As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processin...As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processing and enabled significant improvements in various applications.This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models(LLM).Our primary objective is to evaluate the effectiveness of Static Application Security Testing(SAST)by applying various techniques such as prompt persona,structure outputs and zero-shot.To the selection of the LLMs(CodeLlama 7B,DeepSeek coder 7B,Gemini 1.5 Flash,Gemini 2.0 Flash,Mistral 7b Instruct,Phi 38b Mini 128K instruct,Qwen 2.5 coder,StartCoder 27B)with comparison and combination with Find Security Bugs.The evaluation method will involve using a selected dataset containing vulnerabilities,and the results to provide insights for different scenarios according to the software criticality(Business critical,non-critical,minimum effort,best effort)In detail,the main objectives of this study are to investigate if large language models outperform or exceed the capabilities of traditional static analysis tools,if the combining LLMs with Static Application Security Testing(SAST)tools lead to an improvement and the possibility that local machine learning models on a normal computer produce reliable results.Summarizing the most important conclusions of the research,it can be said that while it is true that the results have improved depending on the size of the LLM for business-critical software,the best results have been obtained by SAST analysis.This differs in“NonCritical,”“Best Effort,”and“Minimum Effort”scenarios,where the combination of LLM(Gemini)+SAST has obtained better results.展开更多
The article entitled with OptoGPT:A foundation model for inverse design in optical multilayer thin film structures1,with doi:10.29026/oea.2024.240062,published in No.7,Vol.7,2024 of Opto-Electronic Advances,has attrac...The article entitled with OptoGPT:A foundation model for inverse design in optical multilayer thin film structures1,with doi:10.29026/oea.2024.240062,published in No.7,Vol.7,2024 of Opto-Electronic Advances,has attracted attention from many researchers.As a result,the authors received many requests on the possibility sharing their code,model,and dataset in the mentioned work.To facilitate the needs of the research community,the authors decide to make the code,model,and datasets of OptoGPT public,enabling broader utilization and further development of enhanced models.展开更多
介绍了高温蠕变工况下运行的压力容器可能出现的失效模式,结合工程设计现状,指出了我国当前压力容器标准体系在确定高温蠕变工况许用压应力时存在的技术瓶颈,在此基础之上引出ASME Code Case 3029,对其适用范围、发展历程、产生背景及...介绍了高温蠕变工况下运行的压力容器可能出现的失效模式,结合工程设计现状,指出了我国当前压力容器标准体系在确定高温蠕变工况许用压应力时存在的技术瓶颈,在此基础之上引出ASME Code Case 3029,对其适用范围、发展历程、产生背景及工程意义进行了简单的介绍,以某工程设计项目中的实际结构为例,介绍了该方法的使用过程及注意事项,并结合压力容器工程设计领域的实际需求,对我国标准体系下一步的制定或修订方向提出了展望。展开更多
This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional(1D)barcodes and Quick Response(QR)codes,addressing critical challenges in logistics ope...This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional(1D)barcodes and Quick Response(QR)codes,addressing critical challenges in logistics operations.The proposed solution leverages an efficient Pix2Pix-based framework,a type of conditional Generative Adversarial Network(GAN)optimized for image-to-image translation tasks,enabling the recovery of degraded barcodes and QR codes with minimal computational overhead.A core contribution of this work is the development of a synthetic dataset that simulates realistic damage scenarios frequently encountered in logistics environments,such as low contrast,misalignment,physical wear,and environmental interference.By training on this diverse and realistic dataset,the model demonstrates exceptional performance in restoring readability and decoding accuracy.The lightweight architecture,featuring a U-Net-based encoder-decoder with separable convolutions,ensures computational efficiency,making the approach suitable for real-time deployment on embedded and resource-constrained devices commonly used in logistics systems.Experimental results reveal significant improvements:QR code decoding ratios increased from 14%to 99%on training data and from 15%to 68%on validation data,while 1D barcode decoding ratios improved from 7%to 73%on training data and from 9%to 44%on validation data.By providing a robust,resource-efficient solution for restoring damaged barcodes and QR codes,this study offers practical advancements for enhancing the reliability of automated scanning systems in logistics operations,particularly under challenging conditions.展开更多
Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and ...Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and cyberattacks over these networks have become pressing concerns.Therefore,protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention.The Intelligent Reflecting Surface(IRS)is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment.This study investigates the security performance of an uplink Non-Orthogonal Multiple Access(NOMA)system integrated with an IRS and employing Fountain Codes(FCs).Specifically,two users send signals to the base station at separate distances.A relay receives the signal from the nearby user first and then relays it to the base station.The IRS receives the signal from the distant user and reflects it to the relay,which then sends the reflected signal to the base station.Furthermore,a malevolent eavesdropper intercepts both user and relay communications.We construct mathematical equations for Outage Probability(OP),throughput,diversity evaluation,and Interception Probability(IP),offering quantitative insights to assess system security and performance.Additionally,OP and IP are analyzed using a Deep Neural Network(DNN)model.A deeper comprehension of the security performance of the IRS-assisted NOMA systemin signal transmission is provided by Monte Carlo simulations,which are also carried out to confirm the theoretical conclusions.展开更多
For computer science majors in higher education institutions,programming courses are one of the most important professional foundation courses.Proficiency in independent programming skills is of great help to the stud...For computer science majors in higher education institutions,programming courses are one of the most important professional foundation courses.Proficiency in independent programming skills is of great help to the study of subsequent courses and the personal development of students.In the teaching process of programming courses,online judgement systems are often used to improve students’programming level.Traditional online judgement systems lack guidance for students,and it is often difficult for inexperienced students to find and correct errors in their codes by themselves.We propose an online judgement system that integrates a large model of error correction to help students find errors and improve their programming skills.展开更多
Two dimensional(2D) materials based on boron and carbon have attracted wide attention due to their unique properties. BC compounds have rich active sites and diverse chemical coordination, showing great potential in o...Two dimensional(2D) materials based on boron and carbon have attracted wide attention due to their unique properties. BC compounds have rich active sites and diverse chemical coordination, showing great potential in optoelectronic applications. However, due to the limitation of calculation and experimental conditions, it is still a challenging task to predict new 2D BC monolayer materials. Specifically, we utilized Crystal Diffusion Variational Autoencoder(CDVAE) and pre-trained Materials Graph Neural Network with 3-Body Interactions(M3GNet) model to generate novel and stable BCP materials. Each crystal structure was treated as a high-dimensional vector, where the encoder extracted lattice information and element coordinates, mapping the high-dimensional data into a low-dimensional latent space. The decoder then reconstructed the latent representation back into the original data space. Additionally, our designed attribute predictor network combined the advantages of dilated convolutions and residual connections,effectively increasing the model's receptive field and learning capacity while maintaining relatively low parameter count and computational complexity. By progressively increasing the dilation rate, the model can capture features at different scales. We used the DFT data set of about 1600 BCP monolayer materials to train the diffusion model, and combined with the pre-trained M3GNet model to screen the best candidate structure. Finally, we used DFT calculations to confirm the stability of the candidate structure.The results show that the combination of generative deep learning model and attribute prediction model can help accelerate the discovery and research of new 2D materials, and provide effective methods for exploring the inverse design of new two-dimensional materials.展开更多
Mobile communications are reaching out to every aspect of our daily life,necessitating highefficiency data transmission and support for diverse data types and communication scenarios.Polar codes have emerged as a prom...Mobile communications are reaching out to every aspect of our daily life,necessitating highefficiency data transmission and support for diverse data types and communication scenarios.Polar codes have emerged as a promising solution due to their outstanding error-correction performance and low complexity.Unequal error protection(UEP)involves nonuniform error safeguarding for distinct data segments,achieving a fine balance between error resilience and resource allocation,which ultimately enhancing system performance and efficiency.In this paper,we propose a novel class of UEP rateless polar codes.The codes are designed based on matrix extension of polar codes,and elegant mapping and duplication operations are designed to achieve UEP property while preserving the overall performance of conventional polar codes.Superior UEP performance is attained without significant modifications to conventional polar codes,making it straightforward for compatibility with existing polar codes.A theoretical analysis is conducted on the block error rate and throughput efficiency performance.To the best of our knowledge,this work provides the first theoretical performance analysis of UEP rateless polar codes.Simulation results show that the proposed codes significantly outperform existing polar coding schemes in both block error rate and throughput efficiency.展开更多
In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challen...In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challenging task. However, previous work has primarily focused on the independent recognition of user intent and emotion, making it difficult to simultaneously track both aspects in the dialogue tracking module and to effectively utilize user emotions in subsequent dialogue strategies. We propose a Multi-Head Encoder Shared Model (MESM) that dynamically integrates features from emotion and intent encoders through a feature fusioner. Addressing the scarcity of datasets containing both emotion and intent labels, we designed a multi-dataset learning approach enabling the model to generate dialogue summaries encompassing both user intent and emotion. Experiments conducted on the MultiWoZ and MELD datasets demonstrate that our model effectively captures user intent and emotion, achieving extremely competitive results in dialogue state tracking tasks.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation ...In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
Differential pulse-position modulation(DP PM)can achieve a good compromise between power and bandwidth requirements.However,the output sequence has undetectable insertions and deletions.This paper proposes a successiv...Differential pulse-position modulation(DP PM)can achieve a good compromise between power and bandwidth requirements.However,the output sequence has undetectable insertions and deletions.This paper proposes a successive cancellation(SC)decoding scheme based on the weighted levenshtein distance(WLD)of polar codes for correcting insertions/deletions in DPPM systems.In this method,the WLD is used to calculate the transfer probabilities recursively to obtain likelihood ratios,and the low-complexity SC decoding method is built according to the error characteristics to match the DPPM system.Additionally,the proposed SC decoding scheme is extended to list decoding,which can further improve error correction performance.Simulation results show that the proposed scheme can effectively correct insertions/deletions in the DPPM system,which enhances its reliability and performance.展开更多
The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpec...The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpected non-physical phase points in practice.We conduct an analysis on the phase-space characteristics and the fixed-point theory underlying the k-ε turbulence model,and employ the classical Kolmogorov flow as a framework,leveraging its direct numerical simulation(DNS)data to construct a one-dimensional(1D)system under periodic/fixed boundary conditions.The RANS results demonstrate that under periodic boundary conditions,the k-ε model exhibits only a unique trivial fixed point,with asymptotes capturing the phase portraits.The stability of this trivial fixed point is determined by a mathematically derived stability phase diagram,indicating the fact that the k-ε model will never converge to correct values under periodic conditions.In contrast,under fixed boundary conditions,the model can yield a stable non-trivial fixed point.The evolutionary mechanisms and their relationship with boundary condition settings systematically explain the inherent limitations of the k-ε model,i.e.,its deficiency in computing the flow field under periodic boundary conditions and sensitivity to boundary-value specifications under fixed boundary conditions.These conclusions are finally validated with the open-source code OpenFOAM.展开更多
Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is empl...Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is employed to simulate the ballistic impact of 7.62 mm armor-piercing projectiles on Aluminum AA5083-H116 and Steel Secure 500 armors,focusing on the evaluation of material deformation and penetration resistance at varying impact points.While the D-shaped armor plate is penetrated by the armor-piercing projectiles,the combination of the perforated D-shaped and base armor plates successfully halts penetration.A numerical model based on the finite element method is developed using software such as SolidWorks and ANSYS to analyze the interaction between radiator armor and bullet.The perforated design of radiator armor is to maintain airflow for radiator function,with hole sizes smaller than the bullet core diameter to protect radiator assemblies.Predictions are made regarding the brittle fracture resulting from the projectile core′s bending due to asymmetric impact,and the resulting fragments failed to penetrate the perforated base armor plate.Craters are formed on the surface of the perforated D-shaped armor plate due to the impact of projectile fragments.The numerical model accurately predicts hole growth and projectile penetration upon impact with the armor,demonstrating effective protection of the radiator assemblies by the radiator armor.展开更多
Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Althoug...Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code,existing techniques predominantly depend on inserting artificial instructions,which incur high computational costs and offer limited diversity of perturbations.To address these limitations,we propose AIMA,a novel gradient-guided assembly instruction relocation method.Our method decouples the detection model into tokenization,embedding,and encoding layers to enable efficient gradient computation.Since token IDs of instructions are discrete and nondifferentiable,we compute gradients in the continuous embedding space to evaluate the influence of each token.The most critical tokens are identified by calculating the L2 norm of their embedding gradients.We then establish a mapping between instructions and their corresponding tokens to aggregate token-level importance into instructionlevel significance.To maximize adversarial impact,a sliding window algorithm selects the most influential contiguous segments for relocation,ensuring optimal perturbation with minimal length.This approach efficiently locates critical code regions without expensive search operations.The selected segments are relocated outside their original function boundaries via a jump mechanism,which preserves runtime control flow and functionality while introducing“deletion”effects in the static instruction sequence.Extensive experiments show that AIMA reduces similarity scores by up to 35.8%in state-of-the-art BCSD models.When incorporated into training data,it also enhances model robustness,achieving a 5.9%improvement in AUROC.展开更多
Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interact...Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interactions to predict future items of interest.However,many current methods rely on unique user and item IDs,limiting their ability to represent users and items effectively,especially in zero-shot learning scenarios where training data is scarce.With the rapid development of Large Language Models(LLMs),researchers are exploring their potential to enhance recommendation systems.However,there is a semantic gap between the linguistic semantics of LLMs and the collaborative semantics of recommendation systems,where items are typically indexed by IDs.Moreover,most research focuses on item representations,neglecting personalized user modeling.To address these issues,we propose a sequential recommendation framework using LLMs,called CIT-Rec,a model that integrates Collaborative semantics for user representation and Image and Text information for item representation to enhance Recommendations.Specifically,by aligning intuitive image information with text containing semantic features,we can more accurately represent items,improving item representation quality.We focus not only on item representations but also on user representations.To more precisely capture users’personalized preferences,we use traditional sequential recommendation models to train on users’historical interaction data,effectively capturing behavioral patterns.Finally,by combining LLMs and traditional sequential recommendation models,we allow the LLM to understand linguistic semantics while capturing collaborative semantics.Extensive evaluations on real-world datasets show that our model outperforms baseline methods,effectively combining user interaction history with item visual and textual modalities to provide personalized recommendations.展开更多
The National Geophysical Data Center(NGDC)of the United States has collected aeromagnetic data for input into a series of geomagnetic models to improve model resolution;however,in the Tibetan Plateau region,ground-bas...The National Geophysical Data Center(NGDC)of the United States has collected aeromagnetic data for input into a series of geomagnetic models to improve model resolution;however,in the Tibetan Plateau region,ground-based observations remain insufficient to clearly reflect the characteristics of the region’s lithospheric magnetism.In this study,we evaluate the lithospheric magnetism of the Tibetan Plateau by using a 3D surface spline model based on observations from>200 newly constructed repeat stations(portable stations)to determine the spatial distribution of plateau geomagnetism,as well as its correlation with the tectonic features of the region.We analyze the relationships between M≥5 earthquakes and lithospheric magnetic field variations on the Tibetan Plateau and identify regions susceptible to strong earthquakes.We compare the geomagnetic results with those from an enhanced magnetic model(EMM2015)developed by the NGDC and provide insights into improving lithospheric magnetic field calculations in the Tibetan Plateau region.Further research reveals that these magnetic anomalies exhibit distinct differences from the magnetic-seismic correlation mechanisms observed in other tectonic settings;here,they are governed primarily by the combined effects of compressional magnetism,thermal magnetism,and deep thermal stress.This study provides new evidence of geomagnetic anomalies on the Tibetan Plateau,interprets them physically,and demonstrates their potential for identifying seismic hazard zones on the Plateau.展开更多
文摘As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processing and enabled significant improvements in various applications.This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models(LLM).Our primary objective is to evaluate the effectiveness of Static Application Security Testing(SAST)by applying various techniques such as prompt persona,structure outputs and zero-shot.To the selection of the LLMs(CodeLlama 7B,DeepSeek coder 7B,Gemini 1.5 Flash,Gemini 2.0 Flash,Mistral 7b Instruct,Phi 38b Mini 128K instruct,Qwen 2.5 coder,StartCoder 27B)with comparison and combination with Find Security Bugs.The evaluation method will involve using a selected dataset containing vulnerabilities,and the results to provide insights for different scenarios according to the software criticality(Business critical,non-critical,minimum effort,best effort)In detail,the main objectives of this study are to investigate if large language models outperform or exceed the capabilities of traditional static analysis tools,if the combining LLMs with Static Application Security Testing(SAST)tools lead to an improvement and the possibility that local machine learning models on a normal computer produce reliable results.Summarizing the most important conclusions of the research,it can be said that while it is true that the results have improved depending on the size of the LLM for business-critical software,the best results have been obtained by SAST analysis.This differs in“NonCritical,”“Best Effort,”and“Minimum Effort”scenarios,where the combination of LLM(Gemini)+SAST has obtained better results.
文摘The article entitled with OptoGPT:A foundation model for inverse design in optical multilayer thin film structures1,with doi:10.29026/oea.2024.240062,published in No.7,Vol.7,2024 of Opto-Electronic Advances,has attracted attention from many researchers.As a result,the authors received many requests on the possibility sharing their code,model,and dataset in the mentioned work.To facilitate the needs of the research community,the authors decide to make the code,model,and datasets of OptoGPT public,enabling broader utilization and further development of enhanced models.
文摘介绍了高温蠕变工况下运行的压力容器可能出现的失效模式,结合工程设计现状,指出了我国当前压力容器标准体系在确定高温蠕变工况许用压应力时存在的技术瓶颈,在此基础之上引出ASME Code Case 3029,对其适用范围、发展历程、产生背景及工程意义进行了简单的介绍,以某工程设计项目中的实际结构为例,介绍了该方法的使用过程及注意事项,并结合压力容器工程设计领域的实际需求,对我国标准体系下一步的制定或修订方向提出了展望。
基金supported by the Scientific and Technological Research Council of Turkey(TÜB˙ITAK)through the Industrial R&D Projects Grant Program(TEYDEB)under Project No.3211077(grant recipient:Metin Kahraman)。
文摘This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional(1D)barcodes and Quick Response(QR)codes,addressing critical challenges in logistics operations.The proposed solution leverages an efficient Pix2Pix-based framework,a type of conditional Generative Adversarial Network(GAN)optimized for image-to-image translation tasks,enabling the recovery of degraded barcodes and QR codes with minimal computational overhead.A core contribution of this work is the development of a synthetic dataset that simulates realistic damage scenarios frequently encountered in logistics environments,such as low contrast,misalignment,physical wear,and environmental interference.By training on this diverse and realistic dataset,the model demonstrates exceptional performance in restoring readability and decoding accuracy.The lightweight architecture,featuring a U-Net-based encoder-decoder with separable convolutions,ensures computational efficiency,making the approach suitable for real-time deployment on embedded and resource-constrained devices commonly used in logistics systems.Experimental results reveal significant improvements:QR code decoding ratios increased from 14%to 99%on training data and from 15%to 68%on validation data,while 1D barcode decoding ratios improved from 7%to 73%on training data and from 9%to 44%on validation data.By providing a robust,resource-efficient solution for restoring damaged barcodes and QR codes,this study offers practical advancements for enhancing the reliability of automated scanning systems in logistics operations,particularly under challenging conditions.
基金supported in part by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant 102.04-2021.57in part by Culture,Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports and Tourism in 2024(Project Name:Global Talent Training Program for Copyright Management Technology in Game Contents,Project Number:RS-2024-00396709,Contribution Rate:100%).
文摘Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and cyberattacks over these networks have become pressing concerns.Therefore,protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention.The Intelligent Reflecting Surface(IRS)is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment.This study investigates the security performance of an uplink Non-Orthogonal Multiple Access(NOMA)system integrated with an IRS and employing Fountain Codes(FCs).Specifically,two users send signals to the base station at separate distances.A relay receives the signal from the nearby user first and then relays it to the base station.The IRS receives the signal from the distant user and reflects it to the relay,which then sends the reflected signal to the base station.Furthermore,a malevolent eavesdropper intercepts both user and relay communications.We construct mathematical equations for Outage Probability(OP),throughput,diversity evaluation,and Interception Probability(IP),offering quantitative insights to assess system security and performance.Additionally,OP and IP are analyzed using a Deep Neural Network(DNN)model.A deeper comprehension of the security performance of the IRS-assisted NOMA systemin signal transmission is provided by Monte Carlo simulations,which are also carried out to confirm the theoretical conclusions.
基金supported by Research and Construction of Experimental Teaching Aid Platform for Programming under the Teaching Reform Research Project of Shandong University。
文摘For computer science majors in higher education institutions,programming courses are one of the most important professional foundation courses.Proficiency in independent programming skills is of great help to the study of subsequent courses and the personal development of students.In the teaching process of programming courses,online judgement systems are often used to improve students’programming level.Traditional online judgement systems lack guidance for students,and it is often difficult for inexperienced students to find and correct errors in their codes by themselves.We propose an online judgement system that integrates a large model of error correction to help students find errors and improve their programming skills.
基金supported by the National Nature Science Foundation of China (Nos. 61671362 and 62071366)。
文摘Two dimensional(2D) materials based on boron and carbon have attracted wide attention due to their unique properties. BC compounds have rich active sites and diverse chemical coordination, showing great potential in optoelectronic applications. However, due to the limitation of calculation and experimental conditions, it is still a challenging task to predict new 2D BC monolayer materials. Specifically, we utilized Crystal Diffusion Variational Autoencoder(CDVAE) and pre-trained Materials Graph Neural Network with 3-Body Interactions(M3GNet) model to generate novel and stable BCP materials. Each crystal structure was treated as a high-dimensional vector, where the encoder extracted lattice information and element coordinates, mapping the high-dimensional data into a low-dimensional latent space. The decoder then reconstructed the latent representation back into the original data space. Additionally, our designed attribute predictor network combined the advantages of dilated convolutions and residual connections,effectively increasing the model's receptive field and learning capacity while maintaining relatively low parameter count and computational complexity. By progressively increasing the dilation rate, the model can capture features at different scales. We used the DFT data set of about 1600 BCP monolayer materials to train the diffusion model, and combined with the pre-trained M3GNet model to screen the best candidate structure. Finally, we used DFT calculations to confirm the stability of the candidate structure.The results show that the combination of generative deep learning model and attribute prediction model can help accelerate the discovery and research of new 2D materials, and provide effective methods for exploring the inverse design of new two-dimensional materials.
基金supported by National Natural Science Foundation of China(No.62301008)China Postdoctoral Science Foundation(No.2022M720272)New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘Mobile communications are reaching out to every aspect of our daily life,necessitating highefficiency data transmission and support for diverse data types and communication scenarios.Polar codes have emerged as a promising solution due to their outstanding error-correction performance and low complexity.Unequal error protection(UEP)involves nonuniform error safeguarding for distinct data segments,achieving a fine balance between error resilience and resource allocation,which ultimately enhancing system performance and efficiency.In this paper,we propose a novel class of UEP rateless polar codes.The codes are designed based on matrix extension of polar codes,and elegant mapping and duplication operations are designed to achieve UEP property while preserving the overall performance of conventional polar codes.Superior UEP performance is attained without significant modifications to conventional polar codes,making it straightforward for compatibility with existing polar codes.A theoretical analysis is conducted on the block error rate and throughput efficiency performance.To the best of our knowledge,this work provides the first theoretical performance analysis of UEP rateless polar codes.Simulation results show that the proposed codes significantly outperform existing polar coding schemes in both block error rate and throughput efficiency.
基金funded by the Science and Technology Foundation of Chongqing EducationCommission(GrantNo.KJQN202301153)the ScientificResearch Foundation of Chongqing University of Technology(Grant No.2021ZDZ025)the Postgraduate Innovation Foundation of Chongqing University of Technology(Grant No.gzlcx20243524).
文摘In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challenging task. However, previous work has primarily focused on the independent recognition of user intent and emotion, making it difficult to simultaneously track both aspects in the dialogue tracking module and to effectively utilize user emotions in subsequent dialogue strategies. We propose a Multi-Head Encoder Shared Model (MESM) that dynamically integrates features from emotion and intent encoders through a feature fusioner. Addressing the scarcity of datasets containing both emotion and intent labels, we designed a multi-dataset learning approach enabling the model to generate dialogue summaries encompassing both user intent and emotion. Experiments conducted on the MultiWoZ and MELD datasets demonstrate that our model effectively captures user intent and emotion, achieving extremely competitive results in dialogue state tracking tasks.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
文摘In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
基金supported by National Natural Science Foundation of China(No.61801327).
文摘Differential pulse-position modulation(DP PM)can achieve a good compromise between power and bandwidth requirements.However,the output sequence has undetectable insertions and deletions.This paper proposes a successive cancellation(SC)decoding scheme based on the weighted levenshtein distance(WLD)of polar codes for correcting insertions/deletions in DPPM systems.In this method,the WLD is used to calculate the transfer probabilities recursively to obtain likelihood ratios,and the low-complexity SC decoding method is built according to the error characteristics to match the DPPM system.Additionally,the proposed SC decoding scheme is extended to list decoding,which can further improve error correction performance.Simulation results show that the proposed scheme can effectively correct insertions/deletions in the DPPM system,which enhances its reliability and performance.
基金Project supported by the National Natural Science Foundation of China(Nos.12372214 and U2341231)。
文摘The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpected non-physical phase points in practice.We conduct an analysis on the phase-space characteristics and the fixed-point theory underlying the k-ε turbulence model,and employ the classical Kolmogorov flow as a framework,leveraging its direct numerical simulation(DNS)data to construct a one-dimensional(1D)system under periodic/fixed boundary conditions.The RANS results demonstrate that under periodic boundary conditions,the k-ε model exhibits only a unique trivial fixed point,with asymptotes capturing the phase portraits.The stability of this trivial fixed point is determined by a mathematically derived stability phase diagram,indicating the fact that the k-ε model will never converge to correct values under periodic conditions.In contrast,under fixed boundary conditions,the model can yield a stable non-trivial fixed point.The evolutionary mechanisms and their relationship with boundary condition settings systematically explain the inherent limitations of the k-ε model,i.e.,its deficiency in computing the flow field under periodic boundary conditions and sensitivity to boundary-value specifications under fixed boundary conditions.These conclusions are finally validated with the open-source code OpenFOAM.
文摘Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is employed to simulate the ballistic impact of 7.62 mm armor-piercing projectiles on Aluminum AA5083-H116 and Steel Secure 500 armors,focusing on the evaluation of material deformation and penetration resistance at varying impact points.While the D-shaped armor plate is penetrated by the armor-piercing projectiles,the combination of the perforated D-shaped and base armor plates successfully halts penetration.A numerical model based on the finite element method is developed using software such as SolidWorks and ANSYS to analyze the interaction between radiator armor and bullet.The perforated design of radiator armor is to maintain airflow for radiator function,with hole sizes smaller than the bullet core diameter to protect radiator assemblies.Predictions are made regarding the brittle fracture resulting from the projectile core′s bending due to asymmetric impact,and the resulting fragments failed to penetrate the perforated base armor plate.Craters are formed on the surface of the perforated D-shaped armor plate due to the impact of projectile fragments.The numerical model accurately predicts hole growth and projectile penetration upon impact with the armor,demonstrating effective protection of the radiator assemblies by the radiator armor.
基金supported by Key Laboratory of Cyberspace Security,Ministry of Education,China。
文摘Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code,existing techniques predominantly depend on inserting artificial instructions,which incur high computational costs and offer limited diversity of perturbations.To address these limitations,we propose AIMA,a novel gradient-guided assembly instruction relocation method.Our method decouples the detection model into tokenization,embedding,and encoding layers to enable efficient gradient computation.Since token IDs of instructions are discrete and nondifferentiable,we compute gradients in the continuous embedding space to evaluate the influence of each token.The most critical tokens are identified by calculating the L2 norm of their embedding gradients.We then establish a mapping between instructions and their corresponding tokens to aggregate token-level importance into instructionlevel significance.To maximize adversarial impact,a sliding window algorithm selects the most influential contiguous segments for relocation,ensuring optimal perturbation with minimal length.This approach efficiently locates critical code regions without expensive search operations.The selected segments are relocated outside their original function boundaries via a jump mechanism,which preserves runtime control flow and functionality while introducing“deletion”effects in the static instruction sequence.Extensive experiments show that AIMA reduces similarity scores by up to 35.8%in state-of-the-art BCSD models.When incorporated into training data,it also enhances model robustness,achieving a 5.9%improvement in AUROC.
基金supported by the National Key R&D Program of China[2022YFF0902703]the State Administration for Market Regulation Science and Technology Plan Project(2024MK033).
文摘Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interactions to predict future items of interest.However,many current methods rely on unique user and item IDs,limiting their ability to represent users and items effectively,especially in zero-shot learning scenarios where training data is scarce.With the rapid development of Large Language Models(LLMs),researchers are exploring their potential to enhance recommendation systems.However,there is a semantic gap between the linguistic semantics of LLMs and the collaborative semantics of recommendation systems,where items are typically indexed by IDs.Moreover,most research focuses on item representations,neglecting personalized user modeling.To address these issues,we propose a sequential recommendation framework using LLMs,called CIT-Rec,a model that integrates Collaborative semantics for user representation and Image and Text information for item representation to enhance Recommendations.Specifically,by aligning intuitive image information with text containing semantic features,we can more accurately represent items,improving item representation quality.We focus not only on item representations but also on user representations.To more precisely capture users’personalized preferences,we use traditional sequential recommendation models to train on users’historical interaction data,effectively capturing behavioral patterns.Finally,by combining LLMs and traditional sequential recommendation models,we allow the LLM to understand linguistic semantics while capturing collaborative semantics.Extensive evaluations on real-world datasets show that our model outperforms baseline methods,effectively combining user interaction history with item visual and textual modalities to provide personalized recommendations.
基金supported by the CAS Pioneer Hundred Talents Program and Second Tibetan Plateau Scientific Expedition Research Program(2019QZKK0708)as well as the Basic Research Program of Qinghai Province:Lithospheric Geomagnetic Field of the Qinghai-Tibet Plateau and the Relationship with Strong Earthquakes(2021-ZJ-969Q).
文摘The National Geophysical Data Center(NGDC)of the United States has collected aeromagnetic data for input into a series of geomagnetic models to improve model resolution;however,in the Tibetan Plateau region,ground-based observations remain insufficient to clearly reflect the characteristics of the region’s lithospheric magnetism.In this study,we evaluate the lithospheric magnetism of the Tibetan Plateau by using a 3D surface spline model based on observations from>200 newly constructed repeat stations(portable stations)to determine the spatial distribution of plateau geomagnetism,as well as its correlation with the tectonic features of the region.We analyze the relationships between M≥5 earthquakes and lithospheric magnetic field variations on the Tibetan Plateau and identify regions susceptible to strong earthquakes.We compare the geomagnetic results with those from an enhanced magnetic model(EMM2015)developed by the NGDC and provide insights into improving lithospheric magnetic field calculations in the Tibetan Plateau region.Further research reveals that these magnetic anomalies exhibit distinct differences from the magnetic-seismic correlation mechanisms observed in other tectonic settings;here,they are governed primarily by the combined effects of compressional magnetism,thermal magnetism,and deep thermal stress.This study provides new evidence of geomagnetic anomalies on the Tibetan Plateau,interprets them physically,and demonstrates their potential for identifying seismic hazard zones on the Plateau.
