Engaging users in threat reporting is important in order to improve threat monitoring in urban environments. Today, mobile applications are mostly used to provide basic reporting interfaces. With a rapid evolution of mobile devices, the idea of context awareness has gained a remarkable popularity in recent years. Modern smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful which allows for real-time processing of data gathered by their sensors. Universal access to the Internet via WiFi hot-spots and GSM network makes mobile devices perfect platforms for ubiquitous computing. Although there exist numerous frameworks for context-aware systems, they are usually dedicated to static, centralized, client-server architectures. There is still space for research in the field of context modeling and reasoning for mobile devices. In this paper, we propose a lightweight context-aware framework for mobile devices that uses data gathered by mobile device sensors and performs on-line reasoning about possible threats based on the information provided by the Social Threat Monitor system developed in the INDECT project.

Formalized knowledge representation methods allow to build useful and semantically enriched knowledge bases which can be shared and reasoned upon. Unfortunately, knowledge acquisition for such formalized systems is often a time-consuming and tedious task. The process requires a domain expert to provide terminological knowledge, a knowledge engineer capable of modeling knowledge in a given formalism, and also a great amount of instance data to populate the knowledge base. We propose a CAPTCHA-like system called AceCAPTCHA in which users are asked questions in a controlled natural language. The questions are generated automatically based on a terminology stored in a knowledge base of the system, and the answers provided by users serve as instance data to populate it. The implementation uses AceWiki semantic wiki and a reasoning engine written in Prolog.

Semantic wikis constitute an increasingly popular class of systems for collaborative knowledge engineering. We developed Loki, a semantic wiki that uses a logic-based knowledge representation. It is compatible with semantic annotations mechanism as well as Semantic Web languages. We integrated the system with a rule engine called Heart that supports inference with production rules. Several modes for modularized rule bases, suitable for the distributed rule bases present in a wiki, are considered. Embedding the rule engine enables strong reasoning and allows to run production rules over semantic knowledge bases. In the paper, we demonstrate the system concepts and functionality using an illustrative example.