1,721,086 research outputs found
Community detection over feature-rich information networks: An eHealth case study
No full textIn 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%)
A benchmark of machine learning approaches for credit score prediction
No full textCredit 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
No full textExploiting 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
Detecting malicious reviews and users affecting social reviewing systems: A survey
No full textThe proliferation of attacks on On-line Social Networks (OSNs) has imposed particular attention by providers and users. This has an even higher importance for Social Reviewing Systems (SRSs), where users can be strongly conditioned by means of malevolent reviews and behavior of fake or camouflage accounts. There is a rich literature on the use of strong authentication means, encryption or privacy-preserving schemes for OSNs and SRSs, but these mechanisms only represent a first defense line. Advanced attacks may be able to bypass such a defense and to successfully threaten the system and harm its users. Therefore, it is needed to embed a second defence line to peculiar attack scenarios of SRSs that take advantage of user dynamics occurring within social networks. This survey focuses on the issue and solutions to detect malicious reviews and users so as to exclude them from social networks, protect legitimate and honest users and keep the credibility of the protected SRS as high as possible. Therefore, we provide an evaluation of the main detection solutions for the mentioned specific attacks against SRSs in according to different metrics on several standard datasets
Biomedical Spanish Language Models for entity recognition and linking at BioASQ DisTEMIST
No full textNamed 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
An Hypergraph Data Model for Expert Finding in Multimedia Social Networks
No full textNowadays, 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
Few-shot Named Entity Recognition: Definition, Taxonomy and Research Directions
Full text linkRecent years have seen an exponential growth (+98% in 2022 w.r.t. the previous year) of the number of research articles in the few-shot learning field, which aims at training machine learning models with extremely limited available data. The research interest toward few-shot learning systems for Named Entity Recognition (NER) is thus at the same time increasing. NER consists in identifying mentions of pre-defined entities from unstructured text, and serves as a fundamental step in many downstream tasks, such as the construction of Knowledge Graphs, or Question Answering. The need for a NER system able to be trained with few-annotated examples comes in all its urgency in domains where the annotation process requires time, knowledge and expertise (e.g., healthcare, finance, legal), and in low-resource languages. In this survey, starting from a clear definition and description of the few-shot NER (FS-NER) problem, we take stock of the current state-of-the-art and propose a taxonomy which divides algorithms in two macro-categories according to the underlying mechanisms: model-centric and data-centric. For each category, we line-up works as a story to show how the field is moving toward new research directions. Eventually, techniques, limitations, and key aspects are deeply analyzed to facilitate future studies
Evolutionary game theoretical on-line event detection over tweet streams
No full textCurrent Online Social Networks represent a means for the continuous generation and distribution of information, which is slightly changed when moving from a user to another during the traversing of the network. Such an amount of information can overcome the capacity of a single user to manage it, so it would be useful to reduce it so that the user is able to have a summary of the information flowing the network. To this aim, it is of crucial importance to detect events within such an information stream, composing of the most representative words containing in each information instance, representing the event described by the set of tweet categorized together. There is a vast literature on off-line event detection on data-sets acquired from online social networks, but a similar solid set of approaches is missing if the detection has to be done on-line, which is demanding by the current applications. The driving idea described in this paper is to realize on-line clustering of tweets by leveraging on evolutionary game theory and the replicator dynamics, which have been used with success in many classification problems and/or multiobjective optimizations. We have adapted and enhanced a evolutionary clustering from the literature to meet the needs of on-line tweet clustering. Such a solution has been implemented according to the Kappa architectural model and assessed against state-of-the art approaches showing higher values of topic and keyword recall on two realistic data-sets
On Attacks To Federated Learning and a Blockchain-empowered Protection
No full textFederated learning has been increasingly studied to cope with the scalability and privacy issues characterizing current and upcoming large-scale infrastructures, such as the Internet of Things, 5G networks, vehicular applications, and so on. This approach partitions the data storage and AI model training in a series of local learners, whose results are aggregated by a central server and then redistributed back to the learners to have a trained global model. However, despite avoiding outsourcing sensitive data to cloud-hosted services and fragmenting the workload for data processing, such a decentralized learning approach lays the overall solution open to various kinds of attacks, able to fully compromise the accuracy of the obtained global model. This study aims to quantitatively assess the impact of two widely-recognized attacks against federated learning and propose a tentative protection means by using blockchain
MOWIS: A system for building multimedia ontologies from web information sources
No full textDefining ontologies within the multimedia domain still remains a challenging task, due to the complexity of multimedia data and the related associated knowledge. In this paper, we propose: i) a novel multimedia ontology model that combine both low level descriptors and high level semantic concepts; ii) an automatic construction of ontologies using the Flickrweb services, that provide images, tags, Keywords: and sometimes useful annotation describing both the content of an image and personal interesting information. Eventually, we describe an example of automatic ontology construction in a specific domain. Copyright owned by the authors
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