Ofertes de projectes

La interacció en entorns de Realitat Virtual és complexa perquè els dispositius amb els quals s'ha d'operar són diferents, i a la vegada permeten obtenir informació sobre la posició de l'usuari, la orientació del seu cap, etc.
L'objectiu del projecte és la implementació i avaluació de diferents mètodes de manipulació i interacció que siguin intuïtius i fàcils d'usar en entorns de realitat virtual per models mèdics. També s'estudiarà la viabilitat i usabilitat de l'aplicació per a ser utilitzada en diagnòstic.

Recently, one of the leaders in Robotic Process Automation has adquired one of the main process mining tools (https://www.uipath.com/newsroom/uipath-acquires-process-gold-unparalleled-process-understanding). This is a confirmation of the potential link between the field of process mining and the field of robotic process automation.

In this project we will try to find out how strong is this link. By using real data from a company that is in trying to automate its processes, the student will dig into the field of process mining to propose a methodology to unleash the application of RPA.

In this project, there is a possibility to have a grant that covers the time invested.

This is precisely why companies such as Google have invested a lot of resources on exploring applications of GNN, some of which are as mediatic as Alpha Fold (recently solving the Protein-Folding problem), or used by Goolge Maps to make predictions of the expected traffic of a road. In this regard, the goal of this thesis is to continue investigating about the potential of GNNs when applied to the field of Computer Networks. Our goal is to provide GNN-based solutions that make computer networks run more autonomously and in a more efficient way.

Current quantum hardware, which has on the order of tens to hundreds of physical qubits, is insufficient to run these important quantum algorithms. Scaling these devices even to a moderate size with low error rates has proven extremely challenging. Due to these challenges, as well as developing technology for communicating between different quantum chips, we expect quantum hardware to scale via a modular approach similar to how a classical computer can be scaled increasing the number of processors not just the size of the processors.

Goal:

The goal of the thesis is to understand the communications pattern of a set of quantum computers communicating in a modular approach. This will be achieved by a quantum simulator running a set of relevant quantum algorithms and generating a dataset of the required communications. Then analyzing this dataset and proposing early solutions, both from a computer architecture perspective as well as from the communication technologies needed for this. In addition we will also consider ML-based solution for scheduling, therefore the dataset generated becomes a training set for a  Graph Neural Network-based solution.

Graph Neural Networks (GNN) have been recently proposed to learn, model and generalize over graph structured data. Computer Networks are fundamentally graphs, and many of its relevant characteristics -such as topology and routing- are represented as graph-structured data. 

GNN are a central tool to apply ML techniques to Computer Networks. GNN can learn the relationship of complex network characteristics and build relevant models that can be useful to plan and manage a network. In combination with Deep-Reinforcement Learning (DRL) techniques, GNN can help developing autonomous network optimization mechanisms that will result in unprecedented performance, achieving the ultimate vision of self-driving networks.

The Barcelona Neural Networking Center (https://bnn.upc.edu) is a new research initiative of UPC with the main goal of carrying out fundamental research in the field of Graph Neural Networks applied to Computer Networks, and providing education and training to the new generation of Computer Networking students.

The goal of this thesis is to enable the application of Deep Reinforcement Learning techniques for optimization problems in very large and complex scenarios, in this case LEO satellite communication networks. Inspired by the last related work from OpenAI (https://openai.com/blog/evolution-strategies/), we will explore time-efficient alternatives to train a Graph Neural Network based DRL agent on large optimization scenarios in a reasonable amount of time.

Improvement of OpenMP library for HPC on many core systems

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.

The goal of this thesis is to enable the application of Deep Reinforcement Learning techniques for optimization problems in very large and complex scenarios. Inspired by the last related work from OpenAI (https://openai.com/blog/evolution-strategies/), we will explore time-efficient alternatives to train a Graph Neural Network based DRL agent on large optimization scenarios in a reasonable amount of time.

It is known that humans cannot interpret visual depictions equally if some parameters are changed such as color palettes, size of marks, etc. However, it is still unkown the value ranges of certain parameters for a set of commonly used visualization techniques such as heatmaps, for example. The goal of this project is to explore the ability to carry out visualization tasks using different visualization techniques in desktop and Virtual Reality environments according to the visual variables chosen.

The medical records of each patient contain textual information about the clinical evolution of the patient (including drugs, chemicals, diseases, symptoms and body parts). This corpus has already been represented in a structured format as a set of semantic graphs, using Neo4J graph database.

This thesis would aim at the acquisition of patterns of clinical behavior from these graphs. These patterns would be specifically devoted to provide help in diagnosis to the medical community in primary care. For example, we can get to automatically infer that a certain drug has a previously unknown side effect, or that patients suffering from a certain disease develop certain symptoms in a certain period of time. A simple example of pattern might be "patient has fever and is prescribed ibuprofen -(after x days) - patient has fever and soreness - (after y days) - patient has fever and breathing difficulty -> patient diagnosed with COVID".

The thesis would be a continuation of a previous master thesis in which we have built a baseline system that is able to extract simple rules whose elements are generalized codes. However, we want to extend this system to deal with sequence patterns as the ones mentioned before, eventually composed by sentences in Natural Language.

Since we have an annotated corpus of medical records, the project might use supervised and semi-supervised machine learning techniques as well as more standard data mining techniques.

The main objective of this project is to apply the most recent advances in Deep Learning, including Deep Reinforcement Learning and Graph Neural Networks, to uncover the particular methods used to track Internet users and collect PI.

This project will be useful for both Internet users and the research community, and will produce open source tools, real data sets, and publications revealing most privacy attempting practices.

Some preliminary results of our work in this area were recently published in Proceedings of the IEEE (IF: 9.237) and featured in a Wall Street Journal article.

More info at:

http://personals.ac.upc.edu/pbarlet/papers/web-tracking.survey2015.pdf

http://blogs.wsj.com/digits/2015/08/04/7-ways-youre-being-tracked-online-and-how-to-stop-it/

Wireless mesh networks consist of mesh devices that opportunistically create wireless links to build the network topology. Due to its nature, such networks are specially dynamic and prone to anomalies. It is thus essential to continuously monitor the network conditions and detect misbehavior of its components, as nodes, links and routing protocol.

During the project it will be set up a dataset with measurements obtained from a production wireless mesh network. Different machine learning techniques will be used to produce quality parameters, and identify network anomalies.

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 goal of this thesis is to enable the application of Deep Reinforcement Learning techniques for optimization problems in very large and complex scenarios, in this case Data Center Networks. Inspired by the last related work from OpenAI (https://openai.com/blog/evolution-strategies/), we will explore time-efficient alternatives to train a Graph Neural Network based DRL agent on large optimization scenarios in a reasonable amount of time.

In many teaching applications such as the study of anatomy, the student is required to obtain a three-dimensional view of the models or structures to be analyzed. This goal is necessary for both individual study and class follow-up. There are some VR-based systems that usually use immersive VR HMD. However, these systems require an infrastructure and cost that is often not affordable.

This master's thesis aims to analyze the feasibility of a remote VR system based on the use of mobile devices with cardboard glasses and low-cost interaction devices. It will start from a system based on HTC-VIVES programmed with Unity. Different portability alternatives to the new platform will be analyzed both in terms of the rendering of the models (locally or on a server) and the limitations of the interaction and connection between students and teacher. Once the advantages/limitations have been analyzed, a prototype will be developed with basic interaction techniques and its usability will be analyzed. The models that will be visualized will be anatomical structures modeled with triangles. For more information contact the directors.

How to apply: Please send an email to pbarlet@ac.upc.edu with your CV and academic file (pdf can be generated from the Raco).