Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers

Picasso is a free open-source (Eclipse Public License) web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks. Picasso ships with occlusion maps and saliency maps, two visualizations which help reveal issues that evaluation metrics like loss and accuracy might hide: for example, learning a proxy classification task. Picasso works with the Tensorflow deep learning framework, and Keras (when the model can be loaded into the Tensorflow backend). Picasso can be used with minimal configuration by deep learning researchers and engineers alike across various neural network architectures. Adding new visualizations is simple: the user can specify their visualization code and HTML template separately from the application code.Comment: 9 pages, submission to the Journal of Open Research Software, github.com/merantix/picass

Terrain Database Correlation Assessment Using an Open Source Tool

Configuring networked simulators for training military teams in a distributed environment requires the usage of a set of terrain databases to represent the same training area. The results of simulation exercises can be degraded if the terrain databases are poorly correlated. A number of methodologies for determining the correlation between terrain databaHowever, there are few computational tools for this task and most of them were developed to address government needs, have limited availability, and handle specific digital formats. The goal of this paper is thus to present a novel open source tool developed as part of an academic research project.Comment: 12 pages, I/ITSEC 201

Matching Natural Language Sentences with Hierarchical Sentence Factorization

Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Depending on whether training data is available, prior research has proposed both unsupervised distance-based schemes and supervised deep learning schemes for sentence matching. However, previous approaches either omit or fail to fully utilize the ordered, hierarchical, and flexible structures of language objects, as well as the interactions between them. In this paper, we propose Hierarchical Sentence Factorization---a technique to factorize a sentence into a hierarchical representation, with the components at each different scale reordered into a "predicate-argument" form. The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies. We apply our techniques to text-pair similarity estimation and text-pair relationship classification tasks, based on multiple datasets such as STSbenchmark, the Microsoft Research paraphrase identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments show that the proposed hierarchical sentence factorization can be used to significantly improve the performance of existing unsupervised distance-based metrics as well as multiple supervised deep learning models based on the convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page

Managing Global Training Utilizing Distance Learning Technologies and Techniques: The United States Army Readiness Training

Distance learning (e-learning) is expanding at a very rapid pace as organizations throughout the world search for economical, responsive, and effective means to train workers to meet the challenges of the information age workplace. The Army Distance Learning Program (TADLP) model is discussed in the context of the global e-learning environment. Both e-learning infrastructure and management issues are identified, with emphasis on: (1) developing policy, (2) measuring performance, (3) managing resources, (4) maintaining standards, and (5) satisfying users. The TADLP program is challenging to manage effectively, and difficult to accurately assess program outcomes. The TADLP program is shown to have a well-executed infrastructure plan, quality management of both facilities and services by contractor-supplied staff, and well-designed classrooms. However, the program suffers from limited courseware, creating a bottleneck for full program utilization. A discussion follows relating the Army program to public and private e-learning programs and expectations.

Managing at the Speed of Light: Improving Mission-Support Performance

The House and Senate Energy and Water Development Appropriations Subcommittees requested this study to help DOE's three major mission-support organizations improve their operations to better meet the current and future needs of the department. The passage of the Recovery Act only increased the importance of having DOE's mission-support offices working in the most effective, efficient, and timely manner as possible. While following rules and regulations is essential, the foremost task of the mission-support offices is to support the department's mission, i.e., the programs that DOE is implementing, whether in Washington D.C. or in the field. As a result, the Panel offered specific recommendations to strengthen the mission-focus and improve the management of each of the following support functions based on five "management mandates":- Strategic Vision- Leadership- Mission and Customer Service Orientation- Tactical Implementation- Agility/AdaptabilityKey FindingsThe Panel made several recommendations in each of the functional areas examined and some overarching recommendations for the corporate management of the mission-support offices that they believed would result in significant improvements to DOE's mission-support operations. The Panel believed that adopting these recommendations will not only make DOE a better functioning organization, but that most of them are essential if DOE is to put its very large allocation of Recovery Act funding to its intended uses as quickly as possible

The ergonomics of command and control

Since its inception, just after the Second World War, ergonomics research has paid special attention to the issues surrounding human control of systems. Command and Control environments continue to represent a challenging domain for Ergonomics research. We take a broad view of Command and Control research, to include C2 (Command and Control), C3 (Command, Control and Communication), and C4 (Command, Control, Communication and Computers) as well as human supervisory control paradigms. This special issue of ERGONOMICS aims to present state-of-the-art research into models of team performance, evaluation of novel interaction technologies, case studies, methodologies and theoretical review papers. We are pleased to present papers that detail research on these topics in domains as diverse as the emergency services (e.g., police, fire, and ambulance), civilian applications (e.g., air traffic control, rail networks, and nuclear power) and military applications (e.g., land, sea and air) of command and control. While the domains of application are very diverse, many of the challenges they face share interesting similarities