The reliability of information systems(IS)is a key factor in the sustainable operation of modern digital services.However,existing assessment methods remain fragmented and are often limited to individual indicators or...The reliability of information systems(IS)is a key factor in the sustainable operation of modern digital services.However,existing assessment methods remain fragmented and are often limited to individual indicators or expert judgments.This paper proposes a hybridmethodology for a comprehensive assessment of IS reliability based on the integration of the international standard ISO/IEC 25010:2023,multicriteria analysismethods(ARAS,CoCoSo,and TOPSIS),and theXGBoostmachine learning algorithmfor missing data imputation.Thestructure of the ISO/IEC 25010 standard is used to formalize reliability criteria and subcriteria,while theAHP method allows for the calculation of their weighting coefficients based on expert assessments.The XGBoost algorithm ensures the correct filling of gaps in the source data,increasing the completeness and reliability of the subsequent assessment.The resulting weighted indicators are aggregated using threeMCDMmethods,after which an integral reliability indicator is formed as a percentage.The methodology was tested on six real-world information systems with different architectures.The results demonstrated high consistency between the ARAS,CoCoSo,and TOPSISmethods,as well as the stability of the final rating when the criterion weights vary by±10%.The proposed approach provides a reproducible,transparent,and objective assessment of information system reliability and can be used to identify system bottlenecks,make modernization decisions,and manage the quality of digital infrastructure.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
Due to the centralization of charging stations(CSs),CSs are organized as charging station alliances(CSAs)in the commercial competition.Under this situation,this paper studies the profit-oriented dynamic pricing strate...Due to the centralization of charging stations(CSs),CSs are organized as charging station alliances(CSAs)in the commercial competition.Under this situation,this paper studies the profit-oriented dynamic pricing strategy of CSAs.As the practicability basis,a privacy-protected bidirectional real-time information interaction framework is designed,under which the status of EVs is utilized as the reference for pricing,and the prices of CSs are the reference for charging decisions.Based on this framework,the decision-making models of EVs and CSs are established,in which the uncertainty caused by the information asymmetry between EVs and CSs and the bounded rationality of EV users are integrated.To solve the pricing decision model,the evolutionary game theory is adopted to describe the dynamic pricing game among CSAs,the equilibrium of which gives the optimal pricing strategy.Finally,the case study conducted in an urban area of Shanghai,China,validates the practicability of the framework and the effectiveness of the dynamic pricing strategy.展开更多
Graph neural networks(GNNs)have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control-and data-flow graphs.Despite their...Graph neural networks(GNNs)have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control-and data-flow graphs.Despite their effectiveness,most GNN-based vulnerability detectors operate as black boxes,making their decisions difficult to interpret and thus less suitable for critical security auditing.The information bottleneck(IB)principle provides a theoretical framework for isolating task-relevant graph components.However,existing IB-based implementations often encounter unstable optimization and limited understanding of code semantics.To address these issues,we introduce ContractGIB,an interpretable graph information bottleneck framework for function-level vulnerability analysis.ContractGIB introduces three main advances.First,ContractGIB introduces an Hilbert–Schmidt Independence Criterion(HSIC)based estimator that provides stable dependence measurement.Second,it incorporates a CodeBERT semantic module to improve node representations.Third,it initializes all nodes with pretrained CodeBERT embeddings,removing the need for hand-crafted features.For each contract function,ContractGIB identifies themost informative nodes forming an instance-specific explanatory subgraph that supports the model’s prediction.Comprehensive experiments on public smart contract datasets,including ESC andVSC,demonstrate thatContractGIB achieves superior performance compared to competitive GNN baselines,while offering clearer,instance-level interpretability.展开更多
Topological information is very important for understanding different types of online web services,in particular,for online social networks(OSNs).People leverage such information for various applications,such as socia...Topological information is very important for understanding different types of online web services,in particular,for online social networks(OSNs).People leverage such information for various applications,such as social relationship modeling,community detection,user profiling,and user behavior prediction.However,the leak of such information will also pose severe challenges for user privacy preserving due to its usefulness in characterizing users.Large-scale web crawling-based information probing is a representative way for obtaining topological information of online web services.In this paper,we explore how to defend against topological information probing for online web services,with a particular focus on online decentralized web services such as Mastodon.Different from traditional centralized web services,the federated nature of decentralized web services makes the identification of distributed crawlers even more difficult.We analyze the behavioral differences between legitimate users and crawlers in decentralized web services and highlight two key behavioral attributes that distinguish crawlers from legitimate users:instance interaction preferences and hop count in profile viewing patterns.Based on these insights:we propose a supervised machine learning-based framework for crawler detection,which is able to learn the federation-aware feature representations for users.To validate the framework’s effectiveness,we construct a labeled dataset that integrates real users with real-trace driven simulated crawlers in Mastodon.We use this dataset to train various supervised classifiers for crawler detection.Experimental results demonstrate that our framework can achieve an excellent classification performance.Moreover,it is observed that federation-aware features are effective in improving detection performance.展开更多
Sepsis poses a serious threat to patient survival,making timely risk assessment crucial.Predicting in-hospital mortality based on clinical indicators can aid in making better clinical decisions.Previous studies have f...Sepsis poses a serious threat to patient survival,making timely risk assessment crucial.Predicting in-hospital mortality based on clinical indicators can aid in making better clinical decisions.Previous studies have focused on classifier selection but lacked a comprehensive analysis of feature selection and data preprocessing.This study optimized machine learning models for sepsis mortality prediction by:(1)comprehensively comparing feature selection and classification methods to identify the best combination,(2)building a high-performing model with fewer features,and(3)identifying key clinically relevant indicators.Methods:Using the MIMIC-III sepsis cohort,we conducted a comprehensive analysis to determine the optimal model,including data preprocessing,data balance,classifier selection,and feature selection.Feature importance was further analyzed to identify the key predictors of in-hospital mortality.Results:The proposed Synthetic Minority Oversampling Technique-Random Forest Recursive Feature Elimination-Extreme Gradient Boosting(SMOTE-(RF-RFE)-XGB)model achieved high predictive performance with a mean Area Under the Curve(AUC)of 0.8507,while reducing the number of features from 78 to 39.Compared to other feature selection methods evaluated in this study and those reported in related literature,Random Forest Recursive Feature Elimination(RF-RFE)offers the best trade-off between accuracy,feature compactness,and stability.Additionally,feature importance rankings consistently identified Acute Physiology Score Ⅲ(APS Ⅲ),Ventilation on First Day,and Depression as the top three most influential predictors,besides the Length of Stay in ICU and Hospital.Conclusions:This study addresses key gaps by conducting a comprehensive evaluation of classifiers and feature selection methods for predicting in-hospital mortality in patients with sepsis.The proposed SMOTE-(RFRFE)-XGB model achieved a high predictive performance and stability with a compact feature set.APS III,Ventilation on First Day,and Depression were consistently identified as key predictors besides Length of Stay in ICU and Hospital.展开更多
Compared to monocular depth estimation,multi-view depth estimation often yields more accurate results.However,traditional multi-view depth estimation methods often fail to leverage semantic information fully and strug...Compared to monocular depth estimation,multi-view depth estimation often yields more accurate results.However,traditional multi-view depth estimation methods often fail to leverage semantic information fully and struggle to effectively fuse information from multiple views,leading to suboptimal prediction performance in challenging scenarios such as texture-less regions and reflective surfaces.To address these limitations,we present MVI-Depth,a novel framework with two core innovations:(1)a Semantic Fusion Module(SFM)that establishes semantic correspondence,and(2)a Depth Updating Module(DUM)enabling iterative depth refinement.Specifically,MVI-Depth initially establishes a main view representation that integrates single-view depth,depth features,and semantic features.Subsequent feature extraction from neighbouring views enables the construction of the original cost volume.Recognising the inherent limitations of direct cost volume utilisation in complex scenes,the proposed SFM constructs an aligned semantic cost volume to utilise the complementarity between semantic and depth information,forming an improved final cost volume.The final cost volume is updated through the proposed DUM to achieve iterative depth optimisation.Comprehensive evaluations demonstrate that MVI-Depth achieves superior performance across all standard metrics on both ScanNet and KITTI benchmarks,outperforming existing methods.Additional experiments on the 7-Scenes dataset further confirm the framework's robust generalisation capabilities in diverse environments.展开更多
With the purpose to smooth the way of a correct understanding of information concepts and their evolution,in this paper,is discussed the evolution and development of the concept of information in biological systems,sh...With the purpose to smooth the way of a correct understanding of information concepts and their evolution,in this paper,is discussed the evolution and development of the concept of information in biological systems,showing that this concept was intuitively perceived even since ancient times by our predecessors,and described according to their language level of that times,but the crystallization of the real meaning of information is an achievement of our nowadays,by successive contribution of various scientific branches and personalities of the scientific community of the world,leading to a modern description/modeling of reality,in which information plays a fundamental role.It is shown that our reality can be understood as a contribution of matter/energy/information and represented/discussed as the model of the Universal Triangle of Reality(UTR),where various previous models can be suggestively inserted,as a function of their basic concern.The modern concepts on information starting from a theoretic experiment which would infringe the thermodynamics laws and reaching the theory of information and modern philosophic concepts on the world structuration allow us to show that information is a fundamental component of the material world and of the biological structures,in correlation with the structuration/destructuration processes of matter,involving absorption/release of information.Based on these concepts,is discussed the functionality of the biologic structures and is presented the informational model of the human body and living structures,as a general model of info-organization on the entire biological scale,showing that a rudimentary proto-consciousness should be operative even at the low-scale biological systems,because they work on the same principles,like the most developed bio-systems.The operability of biologic structures as informational devices is also pointed out.展开更多
Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the con...Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the context of fewshot and zero-shot scenarios remains highly challenging due to the scarcity of training data.Large language models(LLMs),on the other hand,can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks.Nevertheless,it is unclear,whether such effectiveness can be replicated in the task of IE,where the target tasks involve specialized schema and quite abstractive entity or relation concepts.In this paper,we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods,including the structured fundamental prompt(SFP),the structured interactive reasoning prompt(SIRP),and the voting-enabled structured interactive reasoning prompt(VESIRP).The experimental results demonstrate that while directly promotes inferior performance,the proposed augmented prompt methods significantly improve the extraction accuracy,achieving comparable or even better performance(e.g.,zero-shot FewNERD,FewNERD-INTRA)than state-of-theart methods that require large-scale training samples.This study represents a systematic exploration of employing instruction-following LLM for the task of IE.It not only establishes a performance benchmark for this novel paradigm but,more importantly,validates a practical technical pathway through the proposed prompt enhancement method,offering a viable solution for efficient IE in low-resource settings.展开更多
In this paper,there are discussed the informational functions of the living structures,analyzing the properties of the simplest eukaryotic cell as an example of a structural unit of the living unicellular and multicel...In this paper,there are discussed the informational functions of the living structures,analyzing the properties of the simplest eukaryotic cell as an example of a structural unit of the living unicellular and multicellular systems.The initiation of this analysis starts from an older example of an imaginary mechanism,particularly that described by the Maxwell’s demon experiment,which along the history of the information development concepts accompanied the philosophic vision on the structuration of matter and of the living entities,showing that these are actually the result of the intervention of information on the matter available substrate.Particularly,it is shown that the deoxyribonucleic acid(DNA)structure is appropriate to store a large quantity of structural information,allowing the transfer of this information by transcription and translation mechanisms to proteins,which act as(re)structuration/transmission informational agents,or the generation of a new cellular daughter structure by a replication process.On the basis of the theory of information in communication channels,applicable also in biological systems,it was discussed the followed line for the evaluation of the quantity of structural information in various cells,demonstrating the evolution of organism complexity by the increase of the structural information quantity from unicellular(bacterium)to human cell.Applying a natural strategy of entropy lowering mainly by heat elimination,folding protein structuration and compartmentalization on the evolutionary scale,the living structures act as dynamic entities assuring their self-organizational structure by a permanent change of matter,energy and information with the environment in an efficient way,following a negative entropic process by internal structuration,similarly with Maxwell’s demon work.It is shown that to assure such a communication with external and internal intracellular structure,it was necessary the development of an own info-operational system of communication and decision,in which the operational“Yes/No”decisional binary(Bit)unit is essential.These revolutionary results show that the cell unit complies with the similar informational functions like the multicellular structure of the human body,organized in seven-type informational components,allowing the informational modeling of the activity of the living biologic structures and the opening of a shortcutting way to mimic the biologic functions in artificial cells.展开更多
High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelations...High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.展开更多
Purpose: The aim of this paper is to develop a standardized and reliable measurement tool for assessing information-seeking behavior of undergraduate students.Design/methodology/approach: Based on information literacy...Purpose: The aim of this paper is to develop a standardized and reliable measurement tool for assessing information-seeking behavior of undergraduate students.Design/methodology/approach: Based on information literacy and information-seeking behavior theories, expert advice and students' interview, items of undergraduates' informationseeking behavior indicators were selected. With the analysis of homogeneity reliability, item analysis and factor analysis, this study constructs an assessment system to evaluate reliability and validity of the scale.Findings: The information-seeking behavior scale for undergraduates has divided undergraduates' information-seeking behavior into seven dimensions, which include 46 items. The reliability analysis of Cronbach's α was 0.910, and the coefficient of split-half reliability was0.817. The results of factor analysis showed that Kaiser-Meyer-Olkin(KMO) was 0.864,which indicates 55.536% of the total variation could be explained by the above seven dimensions.Research limitations: Due to a small sample size and limited sample distribution, further research need be conducted in an expanded sample size in order to explore the application scope of this evaluation system; in addition, the stability of the scale also need be confirmed.Practical implications: The paper sets up an information-seeking behavior evaluation system for undergraduates and explores the characteristics of their information-seeking behavior.This study provides guidance for the development of future information literacy education and the improvement of the information literacy level of undergraduates.Originality/value: An information-seeking behavior scale for undergraduates has been developed, which comprehensively covers information need, information source, information evaluation, information retrieval, information management, information utilization and information morality. The scale is proved to have good reliability, validity, popularity anddiscrimination that it is qualified to be an assessment tool of information-seeking behavior for Chinese undergraduates.展开更多
Our study aims to take a closer look at China's current information literacy(IL) program standards at secondary schools and to analyze their level of success and/or failures in a comparative way with those of the ...Our study aims to take a closer look at China's current information literacy(IL) program standards at secondary schools and to analyze their level of success and/or failures in a comparative way with those of the United States in terms of fulfilling their each other's mission-oriented mandates. Our research findings show that China's current IL standards of high schools contain a disproportionate emphasis on information technology(IT). Moreover, the stipulations of these IL standards are narrowly construed and without being solidly grounded on a broad and comprehensive educational perspective. We also suggest that there are two underlying causes for this set of unsound IL standards in China.Firstly, there is a lack of collaboration between two major competing forces engaged in the curricular development and research of IL in China: Those professionals in educational IT discipline vis-à-vis those in Library and Information Science. Secondly, library professionals have a very limited influence on major socio-cultural policies, even at their own institutions. As a result, this paper recommends the following three possible measures,which may help remedy this situation strategically: 1) Establishing a set of new IL curriculum standards based on an IL-centered educational perspective; 2) establishing a teacher-librarian's training program to promote school librarians' role in IL education; and 3) strengthening the research and development of an online IL education program and an accompanied evaluation mechanism.展开更多
Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for ...Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for comprehensively obtaining the porosity. Deep learning methods provide an intelligent approach to suppress the ambiguity of the conventional inversion method. However, under the trace-bytrace inversion strategy, there is a lack of constraints from geological structural information, resulting in poor lateral continuity of prediction results. In addition, the heterogeneity and the sedimentary variability of subsurface media also lead to uncertainty in intelligent prediction. To achieve fine prediction of porosity, we consider the lateral continuity and variability and propose an improved structural modeling deep learning porosity prediction method. First, we combine well data, waveform attributes, and structural information as constraints to model geophysical parameters, constructing a high-quality training dataset with sedimentary facies-controlled significance. Subsequently, we introduce a gated axial attention mechanism to enhance the features of dataset and design a bidirectional closed-loop network system constrained by inversion and forward processes. The constraint coefficient is adaptively adjusted by the petrophysical information contained between the porosity and impedance in the study area. We demonstrate the effectiveness of the adaptive coefficient through numerical experiments.Finally, we compare the performance differences between the proposed method and conventional deep learning methods using data from two study areas. The proposed method achieves better consistency with the logging porosity, demonstrating the superiority of the proposed method.展开更多
The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and e...The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.展开更多
The security of information transmission and processing due to unknown vulnerabilities and backdoors in cyberspace is becoming increasingly problematic.However,there is a lack of effective theory to mathematically dem...The security of information transmission and processing due to unknown vulnerabilities and backdoors in cyberspace is becoming increasingly problematic.However,there is a lack of effective theory to mathematically demonstrate the security of information transmission and processing under nonrandom noise(or vulnerability backdoor attack)conditions in cyberspace.This paper first proposes a security model for cyberspace information transmission and processing channels based on error correction coding theory.First,we analyze the fault tolerance and non-randomness problem of Dynamic Heterogeneous Redundancy(DHR)structured information transmission and processing channel under the condition of non-random noise or attacks.Secondly,we use a mathematical statistical method to demonstrate that for non-random noise(or attacks)on discrete memory channels,there exists a DHR-structured channel and coding scheme that enables the average system error probability to be arbitrarily small.Finally,to construct suitable coding and heterogeneous channels,we take Turbo code as an example and simulate the effects of different heterogeneity,redundancy,output vector length,verdict algorithm and dynamism on the system,which is an important guidance for theory and engineering practice.展开更多
This paper focuses on the research of MPLS VPN technology in the ocean information communication network.Through the analysis of the current situation of the ocean information communication network,the architecture de...This paper focuses on the research of MPLS VPN technology in the ocean information communication network.Through the analysis of the current situation of the ocean information communication network,the architecture design of MPLS VPN technology in the ocean information communication network and the important role of RD value and RT value in the VPN instances,the matching strategies of import RT and export RT of different VPN instances are verified through experiments.展开更多
To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-l...To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-line phase and an on-line phase.In the off-line phase,three APs were selected from the four APs in the localization area based on the received signal strength indication(RSSI).Next,CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card.A single-channel subimage was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas.These sub-images were then merged to form a three-channel RGB image,which was subsequently fed into the convolutional neural network(CNN)for training.The CNN model was saved upon completion of training.In the on-line phase,the CSI data from the target device was collected,converted into images using the same process as in the off-line phase,and fed into the well-trained CNN model.Finally,the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities.The proposed method was validated in indoor environments using two datasets,achieving good localization accuracy.展开更多
In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data,a Kalman filter multi-information fusion algorithm based on opti...In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data,a Kalman filter multi-information fusion algorithm based on optimized P-Huber weight function was proposed.The algorithm took Kalman filter(KF)as the whole frame,and established the decision threshold based on the confidence level of Chi-square distribution.At the same time,the abnormal error judgment value was constructed by Mahalanobis distance function,and the three segments of Huber weight function were formed.It could improve the accuracy of the interval judgment of outliers,and give a reasonable weight,so as to improve the tracking accuracy of the algorithm.The data values of four important locations in the vehicle obtained after optimized filtering were processed by information fusion.According to theoretical analysis,compared with Kalman filtering algorithm,the proposed algorithm could accurately track the actual temperature in the case of abnormal error,and multi-station data fusion processing could improve the overall fault tolerance of the system.The results showed that the proposed algorithm effectively reduced the interference of abnormal errors on filtering,and the synthetic value of fusion processing was more stable and critical.展开更多
Mathematics is a basic course for cultivating advanced technical talents,a core course for students in the basic education stage,mathematical knowledge content is the foundation of professional courses,mathematical th...Mathematics is a basic course for cultivating advanced technical talents,a core course for students in the basic education stage,mathematical knowledge content is the foundation of professional courses,mathematical thinking ability is one of the abilities for students’sustainable development,mathematical literacy is a basic quality that students should possess,and it carries the function of implementing the fundamental task of fostering virtue and nurturing talent and developing quality-oriented education.It has the characteristics of being fundamental,developmental,applied,and vocational.In today’s era of rapid development of artificial intelligence and big data,mathematics plays a huge role in production and life.This paper briefly expounds and analyzes the current situation of mathematics teaching,explores the significance of information-based teaching for mathematics teaching,and on this basis,proposes relevant strategies for information-based mathematics teaching,including knowledge visualization,the use of information technology to create mathematics teaching scenarios,the realization of efficient mathematics teaching through micro-lessons,and the realization of teaching interaction through network platforms.展开更多
基金the financial support of the Ministry of Science and Higher Education of the Republic of Kazakhstan,grant No.AP25793823.
文摘The reliability of information systems(IS)is a key factor in the sustainable operation of modern digital services.However,existing assessment methods remain fragmented and are often limited to individual indicators or expert judgments.This paper proposes a hybridmethodology for a comprehensive assessment of IS reliability based on the integration of the international standard ISO/IEC 25010:2023,multicriteria analysismethods(ARAS,CoCoSo,and TOPSIS),and theXGBoostmachine learning algorithmfor missing data imputation.Thestructure of the ISO/IEC 25010 standard is used to formalize reliability criteria and subcriteria,while theAHP method allows for the calculation of their weighting coefficients based on expert assessments.The XGBoost algorithm ensures the correct filling of gaps in the source data,increasing the completeness and reliability of the subsequent assessment.The resulting weighted indicators are aggregated using threeMCDMmethods,after which an integral reliability indicator is formed as a percentage.The methodology was tested on six real-world information systems with different architectures.The results demonstrated high consistency between the ARAS,CoCoSo,and TOPSISmethods,as well as the stability of the final rating when the criterion weights vary by±10%.The proposed approach provides a reproducible,transparent,and objective assessment of information system reliability and can be used to identify system bottlenecks,make modernization decisions,and manage the quality of digital infrastructure.
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sponsored in part by the National Natural Science Foundation of China(52167014)in part by the Science and Technology Commission of Shanghai Municipality(23XD1422000,23QB1400500).
文摘Due to the centralization of charging stations(CSs),CSs are organized as charging station alliances(CSAs)in the commercial competition.Under this situation,this paper studies the profit-oriented dynamic pricing strategy of CSAs.As the practicability basis,a privacy-protected bidirectional real-time information interaction framework is designed,under which the status of EVs is utilized as the reference for pricing,and the prices of CSs are the reference for charging decisions.Based on this framework,the decision-making models of EVs and CSs are established,in which the uncertainty caused by the information asymmetry between EVs and CSs and the bounded rationality of EV users are integrated.To solve the pricing decision model,the evolutionary game theory is adopted to describe the dynamic pricing game among CSAs,the equilibrium of which gives the optimal pricing strategy.Finally,the case study conducted in an urban area of Shanghai,China,validates the practicability of the framework and the effectiveness of the dynamic pricing strategy.
基金supported by the National Natural Science Foundation of China(Grant Nos.52208424,52208416,52078091,and 52108399)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0102).
文摘Graph neural networks(GNNs)have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control-and data-flow graphs.Despite their effectiveness,most GNN-based vulnerability detectors operate as black boxes,making their decisions difficult to interpret and thus less suitable for critical security auditing.The information bottleneck(IB)principle provides a theoretical framework for isolating task-relevant graph components.However,existing IB-based implementations often encounter unstable optimization and limited understanding of code semantics.To address these issues,we introduce ContractGIB,an interpretable graph information bottleneck framework for function-level vulnerability analysis.ContractGIB introduces three main advances.First,ContractGIB introduces an Hilbert–Schmidt Independence Criterion(HSIC)based estimator that provides stable dependence measurement.Second,it incorporates a CodeBERT semantic module to improve node representations.Third,it initializes all nodes with pretrained CodeBERT embeddings,removing the need for hand-crafted features.For each contract function,ContractGIB identifies themost informative nodes forming an instance-specific explanatory subgraph that supports the model’s prediction.Comprehensive experiments on public smart contract datasets,including ESC andVSC,demonstrate thatContractGIB achieves superior performance compared to competitive GNN baselines,while offering clearer,instance-level interpretability.
基金funded by the National Key R&D Program of China under Grant(No.2022YFB3102901)National Natural Science Foundation of China(No.62072115,No.62102094)Shanghai Science and Technology Innovation Action Plan Project(No.22510713600).
文摘Topological information is very important for understanding different types of online web services,in particular,for online social networks(OSNs).People leverage such information for various applications,such as social relationship modeling,community detection,user profiling,and user behavior prediction.However,the leak of such information will also pose severe challenges for user privacy preserving due to its usefulness in characterizing users.Large-scale web crawling-based information probing is a representative way for obtaining topological information of online web services.In this paper,we explore how to defend against topological information probing for online web services,with a particular focus on online decentralized web services such as Mastodon.Different from traditional centralized web services,the federated nature of decentralized web services makes the identification of distributed crawlers even more difficult.We analyze the behavioral differences between legitimate users and crawlers in decentralized web services and highlight two key behavioral attributes that distinguish crawlers from legitimate users:instance interaction preferences and hop count in profile viewing patterns.Based on these insights:we propose a supervised machine learning-based framework for crawler detection,which is able to learn the federation-aware feature representations for users.To validate the framework’s effectiveness,we construct a labeled dataset that integrates real users with real-trace driven simulated crawlers in Mastodon.We use this dataset to train various supervised classifiers for crawler detection.Experimental results demonstrate that our framework can achieve an excellent classification performance.Moreover,it is observed that federation-aware features are effective in improving detection performance.
文摘Sepsis poses a serious threat to patient survival,making timely risk assessment crucial.Predicting in-hospital mortality based on clinical indicators can aid in making better clinical decisions.Previous studies have focused on classifier selection but lacked a comprehensive analysis of feature selection and data preprocessing.This study optimized machine learning models for sepsis mortality prediction by:(1)comprehensively comparing feature selection and classification methods to identify the best combination,(2)building a high-performing model with fewer features,and(3)identifying key clinically relevant indicators.Methods:Using the MIMIC-III sepsis cohort,we conducted a comprehensive analysis to determine the optimal model,including data preprocessing,data balance,classifier selection,and feature selection.Feature importance was further analyzed to identify the key predictors of in-hospital mortality.Results:The proposed Synthetic Minority Oversampling Technique-Random Forest Recursive Feature Elimination-Extreme Gradient Boosting(SMOTE-(RF-RFE)-XGB)model achieved high predictive performance with a mean Area Under the Curve(AUC)of 0.8507,while reducing the number of features from 78 to 39.Compared to other feature selection methods evaluated in this study and those reported in related literature,Random Forest Recursive Feature Elimination(RF-RFE)offers the best trade-off between accuracy,feature compactness,and stability.Additionally,feature importance rankings consistently identified Acute Physiology Score Ⅲ(APS Ⅲ),Ventilation on First Day,and Depression as the top three most influential predictors,besides the Length of Stay in ICU and Hospital.Conclusions:This study addresses key gaps by conducting a comprehensive evaluation of classifiers and feature selection methods for predicting in-hospital mortality in patients with sepsis.The proposed SMOTE-(RFRFE)-XGB model achieved a high predictive performance and stability with a compact feature set.APS III,Ventilation on First Day,and Depression were consistently identified as key predictors besides Length of Stay in ICU and Hospital.
基金supported by the National Natural Science Foundation of China(Nos.62373009 and 62073004).
文摘Compared to monocular depth estimation,multi-view depth estimation often yields more accurate results.However,traditional multi-view depth estimation methods often fail to leverage semantic information fully and struggle to effectively fuse information from multiple views,leading to suboptimal prediction performance in challenging scenarios such as texture-less regions and reflective surfaces.To address these limitations,we present MVI-Depth,a novel framework with two core innovations:(1)a Semantic Fusion Module(SFM)that establishes semantic correspondence,and(2)a Depth Updating Module(DUM)enabling iterative depth refinement.Specifically,MVI-Depth initially establishes a main view representation that integrates single-view depth,depth features,and semantic features.Subsequent feature extraction from neighbouring views enables the construction of the original cost volume.Recognising the inherent limitations of direct cost volume utilisation in complex scenes,the proposed SFM constructs an aligned semantic cost volume to utilise the complementarity between semantic and depth information,forming an improved final cost volume.The final cost volume is updated through the proposed DUM to achieve iterative depth optimisation.Comprehensive evaluations demonstrate that MVI-Depth achieves superior performance across all standard metrics on both ScanNet and KITTI benchmarks,outperforming existing methods.Additional experiments on the 7-Scenes dataset further confirm the framework's robust generalisation capabilities in diverse environments.
文摘With the purpose to smooth the way of a correct understanding of information concepts and their evolution,in this paper,is discussed the evolution and development of the concept of information in biological systems,showing that this concept was intuitively perceived even since ancient times by our predecessors,and described according to their language level of that times,but the crystallization of the real meaning of information is an achievement of our nowadays,by successive contribution of various scientific branches and personalities of the scientific community of the world,leading to a modern description/modeling of reality,in which information plays a fundamental role.It is shown that our reality can be understood as a contribution of matter/energy/information and represented/discussed as the model of the Universal Triangle of Reality(UTR),where various previous models can be suggestively inserted,as a function of their basic concern.The modern concepts on information starting from a theoretic experiment which would infringe the thermodynamics laws and reaching the theory of information and modern philosophic concepts on the world structuration allow us to show that information is a fundamental component of the material world and of the biological structures,in correlation with the structuration/destructuration processes of matter,involving absorption/release of information.Based on these concepts,is discussed the functionality of the biologic structures and is presented the informational model of the human body and living structures,as a general model of info-organization on the entire biological scale,showing that a rudimentary proto-consciousness should be operative even at the low-scale biological systems,because they work on the same principles,like the most developed bio-systems.The operability of biologic structures as informational devices is also pointed out.
基金supported by the National Natural Science Foundation of China(62222212).
文摘Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the context of fewshot and zero-shot scenarios remains highly challenging due to the scarcity of training data.Large language models(LLMs),on the other hand,can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks.Nevertheless,it is unclear,whether such effectiveness can be replicated in the task of IE,where the target tasks involve specialized schema and quite abstractive entity or relation concepts.In this paper,we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods,including the structured fundamental prompt(SFP),the structured interactive reasoning prompt(SIRP),and the voting-enabled structured interactive reasoning prompt(VESIRP).The experimental results demonstrate that while directly promotes inferior performance,the proposed augmented prompt methods significantly improve the extraction accuracy,achieving comparable or even better performance(e.g.,zero-shot FewNERD,FewNERD-INTRA)than state-of-theart methods that require large-scale training samples.This study represents a systematic exploration of employing instruction-following LLM for the task of IE.It not only establishes a performance benchmark for this novel paradigm but,more importantly,validates a practical technical pathway through the proposed prompt enhancement method,offering a viable solution for efficient IE in low-resource settings.
文摘In this paper,there are discussed the informational functions of the living structures,analyzing the properties of the simplest eukaryotic cell as an example of a structural unit of the living unicellular and multicellular systems.The initiation of this analysis starts from an older example of an imaginary mechanism,particularly that described by the Maxwell’s demon experiment,which along the history of the information development concepts accompanied the philosophic vision on the structuration of matter and of the living entities,showing that these are actually the result of the intervention of information on the matter available substrate.Particularly,it is shown that the deoxyribonucleic acid(DNA)structure is appropriate to store a large quantity of structural information,allowing the transfer of this information by transcription and translation mechanisms to proteins,which act as(re)structuration/transmission informational agents,or the generation of a new cellular daughter structure by a replication process.On the basis of the theory of information in communication channels,applicable also in biological systems,it was discussed the followed line for the evaluation of the quantity of structural information in various cells,demonstrating the evolution of organism complexity by the increase of the structural information quantity from unicellular(bacterium)to human cell.Applying a natural strategy of entropy lowering mainly by heat elimination,folding protein structuration and compartmentalization on the evolutionary scale,the living structures act as dynamic entities assuring their self-organizational structure by a permanent change of matter,energy and information with the environment in an efficient way,following a negative entropic process by internal structuration,similarly with Maxwell’s demon work.It is shown that to assure such a communication with external and internal intracellular structure,it was necessary the development of an own info-operational system of communication and decision,in which the operational“Yes/No”decisional binary(Bit)unit is essential.These revolutionary results show that the cell unit complies with the similar informational functions like the multicellular structure of the human body,organized in seven-type informational components,allowing the informational modeling of the activity of the living biologic structures and the opening of a shortcutting way to mimic the biologic functions in artificial cells.
基金supported by the Aeronautical Science Foundation of China(2020Z023053002).
文摘High complexity and uncertainty of air combat pose significant challenges to target intention prediction.Current interpolation methods for data pre-processing and wrangling have limitations in capturing interrelationships among intricate variable patterns.Accordingly,this study proposes a Mogrifier gate recurrent unit-D(Mog-GRU-D)model to address the com-bat target intention prediction issue under the incomplete infor-mation condition.The proposed model directly processes miss-ing data while reducing the independence between inputs and output states.A total of 1200 samples from twelve continuous moments are captured through the combat simulation system,each of which consists of seven dimensional features.To bench-mark the experiment,a missing valued dataset has been gener-ated by randomly removing 20%of the original data.Extensive experiments demonstrate that the proposed model obtains the state-of-the-art performance with an accuracy of 73.25%when dealing with incomplete information.This study provides possi-ble interpretations for the principle of target interactive mecha-nism,highlighting the model’s effectiveness in potential air war-fare implementation.
基金supported by the National Social Science Foundation of China(Grant No.:11BTQ044)the Innovative Training Program for College Students in Changsha University(Grant No:CW11255)
文摘Purpose: The aim of this paper is to develop a standardized and reliable measurement tool for assessing information-seeking behavior of undergraduate students.Design/methodology/approach: Based on information literacy and information-seeking behavior theories, expert advice and students' interview, items of undergraduates' informationseeking behavior indicators were selected. With the analysis of homogeneity reliability, item analysis and factor analysis, this study constructs an assessment system to evaluate reliability and validity of the scale.Findings: The information-seeking behavior scale for undergraduates has divided undergraduates' information-seeking behavior into seven dimensions, which include 46 items. The reliability analysis of Cronbach's α was 0.910, and the coefficient of split-half reliability was0.817. The results of factor analysis showed that Kaiser-Meyer-Olkin(KMO) was 0.864,which indicates 55.536% of the total variation could be explained by the above seven dimensions.Research limitations: Due to a small sample size and limited sample distribution, further research need be conducted in an expanded sample size in order to explore the application scope of this evaluation system; in addition, the stability of the scale also need be confirmed.Practical implications: The paper sets up an information-seeking behavior evaluation system for undergraduates and explores the characteristics of their information-seeking behavior.This study provides guidance for the development of future information literacy education and the improvement of the information literacy level of undergraduates.Originality/value: An information-seeking behavior scale for undergraduates has been developed, which comprehensively covers information need, information source, information evaluation, information retrieval, information management, information utilization and information morality. The scale is proved to have good reliability, validity, popularity anddiscrimination that it is qualified to be an assessment tool of information-seeking behavior for Chinese undergraduates.
文摘Our study aims to take a closer look at China's current information literacy(IL) program standards at secondary schools and to analyze their level of success and/or failures in a comparative way with those of the United States in terms of fulfilling their each other's mission-oriented mandates. Our research findings show that China's current IL standards of high schools contain a disproportionate emphasis on information technology(IT). Moreover, the stipulations of these IL standards are narrowly construed and without being solidly grounded on a broad and comprehensive educational perspective. We also suggest that there are two underlying causes for this set of unsound IL standards in China.Firstly, there is a lack of collaboration between two major competing forces engaged in the curricular development and research of IL in China: Those professionals in educational IT discipline vis-à-vis those in Library and Information Science. Secondly, library professionals have a very limited influence on major socio-cultural policies, even at their own institutions. As a result, this paper recommends the following three possible measures,which may help remedy this situation strategically: 1) Establishing a set of new IL curriculum standards based on an IL-centered educational perspective; 2) establishing a teacher-librarian's training program to promote school librarians' role in IL education; and 3) strengthening the research and development of an online IL education program and an accompanied evaluation mechanism.
基金the support of Research Program of Fine Exploration and Surrounding Rock Classification Technology for Deep Buried Long Tunnels Driven by Horizontal Directional Drilling and Magnetotelluric Methods Based on Deep Learning under Grant E202408010the Sichuan Science and Technology Program under Grant 2024NSFSC1984 and Grant 2024NSFSC1990。
文摘Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for comprehensively obtaining the porosity. Deep learning methods provide an intelligent approach to suppress the ambiguity of the conventional inversion method. However, under the trace-bytrace inversion strategy, there is a lack of constraints from geological structural information, resulting in poor lateral continuity of prediction results. In addition, the heterogeneity and the sedimentary variability of subsurface media also lead to uncertainty in intelligent prediction. To achieve fine prediction of porosity, we consider the lateral continuity and variability and propose an improved structural modeling deep learning porosity prediction method. First, we combine well data, waveform attributes, and structural information as constraints to model geophysical parameters, constructing a high-quality training dataset with sedimentary facies-controlled significance. Subsequently, we introduce a gated axial attention mechanism to enhance the features of dataset and design a bidirectional closed-loop network system constrained by inversion and forward processes. The constraint coefficient is adaptively adjusted by the petrophysical information contained between the porosity and impedance in the study area. We demonstrate the effectiveness of the adaptive coefficient through numerical experiments.Finally, we compare the performance differences between the proposed method and conventional deep learning methods using data from two study areas. The proposed method achieves better consistency with the logging porosity, demonstrating the superiority of the proposed method.
基金supported by the Natural Science Foundation Research Plan of Shanxi Province (2023JCQN0728)。
文摘The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.
基金supported by National Key R&D Program of China for Young Scientists:Cyberspace Endogenous Security Mechanisms and Evaluation Methods(No.2022YFB3102800).
文摘The security of information transmission and processing due to unknown vulnerabilities and backdoors in cyberspace is becoming increasingly problematic.However,there is a lack of effective theory to mathematically demonstrate the security of information transmission and processing under nonrandom noise(or vulnerability backdoor attack)conditions in cyberspace.This paper first proposes a security model for cyberspace information transmission and processing channels based on error correction coding theory.First,we analyze the fault tolerance and non-randomness problem of Dynamic Heterogeneous Redundancy(DHR)structured information transmission and processing channel under the condition of non-random noise or attacks.Secondly,we use a mathematical statistical method to demonstrate that for non-random noise(or attacks)on discrete memory channels,there exists a DHR-structured channel and coding scheme that enables the average system error probability to be arbitrarily small.Finally,to construct suitable coding and heterogeneous channels,we take Turbo code as an example and simulate the effects of different heterogeneity,redundancy,output vector length,verdict algorithm and dynamism on the system,which is an important guidance for theory and engineering practice.
文摘This paper focuses on the research of MPLS VPN technology in the ocean information communication network.Through the analysis of the current situation of the ocean information communication network,the architecture design of MPLS VPN technology in the ocean information communication network and the important role of RD value and RT value in the VPN instances,the matching strategies of import RT and export RT of different VPN instances are verified through experiments.
基金supported by Lanzhou Science and Technology Plan Project(No.2023-3-104)Gansu Province Higher Education Industry Support Plan Project(No.2023CYZC-40)Gansu Province Excellent Graduate“Innovation Star”Program(No.2023CXZX-546)。
文摘To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-line phase and an on-line phase.In the off-line phase,three APs were selected from the four APs in the localization area based on the received signal strength indication(RSSI).Next,CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card.A single-channel subimage was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas.These sub-images were then merged to form a three-channel RGB image,which was subsequently fed into the convolutional neural network(CNN)for training.The CNN model was saved upon completion of training.In the on-line phase,the CSI data from the target device was collected,converted into images using the same process as in the off-line phase,and fed into the well-trained CNN model.Finally,the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities.The proposed method was validated in indoor environments using two datasets,achieving good localization accuracy.
基金supported by Natural Science Foundation of Gansu Province(No.20JR5RA407).
文摘In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data,a Kalman filter multi-information fusion algorithm based on optimized P-Huber weight function was proposed.The algorithm took Kalman filter(KF)as the whole frame,and established the decision threshold based on the confidence level of Chi-square distribution.At the same time,the abnormal error judgment value was constructed by Mahalanobis distance function,and the three segments of Huber weight function were formed.It could improve the accuracy of the interval judgment of outliers,and give a reasonable weight,so as to improve the tracking accuracy of the algorithm.The data values of four important locations in the vehicle obtained after optimized filtering were processed by information fusion.According to theoretical analysis,compared with Kalman filtering algorithm,the proposed algorithm could accurately track the actual temperature in the case of abnormal error,and multi-station data fusion processing could improve the overall fault tolerance of the system.The results showed that the proposed algorithm effectively reduced the interference of abnormal errors on filtering,and the synthetic value of fusion processing was more stable and critical.
文摘Mathematics is a basic course for cultivating advanced technical talents,a core course for students in the basic education stage,mathematical knowledge content is the foundation of professional courses,mathematical thinking ability is one of the abilities for students’sustainable development,mathematical literacy is a basic quality that students should possess,and it carries the function of implementing the fundamental task of fostering virtue and nurturing talent and developing quality-oriented education.It has the characteristics of being fundamental,developmental,applied,and vocational.In today’s era of rapid development of artificial intelligence and big data,mathematics plays a huge role in production and life.This paper briefly expounds and analyzes the current situation of mathematics teaching,explores the significance of information-based teaching for mathematics teaching,and on this basis,proposes relevant strategies for information-based mathematics teaching,including knowledge visualization,the use of information technology to create mathematics teaching scenarios,the realization of efficient mathematics teaching through micro-lessons,and the realization of teaching interaction through network platforms.
