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In voting theory and social choice theory, decision systems can be represented as simple games, i.e., cooperative games defined through their players or voters and their set of winning coalitions. The weighted voting games form a well-known strict subclass of simple games, where each player has a voting weight so that a coalition wins if the sum of weights of their members exceeds a given quota. Since the number of winning coalitions can be exponential in the number of players, simple games can be represented much more compactly as intersections or unions of weighted voting games. A simple game's dimension (codimension) is the minimum number of weighted voting games such that their intersection (union) is the given game. It is known there are voting systems with a high (co)dimension. This work introduces the multidimension as the minimum size of an expression with intersections and unions on weighted voting games necessary to obtain the considered simple game. We generalize this notion to subclasses of weighted voting games and analyze the generative properties of these subclasses. We also characterize the simple games with finite generalized multidimension over the set of weighted voting games without dummy players. We provide a comprehensive classification for simple games up to a certain number of players. These results complement similar classification results for generalized (co)dimensions. Our results show how generalized multidimension allows representing more simple games and more compactly, even for a small number of players and for subclasses. [ABSTRACT FROM AUTHOR]

The influence spread in a social network is an iterative process that can take several steps. It begins with an activation seed and finishes when the current activation cannot influence more actors. The multi-objective influence spread problem corresponds to finding the smallest number of actors capable of maximizing the influence spread within the network. This problem has been solved by metaheuristic optimization algorithms using swarm intelligence methods. This article proposes a heuristic to improve the existing solution: when two sets of actors can influence the same number of actors, the one whose spread requires the least number of steps is chosen. The proposed solution is tested on two different real networks. The results show that the heuristic allowed better results for both networks and decreased the average number of steps in the influence spread processes (in 15.5 and 0.07 average steps, respectively), thus improving execution times. Moreover, the heuristic allowed decreasing the number of steps in 83% (against 17% of increasing) and 13% (against 7% of increasing) of the particles, respectively. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Multidimension: a dimensionality extension of simple games" published in the journal Computational & Applied Mathematics. The correction addresses an error in the affiliation of the second author, Dr. Fabián Riquelme. The correct affiliation is stated as "Escuela de Ingeniería Informática, Universidad de Valparaíso, General Cruz 222, 2362905, Valparaíso, Chile." The correction notice is provided by the publisher, Springer Nature, who maintains a neutral stance on jurisdictional claims and institutional affiliations. The article was authored by Xavier Molinero, Fabián Riquelme, Salvador Roura, and Maria Serna. [Extracted from the article]

This document is a correction notice for an article titled "Multidimension: a dimensionality extension of simple games" published in the journal Computational & Applied Mathematics. The correction addresses an error in the affiliation of the second author, Dr. Fabián Riquelme, and provides the correct affiliation as the Escuela de Ingeniería Informática at the Universidad de Valparaíso in Chile. The original article has been corrected, and it is licensed under a Creative Commons Attribution 4.0 International License. The contact information for the authors is also provided. [Extracted from the article]

The concept of "politics of the end" assumes the catastrophe of living in a world that produces new forms of accumulation and allows symbolic and semiotic capital to create value. Currently, various far-right movements worldwide seem to appropriate this concept, employing radical communication strategies as a repertoire to contest the public agenda. These strategies include the massive creation of bots on social networks to spread hate speech and coordinate ideological manifestations. This article seeks to verify the use of these strategies by the Chilean far-right on Twitter. For the above, a social network analysis approach is proposed during the current socio-political crisis in Chile, which began with the massive protests of October 2019 and led to an unprecedented constituent process. For nine months, we studied five opinion leaders on Twitter from the Chilean far-right, who together have more than 600 thousand followers and almost 130 thousand followings. Through descriptive, quantitative, and qualitative techniques, an explicit political action "from the resistance" is revealed in the activity of the network, which includes hundreds of new users and coordinated bots to disseminate identifiable discourses with strongly ideological ideas. This coordination also presents identifiable differences in how opinion leaders interact and communicate with their network environment. [ABSTRACT FROM AUTHOR]

Nature–inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensification generates a local solution around the best solution by exploiting a promising region. Diversification is responsible for finding new solutions when the main procedure is trapped in a local region. This procedure is usually carryout by non-deterministic fundamentals that do not necessarily provide the expected results. Here, we encounter the stagnation problem, which describes a scenario where the search for the optimum solution stalls before discovering a globally optimal solution. In this work, we propose an efficient technique for detecting and leaving local optimum regions based on Shannon entropy. This component can measure the uncertainty level of the observations taken from random variables. We employ this principle on three well–known population–based bio–inspired optimization algorithms: particle swarm optimization, bat optimization, and black hole algorithm. The proposal's performance is evidenced by solving twenty of the most challenging instances of the multidimensional knapsack problem. Computational results show that the proposed exploration approach is a legitimate alternative to manage the diversification of solutions since the improved techniques can generate a better distribution of the optimal values found. The best results are with the bat method, where in all instances, the enhanced solver with the Shannon exploration strategy works better than its native version. For the other two bio-inspired algorithms, the proposal operates significantly better in over 70% of instances. [ABSTRACT FROM AUTHOR]

Developing communication skills in collaborative contexts is of special interest for educational institutions, since these skills are crucial to forming competent professionals for today's world. New and accessible technologies open a way to analyze collaborative activities in face-to-face and non-face-to-face situations, where collaboration and student attitudes are difficult to measure using traditional methods. In this context, Multimodal Learning Analytics (MMLA) appear as an alternative to complement the evaluation and feedback of core skills. We present a MMLA platform to support collaboration assessment based on the capture and classification of non-verbal communication interactions. The developed platform integrates hardware and software, including machine learning techniques, to detect spoken interactions and body postures from video and audio recordings. The captured data is presented in a set of visualizations, designed to help teachers to obtain insights about the collaboration of a team. We performed a case study to explore if the visualizations were useful to represent different behavioral indicators of collaboration in different teamwork situations: a collaborative situation and a competitive situation. We discussed the results of the case study in a focus group with three teachers, to get insights in the usefulness of our proposal. The results show that the measurements and visualizations are helpful to understand differences in collaboration, confirming the feasibility the MMLA approach for assessing and providing collaboration insights based on non-verbal communication. [ABSTRACT FROM AUTHOR]

An influence game is a simple game represented over an influence graph (i.e., a labeled, weighted graph) on which the influence spread phenomenon is exerted. Influence games allow applying different properties and parameters coming from cooperative game theory to the contexts of social network analysis, decision-systems, voting systems, and collective behavior. The exact calculation of several of these properties and parameters is computationally hard, even for a small number of players. Two examples of these parameters are the length and the width of a game. The length of a game is the size of its smaller winning coalition, while the width of a game is the size of its larger losing coalition. Both parameters are relevant to know the levels of difficulty in reaching agreements in collective decision-making systems. Despite the above, new bio-inspired metaheuristic algorithms have recently been developed to solve the NP-hard influence maximization problem in an efficient and approximate way, being able to find small winning coalitions that maximize the influence spread within an influence graph. In this article, we apply some variations of this solution to find extreme winning and losing coalitions, and thus efficient approximate solutions for the length and the width of influence games. As a case study, we consider two real social networks, one formed by the 58 members of the European Union Council under nice voting rules, and the other formed by the 705 members of the European Parliament, connected by political affinity. Results are promising and show that it is feasible to generate approximate solutions for the length and width parameters of influence games, in reduced solving time. [ABSTRACT FROM AUTHOR]

Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses. [ABSTRACT FROM AUTHOR]

In Chile, since the restitution of democracy (1989), the distribution of parliamentary seats is governed by a binomial system, the only one of its kind in the world. In this electoral system, two senators and two deputies are elected for each senatorial constituency and electoral district. By the end of 2017, this system will be replaced by the well-known D'Hondt method, which entails a significant increase in the number of parliamentarians and the redistribution of seats in the legislative chambers. In this study, we discuss the problem of centralism and analyze how this redistribution of seats will affect the voting power of the different regions of the country. To measure the hypothetical voting power of each region we use classical power indices of cooperative game theory. The study shows that although the voting power of the regions in the Senate will remain relatively unchanged, the centralization of power in the Metropolitan Region will be increased in the Chamber of Deputies. Finally, we propose a method to obtain a feasible redistributions of seats, which would effectively increase the voting power of the other regions in the Chamber of Deputies. [ABSTRACT FROM AUTHOR]

We propose the design of a system of systems (SoS) called Seniors Guardian as an integrated environment for healthcare monitoring, in which several non-invasive sensors coexist to monitor falls, nocturia, perseverant behaviors, air quality (carbon monoxide), humidity, and temperature in real time. The modular architecture also allows the integration of new components. This research adheres to the use of Internet of Things to develop ambient assisted living in smart homes. [ABSTRACT FROM AUTHOR]

We propose the design of a system of systems (SoS) called Seniors Guardian as an integrated environment for healthcare monitoring, in which several non-invasive sensors coexist to monitor falls, nocturia, perseverant behaviors, air quality (carbon monoxide), humidity, and temperature in real time. The modular architecture also allows the integration of new components. This research adheres to the use of Internet of Things to develop ambient assisted living in smart homes. [ABSTRACT FROM AUTHOR]

While technology has helped improve process efficiency in several domains, it still has an outstanding debt to education. In this article, we introduce NAIRA, a Multimodal Learning Analytics platform that provides Real-Time Feedback to foster collaborative learning activities' efficiency. NAIRA provides real-time visualizations for students' verbal interactions when working in groups, allowing teachers to perform precise interventions to ensure learning activities' correct execution. We present a case study with 24 undergraduate subjects performing a remote collaborative learning activity based on the Jigsaw learning technique within the COVID-19 pandemic context. The main goals of the study are (1) to qualitatively describe how the teacher used NAIRA's visualizations to perform interventions and (2) to identify quantitative differences in the number and time between students' spoken interactions among two different stages of the activity, one of them supported by NAIRA's visualizations. The case study showed that NAIRA allowed the teacher to monitor and facilitate the learning activity's supervised stage execution, even in a remote learning context, with students working in separate virtual classrooms with their video cameras off. The quantitative comparison of spoken interactions suggests the existence of differences in the distribution between the monitored and unmonitored stages of the activity, with a more homogeneous speaking time distribution in the NAIRA supported stage. [ABSTRACT FROM AUTHOR]

Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review. [ABSTRACT FROM AUTHOR]

Currently, the improvement of core skills appears as one of the most significant educational challenges of this century. However, assessing the development of such skills is still a challenge in real classroom environments. In this context, Multimodal Learning Analysis techniques appear as an attractive alternative to complement the development and evaluation of core skills. This article presents an exploratory study that analyzes the collaboration and communication of students in a Software Engineering course, who perform a learning activity simulating Scrum with Lego® bricks. Data from the Scrum process was captured, and multidirectional microphones were used in the retrospective ceremonies. Social network analysis techniques were applied, and a correlational analysis was carried out with all the registered information. The results obtained allowed the detection of important relationships and characteristics of the collaborative and Non-Collaborative groups, with productivity, effort, and predominant personality styles in the groups. From all the above, we can conclude that the Multimodal Learning Analysis techniques offer considerable feasibilities to support the process of skills development in students. [ABSTRACT FROM AUTHOR]