This paper analyses student cross-assessment with a gender perspective in three different courses along several academic years in an engineering school in Spain. The aim of this study is to contribute to a wider understanding of the hidden effect of gender in higher education by testing if gender has any effect on the way students assess others and themselves. Previous research has demonstrated sex differences in self-image and self-confidence, which are both closely related to self-esteem and same- and other-sex esteem. It follows that gender effects might be expected in investigations on peer and self-assessment (PSA), as both techniques are closely linked to self-image and self-confidence. But a critical review of the literature on PSA with a gender perspective reveals that findings on this field are inconclusive, thus more research is needed. The analysis of the results of our study reveals that women judge themselves too harshly. As the literature on PSA does not show great concern for gender issues, some tentative suggestions are proposed to support PSA with gender-awareness sessions.

Potato quality control has improved in the last years thanks to automation techniques like machine vision, mainly making the classification task between different quality degrees faster, safer and less subjective. We present a system that classifies potatoes depending on their external defects and diseases. Firstly, some image processing techniques are used to segment and analyze the potatoes. Then, a classifier is used to decide the group the potato belongs to. For the feature selection task, we have designed an ad-hoc genetic algorithm which maximizes the classification percentage. This approach is used to perform an optimization in the search of the better feature combination. The system shows to be effective in real operation simulations (working with unwashed potatoes covered with dust and sand,), what seems to be a good starting point in the development of the system.

Social relationships in the digital sphere are becoming more usual and frequent, and they constitute a very important aspect for all of us. Violent interactions in this sphere are very frequent, and have serious effects on the victims. Within this global scenario, there is one kind of digital violence that is becoming really worrying: sexism against women. Sexist comments that are publicly posted in social media (newspaper comments, social networks, etc.), usually obtain a lot of attention and become viral, with consequent damage to the persons involved. In this paper, we introduce an anti-sexism alert system, based on natural language processing (NLP) and artificial intelligence (AI), that analyzes any public post, and decides if it could be considered a sexist comment or not. Additionally, this system also works on analyzing all the public comments linked to any multimedia content (piece of news, video, tweet, etc.) and decides, using a color-based system similar to traffic lights, if there is sexism in the global set of posts. We have created a labeled data set in Spanish, since the majority of studies focus on English, to train our system, which offers a very good performance after the validation experiments.