Distributed averaging for accuracy prediction in networked systems

Distributed averaging is among the most relevant cooperative control problems, with applications in sensor and robotic networks, distributed signal processing, data fusion, and load balancing. Consensus and gossip algorithms have been investigated and successfully deployed in multi-agent systems to perform distributed averaging in synchronous and asynchronous settings. This study proposes a heuristic approach to estimate the convergence rate of averaging algorithms in a distributed manner, relying on the computation and propagation of local graph metrics while entailing simple data elaboration and small message passing. The protocol enables nodes to predict the time (or the number of interactions) needed to estimate the global average with the desired accuracy. Consequently, nodes can make informed decisions on their use of measured and estimated data while gaining awareness of the global structure of the network, as well as their role in it. The study presents relevant applications to outliers identification and performance evaluation in switching topologies

Large-scale assessment of mobile crowdsensed data: a case study

Mobile crowdsensing (MCS) is a well-established paradigm that leverages mobile devices’ ubiquitous nature and processing capabilities for large-scale data collection to monitor phenomena of common interest. Crowd-powered data collection is significantly faster and more cost-effective than traditional methods. However, it poses challenges in assessing the accuracy and extracting information from large volumes of user-generated data. SmartRoadSense (SRS) is an MCS technology that utilises sensors embedded in mobile phones to monitor the quality of road surfaces by computing a crowdsensed road roughness index (referred to as PPE). The present work performs statistical modelling of PPE to analyse its distribution across the road network and elucidate how it can be efficiently analysed and interpreted. Joint statistical analysis of open datasets is then carried out to investigate the effect of both internal and external road features on PPE . Several road properties affecting PPE as predicted are identified, providing evidence that SRS can be effectively applied to assess road quality conditions. Finally, the effect of road category and the speed limit on the mean and standard deviation of PPE is evaluated, incorporating previous results on the relationship between vehicle speed and PPE . These results enable more effective and confident use of the SRS platform and its data to help inform road construction and renovation decisions, especially where a lack of resources limits the use of conventional approaches. The work also exemplifies how crowdsensing technologies can benefit from open data integration and highlights the importance of making coherent, comprehensive, and well-structured open datasets available to the public

Topological network features determine convergence rate of distributed average algorithms

Gossip algorithms are message-passing schemes designed to compute averages and other global functions over networks through asynchronous and randomised pairwise interactions. Gossip-based protocols have drawn much attention for achieving robust and fault-tolerant communication while maintaining simplicity and scalability. However, the frequent propagation of redundant information makes them inefficient and resource-intensive. Most previous works have been devoted to deriving performance bounds and developing faster algorithms tailored to specific structures. In contrast, this study focuses on characterising the effect of topological network features on performance so that faster convergence can be engineered by acting on the underlying network rather than the gossip algorithm. The numerical experiments identify the topological limiting factors, the most predictive graph metrics, and the most efficient algorithms for each graph family and for all graphs, providing guidelines for designing and maintaining resource-efficient networks. Regression analyses confirm the explanatory power of structural features and demonstrate the validity of the topological approach in performance estimation. Finally, the high predictive capabilities of local metrics and the possibility of computing them in a distributed manner and at a low computational cost inform the design and implementation of a novel distributed approach for predicting performance from the network topology

Investigating Participation Mechanisms in EU Code Week

Digital competence (DC) is a broad set of skills, attitudes, and knowledge for confident, critical and responsible use of digital technologies in every aspect of life. DC proves essential in the contemporary digital landscape, yet its diffusion is hindered by biases, misunderstandings, and limited awareness. Teaching Informatics in the educational curriculum is increasingly supported by the institutions but faces serious challenges, such as teacher upskilling and support. In response, grassroots movements promoting computing literacy in an informal setting have grown, including EU Code Week, whose vision is to develop computing skills while promoting diversity and raising awareness of the importance of digital skills. This study extensively analyses EU Code Week editions spanning 2014 to 2021 across European Union member states, pursuing three primary objectives: firstly, to evaluate the teacher engagement in the campaign in terms of penetration, retention, and spatial distribution; secondly, to characterise the multifaceted audience and themes embraced by these initiatives; and lastly, to investigate the influence of socio-economic factors on engagement. The investigation uncovers the underlying mechanisms fostering Code Week’s engagement, providing insights to campaign organisers for strategic planning and resource allocation in future editions. Moreover, the analysis reveals that the most engaged areas are characterised by lower income, as well as lower digital literacy, restricted access to technology, and a less established computer education, suggesting that Code Week thrives precisely where its impact is most needed

Machine Learning-Enabled Prediction of Metabolite Response in Genetic Disorders

Metabolomics has emerged as a promising discipline in pharmaceuticals and preventive healthcare, holding great potential for disease detection and drug testing. However, analysing large metabolomics datasets remains challenging, with available methods generally relying on limited and incompletely annotated biological pathways. This study introduces a novel approach that leverages machine learning classifiers trained on molecular fingerprints of metabolites, to predict their responses under specific experimental conditions. The model is evaluated on mass spectrometry metabolomic data for a cellular model of the genetic disease Ataxia Telangiectasia. In this study, metabolite structures are encoded using the Morgan fingerprint, a well-established technique widely embraced in drug discovery. The suitability of this fingerprinting method, in generating unique structural encodings for detected metabolites, is analysed, and strategies to mitigate resolution limitations inherent to this fingerprint are introduced. Machine learning classifiers are trained on these fingerprints and exhibit satisfactory performance, providing evidence that the structural encoding holds predictive power over the metabolic response. Feature importance analysis, conducted on the best-performing models, identifies the chemical configu- rations that have the greatest influence to the classification process, shedding light on affected biological processes. Remarkably, this analysis not only identifies metabolites known to participate in affected pathways but also discovers metabolites not previously associated with the disease, opening up novel opportunities for further exploration. As an initial exploration of the proposed approach, this work lays the foundation for future research that leverages alternative structural encodings, diverse machine learning models, and explainability tools

Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments

Structure probing coupled with high-throughput sequencing could revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite recent technological advances, intrinsic noise and high sequence coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline that accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome wide. Using two yeast data sets, we demonstrate that our method has increased sensitivity, and thus our pipeline identifies modified regions on many more transcripts than do existing pipelines. Our method also provides confident predictions at much lower sequence coverage levels than those recommended for reliable structural probing. Our results show that statistical modeling extends the scope and potential of transcriptome-wide structure probing experiments