Marin Vuković

Early Forest Fire Detection with Sensor Networks: Sliding Window Skylines Approach

Wireless sensor networks are widely used in environmental applications, like forest fire detection. Although forest fires occur relatively rarely, their number is increasing in Europe in the last years, so their manifestation must be early detected in order to prevent higher damages. To minimize needless communication between the sensor nodes for this usage, new data suppression technique using sliding window skylines is described in this paper. We experimentally evaluate our algorithm for continuous sliding windows skylines computation, and show its usability in practice.

AAC services development: from usability requirements to the reusable components

Intensive development of information and communication technologies can contribute greatly to enhance alternative and augmentative communication for individuals who experience difficulty communicating in an understandable manner. Availability of internet infrastructure and affordability of mobile computer devices provide a sound starting point for planning and building AAC services. In order to make the services more familiar and understandable to users, it is justifiable to build them on top of common service platforms in an adaptive and configurable manner. In this paper we present a component-based AAC service development model that enables building such services by using mandatory AAC components on top of the component framework, and selecting visual or non-visual components from the available component pool. The AAC component selection criteria are based on matching the data and metadata of usability requirements specified with the metadata of available components from the component pool. Registering new components into the reusable component pool extends the pool and helps building the component-based AAC services development platform.

Predicting user movement for advanced location-aware services

-The paper deals with prediction of user movement in the context of enhancing location-aware services. Location and movement information are based on the simplest mechanism provided by mobile networks -broadcasted cell identification. The proposed approach includes the following steps: collecting information about user movement, analysing and learning user movement patterns, and applying movement knowledge to movement prediction. Movement prediction system based on the neural networks used for movement regularity detection and movement prediction is presented. Service architecture and an example service concludes the paper.

Location name extraction for user created digital content services

The increase in the amount of electronically stored textual data over the past decade has opened new communication possibilities in which users significantly participate in services. The result is a variety of digital content services in which new formats, such as free text form, have become particularly accepted and attractive. To support user service interaction, services must be able to obtain specific information from user defined texts. This paper proposes a model for location name extraction, constructed using a neural network trained with a Backpropagation learning algorithm. An analysis of location name interpretation, the semantic and binding problem in Croatian, as well as language specific variations are given.

e-Accessible service system: calibrator and communicator

Augmentative and Alternative Communication (AAC) includes various communication methods used by people with speech and language impairments, and is often aided by electronic devices such as smartphones or tablets. Since skills and limitations vary for each user, it is possible to create an interface on these electronic devices that would adjust to users needs in ways that would significantly improve usability and ease of use. Paper describes e-Accessible service system containing Calibrator and Communicator services. Calibrator measures users preferences and limitations regarding user interface through calibration process and calculates UI parameters adapted to the particular user. The parameters are forwarded to the Communicator service, whose main purpose is to supplement speech or writing for people with complex communication needs.

User movement prediction based on traffic topology for value added services

Value added services are based on user context awareness. Important context aspect is location, which could be extended to future locations if services had the ability to predict movement. We propose a model for user movement prediction based on traffic topology. Benefits of the model are presented on example service, while the performance is evaluated on real user movement data.

User privacy risk calculator

User privacy is becoming an issue on the Internet due to common data breaches and various security threats. Services tend to require private user data in order to provide more personalized content and users are typically unaware of potential risks to their privacy. This paper proposes a risk calculator based on a feedforward neural network that will provide users with an ability to calculate risks to their privacy. The proposed calculator is evaluated on a set of real world example scenarios. Furthermore, to give more insight into privacy issues, each estimated risk is explained by several real life scenarios that might happen if the observed parameters are obtained by an attacker. In turn, this should raise user awareness and knowledge about privacy issues on the Internet.

Estimating real world privacy risk scenarios

User privacy is becoming an issue on the Internet due to common data breaches and various security threats. Services tend to require private user data in order to provide more personalized content and users are typically unaware of potential risks to their privacy. This paper continues our work on the proposed user privacy risk calculator based on a feedforward neural network. Along with risk estimation, we provide the users with real world example scenarios that depict privacy threats according to selected input parameters. In this paper, we present a model for selecting the most probable real world scenario, presented as a comic, and thus avoid overwhelming the user with lots of information that he/she may find confusing. Most probable scenario estimations are performed by artificial neural network that is trained with real world scenarios and estimated probabilities from real world occurrences. Additionally, we group real world scenarios into categories that are presented to the user as further reading regarding privacy risks.

Lightweight and adaptable solution for security agility

Secure communication is an important aspect of today’s interconnected environments and it can be achieved by the use of cryptographic algorithms and protocols. However, many existing cryptographic mechanisms are tightly integrated into communication protocols. Issues emerge when security vulnerabilities are discovered in cryptographic mechanisms because their replacement would eventually require replacing deployed protocols. The concept of cryptographic agility is the solution to these issues because it allows dynamic switching of cryptographic algorithms and keys prior to and during the communication. Most of today’s secure protocols implement cryptographic agility (IPsec, SSL/TLS, SSH), but cryptographic agility mechanisms cannot be used in a standalone manner. In order to deal with the aforementioned limitations, we propose a lightweight cryptographically agile agreement model, which is formally verified. We also present a solution in the Agile Cryptographic Agreement Protocol (ACAP) that can be adapted on various network layers, architectures and devices. The proposed solution is able to provide existing and new communication protocols with secure communication prerequisites in a straightforward way without adding substantial communication overhead. Furthermore, it can be used between previously unknown parties in an opportunistic environment. The proposed model is formally verified, followed by a comprehensive discussion about security considerations. A prototype implementation of the proposed model is demonstrated and evaluated.

Requirements and Architecture Modeling in Software Engineering Courses

Modeling of requirements and software architecture involves abstraction and critical reasoning that is the most complex stage of development. This knowledge area is the hardest for students to acquire within computer science curricula. The main problem is the fact that the full understanding of this knowledge may only be visible while developing and evolving large scale complex software systems. The learning strategy may have significant impact on students ability to capture this knowledge. On the other hand, we may need different learning strategies depending on the context of study programme and students knowledge base. Here, in this paper, we present different strategies taken within software engineering courses but in different study programme context. We discuss lessons learned from two courses that belong to two different study programmes at Croatian Universities. Also, the deviate learning strategies involving approach and modeling tools and techniques used in these courses are different.