Computer Science

Deep-predictive-coding networks (DPCNs) are hierarchical, generative models. They rely on feed-forward and feed-back connections to modulate latent feature representations of stimuli in a dynamic and context-sensitive manner. A crucial element of DPCNs is a forward-backward inference procedure to uncover sparse, invariant features. However, this inference is a major computational bottleneck. It severely limits the network depth due to learning stagnation. Here, we prove why this bottleneck occurs. We then propose a new forward-inference strategy based on accelerated proximal gradients. This strategy has faster theoretical convergence guarantees than the one used for DPCNs. It overcomes learning stagnation. We also demonstrate that it permits constructing deep and wide predictive-coding networks. Such convolutional networks implement receptive fields that capture well the entire classes of objects on which the networks are trained. This improves the feature representations compared with our lab's previous non-convolutional and convolutional DPCNs. It yields unsupervised object recognition that surpass convolutional autoencoders and are on par with convolutional networks trained in a supervised manner.

In this paper, we introduce an optimization problem posed by the Music Building Eindhoven (MBE) to deal with the economical consequences of the COVID-19 pandemic for theatre halls. We propose a model for maximizing the number of guests in a theatre hall that respects social distancing rules, and is based on trapezoid packings. Computational results show that up to 40% of the normal capacity can be used for a single show setting, and up to 70% in case artists opt for two consecutive performances per evening.

Employing multiple workers to label data for machine learning models has become increasingly important in recent years with greater demand to collect huge volumes of labelled data to train complex models while mitigating the risk of incorrect and noisy labelling. Whether it is large scale data gathering on popular crowd-sourcing platforms or smaller sets of workers in high-expertise labelling exercises, there are various methods recommended to gather a consensus from employed workers and establish ground-truth labels. However, there is very little research on how the various parameters of a labelling task can impact said methods. These parameters include the number of workers, worker expertise, number of labels in a taxonomy and sample size. In this paper, Majority Vote, CrowdTruth and Binomial Expectation Maximisation are investigated against the permutations of these parameters in order to provide better understanding of the parameter settings to give an advantage in ground-truth inference. Findings show that both Expectation Maximisation and CrowdTruth are only likely to give an advantage over majority vote under certain parameter conditions, while there are many cases where the methods can be shown to have no major impact. Guidance is given as to what parameters methods work best under, while the experimental framework provides a way of testing other established methods and also testing new methods that can attempt to provide advantageous performance where the methods in this paper did not. A greater level of understanding regarding optimal crowd-sourcing parameters is also achieved.

The formal semantics of an interpreted first-order logic (FOL) statement can be given in Tarskian Semantics or a basically equivalent Game Semantics. The latter maps the statement and the interpretation into a two-player semantic game. Many combinatorial problems can be described using interpreted FOL statements and can be mapped into a semantic game. Therefore, learning to play a semantic game perfectly leads to the solution of a specific instance of a combinatorial problem. We adapt the AlphaZero algorithm so that it becomes better at learning to play semantic games that have different characteristics than Go and Chess. We propose a general framework, Persephone, to map the FOL description of a combinatorial problem to a semantic game so that it can be solved through a neural MCTS based reinforcement learning algorithm. Our goal for Persephone is to make it tabula-rasa, mapping a problem stated in interpreted FOL to a solution without human intervention.

The notion of concept has been studied for centuries, by philosophers, linguists, cognitive scientists, and researchers in artificial intelligence (Margolis & Laurence, 1999). There is a large literature on formal, mathematical models of concepts, including a whole sub-field of AI -- Formal Concept Analysis -- devoted to this topic (Ganter & Obiedkov, 2016). Recently, researchers in machine learning have begun to investigate how methods from representation learning can be used to induce concepts from raw perceptual data (Higgins, Sonnerat, et al., 2018). The goal of this report is to provide a formal account of concepts which is compatible with this latest work in deep learning. The main technical goal of this report is to show how techniques from representation learning can be married with a lattice-theoretic formulation of conceptual spaces. The mathematics of partial orders and lattices is a standard tool for modelling conceptual spaces (Ch.2, Mitchell (1997), Ganter and Obiedkov (2016)); however, there is no formal work that we are aware of which defines a conceptual lattice on top of a representation that is induced using unsupervised deep learning (Goodfellow et al., 2016). The advantages of partially-ordered lattice structures are that these provide natural mechanisms for use in concept discovery algorithms, through the meets and joins of the lattice.

The previous works on formalizing enterprise application integration (EAI) scenarios showed an emerging need for setting up formal foundations for integration patterns, the EAI building blocks, in order to facilitate the model-driven development and ensure its correctness. So far, the formalization requirements were focusing on more "conventional" integration scenarios, in which control-flow, transactional persistent data and time aspects were considered. However, none of these works took into consideration another arising EAI trend that covers social and multimedia computing. In this work we propose a Petri net-based formalism that addresses requirements arising from the multimedia domain. We also demonstrate realizations of one of the most frequently used multimedia patterns and discuss which implications our formal proposal may bring into the area of the multimedia EAI development.

This work is the first to propose the concept of a semantic numeration system (SNS) as a certain class of context-based numeration methods. The development of the SNS concept required the introduction of fundamentally new concepts such as a cardinal abstract entity, a cardinal semantic operator, a cardinal abstract object, a numeration space. The main attention is paid to the key elements of semantic numeration systems - cardinal semantic operators. A classification of semantic numeration systems is given.

With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students' knowledge status and predicts their performance on new questions. Questions are often numerous in online education systems, and are always associated with much fewer skills. However, the previous literature fails to involve question information together with high-order question-skill correlations, which is mostly limited by data sparsity and multi-skill problems. From the model perspective, previous models can hardly capture the long-term dependency of student exercise history, and cannot model the interactions between student-questions, and student-skills in a consistent way. In this paper, we propose a Graph-based Interaction model for Knowledge Tracing (GIKT) to tackle the above probems. More specifically, GIKT utilizes graph convolutional network (GCN) to substantially incorporate question-skill correlations via embedding propagation. Besides, considering that relevant questions are usually scattered throughout the exercise history, and that question and skill are just different instantiations of knowledge, GIKT generalizes the degree of students' master of the question to the interactions between the student's current state, the student's history related exercises, the target question, and related skills. Experiments on three datasets demonstrate that GIKT achieves the new state-of-the-art performance, with at least 1% absolute AUC improvement.

We present a new concept called Game Mechanic Alignment theory as a way to organize game mechanics through the lens of environmental rewards and intrinsic player motivations. By disentangling player and environmental influences, mechanics may be better identified for use in an automated tutorial generation system, which could tailor tutorials for a particular playstyle or player. Within, we apply this theory to several well-known games to demonstrate how designers can benefit from it, we describe a methodology for how to estimate mechanic alignment, and we apply this methodology on multiple games in the GVGAI framework. We discuss how effectively this estimation captures intrinsic/extrinsic rewards and how our theory could be used as an alternative to critical mechanic discovery methods for tutorial generation.

According to GHO (Global Health Observatory (GHO), the high prevalence of a large variety of diseases such as Ischaemic heart disease, stroke, lung cancer disease and lower respiratory infections have remained the top killers during the past decade. The growth in the number of mortalities caused by these disease is due to the very delayed symptoms'detection. Since in the early stages, the symptoms are insignificant and similar to those of benign diseases (e.g. the flu ), and we can only detect the disease at an advanced stage. In addition, The high frequency of improper practices that are harmful to health, the hereditary factors, and the stressful living conditions can increase the death rates. Many researches dealt with these fatal disease, and most of them applied advantage machine learning models to deal with image diagnosis. However the drawback is that imagery permit only to detect disease at a very delayed stage and then patient can hardly be saved. In this Paper we present our new approach "DeepLCP" to predict fatal diseases that threaten people's lives. It's mainly based on raw and heterogeneous data of the concerned (or under-tested) person. "DeepLCP" results of a combination combination of the Natural Language Processing (NLP) and the deep learning paradigm.The experimental results of the proposed model in the case of Lung cancer prediction have approved high accuracy and a low loss data rate during the validation of the disease prediction.