Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geos...Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.展开更多
The proliferation of textual data in society currently is overwhelming, in particular, unstructured textual data is being constantly generated via call centre logs, emails, documents on the web, blogs, tweets, custome...The proliferation of textual data in society currently is overwhelming, in particular, unstructured textual data is being constantly generated via call centre logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, etc.While the amount of textual data is increasing rapidly, users ability to summarise, understand, and make sense of such data for making better business/living decisions remains challenging. This paper studies how to analyse textual data, based on layered software patterns, for extracting insightful user intelligence from a large collection of documents and for using such information to improve user operations and performance.展开更多
A new design solution of data access layer for N-tier architecture is presented. It can solve the problems such as low efficiency of development and difficulties in transplantation, update and reuse. The solution util...A new design solution of data access layer for N-tier architecture is presented. It can solve the problems such as low efficiency of development and difficulties in transplantation, update and reuse. The solution utilizes the reflection technology of .NET and design pattern. A typical application of the solution demonstrates that the new solution of data access layer performs better than the current N-tier architecture. More importantly, the application suggests that the new solution of data access layer can be reused effectively.展开更多
A new B-spline surface reconstruction method from layer data based on deformable model is presented. An initial deformable surface, which is represented as a closed cylinder, is firstly given. The surface is subject t...A new B-spline surface reconstruction method from layer data based on deformable model is presented. An initial deformable surface, which is represented as a closed cylinder, is firstly given. The surface is subject to internal forces describing its implicit smoothness property and external forces attracting it toward the layer data points. And then finite element method is adopted to solve its energy minimization problem, which results a bicubic closed B-spline surface with C^2 continuity. The proposed method can provide a smoothness and accurate surface model directly from the layer data, without the need to fit cross-sectional curves and make them compatible. The feasibility of the proposed method is verified by the experimental results.展开更多
With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also increasing.An intrusion detection system(IDS)is a secondary intelligent system for monitoring,detectin...With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also increasing.An intrusion detection system(IDS)is a secondary intelligent system for monitoring,detecting and alerting against malicious activity.IDS is important in developing advanced security models.This study reviews the importance of various techniques,tools,and methods used in IoT detection and/or prevention systems.Specifically,it focuses on machine learning(ML)and deep learning(DL)techniques for IDS.This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles.To speed up the detection of recent attacks,the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network(CNN),which performs better than a support vector machine(SVM).Processing data are enhanced using the synthetic minority oversampling technique to ensure learning accuracy.The nearest class mean classifier is applied during the testing phase to identify new attacks.Experimental results using the AWID dataset,which is one of the most common open intrusion detection datasets,revealed a higher detection accuracy(94%)compared to SVM and random forest methods.展开更多
Network security has become more of a concern with the rapid growth and expansion of the Internet. While there are several ways to provide security in the application, transport, or network layers of a network, the da...Network security has become more of a concern with the rapid growth and expansion of the Internet. While there are several ways to provide security in the application, transport, or network layers of a network, the data link layer (Layer 2) security has not yet been adequately addressed. Data link layer protocols used in local area networks (LANs) are not designed with security features. Dynamic host configuration protocol (DHCP) is one of the most used network protocols for host configuration that works in data link layer. DHCP is vulnerable to a number of attacks, such as the DHCP rouge server attack, DHCP starvation attack, and malicious DHCP client attack. This work introduces a new scheme called Secure DHCP (S-DHCP) to secure DHCP protocol. The proposed solution consists of two techniques. The first is the authentication and key management technique that is used for entities authentication and management of security key. It is based on using Diffie-Hellman key exchange algorithm supported by the difficulty of Elliptic Curve Discrete Logarithm Problem (ECDLP) and a strong cryptographic one-way hash function. The second technique is the message authentication technique, which uses the digital signature to authenticate the DHCP messages exchanged between the clients and server.展开更多
Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such atta...Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.展开更多
Episodes of tectonic activities since Archaean time in one of the oldest craton,the eastern Yilgarn Craton of Western Australia,have left a complex pattern in the architectural settings.Insights of the crustal scale a...Episodes of tectonic activities since Archaean time in one of the oldest craton,the eastern Yilgarn Craton of Western Australia,have left a complex pattern in the architectural settings.Insights of the crustal scale architectural settings of the Craton have been made through geophysical data modelling and imaging using high resolution aeromagnetic and Bouguer gravity data.The advanced technique of image processing using pseudocolour composition,hill-shading and the multiple data layers compilation in the hue,saturation and intensity(HSI)space has been used for image based analysis of potential field data.Geophysical methods of anomaly enhancement technique along with the imaging technique are used to delineate several regional and as well as local structures.Multiscale analysis in geophysical data processing with the application of varying upward continuation levels,and also anomaly enhancement techniques using spatial derivatives are used delineating major shear zones and regional scale structures.A suitable data based interpretation of basement architecture of the study area is given.展开更多
Agentic AI represents a significant advancement in artificial intelligence,enabling proactive agents that can set goals,make decisions,and adapt to changing situations.However,the performance of these systems is heavi...Agentic AI represents a significant advancement in artificial intelligence,enabling proactive agents that can set goals,make decisions,and adapt to changing situations.However,the performance of these systems is heavily dependent on the quality and relevance of the data they process.This research highlights the critical risk posed by faulty,insecure,or contextually inappropriate input data in modern Agentic AI systems.To address this challenge,this study proposes the Autonomous Data Integrity Layer(ADIL).This flexible architecture integrates best practices from security engineering and data science to ensure that Agentic AI systems operate with clean,validated,and contextually relevant data.By focusing on data integrity,ADIL enhances the reliability,accountability,and effectiveness of Agentic AI systems,leading to more trustworthy and robust intelligent agents.展开更多
Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely us...Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely used for improving the signal to noise ratio(SNR)and discriminating internal layers by radio echo sounding data of ice sheets.This method is not efficient when we use edge detection operators to obtain accurate information of the layers,especially the ice-bed interface.This paper presents a new image processing method via a combined robust principal component analysis-total variation(RPCA-TV)approach for discriminating internal layers of ice sheets by radio echo sounding data.The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method,which is used to discriminate the internal layers.The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics,Chinese Academy of Sciences,collected during CHINARE 28.The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.展开更多
A novel physical layer data encryption scheme using two-level constellation masking in three-dimensional(3D)carrier-less amplitude and phase modulation(CAP)passive optical network(PON)is proposed in this Letter.The ch...A novel physical layer data encryption scheme using two-level constellation masking in three-dimensional(3D)carrier-less amplitude and phase modulation(CAP)passive optical network(PON)is proposed in this Letter.The chaotic sequence generated by Chua’s circuit model realizes two-level encryption of displacement masking and constellation rotation for3 D constellations.We successfully conduct an experiment demonstrating 8.7 Gb/s 3 D-CAP-8 data transmission over25 km standard single-mode fiber.With two-level constellation masking,a key space size of 2.1×1085 is achieved to bring about high security and good encryption performance,suggesting broad application prospects in future short-range secure communications.展开更多
文摘Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.
文摘The proliferation of textual data in society currently is overwhelming, in particular, unstructured textual data is being constantly generated via call centre logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, etc.While the amount of textual data is increasing rapidly, users ability to summarise, understand, and make sense of such data for making better business/living decisions remains challenging. This paper studies how to analyse textual data, based on layered software patterns, for extracting insightful user intelligence from a large collection of documents and for using such information to improve user operations and performance.
基金the Foundation for Key Teachers of Chongqing University (200209055).
文摘A new design solution of data access layer for N-tier architecture is presented. It can solve the problems such as low efficiency of development and difficulties in transplantation, update and reuse. The solution utilizes the reflection technology of .NET and design pattern. A typical application of the solution demonstrates that the new solution of data access layer performs better than the current N-tier architecture. More importantly, the application suggests that the new solution of data access layer can be reused effectively.
基金This project is supported by National Natural Science Foundation of China(No. 10272033) and Provincial Natural Science Foundation of Guangdong,China(No.04105385).
文摘A new B-spline surface reconstruction method from layer data based on deformable model is presented. An initial deformable surface, which is represented as a closed cylinder, is firstly given. The surface is subject to internal forces describing its implicit smoothness property and external forces attracting it toward the layer data points. And then finite element method is adopted to solve its energy minimization problem, which results a bicubic closed B-spline surface with C^2 continuity. The proposed method can provide a smoothness and accurate surface model directly from the layer data, without the need to fit cross-sectional curves and make them compatible. The feasibility of the proposed method is verified by the experimental results.
基金The author extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research atMajmaah University for funding this research work through the project number(R-2024-920).
文摘With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also increasing.An intrusion detection system(IDS)is a secondary intelligent system for monitoring,detecting and alerting against malicious activity.IDS is important in developing advanced security models.This study reviews the importance of various techniques,tools,and methods used in IoT detection and/or prevention systems.Specifically,it focuses on machine learning(ML)and deep learning(DL)techniques for IDS.This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles.To speed up the detection of recent attacks,the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network(CNN),which performs better than a support vector machine(SVM).Processing data are enhanced using the synthetic minority oversampling technique to ensure learning accuracy.The nearest class mean classifier is applied during the testing phase to identify new attacks.Experimental results using the AWID dataset,which is one of the most common open intrusion detection datasets,revealed a higher detection accuracy(94%)compared to SVM and random forest methods.
文摘Network security has become more of a concern with the rapid growth and expansion of the Internet. While there are several ways to provide security in the application, transport, or network layers of a network, the data link layer (Layer 2) security has not yet been adequately addressed. Data link layer protocols used in local area networks (LANs) are not designed with security features. Dynamic host configuration protocol (DHCP) is one of the most used network protocols for host configuration that works in data link layer. DHCP is vulnerable to a number of attacks, such as the DHCP rouge server attack, DHCP starvation attack, and malicious DHCP client attack. This work introduces a new scheme called Secure DHCP (S-DHCP) to secure DHCP protocol. The proposed solution consists of two techniques. The first is the authentication and key management technique that is used for entities authentication and management of security key. It is based on using Diffie-Hellman key exchange algorithm supported by the difficulty of Elliptic Curve Discrete Logarithm Problem (ECDLP) and a strong cryptographic one-way hash function. The second technique is the message authentication technique, which uses the digital signature to authenticate the DHCP messages exchanged between the clients and server.
文摘Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.
基金supported by Spaceage Geoconsulting,a research oriented consulting firm.
文摘Episodes of tectonic activities since Archaean time in one of the oldest craton,the eastern Yilgarn Craton of Western Australia,have left a complex pattern in the architectural settings.Insights of the crustal scale architectural settings of the Craton have been made through geophysical data modelling and imaging using high resolution aeromagnetic and Bouguer gravity data.The advanced technique of image processing using pseudocolour composition,hill-shading and the multiple data layers compilation in the hue,saturation and intensity(HSI)space has been used for image based analysis of potential field data.Geophysical methods of anomaly enhancement technique along with the imaging technique are used to delineate several regional and as well as local structures.Multiscale analysis in geophysical data processing with the application of varying upward continuation levels,and also anomaly enhancement techniques using spatial derivatives are used delineating major shear zones and regional scale structures.A suitable data based interpretation of basement architecture of the study area is given.
文摘Agentic AI represents a significant advancement in artificial intelligence,enabling proactive agents that can set goals,make decisions,and adapt to changing situations.However,the performance of these systems is heavily dependent on the quality and relevance of the data they process.This research highlights the critical risk posed by faulty,insecure,or contextually inappropriate input data in modern Agentic AI systems.To address this challenge,this study proposes the Autonomous Data Integrity Layer(ADIL).This flexible architecture integrates best practices from security engineering and data science to ensure that Agentic AI systems operate with clean,validated,and contextually relevant data.By focusing on data integrity,ADIL enhances the reliability,accountability,and effectiveness of Agentic AI systems,leading to more trustworthy and robust intelligent agents.
基金supported by the National Hi-Tech Research and Development Program of China("863"Project)(Grant No.2011AA040202)the National Natural Science Foundation of China(Grant No.40976114)
文摘Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely used for improving the signal to noise ratio(SNR)and discriminating internal layers by radio echo sounding data of ice sheets.This method is not efficient when we use edge detection operators to obtain accurate information of the layers,especially the ice-bed interface.This paper presents a new image processing method via a combined robust principal component analysis-total variation(RPCA-TV)approach for discriminating internal layers of ice sheets by radio echo sounding data.The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method,which is used to discriminate the internal layers.The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics,Chinese Academy of Sciences,collected during CHINARE 28.The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.
基金the National Key Research and Development Program of China(No.2018YFB1801302)National Natural Science Foundation of China(Nos.61835005,61822507,61522501,61475024,61675004,61705107,61727817,61775098,61720106015,and 61875248)+2 种基金Beijing Young Talent(No.2016000026833ZK15)Open Fund of IPOC(BUPT)(No.IPOC2019A011)Jiangsu Talent of Innovation and Entrepreneurship,Jiangsu Team of Innovation and Entrepreneurship,and Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX200963)。
文摘A novel physical layer data encryption scheme using two-level constellation masking in three-dimensional(3D)carrier-less amplitude and phase modulation(CAP)passive optical network(PON)is proposed in this Letter.The chaotic sequence generated by Chua’s circuit model realizes two-level encryption of displacement masking and constellation rotation for3 D constellations.We successfully conduct an experiment demonstrating 8.7 Gb/s 3 D-CAP-8 data transmission over25 km standard single-mode fiber.With two-level constellation masking,a key space size of 2.1×1085 is achieved to bring about high security and good encryption performance,suggesting broad application prospects in future short-range secure communications.
