Prilagodljivi računalniški sistem za priporočanje učnih objektov v konstruktivističnem učnem okolju – ALECA

Today there are increasingly more learning environments which support active learning, taking into account student characteristics, preferences and activities. In this paper, we present a concept of a learning recommender system, which combines knowledge from pedagogy and recommending systems. We analyse the influence of combining different learning styles models on preferred types of multimedia materials. The results reveal that students prefer well-structured learning texts with color discrimination, and that the hemispheric learning style model is the most important criterion in determining student preferences for different multimedia learning materials. In the second part of our research, we describe an approach to alleviating the new user problem in terms of better recommendation accuracy of the system for recommending learning materials in environments where the system has no prior information about learners. Our findings present the concept of an adaptive learning system, with an analysis of its possible effects in learning practice.Dandanes se pojavlja vse več učnih sistemov, ki podpirajo aktivno učenje in upoštevajo učenčeve učne lastnosti, značilnosti in aktivnosti. V prispevku predstavljamo zasnovo učnega priporočilnega sistema, ki združuje znanja pedagogike in računalniških priporočilnih algoritmov. Proučujemo, kako združevanje modelov učnih stilov vpliva na izbiro različnih tipov večpredstavnih učnih gradiv. Rezultati kažejo, da študentje za učenje najpogosteje uporabljajo dobro strukturirana učna gradiva, ki vsebujejo barvno diskriminacijo, in da je hemisferični model učnih stilov najpomembnejši odločitveni kriterij. V nadaljevanju opisujemo postopek za reševanje t. i. problema hladnega zagona, s katerim je mogoče izboljšati točnost sistema za priporočanje učnih gradiv v okoljih, kjer o učencih nimamo predhodnih podatkov. Namen prispevka je predstaviti idejno zasnovo prilagodljivega učnega sistema z analizo njegovih predvidenih učinkov na učno prakso

Discovering motion hierarchies via tree-structured coding of trajectories

International audienceThe dynamic content of physical scenes is largely compositional, that is, the movements of the objects and of their parts are hierarchically organised and relate through composition along this hierarchy. This structure also prevails in the apparent 2D motion that a video captures. Accessing this visual motion hierarchy is important to get a better understanding of dynamic scenes and is useful for video manipulation. We propose to capture it through learned, tree-structured sparse coding of point trajectories. We leverage this new representation within an unsupervised clustering scheme to partition hierarchically the trajectories into meaningful groups. We show through experiments on motion capture data that our model is able to extract moving segments along with their organisation. We also present competitive results on the task of segmenting objects in video sequences from trajectories

Semi and weighted semi-nonnegative matrix factorization : comparative study

Orientador: Jacques WainerDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Algoritmos que envolvem fatoração de matrizes tem sido objeto de intensos estudos nos anos recentes, gerando uma ampla variedade de técnicas e aplicações para diversos tipos de problemas. Dada uma matriz de dados de entrada X, a forma mais simples do problema de fatoração de matrizes pode ser definido como a tarefa de encontrar as matrizes F e G, usualmente com posto baixo, tal que X ~ FG. São consideradas duas variações principais do problema de fatoração de matrizes: a fatoração de matrizes semi-não-negativa (Semi Nonnegative Matrix Factorization (SNMF) ), que requer que a matriz G seja não-negativa, e a fatoração de matrizes semi-não-negativa com pesos ( Weighted Nonnegative Matriz Factorization(WSNMF) ), que lida adicionalmente com casos onde há dados de entrada faltantes ou incertos. Essa dissertação tem como principal objetivo comparar diferentes algoritmos e estratégias para resolver esses problemas, focando em duas estratégias principais: Mínimos Quadrados Alternado com Restrição Constrained Alternating Least Squares e Atualização Multiplicativa Multiplicative UpdatesAbstract: Algorithms that involve matrix factorization have been the object of intense study in the recent years, generating a wide range of techniques and applications for many different problems. Given an input data matrix X, the simplest matrix factorization problem can be defined as the task to find matrices F and G, usually of low rank, such that X ? F G. I consider two different variations of the matrix factorization problem, the Semi- Nonnegative Matrix Factorization, which requires the matrix G to be nonnegative, and the Weighted Semi-Nonnegative Matrix Factorization, which deals additionally with cases where the input data has missing or uncertain values. This dissertation aims to compare different algorithms and strategies to solve these problems, focusing on two main strategies: Constrained Alternating Least Squares and Multiplicative UpdatesMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

A model of an adaptive system for recommending learning objects in a constructivist learning environment

Computer-based multimedia learning environments support the idea that people learn better and more deeply when appropriate pictures (i.e., animations, video, static graphics) are added to text or narration. There are many adaptive learning systems that adapt learning materials to student properties, preferences, and activities. Adaptive learning environments mostly support only traditional concepts of learning. There is a need to design and develop an e-learning system that embodies principles of constructivist learning approach. The solution is in recommenders systems, which suggest items of interest to users based on their preferences (i.e. previous ratings). If there are no ratings for a certain user or item/object, there is a situation called a cold start problem, which leads to unreliable recommendations. Researchers mostly avoid tackling the absolute cold start in recommender systems. The topic of presented dissertation is designing a recommender system with a novel approach to avoid cold start problem. Approaches for solving the new user cold start problem can be divided into two main groups: the first group performs additional inquiries to gather more information about the users; and the second group uses dedicated algorithms for users in the cold start state. The first group of approaches aims at performing additional inquiries about the user. According to this approach, we relate combinations of different learning styles (taking into account four different learning styles models) to preferred multimedia types. We explore a decision model aimed at proposing learning material of an appropriate multimedia type. The study includes 272 student participants. The resulting decision model shows that students prefer well-structured learning texts with colour discrimination, and that the hemispheric learning style model is the most important criterion in deciding student preferences for different multimedia learning materials. To provide a more accurate and reliable model for recommending different multimedia types more learning style models must be combined. Kolb’s classification and the VAK classification allow us to learn if students prefer an active role in the learning process, and what multimedia type they prefer. The results also shows that there is an obvious need to combine learning styles model in order to get a wider view of the student’s characteristics: an approach to problem solving problems, cognitive modes, way of thinking, and a dominant mode of perceiving information. On another hand, model recommends same multimedia material regardless of the learning topic. In the second part of our research, we have designed and developed a novel approach for alleviating the cold start problem by imputing missing values into the input matrix, thereby improving recommendation performance. Our approach has three steps: 1) finding similar users to given user in cold start state; 2) selecting relevant attributes for the imputation process; 3) aggregate ratings to input matrix for a user in the cold start state. We separate our approach for solving cold start problem into solving absolute cold start problem and solving partial cold start problem. According to the results of our experiments (solving absolute cold start problem), the results indicate that all our proposed methods improve recommending for non-negative matrix factorization with stochastic gradient descent (NG). For semi-non-negative matrix factorization with missing data (SN), combinations FR-ME (imputing attribute's mean value into the attributes that have the highest frequency of the most frequent values) and SD-MF (imputing attribute’s most frequent value into attributes that have the lowest standard deviation) improve recommendations for users in the absolute cold start state. For non-negative matrix factorization with alternating least squares (NS) and matrix factorization by data fusion (DF), none of variations of proposed parameters (methods) improves recommending in absolute cold start state. In the next stage of our research, we evaluated our approach for solving partial cold start problem. Statistical analysis of experimental evaluation of our approach on the artificial domain showed that each parameter significantly improved recommending of matrix factorization methods. The methods that yield improvements in recommendation accuracy compared with the raw matrix factorization are methods that consider 25 % of similar users ($25$-*-*-*), select an attribute according to the frequency (*-FR-*-*) or RReliefF (*-RR-*-*), and impute a value aggregated by mean value (ME) or predicted by using regression trees (RT). For further investigation we chose two method combinations (25-FR-ME-* and 25-RR-RT-*), which were expected to work well, and compared them with other strategies on real domains. Among all approaches evaluated on the artificial domain, we chose the best performing method with the highest average rank – a method that considers 50 % of similar users, selects an attribute for imputation according to the RReliefF, and imputes a value predicted by linear regression (50-RR-LR-*). All three combinations of the selected methods were evaluated on two real domains: Jester in PEFbase. An evaluation showed that method 25-FR-ME-* combined with matrix factorization NG performed statistically better than the raw matrix factorization algorithms (DF, NG, NS in SN) on real domains for users in the partial cold-start state. The results demonstrated the advantage of using imputation approaches in terms of better recommendation accuracy. At the same time, the results have shown that imputing of missing values has no negative impact for recommending to the users, which are not in the cold start state

Robust motion segmentation with subspace constraints

Motion segmentation is an important task in computer vision with many applications such as dynamic scene understanding and multi-body structure from motion. When the point correspondences across frames are given, motion segmentation can be addressed as a subspace clustering problem under an affine camera model. In the first two parts of this thesis, we target the general subspace clustering problem and propose two novel methods, namely Efficient Dense Subspace Clustering (EDSC) and the Robust Shape Interaction Matrix (RSIM) method. Instead of following the standard compressive sensing approach, in EDSC we formulate subspace clustering as a Frobenius norm minimization problem, which inherently yields denser connections between data points. While in the noise-free case we rely on the self-expressiveness of the observations, in the presence of noise we recover a clean dictionary to represent the data. Our formulation lets us solve the subspace clustering problem efficiently. More specifically, for outlier-free observations, the solution can be obtained in closed-form, and in the presence of outliers, we solve the problem by performing a series of linear operations. Furthermore, we show that our Frobenius norm formulation shares the same solution as the popular nuclear norm minimization approach when the data is free of any noise. In RSIM, we revisit the Shape Interaction Matrix (SIM) method, one of the earliest approaches for motion segmentation (or subspace clustering), and reveal its connections to several recent subspace clustering methods. We derive a simple, yet effective algorithm to robustify the SIM method and make it applicable to real-world scenarios where the data is corrupted by noise. We validate the proposed method by intuitive examples and justify it with the matrix perturbation theory. Moreover, we show that RSIM can be extended to handle missing data with a Grassmannian gradient descent method. The above subspace clustering methods work well for motion segmentation, yet they require that point trajectories across frames are known {\it a priori}. However, finding point correspondences is in itself a challenging task. Existing approaches tackle the correspondence estimation and motion segmentation problems separately. In the third part of this thesis, given a set of feature points detected in each frame of the sequence, we develop an approach which simultaneously performs motion segmentation and finds point correspondences across the frames. We formulate this problem in terms of Partial Permutation Matrices (PPMs) and aim to match feature descriptors while simultaneously encouraging point trajectories to satisfy subspace constraints. This lets us handle outliers in both point locations and feature appearance. The resulting optimization problem is solved via the Alternating Direction Method of Multipliers (ADMM), where each subproblem has an efficient solution. In particular, we show that most of the subproblems can be solved in closed-form, and one binary assignment subproblem can be solved by the Hungarian algorithm. Obtaining reliable feature tracks in a frame-by-frame manner is desirable in applications such as online motion segmentation. In the final part of the thesis, we introduce a novel multi-body feature tracker that exploits a multi-body rigidity assumption to improve tracking robustness under a general perspective camera model. A conventional approach to addressing this problem would consist of alternating between solving two subtasks: motion segmentation and feature tracking under rigidity constraints for each segment. This approach, however, requires knowing the number of motions, as well as assigning points to motion groups, which is typically sensitive to motion estimates. By contrast, we introduce a segmentation-free solution to multi-body feature tracking that bypasses the motion assignment step and reduces to solving a series of subproblems with closed-form solutions. In summary, in this thesis, we exploit the powerful subspace constraints and develop robust motion segmentation methods in different challenging scenarios where the trajectories are either given as input, or unknown beforehand. We also present a general robust multi-body feature tracker which can be used as the first step of motion segmentation to get reliable trajectories