Mirco Musolesi

Mirco Musolesi
University College London | UCL · Department of Computer Science

About

213
Publications
34,291
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9,876
Citations

Publications

Publications (213)
Preprint
A key problem in network theory is how to reconfigure a graph in order to optimize a quantifiable objective. Given the ubiquity of networked systems, such work has broad practical applications in a variety of situations, ranging from drug and material design to telecommunications. The large decision space of possible reconfigurations, however, make...
Conference Paper
Full-text available
We present an architecture where a feedback controller derived on an approximate model of the environment assists the learning process to enhance its data efficiency. This architecture, which we term as Control-Tutored Q-learning (CTQL), is presented in two alternative flavours. The former is based on defining the reward function so that a Boolean...
Article
Full-text available
Understanding the shopping motivations behind market baskets has significant commercial value for the grocery retail industry. The analysis of shopping transactions demands techniques that can cope with the volume and dimensionality of grocery transactional data while delivering interpretable outcomes. Latent Dirichlet allocation (LDA) allows proce...
Preprint
Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence. This paper explores the possibility of using generative learning techniques for automatic assessment of creativity. The proposed solution does not involve human judgement, it is modular and of general applicability. We introduce a new measure, namely...
Article
Machine-generated artworks are now part of the contemporary art scene: they are attracting significant investments and they are presented in exhibitions together with those created by human artists. These artworks are mainly based on generative deep learning (GDL) techniques, which have seen a formidable development and remarkable refinement in the...
Preprint
Full-text available
We present an architecture where a feedback controller derived on an approximate model of the environment assists the learning process to enhance its data efficiency. This architecture, which we term as Control-Tutored Q-learning (CTQL), is presented in two alternative flavours. The former is based on defining the reward function so that a Boolean...
Conference Paper
Public goods games represent insightful settings for studying incentives for individual agents to make contributions that, while costly for each of them, benefit the wider society. In this work, we adopt the perspective of a central planner with a global view of a network of self-interested agents and the goal of maximizing some desired property in...
Preprint
Full-text available
Understanding the customer behaviours behind transactional data has high commercial value in the grocery retail industry. Customers generate millions of transactions every day, choosing and buying products to satisfy specific shopping needs. Product availability may vary geographically due to local demand and local supply, thus driving the importan...
Article
Graphs can be used to represent and reason about systems and a variety of metrics have been devised to quantify their global characteristics. However, little is currently known about how to construct a graph or improve an existing one given a target objective. In this work, we formulate the construction of a graph as a decision-making process in wh...
Preprint
The growing number of applications of Reinforcement Learning (RL) in real-world domains has led to the development of privacy-preserving techniques due to the inherently sensitive nature of data. Most existing works focus on differential privacy, in which information is revealed in the clear to an agent whose learned model should be robust against...
Article
Full-text available
Ben Glocker (an expert in machine learning for medical imaging, Imperial College London), Mirco Musolesi (a data science and digital health expert, University College London), Jonathan Richens (an expert in diagnostic machine learning models, Babylon Health) and Caroline Uhler (a computational biology expert, MIT) talked to Nature Communications ab...
Preprint
Public goods games represent insightful settings for studying incentives for individual agents to make contributions that, while costly for each of them, benefit the wider society. In this work, we adopt the perspective of a central planner with a global view of a network of self-interested agents and the goal of maximizing some desired property in...
Preprint
We tackle the problem of goal-directed graph construction: given a starting graph, a global objective function (e.g., communication efficiency), and a budget of modifications, the aim is to find a set of edges whose addition to the graph maximally improves the objective. This problem emerges in many networks of great importance for society such as...
Article
Full-text available
Space, time and the social realm are intrinsically linked. While an array of studies have tried to untangle these factors and their influence on human behaviour, hardly any have taken their effects into account at the same time. To disentangle these factors, we try to predict future encounters between students and assess how important social, spati...
Preprint
Machine-generated artworks are now part of the contemporary art scene: they are attracting significant investments and they are presented in exhibitions together with those created by human artists. These artworks are mainly based on generative deep learning techniques. Also given their success, several legal problems arise when working with these...
Article
Understanding in which circumstances office workers take rest breaks is important for delivering effective mobile notifications and make inferences about their daily lifestyle, e.g., whether they are active and/or have a sedentary life. Previous studies designed for office workers show the effectiveness of rest breaks for preventing work-related co...
Preprint
There is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state of the art of computational creativity theories, machine learning techniques, including generative deep learning, and corresponding automatic evaluation methods. After presenting a critical discussion of the key...
Preprint
Creating incentives for cooperation is a challenge in natural and artificial systems. One potential answer is reputation, whereby agents trade the immediate cost of cooperation for the future benefits of having a good reputation. Game theoretical models have shown that specific social norms can make cooperation stable, but how agents can independen...
Article
Data gathered from smartphones enables service providers to infer a wide range of personal information about their users, such as their traits, their personality, and their demographics. This personal information can be made available to third parties, such as advertisers, sometimes unbeknownst to the users. Leveraging location information, adverti...
Chapter
Mobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the sharing of people’s whereabouts may arise serious privacy concerns. Existing frameworks for privacy risk assessment provide tools to identify and measure privacy risks, but they of...
Conference Paper
Full-text available
Online advertising is an effective way for businesses to find new customers and expand their reach to a great variety of audiences. Due to the large number of participants interacting in the process, advertising networks act as brokers between website owners and businesses facilitating the display of advertisements. Unfortunately, this system is ab...
Preprint
Understanding the shopping motivations behind market baskets has high commercial value in the grocery retail industry. Analyzing shopping transactions demands techniques that can cope with the volume and dimensionality of grocery transactional data while keeping interpretable outcomes. Latent Dirichlet Allocation (LDA) provides a suitable framework...
Article
Social dilemmas have been widely studied to explain how humans are able to cooperate in society. Considerable effort has been invested in designing artificial agents for social dilemmas that incorporate explicit agent motivations that are chosen to favor coordinated or cooperative responses. The prevalence of this general approach points towards th...
Article
There has been an increasing interest in the problem of inferring emotional states of individuals using sensor and user-generated information as diverse as GPS traces, social media data and smartphone interaction patterns. One aspect that has received little attention is the use of visual context information extracted from the surroundings of indiv...
Article
In this study, we investigate the effects of social context, personal and mobile phone usage on the inference of work engagement/challenge levels of knowledge workers and their responsiveness to well-being related notifications. Our results show that mobile application usage is associated to the responsiveness and work engagement/challenge levels o...
Conference Paper
In mobile crowd-sourcing systems, simply relying on people to opportunistically select and perform tasks typically leads to drawbacks such as low task acceptance/completion rates and undesirable spatial skews. In this paper, we utilize data from TASKer, a campus-based mobile crowd-sourcing platform, to empirically study and discover whether and how...
Preprint
Graphs can be used to represent and reason about real world systems. A variety of metrics have been devised to quantify their global characteristics. In general, prior work focuses on measuring the properties of existing graphs rather than the problem of dynamically modifying them (for example, by adding edges) in order to improve the value of an o...
Conference Paper
Mobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the sharing of people’s whereabouts may arise serious privacy concerns. Existing frameworks for privacy risk assessment provide tools to identify and measure privacy risks, but they of...
Chapter
When mobile devices first appeared, they were used merely for calling and messaging purposes. Later, with the advent of sensing capabilities, these devices have graduated from calling instruments to intelligent and highly personal devices performing numerous functions salient to users’ daily requirements. This has provided opportunities to mobile a...
Chapter
Interruptibility management has attracted the interest of HCI researchers well before the advent of mobile devices. However, interruptions received on the desktop have very specific characteristics. In fact, because of their very nature desktops are situated in a constant environment and a user’s physical context (such as surrounding people, locati...
Chapter
Interruptions are an inevitable part of our daily life. As discussed in this book, the effects of disruption caused by interruptions occurring at inopportune moments have been studied thoroughly in the past. Numerous studies have been conducted to investigate the effect of interruptions on users’ ongoing tasks. More specifically, researchers have f...
Chapter
Interruptions are an inevitable part of our everyday life as it is hard to get through the entire day without being interrupted. As suggested by Zabelina et al. in [109], people are sensitive to their surroundings and they receive more information through interruptions, which might help them in their everyday tasks and even boost their creativity....
Chapter
In this chapter we discuss different definitions of interruption, provide a possible classification of interruptions, and give an overview of the sources of interruptions.
Article
Understanding and learning the characteristics of network paths has been of particular interest for decades and has led to several successful applications. Such analysis becomes challenging for urban networks as their size and complexity are significantly higher compared to other networks. The state-of-the-art machine learning techniques allow us t...
Preprint
Understanding and learning the characteristics of network paths has been of particular interest for decades and has led to several successful applications. Such analysis becomes challenging for urban networks as their size and complexity are significantly higher compared to other networks. The state-of-the-art machine learning (ML) techniques allow...
Article
Ubiquitous computing is moving from context-awareness to context-prediction. In order to build truly anticipatory systems developers have to deal with many challenges, from multimodal sensing to modeling context from sensed data, and, when necessary, coordinating multiple predictive models across devices. Novel expressive programming interfaces and...
Conference Paper
Mental health issues affect a significant portion of the world's population and can result in debilitating and life-threatening outcomes. To address this increasingly pressing healthcare challenge, there is a need to research novel approaches for early detection and prevention. Toward this, ubiquitous systems can play a central role in revealing an...
Article
Recent years have seen an explosion in the use of data science and AI as a central tenant in numerous computing applications, products, research, and innovation. Examples of the success of data science abound—applying new machine-learning techniques to problems such as vision and speech recognition and translation has achieved commonplace levels of...
Conference Paper
Full-text available
In recent years, numerous studies have explored the use of machine learning algorithms for supporting applications in social and clinical psychology. In particular, there is an increasing prevalence of smartphone-based techniques for collecting data through embedded sensors and efficient in-situ questionnaires. Models are then built to explore the...
Conference Paper
Multi-agent reinforcement learning has received significant interest in recent years notably due to the advancements made in deep reinforcement learning which have allowed for the developments of new architectures and learning algorithms. In this extended abstract we present our initial efforts towards the development of decentralized architectures...
Conference Paper
Full-text available
Smartphones are increasingly augmented with sensors for a variety of purposes. In this paper, we show how magnetic field emissions can be used to fingerprint smartphones. Previous work on identification rely on specific characteristics that vary with the settings and components available on a device. This limits the number of devices on which one a...
Preprint
Full-text available
Water distribution networks (WDNs) are one of the most important man-made infrastructures. Resilience, the ability to respond to disturbances and recover to a desirable state, is of vital importance to our society. There is increasing evidence that the resilience of networked infrastructures with dynamic signals depends on their network topological...
Preprint
Full-text available
Water distribution networks (WDNs) are one of the most important man-made infrastructures. Resilience, the ability to respond to disturbances and recover to a desirable state, is of vital importance to our society. There is increasing evidence that the resilience of networked infrastructures with dynamic signals depends on their network topological...
Conference Paper
Personal interactions and information access are happening more and more through the mediation of computing devices of various types all around us. In our daily life we use many computing devices running different versions of the same application such as email clients or social media platforms, which alert users about a new piece of information or...
Article
Full-text available
Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as first rounds of staffing and resource allocation. Traditionally, this estimation has been performed through coarse-grained measures such as observing numbers in local venues or venues at similar places (e.g., co...
Article
Full-text available
In the hospitality industry, the room and apartment sharing platform of Airbnb has been accused of unfair competition. Detractors have pointed out the chronic lack of proper legislation. Unfortunately, there is little quantitative evidence about Airbnb’s spatial penetration upon which to base such a legislation. In this study, we analyze Airbnb’s s...
Article
Full-text available
Abstract The world has seen a dramatic increase in cybercrime, in both the Surface Web, which is the portion of content on the World Wide Web that may be indexed by popular engines, and lately in the Dark Web, a portion that is not indexed by conventional search engines and is accessed through network overlays such as the Tor network. For instance,...
Conference Paper
Mental health issues affect a significant portion of the world's population and can result in debilitating and life-threatening outcomes. To address this increasingly pressing healthcare challenge, there is a need to research novel approaches for early detection and prevention. Toward this, ubiquitous systems can play a central role in revealing an...
Preprint
Multi-agent reinforcement learning has received significant interest in recent years notably due to the advancements made in deep reinforcement learning which have allowed for the developments of new architectures and learning algorithms. Using social dilemmas as the training ground, we present a novel learning architecture, Learning through Probin...
Article
Recent studies have shown the potential of exploiting GPS data for passively inferring people's mental health conditions. However, feature extraction for characterizing human mobility remains a heuristic process that relies on the domain knowledge of the condition under consideration. Moreover, we do not have guarantees that these "hand-crafted" me...
Article
Full-text available
Allogrooming is a key aspect of chimpanzee sociality and many studies have investigated the role of reciprocity in a biological market. One theoretical form of reciprocity is time-matching, where payback consists of an equal duration of effort (e.g. twenty seconds of grooming repaid with twenty seconds of grooming). Here, we report a study of allog...
Data
(1) Alternative event-pairing results and (2) Reciprocity over different delay periods. (PDF)
Conference Paper
While mobile apps have become an integral part of everyday life, little is known about the factors that govern their usage. Particularly the role of geographic and cultural factors has been understudied. This article contributes by carrying out a large-scale analysis of geographic, cultural, and demographic factors in mobile usage. We consider app...
Preprint
Full-text available
Understanding the movement patterns of animals across different spatio-temporal scales, conditions, habitats and contexts is becoming increasingly important for addressing a series of questions in animal behaviour studies, such as mapping migration routes, evaluating resource use, modelling epidemic spreading in a population, developing strategies...
Conference Paper
Harnessing the research opportunities provided by the large datasets generated by users of self-tracking technologies is a challenge for researchers of both human-computer interaction (HCI) and data science. While HCI is concerned with facilitating the insights gathered from data produced by self-tracking systems, data scientists rely on the qualit...
Conference Paper
Full-text available
Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world. Yet, surprisingly, metadata are often still catergorized as non-sensitive. Indeed, in the past, researchers and practitioners have mainly focused on the problem of the identification of a user from the content of a message...
Book
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the VI International Conference on Complex Networks and their Applications (COMPLEX NE...
Article
Full-text available
Understanding and modeling the mobility of individuals is of paramount importance for public health. In particular, mobility characterization is key to predict the spatial and temporal diffusion of human-transmitted infections. However, the mobility behavior of a person can also reveal relevant information about her/his health conditions. In this p...
Article
Notifications provide a unique mechanism for increasing the effectiveness of real-time information delivery systems. However, notifications that demand users' attention at inopportune moments are more likely to have adverse effects and might become a cause of potential disruption rather than proving beneficial to users. In order to address these ch...
Conference Paper
Full-text available
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investig...
Conference Paper
Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as !rst rounds of sta"ng and resource allocation. Traditionally, this estimation has been performed through coarse measures such as observing numbers in local venues. The advent of crowdsourced data from devices an...
Article
The presence of pervasive computing in our everyday lives and emergence of the Internet of Things, such as the interaction of users with connected devices like smartphones or home appliances generate increasing amounts of traces that reflect users' behavior. A plethora of machine learning techniques enable service providers to process these traces...
Chapter
Recent advances in healthcare illuminated the role that individual traits and behaviors play in a person’s health. Consequently, a need has arisen for, currently expensive and non-scalable, continuous long-term patient monitoring and individually tailored therapies. Equipped with an array of sensors, high-performance computing power, and carried by...
Article
Full-text available
Traditional ways to study urban social behavior, e.g. surveys, are costly and do not scale. Recently, some studies have been showing new ways of obtaining data through location-based social networks (LBSNs), such as Foursquare, which could revolutionize the study of urban social behavior. We use Foursquare check-ins to represent user preferences re...
Article
According to behavioral finance, stock market returns are influenced by emotional, social and psychological factors. Several recent works support this theory by providing evidence of correlation between stock market prices and collective sentiment indexes measured using social media data. However, a pure correlation analysis is not sufficient to pr...
Conference Paper
This paper showcases an approach to combining smart-phone sensing technology, web mapping services, and psychological assessments to enhance our understanding of the psychological characteristics of places. For two weeks, twenty-six students used a smartphone app that passively collected GPS sensor data. Human raters then characterized their most f...
Conference Paper
Mental health issues affect a significant portion of the world's population and can result in debilitating and life-threatening outcomes. To address this increasingly pressing healthcare challenge, there is a need to research novel approaches for early detection and prevention. In particular, ubiquitous systems can play a central role in revealing...
Article
User interaction patterns with mobile apps and notifications are generally complex due to the many factors involved. However a deep understanding of what influences them can lead to more acceptable applications that are able to deliver information at the right time. In this paper, we present for the first time an in-depth analysis of interaction be...
Article
Most of the existing work concerning the analysis of emotional states and mobile phone interaction has been based on correlation analysis. In this paper, for the first time, we carry out a causality study to investigate the causal links between users’ emotional states and their interaction with mobile phones, which could provide valuable informatio...
Article
Mobile notifications are increasingly used by a variety of applications to inform users about events, news or just to send alerts and reminders to them. However, many notifications are neither useful nor relevant to users' interests and, for this reason, they are considered disruptive and potentially annoying, as well. PrefMiner is a novel interrup...
Conference Paper
Mobile sensing technologies and machine learning techniques have been successfully exploited to build effective systems for mental health monitoring and intervention. Various approaches have recently been proposed to effectively exploit contextual information such as mobility, communication and mobile usage patterns for quantifying users' emotional...
Article
Predicting investors reactions to financial and political news is important for the early detection of stock market jitters. Evidence from several recent studies suggests that online social media could improve prediction of stock market movements. However, utilizing such information to predict strong stock market fluctuations has not been explored...