5 September 2019 – CITIC

09:00 h – 09:30 h.- Registration (CITIC. First Floor. Cloud Room)
09:30 h – 10:00 h.- Welcome address (CITIC. First Floor. Cloud Room)
10:00 h – 11:00 h.- Keynote address: (CITIC. First Floor. Cloud Room)

“Digital age innovation”

Francisco Servia FiuzaSenior Product Manager, Amazon

This conference will address the concept of innovation in the digital age, going over the main changes that motivate it and the processes and mechanisms that allow companies to innovate faster and faster in an interconnected and global world. All this, with a clear focus on the client and their experience as the main catalysts for the success or failure of a product or service, as well as organizations as a whole.

11:00 h – 12:00 h.- Oral presentations: (CITIC. First Floor. Cloud Room)

  • 11:00 h – 11:10 h.-  Liñares‐Blanco, J.; Fernandez‐Lozano, C. Prediction of Peptide Vascularization Inhibitory Activity in Tumor Tissue as a Possible Target for Cancer Treatment. Proceedings 2019, 21(1), 15;
  • 11:10 h – 11:20 h.- Puente‐Castro, A.; Munteanu, C.; Fernandez‐Blanco, E. System for Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques. Proceedings 2019, 21(1), 28;
  • 11:20 h – 11:30 h.- Fraga, I.; Alvarellos, A.; González‐Coma, J. Exploring the Feasibility of Low Cost Technology in Rainfall Monitoring: The TREBOADA Observing System. Proceedings 2019, 21(1), 5;
  • 11:30 – 11:40.- Landin, A.; Valcarce, D.; Parapar, J.; Barreiro, Á. Priors for Diversity and Novelty on Neural Recommender Systems. Proceedings 2019, 21(1), 20;
  • 11:40 – 11:50.- Vidal, P.; Moura, J.; Novo, J.; Ortega, M. Intraretinal Fluid Detection by Means of a Densely Connected Convolutional Neural Network Using Optical Coherence Tomography Images. Proceedings 2019, 21(1), 34;
  • 11:50 – 12:00.- Cedron, F.; Alvarez‐Gonzalez, S.; Pazos, A.; Porto‐Pazos, A. Use of Multiple Astrocytic Configurations within an Artificial Neuro‐Astrocytic Network. Proceedings 2019, 21(1), 46;
  • 12:00 – 12:10.- Pérez, M.; Dafonte, C.; Gómez, Á. The Integration of RFID Technology into Business Settings. Proceedings 2019, 21(1), 6;

12:00 h – 12:30 h.- Coffee break (CITIC. Ground Floor)
12:30 h – 14:30 h.- Oral presentations: (CITIC. First Floor. Cloud Room)

14:30 h – 16:00 h.- Networking lunch (Faculty of Economics and Business. Campus de Elviña)
16:00 h – 18:00 h.- Workshops

“Object Tracking for automatic dataset generation”, taught by ITG technicians (CITIC. First Floor. CONNECT Room)

Object detection (within the field of computer vision) enables the location of objects in images or videos and is a key technology in the field of artificial intelligence, facilitating the development of innovative systems and applications, such as advanced driving assistance and / or navigation, surveillance, etc.
Although there are many techniques for object detection, the use of convolutional neural networks (CNNs) has been extended in recent years, allowing machine learning for object detection in images.
One of CNN’s big problems is the need for a lot of data for training. Even with the use of Transfer Learning techniques, thousands of images labeled with the high cost associated with generating them are required.
In this workshop we will look at how to use Object Tracking techniques to transform video sequences into automatically labeled image sets, which allow to train CNN to detect that object.
• Laptop
• Chrome, Firefox or Safari is recommended.

Document download:—-?usp=sharing

“Augmented Reality, Principles and Practical Application”, taught by Xoia Software Development (CITIC. First Floor. Cloud Room)

6 September 2019 – Palexco

09:00 h – 10:00 h.- Keynote address: (PALEXCO. 2nd Floor. Auditorium ARAO)

Graph Neural Networks”

Alejandro Ribeiro, Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA

Convolutional Neural Networks (CNN) are layered information processing architectures in which each of the layers is itself the composition of a convolution operation with a pointwise nonlinearity. Graph Neural Networks (GNNs) replace the regular convolution operation with a graph convolution operation. We will discuss graph convolutions, their use in building GNN architectures, and explore stability properties of GNN operators. The stability results establish that a GNN is stable to graph deformations that are close to permutations. This result provides a theoretical basis to characterize classes of machine learning problems in which we expect GNNs to work well. We will discuss applications to control of large scale collaborative autonomous systems and wireless networks

10:00 h – 10:30 h.- Coffee break (PALEXCO. Ground Floor and 2nd Floor)
10:30 h – 12:30 h.- Oral presentations: (PALEXCO. 2nd Floor. Room 4)

12:30 h – 13:00 h.- Closing address and prizes (PALEXCO. 2nd Floor. Room 4)
13:00 h – 14:00 h.- Networking lunch (PALEXCO. 2nd Floor. Welcome Reception)
14:00 h – 15:30 h.- Poster session: (PALEXCO. Ground Floor. Poster Area)

  • Liñares‐Blanco, J.; Fernandez‐Lozano, C.; “Gene Signatures Research Involved in Cancer Using Machine Learning”. Proceedings 2019, 21(1), 19;
  • Kuriyozov, E.; Matlatipov, S.; ” Building a New Sentiment Analysis Dataset for Uzbek Language and Creating Baseline Models”. Proceedings 2019, 21(1), 37;
  • Rodriguez‐Fernandez, N.; Santos, I.; Torrente, A.; “Dataset for the Aesthetic Value Automatic Prediction”. Proceedings 2019, 21(1), 31;
  • Yolanda Rodriguez‐Vaqueiro, José Vazquéz Cabo, Borja Gonzalez‐Valdes and Antonio Pino García; “Array of Antennas for GPR”.
  • Otero, D.; Valcarce, D.; Parapar, J.; Barreiro, Á.; “Building High‐Quality Datasets for Information Retrieval Evaluation at a Reduced Cost”. Proceedings 2019, 21(1), 33;
  • Pedrouzo‐Ulloa, A.; Masciopinto, M.; Troncoso‐Pastoriza, J.; Pérez‐González, F.; “Efficient PRNU Matching in the Encrypted Domain”. Proceedings 2019, 21(1), 17;
  • Brais Castiñeiras Galdo, Daniel Rivero Cebrián and Enrique Fernández Blanco; “Estimation of the alcoholic degree in beers through near infrared spectrometry using machine learning”. Proceedings 2019, 21(1)
  • Hervella, Á.; Rouco, J.; Novo, J.; Ortega, M.; “Paired and Unpaired Deep Generative Models on Multimodal Retinal Image Reconstruction”. Proceedings 2019, 21(1), 45;
  • Moura, J.; Vidal, P.; Novo, J.; Ortega, M.; “Automatic Identification of Diabetic Macular Edema Using a Transfer Learning‐Based Approach”. Proceedings 2019, 21(1), 16;
  • López, J.; Pérez, Y.; ” Integration of Asterisk IP‐PBX with ESP32 Embedded System for Remote Code Execution”. Proceedings 2019, 21(1), 38;
  • Barbeito, I.; Cao, R.; Sperlich, S.; “Bandwidth Selection for Prediction in Regression”. Proceedings 2019, 21(1), 42;
  • Morales, L.; López‐Vizcaíno, M.; Iglesias, D.; Díaz, V.; “Anomaly Detection in IoT: Methods, Techniques and Tools”. Proceedings 2019, 21(1), 4;
  • Sanmartín, C.; Briceño, M.; “Development of an Artificial Vision System for Underwater Vehicles”. Proceedings 2019, 21(1), 1;
  • Castro, R.; Canosa, D.; ” Using Artificial Vision Techniques for Individual Player Tracking in Sport Events”. Proceedings 2019, 21(1), 21;
  • Concheiro‐Moscoso, P.; Groba, B.; Canosa, N.; “Sleep Disturbances in Nursing Home Residents: Links to Quality of Life and Daily Functioning”. Proceedings 2019, 21(1), 12;
  • Marc Bernice Angoue Avele, Julio Claudio Brégains Rodríguez, Roberto Maneiro Catoira, José Antonio García Naya and Luís Castedo Ribas; “Numerical Simulation of a Bipolar‐Sequence TMA System”.
  • Coelho, T.; Marques, C.; Moreira, D.; Soares, M.; Portugal, P.; Marques, A.;  “Promoting Reminiscences with Virtual Reality: Feasibility Study with People with Dementia”. Proceedings 2019, 21(1), 11;
  • Carro, E.; Miranda‐Duro, M.; Concheiro‐Moscoso, P.; Castro, A.; Cardoso, P.; Coelho, T.; “Internationalization of the ClepiTO Web Platform”. Proceedings 2019, 21(1), 30;
  • Santovena, R.; Manchado, A.; Dafonte, C.; “Signal Processing Techniques Intended for Peculiar Star Detection in APOGEE Survey”. Proceedings 2019, 21(1), 32;