Scrybe: A Secure Audit Trail for Clinical Trial Data Fusion.

Clinical trials are a multi-billion dollar industry. One of the biggest challenges facing the clinical trial research community is satisfying Part 11 of Title 21 of the Code of Federal Regulations [7] and ISO 27789 [40]. These controls provide audit requirements that guarantee the reliability of the data contained in the electronic records. Context-aware smart devices and wearable IoT devices have become increasingly common in clinical trials. Electronic Data Capture (EDC) and Clinical Data Management Systems (CDMS) do not currently address the new challenges introduced using these devices. The healthcare digital threat landscape is continually evolving, and the prevalence of sensor fusion and wearable devices compounds the growing attack surface. We propose Scrybe, a permissioned blockchain, to store proof of clinical trial data provenance. We illustrate how Scrybe addresses each control and the limitations of the Ethereum-based blockchains. Finally, we provide a proof-of-concept integration with REDCap to show tamper resistance.

Radicalism: The asymmetric stances of radicals versus conventionals.

We study the conditions of propagation of an initial emergent practice qualified as extremist within a population adept at a practice perceived as moderate, whether political, societal, or religious. The extremist practice is carried by an initial ultraminority of radicals (R) dispersed among conventionals (C) who are the overwhelming majority in the community. Both R and C are followers, that is, agents who, while having arguments to legitimize their current practice, are likely to switch to the other practice if given more arguments during a debate. The issue being controversial, most C tend to avoid social confrontation with R about it. They maintain a neutral indifference, assuming it is none of their business. On the contrary, R aim to convince C through an expansion strategy to spread their practice as part of a collective agenda. However, aware of being followers, they implement an appropriate strategy to maximize their expansion and determine when to force a debate with C. The effect of this asymmetry between initiating or avoiding an update debate among followers is calculated using a weighted version of the Galam model of opinion dynamics. An underlying complex landscape is obtained as a function of the respective probabilities to engage in a local discussion by R and C. It discloses zones where R inexorably expand and zones where they get extinct. The results highlight the instrumental character of the above asymmetry in providing a decisive advantage to R against C. It also points to a barrier in R initial support to reach the extension zone. In parallel, the landscape reveals a path for C to counter R expansion, pushing them back into their extinction zone. It relies on the asymmetry of C being initially a large majority which puts the required involvement of C at a rather low level.

A normalized statistical metric space for hidden Markov models.

In this paper, we present a normalized statistical metric space for hidden Markov models (HMMs). HMMs are widely used to model real-world systems. Like graph matching, some previous approaches compare HMMs by evaluating the correspondence, or goodness of match, between every pair of states, concentrating on the structure of the models instead of the statistics of the process being observed. To remedy this, we present a new metric space that compares the statistics of HMMs within a given level of statistical significance. Compared with the Kullback-Leibler divergence, which is another widely used approach for measuring model similarity, our approach is a true metric, can always return an appropriate distance value, and provides a confidence measure on the metric value. Experimental results are given for a sample application, which quantify the similarity of HMMs of network traffic in the Tor anonymization system. This application is interesting since it considers models extracted from a system that is intentionally trying to obfuscate its internal workings. In the conclusion, we discuss applications in less-challenging domains, such as data mining.

Methods to window data to differentiate between Markov models.

In this paper, we consider how we can detect patterns in data streams that are serial Markovian, where target behaviors are Markovian, but targets may switch from one Markovian behavior to another. We want to reliably and promptly detect behavior changes. Traditional Markov-model-based pattern detection approaches, such as hidden Markov models, use maximum likelihood techniques over the entire data stream to detect behaviors. To detect changes between behaviors, we use statistical pattern matching calculations performed on a sliding window of data samples. If the window size is very small, the system will suffer from excessive false-positive rates. If the window is very large, change-point detection is delayed. This paper finds both necessary and sufficient bounds on the window size. We present two methods of calculating window sizes based on the state and transition structures of the Markov models. Two application examples are presented to verify our results. Our first example problem uses simulations to illustrate the utility of the proposed approaches. The second example uses models extracted from a database of consumer purchases to illustrate their use in a real application.

Behavior detection using confidence intervals of hidden Markov models.

Markov models are commonly used to analyze real-world problems. Their combination of discrete states and stochastic transitions is suited to applications with deterministic and stochastic components. Hidden Markov models (HMMs) are a class of Markov models commonly used in pattern recognition. Currently, HMMs recognize patterns using a maximum-likelihood approach. One major drawback with this approach is that data observations are mapped to HMMs without considering the number of data samples available. Another problem is that this approach is only useful for choosing between HMMs. It does not provide a criterion for determining whether or not a given HMM adequately matches the data stream. In this paper, we recognize complex behaviors using HMMs and confidence intervals. The certainty of a data match increases with the number of data samples considered. Receiver operating characteristic curves are used to find the optimal threshold for either accepting or rejecting an HMM description. We present one example using a family of HMMs to show the utility of the proposed approach. A second example using models extracted from a database of consumer purchases provides additional evidence that this approach can perform better than existing techniques.

Markovian search games in heterogeneous spaces.

In this paper, we consider how to search for a mobile evader in a large heterogeneous region when sensors are used for detection. Sensors are modeled using probability of detection. Due to environmental effects, this probability will not be constant over the entire region. We map this problem to a graph-search problem, and even though deterministic graph search is NP-complete, we derive a tractable optimal probabilistic search strategy. We do this by defining the problem as a dynamic game played on a Markov chain. We prove that this strategy is optimal in the sense of Nash. Simulations of an example problem illustrate our approach and verify our claims.