Despite much creative work on methods and tools, reproducibility-the ability to repeat the computational steps used to obtain a research result-remains elusive. One reason for these difficulties is that extant tools for capturing research processes, while powerful, often fail to capture vital connections as research projects grow in extent and complexity. We explain here how these interstitial connections can be preserved via simple methods that integrate easily with current work practices to capture basic information about every data product consumed or produced in a project. By thus extending the scope of findable, accessible, interoperable, and reusable (FAIR) data in both time and space to enable the creation of a continuous chain of Continuous and Ubiquitous FAIRness linkages (CUF-links) from inputs to outputs, such mechanisms can facilitate capture of the provenance linkages that are essential to reproducible research. We give examples of mechanisms that can facilitate the use of these methods, and review how they have been applied in practice.

A Deep Neural Network (DNN) based system, such as the one used for autonomous vehicle operations, is a "black box" of complex interactions resulting in a classification or prediction. An important question for any such system is how to increase the reliability of, and consequently the trust in, the underlying model. To this end, researchers have largely resorted to adapting existing testing techniques. For example, similar to statement or branch coverage in traditional software testing, neuron coverage has been hypothesized as an effective metric for assessing a test suite's strength toward uncovering failures and anomalies in the DNN. We investigate the use of realistic transformations to create new images for testing a trained autonomous vehicle DNN, and its impact on neuron coverage as well as the model output.

While the threats may appear to be vastly different, further investigation reveals that the cybersecurity community can learn much from the COVID-19 messaging response.

Sharing data between different organizations is a challenge primarily due to database management systems (DBMSs) being different types that impose different schemas to represent and retrieve data. In addition, maintaining security and privacy is a concern. The authors leverage two proven National Institute of Standards and Technology (NIST) tools to address this challenge: Next Generation Database Access Control (NDAC) and data block matrix.

Security awareness training requirements set a minimum baseline for introducing security practices to an organization's workforce. But is simple compliance enough to result in behavior change?

Testing is the most commonly used approach for software assurance, yet it remains as much judgement and art as science. Structural coverage adds some rigor to the process by establishing formally defined criteria for some notion of test completeness, but even full coverage, however defined, may miss faults related to rare inputs that were not included in the test suite. We suggest that structural coverage measures must be supplemented with measures of input space coverage. Useful input space measures exist and have a relationship with structural coverage measures, providing a means of verifying that an adequate input model has been defined.

The automatic and unsupervised analysis of biomedical time series is of primary importance for diagnostic and preventive medicine, enabling fast and reliable data processing to reveal clinical insights without the need for human intervention. Representation learning (RL) methods perform an automatic extraction of meaningful features that can be used, e.g., for a subsequent classification of the measured data. The goal of this study is to explore and quantify the benefits of RL techniques of varying degrees of complexity, focusing on modern deep learning (DL) architectures. We focus on the automatic classification of atrial fibrillation (AF) events from noisy single-lead electrocardiographic signals (ECG) obtained from wireless sensors. This is an important task as it allows the detection of sub-clinical AF which is hard to diagnose with a short in-clinic 12-lead ECG. The effectiveness of the considered architectures is quantified and discussed in terms of classification performance, memory/data efficiency and computational complexity.

Human eye movements indicate important spatial information in static images as well as videos. Yet videos contain additional temporal information and convey a storyline. Video summarization is a technique that reduces video size, but maintains the essence of the storyline. Here, the authors explore whether eye movement patterns reflect frame importance during video viewing and facilitate video summarization. Eye movements were recorded while subjects watched videos from the SumMe video summarization dataset. The authors find more gaze consistency for selected than unselected frames. They further introduce a novel multi-stream deep learning model for video summarization that incorporates subjects' eye movement information. Gaze data improved the model's performance over that observed when only the frames' physical attributes were used. The results suggest that eye movement patterns reflect cognitive processing of sequential information that helps select important video frames, and provide an innovative algorithm that uses gaze information in video summarization.

Health and well-being are shaped by how lifestyle and the environment interact with biological machines. A navigational paradigm can help users reach a specific health goal by using constantly captured measurements to estimate how their health is continuously changing and provide actionable guidance.

Blockchains were designed to solve the problem of double-spending in cryptocurrencies, and the success of the Bitcoin design has generated vastly more interest than previous proposals for digital currencies. Blockchains are being used in other areas as well, but the design choices that made blockchains effective for cryptocurrencies often do not fit well with other applications. In this paper we review the properties of distributed ledger technology (DLT) for use in typical data management applications and show how two recently developed distributed ledger ideas can be used to retain valuable aspects of blockchain while simplifying design and adding new but often necessary capabilities to permissioned distributed ledger applications. In particular, we are interested in the ability to delete or modify blocks, and the ability to provide a timestamping mechanism to provide a highly accurate time for applications that are time order dependent.

Just as yeast, flour, water, and salt are to bread, algorithms, data structures, operating systems, database design, compiler design, and programming languages were computer science (CS) education's core ingredients in past years. Then, universities led the computer technology revolution by producing the inventors for Yahoo, Google, Facebook, and others. The overarching question that we pose in this roundtable is: Is university computer science education leading technology forward or are commercial technology demands leaving university computer science programs "in the dust"?

The security of encrypted data depends not only on the theoretical properties of cryptographic primitives but also on the robustness of their implementations in software and hardware. Threshold cryptography introduces a computational paradigm that enables higher assurance for such implementations.

Security diagnostics expose vulnerabilities and privacy threats that exist in commercial Intelligent Virtual Assistants (IVA) - diagnostics offer the possibility of securer IVA ecosystems.

Microscopy could be an important tool for characterizing stem cell products if quantitative measurements could be collected over multiple spatial and temporal scales. With the cells changing states over time and being several orders of magnitude smaller than cell products, modern microscopes are already capable of imaging large spatial areas, repeat imaging over time, and acquiring images over several spectra. However, characterizing stem cell products from such large image collections is challenging because of data size, required computations, and lack of interactive quantitative measurements needed to determine release criteria. We present a measurement web system consisting of available algorithms, extensions to a client-server framework using Deep Zoom, and the configuration know-how to provide the information needed for inspecting the quality of a cell product. The cell and other data sets are accessible via the prototype web-based system at http://isg.nist.gov/deepzoomweb.

Mobile health technology has great potential to increase healthcare quality, expand access to services, reduce costs, and improve personal wellness and public health. However, mHealth also raises significant privacy and security challenges.

As phishing continues to evolve, what's your organization doing to stay off the hook?

How to model and encode the semantics of human-written text and select the type of neural network to process it are not settled issues in sentiment analysis. Accuracy and transferability are critical issues in machine learning in general and are closely related to the viability the trained model. I present a computationally-efficient and accurate feedforward neural network for sentiment prediction capable of maintaining high transfer accuracy when coupled with an effective semantics model of the text. Experimental results on representative benchmark datasets and comparisons to other methods show the advantages of the new approach. Applications to security validation programs are discussed.

Technologies such as BigData, Cloud, Grid, and IoT are reshaping current data systems and practices, and IT experts are just as keen on harnessing the power of distributed systems to boost security and prevent fraud. How can massive distributed system capabilities be used to improve processing, instead of inflating risk?