At the core of augmented reality audio (ARA) technology lies the ARA mix, a process responsible for the assignment of a virtual environment to a real one. Legacy ARA mix models have focused on the ...natural reproduction of the real environment, whereas the virtual environment is simply mixed through fixed gain methods. This study presents a novel approach of a dynamic ARA mix that facilitates a smooth adaptation of the virtual environment to the real one, as well as dynamic control of the virtual audio engine, by taking into account the inherent characteristics of both ARA technology and binaural auditory perception. A prototype feature extraction technique of auditory perception characteristics through a real-time binaural loudness prediction method was used to upgrade the legacy ARA mix model into a dynamic model, which was evaluated through benchmarks and subjective tests and showed encouraging results in terms of functionality and acceptance.
In this study, we addressed the problem of point and probabilistic forecasting by describing a blending methodology for machine learning models from the gradient boosted trees and neural networks ...families. These principles were successfully applied in the recent M5 Competition in both the Accuracy and Uncertainty tracks. The key points of our methodology are: (a) transforming the task into regression on sales for a single day; (b) information-rich feature engineering; (c) creating a diverse set of state-of-the-art machine learning models; and (d) carefully constructing validation sets for model tuning. We show that the diversity of the machine learning models and careful selection of validation examples are most important for the effectiveness of our approach. Forecasting data have an inherent hierarchical structure (12 levels) but none of our proposed solutions exploited the hierarchical scheme. Using the proposed methodology, we ranked within the gold medal range in the Accuracy track and within the prizes in the Uncertainty track. Inference code with pre-trained models are available on GitHub.11https://github.com/IoannisNasios/M5_Uncertainty_3rd_place.
In this paper we tackle the problem of point and probabilistic forecasting by describing a blending methodology of machine learning models that belong to gradient boosted trees and neural networks ...families. These principles were successfully applied in the recent M5 Competition on both Accuracy and Uncertainty tracks. The keypoints of our methodology are: a) transform the task to regression on sales for a single day b) information rich feature engineering c) create a diverse set of state-of-the-art machine learning models and d) carefully construct validation sets for model tuning. We argue that the diversity of the machine learning models along with the careful selection of validation examples, where the most important ingredients for the effectiveness of our approach. Although forecasting data had an inherent hierarchy structure (12 levels), none of our proposed solutions exploited that hierarchical scheme. Using the proposed methodology, our team was ranked within the gold medal range in both Accuracy and the Uncertainty track. Inference code along with already trained models are available at https://github.com/IoannisNasios/M5_Uncertainty_3rd_place
In the present era of sustainable innovation, the circular economy paradigm dictates the optimal use and exploitation of existing finite resources. At the same time, the transition to smart ...infrastructures requires considerable investment in capital, resources and people. In this work, we present a general machine learning approach for offering indoor location awareness without the need to invest in additional and specialised hardware. We explore use cases where visitors equipped with their smart phone would interact with the available WiFi infrastructure to estimate their location, since the indoor requirement poses a limitation to standard GPS solutions. Results have shown that the proposed approach achieves a less than 2m accuracy and the model is resilient even in the case where a substantial number of BSSIDs are dropped.
In this work we investigate how IoT in conjunction with the data-driven Circular Economy (CE) model can contribute towards a more sustainable Built Environment (ΒΕ). We address longstanding ...challenges related to the distribution of resources and the multi-sectoral impact of the buildings sector. We first discuss recent developments in policy making that underpin the recently introduced Green Deal by the European Commission and the paradigm of Circular Economy. This motivates the development of ISUMS; an Indoor Space Usage Monitoring System. The system provides the facilities and the estates management teams of commercial and office buildings with an IoT-enabled system able to provide fine grained and timely data on occupancy rates of shared building spaces. This type of data can then be used to develop new or inform existing action plans towards increasing building sustainability. The development of the system comprises a) a Pre-Analysis Plan (PAP) for a smart campus use case at the Talbot Campus of Bournemouth University; b) a proof of concept IoT end-device that can be integrated in pieces of furniture for occupancy monitoring; and c) a measurements campaign for evaluating the use of LoRaWAN in indoor environments. ISUMS expands the notions of smart buildings and buildings management beyond interconnected actuators and towards adaptive space management with dynamic changes in the use requirements.
Citizen Science aims to engage people in research activities on important issues related to their well-being. Smart Cities aim to provide them with services that improve the quality of their life. ...Both concepts have seen significant growth in the last years, and can be further enhanced by combining their purposes with IoT technologies that allow for dynamic and large-scale communication and interaction. However, exciting and retaining the interest of participants is a key factor for such initiatives. In this paper we suggest that engagement in Citizen Science projects applied on Smart Cities infrastructure can be enhanced through contextual and structural game elements realized through augmented audio interactive mechanisms. Our inter-disciplinary framework is described through the paradigm of a collaborative bird call recognition game, in which users collect and submit audio data, which are then classified and used for augmenting physical space with virtual soundscape maps. We discuss the Playful Learning, Internet of Audio Things, and Bird Monitoring principles that shaped the design of our paradigm, and analyze its potential technical implementation.
Yolo architecture has presented promising results in various segmentation and detection tasks, including biomedical image analysis. This work studies the effectiveness and applicability of the method ...on cancer data, by comparative and experimental testing on the complex pan-cancer benchmark Pan-nuke for the recent Yolov8 algorithm variation. The experimental protocol encompasses a comprehensive exploration of various v8 approaches and subsequently provides an evaluative overview of the models employing a plug-and-play utilization paradigm. The results illustrate the performance characteristics of each separate model and explores the effect of the architecture in conjunction with the model development load and tradeoff.
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