Et Cetera

Et Cetera is woven together with five works that are essentially five bodies of writings as digital poetry -- a poetic practice that is made possible by digital media and technology in which aesthetic possibilities are extended through the semantic impact of data, alphabets, visuals, sound, etc. Interlaced by multimedial meaning-making, Et Cetera re(produces) installations that are engineered with algorithmic materials utilizing real-time data feeds, animated letterforms, performative instructions and sensory synthesis. Exploring different scenarios of human-machine coupling that consequently lead to multifarious illegibilities, Et Cetera amplifies the noise of information overflow in the concurrent mediascape with its rhizomatic networks largely beyond human conscious apprehension. On the B-side, Et Cetera is also involved with writing about the alphabetic writing apparatus, the role of artist as author as human-machine-centaur and networked subjectivity

ToyArchitecture: Unsupervised Learning of Interpretable Models of the World

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usually uncomputable, incompatible with theories of biological intelligence, or lack practical implementations. The goal of this work is to combine the main advantages of the two: to follow a big picture view, while providing a particular theory and its implementation. In contrast with purely theoretical approaches, the resulting architecture should be usable in realistic settings, but also form the core of a framework containing all the basic mechanisms, into which it should be easier to integrate additional required functionality. In this paper, we present a novel, purposely simple, and interpretable hierarchical architecture which combines multiple different mechanisms into one system: unsupervised learning of a model of the world, learning the influence of one's own actions on the world, model-based reinforcement learning, hierarchical planning and plan execution, and symbolic/sub-symbolic integration in general. The learned model is stored in the form of hierarchical representations with the following properties: 1) they are increasingly more abstract, but can retain details when needed, and 2) they are easy to manipulate in their local and symbolic-like form, thus also allowing one to observe the learning process at each level of abstraction. On all levels of the system, the representation of the data can be interpreted in both a symbolic and a sub-symbolic manner. This enables the architecture to learn efficiently using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl

Practical, appropriate, empirically-validated guidelines for designing educational games

There has recently been a great deal of interest in the potential of computer games to function as innovative educational tools. However, there is very little evidence of games fulfilling that potential. Indeed, the process of merging the disparate goals of education and games design appears problematic, and there are currently no practical guidelines for how to do so in a coherent manner. In this paper, we describe the successful, empirically validated teaching methods developed by behavioural psychologists and point out how they are uniquely suited to take advantage of the benefits that games offer to education. We conclude by proposing some practical steps for designing educational games, based on the techniques of Applied Behaviour Analysis. It is intended that this paper can both focus educational games designers on the features of games that are genuinely useful for education, and also introduce a successful form of teaching that this audience may not yet be familiar with

Editorial: Rendering Research

To render is to give something “cause to be” or “hand over” (from the Latin reddere “give back”) and enter into an obligation to do or make something like a decision. More familiar perhaps in computing, to render is to take an image or file and convert it into another format or apply a modification of some kind; or in the case of 3D animation or scanning, to render is to animate it or give it volume. In this issue, we ask, what does it mean to render research? How does the rendering of research reinforce certain limitations of thought and action? We ask these questions in the context of more and more demands on researchers to produce academic outputs in standardised forms, in peer-reviewed journals and such like that are legitimised by normative values. So, then, how to render research otherwise

Local feature selection for multiple instance learning with applications.

Feature selection is a data processing approach that has been successfully and effectively used in developing machine learning algorithms for various applications. It has been proven to effectively reduce the dimensionality of the data and increase the accuracy and interpretability of machine learning algorithms. Conventional feature selection algorithms assume that there is an optimal global subset of features for the whole sample space. Thus, only one global subset of relevant features is learned. An alternative approach is based on the concept of Local Feature Selection (LFS), where each training sample can have its own subset of relevant features. Multiple Instance Learning (MIL) is a variation of traditional supervised learning, also known as single instance learning. In MIL, each object is represented by a set of instances, or a bag. While bags are labeled, the labels of their instances are unknown. The ambiguity of the instance labels makes the feature selection for MIL challenging. Although feature selection in traditional supervised learning has been researched extensively, there are only a few methods for the MIL framework. Moreover, localized feature selection for MIL has not been researched. This dissertation focuses on developing a local feature selection method for the MIL framework. Our algorithm, called Multiple Instance Local Salient Feature Selection (MI-LSFS), searches the feature space to find the relevant features within each bag. We also propose a new multiple instance classification algorithm, called MILES-LFS, that integrates information learned by MI-LSFS during the feature selection process to identify a reduced subset of representative bags and instances. We show that using a more focused subset of prototypes can improve the performance while significantly reducing the computational complexity. Other applications of the proposed MI-LSFS include a new method that uses our MI-LSFS algorithm to explore and investigate the features learned by a Convolutional Neural Network (CNN) model; a visualization method for CNN models, called Gradient-weighted Sample Activation Map (Grad-SAM), that uses the locally learned features of each sample to highlight their relevant and salient parts, and a novel explanation method, called Classifier Explanation by Local Feature Selection (CE-LFS), to explain the decisions of trained models. The proposed MI-LSFS and its applications are validated using several synthetic and real data sets. We report and compare quantitative measures such as Rand Index, Area Under Curve (AUC), and accuracy. We also provide qualitative measures by visualizing and interpreting the selected features and their effects