Preference learning based decision map algebra: specification and implementation

Decision Map Algebra (DMA) is a generic and context independent algebra, especially devoted to spatial multicriteria modelling. The algebra defines a set of operations which formalises spatial multicriteria modelling and analysis. The main concept in DMA is decision map, which is a planar subdivision of the study area represented as a set of non-overlapping polygonal spatial units that are assigned, using a multicriteria classification model, into an ordered set of classes. Different methods can be used in the multicriteria classification step. In this paper, the multicriteria classification step relies on the Dominance-based Rough Set Approach (DRSA), which is a preference learning method that extends the classical rough set theory to multicriteria classification. The paper first introduces a preference learning based approach to decision map construction. Then it proposes a formal specification of DMA. Finally, it briefly presents an object oriented implementation of DMA

A Clustering Approach for Protecting GIS Vector Data

The availability of Geographic Information System (GIS) data has increased in recent years, as well as the need to prevent its unauthorized use. One way of protecting this type of data is by embedding within it a digital watermark. In this paper, we build on our previous work on watermarking vector map data, to improve the robustness to (unwanted) modifications to the maps that may prevent the identification of the rightful owner of the data. More specifically, we address the simplification (removing some vertices from GIS vector data) and interpolation (adding new vertices to GIS data) modifications by exploiting a particular property of vector data called a bounding box. In addition, we experiment with bigger maps to establish the feasibility of the approach for larger maps

Exploiting Vector Map Properties for GIS Data Copyright Protection

Geographic Information System (GIS) vector maps have become more widely available, prompting a need to prevent their unauthorized use. This is commonly done through the use of a digital watermark, with many approaches applying techniques from image map watermarking, without exploiting the particular properties of vector map data. In previous work we showed that using k-medoids clustering and the bounding box property of vector maps in the embedding process leads to increased robustness against simplification (removing vertices from vector data) and interpolation (adding new vertices to the data) attacks, which may distort the watermark and prevent the identification of the map owner. In this paper we show that the advantages of using the bounding box property are maintained even with a different clustering approach (k-means), and argue that they would hold regardless of the method used for identifying the watermark embedding locations in the map

Evaluating the topological quality of watermarked vector maps

The pervasive use and exchange of digital content led to increased efforts in the research community for efficient approaches to protect intellectual property rights. While watermarking techniques have been used extensively for raster image format, watermarking approaches for the vector map format have been largely inspired from existing image watermarking techniques, without due consideration to the suitability of these techniques for this different data format. A key requirement of any watermarking approach of vector data is the preservation of the topological quality of the watermarked data. This is sometimes referred to as the invisibility of the watermark. For vector map data, the topological quality and invisibility are fundamentally different, but currently submerged into one and measured with error metrics borrowed from image watermarking, such as Root Mean Squared Error (RMSE) and Peak Signal to Noise Ratio (PSNR). Over the last 10 year, the research community on watermarking vector map data has repeatedly posed that error metrics alone are not appropriate for the evaluation of watermarked vector map topological quality. In this paper, a metric for measuring topological quality by measuring topological distortions is proposed based on topological properties of polygon-based vector maps. To evaluate the proposed metric, experiments with controlled watermarking capacity (i.e. how much is embedded) were run on maps of various sizes, using two popular embedding approaches, i.e. coordinate-based and distance-based embedding. The results indicate that the metrics allow comparisons between watermarked maps of different sizes and of different watermark sizes, and, thus, can be used to assess the quality of watermarked vector maps. The advantages and limitations of the proposed metric are discussed and further research directions are highlighted toward an agreed metric by the research community

Decision Making by Applying Machine Learning Techniques to Mitigate Spam SMS Attacks

Due to exponential developments in communication networks and computer technologies, spammers have more options and tools to deliver their spam SMS attacks. This makes spam mitigation seen as one of the most active research areas in recent years. Spams also affect people’s privacy and cause revenue loss. Thus, tools for making accurate decisions about whether spam or not are needed. In this paper, a spam mitigation model is proposed to find spam from non-spam and the different processes used to mitigate spam SMS attacks. Also, anti-spam measures are applied to classify spam with the aim to have high classification accuracy performance using different classification methods. This paper seeks to apply the most appropriate machine learning (ML) techniques using decision-making paradigms to produce a ML model for mitigating spam attacks. The proposed model combines ML techniques and the Delphi method along with Agile to formulate the solution model. Also, three ML classifiers were used to cluster the dataset, which are Naive Bayes, Random Forests, and Support Vector Machine. These ML techniques are renowned as easy to apply, efficient and more accurate in comparison with other classifiers. The findings indicated that the number of clusters combined with the number of attributes has revealed a significant influence on the classification accuracy performance

Global IoT Mobility: A Path Based Forwarding Approach

With the huge proliferation of mobile Internet of Things (IoT) devices such as connected vehicles, drones, and healthcare wearables, IoT networks are promising mobile connectivity capacity far beyond the conventional computing platforms. The success of this service provisioning is highly dependent on the flexibility offered by such enabling technologies to support IoT mobility using different devices and protocol stacks. Many of the connected mobile IoT devices are autonomous, and resource constrained, which poses additional challenges for mobile IoT communication. Therefore, given the unique mobility requirements of IoT devices and applications, many challenges are still to be addressed. This paper presents a global mobility management solution for IoT networks that can handle both micro and macro mobility scenarios. The solution exploits a path-based forwarding fabric together with mechanisms from Information-Centric Networking. The solution is equally suitable for legacy session-based mobile devices and emerging information-based IoT devices such as mobile sensors. Simulation evaluations have shown minimum overhead in terms of packet delivery and signalling costs to support macro mobility handover across different IoT domains

Decision Making by Applying Machine Learning Techniques to Mitigate Spam SMS Attacks

Due to exponential developments in communication networks and computer technologies, spammers have more options and tools to deliver their spam SMS attacks. This makes spam mitigation seen as one of the most active research areas in recent years. Spams also affect people’s privacy and cause revenue loss. Thus, tools for making accurate decisions about whether spam or not are needed. In this paper, a spam mitigation model is proposed to find spam from non-spam and the different processes used to mitigate spam SMS attacks. Also, anti-spam measures are applied to classify spam with the aim to have high classification accuracy performance using different classification methods. This paper seeks to apply the most appropriate machine learning (ML) techniques using decision-making paradigms to produce a ML model for mitigating spam attacks. The proposed model combines ML techniques and the Delphi method along with Agile to formulate the solution model. Also, three ML classifiers were used to cluster the dataset, which are Naïve Bayes, Random Forests, and Support Vector Machine. These ML techniques are renowned as easy to apply, efficient and more accurate in comparison with other classifiers. The findings indicated that the number of clusters combined with the number of attributes has revealed a significant influence on the classification accuracy performance