The Generic Integral Tunnel Design (GITO) contains generic models for the tunnel control systems of Rijkswaterstaat, part of the Dutch Ministry of Infrastructure and Water Management. A formal verification of these models advances the safety and reliability of GITO derived tunnel control systems. In this paper, the first known large-scale formalisation of tunnel control systems is presented which transforms GITO models to the formal specification language mCRL2. This transformation is applied to two sub-systems of the GITO to analyse the correctness of the supplied models. In this formal analysis, several deficiencies in the specifications and faults in the existing models are revealed and verified solutions are proposed. Some of the presented faults even find their origin in the legally required standards.
{"title":"A formal analysis of Dutch Generic Integral Tunnel Design models","authors":"Kevin H. J. Jilissen, P. Dieleman, J. F. Groote","doi":"10.1145/3555776.3577786","DOIUrl":"https://doi.org/10.1145/3555776.3577786","url":null,"abstract":"The Generic Integral Tunnel Design (GITO) contains generic models for the tunnel control systems of Rijkswaterstaat, part of the Dutch Ministry of Infrastructure and Water Management. A formal verification of these models advances the safety and reliability of GITO derived tunnel control systems. In this paper, the first known large-scale formalisation of tunnel control systems is presented which transforms GITO models to the formal specification language mCRL2. This transformation is applied to two sub-systems of the GITO to analyse the correctness of the supplied models. In this formal analysis, several deficiencies in the specifications and faults in the existing models are revealed and verified solutions are proposed. Some of the presented faults even find their origin in the legally required standards.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"56 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74734020","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}
Intelligent Transportation Systems (ITS) are systems that consist on an complex set of technologies that are applied to road agents, aiming to provide a more efficient and safe usage of the roads. The aspect of safety is particularly important for Vulnerable Road Users (VRUs), which are entities for whose implementation of automatic safety solutions is challenging for their agility and hard to anticipate behavior. However, the usage of ML techniques on Vehicle to Anything (V2X) data has the potential to implement such systems. This paper proposes a VRUs (motorcycles) accident prediction system by using Long Short-Term Memorys (LSTMs) on top of communication data that is generated using the VEINS simulation framework (pairing SUMO and ns-3). Results show that the proposed system is able to predict 96% of the accidents on Scenario A (with a 4.53s Average Prediction Time and a 41% Correct Decision Percentage (CDP) - 78 False Positives (FP)) and 95% on Scenario B (with a 4.44s Average Prediction Time and a 43% CDP - 68 FP).
{"title":"Machine Learning for VRUs accidents prediction using V2X data","authors":"B. Ribeiro, M. J. Nicolau, Alexandre J. T. Santos","doi":"10.1145/3555776.3578263","DOIUrl":"https://doi.org/10.1145/3555776.3578263","url":null,"abstract":"Intelligent Transportation Systems (ITS) are systems that consist on an complex set of technologies that are applied to road agents, aiming to provide a more efficient and safe usage of the roads. The aspect of safety is particularly important for Vulnerable Road Users (VRUs), which are entities for whose implementation of automatic safety solutions is challenging for their agility and hard to anticipate behavior. However, the usage of ML techniques on Vehicle to Anything (V2X) data has the potential to implement such systems. This paper proposes a VRUs (motorcycles) accident prediction system by using Long Short-Term Memorys (LSTMs) on top of communication data that is generated using the VEINS simulation framework (pairing SUMO and ns-3). Results show that the proposed system is able to predict 96% of the accidents on Scenario A (with a 4.53s Average Prediction Time and a 41% Correct Decision Percentage (CDP) - 78 False Positives (FP)) and 95% on Scenario B (with a 4.44s Average Prediction Time and a 43% CDP - 68 FP).","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"25 5 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80725191","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}
Mahyar Tourchi Moghaddam, Andreas Edal Pedersen, William Walter Lillebroe Bolding, T. Worm
The Single Sign-On (SSO) method eases the authentication and authorization process. The solution substantially impacts the users' experience since they only need to authenticate once to access multiple services without re-authenticating. This paper adopts an incremental prototyping approach to develop an SSO system. The research reveals that while SSO improves users' quality of experience, it could imply performance and security issues if traditional architectures are adopted. Thus, a Microservices-based approach with containerization is subsequently proposed to overcome SSO's quality issues in practice. The SSO system is containerized using Docker and managed using Docker Compose. The results show a significant performance and security improvement.
{"title":"A Performant and Secure Single Sign-On System Using Microservices","authors":"Mahyar Tourchi Moghaddam, Andreas Edal Pedersen, William Walter Lillebroe Bolding, T. Worm","doi":"10.1145/3555776.3577869","DOIUrl":"https://doi.org/10.1145/3555776.3577869","url":null,"abstract":"The Single Sign-On (SSO) method eases the authentication and authorization process. The solution substantially impacts the users' experience since they only need to authenticate once to access multiple services without re-authenticating. This paper adopts an incremental prototyping approach to develop an SSO system. The research reveals that while SSO improves users' quality of experience, it could imply performance and security issues if traditional architectures are adopted. Thus, a Microservices-based approach with containerization is subsequently proposed to overcome SSO's quality issues in practice. The SSO system is containerized using Docker and managed using Docker Compose. The results show a significant performance and security improvement.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"15 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87396157","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}
A microservices-based architecture is a set of small components that communicate with each other using a programming language-independent API [1]. It has been gaining popularity for more than a decade. One of its advantages is greater agility in software development and following modern, agile software development practices [2]. The article presents an experimental study. Two applications with the same business logic and different architecture were developed. Both applications were tested using prepared test cases on the local computer of one of the authors and the Microsoft Azure platform. The results were collected and compared using the JMeter tool. In almost all cases, the monolithic architecture proved to be more efficient. The comparable performance of both architectures occurred when queries were handled by the business logic layer for a relatively long time.
{"title":"Differences in performance, scalability, and cost of using microservice and monolithic architecture","authors":"Przemysław Jatkiewicz, Szymon Okrój","doi":"10.1145/3555776.3578725","DOIUrl":"https://doi.org/10.1145/3555776.3578725","url":null,"abstract":"A microservices-based architecture is a set of small components that communicate with each other using a programming language-independent API [1]. It has been gaining popularity for more than a decade. One of its advantages is greater agility in software development and following modern, agile software development practices [2]. The article presents an experimental study. Two applications with the same business logic and different architecture were developed. Both applications were tested using prepared test cases on the local computer of one of the authors and the Microsoft Azure platform. The results were collected and compared using the JMeter tool. In almost all cases, the monolithic architecture proved to be more efficient. The comparable performance of both architectures occurred when queries were handled by the business logic layer for a relatively long time.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"66 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74103822","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}
Yuning Wang, I. Azimi, M. Feli, A. Rahmani, P. Liljeberg
Internet-of-Things-based systems have recently emerged, enabling long-term health monitoring systems for the daily activities of individuals. The data collected from such systems are multivariate and longitudinal, which call for tailored analysis techniques to extract the trends and abnormalities in the monitoring. Different methods in the literature have been proposed to identify trends in data. However, they do not include the time dependency and cannot distinguish changes in long-term health data. Moreover, their evaluations are limited to lab settings or short-term analysis. Long-term health monitoring applications require a modeling technique to merge the multisensory data into a meaningful indicator. In this paper, we propose a personalized neural network method to track changes and abnormalities in multivariate health data. Our proposed method leverages convolutional and graph attention layers to produce personalized scores indicating the abnormality level (i.e., deviations from the baseline) of users' data throughout the monitoring. We implement and evaluate the proposed method via a case study on long-term maternal health monitoring. Sleep and stress of pregnant women are remotely monitored using a smartwatch and a mobile application during pregnancy and 3-months postpartum. Our analysis includes 46 women. We build personalized sleep and stress models for each individual using the data from the beginning of the monitoring. Then, we compare the two groups by measuring the data variations. The abnormality scores produced by the proposed method are compared with the findings from the self-report questionnaire data collected in the monitoring and abnormality scores generated by an autoencoder method. The proposed method outperforms the baseline methods in exploring the changes between high-risk and low-risk pregnancy groups. The proposed method's scores also show correlations with the self-report data. Consequently, the results indicate that the proposed method effectively detects the abnormality in multivariate long-term health monitoring.
{"title":"Personalized Graph Attention Network for Multivariate Time-series Change Analysis: A Case Study on Long-term Maternal Monitoring","authors":"Yuning Wang, I. Azimi, M. Feli, A. Rahmani, P. Liljeberg","doi":"10.1145/3555776.3577675","DOIUrl":"https://doi.org/10.1145/3555776.3577675","url":null,"abstract":"Internet-of-Things-based systems have recently emerged, enabling long-term health monitoring systems for the daily activities of individuals. The data collected from such systems are multivariate and longitudinal, which call for tailored analysis techniques to extract the trends and abnormalities in the monitoring. Different methods in the literature have been proposed to identify trends in data. However, they do not include the time dependency and cannot distinguish changes in long-term health data. Moreover, their evaluations are limited to lab settings or short-term analysis. Long-term health monitoring applications require a modeling technique to merge the multisensory data into a meaningful indicator. In this paper, we propose a personalized neural network method to track changes and abnormalities in multivariate health data. Our proposed method leverages convolutional and graph attention layers to produce personalized scores indicating the abnormality level (i.e., deviations from the baseline) of users' data throughout the monitoring. We implement and evaluate the proposed method via a case study on long-term maternal health monitoring. Sleep and stress of pregnant women are remotely monitored using a smartwatch and a mobile application during pregnancy and 3-months postpartum. Our analysis includes 46 women. We build personalized sleep and stress models for each individual using the data from the beginning of the monitoring. Then, we compare the two groups by measuring the data variations. The abnormality scores produced by the proposed method are compared with the findings from the self-report questionnaire data collected in the monitoring and abnormality scores generated by an autoencoder method. The proposed method outperforms the baseline methods in exploring the changes between high-risk and low-risk pregnancy groups. The proposed method's scores also show correlations with the self-report data. Consequently, the results indicate that the proposed method effectively detects the abnormality in multivariate long-term health monitoring.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"64 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85777674","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}
Hosting popular Meetup events is one of the primary objectives of the Meetup organizers. This paper explores the possibility of inviting a few key influential members to attend Meetup events, who may further influence their followers to attend and boost the popularity of those Meetup events. Importantly, our pilot study reveals that topics of the Meetup events play a key role behind the effectiveness of the influential members. Leveraging this observation, in this paper, we develop Topic Aware Influencer Detection (TAID) heuristics, which recommends (i) top-k influential members Ik, and (ii) top-b influence badges Rb based on the topical interest of a Meetup group. This indicates that Ik. will be most effective in influencing the Meetup members to attend the events hosted on topic Rb. TAID heuristics contains two major blocks (a) influence propagation graph construction, and (b) recommendation generation. Rigorous evaluation of TAID on 1447 Meetup groups with three different topics reveals that TAID comfortably outperforms the baselines by influencing (on average) 15% more Meetup members.
{"title":"Topic Aware Influential Member Detection in Meetup","authors":"Arpan Dam, Surya Kumar, Debjyoti Bhattacharjee, Sayan D. Pathak, Bivas Mitra","doi":"10.1145/3555776.3577684","DOIUrl":"https://doi.org/10.1145/3555776.3577684","url":null,"abstract":"Hosting popular Meetup events is one of the primary objectives of the Meetup organizers. This paper explores the possibility of inviting a few key influential members to attend Meetup events, who may further influence their followers to attend and boost the popularity of those Meetup events. Importantly, our pilot study reveals that topics of the Meetup events play a key role behind the effectiveness of the influential members. Leveraging this observation, in this paper, we develop Topic Aware Influencer Detection (TAID) heuristics, which recommends (i) top-k influential members Ik, and (ii) top-b influence badges Rb based on the topical interest of a Meetup group. This indicates that Ik. will be most effective in influencing the Meetup members to attend the events hosted on topic Rb. TAID heuristics contains two major blocks (a) influence propagation graph construction, and (b) recommendation generation. Rigorous evaluation of TAID on 1447 Meetup groups with three different topics reveals that TAID comfortably outperforms the baselines by influencing (on average) 15% more Meetup members.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"316 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84482779","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}
The application of deep learning-based (DL) network intrusion detection systems (NIDS) enables effective automated detection of cyberattacks. Such models can extract valuable features from high-dimensional and heterogeneous network traffic with minimal feature engineering and provide high accuracy detection rates. However, it has been shown that DL can be vulnerable to adversarial examples (AEs), which mislead classification decisions at inference time, and several works have shown that AEs are indeed a threat against DL-based NIDS. In this work, we argue that these threats are not necessarily realistic. Indeed, some general techniques used to generate AE manipulate features in a way that would be inconsistent with actual network traffic. In this paper, we first implement the main AE attacks selected from the literature (FGSM, BIM, PGD, NewtonFool, CW, DeepFool, EN, Boundary, HSJ, ZOO) for two different datasets (WSN-DS and BoT-IoT) and we compare their relative performance. We then analyze the perturbation generated by these attacks and use the metrics to establish a notion of "attack unrealism". We conclude that, for these datasets, some of these attacks are performant but not realistic.
{"title":"Realism versus Performance for Adversarial Examples Against DL-based NIDS","authors":"Huda Ali Alatwi, C. Morisset","doi":"10.1145/3555776.3577671","DOIUrl":"https://doi.org/10.1145/3555776.3577671","url":null,"abstract":"The application of deep learning-based (DL) network intrusion detection systems (NIDS) enables effective automated detection of cyberattacks. Such models can extract valuable features from high-dimensional and heterogeneous network traffic with minimal feature engineering and provide high accuracy detection rates. However, it has been shown that DL can be vulnerable to adversarial examples (AEs), which mislead classification decisions at inference time, and several works have shown that AEs are indeed a threat against DL-based NIDS. In this work, we argue that these threats are not necessarily realistic. Indeed, some general techniques used to generate AE manipulate features in a way that would be inconsistent with actual network traffic. In this paper, we first implement the main AE attacks selected from the literature (FGSM, BIM, PGD, NewtonFool, CW, DeepFool, EN, Boundary, HSJ, ZOO) for two different datasets (WSN-DS and BoT-IoT) and we compare their relative performance. We then analyze the perturbation generated by these attacks and use the metrics to establish a notion of \"attack unrealism\". We conclude that, for these datasets, some of these attacks are performant but not realistic.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"198 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86228950","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}
Nowadays, more and more smart cities around the world are being built. As a part of the smart city, intelligent public transportation plays a very important role. Improving the quality of public transportation by reducing crowdedness and total transit time is a critical issue. To this end, we propose a bus operation prediction model based on deep learning techniques, and use this model to dynamically adjust the bus departure time to improve the bus service quality. Specifically, we first combine bus fare card data and open data, such as weather conditions and traffic accidents, to build models for predicting the number of passengers who board/alight the bus at a stop, the boarding and alighting time, and the bus running time between stops. Then we combine these models to predict the operation of the bus for deciding the best bus departure time within the bus departure interval. Experimental results on real-world data of Taichung City bus route #300 show that our approach to deciding the bus departure time is effective for improving its service quality.
{"title":"Improving the Quality of Public Transportation by Dynamically Adjusting the Bus Departure Time","authors":"Shuheng Cao, S. Thamrin, Arbee L. P. Chen","doi":"10.1145/3555776.3577596","DOIUrl":"https://doi.org/10.1145/3555776.3577596","url":null,"abstract":"Nowadays, more and more smart cities around the world are being built. As a part of the smart city, intelligent public transportation plays a very important role. Improving the quality of public transportation by reducing crowdedness and total transit time is a critical issue. To this end, we propose a bus operation prediction model based on deep learning techniques, and use this model to dynamically adjust the bus departure time to improve the bus service quality. Specifically, we first combine bus fare card data and open data, such as weather conditions and traffic accidents, to build models for predicting the number of passengers who board/alight the bus at a stop, the boarding and alighting time, and the bus running time between stops. Then we combine these models to predict the operation of the bus for deciding the best bus departure time within the bus departure interval. Experimental results on real-world data of Taichung City bus route #300 show that our approach to deciding the bus departure time is effective for improving its service quality.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"23 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86297252","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}
Salatiel Dantas Silva, C. E. Campelo, Maxwell Guimarães De Oliveira
Representing Points of Interest (POI) types, such as restaurants and shopping malls, is crucial to develop computational mechanisms that may assist in tasks such as urban planning and POI recommendation. The POI co-occurrences in different spatial regions have been used to represent POI types in high-dimensional vectors. However, such representations do not consider the geographic features (e.g. streets, buildings, rivers, parks) in the vicinity of POIs which may contribute to characterize such types. In this context, we propose the Geographic Context to Vector (GeoContext2Vec), an approach that relies on geographic features in the POIs' vicinity to generate POI types representation based on embeddings. We carried out an experiment to evaluate the GeoContext2Vec by using a POI type representation from the state-of-the-art that it does not consider geographic features. The promising results show that the geographic information provided by the GeoContext2Vec outperforms the state-of-the-art and demonstrates the relevance of surrouding geographic features on representing POI type more precisely.
{"title":"POI types characterization based on geographic feature embeddings","authors":"Salatiel Dantas Silva, C. E. Campelo, Maxwell Guimarães De Oliveira","doi":"10.1145/3555776.3577659","DOIUrl":"https://doi.org/10.1145/3555776.3577659","url":null,"abstract":"Representing Points of Interest (POI) types, such as restaurants and shopping malls, is crucial to develop computational mechanisms that may assist in tasks such as urban planning and POI recommendation. The POI co-occurrences in different spatial regions have been used to represent POI types in high-dimensional vectors. However, such representations do not consider the geographic features (e.g. streets, buildings, rivers, parks) in the vicinity of POIs which may contribute to characterize such types. In this context, we propose the Geographic Context to Vector (GeoContext2Vec), an approach that relies on geographic features in the POIs' vicinity to generate POI types representation based on embeddings. We carried out an experiment to evaluate the GeoContext2Vec by using a POI type representation from the state-of-the-art that it does not consider geographic features. The promising results show that the geographic information provided by the GeoContext2Vec outperforms the state-of-the-art and demonstrates the relevance of surrouding geographic features on representing POI type more precisely.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"185 3 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80002939","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}
Hind Bangui, Emilia Cioroaica, Mouzhi Ge, Barbora Buhnova
Internet of Behavior (IoB) has emerged as a new research paradigm within the context of digital ecosystems, with the support for understanding and positively influencing human behavior by merging behavioral sciences with information technology, and fostering mutual trust building between humans and technology. For example, when automated systems identify improper human driving behavior, IoB can support integrated behavioral adaptation to avoid driving risks that could lead to hazardous situations. In this paper, we propose an ecosystem-level self-adaptation mechanism that aims to provide runtime evidence for trust building in interaction among IoB elements. Our approach employs an indirect trust management scheme based on deep learning, which has the ability to mimic human behaviour and trust building patterns. In order to validate the model, we consider Pay-How-You-Drive vehicle insurance as a showcase of a IoB application aiming to advance the adaptation of business incentives based on improving driver behavior profiling. The experimental results show that the proposed model can identify different driving states with high accuracy, to support the IoB applications.
行为互联网(Internet of Behavior, IoB)是数字生态系统背景下的一种新的研究范式，通过将行为科学与信息技术相结合，促进人与技术之间的相互信任，支持理解和积极影响人类行为。例如，当自动系统识别出人类不当驾驶行为时，IoB可以支持综合行为适应，以避免可能导致危险情况的驾驶风险。在本文中，我们提出了一种生态系统级的自适应机制，旨在为IoB元素之间相互作用中的信任建立提供运行时证据。我们的方法采用了基于深度学习的间接信任管理方案，该方案具有模仿人类行为和信任建立模式的能力。为了验证该模型，我们将按需付费汽车保险作为IoB应用的一个展示，该应用旨在通过改进驾驶员行为分析来促进商业激励的适应。实验结果表明，该模型能较准确地识别不同的驾驶状态，支持IoB应用。
{"title":"Deep-Learning based Trust Management with Self-Adaptation in the Internet of Behavior","authors":"Hind Bangui, Emilia Cioroaica, Mouzhi Ge, Barbora Buhnova","doi":"10.1145/3555776.3577694","DOIUrl":"https://doi.org/10.1145/3555776.3577694","url":null,"abstract":"Internet of Behavior (IoB) has emerged as a new research paradigm within the context of digital ecosystems, with the support for understanding and positively influencing human behavior by merging behavioral sciences with information technology, and fostering mutual trust building between humans and technology. For example, when automated systems identify improper human driving behavior, IoB can support integrated behavioral adaptation to avoid driving risks that could lead to hazardous situations. In this paper, we propose an ecosystem-level self-adaptation mechanism that aims to provide runtime evidence for trust building in interaction among IoB elements. Our approach employs an indirect trust management scheme based on deep learning, which has the ability to mimic human behaviour and trust building patterns. In order to validate the model, we consider Pay-How-You-Drive vehicle insurance as a showcase of a IoB application aiming to advance the adaptation of business incentives based on improving driver behavior profiling. The experimental results show that the proposed model can identify different driving states with high accuracy, to support the IoB applications.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"163 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80312584","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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