Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.

Substance abuse is an established precursor to violent and non-violent crimes. Typical violent crimes are usually related to narcotics and alcohol sale, while non-violent crimes are related to the purchase. This study investigates correlations between substance abuse and crime rate in the United States. Data from the Federal Bureau of Investigation's Uniform Crime Reports (UCR) Program was used in the study to compare drug usage, alcohol consumption, and crime rate. The researchers used Tableau (Visualization Application) to analyze the data and visual the comparison between the three entities. The main goal of the study was to seek a correlation that could provide insight to the increase in crime rate when people abuse substances.

Stratum is an open source network operating system (NOS)that provides a common implementation of P4Runtime and OpenConfig interfaces for white box switches. This demonstration will show an SDN leaf-spine fabric of Stratum-enabled white box switches managed by the ONOS SDN controller. The switching chips (ASICs)and platforms will come from different vendors, but they will share a common P4-defined pipeline and set of OpenConfig models.

Management approaches to modern networks are increasingly influenced by software-defined networks (SDNs), and this increased influence is reflected in the growth of commercially available innovative SDN-based switches, controllers and applications. To date, there have been a number of commercial and open-source SDN operating systems (NOS) introduced for various purposes, including distributed controller frameworks targeting large, carrier-grade networks such as the Open Network Operating System (ONOS) and OpenDayLight (ODL). These frameworks are distinguished by their (i) elastic cluster controller architecture, (ii) network virtualization support, and (iii) modular design. Given their flexible design, growing list of supported features, and collaborative community support, these are attractive hosting platforms for a wide range of third-party distributed network management applications. This paper identifies the common security requirements for policy enforcement in such distributed controller environments. We present the design of a network application permission-enforcement model and an integrated security subsystem (SM-ONOS) for managing distributed applications running on an ONOS controller. We discuss the underlying motivations of its security extensions and their implications for improving our understanding of how to securely manage large-scale SDNs. Our performance assessments demonstrate that the security-mode extension imposed reasonable overheads (ranging from 5 to 20% for 1-7 node clusters).

The need for fault tolerance and scalability is leading to the development of distributed SDN operating systems and applications. But how can you develop such systems and applications reliably without access to an expensive testbed? We continue to observe SDN development practices using full system virtualization or heavyweight containers, increasing complexity and overhead while decreasing usability. We demonstrate a simpler and more efficient approach: using Mininet's cluster mode to easily deploy a virtual testbed of lightweight containers on a single machine, an ad hoc cluster, or a dedicated hardware testbed. By adding an open source, distributed network operating system such as ONOS, we can create a flexible and scalable open source development platform for distributed SDN system and application software development.

In this paper, we deploy a Fuzzy C-Means Clustering with a Level Set (FCMLS) method in an effort to localize the nonideal iris images accurately. We apply Genetic and Evolutionary Feature Extraction (GEFE), a method that evolves Local Binary Pattern (LBP) based feature extractors in order to elicit the most discriminating biometric features. In addition, a hybrid Genetic and Evolutionary Feature Weighting/Selection (GEFeWS) method is applied to select and weight the most important features. GEFeWS uses a genetic and evolutionary computation (GEC) to evolve a population of real-coded feature masks (FMs). We apply GEFeWS on features extracted by GEFE, and we refer to this technique as hybrid GEFE/GEFeWS, or hGEFE. Results show that hGEFE provides a significant increase in recognition accuracy while reducing the number of features being used when compared to just using GEFE alone.