AI for Zero-Touch Management of Satellite Networks in B5G and 6G Infrastructures

Satellite Communication (SatCom) networks are become more and more integrated with the terrestrial telecommunication infrastructure. In this paper, we shows the current status of the still ongoing European Space Agency (ESA) project”Data-driven Network Controller Orchestration for Real time Network Management-ANChOR”. In particular, we propose a Long Short-Term Memory (LSTM)based methodology to drive the dynamic selection of the optimal satellite gateway station, which will be performed by combining different kinds of information (i.e. traffic profile, network and weather conditions). Some preliminary results on the real world dataset shows the effectiveness of the proposed approach

A hypergraph data model for expert-finding in multimedia social networks

Online Social Networks (OSNs) have found widespread applications in every area of our life. A large number of people have signed up to OSN for different purposes, including to meet old friends, to choose a given company, to identify expert users about a given topic, producing a large number of social connections. These aspects have led to the birth of a new generation of OSNs, called Multimedia Social Networks (MSNs), in which user-generated content plays a key role to enable interactions among users. In this work, we propose a novel expert-finding technique exploiting a hypergraph-based data model for MSNs. In particular, some user-ranking measures, obtained considering only particular useful hyperpaths, have been profitably used to evaluate the related expertness degree with respect to a given social topic. Several experiments on Last.FM have been performed to evaluate the proposed approach's effectiveness, encouraging future work in this direction for supporting several applications such as multimedia recommendation, influence analysis, and so on

Smart conversational user interface for recommending cultural heritage points of interest

Researchers and companies are making great efforts to create new ways of interaction with a increasing number and types of electronic devices, with particular attention to the conversational interface, whether spoken or written. The cultural heritage domain can bring many benefits from the great effort in this field, as a smart conversational system can provide both room/hotel/apartment information and information related to the area, natural or cultural points of interest, tourist routes, social events, history etc. The paper’s main goal is to present a quick and effective service to the tourists visiting a city for the first time. Learning the cultural preferences of the users can enhance customer satisfaction, since a smart system can propose them customised tours in order to reach point of interests according to the expressed preferences. The interaction with the user is simplified by a conversational interface (chatbot)

A prototype for anomaly detection in video surveillance context

Security has been raised at major public buildings in the most famous and crowded cities all over the world following the terrorist attacks of the last years, the latest one at the Bardo museum in the centre of Tunis. For that reason, video surveillance systems have become more and more essential for detecting and hopefully even prevent dangerous events in public areas. In this paper, we present a prototype for anomaly detection in video surveillance context. The whole process is described, starting from the video frames captured by sensors/cameras till at the end some well-known reasoning algorithms for finding potentially dangerous activities are applied. The conducted experiments confirm the efficiency and the effectiveness achieved by our prototype

Summarizing social media content for multimedia stories creation

This article represents an extended abstract of our previous work on multimedia summarization. In particular, we propose a novel summarization technique of social media content for multimedia stories creation, using a graph- based modeling approach and influence analysis methodologies to detect the most important multimedia objects related to one or more topics of interest. Consecutively, from the list of candidates, we obtain a multimedia summary exploiting a summarization model that satisfies several properties such as Priority (w.r.t. user keywords), Continuity, Variety and not Repetitiveness. The summary objects are finally arranged in a multimedia story

Summarizing social media content for multimedia stories creation

This article represents an extended abstract of our previous work on multimedia summarization. In particular, we propose a novel summarization technique of social media content for multimedia stories creation, using a graph- based modeling approach and influence analysis methodologies to detect the most important multimedia objects related to one or more topics of interest. Consecutively, from the list of candidates, we obtain a multimedia summary exploiting a summarization model that satisfies several properties such as Priority (w.r.t. user keywords), Continuity, Variety and not Repetitiveness. The summary objects are finally arranged in a multimedia story

A Community Detection Approach for Smart-Phone Addiction Recognition

In this paper, we present a novel approach for Smart-Phone Addiction recognition that leverages community detection algorithms from the Social Network Analysis (SNA) theory. Our basic idea is to model data concerning users’ behavior while they are using mobile devices as a particular social graph, discovering by means of SNA facilities patterns that better identify users with a high predisposition to smart phone addiction. Eventually, several experiments on a sample of users monitored for several weeks have been carried out to verify effectiveness of the proposed approach in correctly recognizing the related addiction degree

Credit Score Prediction Relying on Machine Learning

Financial institutions use a variety of methodologies to define their commercial and strategic policies, and a significant role is played by credit risk assessment. In recent years, different credit risk assessment services arose, providing Social Lending platforms to connect lenders and borrowers in a direct way without assisting of financial institutions. Despite the pros of these platforms in supporting fundraising process, there are different stems from multiple factors including lack of experience of lenders, missing or uncertain information about the borrower's credit history. In order to handle these problems, credit risk assessments of financial transactions are usually modeled as a binary problem based on debt repayment, going to apply Machine Learning (ML) techniques. The paper represents an extended abstract of a recent work, where some of the authors performed a benchmarking among the most used credit risk assessment ML models in the field of predicting whether a loan will be repaid in a P2P platform. The experimental analysis is based on a real dataset of Social Lending (Lending Club), going to evaluate several evaluation metrics including AUC, sensitivity, specificity and explainability of the models

Sentiment analysis on yelp social network

In this paper, we propose a novel data model for Multimedia Social Networks, i.e. particular social media networks that combine information on users belonging to one or more social communities together with the content that is generated and used within the related environments. The proposed model relies on the hypergraph data structure to capture and represent in a simple way all the different kinds of relationships that are typical of social media networks, and in particular among users and multimedia content. We also introduce some user and multimedia ranking functions to enable different applications. Finally, some experiments concerning effectiveness of the approach for supporting relevant information retrieval activities are reported and discussed