A blockchain-based distributed paradigm to secure localization services

In recent decades, modern societies are experiencing an increasing adoption of interconnected smart devices. This revolution involves not only canonical devices such as smartphones and tablets, but also simple objects like light bulbs. Named the Internet of Things (IoT), this ever-growing scenario offers enormous opportunities in many areas of modern society, especially if joined by other emerging technologies such as, for example, the blockchain. Indeed, the latter allows users to certify transactions publicly, without relying on central authorities or intermediaries. This work aims to exploit the scenario above by proposing a novel blockchain-based distributed paradigm to secure localization services, here named the Internet of Entities (IoE). It represents a mechanism for the reliable localization of people and things, and it exploits the increasing number of existing wireless devices and blockchain-based distributed ledger technologies. Moreover, unlike most of the canonical localization approaches, it is strongly oriented towards the protection of the users’ privacy. Finally, its implementation requires minimal efforts since it employs the existing infrastructures and devices, thus giving life to a new and wide data environment, exploitable in many domains, such as e-health, smart cities, and smart mobility

CulturAI: Semantic Enrichment of Cultural Data Leveraging Artificial Intelligence

In this paper, we propose an innovative tool able to enrich cultural and creative spots (gems, hereinafter) extracted from the European Commission Cultural Gems portal, by suggesting relevant keywords (tags) and YouTube videos (represented with proper thumbnails). On the one hand, the system queries the YouTube search portal, selects the videos most related to the given gem, and extracts a set of meaningful thumbnails for each video. On the other hand, each tag is selected by identifying semantically related popular search queries (i.e., trends). In particular, trends are retrieved by querying the Google Trends platform. A further novelty is that our system suggests contents in a dynamic way. Indeed, as for both YouTube and Google Trends platforms the results of a given query include the most popular videos/trends, such that a gem may constantly be updated with trendy content by periodically running the tool. The system has been tested on a set of gems and evaluated with the support of human annotators. The results highlighted the effectiveness of our proposal

Explainable Machine Learning Exploiting News and Domain-Specific Lexicon for Stock Market Forecasting

In this manuscript, we propose a Machine Learning approach to tackle a binary classification problem whose goal is to predict the magnitude (high or low) of future stock price variations for individual companies of the SP 500 index. Sets of lexicons are generated from globally published articles with the goal of identifying the most impactful words on the market in a specific time interval and within a certain business sector. A feature engineering process is then performed out of the generated lexicons, and the obtained features are fed to a Decision Tree classifier. The predicted label (high or low) represents the underlying company's stock price variation on the next day, being either higher or lower than a certain threshold. The performance evaluation we have carried out through a walk-forward strategy, and against a set of solid baselines, shows that our approach clearly outperforms the competitors. Moreover, the devised Artificial Intelligence (AI) approach is explainable, in the sense that we analyze the white-box behind the classifier and provide a set of explanations on the obtained results

Data-Driven Methodology for Knowledge Graph Generation Within the Tourism Domain

The tourism and hospitality sectors have become increasingly important in the last few years and the companies operating in this field are constantly challenged with providing new innovative services. At the same time, (big-) data has become the 'new oil' of this century and Knowledge Graphs are emerging as the most natural way to collect, refine, and structure this heterogeneous information. In this paper, we present a methodology for semi-automatic generating a Tourism Knowledge Graph (TKG), which can be used for supporting a variety of intelligent services in this space, and a new ontology for modelling this domain, the Tourism Analytics Ontology (TAO). Our approach processes and integrates data from Booking.com, Airbnb, DBpedia, and GeoNames. Due to its modular structure, it can be easily extended to include new data sources or to apply new enrichment and refinement functions. We report a comprehensive evaluation of the functional, logical, and structural dimensions of TKG and TAO

Ensembling and Dynamic Asset Selection for Risk-Controlled Statistical Arbitrage

In recent years, machine learning algorithms have been successfully employed to leverage the potential of identifying hidden patterns of financial market behavior and, consequently, have become a land of opportunities for financial applications such as algorithmic trading. In this paper, we propose a statistical arbitrage trading strategy with two key elements: an ensemble of regression algorithms for asset return prediction, followed by a dynamic asset selection. More specifically, we construct an extremely heterogeneous ensemble ensuring model diversity by using state-of-the-art machine learning algorithms, data diversity by using a feature selection process, and method diversity by using individual models for each asset, as well models that learn cross-sectional across multiple assets. Then, their predictive results are fed into a quality assurance mechanism that prunes assets with poor forecasting performance in the previous periods. We evaluate the approach on historical data of component stocks of the SP500 index. By performing an in-depth risk-return analysis, we show that this setup outperforms highly competitive trading strategies considered as baselines. Experimentally, we show that the dynamic asset selection enhances overall trading performance both in terms of return and risk. Moreover, the proposed approach proved to yield superior results during both financial turmoil and massive market growth periods, and it showed to have general application for any risk-balanced trading strategy aiming to exploit different asset classes

A Two-Step Feature Space Transforming Method to Improve Credit Scoring Performance

The increasing amount of credit offered by financial institutions has required intelligent and efficient methodologies of credit scoring. Therefore, the use of different machine learning solutions to that task has been growing during the past recent years. Such procedures have been used in order to identify customers who are reliable or unreliable, with the intention to counterbalance financial losses due to loans offered to wrong customer profiles. Notwithstanding, such an application of machine learning suffers with several limitations when put into practice, such as unbalanced datasets and, specially, the absence of sufficient information from the features that can be useful to discriminate reliable and unreliable loans. To overcome such drawbacks, we propose in this work a Two-Step Feature Space Transforming approach, which operates by evolving feature information in a twofold operation: (i) data enhancement; and (ii) data discretization. In the first step, additional meta-features are used in order to improve data discrimination. In the second step, the goal is to reduce the diversity of features. Experiments results performed in real-world datasets with different levels of unbalancing show that such a step can improve, in a consistent way, the performance of the best machine learning algorithm for such a task. With such results we aim to open new perspectives for novel efficient credit scoring systems

Workshop on Deep Learning and Large Language Models for Knowledge Graphs (DL4KG)

The use of Knowledge Graphs (KGs) which constitute large networks of real-world entities and their interrelationships, has grown rapidly. A substantial body of research has emerged, exploring the integration of deep learning (DL) and large language models (LLMs) with KGs. This workshop aims to bring together leading researchers in the field to discuss and foster collaborations on the intersection of KG and DL/LLMs

Evaluating neural word embeddings created from online course reviews for sentiment analysis

Social media are providing the humus for the sharing of knowledge and experiences and the growth of community activities (e.g., debating about different topics). The analysis of the user-generated content in this area usually relies on Sentiment Analysis. Word embeddings and Deep Learning have attracted extensive attention in various sentiment detection tasks. In parallel, the literature exposed the drawbacks of traditional approaches when content belonging to specific contexts is processed with general techniques. Thus, ad-hoc solutions are needed to improve the effectiveness of such systems. In this paper, we focus on user-generated content coming from the e-learning context to demonstrate how distributional semantic approaches trained on smaller context-specific textual resources are more effective with respect to approaches trained on bigger general-purpose ones. To this end, we build context-trained embeddings from online course reviews using state-of-the-art generators. Then, those embeddings are integrated in a deep neural network we designed to solve a polarity detection task on reviews in the e-learning context, modeled as a regression. By applying our approach on embeddings trained using background corpora from different contexts, we show that the performance is better when the background context is aligned with the regression context