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The toll of human casualties and psychological impacts on societies make any study on violent extremism worthwhile, let alone attempting to detect patterns among them. This paper is an effort to predict which violent extremist organization (VEO), among 14 currently active ones throughout the world, is responsible for a violent act based on 14 features, including its human and structural tolls, its target type and value, intelligence, and weapons utilized in the attack. Three main steps in our paper include: 1) the visualization of the violent acts through linear and non-linear dimensionality reduction techniques; 2) sequential forward feature selection based on the generalization accuracy of three machine learning models-decision tree, and linear and nonlinear SVM; and 3) employing multilayer perceptron to predict the VEO based on the selected features of a violent act. Top-ranked selected features were related to the target type and plan and the multilayer perceptron achieved up to 40% test accuracy.

Public fog nodes can be deployed in public places closer to the edge where many personal and commercial devices (e.g., a sensor, an application, or a device) can connect to. These public fog nodes can provide real-time and localized services for networking, computing, storage and content delivery to the connected devices. The monetization and payment of such services is typically manual, centralized, and lacks the necessary trust. The providers of the public fog nodes typically offer fixed pricing models for their services, and the customers manually select and pay for the used services, with little or no transparency and trust in the provided service in terms of the used time, network bandwidth, and quality of service (QoS). This paper presents a novel scheme to enable blockchain-based monetization and automated payment in cryptocurrency for services provided by public fog nodes. The proposed scheme is decentralized, trustworthy, automated, and with certain guarantees for QoS, customer satisfaction, and dispute resolutions through a reputation system. The proposed solution uses the Ethereum blockchain and its native smart contract features to govern the interactions between devices and fog nodes. The proposed solution is implemented, tested and evaluated to show correct behavior and functionality. We also provide cost and security analysis and show that our solution is resilient against major security attacks. Our smart contract is made publicly available on Github 1 . 1 https://github.com/mazendb/fognodemonetization

Robots are meant to replace humans for a broad variety of everyday tasks, such as environmental monitoring or patrolling large public areas for security assurance. The main focus of researchers and practitioners has been on providing tailored software and hardware solutions for very specific and often complex tasks. On one hand, these solutions show great potential and provide advanced capabilities for solving the specific task. On the other hand, the polarized attention to task-specific solutions makes them hard to reuse, customize, and combine. In this paper we propose a family of domain-specific modeling languages for the specification of civilian missions of mobile multi-robot systems. These missions are meant to be described in terms of models that are: 1) closer to the general problem domain; 2) independent from the underlying technologies; 3) ready to be analyzed, simulated, and executed; and 4) extensible to new application domains, thus opening up the use of robots to even non-technical operators. Moreover, we show the applicability of the proposed family of languages in two real-world application domains: unmanned multicopters and autonomous underwater vehicles.