Time-aware recommender systems extend traditional recommendation methods by revealing user preferences over time or observing a specific temporal context. Among other features and advantages, they can be used to provide rating predictions based on changes in recurring time periods. Their underlying assumption is that users are similar if their behavior is similar in the same temporal context. Existing approaches usually consider separate temporal contexts and generated user profiles. In this paper, we create user profiles based on multidimensional temporal contexts and use their combined presentation in a user-based collaborative filtering method. The proposed model provides user preferences at a future point in time that matches temporal profiles. The experimental validation demonstrates that the proposed model is able to outperform the usual collaborative filtering algorithms in prediction accuracy.

The development of blockchain has allowed for the development of new concepts and ideas. A completely immutable ledger might not be appropriate for all new applications that are being envisaged for the blockchain. One of them is self-sovereign identity. The aim of this paper is to analyze the possible use cases for blockchain redaction in SSI. Main concepts of redaction and a summary of the current research progress are given. Use cases for redaction in SSI are categorized and described alongside their existing solutions. Detailed proposal for possible use cases is given and comparison is drawn between this solution and existing solution. Future challenges are introduced.

Social media is an important source of real-world data for sentiment analysis. Hate speech detection models can be trained on data from Twitter and then utilized for content filtering and removal of posts which contain hate speech. This work proposes a new algorithm for calculating user hate speech index based on user post history. Three available datasets were merged for the purpose of acquiring Twitter posts which contained hate speech. Text preprocessing and tokenization was performed, as well as outlier removal and class balancing. The proposed algorithm was used for determining hate speech index of users who posted tweets from the dataset. The preprocessed dataset was used for training and testing multiple machine learning models: k-means clustering without and with principal component analysis, naïve Bayes, decision tree and random forest. Four different feature subsets of the dataset were used for model training and testing. Anomaly detection, data transformation and parameter tuning were used in an attempt to improve classification accuracy. The highest F1 measure was achieved by training the model using a combination of user hate speech index and other user features. The results show that the usage of user hate speech index, with or without other user features, improves the accuracy of hate speech detection.

Vehicle routing problem as the generalization of the Travelling Salesman Problem (TSP) is one of the most studied optimization problems. Industry itself pays special attention to this problem, since transportation is one of the most crucial segments in supplying goods. This paper presents an innovative cluster-based approach for the successful solving of real-world vehicle routing problems that can involve extremely complex VRP problems with many customers needing to be served. The validation of the entire approach was based on the real data of a distribution company, with transport savings being in a range of 10-20 %. At the same time, the transportation routes are completely feasible, satisfying all the realistic constraints and conditions.

Identifying at-risk students is a crucial step in different learning settings. Predictive modeling technique can be used to create an early warning system which predicts students’ success in courses and informs both the teacher and the student of their performance. In this paper we describe a course-specific model for prediction of at-risk students. The proposed model uses the case-based reasoning (CBR) methodology to predict at-risk students at three specific points in time during the first half of the semester. In general, CBR is an approach of solving new problems based on solutions of similar previously experienced problem situation encoded in the form of cases. The proposed model classifies students as at-risk based on the most similar past cases retrieved from the casebase by using the k-NN algorithm. According to the experimental evaluation of the model accuracy, CBR model that is being developed for a specific course showed potential for an early prediction of at-risk students. Although the presented CBR model has been applied for one specific course, the key elements of predictive model can be easily reused by other courses.

In a traditional teaching models students are passive learners and usually share a common learning path that is in most cases predefined and fixed. In this paper we describe the case-based model for personalized learning path recommendation in example-based learning activities. According to the proposed model, the primary source of the knowledge for recommendation is represented in the form of cases that represent previously experienced learning paths. A recommended learning path comprises appropriate learning examples and learning resources for a student to go through in order to achieve desired learning outcomes. To validate the effectiveness of the proposed model, we conducted an experimentation to evaluate the impact of the model to student learning progress.

Collaborative filtering methods are widely accepted and used for item recommendation in various applications and domains. Their simplicity and ability to provide recommendations without the need fo...

The continually changing learner's mobile environment gives rise to many learning context. This paper describes a model of a case-based approach to context-aware content sequencing in mobile learning environments. The design of mobile learning teaching systems with adaptive content sequencing requires significant effort, since dependencies between educational characteristics of learning resources, learning context and learner characteristics are too complex. According to the proposed approach, an prototype of Mobile Finite Automata Tutoring (MoFAT) system using a context-aware case-based content sequencing is presented. The MoFAT's learning material organization has a hierarchical structure with four levels. A new content sequencing problem (a new case) is solved by finding a similar past case, and reusing it in the new content sequencing problem context.

A hybrid colour model is a colour descriptor formed by combining channels from several different colour models. Although rarely used in computer graphics applications due to redundancy, hybrid colour models may be of interest for the Content‐Based Image Retrieval (CBIR). Best features of each colour model can be combined to obtain optimal retrieval performance. This paper evaluates several approaches to the construction of a hybrid colour model that is used to construct a fuzzy colour histogram of image as a compact feature for retrieval. By evaluating each channel separately, a colour model named HSY is proposed. Various parameters of fuzzy histogram are further improved using Genetic algorithm (GA). Using standard data sets and the Average Normalized Modified Retrieval Rank (ANMRR) as a metric for retrieval performance, it is shown that this novel approach can give an improved retrieval performance.

Using contextual information in recommender systems is a subject of continuous improvement of rating prediction accuracy. Among others, information on temporal rating dynamics contain valuable data that establish foundation for discovering changes in both individual and group user's preferences. Such changes can be caused by multiple factors such as changes of individual user interests, changes in item popularity or other hidden patterns or events. In this paper an improved user-based collaborative filtering algorithm is presented that utilizes changes of group user's preferences over time. We also investigate temporal dynamics of changes in user's preferences within different item categories and propose time weight function that improves prediction accuracy of recommender systems.

Traditional recommender systems use collaborative filtering or content-based methods to recommend new items for users. New users and items are continuously updated to the system bringing changes in user's preferences, as well as the additional context in form of temporal information. The continuous system updates change not just individual user's preferences, but also group user's preferences affecting prediction of ratings for individual users. In this work is presented improved user-based collaborative filtering algorithm using temporal contextual information. With difference to other approaches, we propose using weight function based on changes in the group user's preferences over time that increases prediction accuracy of collaborative filtering prediction algorithm.

A hybrid color model is a color descriptor formed by combining different channels from several other color models. In computer graphics applications such models are rarely used due to redundancy. However, hybrid color models may be of interest for Content-Based Image Retrieval (CBIR). Best features of each color model can be combined to obtain optimum retrieval performance. In this paper, a novel algorithm is proposed for selection of channels for a hybrid color model used in construction of a fuzzy color histogram. This algorithm is elaborated and implemented for use with several common reference datasets consisting of photographs of natural scenes. Result of this experimental procedure is a new hybrid color model named HSY. Using standard datasets and a standard metric for retrieval performance (ANMRR), it is shown that this new model can give an improved retrieval performance. In addition, this model is of interest for use in JPEG compressed domain due to simpler calculation.

Energy efficiency became more relevant recently. This also includes the construction of energy efficient buildings in terms of heat conservation and dissipation. For analysing the energy efficiency several mapping algorithms are proposed that map indoor environments with added thermal information. Also, several algorithms that generate virtual 3D models are recently presented. One of the main parts of these algoritms are nearest neighbour searching techniques. There are several algorithms that enables the use of nearest neighbour (NN) search. In this paper we present the assessment of R-tree based NN queries in the problem of scalar field mapping that maps a measured temperatures onto reconstructed 3D-mesh of indoor environment. The mesh is reconstructed from the point cloud recorded with 3D laser scanner and thermal imaging camera. We present the performance analysis of the R-tree based NN search with different R-tree types. Also, we present the quality of the scalar field mapping produced with employed R-tree based NN search techniques.