Bushfires are devastating to forest managers,owners,residents,and the natural environment.Recent tech-nological advances indicate a potential for faster response times in terms of detecting and suppressing fires.Howev...Bushfires are devastating to forest managers,owners,residents,and the natural environment.Recent tech-nological advances indicate a potential for faster response times in terms of detecting and suppressing fires.However,to date,all these technologies have been applied in isola-tion.This paper introduces the latest fire detection and sup-pression technologies from ground to space.An operations research method was used to assemble these technologies into a theoretical framework for fire detection and suppres-sion.The framework harnesses the advantages of satellite-based,drone,sensor,and human reporting technologies as well as image processing and artificial intelligence machine learning.The study concludes that,if a system is designed to maximise the use of available technologies and carefully adopts them through complementary arrangements,a fire detection and resource suppression system can achieve the ultimate aim:to reduce the risk of fire hazards and the dam-age they may cause.展开更多
With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges su...With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges such as slow updates,usability issues,and limited installation methods.These challenges hinder the adoption and practicality of these tools.This paper examines smart contract vulnerability detection tools from 2016 to 2023,sourced from the Web of Science(WOS)and Google Scholar.By systematically collecting,screening,and synthesizing relevant research,38 open-source tools that provide installation methods were selected for further investigation.From a developer’s perspective,this paper offers a comprehensive survey of these 38 open-source tools,discussing their operating principles,installation methods,environmental dependencies,update frequencies,and installation challenges.Based on this,we propose an Ethereum smart contract vulnerability detection framework.This framework enables developers to easily utilize various detection tools and accurately analyze contract security issues.To validate the framework’s stability,over 1700 h of testing were conducted.Additionally,a comprehensive performance test was performed on the mainstream detection tools integrated within the framework,assessing their hardware requirements and vulnerability detection coverage.Experimental results indicate that the Slither tool demonstrates satisfactory performance in terms of system resource consumption and vulnerability detection coverage.This study represents the first performance evaluation of testing tools in this domain,providing significant reference value.展开更多
With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, i...With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anonymity has provided new ways for Ponzi schemes to commit fraud, posing significant risks to investors. Current research still has some limitations, for example, Ponzi schemes are difficult to detect in the early stages of smart contract deployment, and data imbalance is not considered. In addition, there is room for improving the detection accuracy. To address the above issues, this paper proposes LT-SPSD (LSTM-Transformer smart Ponzi schemes detection), which is a Ponzi scheme detection method that combines Long Short-Term Memory (LSTM) and Transformer considering the time-series transaction information of smart contracts as well as the global information. Based on the verified smart contract addresses, account features, and code features are extracted to construct a feature dataset, and the SMOTE-Tomek algorithm is used to deal with the imbalanced data classification problem. By comparing our method with the other four typical detection methods in the experiment, the LT-SPSD method shows significant performance improvement in precision, recall, and F1-score. The results of the experiment confirm the efficacy of the model, which has some application value in Ethereum Ponzi scheme smart contract detection.展开更多
Urban railways are vital means of public transportation in Korea.More than 30%of metropolitan residents use the railways,and this proportion is expected to increase.To enhance safety,the government has mandated the in...Urban railways are vital means of public transportation in Korea.More than 30%of metropolitan residents use the railways,and this proportion is expected to increase.To enhance safety,the government has mandated the installation of closed-circuit televisions in all carriages by 2024.However,cameras still monitored humans.To address this limitation,we developed a dataset of risk factors and a smart detection system that enables an immediate response to any abnormal behavior and intensive monitoring thereof.We created an innovative learning dataset that takes into account seven unique risk factors specific to Korean railway passengers.Detailed data collection was conducted across the Shinbundang Line of the Incheon Transportation Corporation,and the Ui-Shinseol Line.We observed several behavioral characteristics and assigned unique annotations to them.We also considered carriage congestion.Recognition performance was evaluated by camera placement and number.Then the camera installation plan was optimized.The dataset will find immediate applications in domestic railway operations.The artificial intelligence algorithms will be verified shortly.展开更多
The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved d...The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved due to the development of deep learning algorithms.Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or events.The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection.In this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking images.We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces.Image augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through video-camera.The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported methodologies.Moreover,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection.The results are verified using Python,TensorFlow,OpenCV computer simulation frameworks.展开更多
基金supported by the National Institute for Forest Products Innovation (NIFPI) Australia (Project No. NS034),titled Scoping an Automated Forest Fire Detection and Suppression Framework for the Green Triangle.
文摘Bushfires are devastating to forest managers,owners,residents,and the natural environment.Recent tech-nological advances indicate a potential for faster response times in terms of detecting and suppressing fires.However,to date,all these technologies have been applied in isola-tion.This paper introduces the latest fire detection and sup-pression technologies from ground to space.An operations research method was used to assemble these technologies into a theoretical framework for fire detection and suppres-sion.The framework harnesses the advantages of satellite-based,drone,sensor,and human reporting technologies as well as image processing and artificial intelligence machine learning.The study concludes that,if a system is designed to maximise the use of available technologies and carefully adopts them through complementary arrangements,a fire detection and resource suppression system can achieve the ultimate aim:to reduce the risk of fire hazards and the dam-age they may cause.
基金supported by the Major Public Welfare Special Fund of Henan Province(No.201300210200)the Major Science and Technology Research Special Fund of Henan Province(No.221100210400).
文摘With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges such as slow updates,usability issues,and limited installation methods.These challenges hinder the adoption and practicality of these tools.This paper examines smart contract vulnerability detection tools from 2016 to 2023,sourced from the Web of Science(WOS)and Google Scholar.By systematically collecting,screening,and synthesizing relevant research,38 open-source tools that provide installation methods were selected for further investigation.From a developer’s perspective,this paper offers a comprehensive survey of these 38 open-source tools,discussing their operating principles,installation methods,environmental dependencies,update frequencies,and installation challenges.Based on this,we propose an Ethereum smart contract vulnerability detection framework.This framework enables developers to easily utilize various detection tools and accurately analyze contract security issues.To validate the framework’s stability,over 1700 h of testing were conducted.Additionally,a comprehensive performance test was performed on the mainstream detection tools integrated within the framework,assessing their hardware requirements and vulnerability detection coverage.Experimental results indicate that the Slither tool demonstrates satisfactory performance in terms of system resource consumption and vulnerability detection coverage.This study represents the first performance evaluation of testing tools in this domain,providing significant reference value.
基金This work was granted by Qin Xin Talents Cultivation Program(No.QXTCP C202115)Beijing Information Science and Technology University+1 种基金the Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Fund(No.GJJ-23)National Social Science Foundation,China(No.21BTQ079).
文摘With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anonymity has provided new ways for Ponzi schemes to commit fraud, posing significant risks to investors. Current research still has some limitations, for example, Ponzi schemes are difficult to detect in the early stages of smart contract deployment, and data imbalance is not considered. In addition, there is room for improving the detection accuracy. To address the above issues, this paper proposes LT-SPSD (LSTM-Transformer smart Ponzi schemes detection), which is a Ponzi scheme detection method that combines Long Short-Term Memory (LSTM) and Transformer considering the time-series transaction information of smart contracts as well as the global information. Based on the verified smart contract addresses, account features, and code features are extracted to construct a feature dataset, and the SMOTE-Tomek algorithm is used to deal with the imbalanced data classification problem. By comparing our method with the other four typical detection methods in the experiment, the LT-SPSD method shows significant performance improvement in precision, recall, and F1-score. The results of the experiment confirm the efficacy of the model, which has some application value in Ethereum Ponzi scheme smart contract detection.
基金supported by a Korean Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure and Transport(grant no.RS-2023-00239464).
文摘Urban railways are vital means of public transportation in Korea.More than 30%of metropolitan residents use the railways,and this proportion is expected to increase.To enhance safety,the government has mandated the installation of closed-circuit televisions in all carriages by 2024.However,cameras still monitored humans.To address this limitation,we developed a dataset of risk factors and a smart detection system that enables an immediate response to any abnormal behavior and intensive monitoring thereof.We created an innovative learning dataset that takes into account seven unique risk factors specific to Korean railway passengers.Detailed data collection was conducted across the Shinbundang Line of the Incheon Transportation Corporation,and the Ui-Shinseol Line.We observed several behavioral characteristics and assigned unique annotations to them.We also considered carriage congestion.Recognition performance was evaluated by camera placement and number.Then the camera installation plan was optimized.The dataset will find immediate applications in domestic railway operations.The artificial intelligence algorithms will be verified shortly.
文摘The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved due to the development of deep learning algorithms.Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or events.The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection.In this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking images.We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces.Image augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through video-camera.The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported methodologies.Moreover,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection.The results are verified using Python,TensorFlow,OpenCV computer simulation frameworks.
