Maja Matetic

Development and evaluation on a wireless multi-gas-sensors system for improving traceability and transparency of table grape cold chain

A WGS2 for table grape cold chain was developed and evaluated.The key traceability indicators include temperature, humidity, CO2, O2, and SO2.The shelf life model was built to predict the quality indicators.Tested and analyzed in two sample table grapes cold chains in China.Improving the traceability and transparency of the table grape cold chain. There is increasing requirement to improve traceability and transparency of table grapes cold chain. Key traceability indicators including temperature, humidity and gas microenvironments (e.g., CO2, O2, and SO2) based on table grape cold chain management need to be monitored and controlled. This paper presents a Wireless Multi-Gas-Sensors System (WGS2) as an effective real-time cold chain monitoring system, which consists of three units: (1) the WMN which applies the 433MHz as the radio frequency to increase the transmission performance and forms a wireless sensor network; (2) the WAN which serves as the intermediary to connect the users and the sensor nodes to keep the sensor data without delay by the GPRS remote transmission module; (3) the signal processing unit which contains embedded software to drive the hardware to normal operation and shelf life prediction for table grapes. Then the study evaluates the WGS2 in a cold chain scenario and analyses the monitoring data. The results show that the WGS2 is effective in monitoring quality, and improving transparency and traceability of table grape cold chains. Its deploy ability and efficiency in implantation can enable the establishment of a more efficient, transparent and traceable table grape supply chain.

Qualitative modelling of object behaviour in the dynamic vision system using hidden Markov models

The subject of this paper is cognitive analysis of object behaviour in a dynamic scene. Based on the identified motions of an animal and calculated trajectory attributes we need to analyse, evaluate and reconstruct the behaviour pattern implementing artificial intelligence methods. Dynamic scene analysis has traditionally been quantitative and it generates large amounts of temporally evolving data. The system presented in the paper uses the qualitative modelling method, based on the model of space and time and incomplete domain background knowledge, in the process of discovering qualitative behaviour patterns. The Markov model (HMM) has been used as a model of object behaviour, based on qualitative spatial and temporal representation and conceptual grouping method.

Croatian speech technologies

Speech technologies deal with designing computer systems that can recognize spoken words, comprehend human language and generate intelligible speech. There is a wide range of applications speech technology systems were successfully implemented in. One of the most complex applications in speech technology is a spoken dialog system, which can be used for information inquiry services. In the paper we present the work in the development of a spoken dialog system for Croatian language. We propose an approach for development of modules in a spoken dialog system for the limited domain which uses the same acoustic model for speech recognition and speech synthesis. For the linguistic analysis an approach is proposed which is based on qualitative language modelling, while the development of the dialog module is based on the formalism of the object-oriented frame logic language. Some experimental results for Croatian speech recognition and understanding are presented and discussed.

Qualitative Modelling and Analysis of Animal Behaviour

The paper presents the LABAQM system developed for the analysis of laboratory animal behaviours. It is based on qualitative modelling of animal motions. We are dealing with the cognitive phase of the laboratory animal behaviour analysis as a part of the pharmacological experiments. The system is based on the quantitative data from the tracking application and incomplete domain background knowledge. The LABAQM system operates in two main phases: behaviour learning and behaviour analysis. The behaviour learning and behaviour analysis phase are based on symbol sequences, obtained by the transformation of the quantitative data. Behaviour learning phase includes supervised learning procedure, unsupervised learning procedure and their combination. We have shown that the qualitative model of behaviour can be modelled by hidden Markov models. The fusion of supervised and unsupervised learning procedures produces more robust models of characteristic behaviours, which are used in the behaviour analysis phase.

Association rule mining and visualization of introductory programming course activities

In introductory programming courses instructors usually experiment by introducing new activities based on new technologies in order to promote learning and achievement. This paper presents a case study from the Department of Informatics at the University of Rijeka where a blended course Programming 2 offers new supportive activities to students. The work gives objective evaluation of these activities implementing data mining on Moodle log data using the Apriori method and interactive visualization.

Data mining of computer game assisted e/m-learning systems in higher education

One of the major challenges in the implementation of e-learning systems is motivating students to use it. E-learning systems are mostly unadapted to mobile platforms because they do not use responsive web design and progressive enhancement based on browser, device or feature detection. This paper presents the evolutionary cycle, current status and potential development of new platforms for e/m-learning in higher education. Educational computer games that serve as a motivational element in e/m-learning are presented, as well as the data structures that can complement the existing models of e/m-learning. The University of Rijeka uses MudRi e-learning system, which is based on Moodle open source software. This system collects data on the activity of participants in the educational process, which is suitable for data mining. A preliminary research on the correlation between the activity in forum discussions and the course pass or fail grade is carried out. The possibility of using data mining based on the data structure of the Moodle system and the models of e/m-learning and learning through play is described, aiming at finding optimal ways of learning and optimal learning results. Finally, a model of integrated system user interaction on which our future research will be based is presented.

Development of an online multimedia course

At the University of Rijeka the theoretical knowledge is mainly transferred by the traditional model consisting of professor explaining material to a group of students in front of a blackboard. We believe that this model dates from the period of the industrial revolution when it was necessary to educate large number of people to work on new machines, but there was no technology for more efficient knowledge transfer. Nowadays, such a model cannot keep up with the speed of generating new knowledge and technology. A larger number of participants cannot be educated due to time and space constraints, and the knowledge usually becomes obsolete by the end of studies. The result of this model are students with skills unadapted to the labor market requirements. We believe this is an extremely important issue for our university. One of the possible solutions might be digitizing theoretical parts of courses. Using web technologies, theoretical lectures can be held online and in that way more efficient methods of teaching on campus can be used. In this paper, the process of digitizing the university course Introduction to Psychology is presented, as well as the benefits that can be achieved by creating and using multimedia online courses.

Can we beat google translate

This paper presents a machine translation evaluation study for Croatian-English language pair. In-domain and out-of-domain translations from Croatian into English have been obtained from Google Translate, our own statistical machine translation system LegTran, and from a professional translator. These translations have been evaluated by six different automatic metrics. The gains obtained from increasing the number of reference translations have been explored and measured. System level correlation between automatic evaluation metrics is given and the significance of the results is discussed. Bootstrapping, approximate randomization and the sign test have been used for confidence intervals and hypothesis testing.

VoiceXML for Slavic languages application development

Since Croatian language, as well as other Slavic languages, is essentially very different from English, we are in the need of developing language specific tools and systems. In this paper we point out the benefits that speech applications have and describe our future platform. Furthermore, we create domain-specific dialogues using VoiceXML. VoiceXML is a language for creating voice user interfaces. We opted for VoiceXML because it simplifies application development by permitting developers to use familiar Web techniques, tools and infrastructure.

Clustering of imbalanced moodle data for early alert of student failure

This paper is an attempt of applying EDM methods on Moodle data in order to detect specific behaviours within student groups with the tendency to fail the course. The research is conducted on Moodle logs gathered in the blended course Programming 1. Extracting and using crucial information on time can be a turning point for students in at-risk stage, which is what we tried to achieve in this research.