A deep attention based approach for predictive maintenance applications in IoT scenarios

Purpose: The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware. Design/methodology/approach: In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware. Findings: The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques. Research limitations/implications: A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time. Practical implications: The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature. Originality/value: The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness

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

Community detection over feature-rich information networks: An eHealth case study

In this paper, we present a novel graph data model to analyze eating habits and physical activities of a large number of persons, aiming at automatically detect groups of users sharing the same lifestyle using Social Network Analysis facilities. We focus our attention on physical activities and dietary habits of users because they often can be correlated to several types of diseases. Indeed, they constitute a real example of feature-rich information network (containing multi-relational and heterogeneous data) that can support different analytics. Furthermore, a novel community detection approach has been exploited to detect groups of users sharing same behaviors/habits within the obtained information network by leveraging nodes’ and edges’ properties. Finally, an extensive experimentation on simulated and real networks has been performed for evaluating the proposed approach in terms of efficiency and effectiveness, outperforming some of the most diffused state-of-the-art approaches (up to 8%)

An Hypergraph Data Model for Expert Finding in Multimedia Social Networks

Nowadays, the tremendous usage of multimedia data within Online Social Networks (OSNs) has led the born of a new generation of OSNs, called Multimedia Social Networks (MSNs). They represent particular social media networks – particularly interesting for Social Network Analysis (SNA) applications – that combine information on users, belonging to one or more social communities, together with all the multimedia contents that can be generated and used in the related environments. In this work, we present 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 preliminary experiments on Last.fm show the effectiveness of the proposed approach, encouraging the future work in this direction

A survey of Big Data dimensions vs Social Networks analysis

The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated data (text, video, image and audio), data heterogeneity and high speed generation rate. More in detail, the analysis of user generated data by popular social networks (i.e Facebook (https://www.facebook.com/), Twitter (https://www.twitter.com/), Instagram (https://www.instagram.com/), LinkedIn (https://www.linkedin.com/)) poses quite intriguing challenges for both research and industry communities in the task of analyzing user behavior, user interactions, link evolution, opinion spreading and several other important aspects. This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V’s)

Biomedical Spanish Language Models for entity recognition and linking at BioASQ DisTEMIST

Named Entity Recognition and Entity Linking systems usually require a rich and annotated dataset to be trained and produce high-quality results, but the annotation process is time consuming and expensive, especially when it needs the effort of domain experts, such as in the medical field. However, recent developments in Natural Language Processing (NLP) allow us to easily use transformer language models which have been pre-trained on a huge quantity of data (often coming from specialized domains), and thus obtain high performance without excessive efforts. In this work, we outline our approach to NER and EL tasks on Spanish clinical notes for the DisTEMIST track at the BioASQ 2022 challenge. Our results demonstrate that the proposed methodology based on biomedical pre-trained language models turned out the best for the NER task with a ∼ 3% higher F1 w.r.t. the second-best solution

Analysis of community in social networks based on game theory

In this paper we describe a novel algorithm based on Game Theory for Community Detection in Social Networks. Extending several Game Theoretic approaches that are well established in the literature, we propose a novel algorithm that outperforms the previous ones in terms of computational complexity and efficiency of results, as proved by a number of experiments on standard data sets. © 2019 IEEE

A benchmark of machine learning approaches for credit score prediction

Credit risk assessment plays a key role for correctly supporting financial institutes in defining their bank policies and commercial strategies. Over the last decade, the emerging of social lending platforms has disrupted traditional services for credit risk assessment. Through these platforms, lenders and borrowers can easily interact among them without any involvement of financial institutes. In particular, they support borrowers in the fundraising process, enabling the participation of any number and size of lenders. However, the lack of lenders’ experience and missing or uncertain information about borrower's credit history can increase risks in social lending platforms, requiring an accurate credit risk scoring. To overcome such issues, the credit risk assessment problem of financial operations is usually modeled as a binary problem on the basis of debt's repayment and proper machine learning techniques can be consequently exploited. In this paper, we propose a benchmarking study of some of the most used credit risk scoring models to predict if a loan will be repaid in a P2P platform. We deal with a class imbalance problem and leverage several classifiers among the most used in the literature, which are based on different sampling techniques. A real social lending platform (Lending Club) data-set, composed by 877,956 samples, has been used to perform the experimental analysis considering different evaluation metrics (i.e. AUC, Sensitivity, Specificity), also comparing the obtained outcomes with respect to the state-of-the-art approaches. Finally, the three best approaches have also been evaluated in terms of their explainability by means of different eXplainable Artificial Intelligence (XAI) tools

Deep-learning-based community detection approach on multimedia social networks

Exploiting multimedia data to analyze social networks has recently become one the most challenging issues for Social Network Analysis (SNA), leading to defining Multimedia Social Networks (MSNs). In particular, these networks consider new ways of interaction and further relationships among users to support various SNA tasks: influence analysis, expert finding, community identifica-tion, item recommendation, and so on. In this paper, we present a hypergraph-based data model to represent all the different types of relationships among users within an MSN, often mediated by multimedia data. In particular, by considering only user-to-user paths that exploit particular hyperarcs and relevant to a given application, we were able to transform the initial hypergraph into a proper adjacency matrix, where each element represents the strength of the link between two users. This matrix was then computed in a novel way through a Convolutional Neural Network (CNN), suitably modified to handle high data sparsity, in order to generate communities among users. Several experiments on standard datasets showed the effectiveness of the proposed methodology compared to other approaches in the literature