Learning by correlation for computer vision applications: from Kernel methods to deep learning

Learning to spot analogies and differences within/across visual categories is an arguably powerful approach in machine learning and pattern recognition which is directly inspired by human cognition. In this thesis, we investigate a variety of approaches which are primarily driven by correlation and tackle several computer vision applications

Generative Probabilistic Models of Biological and Social Network Data

Useat monimutkaiset systeemit voidaan esittää verkkona, jossa kaaret yhdistävät solmuja. Soluissa molekyylien, kuten proteiinien, vuorovaikutukset muodostavat verkon, ja sosiaalinen systeemi voi koostua yksittäisten toimijoiden suhteista. Verkkojen analysointi on kehittynyt pienen ihmisjoukon välisten suhteiden tutkimisesta valtavien monimutkaisten verkkojen, kuten Facebookin ja My- Spacen tapaisten kommunikaatioverkkojen tai solun laajuisten molekyyliverkkojen, analysointiin. Sen lisäksi, että käytännön verkot ovat erittäin suuria, ne ovat tyypillisesti harvoja ja epätäydellistä. Tällaisten verkkojen menestyksekäs analysointi vaatii kehittyneiden laskennallisten menetelmien käyttöä. Tämän diplomityön aiheena on uusi generatiivinen todennäköisyysmalliperhe, vuorovaikutuskomponenttimallit. Se on suunniteltu tiheästi kytkettyjen aliverkkojen löytämiseen kohinaisesta verkkodatasta. Tällaisilla aliverkoilla on monia tulkintoja käytännön sovelluksissa, kuten toiminnalliset geenimoduulit proteiinien vuorovaikutusverkoissa tai yhteisöt sosiaalisissa verkoissa. Malliperhe on suunniteltu mahdollisimman yksinkertaiseksi, jotta se olisi ymmärrettävä ja laskennallisesti toteutettavissa. Tässä työssä mallia sovelletaan uuteen ongelmaan, proteiinien vuorovaikutusverkkoihin, ja tavoitteena on löytää biologisesti järkeviä toiminnallisia moduuleita. Vaihtoehtoja mallin laajentamiseksi ymmärtämään myös verkkoja rikkaampaa dataa, kuten solmujen ominaisuuksia, esitellään ja kokeillaan. Tehdyissä kokeissa mallit löytävät tulkittavia klusterirakenteita verkoista useilla sovellusalueilla. Ehdotetut muutokset parantavat mallin suorituskykyä.Many complex systems can be represented as networks in which nodes are connected with edges. In cells, interactions between molecules, such as proteins, form a network, and social systems can consist of relationships between individual actors. Network analysis has developed from early studies of relationships between a small group of people to the analysis of huge complex networks, such as communication networks like Facebook and MySpace, or cell-wide biomolecular networks. In addition to being very large, the networks arising from real-world systems are typically sparse and contain missing and incomplete data. Successful analysis of such networks thus requires advanced computational methods. The topic of this thesis is a new generative probabilistic modeling framework, interaction component models, which is designed to detect densely connected subnetworks from noisy network data. Such subnetworks have many interpretations in practical applications, such as functional gene modules in protein interaction networks or communities in social networks. The model family is designed to be as simple as possible, to keep it understandable and computationally feasible. In this thesis, the model is applied to a new problem domain, namely protein interaction networks, in order to detect biologically relevant functional modules. Extensions to include additional data, such as attributes of the nodes, into the analysis are proposed and tested. Improvements to model inference are also introduced and their effect studied. In the experiments, models are able to find meaningful cluster structures from networks in several problem domains. The proposed modifications improve model performance

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Proceedings of the 19th Sound and Music Computing Conference

Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios