112 research outputs found
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
In many technical fields, single-objective optimization procedures in
continuous domains involve expensive numerical simulations. In this context, an
improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial
super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide
fast convergence speed, high solution accuracy and robust performance over a
wide range of problems. It implements enhancements of the ABC structure and
hybridizations with interpolation strategies. The latter are inspired by the
quadratic trust region approach for local investigation and by an efficient
global optimizer for separable problems. Each modification and their combined
effects are studied with appropriate metrics on a numerical benchmark, which is
also used for comparing AsBeC with some effective ABC variants and other
derivative-free algorithms. In addition, the presented algorithm is validated
on two recent benchmarks adopted for competitions in international conferences.
Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc
The Exploitation of Web Navigation Data: Ethical Issues and Alternative Scenarios
Nowadays, the users' browsing activity on the Internet is not completely
private due to many entities that collect and use such data, either for
legitimate or illegal goals. The implications are serious, from a person who
exposes unconsciously his private information to an unknown third party entity,
to a company that is unable to control its information to the outside world. As
a result, users have lost control over their private data in the Internet. In
this paper, we present the entities involved in users' data collection and
usage. Then, we highlight what are the ethical issues that arise for users,
companies, scientists and governments. Finally, we present some alternative
scenarios and suggestions for the entities to address such ethical issues.Comment: 11 pages, 1 figur
Human Behaviour on the Web: Evolution, Interactions and Exploitation
The Web has a fundamental impact on our life, and its usage is quite dynamic and heterogeneous.
Moreover, the Web, and in particular Online Social Networks allow people to communicate directly with the public, bypassing filters of traditional medias. Among the others, politicians and companies are exploiting this technologies to widen their influence.
In the talk I will show techniques to capture such usage evolution and analyze people interaction on the Internet. This information allows us to understand how users and web services change over time, and how someone can take advantage of these behaviours
Message passing optimization of Harmonic Influence Centrality
This paper proposes a new measure of node centrality in social networks, the
Harmonic Influence Centrality, which emerges naturally in the study of social
influence over networks. Using an intuitive analogy between social and
electrical networks, we introduce a distributed message passing algorithm to
compute the Harmonic Influence Centrality of each node. Although its design is
based on theoretical results which assume the network to have no cycle, the
algorithm can also be successfully applied on general graphs.Comment: 11 pages; 10 figures; to appear as a journal publicatio
Biases in the frequency of fruits and seeds in modern fluvial sediments in NW Italy: the key to interpret analogous fossil assemblages
RL-IoT: Reinforcement Learning to Interact with IoT Devices
Our life is getting filled by Internet of Things (IoT) devices. These devices often rely on closed or poorly documented protocols, with unknown formats and semantics. Learning how to interact with such devices in an autonomous manner is the key for interoperability and automatic verification of their capabilities. In this paper, we propose RL-IoT, a system that explores how to automatically interact with possibly unknown IoT devices. We leverage reinforcement learning (RL) to recover the semantics of protocol messages and to take control of the device to reach a given goal, while minimizing the number of interactions. We assume to know only a database of possible IoT protocol messages, whose semantics are however unknown. RL-IoT exchanges messages with the target IoT device, learning those commands that are useful to reach the given goal. Our results show that RL-IoT is able to solve both simple and complex tasks. With properly tuned parameters, RL-IoT learns how to perform actions with the target device, a Yeelight smart bulb in our case study, completing non-trivial patterns with as few as 400 interactions. RL-IoT paves the road for automatic interactions with poorly documented IoT protocols, thus enabling interoperable systems
Modeling communication asymmetry and content personalization in online social networks
The increasing popularity of online social networks (OSNs) attracted growing
interest in modeling social interactions. On online social platforms, a few
individuals, commonly referred to as influencers, produce the majority of
content consumed by users and hegemonize the landscape of the social debate.
However, classical opinion models do not capture this communication asymmetry.
We develop an opinion model inspired by observations on social media platforms
{with two main objectives: first, to describe this inherent communication
asymmetry in OSNs, and second, to model the effects of content personalization.
We derive a Fokker-Planck equation for the temporal evolution of users' opinion
distribution and analytically characterize the stationary system behavior.
Analytical results, confirmed by Monte-Carlo simulations, show how strict forms
of content personalization tend to radicalize user opinion, leading to the
emergence of echo chambers, and favor structurally advantaged influencers. As
an example application, we apply our model to Facebook data during the Italian
government crisis in the summer of 2019. Our work provides a flexible framework
to evaluate the impact of content personalization on the opinion formation
process, focusing on the interaction between influential individuals and
regular users. This framework is interesting in the context of marketing and
advertising, misinformation spreading, politics and activism
The stock exchange of influencers: a financial approach for studying fanbase variation trends
In many online social networks (OSNs), a limited portion of profiles emerges and reaches a large base of followers, i.e., the so-called social influencers. One of their main goals is to increase their fanbase to increase their visibility, engaging users through their content. In this work, we propose a novel parallel between the ecosystem of OSNs and the stock exchange market. Followers act as private investors, and they follow influencers, i.e., buy stocks, based on their individual preferences and on the information they gather through external sources. In this preliminary study, we show how the approaches proposed in the context of the stock exchange market can be successfully applied to social networks. Our case study focuses on 60 Italian Instagram influencers and shows how their followers short-term trends obtained through Bollinger bands become close to those found in external sources, Google Trends in our case, similarly to phenomena already observed in the financial market. Besides providing a strong correlation between these different trends, our results pose the basis for studying social networks with a new lens, linking them with a different domain
Disentangling the Information Flood on OSNs: Finding Notable Posts and Topics
Online Social Networks (OSNs) are an integral part of modern life for sharing thoughts, stories, and news. An ecosystem of influencers generates a flood of content in the form of posts, some of which have an unusually high level of engagement with the influencer’s fan base. These posts relate to blossoming topics of discussion that generate particular interest among users: The COVID-19 pandemic is a prominent example. Studying these phenomena provides an understanding of the OSN landscape and requires appropriate methods. This paper presents a methodology to discover notable posts and group them according to their related topic. By combining anomaly detection, graph modelling and community detection techniques, we pinpoint salient events automatically, with the ability to tune the amount of them. We showcase our approach using a large Instagram dataset and extract some notable weekly topics that gained momentum from 1.4 million posts. We then illustrate some use cases ranging from the COVID-19 outbreak to sporting events
Mean-field analysis of the convergence time of message-passing computation of harmonic influence in social networks
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