Predictive Maintenance

The remaining useful life (RUL) estimation of a component is an interesting problem within the Prognostics and Health Management (PHM) field, which consists in estimating the number of time steps occurring between the current time step and the end of the component life. Being able to reliably estimate this value can lead to an improvement of the maintenance scheduling and a reduction of the costs associated with it. Data driven approaches are often used in the literature and they are the preferred choice over model-based approaches: in fact, not only they are easier to build, but the data over which they are built can be gathered easily in many industrial applications. During the last years, Long-Short Term Memory (LSTM) networks have found many applications in this area because they are suitable to deal with time-series data and because they inherently learn how to remember long-term dependencies, which can be really useful to uncover hidden patterns within the sensor data. Here […]


Visual Saliency Prediction

When human observers look at an image, attentive mechanisms drive their gazes towards salient regions. Emulating such ability has been studied for more than 80 years by neuroscientists and by computer vision researchers, while only recently, thanks to the large spread of deep learning, saliency prediction models have achieved a considerable improvement. Data-driven saliency has recently gained a lot of attention thanks to the use of Convolutional Neural Networks for predicting gaze fixations. In this project we go beyond standard approaches to saliency prediction, in which gaze maps are computed with a feed-forward network, and we present a novel model which can predict accurate saliency maps by incorporating neural attentive mechanisms. The core of our solution is a Convolutional LSTM that focuses on the most salient regions of the input image to iteratively refine the predicted saliency map. Additionally, to tackle the center bias present in human eye fixations, our model can learn a set of prior maps generated with […]


Ambient Assisted Living ChatBot

A chatbot is a computer program or an Artificial Intelligence agent which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. They may use sophisticated natural language processing systems and are either accessed via virtual assistants, via messaging apps or via individual organizations’ apps and websites. The aim of our project is to study and analyze a series of innovative technologies which, integrated together in a prototype managed by a virtual assistant, allows to renovate the concept of domotics as we know it. Also, since chatbots are user more and more in a working environment, our aim includes developing a virtual assistant integrated with a chatbot to back up maintenance activities in an industrial setting. The project is supported by POR FESR FVG Project and conducted as […]


Arabic Keyphrase Extraction

Arabic keyphrase extraction is a crucial task due to the significant and growing amount of Arabic text on the web generated by a huge population. It is becoming a challenge for the community of Arabic natural language processing because of the severe shortage of resources and published processing systems. In this paper we propose a deep learning based approach for Arabic keyphrase extraction that achieves better performance compared to the related competitive approaches. We also introduce the community with an annotated large-scale dataset of about 6000 scientific abstracts which can be used for training, validating and evaluating deep learning approaches for Arabic keyphrase extraction. Related publications: Helmy M., Vigneshram R. M., Serra G., Tasso C. Applying Deep Learning for Arabic Keyphrase Extraction. In: Proc. of the 4th International Conference on Arabic Computational Linguistics (ACLing 2018), November 17-19 2018, Dubai, United Arab Emirates. Resources: Arabic Abstracts Dataset


Automatic Keyphrase Extraction

Keyphrases (KPs) are phrases that “capture the main topic discussed on a given document”. More specifically, KPs are phrases typically one to five words long that appear verbatim in a document, and can be used to briefly summarize its content. The task of finding such KPs is called Automatic Keyphrase Extraction (AKE). Recently, AKE has received a lot of attention, because it has been successfully used in many natural language processing (NLP) tasks, such as text summarization, document clustering, or non-NLP tasks such as social network analysis or user modeling. AKE  approaches have been also applied in Information Retrieval of relevant documents in digital document archives which can contain heterogeneous types of items, such as books articles, papers etc. However, given the wide variety of lexical, linguistic and semantic aspects that can contribute to define a keyphrase, it difficult to design hand-crafted feature, and even the best performing algorithms hardly reach F1-Scores of 50% on the most common evaluation sets. […]


Predicting the Usefulness of Amazon Reviews Using Argumentation Mining

Argumentation is the discipline that studies the way in which humans debate and articulate their opinions and beliefs. Argumentation mining is a research area at the cross-road of many fields, such as computational linguistics, machine learning, artificial intelligence, natural-language processing. The main goal of argumentation mining is the automatically extraction and identification of arguments and their relations from natural language text documents. Internet users generate content at unprecedented rates. Building intelligent systems capable of discriminating useful content within this ocean of information is thus becoming a urgent need. In this paper, we aim to predict the usefulness of Amazon reviews, and to do this we exploit features coming from an off-the-shelf argumentation mining system. We argue that the usefulness of a review, in fact, is strictly related to its argumentative content, whereas the use of an already trained system avoids the costly need of relabeling a novel dataset. Results obtained on a large publicly available corpus support this hypothesis. Related […]