Comparison morphisms and the Hochschild cohomology ring of truncated quiver algebras

A main contribution of this paper is the explicit construction of comparison morphisms between the standard bar resolution and Bardzell's minimal resolution for truncated quiver algebras (TQA's). As a direct application we describe explicitely the Yoneda product and derive several results on the structure of the cohomology ring of TQA's. For instance, we show that the product of odd degree cohomology classes is always zero. We prove that TQA's associated with quivers with no cycles or with neither sinks nor sources have trivial cohomology rings. On the other side we exhibit a fundamental example of a TQA with non trivial cohomology ring. Finaly, for truncated polyniomial algebras in one variable, we construct explicit cohomology classes in the bar resolution and give a full description of their cohomology ring.Comment: 32 pages, Final Versio

Correlating Extreme Weather Conditions With Road Traffic Safety: A Unified Latent Space Model

The presence of extreme weather conditions is known to expose drivers to a higher risk to incur in road accidents. Quantifying the correlation between adverse weather conditions and road traffic safety is useful for several reasons such as planning preventive actions, managing vehicle fleets, and configuring alerting systems. However, since the risk of road accidents occurrences within a specific spatial region is influenced by several factors other than the weather conditions, quantifying the actual impact of adverse weather phenomena regardless of the effect of weather-unrelated conditions can be challenging. To tackle the aforesaid issue, this paper proposes to adopt a unified latent space model based on time series embeddings. Firstly, it encodes a subset of historical series reporting weather-related accident occurrences in specific risky areas into the high-dimensional vector representation. It also encodes the weather element measurements acquired by meteorological stations spread over the analyzed area. Then, to estimate the risk level of each region within the same spatial context it seeks the temporal risk patterns that are most similar to those observed in risky areas. The experiments carried out in a real case study confirm the applicability of the proposed approach

Infrequent Weighted Itemset Mining Using Frequent Pattern Growth

Frequent weighted itemsets represent correlations frequently holding in data in which items may weight differently. However, in some contexts, e.g., when the need is to minimize a certain cost function, discovering rare data correlations is more interesting than mining frequent ones. This paper tackles the issue of discovering rare and weighted itemsets, i.e., the infrequent weighted itemset (IWI) mining problem. Two novel quality measures are proposed to drive the IWI mining process. Furthermore, two algorithms that perform IWI and Minimal IWI mining efficiently, driven by the proposed measures, are presented. Experimental results show efficiency and effectiveness of the proposed approach

Leveraging full-text article exploration for citation analysis

Scientific articles often include in-text citations quoting from external sources. When the cited source is an article, the citation context can be analyzed by exploring the article full-text. To quickly access the key information, researchers are often interested in identifying the sections of the cited article that are most pertinent to the text surrounding the citation in the citing article. This paper first performs a data-driven analysis of the correlation between the textual content of the sections of the cited article and the text snippet where the citation is placed. The results of the correlation analysis show that the title and abstract of the cited article are likely to include content highly similar to the citing snippet. However, the subsequent sections of the paper often include cited text snippets as well. Hence, there is a need to understand the extent to which an exploration of the full-text of the cited article would be beneficial to gain insights into the citing snippet, considering also the fact that the full-text access could be restricted. To this end, we then propose a classification approach to automatically predicting whether the cited snippets in the full-text of the paper contain a significant amount of new content beyond abstract and title. The proposed approach could support researchers in leveraging full-text article exploration for citation analysis. The experiments conducted on real scientific articles show promising results: the classifier has a 90% chance to correctly distinguish between the full-text exploration and only title and abstract cases

Predicting student academic performance by means of associative classification

The Learning Analytics community has recently paid particular attention to early predict learners’ performance. An established approach entails training classification models from past learner-related data in order to predict the exam success rate of a student well before the end of the course. Early predictions allow teachers to put in place targeted actions, e.g., supporting at-risk students to avoid exam failures or course dropouts. Although several machine learning and data mining solutions have been proposed to learn accurate predictors from past data, the interpretability and explainability of the best performing models is often limited. Therefore, in most cases, the reasons behind classifiers’ decisions remain unclear. This paper proposes an Explainable Learning Analytics solution to analyze learner-generated data acquired by our technical university, which relies on a blended learning model. It adopts classification techniques to early predict the success rate of about 5000 students who were enrolled in the first year courses of our university. It proposes to apply associative classifiers at different time points and to explore the characteristics of the models that led to assign pass or fail success rates. Thanks to their inherent interpretability, associative models can be manually explored by domain experts with the twofold aim at validating classifier outcomes through local rule-based explanations and identifying at-risk/successful student profiles by interpreting the global rule-based model. The results of an in-depth empirical evaluation demonstrate that associative models (i) perform as good as the best performing classification models, and (ii) give relevant insights into the per-student success rate assignments