Woburn, MA, United States
Woburn, MA, United States

Time filter

Source Type

Grant
Agency: Department of Defense | Branch: Navy | Program: STTR | Phase: Phase I | Award Amount: 80.00K | Year: 2016

The introduction of new systems and technologies is critical for maintaining superiority, yet this brings with it uncertainty regarding the impact on users, teams, and organizations. Rigorous test and evaluation (T&E) practices are therefore essential prior to acquiring and instituting new technologies, particularly to assess the workload imposed on end users. Traditionally, workload assessment has employed highly subjective techniques dependent on self-reported responses provided by users of a system; however more objective techniques for workload measurement are greatly needed. In response to this need, Aptima will develop TOME: Tools for Objective Measurement and Evaluation. TOME will provide a diagnostic toolset for cost-effectively supporting T&E practitioners and augmenting system acquisition decisions through advanced workload measurement and performance assessment strategies. TOME will help guide T&E personnel and acquisition decision-makers to accurate and more thorough assessments than currently available. This in turn will lead to more valid conclusions, thus allowing evaluators to weigh competing factors such as acquisition cost and a given systems ability to meet performance and safety criteria.


Grant
Agency: Department of Defense | Branch: Defense Health Program | Program: SBIR | Phase: Phase II | Award Amount: 996.99K | Year: 2015

Although great strides have been made in the development of simulators for training technical and teamwork skills, substantially less progress has been made with regard to their assessment. Typically, skills and performance are assessed by an expert observer using a Likert-type rating scale with anchors ranging from low to high. However, such methods are extremely coarse, and often provide little insight into the underlying causes of success or failure. What is needed are much more fine-grained, unobtrusive, and real-time measures of individual and team states such as cognitive load (CL), which can supplement expert observer ratings to provide a more holistic assessment of individual and team performance. By automatically alerting instructors to changes in CL, instructors can dynamically modify training scenarios on-the-fly, thereby ensuring that learners remain within the Zone of Proximal Development (ZPD) at all times. Further, by integrating CL measures during the post-training After Action Review (AAR), instructors and learners will gain a better understanding into the causes of effective and ineffective performance. With this in mind, Aptima proposes to extend, harden, and commercialize ACLAMATE, our real-time CL assessment, alerting, and visualization tool.


Grant
Agency: Department of Defense | Branch: Office of the Secretary of Defense | Program: SBIR | Phase: Phase II | Award Amount: 1.00M | Year: 2014

As analysts and operators move from data to insights, tools are needed for supervisory control, command and control, and intelligence analysis. Intelligence, Surveillance, and Reconnaissance (ISR) requires the ability to navigate and interpret mounds of data to produce actionable decisions. Through the Urban Telepresence program, the Air Force Research Laboratory (AFRL) is redefining a concept of operations for ISR operations by enabling remote, virtual operators to interact with operational environments without being physically present. However, redesigning this workflow requires advancements to human-machine interfaces. To support this need, the Aptima team is developing the Sensor Operations via Naturalistic Interactive Control (SONIC) platform. SONIC is a multimodal user interaction framework optimized for use within highly immersive and data-rich environments to provide an intuitive, naturalistic way for users to interact and collaborate with distributed sensors, unmanned systems, and teammates in the operational environment. SONIC integrates an immersive multimodal workstation with a context-driven interaction service, and is built on top of scientifically-grounded human-machine interface guidelines for hybrid reality environments. Ultimately, the objective of SONIC is to enable analysts and operators to provide mission support in real-time from remote locations more effectively, without an increase in workload or a decrease in performance.


Grant
Agency: Department of Defense | Branch: Office of the Secretary of Defense | Program: SBIR | Phase: Phase II | Award Amount: 993.12K | Year: 2014

There is an increasing need for fast and accurate analysis of large volumes of disparate data containing critical information. Existing tools for information search, retrieval, and exploitation are inadequate because the tools have limited ability to (1) help the analyst understand the semantics within the information, (2) reveal the relationships between the information and the analytic task, or (3) demonstrate the best ways to fuse the information into an assessment. Cognitive biases that result from limitations inherent in human cognitive processes subconsciously influence intelligence analysis, and current tools provide little or no help to prevent these biases from influencing results. Aptima and our partners propose to further develop an Adaptive Workspace for Analyst Knowledge and Engagement (AWAKE) capability. AWAKE will provide the next generation of cognitive, knowledge-aided analyst support systems to promote a more effective human-machine partnership, enabling analysts to focus on what they uniquely do best as humans, while the autonomous system looks over their shoulder to provide them cognitive aid. AWAKE provides a capability for measuring the analysts level of rigor; automatically identifying indicators of cognitive biases and vulnerabilities, based on a semantic interpretation of the users interactions with the system; and personalized agents to support analyst activities.


Grant
Agency: Department of Defense | Branch: Office of the Secretary of Defense | Program: SBIR | Phase: Phase II | Award Amount: 990.83K | Year: 2014

The volume of data collected by Air Force ISR has exceeded the capacities of traditional analysis methods. New enhancements to the Air Forces PCPAD intelligence cycle are, thus, critical to providing the analyst with the ability to strategically process, exploit and analyze the most critical information. Aptimas SCAAN system seeks to enhance PCPAD by distributing data exploitation and analysis tasks across a network of semi-autonomous agents (i.e., processing software resources) efficiently managed by a command, control, and communication (C3) software organization. SCAAN ensures robustness of distributed data analysis by balancing agent workload and building resilience to failures. SCAAN supports accuracy and optimality of large-scale data analysis by efficiently partitioning a global search problem into distributed interdependent tasks across multiple agents. SCAAN also provides agents with autonomy for learning patterns in a distributed fashion, and reduces the time of analysis by implementing algorithms that minimize processing of irrelevant data and communication requirements. Additionally, SCAAN operates in a hybrid infrastructure, leveraging service-oriented and cloud-enabled frameworks to support processing of large-scale data in accessible and denied environments. In summary, SCAAN is a processing, exploitation and analysis tool to assist analysts in efficiently and accurately extracting critical information from large-scale, distributed, multi-modal, multi-source data.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2015

ABSTRACT:Cyber operators face a deluge of data about complex networks and need to efficiently identify, analyze, and mitigate anomalies. They are overloaded with information, and are often faced with situations that require immediate action to mitigate network effects. While computational techniques such as filters and fusion algorithms can help, the dynamic nature of cyber operations means that data still requires human interpretation to determine the best course of action. To address these challenges the Aptima team proposes to design and build Advanced Displays for Visualizing Information in Cyber Environments (ADVICE). ADVICE uses context-aware interactive visualization, with computational awareness of context drives which visualizations are presented and what data is shown. These visualizations will be human-centered not only in the visual design, but also in the interactions. As cyber operators perform their tasks, ADVICE will provide an integrated context-sensitive picture by facilitating connections across different sources of data, and prioritizing that information based on the users tasks and interactions. The goal of ADVICE is to enable cyber operators to more efficiently perform their jobs by providing intuitive representations of data that are tailored to the operators needs.BENEFIT:ADVICE provides human-centered, context-aware visualization of data for cyber operators. Through computational awareness of context, visualizations are tailored to the task and needs of the human operator. As cyber operators perform their tasks, ADVICE will provide an integrated context-sensitive picture of network activity by facilitating connections across different sources of data, and prioritizing that information based on the users tasks and interactions. This will enable more effective cyberspace operations, where operators can focus their effort on their current task and fluidly interact with the data.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2015

ABSTRACT:Despite ever-shrinking training budgets, military personnel must continue to develop and maintain proficiency to ensure success in increasingly complex operational environments. To achieve proficiency within these constraints, training methods and technologies must enable efficient and personalized learning. To do so, training approaches must track and asses competence and account for individual differences throughout the training development and delivery lifecycle. Aptima proposes to develop the Adaptive Module for Personalized Learning Environments (AMPLE), an adaptive training management module that plugs into training systems and learning management systems, and assesses individual competence, accounts for individual differences, and provides training recommendations enabling personalized training across training environments. To build such a system, Aptima will leverage existing documentation to understand the different training requirements across positions. AMPLE will integrate with existing performance measurement technologies to collect, consolidate, and store multi-sourced data and assessments, such as the individual learners competence, position, and previous learning experiences, and will apply adaptive algorithms to these data and assessments to provide intelligent, data-driven training recommendations. When fully implemented, AMPLE will enable training designers and instructors to maximize training resources by providing to individual learners personalized training that supports development and maintenance of competence.BENEFIT:AMPLE will be an adaptive training management module that plugs into candidate training systems and existing learning management systems, and provides personalized training recommendations based on the continuous assessment of an individuals competence, accounting for individual differences, and will do so within the context of an integrated training environment (i.e., a set of training systems). Specifically, AMPLE will enable training designers, instructors, and researchers to maximize training resources by providing intelligent, data-driven training recommendations and by allowing users to examine training questions related to specific training goals, such as minimizing skill decay, maximizing retention, or achieving broad coverage of training requirements across positions. This hybrid approach will allow users to understand the impacts of maximizing certain learning outcomes over others. While our primary focus is to address the needs of the Air Force training and training-research communities, the adaptive training management module and underlying approaches will be designed to be generalizable to address personalized training needs across other DoD agencies and commercial organizations. Our initial efforts will focus on the Processing, Exploitation, and Dissemination domain, which represents a complex mission involving a variety of roles, disciplines, and training needs. End users will benefit by receiving recommendations for personalizing training, understanding where learners are in the development of competence, and understanding how best to optimize future training within the constraints of available resources.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2015

ABSTRACT:Operators and analysts in modern Air and Space Operations Centers (AOC) must gather and synthesize information from a variety of disconnected sources, including networks, in order to make effective decisions in a complex, distributed operations environment. Current approaches struggle to integrate air, space, and cyber resources in an effective and timely manner. We propose the Tailored Augmentation Leveraging Integrated Information (TALII) system, an adaptive visual analytics tool that leverages context-aware course of action (COA) recommendation and perceptually grounded cyber-spatial visualization design to support situation awareness and efficient decision-making in the future AOC. TALII integrates multiple sources of incoming information into a context engine that maintains awareness of the current mission state and the state of incoming data. This model of context is then combined with perceptually-driven design principles to tailor a visual analytics interface for users in the AOC and distributed users that highlights important information and selects appropriate level of detail to maintain situation awareness. COA visualization and recommendation helps users make faster and more effective decisions under changing conditions.BENEFIT:TALII employs adaptive, context-aware visual analytics that use integrated information structures and COA recommendations to enable faster decision speeds under highly dynamic conditions. It will allow for greater integration of currently separate components of air, space, and cyber operations so that users in AOCs can identify patterns that are not currently visible. By employing a context model that maintains information on data uncertainty and disruptions, TALII can be more resilient to network attacks and conflicting information streams. Commercial applications of visual analytics tools are growing rapidly as data size and complexity increases across domains. At the same time, there are more situations in which decision-makers must act on this data in real time. Such large-scale streaming data analysis is increasingly important to intelligence analysis, financial analytics, and social media analytics. TALII will accelerate decision-making by helping users become more proactive in responding to unexpected events.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 849.98K | Year: 2014

This proposal describes a series of inter-related subprojects aimed at developing an empirical understanding of the MAGIC CARPET system, including its training requirements, effectiveness, and safety. An important overall objective is to estimate cost, throughput, and readiness considerations compared to conventional landing technology. To accomplish this, the work, including work in future studies, is organized according to Kirkpatricks four levels of learning evaluation. To address levels 1 and 2, we will design and execute a formal experiment aimed at developing an empirical understanding of the training and performance requirements of MAGIC CARPET as compared with conventional landing technology. We will address Level 3 by developing and planning the validation for a model for predicting the effects of different schedules of initial and refresher training, and by planning a transfer-of-training study that involves both live and simulated carrier landings. The level 4 investigation will be undertaken in future studies that will create a model of the organizational training pipeline for MAGIC CARPET.


Grant
Agency: Department of Defense | Branch: Defense Advanced Research Projects Agency | Program: SBIR | Phase: Phase II | Award Amount: 999.99K | Year: 2014

Efficient and accurate indexing is fundamental to the Big Data enterprise, but traditional indexing techniques are often foiled by noise and missing information. Aptima proposes Phase II of CERTAIN (Certainty Enrichment via Relational and Temporal Analyti

Loading Aptima, Inc. collaborators
Loading Aptima, Inc. collaborators