Search

Electronic voting systems are essential for holding virtual elections, and the need for such systems increases due to the COVID-19 pandemic and the social distancing that it mandates. One of the main challenges in e-voting systems is to secure the voting process: namely, to certify that the computed results are consistent with the cast ballots, and that the privacy of the voters is preserved. We propose herein a secure voting protocol for elections that are governed by order-based voting rules. Our protocol offers perfect ballot secrecy, in the sense that it issues only the required output, while no other information on the cast ballots is revealed. Such perfect secrecy, which is achieved by employing secure multiparty computation tools, may increase the voters' confidence and, consequently, encourage them to vote according to their true preferences. Evaluation of the protocol's computational costs establishes that it is lightweight and can be readily implemented in real-life electronic elections.

In higher education courses, peer assessment activities are common for keeping students engaged during presentations. Defining precisely how students assess the work of others requires careful consideration. Asking the student for numeric grades is the most common method. However, students tend to assign high grades to most projects. Aggregating peer assessments, therefore, results in all projects receiving the same grade. Moreover, students might strategically assign low grades to the projects of others so that their projects will shine. Asking students to order all projects from best to worst imposes a high cognitive load on them, as studies have shown that people find it difficult to order more than a handful of items. To address these issues, we propose a novel peer rating model, R2R, consisting of (a) an algorithm that elicits student assessments and (b) a protocol for aggregating grades to produce a single order. The algorithm asks students to evaluate projects and answer pairwise comparison queries. These are then aggregated into a ranking over the projects. $R2R$ was deployed and tested in a university course and showed promising results, including fewer ties between alternatives and a significant reduction in the communication load on students.

We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the-art methods in deep learning multi-label mining, extreme multi-label classification and label ranking. We conclude by offering a few future research directions.

Label ranking is a prediction task which deals with learning a mapping between an instance and a ranking (i.e., order) of labels from a finite set, representing their relevance to the instance. Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms. While boosting algorithms were developed for a multitude of machine learning tasks, label ranking tasks were overlooked. In this paper, we propose a boosting algorithm which was specifically designed for label ranking tasks. Extensive evaluation of the proposed algorithm on 24 semi-synthetic and real-world label ranking datasets shows that it significantly outperforms existing state-of-the-art label ranking algorithms.

One characteristic of human nature is the ability to align our behavior with others. Previous research has linked poor communication skills to alexithymia. This may suggest the possibility that individuals with high alexithymia do not adhere to the principles of social alignment. One form of cognitive alignment is consensus with a group. So far, little research attention has been given to the possible link between alexithymia and this form of cognitive social alignment. In this study, we address this gap by investigating the association between consensus-reaching abilities and alexithymia. A sample comprising of 122 participants completed the Toronto Alexithymia Scale and then played a specially designed game called “Consensus under a deadline”. In each game, a participant played with either seven bots designed to act rationally and always seek a consensus, or with seven other participants. The participants were unaware who they were playing with. The results of the study confirm the link between alexithymia and impaired cognitive social alignment, showing that the alexithymia cognitive component (EOT) is associated with a deficit in reaching a consensus with humans (that sometimes act irrationally). However, this association was not evident when group members were bots (that always act rationally).