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نتیجه جستجو - Context-aware

تعداد مقالات یافته شده: 34
ردیف عنوان نوع
1 Intelligent context-aware fog node discovery
کشف گره مه آگاه از زمینه هوشمند-2022
Fog computing has been proposed as a mechanism to address certain issues in cloud computing such as latency, storage, network bandwidth, etc. Fog computing brings the processing, storage, and networking to the edge of the network near the edge devices, which we called fog consumers. This decreases latency, network bandwidth, and response time. Discovering the most relevant fog node, the nearest one to the fog consumers, is a critical challenge that is yet to be addressed by the research. In this study, we present the Intelligent and Distributed Fog node Discovery mechanism (IDFD) which is an intelligent approach to enable fog consumers to discover appropriate fog nodes in a context-aware manner. The proposed approach is based on the distributed fog registries between fog consumers and fog nodes that can facilitate the discovery process of fog nodes. In this study, the KNN, K-d tree, and brute force algorithms are used to discover fog nodes based on the context-aware criteria of fog nodes and fog consumers. The proposed framework is simulated using OMNET++, and the performance of the proposed algorithms is compared based on performance metrics and execution time. The accuracy and execution time are the major points of consideration in the selection of an optimal fog search algorithm. The experiment results show that the KNN and K-d tree algorithms achieve the same accuracy results of 95 %. However, the K-d tree method takes less time to find the nearest fog nodes than KNN and brute force. Thus, the K-d tree is selected as the fog search algorithm in the IDFD to discover the nearest fog nodes very efficiently and quickly.
keywords: Fog node | Discovery | Context-aware | Intelligent | Fog node discovery
مقاله انگلیسی
2 A comprehensive pseudonym changing scheme for improving location privacy in vehicular networks
یک طرح جامع تغییر نام مستعار برای بهبود حریم خصوصی مکان در شبکه های وسایل نقلیه-2022
In an Intelligent Transportation System (ITS), many applications, such as collision avoidance and lane change warning, require real-time information from vehicles on the road. This typically includes detailed status information such as location, speed, heading and a vehicle identifier, which can be used to infer precise movement patterns leading to long-term driver profiling and vehicle tracking. The use of pseudonyms, instead of actual vehicle IDs, allows vehicles to protect their privacy while sharing relevant information. A pseudonym changing scheme (PCS) determines when and how a vehicle should change its pseudonyms to maintain an appropriate privacy level. In this paper, we propose a new comprehensive PCS that utilizes vehicle context and current traffic patterns to leverage the optimum situation for changing pseudonyms. Simulation results indicate that the proposed PCS outperforms existing approaches both in terms of user-centric and adversary-centric performance metrics.
Keywords: Location privacy | Pseudonym changing scheme | VANET | V2V communication | Context-aware
مقاله انگلیسی
3 CACDA: A knowledge graph for a context-aware cognitive design assistant
CACDA: یک گراف دانش برای دستیار طراحی شناختی زمینه آگاه-2021
The design of complex engineered systems highly relies on a laborious zigzagging between computeraided design (CAD) software and design rules prescribed by design manuals. Despite the emergence of knowledge management techniques (ontology, expert system, text mining, etc.), companies continue to store design rules in large and unstructured documents. To facilitate the integration of design rules and CAD software, we propose a knowledge graph that structures a large set of design rules in a computable format. The knowledge graph organises entities of design rules (nodes), relationships among design rules (edges), as well as contextual information. The categorisation of entities and relationships in four subcontexts: semantic, social, engineering, and IT – facilitates the development of the data model, especially the definition of the “design context” concept. The knowledge graph paves the way to a context-aware cognitive design assistant. Indeed, connected to or embedded in a CAD software, a context-aware cognitive design assistant will capture the design context in near real time and run reasoning operations on the knowledge graph to extend traditional CAD capabilities, such as the recommendation of design rules, the verification of design solutions, or the automation of design routines. Our validation experiment shows that the current version of the context-aware cognitive design assistant is more efficient than the traditional document-based design. On average, participants using an unstructured design rules document have a precision of 0.36 whereas participants using our demonstrator obtain a 0.61 precision score. Finally, designers supported by the design assistant spend more time designing than searching for applicable design rules compared to the traditional design approach.
keywords: قانون طراحی | نمودار دانش | مدیریت دانش | آگاهی متقابل | دستیار شناختی | Design rule | Knowledge graph | Knowledge management | Context-awareness | Cognitive assistant
مقاله انگلیسی
4 An extensive study on the evolution of context-aware personalized travel recommender systems
یک مطالعه گسترده در مورد تکامل سیستمهای توصیه گر سفر شخصی آگاه از متن-2020
Ever since the beginning of civilization, travel for various causes exists as an essential part of human life so as travel recommendations, though the early form of recommendations were the accrued experiences shared by the community. Modern recommender systems evolved along with the growth of Information Technology and are contributing to all industry and service segments inclusive of travel and tourism. The journey started with generic recommender engines which gave way to personalized recommender systems and further advanced to contextualized personalization with advent of artificial intelligence. Current era is also witnessing a boom in social media usage and the social media big data is acting as a critical input for various analytics with no exception for recommender systems. This paper details about the study conducted on the evolution of travel recommender systems, their features and current set of limitations. We also discuss on the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders.
Keywords: Recommender system | Personalization | Context aware | Big data | Travel and tourism
مقاله انگلیسی
5 An extensive study on the evolution of context-aware personalized travel recommender systems
یک مطالعه گسترده در مورد تکامل سیستمهای توصیه گر سفر شخصی آگاه از متن-2019
Ever since the beginning of civilization, travel for various causes exists as an essential part of human life so as travel recommendations, though the early form of recommendations were the accrued experiences shared by the community. Modern recommender systems evolved along with the growth of Information Technology and are contributing to all industry and service segments inclusive of travel and tourism. The journey started with generic recommender engines which gave way to personalized recommender systems and further advanced to contextualized personalization with advent of artificial intelligence. Current era is also witnessing a boom in social media usage and the social media big data is acting as a critical input for various analytics with no exception for recommender systems. This paper details about the study conducted on the evolution of travel recommender systems, their features and current set of limitations. We also discuss on the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders.
Keywords: Recommender system | Personalization | Context aware | Big data | Travel and tourism
مقاله انگلیسی
6 Context-aware recommender systems using hierarchical hidden Markov model
سیستم های توصیه گر آگاه از زمینه با استفاده از مدل مارکوف مخفی سلسله مراتبی-2019
Recommender systems often generate recommendations based on user’s prior preferences. Users’ preferences may change over time due to user mode change or context change, identification of such a change is important for generating personalized recommendations. Many earlier methods have been developed under the assumption that each user has a fixed pattern. Regardless of these changes, the recommendation may not match the user’s personal preference and this recommendation will not be useful to the user based on the current context of the user. Context-aware recommender systems deal with this problem by utilizing contextual information that affects user preferences and states. Using contextual information is challenging because it is not always possible to obtain all the contextual information. Also, adding various types of contexts to recommender systems increases its dimensionality and sparsity. This paper presents a novel hierarchical hidden Markov model to identify changes in user’s preferences over time by modeling the latent context of users. Using the user-selected items, the proposed method models the user as a hidden Markov process and considers the current context of the user as a hidden variable. The latent contexts are automatically learned for each user utilizing hidden Markov model on the data collected from the user’s feedback sequences. The results of the experiments, on the benchmark data sets, show that the proposed model has a better performance compared to other methods.
Keywords: Context-aware recommender system | Hidden Markov model | Latent context | Recommender systems
مقاله انگلیسی
7 Achieving security-by-design through ontology-driven attribute-based access control in cloud environments
دستیابی به امنیت توسط طراحی از طریق کنترل دسترسی مبتنی بر ویژگی شناسی مبتنی بر هستی شناسی در محیط های ابری-2019
The constantly increasing number of cyberattacks worldwide raise significant security concerns that generally deter small, medium and large enterprises from adopting the cloud paradigm and benefitting from the numerous advantages that it offers. One way to alleviate these concerns is to devise suitable policies that infuse adequate access controls into cloud services. However, the dynamicity inherent in cloud environments, coupled with the heterogeneous nature of cloud services, hinders the formulation of effective and interoperable access control policies that are suitable for the underlying domain of application. To this end, this work proposes an approach to the semantic representation of access control policies and, in particular, to the semantic representation of the context expressions incorporated in such policies. More specifically, the proposed approach enables stakeholders to accurately define the structure of their policies, in terms of relevant knowledge artefacts, and thus infuse into these policies their particular security and business requirements. This clearly leads to more effective policies, whilst it enables semantic reasoning about the abidance of policies by the prescribed structure. In order to alleviate the scalability concerns associated with semantic reasoning, the proposed approach introduces a reference implementation that extends XACML 3.0 with an expert system fused with reasoning capabilities through the incorporation of suitable meta-rules.
Keywords: Context-aware security | Ontologies | Access control policies | Data privacy | Security-by-design | Semantic reasoning
مقاله انگلیسی
8 CD-CARS: Cross-domain context-aware recommender systems
CD-CARS: سیستم های توصیه گر متشکل از آگاهی از حوزه متقابل-2019
In this paper, we address two research topics in Recommender Systems (RSs) which have been developed in parallel without a deeper integration: Cross-Domain RS (CDRS) and Context-Aware RS (CARS). CDRS have emerged to enhance the quality of recommendations in a target domain by leveraging sources of information in different domains. CDRS are especially useful to address cold-start, sparsity and diversity problems in target domains with scarce information. CARS, on its turn, have been proposed to consider contextual information for recommendations. Such systems are suitable when the users’ interests change according to factors like time, location, among others. By combining these two approaches, better RSs can be developed, considering both the availability of useful data from multiple domains and the use of con- textual information. In this paper, we formalize the combination of CDRS and CARS, which represents a more systematic integration of these approaches compared to previous work. Based on this formulation, we developed novel RSs techniques, named CD-CARS. To evaluate the developed CD-CARS techniques, we performed extensive experimentation through real datasets taking into account several scenarios. The recommendations were evaluated in terms of predictive and ranking performance, respectively achieving up to 62.6% and 45%, depending on the scenario, in comparison to traditional cross-domain collaborative filtering techniques. Therefore, the experimental results have shown that the integration of techniques de- veloped in isolation can be useful in a variety of situations, in which recommendations can be improved by information gathered from different sources and can be refined by considering specific contextual in- formation.
Keywords: Cross-domain recommendation | Context-aware recommendation | Collaborative filtering recommendation | Cross-domain context-aware | recommendation
مقاله انگلیسی
9 Progress in context-aware recommender systems — An overview
پیشرفت در سیستم های توصیه کننده آگاه از متن - یک مرور کلی-2019
Recommender Systems are the set of tools and techniques to provide useful recommendations and suggestions to the users to help them in the decision-making process for choosing the right products or services. The recommender systems tailored to leverage contextual information (such as location, time, companion or such) in the recommendation process are called context-aware recommender systems. This paper presents a review on the continual development of context-aware recommender systems by analyzing different kinds of contexts without limiting to any specific application domain. First, an in-depth analysis is conducted on different recommendation algorithms used in context-aware recommender systems. Then this information is used to find out that how these techniques deals with the curse of dimensionality, which is an inherent issue in such systems. Since contexts are primarily based on users’ activity patterns that leads to the development of personalized recommendation services for the users. Thus, this paper also presents a review on how this contextual information is represented (either explicitly or implicitly) in the recommendation process. We also presented a list of datasets and evaluation metrics used in the setting of CARS.Wetried to highlight thathowalgorithmic approaches used in CARS differ from those of conventional RS. In that, we presented what modification or additions are being applied on the top of conventional recommendation approaches to produce context-aware recommendations. Finally, the outstanding challenges and research opportunities are presented in front of the research community for analysis
Keywords: Context | Recommender systems | Context-aware | Dimensionality reduction | Contextual modeling | User modeling
مقاله انگلیسی
10 Cloud-based IoT solution for state estimation in smart grids: Exploiting virtualization and edge-intelligence technologies
راه حل مبتنی بر ابر برای اینترنت اشیا برای تخمین حالت در شبکه های هوشمند: بررسی مجازی سازی و روشهای هوش لبه ای-2018
Smart Grids (SGs) are expected to be equipped with a number of smart devices able to generate vast amounts of data about the network status, becoming the key components for an efficient State Estimation (SE) of complex grids. To exploit their potentials, the ICT infrastructure needs to be scalable to follow the increasing amount of data flows and flexible to give the possibility to assign and re-assign grid functions and data flow control policies at runtime, possibly in a context-aware manner. In this scenario, this paper proposes and validates a Cloud-IoT-based architectural solution for SE in SG that combines cloud-capabilities and edge-computing advantages and uses virtualization technologies to decouple the handling of measurement data from the underlying physical devices. Case studies in the field of distribution networks monitoring are also analyzed, demonstrating that the proposed architecture is capable to accomplish the assigned operational tasks, while satisfying the needed quality level from both the communication and the grid perspectives with a significant degree of flexibility and adaptability with respect to state of the art solutions.
keywords: Smart grid| Internet of Things| Cloud| Edge| Virtualization| State estimation| Phasor measurement unit
مقاله انگلیسی
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