1,720,972 research outputs found
Context-aware access control with imprecise context characterization for cloud-based data resources
No full textComputing technologies are increasingly dynamic and ubiquitous in everyday life nowadays. Context information plays a crucial role in such dynamically changing environments and the different types of contextual conditions bring new challenges to context-sensitive access control. This information mostly can be derived from the crisp sets. For example, we can utilize a crisp set to derive a patient and nurse are co-located in the general ward of the hospital or not. Some of the context information characterizations cannot be made using crisp sets, however, they are equally important in order to make access control decisions. Towards this end, this article proposes an approach to Context-Aware Access Control using Fuzzy logic (FCAAC) for data and information resources. We introduce a formal context model to represent the fuzzy and other contextual conditions. We also introduce a formal policy model to specify the policies by utilizing these conditions. Using our formal approach, we combine the fuzzy model with an ontology-based approach that captures such contextual conditions and incorporates them into the policies, utilizing the ontology languages and the fuzzy logic-based reasoning. We introduce a unified data ontology and its associated mapping ontology in terms of facilitating access control to cloud-based data resources. We justify the feasibility of our approach by demonstrating the practicality through a prototype implementation, several healthcare case studies and a usability study. Finally, we demonstrate an experimental evaluation in terms of query response time. The experiment results demonstrate the satisfactory performance of our proposed FCAAC approach.Full Tex
Achieving security scalability and flexibility using Fog-Based Context-Aware Access Control
No full textIn the cyberspace environment, access control is one of the foremost fundamental safeguards used to prevent unauthorized access and to minimize the impact from security breaches. Fog computing preserves many benefits for the integration of both internet of things (IoT) and cloud computing platforms. Security in Fog computing environment remains a significant concern among practitioners from academia and industry. The current existing access control models, like the traditional Context-Aware Access Control (CAAC), are limited to access data from centralized sources, and not robust due to lack of semantics and cloud-based service. This major concern has not been addressed in the literature, also literature still lacks a practical solution to control fog data view from multiple sources. This paper critically reviews and investigates the limitations of current fog-based access control. It considers the trade-off between latency and processing overheads which has not been thoroughly studied before. In this paper, a new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework is proposed to enable flexible access control data from multiple sources. To fill the gap in the literature this paper introduces (i) a general data model and its associated mapping model to collate data from multiple sources. (ii) a data view model to provide an integrated result to the users, dealing with the privacy requirements of the associated stakeholders, (iii) a unified set of CAAC policies with an access controller to reduce both administrative and processing overheads, and (iv) a data ontology to represent the common classes in the relevant data sets. The applicability of FB-CAAC proposal is demonstrated via a walkthrough of the entire mechanism along with several case studies and a prototype testing. The results show the efficiency, flexibility, effectiveness, and practicality of FB-CAAC for data access control in fog computing environment.Full Tex
A deep learning model for mining and detecting causally related events in tweets
No full textNowadays, public gatherings and social events are an integral part of a modern city life. To run such events seamlessly, it requires real time mining and monitoring of causally related events so that the management can make informed decisions and take appropriate actions. The automatic detection of event causality from short text such as tweets could be useful for event management in this context. However, detecting event causality from tweets is a challenging task. Tweets are short, unstructured, and often written in highly informal language which lacks enough contextual information to detect causality. The existing approaches apply different techniques including hand‐crafted linguistic rules and machine learning models. However, none of the approaches tackle the issue related to the lack of contextual information. In this paper, we detect event causality in tweets by applying a context word extension technique and a deep causal event detection model. The context word extension technique is driven by background knowledge extracted from one million news articles. Our model achieves 79.35% recall and 67.28% f1‐score, which are 17.39% and 2.33% improvements to the state‐of‐the‐art approach.No Full Tex
Answering Binary Causal Questions: A Transfer Learning Based Approach
No full textCausal question answering is a task of answering causality related questions. The questions are referred to as binary causal questions when the questions e.g., "Could X cause Y?" can be answered by yes/no answers. Answer to the previous question is yes if X is a cause of Y, and otherwise no. The binary causal question answering systems can be used to validate causal relationships, which can be particularly useful for decision making. For example, it could be useful for the tourism authorities to know the answer to the question "Could growing social tension cause reduction in tourism?". We aim to automatically answer such binary causal questions by developing a machine learning model. However, training a machine learning model to detect causal relationships is challenging due to the lack of large and high quality labeled datasets. In this paper, we propose a transfer learning-based approach which fine-tunes pretrained transformer based language models on a small dataset of cause-effect pairs to detect causality and answer binary causal questions. The proposed approach achieves performance comparable to a number of benchmark approaches on five benchmark test datasets extracted by human experts conditioned on the same small training dataset.Full Tex
Automated measurement of attitudes towards social distancing using social media: A COVID-19 case study
No full textThe COVID-19 outbreak has focused attention on the use of social distancing as the primary defence against community infection. Forcing social animals to maintain physical distance has presented significant challenges for health authorities and law enforcement. Anecdotal media reports suggest widespread dissatisfaction with social distancing as a policy, yet there is little prior work aimed at measuring community acceptance of social distancing. In this paper, we propose a new approach to measuring attitudes towards social distancing by using social media and sentiment analysis. Over a four-month period, we found that 82.5 percent of tweets were in favour of social distancing. The results indicate a widespread acceptance of social distancing in a selected community. We examine options for estimating the optimal (minimal) social distance required at scale, and the implications for securing widespread community support and for appropriate crisis management during emergency health events.Full Tex
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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