Air Force Institute of Technology Research Report 2018

This Research Report presents the FY18 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

Mediating the Spatiality of Conflicts:

Conflict, when dislodged from its conventional understanding as a process and system of war and destruction exclusively, may be apprehended as an experimental method for analysis and synthesis, as a potent resource for pedagogy, for disruptive design and for the production of theory. In this sense, conflict produces more than the eradication of (the possibility of) life and its supporting structures: conflict produces transitional spaces at different scales, of differentiated material ecologies and site-specific meanings in relation to their global position. Conflicts are both locations and explanations of often ‘seductive’ images of destruction offered by popular (and other) media: ruined architectures, dead (or barely alive) bodies, forced migratory movements, impermanent infrastructures and settlements, as well as the tracing and construction of borders, real-estate driven post-war reconstruction processes, etc. The emphasis on the mediatic aspect in the concept of conflict, may be seen as a way of triggering trans- and interdisciplinary discussions, conversations and encounters that serve as a negotiation between conditions of violence and new — or alternative — possibilities for everyday life. Artistic mediations could be as effective as violence in resolving conflicts, but operate through other means and through other channels, thus truly producing new power relations and alternative ways of political struggle. This exposing of conflict and violence through the artistic work is an activist act, but more importantly an artistic and technological mediation. The agency of the artistic work in terms of conflict, then, is situated in the capacity of visualising the conflict, creating awareness of its consequences, its side-effects, its collateral damage. And the creating of awareness and the becoming of fertile ground for protest and the creation of alternative realities. At a three-day conference held at the TU Delft on November 6-8, 2019 researchers, scholars, activists, practitioners and artists presented individual papers that addressed the relationships between spatiality, mediation and conflict from a variety of perspectives. In addition to academic paper contributions, the conference welcomed other proposals in different formats and media: audio-visual material (film, video, photography), digital or physical archives, experimental design proposals, installations, performances, etc. The thematic core of the conference explored new — or innovative — theoretical and methodological approaches and insights on: (1) Spaces of conflict as transitional spaces of material interactions between violence and everyday life; and (2) Spaces of memory as transformative space of violence)

Mediating the Spatiality of Conflicts:

Conflict, when dislodged from its conventional understanding as a process and system of war and destruction exclusively, may be apprehended as an experimental method for analysis and synthesis, as a potent resource for pedagogy, for disruptive design and for the production of theory. In this sense, conflict produces more than the eradication of (the possibility of) life and its supporting structures: conflict produces transitional spaces at different scales, of differentiated material ecologies and site-specific meanings in relation to their global position. Conflicts are both locations and explanations of often ‘seductive’ images of destruction offered by popular (and other) media: ruined architectures, dead (or barely alive) bodies, forced migratory movements, impermanent infrastructures and settlements, as well as the tracing and construction of borders, real-estate driven post-war reconstruction processes, etc. The emphasis on the mediatic aspect in the concept of conflict, may be seen as a way of triggering trans- and interdisciplinary discussions, conversations and encounters that serve as a negotiation between conditions of violence and new — or alternative — possibilities for everyday life. Artistic mediations could be as effective as violence in resolving conflicts, but operate through other means and through other channels, thus truly producing new power relations and alternative ways of political struggle. This exposing of conflict and violence through the artistic work is an activist act, but more importantly an artistic and technological mediation. The agency of the artistic work in terms of conflict, then, is situated in the capacity of visualising the conflict, creating awareness of its consequences, its side-effects, its collateral damage. And the creating of awareness and the becoming of fertile ground for protest and the creation of alternative realities. At a three-day conference held at the TU Delft on November 6-8, 2019 researchers, scholars, activists, practitioners and artists presented individual papers that addressed the relationships between spatiality, mediation and conflict from a variety of perspectives. In addition to academic paper contributions, the conference welcomed other proposals in different formats and media: audio-visual material (film, video, photography), digital or physical archives, experimental design proposals, installations, performances, etc. The thematic core of the conference explored new — or innovative — theoretical and methodological approaches and insights on: (1) Spaces of conflict as transitional spaces of material interactions between violence and everyday life; and (2) Spaces of memory as transformative space of violence)

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

Deep Networks and Knowledge: from Rule Learning to Neural-Symbolic Argument Mining

Deep Learning has revolutionized the whole discipline of machine learning, heavily impacting fields such as Computer Vision, Natural Language Processing, and other domains concerned with the processing of raw inputs. Nonetheless, Deep Networks are still difficult to interpret, and their inference process is all but transparent. Moreover, there are still challenging tasks for Deep Networks: contexts where the success depends on structured knowledge that can not be easily provided to the networks in a standardized way. We aim to investigate the behavior of Deep Networks, assessing whether they are capable of learning complex concepts such as rules and constraints without explicit information, and then how to improve them by providing such symbolic knowledge in a general and modular way. We start by addressing two tasks: learning the rule of a game and learning to construct the solution to Constraint Satisfaction Problems. We provide the networks only with examples, without encoding any information regarding the task. We observe that the networks are capable of learning to play by the rules and to make feasible assignments in the CSPs. Then, we move to Argument Mining, a complex NLP task which consists of finding the argumentative elements in a document and identifying their relationships. We analyze Neural Attention, a mechanism widely used in NLP to improve networks' performance and interpretability, providing a taxonomy of its implementations. We exploit such a method to train an ensemble of deep residual networks and test them on four different corpora for Argument Mining, reaching or advancing the state of the art in most of the datasets we considered for this study. Finally, we realize the first implementation of neural-symbolic argument mining. We use the Logic Tensor Networks framework to introduce logic rules during the training process and establish that they give a positive contribution under multiple dimensions

An Agile Roadmap for Live, Virtual and Constructive-Integrating Training Architecture (LVC-ITA): A Case Study Using a Component based Integrated Simulation Engine

Conducting seamless Live Virtual Constructive (LVC) simulation remains the most challenging issue of Modeling and Simulation (M&S). There is a lack of interoperability, limited reuse and loose integration between the Live, Virtual and/or Constructive assets across multiple Standard Simulation Architectures (SSAs). There have been various theoretical research endeavors about solving these problems but their solutions resulted in complex and inflexible integration, long user-usage time and high cost for LVC simulation. The goal of this research is to provide an Agile Roadmap for the Live Virtual Constructive-Integrating Training Architecture (LVC-ITA) that will address the above problems and introduce interoperable LVC simulation. Therefore, this research describes how the newest M&S technologies can be utilized for LVC simulation interoperability and integration. Then, we will examine the optimal procedure to develop an agile roadmap for the LVC-ITA. In addition, this research illustrated a case study using an Adaptive distributed parallel Simulation environment for Interoperable and reusable Model (AddSIM) that is a component based integrated simulation engine. The agile roadmap of the LVC-ITA that reflects the lessons learned from the case study will contribute to guide M&S communities to an efficient path to increase interaction of M&S simulation across systems