Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing.To address these concerns,this paper presents the mathematica...Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing.To address these concerns,this paper presents the mathematical and computer modeling of a novel two-dimensional(2D)chaotic system for secure key generation in quantum image encryption(QIE).The proposed map employs trigonometric perturbations in conjunction with rational-saturation functions and hence,named as Trigonometric-Rational-Saturation(TRS)map.Through rigorous mathematical analysis and computational simulations,the map is extensively evaluated for bifurcation behaviour,chaotic trajectories,and Lyapunov exponents.The security evaluation validates the map’s non-linearity,unpredictability,and sensitive dependence on initial conditions.In addition,the proposed TRS map has further been tested by integrating it in a QIE scheme.The QIE scheme first quantum-encodes the classic image using the Novel Enhanced Quantum Representation(NEQR)technique,the TRS map is used for the generation of secure diffusion key,which is XOR-ed with the quantum-ready image to obtain the encrypted images.The security evaluation of the QIE scheme demonstrates superior security of the encrypted images in terms of statistical security attacks and also against Differential attacks.The encrypted images exhibit zero correlation and maximum entropy with demonstrating strong resilience due to 99.62%and 33.47%results for Number of Pixels Change Rate(NPCR)and Unified Average Changing Intensity(UACI).The results validate the effectiveness of TRS-based quantum encryption scheme in securing digital images against emerging quantum threats,making it suitable for secure image encryption in IoT and edge-based applications.展开更多
This article takes the current autonomous driving technology as the research background and studies the collaborative protection mechanism between its system-on-chip(SoC)functional safety and information security.It i...This article takes the current autonomous driving technology as the research background and studies the collaborative protection mechanism between its system-on-chip(SoC)functional safety and information security.It includes an introduction to the functions and information security of autonomous driving SoCs,as well as the main design strategies for the collaborative prevention and control mechanism of SoC functional safety and information security in autonomous driving.The research shows that in the field of autonomous driving,there is a close connection between the functional safety of SoCs and their information security.In the design of the safety collaborative protection mechanism,the overall collaborative protection architecture,SoC functional safety protection mechanism,information security protection mechanism,the workflow of the collaborative protection mechanism,and its strategies are all key design elements.It is hoped that this analysis can provide some references for the collaborative protection of SoC functional safety and information security in the field of autonomous driving,so as to improve the safety of autonomous driving technology and meet its practical application requirements.展开更多
Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and ...Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and events—while also involving reasoning tasks like personnel classification,relationship judgment,and implicit inference.Moreover,utilizing models for extracting information from police incident data poses a significant challenge—data scarcity,which limits the effectiveness of traditional rule-based and machine-learning methods.To address these,we propose TIPS.In collaboration with public security experts,we used de-identified police incident data to create templates that enable large language models(LLMs)to populate data slots and generate simulated data,enhancing data density and diversity.We then designed schemas to efficiently manage complex extraction and reasoning tasks,constructing a high-quality dataset and fine-tuning multiple open-source LLMs.Experiments showed that the fine-tuned ChatGLM-4-9B model achieved an F1 score of 87.14%,nearly 30%higher than the base model,significantly reducing error rates.Manual corrections further improved performance by 9.39%.This study demonstrates that combining largescale pre-trained models with limited high-quality domain-specific data can greatly enhance information extraction in low-resource environments,offering a new approach for intelligent public security applications.展开更多
[目的/意义]梳理国际国家安全情报研究发展脉络与知识生产特征,揭示关键学者的群体画像、职业发展模式、合作网络结构与核心研究议题演进,以期为推动我国安全情报学科建设提供借鉴。[方法/过程]基于发文量标准,从Intelligence and Natio...[目的/意义]梳理国际国家安全情报研究发展脉络与知识生产特征,揭示关键学者的群体画像、职业发展模式、合作网络结构与核心研究议题演进,以期为推动我国安全情报学科建设提供借鉴。[方法/过程]基于发文量标准,从Intelligence and National Security期刊中筛选出核心著者,运用履历分析法将国外核心著者履历划分为学科背景、研究方向、科研成果和工作经历4个核心类属进行比较分析,采用LDA主题模型对发文进行主题挖掘,系统识别出情报研究者关注的核心议题。[结果/结论]核心著者群体呈现显著的男性主导、中老年资深学者为主、机构高度集中、学科背景偏重传统人文社科的特征;安全情报研究面临跨学科深度融合不足、学界与实践存在隔阂、技术伦理与法律探讨滞后等问题。展开更多
Purpose-Amidst an increasingly severe cybersecurity landscape,the widespread adoption of Xinchuang endpoints has become a strategic imperative.Governments and enterprises have established terminal localization as a cr...Purpose-Amidst an increasingly severe cybersecurity landscape,the widespread adoption of Xinchuang endpoints has become a strategic imperative.Governments and enterprises have established terminal localization as a critical objective,aiming for comprehensive indigenous replacement through rapid technological iteration.Consequently,Xinchuang systems and Windows platforms are expected to coexist over an extended period.This study seeks to establish an automated verification framework for multi-version operating systems and validate the efficacy of baseline hardening in mitigating security risks.Design/methodology/approach-Based on the Classified Protection 2.0 framework and relevant national standards for endpoint security,this study proposes an endpoint security baseline verification scheme applicable to multiple operating systems.The scheme addresses divergent security policies and implementation methodologies across heterogeneous environments.It automates the inspection of core baselines,including account password complexity,default shared service status and patch installation status.Furthermore,a comprehensive scoring model is established by incorporating differentiated weights for account security,patch management and log auditing,ultimately generating visualized risk reports to facilitate remediation prioritization.Findings-This study reveals that baseline configuration serves as the fundamental prerequisite in endpoint security practices.Through a scalable detection engine and quantitative scoring model,the system can promptly identify and remediate potential risks,thereby reducing the attack surface and mitigating intrusion risks.However,on certain domestic chip architectures,compatibility issues persist in detecting specific configuration items.Further improvement in hardware-software co-adaptation for domestic platforms is required to advance the development of localized security protection systems.Originality/value-Through in-depth research on security baseline configurations across multiple operating systems,this study implements an automated and visualized baseline verification methodology.This approach significantly strengthens the security posture of domestic operating systems and supports the establishment of a more robust,national-level cybersecurity defense framework.展开更多
Malnutrition remains a significant global challenge,particularly in developing countries.Policymakers have increasingly focused on improving household food security and nutrition through farm production diversity(FPD)...Malnutrition remains a significant global challenge,particularly in developing countries.Policymakers have increasingly focused on improving household food security and nutrition through farm production diversity(FPD).While research indicates that FPD correlates positively with reduced malnutrition,other studies emphasize the importance of market access for improved nutritional outcomes.However,this evidence varies by region and remains inconsistent.To address this knowledge gap,this study analyzed survey data from 450 smallholder farmers in Punjab,Pakistan,using regression models to examine the relationship between FPD and dietary diversity,as well as the underlying impact pathways.The findings demonstrate that FPD significantly correlates with increased household dietary diversity score(HDDS).FPD influences dietary diversification through both own-farm production and market food consumption pathways,with the ownfarm production pathway showing greater impact.The increase in food expenditure through own-farm production yielded a marginal return of 8% in household dietary diversity compared to 5.3% through marketing.Gender differences emerged as significant,with male-headed households showing relatively lower dietary diversity.These findings have substantial implications for countries with smallholder farming systems,providing valuable insights for the formation of agricultural policies,resource optimization,and rural development initiatives.展开更多
Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,B...Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.展开更多
The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreser...The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreserving computation.Classical MPC relies on cryptographic techniques such as homomorphic encryption,secret sharing,and oblivious transfer,which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries.This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI,IEEE Explore,Springer,and Elsevier,examining the applications,types,and security issues with the solution of Quantum computing in different fields.This review explores the impact of quantum computing on MPC security,assesses emerging quantum-resistant MPC protocols,and examines hybrid classicalquantum approaches aimed at mitigating quantum threats.We analyze the role of Quantum Key Distribution(QKD),post-quantum cryptography(PQC),and quantum homomorphic encryption in securing multiparty computations.Additionally,we discuss the challenges of scalability,computational efficiency,and practical deployment of quantumsecure MPC frameworks in real-world applications such as privacy-preserving AI,secure blockchain transactions,and confidential data analysis.This review provides insights into the future research directions and open challenges in ensuring secure,scalable,and quantum-resistant multiparty computation.展开更多
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
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
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
1 General information Journal of Geographical Sciences is an international academic journal that publishes papers of the highest quality in physical geography, natural resources, environmental sciences, geographic inf...1 General information Journal of Geographical Sciences is an international academic journal that publishes papers of the highest quality in physical geography, natural resources, environmental sciences, geographic information sciences, remote sensing and cartography. Manuscripts come from different parts of the world.展开更多
Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automaticall...Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.展开更多
With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I...With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.展开更多
The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)...The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.展开更多
The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To...The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.展开更多
In order to solve principal-agent problems caused by interest inconformity and information asymmetry during information security outsourcing, it is necessary to design a reasonable incentive mechanism to promote clien...In order to solve principal-agent problems caused by interest inconformity and information asymmetry during information security outsourcing, it is necessary to design a reasonable incentive mechanism to promote client enterprises to complete outsourcing service actively. The incentive mechanism model of information security outsourcing is designed based on the principal-agent theory. Through analyzing the factors such as enterprise information assets value, invasion probability, information security environment, the agent cost coefficient and agency risk preference degree how to impact on the incentive mechanism, conclusions show that an enterprise information assets value and invasion probability have a positive influence on the fixed fee and the compensation coefficient; while information security environment, the agent cost coefficient and agency risk preference degree have a negative influence on the compensation coefficient. Therefore, the principal enterprises should reasonably design the fixed fee and the compensation coefficient to encourage information security outsourcing agency enterprises to the full extent.展开更多
Two complementary firms＇ information sharing and security investment are investigated. When two complementary firms with heterogeneous assets are both breached, it is assumed that they suffer different losses which a...Two complementary firms＇ information sharing and security investment are investigated. When two complementary firms with heterogeneous assets are both breached, it is assumed that they suffer different losses which are associated with their information assets. Some insights about optimal strategies for the firms and the attacker are obtained by the game theory, which forms a comparison with those derived from substitutable firms, and those derived from complementary firms with homogenous loss. In addition, both the unit transform cost of investment and the extent of firms＇loss affect the optimal strategies.Assuming that firms can control information sharing, security investments and both of them, respectively, the effect of the social planner is further analyzed on the information sharing, firms＇ aggregate defence, the aggregate attack and social total cost. Finally, some policy advice is provided through numerical simulation. Results show that firms are willing to choose security investment centrally rather than individually, but an intervention in information sharing by the social planner may not necessarily be preferable.展开更多
The impact of risk correlation on firm＇s investments in information system security is studied by using quantification models combining the ideas of the risk management theory and the game theory. The equilibrium lev...The impact of risk correlation on firm＇s investments in information system security is studied by using quantification models combining the ideas of the risk management theory and the game theory. The equilibrium levels of self-protection and insurance coverage under the non- cooperative condition are compared with socially optimal solutions, and the associated coordination mechanisms are proposed. The results show that self-protection investment increases in response to an increase in potential loss when the interdependent risk is small; the interdependent risk of security investments often induce firms to underinvest in security relative to the socially efficient level by ignoring marginal external costs or benefits conferred on others. A subsidy on self-protection investment from the government can help coordinate a firm＇s risk management decision and, thereby, improve individual security level and overall social welfare.展开更多
The development of the Internet of Things(IoT)calls for a comprehensive in-formation security evaluation framework to quantitatively measure the safety score and risk(S&R)value of the network urgently.In this pape...The development of the Internet of Things(IoT)calls for a comprehensive in-formation security evaluation framework to quantitatively measure the safety score and risk(S&R)value of the network urgently.In this paper,we summarize the architecture and vulnerability in IoT and propose a comprehensive information security evaluation model based on multi-level decomposition feedback.The evaluation model provides an idea for information security evaluation of IoT and guides the security decision maker for dynamic protection.Firstly,we establish an overall evaluation indicator system that includes four primary indicators of threat information,asset,vulnerability,and management,respectively.It also includes eleven secondary indicators of system protection rate,attack detection rate,confidentiality,availability,controllability,identifiability,number of vulnerabilities,vulnerability hazard level,staff organization,enterprise grading and service continuity,respectively.Then,we build the core algorithm to enable the evaluation model,wherein a novel weighting technique is developed and a quantitative method is proposed to measure the S&R value.Moreover,in order to better supervise the performance of the proposed evaluation model,we present four novel indicators includes residual risk,continuous conformity of residual risk,head-to-tail consistency and decrease ratio,respectively.Simulation results show the advantages of the proposed model in the evaluation of information security for IoT.展开更多
基金funded by Deanship of Research and Graduate Studies at King Khalid University.The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Group Project under grant number(RGP.2/556/45).
文摘Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing.To address these concerns,this paper presents the mathematical and computer modeling of a novel two-dimensional(2D)chaotic system for secure key generation in quantum image encryption(QIE).The proposed map employs trigonometric perturbations in conjunction with rational-saturation functions and hence,named as Trigonometric-Rational-Saturation(TRS)map.Through rigorous mathematical analysis and computational simulations,the map is extensively evaluated for bifurcation behaviour,chaotic trajectories,and Lyapunov exponents.The security evaluation validates the map’s non-linearity,unpredictability,and sensitive dependence on initial conditions.In addition,the proposed TRS map has further been tested by integrating it in a QIE scheme.The QIE scheme first quantum-encodes the classic image using the Novel Enhanced Quantum Representation(NEQR)technique,the TRS map is used for the generation of secure diffusion key,which is XOR-ed with the quantum-ready image to obtain the encrypted images.The security evaluation of the QIE scheme demonstrates superior security of the encrypted images in terms of statistical security attacks and also against Differential attacks.The encrypted images exhibit zero correlation and maximum entropy with demonstrating strong resilience due to 99.62%and 33.47%results for Number of Pixels Change Rate(NPCR)and Unified Average Changing Intensity(UACI).The results validate the effectiveness of TRS-based quantum encryption scheme in securing digital images against emerging quantum threats,making it suitable for secure image encryption in IoT and edge-based applications.
文摘This article takes the current autonomous driving technology as the research background and studies the collaborative protection mechanism between its system-on-chip(SoC)functional safety and information security.It includes an introduction to the functions and information security of autonomous driving SoCs,as well as the main design strategies for the collaborative prevention and control mechanism of SoC functional safety and information security in autonomous driving.The research shows that in the field of autonomous driving,there is a close connection between the functional safety of SoCs and their information security.In the design of the safety collaborative protection mechanism,the overall collaborative protection architecture,SoC functional safety protection mechanism,information security protection mechanism,the workflow of the collaborative protection mechanism,and its strategies are all key design elements.It is hoped that this analysis can provide some references for the collaborative protection of SoC functional safety and information security in the field of autonomous driving,so as to improve the safety of autonomous driving technology and meet its practical application requirements.
文摘Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and events—while also involving reasoning tasks like personnel classification,relationship judgment,and implicit inference.Moreover,utilizing models for extracting information from police incident data poses a significant challenge—data scarcity,which limits the effectiveness of traditional rule-based and machine-learning methods.To address these,we propose TIPS.In collaboration with public security experts,we used de-identified police incident data to create templates that enable large language models(LLMs)to populate data slots and generate simulated data,enhancing data density and diversity.We then designed schemas to efficiently manage complex extraction and reasoning tasks,constructing a high-quality dataset and fine-tuning multiple open-source LLMs.Experiments showed that the fine-tuned ChatGLM-4-9B model achieved an F1 score of 87.14%,nearly 30%higher than the base model,significantly reducing error rates.Manual corrections further improved performance by 9.39%.This study demonstrates that combining largescale pre-trained models with limited high-quality domain-specific data can greatly enhance information extraction in low-resource environments,offering a new approach for intelligent public security applications.
文摘[目的/意义]梳理国际国家安全情报研究发展脉络与知识生产特征,揭示关键学者的群体画像、职业发展模式、合作网络结构与核心研究议题演进,以期为推动我国安全情报学科建设提供借鉴。[方法/过程]基于发文量标准,从Intelligence and National Security期刊中筛选出核心著者,运用履历分析法将国外核心著者履历划分为学科背景、研究方向、科研成果和工作经历4个核心类属进行比较分析,采用LDA主题模型对发文进行主题挖掘,系统识别出情报研究者关注的核心议题。[结果/结论]核心著者群体呈现显著的男性主导、中老年资深学者为主、机构高度集中、学科背景偏重传统人文社科的特征;安全情报研究面临跨学科深度融合不足、学界与实践存在隔阂、技术伦理与法律探讨滞后等问题。
基金supported by scientific research projects of China Academy of Railway Sciences Co.,Ltd.(grant no.2024YJ117).
文摘Purpose-Amidst an increasingly severe cybersecurity landscape,the widespread adoption of Xinchuang endpoints has become a strategic imperative.Governments and enterprises have established terminal localization as a critical objective,aiming for comprehensive indigenous replacement through rapid technological iteration.Consequently,Xinchuang systems and Windows platforms are expected to coexist over an extended period.This study seeks to establish an automated verification framework for multi-version operating systems and validate the efficacy of baseline hardening in mitigating security risks.Design/methodology/approach-Based on the Classified Protection 2.0 framework and relevant national standards for endpoint security,this study proposes an endpoint security baseline verification scheme applicable to multiple operating systems.The scheme addresses divergent security policies and implementation methodologies across heterogeneous environments.It automates the inspection of core baselines,including account password complexity,default shared service status and patch installation status.Furthermore,a comprehensive scoring model is established by incorporating differentiated weights for account security,patch management and log auditing,ultimately generating visualized risk reports to facilitate remediation prioritization.Findings-This study reveals that baseline configuration serves as the fundamental prerequisite in endpoint security practices.Through a scalable detection engine and quantitative scoring model,the system can promptly identify and remediate potential risks,thereby reducing the attack surface and mitigating intrusion risks.However,on certain domestic chip architectures,compatibility issues persist in detecting specific configuration items.Further improvement in hardware-software co-adaptation for domestic platforms is required to advance the development of localized security protection systems.Originality/value-Through in-depth research on security baseline configurations across multiple operating systems,this study implements an automated and visualized baseline verification methodology.This approach significantly strengthens the security posture of domestic operating systems and supports the establishment of a more robust,national-level cybersecurity defense framework.
基金appreciation to the National Natural Science Foundation of China(72071074)Natural Science Foundation of Hunan Province,China(2025JJ30031)for their financial support。
文摘Malnutrition remains a significant global challenge,particularly in developing countries.Policymakers have increasingly focused on improving household food security and nutrition through farm production diversity(FPD).While research indicates that FPD correlates positively with reduced malnutrition,other studies emphasize the importance of market access for improved nutritional outcomes.However,this evidence varies by region and remains inconsistent.To address this knowledge gap,this study analyzed survey data from 450 smallholder farmers in Punjab,Pakistan,using regression models to examine the relationship between FPD and dietary diversity,as well as the underlying impact pathways.The findings demonstrate that FPD significantly correlates with increased household dietary diversity score(HDDS).FPD influences dietary diversification through both own-farm production and market food consumption pathways,with the ownfarm production pathway showing greater impact.The increase in food expenditure through own-farm production yielded a marginal return of 8% in household dietary diversity compared to 5.3% through marketing.Gender differences emerged as significant,with male-headed households showing relatively lower dietary diversity.These findings have substantial implications for countries with smallholder farming systems,providing valuable insights for the formation of agricultural policies,resource optimization,and rural development initiatives.
文摘Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.
文摘The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreserving computation.Classical MPC relies on cryptographic techniques such as homomorphic encryption,secret sharing,and oblivious transfer,which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries.This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI,IEEE Explore,Springer,and Elsevier,examining the applications,types,and security issues with the solution of Quantum computing in different fields.This review explores the impact of quantum computing on MPC security,assesses emerging quantum-resistant MPC protocols,and examines hybrid classicalquantum approaches aimed at mitigating quantum threats.We analyze the role of Quantum Key Distribution(QKD),post-quantum cryptography(PQC),and quantum homomorphic encryption in securing multiparty computations.Additionally,we discuss the challenges of scalability,computational efficiency,and practical deployment of quantumsecure MPC frameworks in real-world applications such as privacy-preserving AI,secure blockchain transactions,and confidential data analysis.This review provides insights into the future research directions and open challenges in ensuring secure,scalable,and quantum-resistant multiparty computation.
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
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.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
文摘1 General information Journal of Geographical Sciences is an international academic journal that publishes papers of the highest quality in physical geography, natural resources, environmental sciences, geographic information sciences, remote sensing and cartography. Manuscripts come from different parts of the world.
文摘Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.
基金supported by National Natural Science Foundation of China(NSFC)under grant U23A20310.
文摘With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.
基金funded and supported by the Ongoing Research Funding program(ORF-2025-314),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.
文摘The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.
基金The National Natural Science Foundation of China(No.71071033)the Youth Foundation of Humanity and Social Scienceof Ministry of Education of China(No.11YJC630234)
文摘In order to solve principal-agent problems caused by interest inconformity and information asymmetry during information security outsourcing, it is necessary to design a reasonable incentive mechanism to promote client enterprises to complete outsourcing service actively. The incentive mechanism model of information security outsourcing is designed based on the principal-agent theory. Through analyzing the factors such as enterprise information assets value, invasion probability, information security environment, the agent cost coefficient and agency risk preference degree how to impact on the incentive mechanism, conclusions show that an enterprise information assets value and invasion probability have a positive influence on the fixed fee and the compensation coefficient; while information security environment, the agent cost coefficient and agency risk preference degree have a negative influence on the compensation coefficient. Therefore, the principal enterprises should reasonably design the fixed fee and the compensation coefficient to encourage information security outsourcing agency enterprises to the full extent.
基金The National Natural Science Foundation of China(No.71371050)
文摘Two complementary firms＇ information sharing and security investment are investigated. When two complementary firms with heterogeneous assets are both breached, it is assumed that they suffer different losses which are associated with their information assets. Some insights about optimal strategies for the firms and the attacker are obtained by the game theory, which forms a comparison with those derived from substitutable firms, and those derived from complementary firms with homogenous loss. In addition, both the unit transform cost of investment and the extent of firms＇loss affect the optimal strategies.Assuming that firms can control information sharing, security investments and both of them, respectively, the effect of the social planner is further analyzed on the information sharing, firms＇ aggregate defence, the aggregate attack and social total cost. Finally, some policy advice is provided through numerical simulation. Results show that firms are willing to choose security investment centrally rather than individually, but an intervention in information sharing by the social planner may not necessarily be preferable.
基金The National Natural Science Foundation of China(No.71071033)
文摘The impact of risk correlation on firm＇s investments in information system security is studied by using quantification models combining the ideas of the risk management theory and the game theory. The equilibrium levels of self-protection and insurance coverage under the non- cooperative condition are compared with socially optimal solutions, and the associated coordination mechanisms are proposed. The results show that self-protection investment increases in response to an increase in potential loss when the interdependent risk is small; the interdependent risk of security investments often induce firms to underinvest in security relative to the socially efficient level by ignoring marginal external costs or benefits conferred on others. A subsidy on self-protection investment from the government can help coordinate a firm＇s risk management decision and, thereby, improve individual security level and overall social welfare.
基金This work was supported in part by National Key R&D Program of China under Grant 2019YFB2102400in part by the BUPT Excellent Ph.D.Students Foundation under Grant CX2019117.
文摘The development of the Internet of Things(IoT)calls for a comprehensive in-formation security evaluation framework to quantitatively measure the safety score and risk(S&R)value of the network urgently.In this paper,we summarize the architecture and vulnerability in IoT and propose a comprehensive information security evaluation model based on multi-level decomposition feedback.The evaluation model provides an idea for information security evaluation of IoT and guides the security decision maker for dynamic protection.Firstly,we establish an overall evaluation indicator system that includes four primary indicators of threat information,asset,vulnerability,and management,respectively.It also includes eleven secondary indicators of system protection rate,attack detection rate,confidentiality,availability,controllability,identifiability,number of vulnerabilities,vulnerability hazard level,staff organization,enterprise grading and service continuity,respectively.Then,we build the core algorithm to enable the evaluation model,wherein a novel weighting technique is developed and a quantitative method is proposed to measure the S&R value.Moreover,in order to better supervise the performance of the proposed evaluation model,we present four novel indicators includes residual risk,continuous conformity of residual risk,head-to-tail consistency and decrease ratio,respectively.Simulation results show the advantages of the proposed model in the evaluation of information security for IoT.
