Ofertas de proyectos

Mobile networks are continuously experiencing a profound transformation, driven by the emergence of groundbreaking applications and communication paradigms, such as the eagerly expected metaverse. In the coming years networks will face daunting challenges to meet the demands of these future applications. In this (r)evolution, Deep Learning is among the most promising techniques for achieving unprecedented levels of connectivity and user experience that can meet such demands.

In this project, you will have the opportunity to work on AI and machine learning applied to real-world problems around 5G and beyond networks. We will focus on hard open challenges, such as AI-based mobility management optimization and planning in future 6G networks, or creating cutting-edge network automation technology to facilitate the day-to-day operations of network engineers around the world.

Internet services are known to collect large amounts of personal information. As a result, more than half of EU citizens are concerned about their online privacy. In this context, data brokers are companies devoted to collecting and selling personal information to other companies. Data brokers are often implemented as third-party trackers, which allow them to gain visibility across the Internet. Recent works show this personal information is not only used for targeted advertising, but also for more obscure practices, such as price discrimination, credit scoring, phishing or identity theft. The project addresses these growing privacy concerns by enhancing ePrivo, a new online privacy observatory. ePrivo will continuously scan the Internet to unveil third-party trackers, the tracking methods they use and the data brokers behind them. A proof-of-concept prototype of ePrivo is already available at https://eprivo.eu. In this project, we will deploy the ePrivo service in production, extend its capabilities for web tracking and phishing detection, and release it under an open-source license. The results will be useful to the RIPE community, including ISPs, network operators, Internet users, policy makers and researchers.

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.

Cybersecurity is becoming an increasingly important challenge for all companies and individuals alike. While big names used to be the main targets in the past, as people's lives move online, anyone is nowadays a potential target for any kind of cyber-attack, ranging from phishing to ransomware or serious privacy issues. In order to fight against those ever-evolving threats, Machine Learning is increasingly being used behind the scenes to design better systems that are able of self-learning to boost detection rates and boost overall resilience to unknown attacks. As AI-based solutions penetrate products across the industry, a new kind of threat that is often overlooked is becoming more and more prominent and dangerous: adversarial machine learning (AML).

AML focuses on designing specific inputs to deceive a previously trained Machine Learning models into misclassifying them for a specific purpose. One of the main flaws of any state-of-the-art Machine Learning or Deep Learning algorithms is that they assume that the nature of the data they receive is systematically benign, which is generally the case but does not hold true when an adversarial input is received. The motivation behind altering a ML model into thinking that, for example, a new sample is benign when in fact is malicious can range from pure research to more serious real-life issues such as an autonomous car wrongly classifying a stop sign (and thus provoking a fatal accident) or a wrongly diagnosed disease because of a slightly manipulated magnetic resonance image.

This problem is no exception for Cybersecurity where companies wrongly assume that once the last AI-based product is deployed in their network, their employees are safe...

The application of Artificial Intelligence (AI) and Machine Learning (ML) to network security (AI4SEC) is paramount against cybercrime. While AI/ML is mainstream in domains such as computer vision and natural language processing, traditional AI/ML has produced below-par results in AI4SEC. Solutions do not properly generalize, are ineffective in real deployments, and are vulnerable to adversarial attacks. A fundamental limitation is the lack of AI/ML technology specific to network security. Due to their unique ability to learn and generalize over graph-structured information, graph-learning approaches, and in particular Graph Neural Networks (GNNs), have recently enabled groundbreaking applications in multiple fields where data are generally represented as graphs. Network security data are intrinsically relational, and initial research suggests that graph-structured representations and GNNs have the potential to become foundational to AI4SEC, in the way convolutional and recursive networks were to computer vision and natural language processing.
The goal of GRAPHS4SEC is to leverage graph data representations and modern GNN technology to conceive a new breed of robust GNN-based network security methods which could radically advance the AI4SEC practice. The objectives of GRAPHS4SEC are: (a) to investigate algorithmic methods that facilitate modeling and learning from graph-based network security data; (b) to compare the benefits and overheads of GNN-based AI4SEC to traditional AI/ML in terms of detection performance, generalization, scalability, and robustness against adversarial attacks; (c) to showcase the benefits and improvements of GRAPHS4SEC technology in four critical, real-world network security applications with significant impact for society, considering (in particular) the detection and early mitigation of phishing and fake/malicious websites, a threat among the most popular and society-wide harmful in today's Internet.