Christian Banse, Immanuel Kunz, Nico Haas, Angelika Schneider
Continuous certification of cloud services requires a high degree of automation in collecting and evaluating evidences. Prior approaches to this topic are often specific to a cloud provider or a certain certification catalog. This makes it costly and complex to achieve conformance to multiple certification schemes and covering multi-cloud solutions. In this paper, we present a novel approach to continuous certification which is scheme- and vendor-independent. Leveraging an ontology of cloud resources and their security features, we generalize vendor- and scheme-specific terminology into a new model of so-called semantic evidence. In combination with generalized metrics that we elicited out of requirements from the EUCS and the CCMv4, we present a framework for the collection and assessment of such semantic evidence across multiple cloud providers. This allows to conduct continuous cloud certification while achieving re-usability of metrics and evidences in multiple certification schemes. The performance benchmark of the framework's prototype implementation shows that up to 200,000 evidences can be processed in less than a minute, making it suitable for short time intervals used in continuous certification.
{"title":"A Semantic Evidence-based Approach to Continuous Cloud Service Certification","authors":"Christian Banse, Immanuel Kunz, Nico Haas, Angelika Schneider","doi":"10.1145/3555776.3577600","DOIUrl":"https://doi.org/10.1145/3555776.3577600","url":null,"abstract":"Continuous certification of cloud services requires a high degree of automation in collecting and evaluating evidences. Prior approaches to this topic are often specific to a cloud provider or a certain certification catalog. This makes it costly and complex to achieve conformance to multiple certification schemes and covering multi-cloud solutions. In this paper, we present a novel approach to continuous certification which is scheme- and vendor-independent. Leveraging an ontology of cloud resources and their security features, we generalize vendor- and scheme-specific terminology into a new model of so-called semantic evidence. In combination with generalized metrics that we elicited out of requirements from the EUCS and the CCMv4, we present a framework for the collection and assessment of such semantic evidence across multiple cloud providers. This allows to conduct continuous cloud certification while achieving re-usability of metrics and evidences in multiple certification schemes. The performance benchmark of the framework's prototype implementation shows that up to 200,000 evidences can be processed in less than a minute, making it suitable for short time intervals used in continuous certification.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"16 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79252397","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}
Crystalline materials, such as metals and semiconductors, nearly always contain a special defect type called dislocation. This defect decisively determines many important material properties, e.g., strength, fracture toughness, or ductility. Over the past years, significant effort has been put into understanding dislocation behavior across different length scales via experimental characterization techniques and simulations. This paper introduces the dislocation ontology (DISO), which defines the concepts and relationships related to linear defects in crystalline materials. We developed DISO using a top-down approach in which we start defining the most general concepts in the dislocation domain and subsequent specialization of them. DISO is published through a persistent URL following W3C best practices for publishing Linked Data. Two potential use cases for DISO are presented to illustrate its usefulness in the dislocation dynamics domain. The evaluation of the ontology is performed in two directions, evaluating the success of the ontology in modeling a real-world domain and the richness of the ontology.
{"title":"DISO: A Domain Ontology for Modeling Dislocations in Crystalline Materials","authors":"Ahmad Zainul Ihsan, S. Fathalla, S. Sandfeld","doi":"10.1145/3555776.3578739","DOIUrl":"https://doi.org/10.1145/3555776.3578739","url":null,"abstract":"Crystalline materials, such as metals and semiconductors, nearly always contain a special defect type called dislocation. This defect decisively determines many important material properties, e.g., strength, fracture toughness, or ductility. Over the past years, significant effort has been put into understanding dislocation behavior across different length scales via experimental characterization techniques and simulations. This paper introduces the dislocation ontology (DISO), which defines the concepts and relationships related to linear defects in crystalline materials. We developed DISO using a top-down approach in which we start defining the most general concepts in the dislocation domain and subsequent specialization of them. DISO is published through a persistent URL following W3C best practices for publishing Linked Data. Two potential use cases for DISO are presented to illustrate its usefulness in the dislocation dynamics domain. The evaluation of the ontology is performed in two directions, evaluating the success of the ontology in modeling a real-world domain and the richness of the ontology.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"33 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":"85565649","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}
Hypervisor vulnerabilities cause severe security issues in multi-tenant cloud environments because hypervisors guarantee isolation among virtual machines (VMs). Unfortunately, hypervisor vulnerabilities are continuously reported, and device emulation in hypervisors is one of the hotbeds because of its complexity. Although applying patches to fix the vulnerabilities is a common way to protect hypervisors, it takes time to develop the patches because the internal knowledge on hypervisors is mandatory. The hypervisors are exposed to the threat of the vulnerabilities exploitation until the patches are released. This paper proposes Nioh-PT, a framework for filtering illegal I/O requests, which reduces the vulnerability windows of the device emulation. The key insight of Nioh-PT is that malicious I/O requests contain illegal I/O sequences, a series of I/O requests that are not issued during normal I/O operations. Nioh-PT filters out those illegal I/O sequences and protects device emulators against the exploitation. The filtering rules, which define illegal I/O sequences for virtual device exploits, can be specified without the knowledge on the internal implementation of hypervisors and virtual devices, because Nioh-PT is decoupled from hypervisors and the device emulators. We develop 11 filtering rules against four real-world vulnerabilities in device emulation, including CVE-2015-3456 (VENOM) and CVE-2016-7909. We demonstrate that Nioh-PT with these filtering rules protects against the virtual device exploits and introduces negligible overhead by up to 8% for filesystem and storage benchmarks.
{"title":"Nioh-PT: Virtual I/O Filtering for Agile Protection against Vulnerability Windows","authors":"Mana Senuki, Ken-Ichi Ishiguro, K. Kono","doi":"10.1145/3555776.3577687","DOIUrl":"https://doi.org/10.1145/3555776.3577687","url":null,"abstract":"Hypervisor vulnerabilities cause severe security issues in multi-tenant cloud environments because hypervisors guarantee isolation among virtual machines (VMs). Unfortunately, hypervisor vulnerabilities are continuously reported, and device emulation in hypervisors is one of the hotbeds because of its complexity. Although applying patches to fix the vulnerabilities is a common way to protect hypervisors, it takes time to develop the patches because the internal knowledge on hypervisors is mandatory. The hypervisors are exposed to the threat of the vulnerabilities exploitation until the patches are released. This paper proposes Nioh-PT, a framework for filtering illegal I/O requests, which reduces the vulnerability windows of the device emulation. The key insight of Nioh-PT is that malicious I/O requests contain illegal I/O sequences, a series of I/O requests that are not issued during normal I/O operations. Nioh-PT filters out those illegal I/O sequences and protects device emulators against the exploitation. The filtering rules, which define illegal I/O sequences for virtual device exploits, can be specified without the knowledge on the internal implementation of hypervisors and virtual devices, because Nioh-PT is decoupled from hypervisors and the device emulators. We develop 11 filtering rules against four real-world vulnerabilities in device emulation, including CVE-2015-3456 (VENOM) and CVE-2016-7909. We demonstrate that Nioh-PT with these filtering rules protects against the virtual device exploits and introduces negligible overhead by up to 8% for filesystem and storage benchmarks.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"13 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76072650","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}
Multi-domain sentiment classification trains a classifier using multiple domains and then tests the classifier on one of the domains. Importantly, no domain is assumed to have sufficient labeled data; instead, the goal is leveraging information between domains, making multi-domain sentiment classification a very realistic scenario. Typically, labeled data is costly because humans must classify it manually. In this context, we propose the MUTUAL approach that learns general and domain-specific sentence embeddings that are also context-aware due to the attention mechanism. In this work, we propose using a stacked BiLSTM-based Autoencoder with an attention mechanism to generate the two above-mentioned types of sentence embeddings. Then, using the Jensen-Shannon (JS) distance, the general sentence embeddings of the four most similar domains to the target domain are selected. The selected general sentence embeddings and the domain-specific embeddings are concatenated and fed into a dense layer for training. Evaluation results on public datasets with 16 different domains demonstrate the efficiency of our model. In addition, we propose an active learning algorithm that first applies the elliptic envelope for outlier removal to a pool of unlabeled data that the MUTUAL model then classifies. Next, the most uncertain data points are selected to be labeled based on the least confidence metric. The experiments show higher accuracy for querying 38% of the original data than random sampling.
{"title":"MUTUAL: Multi-Domain Sentiment Classification via Uncertainty Sampling","authors":"K. Katsarou, Roxana Jeney, K. Stefanidis","doi":"10.1145/3555776.3577765","DOIUrl":"https://doi.org/10.1145/3555776.3577765","url":null,"abstract":"Multi-domain sentiment classification trains a classifier using multiple domains and then tests the classifier on one of the domains. Importantly, no domain is assumed to have sufficient labeled data; instead, the goal is leveraging information between domains, making multi-domain sentiment classification a very realistic scenario. Typically, labeled data is costly because humans must classify it manually. In this context, we propose the MUTUAL approach that learns general and domain-specific sentence embeddings that are also context-aware due to the attention mechanism. In this work, we propose using a stacked BiLSTM-based Autoencoder with an attention mechanism to generate the two above-mentioned types of sentence embeddings. Then, using the Jensen-Shannon (JS) distance, the general sentence embeddings of the four most similar domains to the target domain are selected. The selected general sentence embeddings and the domain-specific embeddings are concatenated and fed into a dense layer for training. Evaluation results on public datasets with 16 different domains demonstrate the efficiency of our model. In addition, we propose an active learning algorithm that first applies the elliptic envelope for outlier removal to a pool of unlabeled data that the MUTUAL model then classifies. Next, the most uncertain data points are selected to be labeled based on the least confidence metric. The experiments show higher accuracy for querying 38% of the original data than random sampling.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89737340","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}
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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