1,721,026 research outputs found
A deep learning based community detection approach
No full textCommunity Detection in On Line Social Networks is a classic feature in networked systems, from the fields of biology, economics, politics and computer science, as well. This paper describes a novel Community Detection method based on a deep learning approach, facing the challenging problems related to the dimensions of the involved data structures, and proposing a novel convolutional technique particularly useful for sparse matrices. Several experiments have been reported and discussed in real scenarios
A deep learning based chatbot for cultural heritage
No full textIn this paper we propose an entertainment Chatbot based on the sequence to sequence model according to the Enconder-Decoder framework based on GRU cells for supporting user's cultural heritage path. A preliminary evaluation about the efficiency of the proposed approach has been performed asking to 10 users to interact with the Chatbot on cultural items of Campania Region
Industrial cyber-physical systems protection: A methodological review
No full textUbiquitous utilization of Information and Communication Technologies in modern manufacturing plants has transformed them into Cyber-Physical Systems (CPSs), making them susceptible to cyber-attacks, which can have huge economic and social impact. In this paper, we focus on the security issues that may affect the Industrial Control System (ICS) supervising an industrial establishment. The purpose of this work is to provide readers with an up-to-date view of the methodologies that current literature suggests as the most appropriate to defend an ICS against cyber-attacks. We firstly provide a classification of existing attacks according to the methodology used by the attacker. Subsequently, we propose a classification of defensive countermeasures in Model and Artificial Intelligence-based approaches by analyzing most recent research contributions. Furthermore, we describe the most used datasets in literature that are available to researchers and practitioners. We conclude the paper by discussing today's open issues, which need to be addressed to cope with the increasing complexity of ICS security
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%)
Harnessing Cognitively Inspired Predictive Models to Improve Investment Decision-Making
No full textIn the last years, researchers and practitioners have focused on defining portfolio optimization approaches. This task aims to identify a suitable distribution of assets for maximizing profits and minimizing risks, also offering protection against unexpected market behaviors. Nevertheless, the state-of-the-art approaches encounter significant limitations due to the complex nature of the task: (1) forecasting of non-stationary, non-linearity and volatile stock price; (2) budget allocation over different stocks satisfying multi-objective objective function; (3) risk costs can significantly affect the effectiveness of the designed approaches. In this paper, we propose a cognitively inspired framework for portfolio optimization by integrating deep learning-based stock forecasting for maximizing the revenue and portfolio diversification and Shape Ratio for minimizing the risk. Furthermore, the cognitively inspired forecasting module relies on the LSTM-based approach which combines historical financial data and technical indicators. Hence, this approach addresses the portfolio optimization task with the aim of designing more and more cognitive agents that perform autonomous actions for supporting decision-making. To make these agents cognitive, we further integrate stock forecasting into the portfolio optimization model, also investigating the main factors affecting both stock forecasting and portfolio optimization tasks. The proposed framework has been evaluated in two stages on a real-world dataset, composed of four years of information about stocks from six different areas. Firstly, we compare the proposed forecasting models based on LSTM and GRU, pointing out that the former achieves higher effectiveness results although the latter has a shorter training time. Finally, the proposed framework has been compared with different baselines, obtaining a net difference of $168 at the maximum. Finally, we compare the proposed approach w.r.t. several baselines in terms of total revenue, also providing an ablation analysis to investigate how stock prediction might support investors in dealing with portfolio optimization task
How does user-generated content on Social Media affect stock predictions? A case study on GameStop
No full textA survey of Big Data dimensions vs Social Networks analysis
No full textThe 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
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
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