241 research outputs found
The Unknown of the Pandemic: An Agent-Based Model of Final Phase Risks
No full textLifting social restrictions is one of the most critical decisions that public health authorities have to face during a pandemic such as COVID-19. This work focuses on the risk associated with such a decision. We have called the period from the re-opening decision to epidemic expiration the ’final epidemic phase’, and considered the critical epidemic conditions which could possibly emerge in this phase. The factors we have considered include: the proportion of asymptomatic cases, a mitigation strategy based on testing and the average duration of infectious states. By assuming hypothetical configurations at the time of the re-opening decision and the partial knowledge concerning epidemic dynamics available to public health authorities, we have analyzed the risk of the re-opening decision based on possibly unreliable estimates. We have presented a discrete-time stochastic model with state-dependent transmission probabilities and multi-agent simulations. Our results show the different outcomes produced by different proportions of undetected asymptomatic cases, different probabilities of asymptomatic cases detected and contained, and a multivariate analysis of risk based on the average duration of asymptomatic and contained states. Finally, our analysis highlights that enduring uncertainty, typical of this pandemic, requires a risk analysis approach to complement epidemiological studies
mc-unimi/epidemics-awareness-paper: Multiagent epidemic simulator
No full textCopyright (C) 2019 Samira Maghool and Marco Cremonini
All files of this release are part of the research project described in the paper titled "A multicomponent model of awareness for different categories of network epidemics", by Samira Maghool, Nahid Maleki-Jirsaraei, and Marco Cremonini
Graph Embeddings in Criminal Investigationn: Extending the Scope of Enquiry Protocols
No full textKnowledge graphs are exploited in criminal investigation to integrate heterogeneous data sources and scale up the operational efficiency of enquiry protocols. Using a declarative perspective, protocols can be viewed as a set of data ingestion procedures and nested exact queries. This meets the probating nature of procedural justice that has to proceed from established facts. At the same time, the exact specification of queries represents a limit for enquiry protocols that can exclusively retrieve those facts in adherence to the designed queries. We then investigated the use of graph em-beddings procedures to extend the scope of a protocol by returning sub-graphs partially matching to its specification. Because exploring the entire set of sub-graphs quickly become computationally intractable, we developed an approach based on a hierarchical filtering procedure. A controlled experiment we executed has shown the feasibility of our approach
Correlation and pattern detection in event networks
No full textEvents happening at defined moments in time and involving specific entities from a social or physical system can be organized in networks or graphs. The study of such event graphs may reveal causal relations between subsequent events or compound events that we define as “typed events”. Moreover, characteristic sequences of events or patterns can arise in consequence of phenomena affecting the system. Methods to build the event graph and to search for the typed events and their significance are described in detail. An embedding strategy to encode typed events in low dimensional vectors is defined, and both supervised and unsupervised learning is applied to search for meaningful patterns. Experiments have been conducted using data from a real investigation and some synthetic data
Data management for continuous learning in EHR systems
No full textTo gain a comprehensive understanding of a patient’s health, advanced analytics must be applied to the data collected by electronic health record (EHR) systems. However, managing and curating this data requires carefully designed workflows. While digitalization and standardization enable continuous health monitoring, missing data values and technical issues can compromise the consistency and timeliness of the data. In this paper, we propose a workflow for developing prognostic models that leverages the SMART BEAR infrastructure and the capabilities of the Big Data Analytics (BDA) engine to homogenize and harmonize data points. Our workflow improves the quality of the data by evaluating different imputation algorithms and selecting one that maintains the distribution and correlation of features similar to the raw data. We applied this workflow to a subset of the data stored in the SMART BEAR repository and examined its impact on the prediction of emerging health states such as cardiovascular disease and mild depression. We also discussed the possibility of model validation by clinicians in the SMART BEAR project, the transmission of subsequent actions in the decision support system, and the estimation of the required number of data points
Graph embeddings in criminal investigation: towards combining precision, generalization and transparency
No full textA Novel Assurance Procedure for Fair Data Augmentation in Machine Learning
No full textIn addressing the limited availability of data for predictive purposes with machine learning, we are concerned with potential biases arising from dataset augmentation. Despite advanced algorithms to generate synthetic data that can preserve the original data distribution, challenges remain, including the risk of perpetuating social biases. Our approach uses a similarity network representation that treats each data point as a node and strategically generates synthetic points near it. A vector label propagation algorithm, complemented by an exponential kernel for adjusting link weights, accurately labels these synthetic points. The primary goal is to reduce the system’s dependence on sensitive features without excluding them, thereby avoiding the risk of exacerbating biases or reducing data variation. Implemented in a big data ecosystem, our methodology enables continuous evaluation in an evolving domain, effectively addressing the challenges of data scarcity with a fairness-aware approach
Enhancing Fairness and Accuracy in Machine Learning Through Similarity Networks
No full textMachine Learning is a powerful tool for uncovering relationships and patterns within datasets. However, applying it to a large datasets can lead to biased outcomes and quality issues, due to confounder variables indirectly related to the outcome of interest. Achieving fairness often alters training data, like balancing imbalanced groups (privileged/unprivileged) or excluding sensitive features, impacting accuracy. To address this, we propose a solution inspired by similarity network fusion, preserving dataset structure by integrating global and local similarities. We evaluate our method, considering data set complexity, fairness, and accuracy. Experimental results show the similarity network’s effectiveness in balancing fairness and accuracy. We discuss implications and future directions
A Comparative Study of Clustering Techniques Applied on Covid-19 Scientific Literature
No full textDue to the current emergency situation, caused by COVID-19, the scientific literature on the topic has rapidly grown. At the same time, purposeful and targeted research plans with strong background knowledge is urgently needed. However, the huge number of documents produced by multiple communities generates a fragmented terminology that may cause confusion in information retrieval. To this aim, in a comparative study, we test different techniques to efficiently cluster these publications for improving their level of findability
Toward a General Framework for Multimodal Big Data Analysis
No full textMultimodal Analytics in Big Data architectures implies compounded configurations of the data processing tasks. Each modality in data requires specific analytics that triggers specific data processing tasks. Scalability can be reached at the cost of an attentive calibration of the resources shared by the different tasks searching for a trade-off with the multiple requirements they impose. We propose a methodology to address multimodal analytics within the same data processing approach to get a simplified architecture that can fully exploit the potential of the parallel processing of Big Data infrastructures. Multiple data sources are first integrated into a unified knowledge graph (KG). Different modalities of data are addressed by specifying ad hoc views on the KG and producing a rewriting of the graph containing merely the data to be processed. Graph traversal and rule extraction are this way boosted. Using graph embeddings methods, the different ad hoc views can be transformed into low-dimensional representation following the same data format. This way a single machine learning procedure can address the different modalities, simplifying the architecture of our system. The experiments we executed demonstrate that our approach reduces the cost of execution and improves the accuracy of analytics
- …
