Elżbieta Kukla

Multidimensional social network: model and analysis

A social network is an abstract concept consisting of set of people and relationships linking pairs of humans. A new multidimensional model, which covers three main dimensions: relation layer, time window and group, is proposed in the paper. These dimensions have a common set of nodes, typically, corresponding to human beings. Relation layers, in turn, reflect various relationship types extracted from different user activities gathered in computer systems. The time dimension corresponds to temporal variability of the social network. Social groups are extracted by means of clustering methods and group people who are close to each other. An atomic component of the multidimensional social network is a view - small social sub-network, which is in the intersection of all dimensions. A view describes the state of one social group, linked by one type of relationship (one layer), and derived from one time period. The multidimensional model of a social network is similar to a general concept of data warehouse, in which a fact corresponds to a view. Aggregation possibilities and usage of the model is also discussed in the paper.

A Generic Model for a Multidimensional Temporal Social Network

A comprehensive generic model for a multidimensional, temporal social network is proposed in the paper. It covers three main dimensions: layers, time windows and social groups. All the dimensions share the same set of nodes corresponding to social entities, usually individuals. Layers correspond to different types of relationships between humans, e.g. social and semantic, that can be derived from different human activities in IT systems; time windows reflect the temporal profile of the social network, whereas groups (social communities) are sets of similar humans. The intersection of all dimensions is called a view; it represents the statement of a single social cluster (group) with connections of only one type (from a single layer) and with the snapshot for a given period. Views can be aggregated by one, two or even all three dimensions simultaneously using filtering of dimension instances. Apart from description of the multidimensional model, its applicability is also considered in the paper.

A model conception for optimal scenario determination in an intelligent learning system

In this paper a conception of the model for learning scenario determination is presented. We define the learning scenario as a sequence of the hypermedia pages, representing particular knowledge units, and tests related to them. The scenario determination is a dynamic process that begins when a new student takes up a course. The opening scenario for this student is chosen as the consensus of the final scenarios of the students, who have already finished this course, and who belong to a class of the learners similar to the new one. We have elaborated the consensus‐based procedure for the scenario determination. Since this procedure operates on a set of similar learners, we have developed the conceptions of learner’s profile and students’ classification. The learner’s profile is proposed to include the attributes describing students’ personal data (as name, birthday etc.), their cognitive and learning styles as well as their usage data (represented by the learning scenarios). The students’ classification is based on a set of the basic attributes that seem to influence the learning effects. Their significance is verified during the learning process. We have also elaborated the procedure of reducing undistinguishable values of the attribute and removing useless attributes from the set of basic attributes. A learning procedure proposed, describes generally the situations when the scenario is modified, and the methods used for its modification.

Determination of learning scenarios in intelligent web-based learning environment

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.

Responsive Web Design: Testing Usability of Mobile Web Applications

Responsive web design (RWD) allows applications for adapting dynamically to diverse screen sizes, proportions, and orientations. RWD is an approach to the problem of designing for the great number of devices ranging from small smartphones to large desktop monitors. The goal of the paper was to test the usability of an application for management of a scientific conference developed using responsive design paradigms. Two versions of the responsive applications were implemented using different design patterns. Various techniques of usability were employed including tests with prospective users, experts’ inspections as well automated tools. The obtained results were thoroughly analysed and recommendations on the utilization of individual design patterns in developing mobile web applications were formulated.

A Model Conception for Learner Profile Construction and Determination of Optimal Scenario in Intelligent Learning Systems

The effectiveness of e-Learning process can be increased by integration of hypermedia presentation possibilities of teaching meth- ods with students learning styles. It may be achieved by applying to user profile and tutoring strategy description the elements representing learners cognitive characteristics. The paper discusses the conception of Felder learning styles model employment for student profile con- struction and consensus based method for optimal learning scenario determination.

Facial Emotion Recognition Based on Cascade of Neural Networks

The chapter presents a method that uses the cascade of neural networks for facial expression recognition. As an input the algorithm receives a normalized image of a face and returns the emotion that the face expresses. To determine the best classifiers for recognizing particular emotions one- and multilayered networks were tested. Experiments covered different resolutions of the images presenting faces as well as the images including regions of mouths and eyes. On the basis of the tests results a cascade of the neural networks was proposed. The cascade recognizes six basic emotions and neutral expression.

User Personalisation for the Web Information Retrieval Using Lexico-Semantic Relations

This contribution presents a new approach to the representation of user interests and preferences at information retrieval process on the Web. The adaptive user profile includes both interests given explicitly by the user, as a query, and also preferences expressed during relevance valuation process, so to express field independent translation between terminology used by the user and terminology accepted in some field of knowledge. Building, modifying, expanding (by semantically related terms) and using procedures for the profile are presented. Experiments concerning the profile, as a personalization mechanism of Web retrieval system, are presented and discussed.