This research to practice work-in-progress paper discusses the potential use of team formation survey responses to predict the development of leaders and social loafers in a first-year engineering team design project. At a large Midwestern university, a robotics cornerstone design project is offered. This project-based and collaborative learning project involves teams designing an autonomous robot. The teams are self-driven and must utilize time and project management skills. To create the teams of four, a team formation survey is administered to the students via the CATME team formation software. Students self-identify levels of prior experience with robotics and technical skills, and levels of preference about leadership, ideas, and details. The instructional staff does not impose specific roles upon team members, but teams are formed so that each team can complete all elements. Anecdotally, this model has allowed for the emergence of both leaders and social loafers within the groups. Students who exhibit leadership qualities keep their teammates on task and schedule, maintain communication and participation, and ensure teammates get help when needed. Students who exhibit characteristics of social loafing, a term used in social psychology, put forth fewer ideas and less effort, require more direction to complete tasks, and do not communicate as well with the team. The absence of leadership or the presence of social loafing can hinder a team's performance and detract from the student experience. Thus, the instructional staff is interested in identifying potential leaders and social loafers on teams early so that appropriate guidance can be given to improve the experience. Throughout the project, students also complete peer evaluations and submit weekly timesheets. This work-in-progress paper presents a methodological plan for analyzing these data to determine if a correlation can be found from specific responses on the team formation survey to student team performance.

We introduce a flexible, scalable Bayesian inference framework for nonlinear dynamical systems characterised by distinct and hierarchical variability at the individual, group, and population levels. Our model class is a generalisation of nonlinear mixed-effects (NLME) dynamical systems, the statistical workhorse for many experimental sciences. We cast parameter inference as stochastic optimisation of an end-to-end differentiable, block-conditional variational autoencoder. We specify the dynamics of the data-generating process as an ordinary differential equation (ODE) such that both the ODE and its solver are fully differentiable. This model class is highly flexible: the ODE right-hand sides can be a mixture of user-prescribed or "white-box" sub-components and neural network or "black-box" sub-components. Using stochastic optimisation, our amortised inference algorithm could seamlessly scale up to massive data collection pipelines (common in labs with robotic automation). Finally, our framework supports interpretability with respect to the underlying dynamics, as well as predictive generalization to unseen combinations of group components (also called “zero-shot" learning). We empirically validate our method by predicting the dynamic behaviour of bacteria that were genetically engineered to function as biosensors.

The Curricular Guidelines for Post-Secondary Degree Programs in Cybersecurity (CSEC 2017) released by the Joint Task Force of the ACM, IEEE Computer Society, AIS and IFIP have begun to define the academic field of cybersecurity. Increasing numbers of institutions are now offering full-fledged undergraduate cybersecurity degree programs. However, as the CSEC 2017 guidelines do not define what constitutes a standalone undergraduate cybersecurity degree, the computing community needs to agree on parameters of what such a degree ought to be. To this end, ABET has created flexible accreditation criteria for cybersecurity programs guided extensively by CSEC 2017 and community input. These criteria were used to conduct pilot accreditation visits of four distinct cybersecurity programs in 2017-18. This panel discusses benefits, challenges, and costs of cybersecurity program accreditation. The panelists, who represent the first four ABET-accredited cybersecurity programs, will discuss major curricular features of their programs, their motivation for participating in the pilot accreditation, the effort in preparing for accreditation, and the benefits anticipated both for their programs and the larger computing community due to program accreditation

Objective: Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised machine learning. The objective of this study is to train and validate an NLP classifier for identifying patients with alcohol misuse. Materials and Methods: An observational cohort of 1422 adult patients admitted to a trauma center between April 2013 and November 2016. Linguistic processing of clinical notes was performed using the clinical Text Analysis and Knowledge Extraction System. The primary analysis was the binary classification of alcohol misuse. The Alcohol Use Disorders Identification Test served as the reference standard. Results: The data corpus comprised 91 045 electronic health record notes and 16 091 features. In the final machine learning classifier, 16 features were selected from the first 24 hours of notes for identifying alcohol misuse. The classifier's performance in the validation cohort had an area under the receiver-operating characteristic curve of 0.78 (95% confidence interval [CI], 0.72 to 0.85). Sensitivity and specificity were at 56.0% (95% CI, 44.1% to 68.0%) and 88.9% (95% CI, 84.4% to 92.8%). The Hosmer-Lemeshow goodness-of-fit test demonstrates the classifier fits the data well (P = .17). A simpler rule-based keyword approach had a decrease in sensitivity when compared with the NLP classifier from 56.0% to 18.2%. Conclusions: The NLP classifier has adequate predictive validity for identifying alcohol misuse in trauma centers. External validation is needed before its application to augment screening.

The complex dynamics of biological systems is primarily driven by molecular interactions that underpin the regulatory networks of cells. These networks typically contain positive and negative feedback loops, which are responsible for switch-like and oscillatory dynamics, respectively. Many computing systems rely on switches and clocks as computational modules. While the combination of such modules in biological systems leads to a variety of dynamical behaviours, it is also driving development of new computing algorithms. Here we present a historical perspective on computation by biological systems, with a focus on switches and clocks, and discuss parallels between biology and computing. We also outline our vision for the future of biological computing.

In this work we describe how a message board can be used to teach a number of important concepts in cyber security in a novel and hands-on manner. The message board is used daily in class and serves the routine functions of disseminating information to students, offering a forum for interactive discussions, allowing a convenient method to copy and paste complex expressions, providing a venue for class interaction, permitting archiving of information, and serving as a general blackboard. We use the message board to illustrate and teach important cyber-security concepts and common attacks such as the following: authentication and cookies, cross-site scripting and injection attacks, man-in-the-middle attack on public-key cryptography, password selection, and password-file management. From the student-learning perspective the tool appears to work very well. We hope that others can make use of the message board in their lessons or incorporate parts of it to improve the educational experience of their students. The message board can be integrated seamlessly into a class to enhance hands-on learning.

The twin rotor MIMO system (TRMS) is a helicopter-like system that is restricted to two degrees of freedom, pitch and yaw. It is a complicated nonlinear, coupled, MIMO system used for the verification of control methods and observers. This paper compares the ability of three adaptive sliding mode controllers (SMC) to suppress chattering. Once the design is complete the controllers are implemented in simulation and experimentally. The performance of the controllers are compared against a PID controller and other controllers in the literature. The ability of the sliding mode controllers (SMC) to suppress chattering is also be explored.

SUMMARY: A host of formal, textual languages for modeling cellular processes have recently emerged, but their simulation tools often require an installation process which can pose a barrier for use. Bio Simulators is a framework for easy online deployment of simulators, providing a uniform web-based user interface to a diverse pool of tools. The framework is demonstrated through two plugins based on the KaSim Kappa simulator, one running directly in the browser and another running in the cloud. AVAILABILITY: Web tool: bsims.azurewebsites.net. KaSim client side simulator: github.com/NicolasOury/KaSimJS. KaSim cloud simulator: github.com/mdpedersen/KaSimCloud. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Due to the high priority of cyber-security education, the United States Naval Academy rapidly developed and implemented a new cyber-security course that is required for all of its first-year students. During the fall semester in 2011, half of the incoming class (about 600 students) took the course through a total of 31 sections offered by 16 instructors from a variety of disciplines and backgrounds. In the following spring semester, the remaining half of the first-year students will take the course. This paper explains the motivation that instigated and drove course development, the curriculum, teaching mechanics implemented, personnel required, as well as challenges and lessons learned from the first offering of the course. The information contained in this paper will be useful to those thinking of implementing a technical course required of all students at the same level in an institution (in our case first-year students) and particularly those interested in implementing such a course in cyber security.

Abstract Summary: The Visual DSD (DNA Strand Displacement) tool allows rapid prototyping and analysis of computational devices implemented using DNA strand displacement, in a convenient web-based graphical interface. It is an implementation of the DSD programming language and compiler described by Lakin et al. (2011) with additional features such as support for polymers of unbounded length. It also supports stochastic and deterministic simulation, construction of continuous-time Markov chains and various export formats which allow models to be analysed using third-party tools. Availability: Visual DSD is available as a web-based Silverlight application for most major browsers on Windows and Mac OS X at http://research.microsoft.com/dna. It can be installed locally for offline use. Command-line versions for Windows, Mac OS X and Linux are also available from the web page. Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online.

Rho GTP-binding proteins play a key role as molecular switches in many cellular activities. In response to extracellular stimuli and with the help of regulators (GEF, GAP, Effector, GDI), these proteins serve as switches that interact with their environment in a complex manner. Based on the structure of a published ordinary differential equations (ODE) model, we first present a generic process model for the Rho GTP-binding proteins, and compare it with the ODE model. We then extend the basic model to include the behaviour of the GDI regulators and explore the parameter space for the extended model with respect to biological data from the literature. We discuss the challenges this extension brings and the directions of further research. In particular, we present techniques for modular representation and refinement of process models, where, for example, different Rho proteins with different rates for regulator interactions can be given as instances of the same parametric model.