Dr. Prieto-Hernández, Yasmany

Background: Older people’s falls are a global public health problem, leading to injuries, disability, and fatalities. Using screening tools to measure predictive factors is essential for assessing the risk of falls among older adults. The literature highlights executive function tests as a way to assess this risk. They are also economical and reliable tools. Therefore, a Machine Learning (ML) technique based on variables obtained from cognitive domains could classify an older adult as at high or low risk of falling. Methodology: The study collected six variables from 50 community-dwelling older adults. The variables included age, educational level, and Trail Making Test (TMT) part B, Digital Span Backward, Stroop Color-Word Interference, and Mini Balance Evaluation Systems Test (Mini-BESTest) tests. These variables fed three ML models to predict if an older adult is at high or low risk of falling. Specifically, we considered Logistic Regression (LR), Decision Trees, and K-Nearest Neighbors. The proposed models were assessed using a bootstrapping sampling method and an aggregated confusion matrix, from which typical performance metrics were derived. The input variables in the best model were selected using a wrapper-based selection method. Results: Of the three models, the LR classifiers were top-ranked based on accuracy, with a maximum value of 71.4%. The best classifiers included the educational level or the TMT part B as input variables. Thus, these variables were strong predictors of fall events in the population study. We tested the input variables to ensure they were significant for the best LR classifiers and assessed model performance, generalization, and stability given the dataset sample size. Discussion: We weighed the performance metric results with a clinical perspective to select the best LR classifier. Thus, the more suitable model resulted in the classifier with TMT part B and educational level as input variables. Besides presenting competitive performance results, it enables us to consider a broader range of clinical information and draw more informed conclusions. Comparing our proposed model with four assessment tools, we observe it was second in Area Under the Receiver Operating Characteristic Curve (AUC) and third in accuracy. Conclusions: In this work, we developed an LR classifier to identify older adults with high or low risk of falling, using the TMT Part B test and the educational level as features. In addition, we provided cut-off values to assess the risk of falling using only the TMT part B test or the educational level. We found that, individually, 8 years or more of schooling or a result of the TMT part B lower than 212 s are associated, on average, with a low risk of falls. The Chilean health system can broadly implement the best classifier since the input variables are easy to collect, and the classification rule can be calculated using simple arithmetic operations.

The empirical entropy is used in network traffic monitoring and classification to detect anomalous events and manage network resources. Computing the entropy of high-speed traffic in real time requires dedicated hardware, such as programmable switches and FPGA-based accelerators. While these devices can achieve high performance by exploiting the parallelism of the algorithm, they possess limited on-chip storage. Thus, designing algorithms that estimate the entropy of network traffic with low error and memory usage is challenging. In this paper, we present an entropy-estimation streaming algorithm that operates on large datasets with sublinear memory usage. We use sketches to estimate the frequency and cardinality of network flows during an observation interval. We only store the frequencies of the most frequent flows and use them to estimate the rest of the frequencies by assuming a power-law distribution. Our results show that, using real network traces with observation intervals of up to 50 million flows, we can estimate their empirical entropy with 0.69% mean relative error, using more than three orders of magnitude less memory than an exact entropy-computation method. We also present an FPGA-based hardware accelerator for the algorithm that can operate at a line rate of more than 200 Gbps and an estimation latency of 16 μs. Using fixed-point arithmetic and function approximations in the accelerator increases the mean estimation error of our algorithm by only 0.07%.

Network reliability has become an important concern to network administrators and service providers, and is prominently considered in network design. Particularly, 0-day vulnerabilities are an increasing threat to software-based networking systems. When shared between node appliances, they can be exploited simultaneously and compromise large portions of the network. Moreover, it has been observed that the number of 0-day vulnerabilities discovered yearly in node appliances tends to increase over time. Thus, we can expect that the reliability to 0-day exploits of a network implemented with these appliances will also worsen over time. In this work, we treat network reliability to 0-day exploits as a service, where he network provider agrees to deliver a reliability-based level of service over time. We propose a network reliability metric based on network connectivity and discovered appliance vulnerabilities. We formulate a strategy to guarantee a reliability value over time, based on heterogeneous networking and periodically running cost-effective partial node migrations. We use numerical evaluations to test our methodology on two software-defined wide-area networks based on known backbone IP topologies. Our significant findings are the following: First, when the network reliability becomes worse than the service guarantee, it can be restored in most cases by combining appliance reallocation and node migration. Second, our evaluations show a direct relationship between a network reliability value and the cost incurred to guarantee it. Third, we noted that, when using our appliance-to-node allocation strategy to guarantee the same reliability on different networks, their post-failure connectivity depends on the underlying network topology.