Similarity Measures In Context Research Articles

Mitigating service-oriented attacks using context-based trust for smart cities in IoT networks

Abstract Smart City technology is an attempt to improve the quality of life of its citizens by providing promising smart solutions for multiple applications. These applications include healthcare monitoring, resource utilization, city resource management, and various public services. Internet of Things (IoT) enables smart city applications to collect data from various sensors and process it for providing numerous smart services to the end-users with improved performance. The diverse nature of IoT network requires the use of multiple types of sensors which produce a huge amount of data. This data is highly vulnerable to multiple service-oriented attacks; therefore, it must be protected during the communication of IoT nodes. This research work has focused on the identification and detection of malicious nodes causing service-oriented attacks in smart city applications and networks. The direct experience of communicating nodes and recommendations from neighboring nodes are collected to formulate a total trust score. The adaptive weights assigned to direct observations and indirect recommendations ensure the effectiveness of the Context-Based Trust Evaluation System Model (CTES) in detecting On–Off attacks. Moreover, context similarity measure calculations filter out those bad nodes which are posing a Sybil Attack. The proposed CTES has also been simulated on Contiki Cooja. The results also validate the effectiveness of CTES in detecting the bad behavior of malicious nodes.

Data-Driven Supervised Learning for Life Science Data

Life science data are often encoded in a non-standard way by means of alpha-numeric sequences, graph representations, numerical vectors of variable length, or other formats. Domain-specific or data-driven similarity measures like alignment functions have been employed with great success. The vast majority of more complex data analysis algorithms require fixed-length vectorial input data, asking for substantial preprocessing of life science data. Data-driven measures are widely ignored in favor of simple encodings. These preprocessing steps are not always easy to perform nor particularly effective, with a potential loss of information and interpretability. We present some strategies and concepts of how to employ data-driven similarity measures in the life science context and other complex biological systems. In particular, we show how to use data-driven similarity measures effectively in standard learning algorithms.

TOPSIS Method and Similarity Measures Based on Cosine Function Using Picture Hesitant Fuzzy Sets and its Applications to Strategic Decision Making

Picture hesitant fuzzy set (PHFS) is a recently developed tool to cope with uncertain and awkward information in realistic decision issues and is applicable where opinions are of more than two types, i.e., yes, no, abstinence and refusal. Similarity measures (SMs) in a data mining context are distance with dimensions representing features of the objects. Keeping the advantages of the above analysis, in this manuscript, the authors proposed SMs for PHFSs, including cosine SMs for PHFSs, SMs for PHFSs based on cosine function, and SMs for PHFSs based on cotangent function. Further, entropy measure, TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based on correlation coefficient are investigated for PHFSs. Further, some weighted SMs are also proposed and then applied to the strategic decision-making problem and the results are discussed. Moreover, we take two illustrative examples to compare the established work with existing drawbacks and also show that the existing drawback cannot solve the problem of established work. But on the other hand, the new approach can easily solve the problem of the existing drawback. Finally, the advantages of the new approach are discussed.

Multiagent Approach to Coreference Resolution Based on the Multifactor Similarity in Ontology Population

The multiagent approach to coreference resolution in the process of extracting information from texts in natural languages for ontology population is described. Special class agents corresponding to ontology classes are defined. They analyze the available information in the corresponding ontology instances. The results of this analysis are used for values to instance attribute, for detecting duplicates and equivalents of instances, for fixing coreferential relations, and for determining the weights of information connections used to resolve ambiguities. Coreferences are resolved taking into account a multifactor similarity measure between extracted objects that combines the semantic, context, positional, and grammar similarity measures. The class agents work within the multiagent approach to text analysis aimed at ontology population.

Context-Aware Location Recommendation Using Geotagged Photos in Social Media

Recently, the increasing availability of digital cameras and the rapid advances in social media have led to the accumulation of a large number of geotagged photos, which may reflect people’s travel experiences in different cities and can be used to generate location recommendations for tourists. Research on this aspect mainly focused on providing personalized recommendations matching a tourist’s travel preferences, while ignoring the context of the visit (e.g., weather, season and time of the day) that potentially influences his/her travel behavior. This article explores context-aware methods to provide location recommendations matching a tourist’s travel preferences and visiting context. Specifically, we apply clustering methods to detect touristic locations and extract travel histories from geotagged photos on Flickr. We then propose a novel context similarity measure to quantify the similarity between any two contexts and develop three context-aware collaborative filtering methods, i.e., contextual pre-filtering, post-filtering and modeling. With these methods, location recommendations like “in similar contexts, other tourists similar to you often visited …” can be provided to the current user. Results of the evaluation with a publicly-available Flickr photo collection show that these methods are able to provide a tourist with location recommendations matching his/her travel preferences and visiting context. More importantly, compared to other state-of-the-art methods, the proposed methods, which employ the introduced context similarity measure, can provide tourists with significantly better recommendations. While Flickr data have been used in this study, these context-aware collaborative filtering (CaCF) methods can also be extended for other kinds of travel histories, such as GPS trajectories and Foursquare check-ins, to provide context-aware recommendations.

An image inpainting method using pLSA-based search space estimation

In this paper, we present a novel exemplar-based image inpainting technique based on the local context measure of the target patch. Three main steps of the proposed method are determination of patch priority, the search space estimation for the candidate patches and the patch completion to fill in the unknown pixels of the target patch. In patch priority, we emphasize on the structure by the spatial relationship of neighborhood similar patches and kernel regression based local image structure. We find the search space, sub-regions of the entire source region similar to the region surrounding the target patch, to find the candidate patches. The said search space is estimated using probabilistic latent semantic analysis (pLSA). Last, we infer the unknown pixels of the target patch using pLSA-based context and histogram similarity measure between the target patch and the candidate patches. Experimental results are found to be good compared to the competitive methods and may be used for digital restoration of images of defective or damaged artifacts.

Building trust from context similarity measures

Trust is an essential feature of any system where entities have to collaborate. Trust can assist entities making decisions before establishing collaborations. It is desirable to simulate the behaviour of users as in social environments where they tend to trust users who have common interests or share some of their opinions, i.e., users similar to them. In this paper, we introduce the concept of context similarity among entities and derive a similarity network. Then, we define a trust model that allows us to establish trust along a path of entities. We validate our model in a proximity-based trust establishment scenario.

Context-aware dissemination of information and services in heterogeneous network environments

The Internet of Things and the advent of cloud computing promotes a global scale Machine-to-Machine (M2M) communication. Dissemination of information and advertising services to the targeted nodes is an important aspect in the performance of the future large scale network systems. With the contextual information inferred from available information, delivery of services can be performed in a more efficient way. Context-awareness is a key ingredient in any ubiquitous and pervasive system and provides intelligence to the system, allowing computing devices make appropriate and timely decisions on behalf of users. Semantic support for M2M communication can be enhanced if it leverages the available information and contextual data. In this paper we present a methodology for a context-aware publish-subscribe based approach to dissemination of information and services and a service selection method which is based on the idea of utilizing network information as services that is delivered via application programming interfaces. We propose a fuzzy MADM method and a context similarity measure. The proposed method of information dissemination is based on mining the value of a node based on information inferred from the contextual information of the node and contextually similar nodes. We take into account the quality of contextual information in aggregating contextual information from different sources.

A Data Fusion Approach to Context-Aware Service Delivery in Heterogeneous Network Environments

Context-awareness is a key ingredient in any ubiquitous and pervasive system and provides intelligence to the system, allowing computing devices make appropriate and timely decisions on behalf of users. Context-awareness in mobile computing refers to internal and external adaptation of the environment and applications to the context state of each other. Such systems should adapt to the changes and variations of user's context such as location, device status, connectivity and etc. In this paper we present our perspective of a context-aware service platform which is based on the idea of utilizing network information as services that is delivered via application programming interfaces and propose a fuzzy MADM method and a context similarity measure. We take into account the quality of contextual information in aggregating contextual information from different sources.

A new method for matching objects in two different geospatial datasets based on the geographic context

Although several methods of handling object matching problems across different datasets have been developed, there is still a need to design new approaches to address the diverse matching applications. Such cases include those where the coordinate differences in datasets are significant, where the shapes of the same objects are dissimilar, or even where the shapes are too similar for different objects. This is especially true, as many large portals worldwide are opening their spatial databases to public access by providing an open application programming interface (API). With this understanding, we propose in this paper a new method for matching objects in different datasets based on geographic context similarity measures. The proposed method employs and combines a set of concepts such as buffer growing, Voronoi diagrams, triangulation, and geometric measurements. This approach is simple in its algorithm but powerful in resolving situations when two datasets have significant coordinate discrepancies. In addition, the concept is highly effective regardless of the shapes of objects. After testing the method for the two major digital datasets in Korea, we found that the matching success rate reached 99.4%.

Similarity-based word sense disambiguation

We describe a method for automatic word sense disambiguation using a text corpus and a machine-readble dictionary (MRD). The method is based on word similarity and context similarity measures. Word...

Data Compression Using a Sort-Based Context Similarity Measure

Every symbol in the data can be predicted by taking the immediately preceding symbols, or context, into account. This paper proposes a new adaptive data-compression method based on a context similarity measure. We measure the similarity of contexts using a context sorting mechanism. The aim of context sorting is to store a set of contexts in a specific order so that contexts more similar to the current one are more accessible. The proposed method predicts the next symbol by ranking the previous context-symbol pairs in order of context similarity. The codeword for the next symbol represents the rank of the symbol in this ordered sequence. The compression performance is evaluated both analytically and empirically. Although the proposed method uses no probability distribution to make a prediction, it gains good compression. It also reveals a strong relation between symbol-ranking compression and the Ziv-Lempel textual substitution method.