ReSS: A tool for discovering relevant sets in complex systems

Abstract A complex system can be composed of inherent dynamical structures, i.e., relevant subsets of variables interacting tightly with one another and loosely with other subsets. In the literature, some effective methods to identify such relevant sets rely on the so-called Relevance Indexes (RIs), measuring subset relevance based on information theory principles. In this paper, we present ReSS, a collection of CUDA-based programs computing two of such RIs, either through an exhaustive search or a niching metaheuristic when the system dimension is too large. ReSS also includes a script that iteratively activates the search and identifies hierarchical relationships among the relevant subsets. The main purpose of ReSS is to establish a common and easy-to-use general RI-based platform for the analysis of complex systems and other possible applications

Emotion-based analysis of programming languages on Stack Overflow

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Anomaly detection in laser-guided vehicles' batteries: a case study

Detecting anomalous data within time series is a very relevant task in pattern recognition and machine learning, with many possible applications that range from disease prevention in medicine, e.g., detecting early alterations of the health status before it can clearly be defined as "illness" up to monitoring industrial plants. Regarding this latter application, detecting anomalies in an industrial plant's status firstly prevents serious damages that would require a long interruption of the production process. Secondly, it permits optimal scheduling of maintenance interventions by limiting them to urgent situations. At the same time, they typically follow a fixed prudential schedule according to which components are substituted well before the end of their expected lifetime. This paper describes a case study regarding the monitoring of the status of Laser-guided Vehicles (LGVs) batteries, on which we worked as our contribution to project SUPER (Supercomputing Unified Platform, Emilia Romagna) aimed at establishing and demonstrating a regional High-Performance Computing platform that is going to represent the main Italian supercomputing environment for both computing power and data volume.Comment: This paper contains a report on the research work carried out as a collaboration between the Department of Engineering and Architecture of the University of Parma and Elettric80 spa within project SUPER (Supercomputing Unified Platform Emilia Romagna

A relevance index method to infer global properties of biological networks

Many complex systems, both natural and artificial, may be represented by networks of interacting nodes. Nevertheless, it is often difficult to find meaningful correspondences between the dynamics expressed by these systems and the topological description of their networks. In contrast, many of these systems may be well described in terms of coordinated behavior of their dynamically relevant parts. In this paper we use the recently proposed Relevance Index approach, based on information-theoretic measures. Starting from the observation of the dynamical states of any system, the Relevance Index is able to provide information about its organization. Moreover, we show how the application of the proposed approach leads to novel and effective interpretations in the T helper network case study

Fine-Grained Agent-Based Modeling to Predict Covid-19 Spreading and Effect of Policies in Large-Scale Scenarios

Modeling and forecasting the spread of COVID-19 remains an open problem for several reasons. One of these concerns the difficulty to model a complex system at a high resolution (fine-grained) level at which the spread can be simulated by taking into account individual features such as the social structure, the effects of the governments’ policies, age sensitivity to Covid-19, maskwearing habits and geographical distribution of susceptible people. Agent-based modeling usually needs to find an optimal trade-off between the resolution of the simulation and the population size. Indeed, modeling single individuals usually leads to simulations of smaller populations or the use of meta-populations. In this article, we propose a solution to efficiently model the Covid-19 spread in Lombardy, the most populated Italian region with about ten million people. In particular, the model described in this paper is, to the best of our knowledge, the first attempt in literature to model a large population at the single-individual level. To achieve this goal, we propose a framework that implements: i. a scale-free model of the social contacts combining a sociability rate, demographic information, and geographical assumptions; ii. a multi-agent system relying on the actor model and the High-Performance Computing technology to efficiently implement ten million concurrent agents. We simulated the epidemic scenario from January to April 2020 and from August to December 2020, modeling the government’s lockdown policies and people’s maskwearing habits. The social modeling approach we propose could be rapidly adapted for modeling future epidemics at their early stage in scenarios where little prior knowledge is available

Social Network and Sentiment Analysis on Twitter: Towards a Combined Approach

Abstract. Twitter is a platform which may contain opinions, thoughts, facts and other information. Within it, many and various communities are originated by users with common interests, or with similar ways to feel part of the community. This paper presents a combined approach between Social Network Analysis and Sentiment Analysis. In particular, we have tried to associate a sentiment to the nodes of the graphs showing the social connections, and this may highlight the potential correlations. The idea behind it is that, on the one hand, the network topology can contextualize and then, in part, unmask some incorrect results of the Sentiment Analysis; on the other hand, the polarity of the feeling on the network can highlight the role of semantic connections in the hierarchy of the communities that are present in the network. In this work, we illustrate the approach to the issue, together with the system architecture and, then, we discuss our first results

Open source technologies as a support for community care

According to current definitions, health can not be merely intended as the absence of disease or infirmity. In this sense, for maintaining health, the coordination and collaboration among social and health systems is becoming essential, and it can happen only though the harmonization of existing and new integrated information systems. In this study, we will discuss the main requirements which are usually expected from applications for health and social care. For this purpose, the features of a number of commercial applications will be presented. Finally, we will confront these requirements and features with those obtainable trough available open source projects, when opportunely integrated