Joint Feature Selection and Parameter Tuning for Short-term Traffic Flow Forecasting based on Heuristically Optimized Multi-layer Neural Networks

Short-term traffic flow forecasting is a vibrant research topic that has been growing in interest since the late 70’s. In the last decade this vibrant field has shifted its focus towards machine learning methods. These techniques often require fine-grained parameter tuning to obtain satisfactory performance scores, a process that usually relies on man- ual trial-and-error adjustment. This paper explores the use of Harmony Search optimization for tuning the parameters of neural network jointly with the selection of the input features from the dataset at hand. Re- sults are discussed and compared to other tuning methods, from which it is concluded that neural predictors optimized via the proposed heuris- tic wrapper outperform those tuned by means of na ̈ıve parametrized algorithms, thus allowing for longer-term predictions. These promising results unfold potential applications of this technique in multi-location neighbor-aware traffic prediction

Exploiting a Stimuli Encoding Scheme of Spiking Neural Networks for Stream Learning

Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios. One of the most promising techniques in stream learn- ing is the Spiking Neural Network, and some of them use an interesting population encod- ing scheme to transform the incoming stimuli into spikes. This study sheds lights on the key issue of this encoding scheme, the Gaussian receptive fields, and focuses on applying them as a pre-processing technique to any dataset in order to gain representativeness, and to boost the predictive performance of the stream learning methods. Experiments with synthetic and real data sets are presented, and lead to confirm that our approach can be applied successfully as a general pre-processing technique in many real cases

Hybrid quantum-classical heuristic for the bin packing problem

Optimization problems is one of the most challenging applications of quantum computers, as well as one of the most relevants. As a consequence, it has attracted huge efforts to obtain a speedup over classical algorithms using quantum resources. Up to now, many problems of different nature have been addressed through the perspective of this revolutionary computation paradigm, but there are still many open questions. In this work, a hybrid classical-quantum approach is presented for dealing with the one-dimensional Bin Packing Problem (1dBPP). The algorithm comprises two modules, each one designed for being executed in different computational ecosystems. First, a quantum subroutine seeks a set of feasible bin configurations of the problem at hand. Secondly, a classical computation subroutine builds complete solutions to the problem from the subsets given by the quantum subroutine. Being a hybrid solver, we have called our method H-BPP. To test our algorithm, we have built 18 different 1dBPP instances as a benchmarking set, in which we analyse the fitness, the number of solutions and the performance of the QC subroutine. Based on these figures of merit we verify that H-BPP is a valid technique to address the 1dBPP.QUANTEK project (ELKARTEK program from the Basque Government, expedient no. KK-2021/00070) Spanish Ramón y Cajal Grant RYC-2020-030503- I QMiCS (820505) and OpenSuperQ (820363) of the EU Flagship on Quantum Technologies EU FET Open project Quromorphic (828826) and EPIQUS (899368

SOFIAS – Herramienta para el análisis de ciclo de vida y la calificación ambiental de edificios

This paper describes the development process of a new software tool, called SOFIAS (Software for a Sustainable Architecture), funded by the Spanish Ministry of Economy and Competitivenes. Following CEN/TC 350 standard on environmental assessment of buildings, the tool aims at assisting building professionals on reducing the life-cycle environmental impact through the design of new buildings and the refurbishment of existing ones. In addition, SOFIAS provides a rating system based on the Life Cycle Assessment (LCA) methodology. This paper explains the innovative aspects of this software, the working methodology and the different type of results that can be obtained using SOFIAS.SOFIAS (Ref. number IPT-2011-0948-380000) project co financed by the Spanish Ministry of Economy and Competitiveness

An active adaptation strategy for streaming time series classification based on elastic similarity measures

In streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line scenarios, data arise from non-stationary processes, which results in a succession of different patterns or events. This work presents an active adaptation strategy that allows time series classifiers to accommodate to the dynamics of streamed time series data. Specifically, our approach consists of a classifier that detects changes between events over streaming time series. For this purpose, the classifier uses features of the dynamic time warping measure computed between the streamed data and a set of reference patterns. When classifying a streaming series, the proposed pattern end detector analyzes such features to predict changes and adapt off-line time series classifiers to newly arriving events. To evaluate the performance of the proposed scheme, we employ the pattern end detection model along with dynamic time warping-based nearest neighbor classifiers over a benchmark of ten time series classification problems. The obtained results present exciting insights into the detection accuracy and latency performance of the proposed strategy.BERC-2022-20

Pedagogical approaches for sustainable development in building in higher education

Education for sustainable development (ESD) is one of the great challenges that university faculties have to face. Therefore, a multidisciplinary team from the faculty of Engineering of Gipuz-koa (EIG) at the University of the Basque Country (UPV/EHU) has developed pedagogical approaches to apply in construction degrees, namely Civil Engineering and Technical Architecture. Pedagogical tools, such as problem-based learning (PBL) or research-based learning (RBL), and environmental tools, such as the life cycle assessment (LCA) and computational thinking (CT), have been used; in doing so, they acquire a sustainable approach to work "œsoft-skills" competencies into sustainability. For example, research-based tools have helped to revalorize waste both outside and inside the university; they have contributed to more sustainable industrial processes, collaborative research projects, and participation in conferences and scientific publications. Based on academic results, the designed tools are appropriate for teaching in Technical Architecture and Civil Engineering degrees; however, to demonstrate their potential in terms of sustainable education, holistic rubrics based on in-depth quantitative educational research are required. Thus, to analyze the abil-ity of the students to incorporate sustainability principles in their work, the multidisciplinary team presenting this paper plans to collaborate with psychologists and sociologists within the framework of the Bizia-Lab program of the UPV/EHU. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Plug and play modular façade construction system for renovation for residential buildings

The present paper focuses on the architectural and constructional features required to ensure that building envelope renovation are safe, functional, and adaptable to the building stock, with particular focus on “plug and play” modular facade construction systems. It presents the design of one such system and how it addresses these issues. The outcome of early-stage functional test with a full-scale mock-up system, as well as its applicability to a real construction project is presented. It is found crucial to obtain high quality information about the status of the existing façade with the use of modern technologies such as topographic surveys or 3D scans and point cloud. Detailed design processes are required to ensure the compatibility of manufacture and installation tolerances, along with anchor systems that deliver flexibility for adjustment, and construction processes adapting standard installation methods to the architectural particularities of each case that may hinder its use or require some modification in each situation. This prefabricated plug and play modular system has been tested by reproducing the holistic methodology and new technologies in the market by means of real demonstrators. When compared to more conventional construction methods, this system achieves savings in a real case of 50% (time), 30% (materials) and 25% (waste), thus achieving significant economic savings