1,720,989 research outputs found
Episodic Memory with Believable Forgetting
Full text linkTitle: Episodic Memory with Believable Forgetting Author: Tomáš Soukup Department: Department of Software and Computer Science Education Supervisor: Mgr. Cyril Brom, Ph.D., DSCSE Abstract: Presented thesis introduces a model of episodic memory for virtual humans extended by believable forgetting based on rating of memories according to their importance. It is inspired by a psychological model of E. Tulving and experiments of W.A. Wagenaar and M. Linton and builds on a memory model for a human-like agent developed by Klára Pešková, which we modified for the needs of forgetting. Our model takes advantage of the level-of-detail approach to forget the parts of memories gradually. In addition to the age and particularity of memories, emotiveness is also considered during the rating of memories. A simple emotional model was created for this purpose. The functionality of our model was verified by implementing it into a prototype application, which simulates the life of a virtual human in a virtual world. Our experiments showed that the behavior of our model, when configured properly, corresponds with the psychological concepts. Keywords: episodical memory, forgetting, virtual human, emotion, believabilit
User Friendly Envioronment for Dynamic Bayesian Networks
Full text linkTitle: User Friendly Environment for Dynamic Bayesian Networks Author: Jan Vinárek Department: Department of Software and Computer Science Education Supervisor: Mgr. Rudolf Kadlec, Department of Software and Computer Science Education Abstract: For open source tools with the graphical interface which are focused on datamining and written in the Java language there is a small support for processing of sequential data. One of the most popular models used for processing of sequential data is the dynamic Bayesian network, with the use of its inference algorithms. The aim of the theoretical part of the thesis was to find a program which supports graphical interface for datamining with a simple control and library which imple- ments inference algorithms of dynamic Bayesian networks in the best way. The aim of the practical part was to design and to program the extension for the chosen program (RapidMiner) with the use of the found library (JSMILE). In the ex- tension the combination of uses of learning algorithm Expectation-Maximization and inference algorithm of dynamic Bayesian network was tested for prediction of sequential data. The combination was compared to the use of learning models Support Vector Machines and Decision Tree on two examples. Keywords: dynamic Bayesian network, sequential data, time series, Jav
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
DyBaNeM: Bayesian Model of Episodic Memory
No full textTitle: DyBaNeM: Bayesian Model of Episodic Memory Author: Mgr. Rudolf Kadlec E-mail: [email protected] Department: Department of Software and Computer Science Education Supervisor: Mgr. Cyril Brom, Ph.D. Department of Software and Computer Science Education Abstract: Artificial agents endowed with episodic (or autobiographic) memory systems have the abilities to remember and recall what happened to them in the past. The existing Episodic Memory (EM) models work as mere data-logs with indexes: they enable record, retrieval and delete operations, but rarely organize events in a hierarchical fashion, let alone abstract automatically detailed streams of "what has just happened" to a "gist of the episode." Consequently, the most interest- ing features of human EM, reconstructive memory retrieval, emergence of false memory phenomena, gradual forgetting and predicting surprising situations are out of their reach. In this work we introduce a computational framework for episodic memory modeling called DyBaNeM. DyBaNeM connects episodic mem- ory abilities and activity recognition algorithms and unites these two computer science themes in one framework. This framework can be conceived as a general architecture of episodic memory systems, it capitalizes on Bayesian statistics and, from the psychological..
Using reinforcement learning to learn how to play text-based games
No full textThe ability to learn optimal control policies in systems where action space is defined by sentences in natural language would allow many interesting real-world applications such as automatic optimisation of dialogue systems. Text-based games with multiple endings and rewards are a promising platform for this task, since their feedback allows us to employ reinforcement learning techniques to jointly learn text representations and control policies. We present a general text game playing agent, testing its generalisation and transfer learning performance and showing its ability to play multiple games at once. We also present pyfiction, an open-source library for universal access to different text games that could, together with our agent that implements its interface, serve as a baseline for future research
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
