Griddly: A platform for AI research in games

In recent years, there have been immense breakthroughs in Game AI research, particularly with Reinforcement Learning (RL). Despite their success, the underlying games are usually implemented with their own preset environments and game mechanics, thus making it difficult for researchers to prototype different game environments… However, testing the RL agents against a variety of game environments is critical for recent effort to study generalization in RL and avoid the problem of overfitting that may otherwise occur. In this paper, we […]

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How to Plot Inline and With Qt – Matplotlib with IPython/Jupyter Notebooks

Introduction There are a number of different data visualization libraries for Python. Out of all of the libraries, however, Matplotlib is easily the most popular and widely used one. With Matplotlib you can create both simple and complex visualizations. Jupyter notebooks are one of the most popular methods of sharing data science and data analysis projects, code, and visualization. Although you may know how to visualize data with Matplotlib, you may not know how to use Matplotlib in a Jupyter […]

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How to Concatenate Two Lists in Python

Introduction List concatenation the act of creating a single list from multiple smaller lists by daisy chaining them together. There are many ways of concatenating lists in Python. Specifically, in this article, we’ll be going over how to concatenate two lists in Python using the plus operator, unpack operator, multiply operator, manual for loop concatenation, the itertools.chain() function and the inbuilt list method extend(). In all the code snippets below, we’ll make use of the following lists: list_a = [1, […]

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Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting

Deep learning is often criticized by two serious issues which rarely exist in natural nervous systems: overfitting and catastrophic forgetting. It can even memorize randomly labelled data, which has little knowledge behind the instance-label pairs… When a deep network continually learns over time by accommodating new tasks, it usually quickly overwrites the knowledge learned from previous tasks. Referred to as the neural variability, it is well-known in neuroscience that human brain reactions exhibit substantial variability even in response to the […]

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Analyzing Neural Discourse Coherence Models

In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation. We devise two datasets of various linguistic alterations that undermine coherence and test model sensitivity to changes in syntax and semantics… We furthermore probe discourse embedding space and examine the knowledge that is encoded in representations of coherence. We hope this study shall provide further insight into how to frame the task and improve models of coherence assessment further. […]

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Turning Transport Data to Comply with EU Standards while Enabling a Multimodal Transport Knowledge Graph

Complying with the EU Regulation on multimodal transportation services requires sharing data on the National Access Points in one of the standards (e.g., NeTEx and SIRI) indicated by the European Commission. These standards are complex and of limited practical adoption… This means that datasets are natively expressed in other formats and require a data translation process for full compliance. This paper describes the solution to turn the authoritative data of three different transport stakeholders from Italy and Spain into a […]

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Exploiting Cross-Dialectal Gold Syntax for Low-Resource Historical Languages: Towards a Generic Parser for Pre-Modern Slavic

This paper explores the possibility of improving the performance of specialized parsers for pre-modern Slavic by training them on data from different related varieties. Because of their linguistic heterogeneity, pre-modern Slavic varieties are treated as low-resource historical languages, whereby cross-dialectal treebank data may be exploited to overcome data scarcity and attempt the training of a variety-agnostic parser… Previous experiments on early Slavic dependency parsing are discussed, particularly with regard to their ability to tackle different orthographic, regional and stylistic features. […]

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Theoretical Knowledge Graph Reasoning via Ending Anchored Rules

Discovering precise and specific rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Language Processing research topics. In this paper, we provide a fundamental theory for knowledge graph reasoning based on ending anchored rules… Our theory provides precise reasons answering why or why not a triple is correct. Then, we implement our theory by what we called the EARDict model. Results […]

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Large-Scale Manual Validation of Bug Fixing Commits: A Fine-grained Analysis of Tangling

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs… Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes […]

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