1,720,964 research outputs found
Interaction and Behaviour Evaluation for Smart Homes: Data Collection and Analytics in the ScaledHome Project
No full textThe smart home concept can significantly benefit from predictive models that take proactive management operations on home actuators, based on users’ behavior evaluation. In this paper, we use a small-scale physical model, the ScaledHome-2 testbed, to experiment with the evolution of measurements in a suburban home under different environmental scenarios. We start from the observation that, for a home to become smart, in addition to IoT sensors and actuators, we also need a predictive model of how actions taken by inhabitants and home actuators affect the internal environment of the home, reflected in the sensor readings. In this paper, we propose a technique to create such a predictive model through machine learning in various simulated weather scenarios. This paper also contributes to the literature in the field by quantitatively comparing several machine learning algorithms (K-nearest neighbor, regression trees, Support Vector Machine regression, and Long Short Term Memory deep neural networks) in their ability to create accurate and generalizable predictive models for smart homes
Reservoir Computing in Real-World Environments: Optimizing the Cost of Offline and Online Training
No full textThe remarkable success of attention-based models in real-world applications has sparked a crucial question for Reservoir Computing (RC): Can its inherent computational efficiency compete with the high-performance, yet energy-intensive, novel deep learning architectures? Can Deep and modular RC neural networks address state of the art challenges in Computer Vision and Natural Language Processing? In the attempt to consolidate RC capabilities towards more complex tasks, this paper delves into the exploration of a comprehensive RC's offlineonline cycle cost analysis. Our investigation highlights hyperparameters (HPs) optimization as a major bottleneck in RC deployment, particularly for those exploring RC capabilities and those who want to maintain user-level knowledge of the solution. To address this, we introduce an adaptive ε-Greedy based search exploration mechanism, significantly streamlining the off-line optimization process while maintaining high accuracy. Furthermore, we enhance existing RC frameworks to support online transfer learning and inference, enabling seamless fast and energy-efficient adaptation to real-world environments. By analyzing the impact of optimized HPs on performance, we aim to demonstrate the viability of RC as a powerful and efficient alternative for many practical applications, including those on devices with limited resources. Experimental results proved that our solution is able to reduce the time required for offline HPs optimization by 70%, enabling energy savings of up to 88%. Moreover, in the online scenario, it guarantees similar performance in terms of accuracy while reducing memory usage by 66%
Unveiling Mental Health Insights: A Novel NLP Tool for Stress Detection through Writing and Speaking Analysis to Prevent Burnout
Full text linkNowadays, innovative approaches that precisely identify and treat health-related problems are becoming more and more necessary in a time of rapid technological advancement and growing mental health awareness. Given the prevalence of mental health issues, different tools that employ Artificial Intelligence to support rapid and effective interventions have been developed. This study focuses on the relationship between language expression and mental health, recognizing subtle nuances in both written and spoken communication as potential stress indicators and presenting a novel AI enhanced tool for autonomous and passive stress detection.Specifically, in our study data scientists and psychologists collaborate to create and validate a groundbreaking knowledge base. This innovative database combines psychometrics, biometrics, and linguistic analysis to provide a comprehensive evaluation of stress levels. We used biomedical indicators, such as blood pressure, heart rate variability (HRV), and cortisol levels correlations to validate the results. The multidisciplinary team brought together expertise from data science and psychology to create a novel database with a wide range of sentences that have been annotated with matching stress levels.Thanks to this strong psychometric framework for correlating language manifestation of stress with clinical diagnosis, we developed the first, to our knowledge, NLP (Natural Language Processing) tool for autonomous and passive stress detection. This includes a variety of emotional and cognitive stress indicators to provide a deeper understanding of stress that takes into account both subjective experiences and objective manifestations. Initial results show a strong relationship between the biomedical markers and the stress scores obtained from language analysis. By combining data science techniques with psychometric insights, our stress detection achieves 83% in terms of F1 score, providing a more complete picture of a person's stress profile.During the entire study, ethical considerations were taken into account, following well defined data privacy and protection protocols. In fact, before any data was added to the database, participants were carefully informed about the purpose of data collection.Workplace communication platforms may be combined with our NLP technology to track employee well-being in a professional context. This includes real-time alerts to managers and HR specialists, allowing for timely interventions and promoting a collaborative and positive work environment. The strong correlation between clinical metrics and linguistic semantic choices represents a significant step toward the reform of mental health care. In addition the impressive accuracy of the tool we developed provides a reliable support system for spotting stress symptoms in both written and spoken communication. This should help us to change the way we think about stress, assisting us to assess the presence of a burnout condition before it escalates into more serious health issues. The implementation of this technology into various elements of daily life has the potential to revolutionize society perceptions on mental health, allowing for a more in-depth knowledge of the multiple components involved with stress
TruFLaaS: Trustworthy Federated Learning as a Service
Full text linkThe increasing availability of data generated by Internet of Things (IoT) and Industrial IoT (IIoT) devices, as well as privacy and law regulations, have significantly boosted the interest in collaborative machine learning (ML) approaches. In this direction, we claim federated learning (FL) as a promising ML paradigm where participants collaboratively train a global model without outsourcing on-premises data. However, setting up and using FL can be extremely costly and time consuming. To effectively promote the adoption of FL in real-world scenarios, while limiting the overhead and knowledge of the underlying technology, service providers should offer FL as a Service (FLaaS). One of the major concerns while designing an architecture that provides FLaaS is achieving trustworthiness among involved typically unknown participants. This article presents a blockchain-based architecture that achieves trustworthy FLaaS (TruFLaaS). Our solution provides trustworthiness among third-party organizations by leveraging blockchain, smart contracts, and a decentralized oracle network. Specifically, during each FL round, the service provider supplies a sample, without overlapping, of its validation set to validate all partial models submitted by clients. By doing so, poor models, which tend to degrade performance or introduce malicious backdoors, are identified and discarded. Due to the transparency of the blockchain, not changing the validation set would enable participants to forge a malicious partial model that passes the validation phase. We evaluate our approach over two well-known IIoT data sets: the reported experimental results show that TruFLaaS outperforms the state-of-the-art literature solutions in the field
A novel middleware for adaptive and efficient split computing for real-time object detection
No full textReal-world applications requiring real-time responsiveness frequently rely on energy-intensive and compute-heavy neural network algorithms. Strategies include deploying distributed and optimized Deep Neural Networks on mobile devices, which can lead to considerable energy consumption and degraded performance, or offloading larger models to edge servers, which requires low-latency wireless channels. Here we present Furcifer, a novel middleware that autonomously adjusts the computing strategy (i.e., local computing, edge computing, or split computing) based on context conditions. Utilizing container-based services and low-complexity predictors that generalize across environments, Furcifer supports supervised compression as a viable alternative to pure local or remote processing in real-time environments. An extensive set of experiments coversdiverse scenarios, including both stable and highly dynamic channel environments with unpredictable changes in connection quality and load. In moderate-varying scenarios, Furcifer demonstrates significant benefits: achieving a 2x reduction in energy consumption, a 30% higher mean Average Precision score compared to local computing, and a three-fold FPS increase over static offloading. In highly dynamic environments with unreliable connectivity and rapid increases in concurrent clients, Furcifer's predictive capabilities preserves up to 30% energy, achieving a 16% higher accuracy rate, and completing 80% more frame inferences compared to pure local computing and approaches without trend forecasting, respectively
Furcifer: a Context Adaptive Middleware for Real-world Object Detection Exploiting Local, Edge, and Split Computing in the Cloud Continuum
No full textModern real-time applications widely embed compute intense neural algorithms at their core. Current solutions to support such algorithms either deploy highly-optimized Deep Neural Networks at mobile devices or offload the execution of possibly larger higher-performance neural models to edge servers. While the former solution typically maps to higher energy consumption and lower performance, the latter necessitates the low-latency wireless transfer of high volumes of data. Time-varying variables describing the state of these systems, such as connection quality and system load, determine the optimality of the different computing configurations in terms of energy consumption, task performance, and latency. Herein, we propose Furcifer, a framework capable of dynamically adapting the cloud continuum computing configuration in response to the perceived state of the system. Our container-based approach incorporates low-complexity predictors that generalize well across operating environments. In addition, we develop a highly optimized split Deep Neural Network model, which achieves in-model supervised compression and enhances task offloading. Experimental results for object detection across diverse conditions, environments, and wireless technologies, show Furcifer's remarkable outcomes, including a 2x energy reduction, 30% higher mean Average Precision score than pure local computing, and a notable three-fold increase in frame per second rate compared to static offloading
Interaction and Behaviour Evaluation for Smart Homes: Data Collection and Analytics in the ScaledHome Project
Full text linkNowadays more and more devices are becoming "smart", in fact they can take autonomous decision and interact proactively with the surrounding environment.
Smart home is just one of the most popular terms related with this relevant change we are witnessing and its relevance in this project is mainly due to the fact that the residential sector account an important percentage in terms of energy consumption.
New ways to share and save energy have to be taken into account in order to optimize the usage of the devices needed by houses to make the environment cozy and comfortable for their inhabitants.
The work done with Professor Turgut's team has improved the knowledge in the smart home system area providing a scalable and reliable architecture, a new dataset and an example of application of these data useful to save energy while satisfying the demands of its inhabitants
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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