feedback from a survey conducted at the end of the courses,we characterized the challenges and benefits of applying LLMs in software engineering education.This research contributes to the ongoing discourse on the integration of LLMs in education,emphasizing both their prominent potential and the need for balanced,mindful usage.展开更多
With the rapid advancement of information technology,the quality assurance and evaluation of software engineering education have become pivotal concerns for higher education institutions.In this paper,we focus on a co...With the rapid advancement of information technology,the quality assurance and evaluation of software engineering education have become pivotal concerns for higher education institutions.In this paper,we focus on a comparative study of software engineering education in China and Europe,aiming to explore the theoretical frameworks and practical pathways employed in both regions.Initially,we introduce and contrast the engineering education accreditation systems of China and Europe,including the Chinese engineering education accreditation framework and the European EUR-ACE(European Accreditation of Engineering Programmes)standards,highlighting their core principles and evaluation methodologies.Subsequently,we provide case studies of several universities in China and Europe,such as Sun Yat-sen University,Tsinghua University,Technical University of Munich,and Imperial College London.Finally,we offer recommendations to foster mutual learning and collaboration between Chinese and European institutions,aiming to enhance the overall quality of software engineering education globally.This work provides valuable insights for educational administrators,faculty members,and policymakers,contributing to the ongoing improvement and innovative development of software engineering education in China and Europe.展开更多
In order to respond to the new engineering construction of the Ministry of Education,and explore the innovative talent training model of collaborative education and multidisciplinary integration,this paper relies on t...In order to respond to the new engineering construction of the Ministry of Education,and explore the innovative talent training model of collaborative education and multidisciplinary integration,this paper relies on the software engineering teaching team of the School of Software Engineering,Beijing University of Posts and Telecommunications,through the implementation of the collaborative education project of the Ministry of Education,and proposes the multi-course collaborative practice teaching system,through the reasonable cross-fusion of the practical links of the 5 software engineering courses in the college,realizes the multi-course collaborative education and reasonable cross-fusion of courses,shares practical project resources,introduces new enterprise technologies,and guides students’innovation and entrepreneurship provide a meaningful reference for the collaborative arrangement of teaching content and cross-disciplinary integration in the current university education system.展开更多
This paper focuses on the problems,opportunities,and challenges faced by software engineering education in the new era.We have studied the core ideas of the new model and reform,the specific measures implemented,and t...This paper focuses on the problems,opportunities,and challenges faced by software engineering education in the new era.We have studied the core ideas of the new model and reform,the specific measures implemented,and the challenges and solutions faced.The new model and reform must focus on cultivating practical abilities,introducing interdisciplinary knowledge,and strengthening innovation awareness and entrepreneurial spirit.The process of reform and innovation is carried out from the aspects of teaching methods,teaching means,and course performance evaluation in the teaching practice of software engineering courses.We adopt a method of“question guiding,simple and easy to understand,flexible and diverse,and emphasizing practical results”,optimizing the curriculum design,providing diverse learning opportunities,and establishing a platform for the industry-university-research cooperation.Our teaching philosophy is to adhere to the viewpoint of innovative teaching ideas,optimizing teaching methods and teaching means,and comprehensively improving the teaching quality and level of software engineering education.展开更多
The concept of“New Engineering”has put forward new challenges to the talents cultivation of universities.Due to some problems of the traditional Software Engineering curriculum,e.g.separated design at undergraduate-...The concept of“New Engineering”has put forward new challenges to the talents cultivation of universities.Due to some problems of the traditional Software Engineering curriculum,e.g.separated design at undergraduate-level and graduate-level courses,poor curriculum structure,lacking of industry characteristics.This paper proposes an integrated undergraduate-graduate education curriculum for Software Engineering Major,which is based on Software Engineering specialty knowledge system(C-SWEBOK)and focuses on the current national strategic demands.Additionally,the curriculum combines with the University’s transportation characteristics,and fuses the discipline of Software Engineering and Intelligent Transportation.The multi-level curriculum designed in this paper is with reasonable structure,complete system,progressive content,and salient feature,which provides the strong support for cultivating high-qualified software talents in line with national strategies and industry needs.展开更多
With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering s...With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering skills in practical contexts.This paper presents an intelligent and interactive learning(Meta-SEE)framework for software engineering education that combines the immersive capabilities of the metaverse with the cognitive processes of metacognition,to create an interactive and engaging learning environment.In the Meta-SEE framework,learners are immersed in a virtual world where they can collaboratively engage with concepts and practices of software engineering.Through the integration of metacognitive strategies,learners are empowered to monitor,regulate,and adapt their learning processes.By incorporating metacognition within the metaverse,learners gain a deeper understanding of their own thinking processes and become self-directed learners.In addition,MetaSEE has the potential to revolutionize software engineering education by offering a dynamic,immersive,and personalized learning experience.It allows learners to engage in realistic software development scenarios,explore complex systems,and collaborate with peers and instructors in virtual spaces.展开更多
As a highly advanced conversational AI chatbot trained on extensive datasets,ChatGPT has garnered significant attention across various domains,including academia,industry,and education.In the field of education,existi...As a highly advanced conversational AI chatbot trained on extensive datasets,ChatGPT has garnered significant attention across various domains,including academia,industry,and education.In the field of education,existing studies primarily focus on 2 areas:Assessing the potential utility of ChatGPT in education by examining its capabilities and limitations;exploring the educational scenarios that could benefit from the integration of ChatGPT.In contrast to these studies,we conduct a user survey targeting undergraduate students specializing in Software Engineering,aiming to gain insights into their perceptions,challenges,and expectations regarding the utilization of ChatGPT.Based on the results of the survey,we provide valuable guidance on the effective incorporation of ChatGPT in the realm of software engineering education.展开更多
"Semester Training"has been adopted as an important part of the personnel training in software engineering majors since it was first put forward.The ultimate goal of semester training is to improve the profe..."Semester Training"has been adopted as an important part of the personnel training in software engineering majors since it was first put forward.The ultimate goal of semester training is to improve the professional quality of students in an all-round way,then eventually achieve the goal of satisfactory employment for both students and enterprises.However,in order to achieve the above purpose,the design of traditional training project still has the following problems:the topic selection of traditional training is designed by teachers in college,which lacks the training of engineering ability aiming at practical problems;the content and technology of traditional project training are out of date,ignoring the urgent demand of software industry development for advanced technology application;the traditional project training inspects the mastery of knowledge in each semester Degree,ignores the incremental of a progressive training system.In view of the above problems,this study proposes an Application-Oriented Software Engineering Semester Training System.Practice has proved that the construction of the training system can effectively improve the quality of teaching,so as to further improve the comprehensive quality of students.展开更多
In view of the increasingly rapid development of global economic integration and combined with the existing modes of training international software engineering talents in China,this paper deeply analyzes and obtains ...In view of the increasingly rapid development of global economic integration and combined with the existing modes of training international software engineering talents in China,this paper deeply analyzes and obtains the existing problems in the current teaching process,and proposes various teaching reform measures under the guidance of CDIO higher engineering education thought.Through many years of teaching practice experience,we can find that our reform has achieved remarkable results.展开更多
This study presents an energy-efficient Internet of Things(IoT)-based wireless sensor network(WSN)framework for autonomous data validation in remote environmental monitoring.We address two critical challenges in WSNs:...This study presents an energy-efficient Internet of Things(IoT)-based wireless sensor network(WSN)framework for autonomous data validation in remote environmental monitoring.We address two critical challenges in WSNs:ensuring data reliability and optimizing energy consumption.Our novel approach integrates an artificial neural network(ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture.The proposed ANN model is designed to simultaneously detect multiple fault types,including spike faults,stuckat faults,outliers,and out-of-range faults.We collected sensor data at 5-minute intervals over three months,using temperature and humidity sensors.The ANN was trained on 70%of the 26,280 data points per sensor,with 15%each for validation and testing.Our framework demonstrated a 97.1%improvement in fault detection accuracy(measured by F1 score)compared to existing methods,including rule-based,moving average,and statistical outlier detection approaches.The energy efficiency of the system was evaluated through 24-h power consumption tests,showing significant savings over traditional WSN architectures.Key contributions include a multi-fault detection ANN model balancing accuracy and computational efficiency,an energy-optimized IoTWSN architecture for remote deployments,and a comprehensive performance evaluation framework.While our approach offers improvements in both data validation and energy efficiency,we acknowledge limitations such as potential scalability issues and the need for further real-world testing.This research advances the field of remote environmental monitoring by providing a robust,energy-efficient solution for ensuring data reliability in challenging deployment scenarios.Future work will explore more advanced machine learning techniques and extended field testing to further validate and improve the system’s performance.展开更多
Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors c...Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors consume less power,but lack the capability to resolve static stimuli.Here,we address this issue by utilizing the unique polarization chemistry of conjugated polymers for the first time and propose a new type of bioinspired,passive,and bio-friendly tactile sensors for resolving both static and dynamic stimuli.Specifically,to emulate the polarization process of natural sensory cells,conjugated polymers(including poly(3,4-ethylenedioxythiophen e):poly(styrenesulfonate),polyaniline,or polypyrrole)are controllably polarized into two opposite states to create artificial potential differences.The controllable and reversible polarization process of the conjugated polymers is fully in situ characterized.Then,a micro-structured ionic electrolyte is employed to imitate the natural ion channels and to encode external touch stimulations into the variation in potential difference outputs.Compared with the currently existing tactile sensing devices,the developed tactile sensors feature distinct characteristics including fully organic composition,high sensitivity(up to 773 mV N^(−1)),ultralow power consumption(nW),as well as superior bio-friendliness.As demonstrations,both single point tactile perception(surface texture perception and material property perception)and two-dimensional tactile recognitions(shape or profile perception)with high accuracy are successfully realized using self-defined machine learning algorithms.This tactile sensing concept innovation based on the polarization chemistry of conjugated polymers opens up a new path to create robotic tactile sensors and prosthetic electronic skins.展开更多
NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large lan...NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials discovery.The platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular informatics.By automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural applications.Additionally,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding expertise.Through automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials research.The latest version(available at https://figshare.com/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction tasks.By utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials datasets.While NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training data.By providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.展开更多
Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resourc...Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.展开更多
In common practice in the oil fields,the injection of water and gas into reservoirs is a crucial technique to increase production.The control of the waterflooding front in oil/gas exploitation is a matter of great con...In common practice in the oil fields,the injection of water and gas into reservoirs is a crucial technique to increase production.The control of the waterflooding front in oil/gas exploitation is a matter of great concern to reservoir engineers.Monitoring the waterflooding front in oil/gas wells plays a very important role in adjusting the well network and later in production,taking advantage of the remaining oil po-tential and ultimately achieving great success in improving the recovery rate.For a long time,micro-seismic monitoring,numerical simulation,four-dimensional seismic and other methods have been widely used in waterflooding front monitoring.However,reconciling their reliability and cost poses a significant challenge.In order to achieve real-time,reliable and cost-effective monitoring,we propose an innovative method for waterflooding front monitoring through the similarity analysis of passive source time-lapse seismic images.Typically,passive source seismic data collected from oil fields have extremely low signal-to-noise ratio(SNR),which poses a serious problem for obtaining structural images.The proposed method aims to visualize and analyze underground changes by highlighting time-lapse images and provide a strategy for underground monitoring using long-term passive source data under low SNR conditions.First,we verify the feasibility of the proposed method by designing a theoretical model.Then,we conduct an analysis of the correlation coefficient(similarity)on the passive source time-lapse seismic imaging results to enhance the image differences and identify the simulated waterflooding fronts.Finally,the proposed method is applied to the actual waterflooding front monitoring tasks in Shengli Oilfield,China.The research findings indicate that the monitoring results are consistent with the actual devel-opment conditions,which in turn demonstrates that the proposed method has great potential for practical application and is very suitable for monitoring common development tasks in oil fields.展开更多
In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing method...In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions.To address these issues,we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition.A dynamic gating mechanism is applied across unimodal encoding,cross-modal alignment,and emotion transfer modeling,substantially improving noise robustness and feature alignment.First,we construct a unimodal encoder based on gated recurrent units and feature-selection gating to suppress intra-modal noise and enhance contextual representation.Second,we design a gated-attention crossmodal encoder that dynamically calibrates the complementary contributions of visual and audio modalities to the dominant textual features and eliminates redundant information.Finally,we introduce a gated enhanced emotion transfer module that explicitly models the temporal dependence of emotional evolution in dialogues via transfer gating and optimizes continuity modeling with a comparative learning loss.Experimental results demonstrate that the proposed method outperforms state-of-the-art models on the public MELD and IEMOCAP datasets.展开更多
Debris flows have increased in frequency within the arid Daheba Basin on the northeastern Tibetan Plateau,but their sediment sources remain poorly quantified.Using high-resolution UAV-derived DEMs from 51 small catchm...Debris flows have increased in frequency within the arid Daheba Basin on the northeastern Tibetan Plateau,but their sediment sources remain poorly quantified.Using high-resolution UAV-derived DEMs from 51 small catchments,this study evaluates the relative contributions of landslide-derived and channel-derived sediment in controlling debris-flow fan magnitude,and quantifies sediment supply during the 2023 rainy season using DEM differencing.A total of 766 landslides occurred predominantly on slopes of 40°-50°and southeast-southwest aspects,generating 36.17×10^(4)m^(3)of material.Gully heads exhibit exceptionally lower landscape dissection thresholds compared with loess and Quaternary regions in China,indicating high susceptibility to failure under intensified runoff.The results show that Landslide area-volume scaling exponent(b)varies with hillslope geometry(K_(u)):b>1.3 for K_(u)<8 and generally b<1.3 for K_(u)>8,indicating more complete scar evacuation upslope and partial erosion downslope.Despite the abundance of landslides,their contribution to debris flow fan magnitude is minor(<25%),with channel debris dominating(>75%).DEM differencing of a small catchment before and after the 2023 rainy season further reveals that sediment supply originates primarily from the main channel(60.6%)and tributaries(23.3%),with smaller contributions from channel banks(6.8%)and channel heads(9.2%).Tributaries exhibit the greatest mean erosion depth(4.2 m),exceeding that of the main channel(3.8 m).These findings demonstrate that debris-flow material supply in the Daheba Basin is transport-limited and controlled mainly by fluvial entrainment rather than slope failures.Climatic warming and wetting may enhance slope instability,but sediment mobilization is dominantly governed by runoff-driven channel erosion.This study underscores the importance of prioritizing channel sediment dynamics in debris flow hazards assessments for arid regions of the Tibetan plateau.展开更多
Although Named Entity Recognition(NER)in cybersecurity has historically concentrated on threat intelligence,vital security data can be found in a variety of sources,such as open-source intelligence and unprocessed too...Although Named Entity Recognition(NER)in cybersecurity has historically concentrated on threat intelligence,vital security data can be found in a variety of sources,such as open-source intelligence and unprocessed tool outputs.When dealing with technical language,the coexistence of structured and unstructured data poses serious issues for traditional BERT-based techniques.We introduce a three-phase approach for improved NER inmulti-source cybersecurity data that makes use of large language models(LLMs).To ensure thorough entity coverage,our method starts with an identification module that uses dynamic prompting techniques.To lessen hallucinations,the extraction module uses confidence-based self-assessment and cross-checking using regex validation.The tagging module links to knowledge bases for contextual validation and uses SecureBERT in conjunction with conditional random fields to detect entity boundaries precisely.Our framework creates efficient natural language segments by utilizing decoderbased LLMs with 10B parameters.When compared to baseline SecureBERT implementations,evaluation across four cybersecurity data sources shows notable gains,with a 9.4%–25.21%greater recall and a 6.38%–17.3%better F1-score.Our refined model matches larger models and achieves 2.6%–4.9%better F1-score for technical phrase recognition than the state-of-the-art alternatives Claude 3.5 Sonnet,Llama3-8B,and Mixtral-7B.The three-stage architecture identification-extraction-tagging pipeline tackles important cybersecurity NER issues.Through effective architectures,these developments preserve deployability while setting a new standard for entity extraction in challenging security scenarios.The findings show how specific enhancements in hybrid recognition,validation procedures,and prompt engineering raise NER performance above monolithic LLM approaches in cybersecurity applications,especially for technical entity extraction fromheterogeneous sourceswhere conventional techniques fall short.Because of itsmodular nature,the framework can be upgraded at the component level as new methods are developed.展开更多
Cascading failure can cause great damage to complex networks, so it is of great significance to improve the network robustness against cascading failure. Many previous existing works on load-redistribution strategies ...Cascading failure can cause great damage to complex networks, so it is of great significance to improve the network robustness against cascading failure. Many previous existing works on load-redistribution strategies require global information, which is not suitable for large scale networks, and some strategies based on local information assume that the load of a node is always its initial load before the network is attacked, and the load of the failure node is redistributed to its neighbors according to their initial load or initial residual capacity. This paper proposes a new load-redistribution strategy based on local information considering an ever-changing load. It redistributes the loads of the failure node to its nearest neighbors according to their current residual capacity, which makes full use of the residual capacity of the network. Experiments are conducted on two typical networks and two real networks, and the experimental results show that the new load-redistribution strategy can reduce the size of cascading failure efficiently.展开更多
Unmanned Aerial Vehicle(UAV)has emerged as a promising novel application for the Sixth-Generation(6G)wireless communication by leveraging more favorable Line-of-Sight(Lo S)propagation.However,the jamming resistance by...Unmanned Aerial Vehicle(UAV)has emerged as a promising novel application for the Sixth-Generation(6G)wireless communication by leveraging more favorable Line-of-Sight(Lo S)propagation.However,the jamming resistance by exploiting UAV’s mobility is a new challenge in the UAV-ground communication.This paper investigates the trajectory planning problem in an UAV communication system,where the UAV is operated by a Ground Control Unit(GCU)to perform certain tasks in the presence of multiple jammers with imperfect power and location information.To ensure the reliability of the GCU-to-UAV link,we formulate the problem as a non-convex semi-infinite optimization,aiming to maximize the average worst-case Signal-toInterference-plus-Noise Ratio(SINR)over a given flight duration by designing the robust trajectory of the UAV under stringent energy availability constraints.To handle this problem efficiently,we develop an iterative algorithm for the solution with the aid of S-procedure and Successive Convex Approximation(SCA)method.Numerous results demonstrate the efficacy of our proposed algorithm and offer some useful design insights to practical system.展开更多
文摘Faculty development serves as a critical foundation for ensuring the quality of higher education.To meet the needs of cultivating specialized software talents and promoting teaching reform,it is particularly crucial to build a faculty team with knowledge in industry application fields and experience in domestic software development.This paper first analyzes the new requirements for the faculty imposed by the cultivation of specialized software talents and the existing problems in the current faculty.Then,in response to these issues,it introduces the reforms and explorations carried out by the School of Software Engineering at Beijing Jiaotong University in the construction of the faculty for cultivating specialized software talents.The aim is to build a high-caliber and diversified faculty that boasts strong political qualities,interdisciplinary integration,complementary advantages between full-time and part-time faculty,and in-depth integration of industry and education.
基金supported in part by the Teaching Reform Project of Chongqing University of Posts and Telecommunications,China under Grant No.XJG23234Chongqing Municipal Higher Education Teaching Reform Research Project under Grant No.203399the Doctoral Direct Train Project of Chongqing Science and Technology Bureau under Grant No.CSTB2022BSXM-JSX0007。
文摘The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education.To do so,there is a need to understand the impact of LLMs on software engineering education.In this paper,we conducted a preliminary case study on three software requirements engineering classes where students are allowed to use LLMs to assist in their projects.Based on the students’experience,performance,and feedback from a survey conducted at the end of the courses,we characterized the challenges and benefits of applying LLMs in software engineering education.This research contributes to the ongoing discourse on the integration of LLMs in education,emphasizing both their prominent potential and the need for balanced,mindful usage.
基金supported by the Guangdong Higher Education Association’s“14th Five Year Plan”2024 Higher Education Research Project(24GYB03)the Natural Science Foundation of Guangdong Province(2024A1515010255)。
文摘With the rapid advancement of information technology,the quality assurance and evaluation of software engineering education have become pivotal concerns for higher education institutions.In this paper,we focus on a comparative study of software engineering education in China and Europe,aiming to explore the theoretical frameworks and practical pathways employed in both regions.Initially,we introduce and contrast the engineering education accreditation systems of China and Europe,including the Chinese engineering education accreditation framework and the European EUR-ACE(European Accreditation of Engineering Programmes)standards,highlighting their core principles and evaluation methodologies.Subsequently,we provide case studies of several universities in China and Europe,such as Sun Yat-sen University,Tsinghua University,Technical University of Munich,and Imperial College London.Finally,we offer recommendations to foster mutual learning and collaboration between Chinese and European institutions,aiming to enhance the overall quality of software engineering education globally.This work provides valuable insights for educational administrators,faculty members,and policymakers,contributing to the ongoing improvement and innovative development of software engineering education in China and Europe.
基金supported in part by Educational Reform Projects of BUPT.
文摘In order to respond to the new engineering construction of the Ministry of Education,and explore the innovative talent training model of collaborative education and multidisciplinary integration,this paper relies on the software engineering teaching team of the School of Software Engineering,Beijing University of Posts and Telecommunications,through the implementation of the collaborative education project of the Ministry of Education,and proposes the multi-course collaborative practice teaching system,through the reasonable cross-fusion of the practical links of the 5 software engineering courses in the college,realizes the multi-course collaborative education and reasonable cross-fusion of courses,shares practical project resources,introduces new enterprise technologies,and guides students’innovation and entrepreneurship provide a meaningful reference for the collaborative arrangement of teaching content and cross-disciplinary integration in the current university education system.
基金supported in part by the postgraduate demonstration course of Guangdong Province Department of Education Programmed Trading(No.2023SFKC_022)the Computer Architecture First Class Course Project,South China Normal University-Baidu Pineapple Talent Training Practice Basethe 2023 Project of Computer Education Research Association of Chinese Universities(No.CERACU2023R02)。
文摘This paper focuses on the problems,opportunities,and challenges faced by software engineering education in the new era.We have studied the core ideas of the new model and reform,the specific measures implemented,and the challenges and solutions faced.The new model and reform must focus on cultivating practical abilities,introducing interdisciplinary knowledge,and strengthening innovation awareness and entrepreneurial spirit.The process of reform and innovation is carried out from the aspects of teaching methods,teaching means,and course performance evaluation in the teaching practice of software engineering courses.We adopt a method of“question guiding,simple and easy to understand,flexible and diverse,and emphasizing practical results”,optimizing the curriculum design,providing diverse learning opportunities,and establishing a platform for the industry-university-research cooperation.Our teaching philosophy is to adhere to the viewpoint of innovative teaching ideas,optimizing teaching methods and teaching means,and comprehensively improving the teaching quality and level of software engineering education.
文摘The concept of“New Engineering”has put forward new challenges to the talents cultivation of universities.Due to some problems of the traditional Software Engineering curriculum,e.g.separated design at undergraduate-level and graduate-level courses,poor curriculum structure,lacking of industry characteristics.This paper proposes an integrated undergraduate-graduate education curriculum for Software Engineering Major,which is based on Software Engineering specialty knowledge system(C-SWEBOK)and focuses on the current national strategic demands.Additionally,the curriculum combines with the University’s transportation characteristics,and fuses the discipline of Software Engineering and Intelligent Transportation.The multi-level curriculum designed in this paper is with reasonable structure,complete system,progressive content,and salient feature,which provides the strong support for cultivating high-qualified software talents in line with national strategies and industry needs.
基金partially funded by the 2023 Teaching Quality Engineering Construction Project of Sun Yat-sen University(No.76250-12230036)the 2023 Project of Computer Education Research Association of Chinese Universities(No.CERACU2023R02)。
文摘With the rapid evolution of technology and the increasing complexity of software systems,there is a growing demand for effective educational approaches that empower learners to acquire and apply software engineering skills in practical contexts.This paper presents an intelligent and interactive learning(Meta-SEE)framework for software engineering education that combines the immersive capabilities of the metaverse with the cognitive processes of metacognition,to create an interactive and engaging learning environment.In the Meta-SEE framework,learners are immersed in a virtual world where they can collaboratively engage with concepts and practices of software engineering.Through the integration of metacognitive strategies,learners are empowered to monitor,regulate,and adapt their learning processes.By incorporating metacognition within the metaverse,learners gain a deeper understanding of their own thinking processes and become self-directed learners.In addition,MetaSEE has the potential to revolutionize software engineering education by offering a dynamic,immersive,and personalized learning experience.It allows learners to engage in realistic software development scenarios,explore complex systems,and collaborate with peers and instructors in virtual spaces.
基金supported in part by the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515012292)the 2023 Teaching Quality Engineering Construction Project of Sun Yat-sen University(Grant No.76250-12230036)the 2023 Project of Computer Education Research Association ofChinese Universities(Grant No.CERACU2023R02)。
文摘As a highly advanced conversational AI chatbot trained on extensive datasets,ChatGPT has garnered significant attention across various domains,including academia,industry,and education.In the field of education,existing studies primarily focus on 2 areas:Assessing the potential utility of ChatGPT in education by examining its capabilities and limitations;exploring the educational scenarios that could benefit from the integration of ChatGPT.In contrast to these studies,we conduct a user survey targeting undergraduate students specializing in Software Engineering,aiming to gain insights into their perceptions,challenges,and expectations regarding the utilization of ChatGPT.Based on the results of the survey,we provide valuable guidance on the effective incorporation of ChatGPT in the realm of software engineering education.
基金supported by the Fundamental Research Funds for the Central Universities under Grant 2020RC011.
文摘"Semester Training"has been adopted as an important part of the personnel training in software engineering majors since it was first put forward.The ultimate goal of semester training is to improve the professional quality of students in an all-round way,then eventually achieve the goal of satisfactory employment for both students and enterprises.However,in order to achieve the above purpose,the design of traditional training project still has the following problems:the topic selection of traditional training is designed by teachers in college,which lacks the training of engineering ability aiming at practical problems;the content and technology of traditional project training are out of date,ignoring the urgent demand of software industry development for advanced technology application;the traditional project training inspects the mastery of knowledge in each semester Degree,ignores the incremental of a progressive training system.In view of the above problems,this study proposes an Application-Oriented Software Engineering Semester Training System.Practice has proved that the construction of the training system can effectively improve the quality of teaching,so as to further improve the comprehensive quality of students.
文摘In view of the increasingly rapid development of global economic integration and combined with the existing modes of training international software engineering talents in China,this paper deeply analyzes and obtains the existing problems in the current teaching process,and proposes various teaching reform measures under the guidance of CDIO higher engineering education thought.Through many years of teaching practice experience,we can find that our reform has achieved remarkable results.
基金supported by King Saud University through Researchers Supporting Project number(RSPD2024R1006),King Saud University,Riyadh,Saudi Arabia.
文摘This study presents an energy-efficient Internet of Things(IoT)-based wireless sensor network(WSN)framework for autonomous data validation in remote environmental monitoring.We address two critical challenges in WSNs:ensuring data reliability and optimizing energy consumption.Our novel approach integrates an artificial neural network(ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture.The proposed ANN model is designed to simultaneously detect multiple fault types,including spike faults,stuckat faults,outliers,and out-of-range faults.We collected sensor data at 5-minute intervals over three months,using temperature and humidity sensors.The ANN was trained on 70%of the 26,280 data points per sensor,with 15%each for validation and testing.Our framework demonstrated a 97.1%improvement in fault detection accuracy(measured by F1 score)compared to existing methods,including rule-based,moving average,and statistical outlier detection approaches.The energy efficiency of the system was evaluated through 24-h power consumption tests,showing significant savings over traditional WSN architectures.Key contributions include a multi-fault detection ANN model balancing accuracy and computational efficiency,an energy-optimized IoTWSN architecture for remote deployments,and a comprehensive performance evaluation framework.While our approach offers improvements in both data validation and energy efficiency,we acknowledge limitations such as potential scalability issues and the need for further real-world testing.This research advances the field of remote environmental monitoring by providing a robust,energy-efficient solution for ensuring data reliability in challenging deployment scenarios.Future work will explore more advanced machine learning techniques and extended field testing to further validate and improve the system’s performance.
基金financially supported by the Sichuan Science and Technology Program(2022YFS0025 and 2024YFFK0133)supported by the“Fundamental Research Funds for the Central Universities of China.”。
文摘Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors consume less power,but lack the capability to resolve static stimuli.Here,we address this issue by utilizing the unique polarization chemistry of conjugated polymers for the first time and propose a new type of bioinspired,passive,and bio-friendly tactile sensors for resolving both static and dynamic stimuli.Specifically,to emulate the polarization process of natural sensory cells,conjugated polymers(including poly(3,4-ethylenedioxythiophen e):poly(styrenesulfonate),polyaniline,or polypyrrole)are controllably polarized into two opposite states to create artificial potential differences.The controllable and reversible polarization process of the conjugated polymers is fully in situ characterized.Then,a micro-structured ionic electrolyte is employed to imitate the natural ion channels and to encode external touch stimulations into the variation in potential difference outputs.Compared with the currently existing tactile sensing devices,the developed tactile sensors feature distinct characteristics including fully organic composition,high sensitivity(up to 773 mV N^(−1)),ultralow power consumption(nW),as well as superior bio-friendliness.As demonstrations,both single point tactile perception(surface texture perception and material property perception)and two-dimensional tactile recognitions(shape or profile perception)with high accuracy are successfully realized using self-defined machine learning algorithms.This tactile sensing concept innovation based on the polarization chemistry of conjugated polymers opens up a new path to create robotic tactile sensors and prosthetic electronic skins.
基金supported by the Jiangsu Provincial Science and Technology Project Basic Research Program(Natural Science Foundation of Jiangsu Province)(No.BK20211283).
文摘NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials discovery.The platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular informatics.By automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural applications.Additionally,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding expertise.Through automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials research.The latest version(available at https://figshare.com/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction tasks.By utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials datasets.While NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training data.By providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.
基金funded by Researchers Supporting Project Number(RSPD2025R947)King Saud University,Riyadh,Saudi Arabia.
文摘Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.
基金supported by the CNPC-SWPU Innovation Alliance Technology Cooperation Project(2020CX020000)the National Natural Science Foundation of China(42022028)+1 种基金the Natural Science Foundation of Sichuan Province(24NSFSC0808)the China Scholarship Council(202306440144)。
文摘In common practice in the oil fields,the injection of water and gas into reservoirs is a crucial technique to increase production.The control of the waterflooding front in oil/gas exploitation is a matter of great concern to reservoir engineers.Monitoring the waterflooding front in oil/gas wells plays a very important role in adjusting the well network and later in production,taking advantage of the remaining oil po-tential and ultimately achieving great success in improving the recovery rate.For a long time,micro-seismic monitoring,numerical simulation,four-dimensional seismic and other methods have been widely used in waterflooding front monitoring.However,reconciling their reliability and cost poses a significant challenge.In order to achieve real-time,reliable and cost-effective monitoring,we propose an innovative method for waterflooding front monitoring through the similarity analysis of passive source time-lapse seismic images.Typically,passive source seismic data collected from oil fields have extremely low signal-to-noise ratio(SNR),which poses a serious problem for obtaining structural images.The proposed method aims to visualize and analyze underground changes by highlighting time-lapse images and provide a strategy for underground monitoring using long-term passive source data under low SNR conditions.First,we verify the feasibility of the proposed method by designing a theoretical model.Then,we conduct an analysis of the correlation coefficient(similarity)on the passive source time-lapse seismic imaging results to enhance the image differences and identify the simulated waterflooding fronts.Finally,the proposed method is applied to the actual waterflooding front monitoring tasks in Shengli Oilfield,China.The research findings indicate that the monitoring results are consistent with the actual devel-opment conditions,which in turn demonstrates that the proposed method has great potential for practical application and is very suitable for monitoring common development tasks in oil fields.
基金funded by“the Fanying Special Program of the National Natural Science Foundation of China,grant number 62341307”“the Scientific research project of Jiangxi Provincial Department of Education,grant number GJJ200839”“theDoctoral startup fund of JiangxiUniversity of Technology,grant number 205200100402”.
文摘In multi-modal emotion recognition,excessive reliance on historical context often impedes the detection of emotional shifts,while modality heterogeneity and unimodal noise limit recognition performance.Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions.To address these issues,we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition.A dynamic gating mechanism is applied across unimodal encoding,cross-modal alignment,and emotion transfer modeling,substantially improving noise robustness and feature alignment.First,we construct a unimodal encoder based on gated recurrent units and feature-selection gating to suppress intra-modal noise and enhance contextual representation.Second,we design a gated-attention crossmodal encoder that dynamically calibrates the complementary contributions of visual and audio modalities to the dominant textual features and eliminates redundant information.Finally,we introduce a gated enhanced emotion transfer module that explicitly models the temporal dependence of emotional evolution in dialogues via transfer gating and optimizes continuity modeling with a comparative learning loss.Experimental results demonstrate that the proposed method outperforms state-of-the-art models on the public MELD and IEMOCAP datasets.
基金supported by the Chengdu University of Information Technology Doctoral Fund‘Study on the Initiation Mechanism of Hydraulic Debris Flow Based on Shields Stress’(KYTZ202275)the Second Tibetan Scientific Expedition and Research Program(Grant No.2019QZKK0902)。
文摘Debris flows have increased in frequency within the arid Daheba Basin on the northeastern Tibetan Plateau,but their sediment sources remain poorly quantified.Using high-resolution UAV-derived DEMs from 51 small catchments,this study evaluates the relative contributions of landslide-derived and channel-derived sediment in controlling debris-flow fan magnitude,and quantifies sediment supply during the 2023 rainy season using DEM differencing.A total of 766 landslides occurred predominantly on slopes of 40°-50°and southeast-southwest aspects,generating 36.17×10^(4)m^(3)of material.Gully heads exhibit exceptionally lower landscape dissection thresholds compared with loess and Quaternary regions in China,indicating high susceptibility to failure under intensified runoff.The results show that Landslide area-volume scaling exponent(b)varies with hillslope geometry(K_(u)):b>1.3 for K_(u)<8 and generally b<1.3 for K_(u)>8,indicating more complete scar evacuation upslope and partial erosion downslope.Despite the abundance of landslides,their contribution to debris flow fan magnitude is minor(<25%),with channel debris dominating(>75%).DEM differencing of a small catchment before and after the 2023 rainy season further reveals that sediment supply originates primarily from the main channel(60.6%)and tributaries(23.3%),with smaller contributions from channel banks(6.8%)and channel heads(9.2%).Tributaries exhibit the greatest mean erosion depth(4.2 m),exceeding that of the main channel(3.8 m).These findings demonstrate that debris-flow material supply in the Daheba Basin is transport-limited and controlled mainly by fluvial entrainment rather than slope failures.Climatic warming and wetting may enhance slope instability,but sediment mobilization is dominantly governed by runoff-driven channel erosion.This study underscores the importance of prioritizing channel sediment dynamics in debris flow hazards assessments for arid regions of the Tibetan plateau.
文摘Although Named Entity Recognition(NER)in cybersecurity has historically concentrated on threat intelligence,vital security data can be found in a variety of sources,such as open-source intelligence and unprocessed tool outputs.When dealing with technical language,the coexistence of structured and unstructured data poses serious issues for traditional BERT-based techniques.We introduce a three-phase approach for improved NER inmulti-source cybersecurity data that makes use of large language models(LLMs).To ensure thorough entity coverage,our method starts with an identification module that uses dynamic prompting techniques.To lessen hallucinations,the extraction module uses confidence-based self-assessment and cross-checking using regex validation.The tagging module links to knowledge bases for contextual validation and uses SecureBERT in conjunction with conditional random fields to detect entity boundaries precisely.Our framework creates efficient natural language segments by utilizing decoderbased LLMs with 10B parameters.When compared to baseline SecureBERT implementations,evaluation across four cybersecurity data sources shows notable gains,with a 9.4%–25.21%greater recall and a 6.38%–17.3%better F1-score.Our refined model matches larger models and achieves 2.6%–4.9%better F1-score for technical phrase recognition than the state-of-the-art alternatives Claude 3.5 Sonnet,Llama3-8B,and Mixtral-7B.The three-stage architecture identification-extraction-tagging pipeline tackles important cybersecurity NER issues.Through effective architectures,these developments preserve deployability while setting a new standard for entity extraction in challenging security scenarios.The findings show how specific enhancements in hybrid recognition,validation procedures,and prompt engineering raise NER performance above monolithic LLM approaches in cybersecurity applications,especially for technical entity extraction fromheterogeneous sourceswhere conventional techniques fall short.Because of itsmodular nature,the framework can be upgraded at the component level as new methods are developed.
基金Project supported by the National Basic Research Program of China(Grant No.2013CB328903)the Special Fund of 2011 Internet of Things Development of Ministry of Industry and Information Technology,China(Grant No.2011BAJ03B13-2)+1 种基金the National Natural Science Foundation of China(Grant No.61473050)the Key Science and Technology Program of Chongqing,China(Grant No.cstc2012gg-yyjs40008)
文摘Cascading failure can cause great damage to complex networks, so it is of great significance to improve the network robustness against cascading failure. Many previous existing works on load-redistribution strategies require global information, which is not suitable for large scale networks, and some strategies based on local information assume that the load of a node is always its initial load before the network is attacked, and the load of the failure node is redistributed to its neighbors according to their initial load or initial residual capacity. This paper proposes a new load-redistribution strategy based on local information considering an ever-changing load. It redistributes the loads of the failure node to its nearest neighbors according to their current residual capacity, which makes full use of the residual capacity of the network. Experiments are conducted on two typical networks and two real networks, and the experimental results show that the new load-redistribution strategy can reduce the size of cascading failure efficiently.
文摘Unmanned Aerial Vehicle(UAV)has emerged as a promising novel application for the Sixth-Generation(6G)wireless communication by leveraging more favorable Line-of-Sight(Lo S)propagation.However,the jamming resistance by exploiting UAV’s mobility is a new challenge in the UAV-ground communication.This paper investigates the trajectory planning problem in an UAV communication system,where the UAV is operated by a Ground Control Unit(GCU)to perform certain tasks in the presence of multiple jammers with imperfect power and location information.To ensure the reliability of the GCU-to-UAV link,we formulate the problem as a non-convex semi-infinite optimization,aiming to maximize the average worst-case Signal-toInterference-plus-Noise Ratio(SINR)over a given flight duration by designing the robust trajectory of the UAV under stringent energy availability constraints.To handle this problem efficiently,we develop an iterative algorithm for the solution with the aid of S-procedure and Successive Convex Approximation(SCA)method.Numerous results demonstrate the efficacy of our proposed algorithm and offer some useful design insights to practical system.
