Automatic short answer grading (ASAG), a hot field of natural language understanding, is a research area within learning analytics. ASAG solutions are conceived to offload teachers and instructors, especially those in higher education, where classes with hundreds of students are the norm and the task of grading (short)answers to open-ended questionnaires becomes tougher. Their outcomes are precious both for the very grading and for providing students with “ad hoc” feedback. ASAG proposals have also enabled different intelligent tutoring systems. Over the years, a variety of ASAG solutions have been proposed, still there are a series of gaps in the literature that we fill in this paper. The present work proposes GradeAid, a framework for ASAG. It is based on the joint analysis of lexical and semantic features of the students’ answers through state-of-the-art regressors; differently from any other previous work, (i) it copes with non-English datasets, (ii) it has undergone a robust validation and benchmarking phase, and (iii) it has been tested on every dataset publicly available and on a new dataset (now available for researchers). GradeAid obtains performance comparable to the systems presented in the literature (root-mean-squared errors down to 0.25 based on the specific tuple $$\langle $$⟨ dataset-question $$\rangle $$⟩ ). We argue it represents a strong baseline for further developments in the field.

Terms of Service (ToS) are fundamental factors in the creation of physical as well as online legally relevant relationships. They not only define mutual rights and obligations but also inform users about contract key issues that, in online settings, span from liability limitations to data management and processing conditions. Despite their crucial role, however, ToS are often neglected by users that frequently accept without even reading what they agree upon, representing a critical issue when there exist potentially unfair clauses. To enhance users’ awareness and uphold legal safeguards, we first propose a definition of ToS unfairness based on a novel unfairness measure computed counting the unfair clauses contained in a ToS, and therefore, weighted according to their direct impact on the customers concrete interests. Secondly, we introduce a novel machine learning-based approach to classify ToS clauses, represented by using sentence embedding, in different categories classes and fairness levels. Results of a test involving well-known machine learning models show that Support Vector Machine is able to classify clauses into categories with a F1-score of 86% outperforming state-of-the-art methods, while Random Forest is able to classify clauses into fairness levels with a F1-score of 81%. With the final goal of making terms of service more readable and understandable, we embedded this approach into ToSware, a prototype of a Google Chrome extension. An evaluation study was performed to measure ToSware effectiveness, efficiency, and the overall users’ satisfaction when interacting with it.

The growth of Internet and the pervasiveness of ICT have led to a radical change in social relationships. One of the drawbacks of this change is the exposure of individuals to threats during online activities. In this context, thetechno-regulationparadigm is inspiring new ways to safeguard legally interests by means of tools allowing to hamper breaches of law. In this paper, we focus on the exposure of individuals to specific online threats when interacting with smartphones. We propose a novel techno-regulatory approach exploiting machine learning techniques to provide safeguards against threats online. Specifically, we study a set of touch-based gestures to distinguish between underages or adults who is accessing a smartphone, and so to guarantee protection. To evaluate the proposed approach’s effectiveness, we developed an Android app to build a dataset consisting of more than 9000 touch-gestures from 147 participants. We experimented bothsingle-viewandmulti-viewlearning techniques to find the best combination of touch-gestures able of distinguishing between adults and underages. Results show that the multi-view learning combining scrolls, swipes, and pinch-to-zoom gestures, achieves the best ROC AUC (0.92) and accuracy (88%) scores.

Gender classification of mobile devices’ users has drawn a great deal of attention for its applications in healthcare, smart spaces, biometric-based access control systems and customization of user interface (UI). Previous works have shown that authentication systems can be more effective when considering soft biometric traits such as the gender, while others highlighted the significance of this trait for enhancing UIs. This paper presents a novel machine learning-based approach to gender classification leveraging the only touch gestures information derived from smartphones’ APIs. To identify the most useful gesture and combination thereof for gender classification, we have considered two strategies:single-viewlearning, analyzing, one at a time, datasets relating to a single type of gesture, andmulti-viewlearning, analyzing together datasets describing different types of gestures. This is one of the first works to apply such a strategy for gender recognition via gestures analysis on mobile devices. The methods have been evaluated on a large dataset of gestures collected through a mobile application, which includes not only scrolls, swipes, and taps but also pinch-to-zooms and drag-and-drops which are mostly overlooked in the literature. Conversely to the previous literature, we have also provided experiments of the solution in different scenarios, thus proposing a more comprehensive evaluation. The experimental results show thatscroll downis the most useful gesture andrandom forestis the most convenient classifier for gender classification. Based on the (combination of) gestures taken into account, we have obtained F1-score up to 0.89 in validation and 0.85 in testing phase. Furthermore, the multi-view approach is recommended when dealing with unknown devices and combinations of gestures can be effectively adopted, building on the requirements of the system our solution is built-into. Solutions proposed turn out to be both an opportunity for gender-aware technologies and a potential risk deriving from unwanted gender classification.

Optimal portfolio selection—composing a set of stocks/assets that provide high yields/returns with a reasonable risk—has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specificallyNondominated Sorting Genetic Algorithm-II (NSGA-II), which tries tomaximizethe yield andminimizethe risk, simultaneously. The system, namedEvoFolio, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators.EvoFoliois aninteractivegenetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users’ preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show howEvoFoliocan significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of return). Furthermore, considering the investor’s preferences has proved to be very effective in the portfolio’s composition and made it more attractive for end-users. We argue thatEvoFoliocan be effectively used by investors as a support tool for portfolio formation.