Saleh ALFahad, Qiyuan Wang, C. Anagnostopoulos, Kostas Kolomvatsos
Mobile edge computing (MEC) paradigm has emerged to improve the quality of service & experience of applications deployed in close proximity to end-users. Due to their restricted computational and communication resources, MEC nodes can provide access to a portion of the entire set of services and data gathered. Therefore, there are several obstacles to their management. Keeping track of all the services offered by the MEC nodes is challenging, particularly if their demand rates change over time. Received tasks (such as, analytics queries, classification tasks, and model learning) require services to be invoked in real MEC use-case scenarios, e.g., smart cities. It is not unusual for a node to lack the necessary services or part of them. Undeniably, not all the requested services may be locally available; thus, MEC nodes must deal with the timely and appropriate choice of whether to carry out a service replication (pull action) or tasks offloading (push action) to peer nodes in a MEC environment. In this study, we contribute with a novel time-optimized mechanism based on the optimal stopping theory, which is built on the cost-based decreasing service demand rates evidenced in various service management situations. Our mechanism tries to optimally solve the decision-making dilemma between pull and push action. The experimental findings of our mechanism and its comparative assessment with other methods found in the literature showcase the achieved optimal decisions with respect to certain cost-based objective functions over dynamic service demand rates.
{"title":"Task offloading in mobile edge computing using cost-based discounted optimal stopping","authors":"Saleh ALFahad, Qiyuan Wang, C. Anagnostopoulos, Kostas Kolomvatsos","doi":"10.1515/comp-2023-0115","DOIUrl":"https://doi.org/10.1515/comp-2023-0115","url":null,"abstract":"\u0000 Mobile edge computing (MEC) paradigm has emerged to improve the quality of service & experience of applications deployed in close proximity to end-users. Due to their restricted computational and communication resources, MEC nodes can provide access to a portion of the entire set of services and data gathered. Therefore, there are several obstacles to their management. Keeping track of all the services offered by the MEC nodes is challenging, particularly if their demand rates change over time. Received tasks (such as, analytics queries, classification tasks, and model learning) require services to be invoked in real MEC use-case scenarios, e.g., smart cities. It is not unusual for a node to lack the necessary services or part of them. Undeniably, not all the requested services may be locally available; thus, MEC nodes must deal with the timely and appropriate choice of whether to carry out a service replication (pull action) or tasks offloading (push action) to peer nodes in a MEC environment. In this study, we contribute with a novel time-optimized mechanism based on the optimal stopping theory, which is built on the cost-based decreasing service demand rates evidenced in various service management situations. Our mechanism tries to optimally solve the decision-making dilemma between pull and push action. The experimental findings of our mechanism and its comparative assessment with other methods found in the literature showcase the achieved optimal decisions with respect to certain cost-based objective functions over dynamic service demand rates.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140518709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the rumor detection based on crowd intelligence, the crowd behavior is constructed as a graph model or probability mode. The detection of rumors is achieved through the collaborative utilization of data and knowledge. Aiming at the problems of insufficient feature extraction ability and data redundancy of current rumor detection methods based on deep learning model, a social network rumor detection method based on bidirectional gated recurrent unit (Bi-GRU) and double self-attention (DSA) mechanism is suggested. First, a combination of application program interface and third-party crawler approach is used to obtain microblogging data from publicly available fake microblogging information pages, including both rumor and non-rumor information. Second, Bi-GRU is used to capture the tendency of medium- and long-term dependence of data and is flexible enough to deal with variable length input. Finally, the DSA mechanism is introduced to help reduce the redundant information in the dataset, thereby enhancing the model’s efficacy. The results of the experiments indicate that the proposed method outperforms existing advanced methods by at least 0.114, 0.108, 0.064, and 0.085 in terms of accuracy, precision, recall, and F1-scores, respectively. Therefore, the proposed method can significantly enhance the ability of social network rumor detection.
在基于人群智能的谣言检测中，人群行为被构建为图模型或概率模型。通过数据和知识的协同利用，实现谣言的检测。针对目前基于深度学习模型的谣言检测方法存在的特征提取能力不足、数据冗余等问题，提出了一种基于双向门控循环单元（Bi-GRU）和双重自我关注（DSA）机制的社交网络谣言检测方法。首先，采用应用程序接口和第三方爬虫相结合的方法，从公开的虚假微博信息页面中获取微博数据，包括谣言信息和非谣言信息。其次，利用 Bi-GRU 捕获数据的中长期依赖趋势，并灵活处理不同长度的输入。最后，引入 DSA 机制来帮助减少数据集中的冗余信息，从而提高模型的有效性。实验结果表明，所提出的方法在准确度、精确度、召回率和 F1 分数方面分别比现有的先进方法高出至少 0.114、0.108、0.064 和 0.085。因此，本文提出的方法可以显著提高社交网络谣言的检测能力。
{"title":"A Bi-GRU-DSA-based social network rumor detection approach","authors":"Xiang Huang, Yan Liu","doi":"10.1515/comp-2023-0114","DOIUrl":"https://doi.org/10.1515/comp-2023-0114","url":null,"abstract":"\u0000 In the rumor detection based on crowd intelligence, the crowd behavior is constructed as a graph model or probability mode. The detection of rumors is achieved through the collaborative utilization of data and knowledge. Aiming at the problems of insufficient feature extraction ability and data redundancy of current rumor detection methods based on deep learning model, a social network rumor detection method based on bidirectional gated recurrent unit (Bi-GRU) and double self-attention (DSA) mechanism is suggested. First, a combination of application program interface and third-party crawler approach is used to obtain microblogging data from publicly available fake microblogging information pages, including both rumor and non-rumor information. Second, Bi-GRU is used to capture the tendency of medium- and long-term dependence of data and is flexible enough to deal with variable length input. Finally, the DSA mechanism is introduced to help reduce the redundant information in the dataset, thereby enhancing the model’s efficacy. The results of the experiments indicate that the proposed method outperforms existing advanced methods by at least 0.114, 0.108, 0.064, and 0.085 in terms of accuracy, precision, recall, and F1-scores, respectively. Therefore, the proposed method can significantly enhance the ability of social network rumor detection.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140524011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Today, the rapid development of computer technology changes with each passing day. In the computer field, computer animation has rapidly grown from a new thing to a leading industry, and animation has entered the era of three-dimensional animation and computer graphics. This article aims to study the application of artificial intelligence-based style transfer algorithm in animation special effects design. It proposes methods such as adaptive loss function, style transfer process, animation special effect design, etc., and conducts related experiments on the application of style transfer algorithm in animation special effect design in the article. The experimental results show that the style transfer algorithm based on AI can effectively improve the effect of animation special effects. In this survey, more than 80% of the people are satisfied with the animation special effects design based on the style transfer algorithm.
{"title":"Application of artificial intelligence-based style transfer algorithm in animation special effects design","authors":"Shan Li","doi":"10.1515/comp-2022-0255","DOIUrl":"https://doi.org/10.1515/comp-2022-0255","url":null,"abstract":"Abstract Today, the rapid development of computer technology changes with each passing day. In the computer field, computer animation has rapidly grown from a new thing to a leading industry, and animation has entered the era of three-dimensional animation and computer graphics. This article aims to study the application of artificial intelligence-based style transfer algorithm in animation special effects design. It proposes methods such as adaptive loss function, style transfer process, animation special effect design, etc., and conducts related experiments on the application of style transfer algorithm in animation special effect design in the article. The experimental results show that the style transfer algorithm based on AI can effectively improve the effect of animation special effects. In this survey, more than 80% of the people are satisfied with the animation special effects design based on the style transfer algorithm.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45177858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract With the rapid development of unmanned aerial vehicle (UAV) technology, there are more and more fields of UAV application. This research mainly discusses the UAV patrol path planning based on machine vision and multi-sensor fusion. This article studies how to apply ultrasonic, a classic ranging sensor, to obstacle avoidance of UAVs. The designed ultrasonic obstacle avoidance system is a complete set of hardware and software systems. The hardware part consists of a forward ultrasonic module and a central signal processing module. Among them, a single-axis stabilization gimbal is designed for the forward ultrasonic module, which decouples the attitude angle of the UAV and the pitch detection angle of the ultrasonic sensor. In the central signal processing module, Kalman filtering is performed on the ultrasonic data in the four directions of front, rear, left, right, and left, and the obstacle avoidance control signal is sent to the flight controller according to the filtered sensor data. At the same time, a human–computer interaction interface is also designed to set various parameters of the obstacle avoidance system. For the route planning method of the tower, the routine steps are used to inspect the tower with a single-circuit line, and the specific targets are the insulator string, the ground wire, and the conductor. In this study, the average statistical result of the straight-line distance of the UAV patrolling 100 m is 99.80 m, and the error is only 0.2%. The fusion obstacle avoidance control method based on machine vision is suitable for the engineering application of UAV perception obstacle avoidance. The obstacle avoidance method adopted in this article can be extended to most flight control platforms, and it is a control method with broad application prospects.
{"title":"UAV patrol path planning based on machine vision and multi-sensor fusion","authors":"Xu Chen","doi":"10.1515/comp-2022-0276","DOIUrl":"https://doi.org/10.1515/comp-2022-0276","url":null,"abstract":"Abstract With the rapid development of unmanned aerial vehicle (UAV) technology, there are more and more fields of UAV application. This research mainly discusses the UAV patrol path planning based on machine vision and multi-sensor fusion. This article studies how to apply ultrasonic, a classic ranging sensor, to obstacle avoidance of UAVs. The designed ultrasonic obstacle avoidance system is a complete set of hardware and software systems. The hardware part consists of a forward ultrasonic module and a central signal processing module. Among them, a single-axis stabilization gimbal is designed for the forward ultrasonic module, which decouples the attitude angle of the UAV and the pitch detection angle of the ultrasonic sensor. In the central signal processing module, Kalman filtering is performed on the ultrasonic data in the four directions of front, rear, left, right, and left, and the obstacle avoidance control signal is sent to the flight controller according to the filtered sensor data. At the same time, a human–computer interaction interface is also designed to set various parameters of the obstacle avoidance system. For the route planning method of the tower, the routine steps are used to inspect the tower with a single-circuit line, and the specific targets are the insulator string, the ground wire, and the conductor. In this study, the average statistical result of the straight-line distance of the UAV patrolling 100 m is 99.80 m, and the error is only 0.2%. The fusion obstacle avoidance control method based on machine vision is suitable for the engineering application of UAV perception obstacle avoidance. The obstacle avoidance method adopted in this article can be extended to most flight control platforms, and it is a control method with broad application prospects.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47040047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In this study, a zebra-crossing detection method based on cascaded Hough transform (CHT) and vanishing point (VP) characteristics is proposed. In this method, the principle of detecting straight lines in the parallel coordinate system is applied to zebra-crossing detection. Each edge point of the image obtained by edge detection is represented in the parallel coordinate system to find the VP. Using the VP coordinate as the judgment condition, those straight lines that do not pass through the VP but meet the straight-line condition are excluded to obtain the straight lines passing through both sides of the zebra crossing, and finally fit the edge points on the straight line, and get the zebra-crossing fitting line segment. Experiments show that CHT has obvious advantages in detection accuracy and speed compared with the Hough transform. At the same time, VPs can be used to eliminate interference segments, which provide support for the accuracy of zebra-crossing detection. This method can get zebra-crossing location information without using region of interest extraction, which also provides a reference method for road detection in some specific cases.
{"title":"Zebra-crossing detection based on cascaded Hough transform principle and vanishing point characteristics","authors":"Chen Zhu, Dong-yuan Ge, Xi-fan Yao, Wenjiang Xiang, Jian Li, Yong-Xiang Li","doi":"10.1515/comp-2022-0260","DOIUrl":"https://doi.org/10.1515/comp-2022-0260","url":null,"abstract":"Abstract In this study, a zebra-crossing detection method based on cascaded Hough transform (CHT) and vanishing point (VP) characteristics is proposed. In this method, the principle of detecting straight lines in the parallel coordinate system is applied to zebra-crossing detection. Each edge point of the image obtained by edge detection is represented in the parallel coordinate system to find the VP. Using the VP coordinate as the judgment condition, those straight lines that do not pass through the VP but meet the straight-line condition are excluded to obtain the straight lines passing through both sides of the zebra crossing, and finally fit the edge points on the straight line, and get the zebra-crossing fitting line segment. Experiments show that CHT has obvious advantages in detection accuracy and speed compared with the Hough transform. At the same time, VPs can be used to eliminate interference segments, which provide support for the accuracy of zebra-crossing detection. This method can get zebra-crossing location information without using region of interest extraction, which also provides a reference method for road detection in some specific cases.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46117175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract As the rapid advancement of information security company is developing quickly and the technology including big information, big computer, large cloud, and artificial intelligence are being widely used, network security has entered a new era. While ushering in huge development opportunities, it also faces severe tests. Network security is a major issue related to the comprehensive realization of a well-off society and national security, and has risen to the national strategic level. The current financial industry network security construction mainly focuses on team, process construction, and the research and development of individual tools and equipment. It lacks system research and implementation guidelines for defense technology construction based on industry IT characteristics. At the same time, there is also a lack of objective and unified measurement and evaluation standards for enterprise security defense capabilities, which restricts the improvement of cybersecurity capabilities in the financial industry to a certain extent. In terms of actual combat exercises, in various actual combat exercises over the years, the technical architecture of the bank’s network security defense has withstood the test of high-intensity confrontation. The defense process achieved zero deductions, and stood out among the participating defending teams through traceability and countermeasures, which effectively improved the network security large-scale group operations and security protection capabilities. The effectiveness of the technical architecture design is verified in actual combat, which shows that the control of the bank’s internet financial security protection system is effective. With digital computing, all banking transactions are fully automatically implemented and the bank’s clientele is systematically self-managed. It predicts that using this system, the speed can be increased by 98% and the accuracy can be increased by 12%.
{"title":"Intelligent cluster construction of internet financial security protection system in banking industry","authors":"Yanzhao Wang","doi":"10.1515/comp-2022-0268","DOIUrl":"https://doi.org/10.1515/comp-2022-0268","url":null,"abstract":"Abstract As the rapid advancement of information security company is developing quickly and the technology including big information, big computer, large cloud, and artificial intelligence are being widely used, network security has entered a new era. While ushering in huge development opportunities, it also faces severe tests. Network security is a major issue related to the comprehensive realization of a well-off society and national security, and has risen to the national strategic level. The current financial industry network security construction mainly focuses on team, process construction, and the research and development of individual tools and equipment. It lacks system research and implementation guidelines for defense technology construction based on industry IT characteristics. At the same time, there is also a lack of objective and unified measurement and evaluation standards for enterprise security defense capabilities, which restricts the improvement of cybersecurity capabilities in the financial industry to a certain extent. In terms of actual combat exercises, in various actual combat exercises over the years, the technical architecture of the bank’s network security defense has withstood the test of high-intensity confrontation. The defense process achieved zero deductions, and stood out among the participating defending teams through traceability and countermeasures, which effectively improved the network security large-scale group operations and security protection capabilities. The effectiveness of the technical architecture design is verified in actual combat, which shows that the control of the bank’s internet financial security protection system is effective. With digital computing, all banking transactions are fully automatically implemented and the bank’s clientele is systematically self-managed. It predicts that using this system, the speed can be increased by 98% and the accuracy can be increased by 12%.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43515390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Due to the popularity of high-performance cameras and the development of computer video pattern recognition technology, intelligent video monitoring technology is widely used in all aspects of social life. It mainly includes the following: industrial control system uses video monitoring technology for remote monitoring and comprehensive monitoring; in addition, intelligent video monitoring technology is also widely used in the agricultural field, for example, farm administrators can view the activities of animals in real time through smart phones, and agricultural experts can predict future weather changes according to the growth of crops. In the implementation of intelligent monitoring system, automatic detection of vehicles in images is an important topic. The construction of China’s Intelligent Transportation System started late, especially in video traffic detection. Although there are many related studies on video traffic detection algorithms, these algorithms usually only analyze and process information from a single sensor. This article describes the application of the single-shot detector (SSD) network algorithm in a panoramic video image vehicle detection system. The purpose of this article is to investigate the effectiveness of the SSD network algorithm in a panoramic video image vehicle detection system. The experimental results show that the detection accuracy of a single convolutional neural network (CNN) algorithm is only 0.7554, the recall rate is 0.9052, and the comprehensive detection accuracy is 0.8235. The detection accuracy of SSD network algorithm is 0.8720, recall rate is 0.9397, and the comprehensive detection accuracy is 0.9046, which is higher than that of single CNN algorithm. Thus, the proposed SSD network algorithm is compared with a single convolution network algorithm. It is more suitable for vehicle detection, and it plays an important role in panoramic video image vehicle detection.
{"title":"Application of SSD network algorithm in panoramic video image vehicle detection system","authors":"Tao Jiang","doi":"10.1515/comp-2022-0270","DOIUrl":"https://doi.org/10.1515/comp-2022-0270","url":null,"abstract":"Abstract Due to the popularity of high-performance cameras and the development of computer video pattern recognition technology, intelligent video monitoring technology is widely used in all aspects of social life. It mainly includes the following: industrial control system uses video monitoring technology for remote monitoring and comprehensive monitoring; in addition, intelligent video monitoring technology is also widely used in the agricultural field, for example, farm administrators can view the activities of animals in real time through smart phones, and agricultural experts can predict future weather changes according to the growth of crops. In the implementation of intelligent monitoring system, automatic detection of vehicles in images is an important topic. The construction of China’s Intelligent Transportation System started late, especially in video traffic detection. Although there are many related studies on video traffic detection algorithms, these algorithms usually only analyze and process information from a single sensor. This article describes the application of the single-shot detector (SSD) network algorithm in a panoramic video image vehicle detection system. The purpose of this article is to investigate the effectiveness of the SSD network algorithm in a panoramic video image vehicle detection system. The experimental results show that the detection accuracy of a single convolutional neural network (CNN) algorithm is only 0.7554, the recall rate is 0.9052, and the comprehensive detection accuracy is 0.8235. The detection accuracy of SSD network algorithm is 0.8720, recall rate is 0.9397, and the comprehensive detection accuracy is 0.9046, which is higher than that of single CNN algorithm. Thus, the proposed SSD network algorithm is compared with a single convolution network algorithm. It is more suitable for vehicle detection, and it plays an important role in panoramic video image vehicle detection.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41813414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Ilyas Fakhir, Syed Asad Raza Kazmi, Awais Qasim, A. Ishaq
Abstract Self-adaptive systems (SASs) have the capability to evaluate and change their behavior according to changes occurring in the environment. Research in this field is being held since mid-60, and over the last decade, the importance of self-adaptivity is being increased. In the proposed research, colored petri nets (CPN) formal language is being used to model self-adaptive multiagent system. CPN is increasingly used to model self-adaptive complex concurrent systems due to its flexible formal specification and formal verification behavior. CPN being visually more expressive than simple, Petri Nets enable diverse modeling approaches and provides a richer framework for such a complex formalism. The main goal of this research is to apply self-adaptive multi-agent concurrent system (SMACS) for complex architectures. In our previous research, the SMACS framework is proposed and verified through traffic monitoring system. All agents of SMACS are also known as intelligent agents due to their self-adaptation behavior. Due to decentralized approach in this framework, each agent will intelligently adapt its behavior in the environment and send updates to other agents. In this research, we are choosing smart computer lab (SCL) as a case study. For internal structure of each agent modal, μ mu -calculus will be used, and then a model checker TAPAs: a tool for the analysis of process algebras will be applied to verify these properties. CPN-based state space analysis will also be done to verify the behavioral properties of the model. The general objective of the proposed system is to maximize the utility generated over some predetermined time horizon.
{"title":"SMACS: A framework for formal verification of complex adaptive systems","authors":"Muhammad Ilyas Fakhir, Syed Asad Raza Kazmi, Awais Qasim, A. Ishaq","doi":"10.1515/comp-2022-0275","DOIUrl":"https://doi.org/10.1515/comp-2022-0275","url":null,"abstract":"Abstract Self-adaptive systems (SASs) have the capability to evaluate and change their behavior according to changes occurring in the environment. Research in this field is being held since mid-60, and over the last decade, the importance of self-adaptivity is being increased. In the proposed research, colored petri nets (CPN) formal language is being used to model self-adaptive multiagent system. CPN is increasingly used to model self-adaptive complex concurrent systems due to its flexible formal specification and formal verification behavior. CPN being visually more expressive than simple, Petri Nets enable diverse modeling approaches and provides a richer framework for such a complex formalism. The main goal of this research is to apply self-adaptive multi-agent concurrent system (SMACS) for complex architectures. In our previous research, the SMACS framework is proposed and verified through traffic monitoring system. All agents of SMACS are also known as intelligent agents due to their self-adaptation behavior. Due to decentralized approach in this framework, each agent will intelligently adapt its behavior in the environment and send updates to other agents. In this research, we are choosing smart computer lab (SCL) as a case study. For internal structure of each agent modal, μ mu -calculus will be used, and then a model checker TAPAs: a tool for the analysis of process algebras will be applied to verify these properties. CPN-based state space analysis will also be done to verify the behavioral properties of the model. The general objective of the proposed system is to maximize the utility generated over some predetermined time horizon.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42980501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In order to improve the ability of computer network intrusion detection, the big data technology for computer intrusion detection was studied. This research uses big data technology to build a network intrusion detection model, using clustering algorithms, classification algorithms, and association rule algorithms in data mining to automatically identify the attack patterns in the network and quickly learn and extract the characteristics of network attacks. The experimental results show that the recognition effect of the classification algorithm is obviously better than that of the clustering algorithm and the association rule. With the increase in the proportion of abnormal commands, the accuracy rate can still be maintained at 90%. As a compromise between the classification algorithm and the clustering algorithm, the accuracy rate of the association rule algorithm is basically maintained at more than 75%. It is proved that the big data technology oriented to computer intrusion detection can effectively improve the detection ability of computer network intrusion.
{"title":"Big data technology for computer intrusion detection","authors":"Ying Chen","doi":"10.1515/comp-2022-0267","DOIUrl":"https://doi.org/10.1515/comp-2022-0267","url":null,"abstract":"Abstract In order to improve the ability of computer network intrusion detection, the big data technology for computer intrusion detection was studied. This research uses big data technology to build a network intrusion detection model, using clustering algorithms, classification algorithms, and association rule algorithms in data mining to automatically identify the attack patterns in the network and quickly learn and extract the characteristics of network attacks. The experimental results show that the recognition effect of the classification algorithm is obviously better than that of the clustering algorithm and the association rule. With the increase in the proportion of abnormal commands, the accuracy rate can still be maintained at 90%. As a compromise between the classification algorithm and the clustering algorithm, the accuracy rate of the association rule algorithm is basically maintained at more than 75%. It is proved that the big data technology oriented to computer intrusion detection can effectively improve the detection ability of computer network intrusion.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42349087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract With the development of urbanization, urban public safety is becoming more and more important. Urban public safety is not only the foundation of urban development, but also the basic guarantee for the stability of citizens’ lives. In the context of today’s artificial intelligence (AI), the concept of smart cities is constantly being practiced. Urban public safety has also ushered in some new problems and challenges. To this end, this article aimed to use AI technology to build an efficient public safety data resource management system in a smart city environment. A major goal of AI research was to enable machines to perform complex tasks that normally require human intelligence. In this article, a data resource management system was constructed according to the city security system and risk data sources, and the data processing method of neural network (NN) was adopted. Factors affecting urban public safety were processed as indicator data. In this article, the feedforward back-propagation neural network (BPNN) was used to predict the index data in real time, which has realized the management functions of risk monitoring and early warning of public safety data indicators. The BPNN model was used to test the urban risk early warning capability of the constructed system. BPNN is a multi-layer feed-forward NN trained according to the error back-propagation algorithm, which is one of the most widely used NN models. The results showed that the average prediction accuracy of the BPNN model for indicator prediction was about 89%, which was 16.1% higher than that of the traditional NN model. The average risk warning accuracy rate of the BPNN model was 90.3%, which was 16.5% higher than that of the traditional NN model. This shows that the BPNN model using AI technology in this article can more efficiently and accurately carry out early warning of risk and management of urban public safety.
{"title":"Artificial intelligence-based public safety data resource management in smart cities","authors":"Hang Zhao","doi":"10.1515/comp-2022-0271","DOIUrl":"https://doi.org/10.1515/comp-2022-0271","url":null,"abstract":"Abstract With the development of urbanization, urban public safety is becoming more and more important. Urban public safety is not only the foundation of urban development, but also the basic guarantee for the stability of citizens’ lives. In the context of today’s artificial intelligence (AI), the concept of smart cities is constantly being practiced. Urban public safety has also ushered in some new problems and challenges. To this end, this article aimed to use AI technology to build an efficient public safety data resource management system in a smart city environment. A major goal of AI research was to enable machines to perform complex tasks that normally require human intelligence. In this article, a data resource management system was constructed according to the city security system and risk data sources, and the data processing method of neural network (NN) was adopted. Factors affecting urban public safety were processed as indicator data. In this article, the feedforward back-propagation neural network (BPNN) was used to predict the index data in real time, which has realized the management functions of risk monitoring and early warning of public safety data indicators. The BPNN model was used to test the urban risk early warning capability of the constructed system. BPNN is a multi-layer feed-forward NN trained according to the error back-propagation algorithm, which is one of the most widely used NN models. The results showed that the average prediction accuracy of the BPNN model for indicator prediction was about 89%, which was 16.1% higher than that of the traditional NN model. The average risk warning accuracy rate of the BPNN model was 90.3%, which was 16.5% higher than that of the traditional NN model. This shows that the BPNN model using AI technology in this article can more efficiently and accurately carry out early warning of risk and management of urban public safety.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41631848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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