Ricerc@Sapienza

In the field of mortality, the Lee–Carter based approach can be considered the milestone
to forecast mortality rates among stochastic models. We could define a “Lee–Carter model family”
that embraces all developments of this model, including its first formulation (1992) that remains the
benchmark for comparing the performance of future models. In the Lee–Carter model, the kt parameter,
describing the mortality trend over time, plays an important role about the future mortality behavior.

In this paper we present a novel mechanism to
get explanations that allow to better understand
network predictions when dealing with sequential
data. Specifically, we adopt memory-based net-
works — Differential Neural Computers — to ex-
ploit their capability of storing data in memory and
reusing it for inference. By tracking both the mem-
ory access at prediction time, and the information
stored by the network at each step of the input
sequence, we can retrieve the most relevant input

The use of mobile phones or smartphones has become so widespread that most people rely on them for many services and applications like sending e-mails, checking the bank account, accessing cloud platforms, health monitoring, buying on-line and many other applications where sharing sensitive data is required. As a consequence, security functions are important in the use of smartphones, especially because most of the applications require the identification and authentication of the device in mobility.

Social Networks (SN) and Instant Messaging Apps (IMA) are more and more engaging people in their personal relations taking possession of an important part of their daily life. Huge amounts of multimedia contents, mainly photos, are poured and successively shared on these networks so quickly that is not possible to follow their paths. This last issue surely grants anonymity and impunity thus it consequently makes easier to commit crimes such as reputation attack and cyberbullying.

Precision agriculture is gaining increasing attention because of the possible reduction of agricultural inputs (e.g., fertilizers and pesticides) that can be obtained by using hightech equipment, including robots. In this paper, we focus on an agricultural robotics system that addresses the weeding problem by means of selective spraying or mechanical removal of the detected weeds. In particular, we describe a deep learning based method to allow a robot to perform an accurate weed/crop classification using a sequence of two Convolutional Neural Networks (CNNs) applied to RGB images.

Due to the stringent requirements imposed by state-of-the-art technologies, most of modern spacecrafts are now equipped with very large substructures such as antennas, deployable booms and solar arrays. However, while the size of these elements increases, their mass is limited by the rocket maximum take-off weight and, therefore, they result to be lightweight and very flexible. A natural concern derived from this trend is that these structures are now more susceptible to possible structural damages during launch phase or operational life (impacts, transient thermal states and fatigue).

Word Sense Disambiguation (WSD) is the task of associating a word in context with one of its meanings. While many works in the past have focused on raising the state of the art, none has even come close to achieving an F-score in the 80% ballpark when using WordNet as its sense inventory. We contend that one of the main reasons for this failure is the excessively fine granularity of this inventory, resulting in senses that are hard to differentiate between, even for an experienced human annotator.

Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling. Nonetheless, an analysis of the latest state-of-the-art multilingual systems reveals the difficulty of bridging the wide gap in performance between high-resource (e.g., English) and low-resource (e.g., German) settings. To overcome this issue, we propose a fully language-agnostic model that does away with morphological and syntactic features to achieve robustness across languages.

Semantic Role Labeling (SRL) is deeply dependent on complex linguistic resources and sophisticated neural models, which makes the task difficult to approach for non-experts. To address this issue we present a new platform named Intelligible Verbs and Roles (InVeRo). This platform provides access to a new verb resource, VerbAtlas, and a state-of-the-art pre-trained implementation of a neural, span-based architecture for SRL.

While contextualized embeddings have produced performance breakthroughs in many Natural Language Processing (NLP) tasks, Word Sense Disambiguation (WSD) has not benefited from them yet. In this paper, we introduce QBERT, a Transformer based architecture for contextualized embeddings which makes use of a coattentive layer to produce more deeply bidirectional representations, better-fitting for the WSD task.