1,721,127 research outputs found
Where You Go Matters: A Study on the Privacy Implications of Continuous Location Tracking
Full text linkData 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, advertisers can serve ads micro-targeted to users based on the places they visited. Understanding the types of information that can be extracted from location data and implications in terms of user privacy is of critical importance.
In this context, we conducted an extensive in-the-wild research study to shed light on the range of personal information that can be inferred from the places visited by users, as well as privacy sensitivity of the personal information. To this end, we developed TrackingAdvisor, a mobile application that continuously collects user location and extracts personal information from it. The app also provides an interface to give feedback about the relevance of the personal information inferred from location data and its corresponding privacy sensitivity. Our findings show that, while some personal information such as social activities is not considered private, other information such as health, religious belief, ethnicity, political opinions, and socio-economic status is considered private by the participants of the study. This study paves the way to the design of privacy-preserving systems that provide contextual recommendations and explanations to help users further protect their privacy by making them aware of the consequences of sharing their personal data
Intelligent Notification Systems
No full textNotifications 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 challenges a variety of intelligent notification mechanisms based on monitoring and learning users' behavior have been proposed. The goal of such mechanisms is maximizing users' receptivity to the delivered information by automatically inferring the right time and the right context for sending a certain type of information. This book presents an overview of the current state of the art in the area of intelligent notification mechanisms that rely on the awareness of users' context and preferences. We first present a survey of studies focusing on understanding and modeling users' interruptibility and receptivity to notifications from desktops and mobile devices. Then, we discuss the existing challenges and opportunities in developing mechanisms for intelligent notification systems in a variety of application scenarios
Welcome from the Program Co-Chairs
No full textSCIRS 2020
1st International Workshop on Smart Computing
for Industrial and Real-World Systems
Welcome from the Program Co-Chairs
It is our great pleasure to welcome you to the 1st IEEE International Workshop on Smart Computing for Industrial and Real-World Systems (SCIRS 2020) co-located with the 6th IEEE International Conference on Smart Computing (SMARTCOMP 2020) held on September 2020. The spread of the Internet of Things (IoT) in industrial environments has fostered the development of new Industrial IoT (IIoT) applications and, more in general, the advent of the Industry 4.0 revolution. The current trend is towards the development of connected machines
able to interact with each other as well as to send data and access cloud and Internet services.
Smart components and services within industrial environments represent a novel and promising solution not only to better support QoS requirements, but also to more easily reconcile safety requirements of Operational Technology with the openness and dynamicity typical of the Information Technology. Moreover, the emergence of edge devices equipped with relatively high computational, memory, and storage capabilities facilitates the adoption of these technologies. They are not only able to increase the efficiency of existing industrial processes, e.g., by more promptly identifying faulty machines to reduce the downtime and maximize OLE/OEE KPIs, but
also to provide new business opportunities, e.g., by widening the market through the provision of industrial equipment based on pay-per-use fees. The objective of the SCIRS 2020 workshop is to provide a forum to exchange ideas, share experience, and enhance collaborations in various aspects of smart computing in industrial
environments, by focusing on real-world use cases and applications.
We thank all the authors for submitting their work. We are grateful to the TPC members for their hard work in reviewing the papers. We hope you enjoy this workshop. Thank you for your participation
Investigating causality in human behavior from smartphone sensor data: a quasi-experimental approach
Full text linkSmartphones and wearables have become an indispensable part of our daily life. Their improved sensing and computing capabilities bring new opportunities for human behavior monitoring and analysis. Most work so far has been focused on detecting correlation rather than causation among features extracted from smartphone data. However, pure correlation analysis does not offer sufficient understanding of human behavior. Moreover, causation analysis could allow scientists to identify factors that have a causal effect on health and well-being issues, such as obesity, stress, depression and so on and suggest actions to deal with them. Finally, detecting causal relationships in this kind of observational data is challenging since, in general, subjects cannot be randomly exposed to an event.
In this article, we discuss the design, implementation and evaluation of a generic quasi-experimental framework for conducting causation studies on human behavior from smartphone data. We demonstrate the effectiveness of our approach by investigating the causal impact of several factors such as exercise, social interactions and work on stress level. Our results indicate that exercising and spending time outside home and working environment have a positive effect on participants stress level while reduced working hours only slightly impact stress
Using Unsupervised Deep Autoencoders to Automatically Extract Mobility Features for Predicting Depressive States
No full textRecent 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" metrics are able to effectively capture mobility behavior of users. Indeed, informative emerging patterns in the data might not be characterized by them. This is also a complex and often time-consuming task, since it usually consists of a lengthy trial-and-error process.
In this paper, we investigate the potential of using autoencoders for automatically extracting features from the raw input data. Through a series of experiments we show the effectiveness of autoencoder-based features for predicting depressive states of individuals compared to "hand-crafted" ones. Our results show that automatically extracted features lead to an improvement of the performance of the prediction models, while, at the same time, reducing the complexity of the feature design task. Moreover, through an extensive experimental performance analysis, we demonstrate the optimal configuration of the key parameters at the basis of the proposed approach
Message from New Ideas Track Chairs: MOBILESoft 2017
No full textPresents the introductory welcome message from the conference proceedings
Towards Decentralized Reinforcement Learning Architectures for Social Dilemmas
Full text linkMulti-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 for multi-agent systems in order to understand and model societies. More specifically, using social dilemmas as the training ground, we present a novel learning architecture, Learning through Probing (LTP), where agents utilize a probing mechanism to incorporate how their opponent's behavior changes when an agent takes an action
Spatio-temporal networks: reachability, centrality and robustness
Full text linkRecent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Full text linkToday’s mobile phones are far from the mere communication devices they were 10 years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users’ location, activity, social setting, and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing
A Measurement Study on the Advertisements Displayed to Web Users Coming from the Regular Web and from Tor
Full text linkOnline 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 abused by cybercriminals to perform illegal activities such as malvertising. In this paper, we perform a measurement of malvertising from the user point of view. Our goal is to collect advertisements from a regular Internet connection and using The Onion Router in an attempt to understand whether using different technologies to access the Web could influence the probability of infection. We compare the data from our experiments to find differences in the malvertising activity observed. We show that the level of maliciousness is similar between the two types of accesses. Nevertheless, there are significant differences related to the malicious landing pages delivered in each type of access. Our results provide the research community with insights into how ad traffic is treated depending on the way users access Web content
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