1,720,971 research outputs found
WiP: Smart services for an augmented campus
No full textTechnological progress in recent years has allowed the design of new intelligent learning systems in smart environments aiming to facilitate users' lives. As a consequence, besides making use of traditional sensors for monitoring the quantities of interest, such systems can also benefit from information obtained from the users' smart devices, which can now be considered as additional sensing tools. In this article, we present the design of a novel system based on the fog computing paradigm that can improve the services offered to users on a smart campus by using different smart devices, i.e., smartphones, smartwatches, tablets, smartcameras and so on. In particular, we will describe a system in which several smart devices will collect sensory and context information, whilst the cloud will aggregate and analyze this data to extract information of particular interest. The main challenge of this project is to create an intelligent platform that allows new software modules to be added without having to re-design the entire architecture, and that can provide new services to campus users or improve existing ones
Towards a smart campus through participatory sensing
No full textIn recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide users with more and more functions that make them real sensing platforms. Exploiting the capabilities offered by smartphones, users can collect data from the surrounding environment and share them with other entities in the network thanks to existing communication infrastructures, i.e., 3G/4G/5G or WiFi. In this work, we present a system based on participatory sensing paradigm using smartphones to collect and share local data in order to monitor make a campus 'smart'. In particular, our system infers the activities performed by users (e.g., students) in a campus in order to identify trends and behavioral patterns. This information allows the system to decide in real-Time which actions are needed to provide the best possible services to users, according to their needs and preferences
A Resilient Smart Architecture for Road Surface Condition Monitoring
No full textNowadays, road surface condition monitoring is a challenging problem that cannot be addressed with traditional techniques. In this paper we propose an architecture for monitoring the condition of road surfaces based on the paradigm of Mobile Crowdsensing. First, a surface detection module extracts high level features from raw data, indicating the presence of hazards. Then, in order to make the system resilient to attacks, the system exploits a reputation module to identify malicious users and filter out unreliable data. Finally, a truth discovery module aggregates the resulting information to obtain the desired truth values. Experiments carried out on a real world dataset prove the resilience of the proposed system to different attacks and the accuracy achieved
A Smart Assistant for Visual Recognition of Painted Scenes
Full text linkNowadays, smart devices allow people to easily interact with the surrounding environment thanks to existing communication infrastructures, i.e., 3G/4G/5G or WiFi. In the context of a smart museum, data shared by visitors can be used to provide innovative services aimed to improve their cultural experience. In this paper, we consider as case study the painted wooden ceiling of the Sala Magna of Palazzo Chiaramonte in Palermo, Italy and we present an intelligent system that visitors can use to automatically get a description of the scenes they are interested in by simply pointing their smartphones to them. As compared to traditional applications, this system completely eliminates the need for indoor positioning technologies, which are unfeasible in many scenarios as they can only be employed when museum items are physically distinguishable. Experimental analysis aimed to evaluate the performance of the system in terms of accuracy of the recognition process, and the obtained results show its effectiveness in a real-world application scenario
Assisted labeling for spam account detection on twitter
Full text linkOnline Social Networks (OSNs) have become increasingly popular both because of their ease of use and their availability through almost any smart device. Unfortunately, these characteristics make OSNs also target of users interested in performing malicious activities, such as spreading malware and performing phishing attacks. In this paper we address the problem of spam detection on Twitter providing a novel method to support the creation of large-scale annotated datasets. More specifically, URL inspection and tweet clustering are performed in order to detect some common behaviors of spammers and legitimate users. Finally, the manual annotation effort is further reduced by grouping similar users according to some characteristics. Experimental results show the effectiveness of the proposed approach
A Federated Learning Approach for Distributed Human Activity Recognition
No full textIn recent years, the widespread diffusion of smart pervasive devices able to provide AI-based services has encouraged research in the definition of new distributed learning paradigms. Federated Learning (FL) is one of the most recent approaches which allows devices to collaborate to train AI-based models, whereas guarantying privacy and lower communication costs. Although different studies on FL have been conducted, a general and modular architecture capable of performing well in different scenarios is still missing. Following this direction, this paper proposes a general FL framework whose validity is assessed by considering a distributed activity recognition scenario in which users' personal devices are employed as the basis of the sensing infrastructure. Experimental analysis was performed to evaluate the effectiveness of the architecture as compared with a centralized approach, under different settings. Results demonstrate the versatility and functionality of the proposed solution
SpADe: Multi-Stage Spam Account Detection for Online Social Networks
Full text linkIn recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest of the network, as well as data and metadata representing the content shared. However, obtaining all this information can be computationally expensive, or even unfeasible, on massive networks. Driven by these motivations, in this paper we propose SpADe, a multi-stage Spam Account Detection algorithm with reject option, whose purpose is to exploit less costly features at the early stages, while progressively extracting more complex information only for those accounts that are difficult to classify. Experimental evaluation shows the effectiveness of the proposed algorithm compared to single-stage approaches, which are much more complex in terms of features processing and classification time
SmartWave: A Smart Platform for Marine Environmental Monitoring
No full textIn recent years, the interest in the study of seas and oceans has dramatically increased as they are considered of primary importance for forecasting catastrophic events or for supporting blue economy, as well as the marine tourism, improving the tourist reception or enhancing any marine-related activity. This led to the development of IT platforms that allow to monitor the marine environment and provide a number of services to different kinds of final users, whether they are private individuals interested in the status of the seas, or companies whose business depends on the marine environmental monitoring. The main limitations of current platforms are due to such a difference between free trials, which often focus only on specific aspects of deep waters, and subscriptions, which provide analyzes whose reliability is generally not proportional to the costs. This paper presents SmartWave, a project funded by Regione Sicilia (European Regional Development Fund), that aims to develop a novel IT platform to observe and predict phenomena that characterize the marine environment, while also providing the consumer with a unified portal to collect, access and analyze marine-related information. To achieve this goal, one of the main challenges of this project is to aggregate and standardize heterogeneous data from multiple sources in order to offer very accurate information to private or business consumers
A Hybrid Recommender System for Cultural Heritage Promotion
No full textAssisting users during their cultural trips is paramount in promoting the heritage of a territory. Recommender Systems offer the automatic tools to guide users in their decision process, by maximizing the adherence of the proposed contents with the particular preferences of every single user. However, traditional recommendation paradigms suffer from several drawbacks which are exacerbated in Cultural Heritage scenarios, due to the extremely wide range of users behaviors, which may also depend on their different educational backgrounds. In this paper, we propose a Hybrid recommender system which combines the four most common recommendation paradigms, namely collaborative filtering, popularity-, knowledge-, and content-based, according to different hybridization strategies. Experimental evaluation shows the versatility of the hybrid recommender with respect to the other paradigms adopted individually
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