No abstract

Programming online judges (POJs) are an emerging application scenario in e-learning recommendation areas. Specifically, they are e-learning tools usually used in programming practices for the automatic evaluation of source code developed by students when they are solving programming problems. Usually, they contain a large collection of such problems, to be solved by students at their own personalized pace. The more problems in the POJ the harder the selection of the right problem to solve according to previous users performance, causing information overload and a widespread discouragement. This paper presents a recommendation framework to mitigate this issue by suggesting problems to solve in programming online judges, through the use of fuzzy tools which manage the uncertainty related to this scenario. The evaluation of the proposal uses real data obtained from a programming online judge, and shows that the new approach improves previous recommendation strategies which do not consider uncertainty management in the programming online judge scenarios. Specifically, the best results were obtained for short recommendation lists.

The paper proposes a recommender system approach to cover online judge's domains. Online judges are e-learning tools that support the automatic evaluation of programming tasks done by individual users, and for this reason they are usually used for training students in programming contest and for supporting basic programming teachings. The proposal pretends to suggest problems assuming that a user must try to solve those problems already successfully solved by similar users. With this goal, the authors adopt the traditional collaborative filtering method with a new similarity measure adapted to the current domain, and the authors propose several transformations in the user-problem matrix to incorporate specific online judge's information. The authors evaluate the effect of the matrix configurations using Precision and Recall metrics, getting better results comparing with the authors method without transformations and with a representative state-of-art approach. Finally, the authors outline possible extensions to the current work.

The treatment of large data streams in the presence of concept drifts is one of the main challenges in the field of data mining, particularly when the algorithms have to deal with concepts that disappear and then reappear. This paper presents a new algorithm, called Fast Adapting Ensemble (FAE), which adapts very quickly to both abrupt and gradual concept drifts, and has been specifically designed to deal with recurring concepts. FAE processes the learning examples in blocks of the same size, but it does not have to wait for the batch to be complete in order to adapt its base classification mechanism. FAE incorporates a drift detector to improve the handling of abrupt concept drifts and stores a set of inactive classifiers that represent old concepts, which are activated very quickly when these concepts reappear. We compare our new algorithm with various well-known learning algorithms, taking into account, common benchmark datasets. The experiments show promising results from the proposed algorithm (regarding accuracy and runtime), handling different types of concept drifts.

Recommender systems are popular applications that help users to identify items that they could be interested in. A recent research area on recommender systems focuses on detecting several kinds of inconsistencies associated with the user preferences. However, the majority of previous works in this direction just process anomalies that are intentionally introduced by users. In contrast, this paper is centered on finding the way to remove non-malicious anomalies, specifically in collaborative filtering systems. A review of the state-of-the-art in this field shows that no previous work has been carried out for recommendation systems and general data mining scenarios, to exactly perform this preprocessing task. More specifically, in this paper we propose a method that is based on the extraction of knowledge from the dataset in the form of rating regularities (similar to frequent patterns), and their use in order to remove anomalous preferences provided by users. Experiments show that the application of the procedure as a preprocessing step improves the performance of a data-mining task associated with the recommendation and also effectively detects the anomalous preferences.

Flavonoids are bioactive compounds that can display antioxidant activity. Their must important source is the vegetal kingdom. Their composition in different foods is compiled into several databases organized by USDA. This information enabled the creation of a data record that was used in the work to predict the total antioxidant capacity of food by the oxygen radical absorbance capacity (ORAC) method, using algorithms of artificial intelligence. K-Nearest Neighbors (KNN) was used. The attributes were: a) amount of flavonoid, b) class of flavonoid, c) Trolox equivalent antioxidant capacity (TEAC) value, d) probability of clastogenicity and clastogenicity classification by Quantitative Structure-Activity Relationship (QSAR) method and e) total polyphenol (TP) value. The selected variable to predict was the ORAC value. For the prediction, a cross-validation method was used. For the KNN algorithm, the optimal K value was 3, making clear the importance of the similarity between objects for the success of the results. It was concluded the successful use of the KNN algorithm to predict the antioxidant capacity in the studied food groups.

No abstract

The growing increase in the amount and type of nutrients in food created the necessity for a more efficient use applied to dietetics and nutrition. Flavonoids are exogenous dietetic antioxidants and contribute to the total antioxidant capacity of the food. This paper aims to explore the data using different algorithms of artificial intelligence to find the one that best predict the total antioxidant capacity of food by the oxygen radical absorbance capacity (ORAC) method. A record of composition data based on the Database for the Flavonoid Content of Selected Foods and the Database for the Isoflavone Content of Selected Foods, was created. The KNN (K-Nearest Neighbors) and supervised unidirectional networks MLP (MultiLayer Perceptron) technics were used. The attributes were: a) amount of flavonoid (mean), b) class of flavonoid, c) Trolox equivalent antioxidant capacity (TEAC) value of each flavonoid, d) probability of clastogenicity and clastogenicity classification by Quantitative Structure-Activity Relationship (QSAR) method and e) total polyphenol (TP) value. The variable to predict the activities was the ORAC value. For the prediction, a cross-validation method was used. For the KNN algorithm the optimal K value was 3, making clear the importance of the similarity between objects for the success of the results. It was concluded the successful use of the MLP and KNN techniques to predict the antioxidant capacity in the studied food groups.