Ofertas de proyectos

Internet services are known to collect large amounts of personal information (PI). As a result, more than half of EU citizens are concerned about their online privacy. In this context, data brokers are companies devoted to collect and sell PI to other companies. Data brokers are often implemented as third-party trackers, which allow them to gain visibility across the Internet. Recent works show that collected PI is not only used for targeted advertising, but also for more obscure practices, such as price discrimination, background scanning, phishing or identity theft.

In this project, you will collaborate in the development of EPRIVO, the first European-wide online privacy observatory. EPRIVO will continuously scan the Internet, from multiple locations across Europe, in the search of third-party trackers that do not respect basic privacy rights and current EU regulations (GDPR 2016/679). Quantitative results will be published through an online service developed within the project. Such results will be useful to Internet users, policy makers, website owners and researchers.

El objetivo principal es diseñar, desarrolar e implementar un producto mínimo viable. Juntamente con los directores se tendran que: 

• Elegir la pila de tecnología
• Diseñar y configurar la infraestructura
• Seleccionar kit de herramientas de desarrollo
• Diseñar e implementar el diseño de la base de datos
• Crear la arquitectura de aplicaciones
• Garantizar la escalabilidad de la aplicación
• Garantizar la escalabilidad de la infraestructura
• Asegurar la calidad
• Garantizar la ciberseguridad
• Explorar nuevas tecnologías para implementar
• Garantizar el uso eficiente de las tecnologías.
• Evaluar la implementación

The last few years have seen an increasing wave of attacks with serious economic and privacy damages, which evinces the need for accurate Network Intrusion Detection Systems (NIDS). Recent works proposed the use of Machine Learning (ML) techniques for building such systems (e.g., decision trees, neural networks). However, existing ML-based NIDS do not generalize well to other network scenarios and they are barely robust to common adversarial attacks. This TFM will explore the potential of using graph representations of network flows together with Graph Neural Networks for building more robust NIDS that can better generalize to other networks.

The syntactic structure of a sentence can be represented as a tree where vertices are words and arcs indicate syntactic dependencies between words (Kübler et al 2012). In languages, these structures are typically planar, namely no two arcs cross when drawn above the sentence (Ferrer-i-Cancho et al 2018). Another property of these structures is that the distance between syntactically related words is much smaller than expected in a shuffling the sentence (Ferrer-i-Cancho et al 2022), a fact that lead to the formulation of the syntactic dependency distance minimization principle (Ferrer-i-Cancho 2004).

Syntactic dependency parsing is the branch of computational linguistic concerned with the extraction of syntactic dependency structures from raw text (Kübler et al 2012). This research proposal is focused on unsupervised syntactic dependency parsing, namely methods to extract syntactic dependency structures from unlabelled data (Han et al 2020). Although these methods can be extremely complex, here we aim to start from simple by taking Yuret's (1998) proposal as starting point to build from it more complex methods. Yuret's (1998) unsupervised parser consists of three steps:
1) training, i.e. learning the statistics of word co-occurrences in texts
2) generating a weighted graph where all vertices are the words that have appeared during training and weights are the mutual information between word pairs.
3) parsing a new sentence and thus obtaining its syntactic dependency structure. This structure is the maximum spanning tree from the subgraph of the weighted graph in Step 2 that is induced by the words of the sentence.

In Yuret's proposal, the maximum spanning tree in Step 3 is restricted to be planar. Interestingly, it has been argued that planarity may be a side-effect of pressure to reduce the distance between syntactically related word rather than a direct pressure of sentence to be planar (Gómez-Rodríguez & Ferrer-i-Cancho 2017, Gómez-Rodriguez, Christiansen & Ferrer-i-Cancho 2022). Therefore, the main goal of this project is to explore alternative unsupervised parsing methods:
a) an unsupervised parser that does not impose planarity. That is, Step 3 consists of computing a maximum spanning tree using classic algorithms for calculating maximum spanning trees.
b) an unsupervised parser that does not impose planarity but incorporates syntactic dependency distance minimization and thus tends to produce planar syntactic dependency structures naturally.

The bulk of the project consists of implementing simple parsers of the sort of a-b) and evaluating their performance. The parsers will be applied to human languages and also to sequences that other species produce (great apes and cetaceans).

This project will be developed in the environment of the LQMC research group https://lqmc.upc.edu/.

REFERENCES

Ferrer-i-Cancho, R (2004). Euclidean distance between syntactically linked words. Physical Review E 70, 056135.
Gómez-Rodríguez, C. & Ferrer-i-Cancho, R. (2017). Scarcity of crossing dependencies: a direct outcome of a specific constraint? Physical Review E 96, 062304.
Gómez-Rodríguez, C., Christiansen, M. & Ferrer-i-Cancho, R. (2022). Memory limitations are hidden in grammar. Glottometrics 52, 39-64.
Ferrer-i-Cancho, R. Gómez-Rodríguez, R., Esteban, J.L. (2018). Are crossing dependencies really scarce? Physica A: Statistical Mechanics and its Applications 493, 311-329.
Ferrer-i-Cancho, R., Gómez-Rodríguez & Esteban, J.L. & Alemany-Puig, L. (2022). Optimality of syntactic dependency distances. Physical Review E 105, 014308.
Han, W., Jiang, Y., Tou Ng, H. & Tu, K. (2020). A survey of unsupervised dependency parsing. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2522¿2533, Barcelona, Spain. International Committee on Computational Linguistics.
Kübler, S., McDonald, R. & Nivre, J. (2009). Dependency parsing. Synthesis Lectures on Human Language Technologies 1, 1-127.
Yuret, D. (1998). Discovery of linguistic relations using lexical attraction. PhD Thesis, MIT.