Visual and Textual Programming Languages: A Systematic Review of the Literature

It is well documented, and has been the topic of much research, that Computer Science courses tend to have higher than average drop out rates at third level. This is a problem that needs to be addressed with urgency but also caution. The required number of Computer Science graduates is growing every year but the number of graduates is not meeting this demand and one way that this problem can be alleviated is to encourage students at an early age towards studying Computer Science courses. This paper presents a systematic literature review on the role of visual and textual programming languages when learning to program, particularly as a first programming language. The approach is systematic, in that a structured search of electronic resources has been conducted, and the results are presented and quantitatively analysed. This study will give insight into whether or not the current approaches to teaching young learners programming are viable, and examines what we can do to increase the interest and retention of these students as they progress through their education.Comment: 18 pages (including 2 bibliography pages), 3 figure

Metropolis-Hastings within Partially Collapsed Gibbs Samplers

The Partially Collapsed Gibbs (PCG) sampler offers a new strategy for improving the convergence of a Gibbs sampler. PCG achieves faster convergence by reducing the conditioning in some of the draws of its parent Gibbs sampler. Although this can significantly improve convergence, care must be taken to ensure that the stationary distribution is preserved. The conditional distributions sampled in a PCG sampler may be incompatible and permuting their order may upset the stationary distribution of the chain. Extra care must be taken when Metropolis-Hastings (MH) updates are used in some or all of the updates. Reducing the conditioning in an MH within Gibbs sampler can change the stationary distribution, even when the PCG sampler would work perfectly if MH were not used. In fact, a number of samplers of this sort that have been advocated in the literature do not actually have the target stationary distributions. In this article, we illustrate the challenges that may arise when using MH within a PCG sampler and develop a general strategy for using such updates while maintaining the desired stationary distribution. Theoretical arguments provide guidance when choosing between different MH within PCG sampling schemes. Finally we illustrate the MH within PCG sampler and its computational advantage using several examples from our applied work

Team Fernando-Pessa at SemEval-2019 Task 4: back to basics in Hyperpartisan News Detection

This paper describes our submission1 to the SemEval 2019 Hyperpartisan News Detection task. Our system aims for a linguistics-based document classification from a minimal set of interpretable features, while maintaining good performance. To this goal, we follow a feature-based approach and perform several experiments with different machine learning classifiers. On the main task, our model achieved an accuracy of 71.7%, which was improved after the task's end to 72.9%. We also participate in the meta-learning sub-task, for classifying documents with the binary classifications of all submitted systems as input, achieving an accuracy of 89.9%

Beyond Toxic: Toxicity Detection Datasets are Not Enough for Brand Safety

The rapid growth in user generated content on social media has resulted in a significant rise in demand for automated content moderation. Various methods and frameworks have been proposed for the tasks of hate speech detection and toxic comment classification. In this work, we combine common datasets to extend these tasks to brand safety. Brand safety aims to protect commercial branding by identifying contexts where advertisements should not appear and covers not only toxicity, but also other potentially harmful content. As these datasets contain different label sets, we approach the overall problem as a binary classification task. We demonstrate the need for building brand safety specific datasets via the application of common toxicity detection datasets to a subset of brand safety and empirically analyze the effects of weighted sampling strategies in text classification

Overcoming mental blocks:A blocks-based approach to experience sampling studies

Experience Sampling Method (ESM) studies repeatedly survey participants on their behaviours and experiences as they go about their everyday lives. Smartphones afford an ideal platform for ESM study applications as devices seldom leave their users, and can automatically sense surrounding context to augment subjective survey responses. ESM studies are employed in fields such as psychology and social science where researchers are not necessarily programmers and require tools for application creation. Previous tools using web forms, text files, or flowchart paradigms are either insufficient to model the potential complexity of study protocols, or fail to provide a low threshold to entry. We demonstrate that blocks programming simultaneously lowers the barriers to creating simple study protocols, while enabling the creation of increasingly sophisticated protocols. We discuss the design of Jeeves, our blocks-based environment for ESM studies, and explain advantages that blocks afford in ESM study design.Postprin