Fiber Optic Tactical Local Network (FOTLAN)

A 100 Mbit/s FDDI (fiber distributed data interface) network interface unit is described that supports real-time data, voice and video. Its high-speed interrupt-driven hardware architecture efficiently manages stream and packet data transfer to the FDDI network. Other enhancements include modular single-mode laser-diode fiber optic links to maximize node spacing, optic bypass switches for increased fault tolerance, and a hardware performance monitor to gather real-time network diagnostics

Improving intrusion detection systems using data mining techniques

Recent surveys and studies have shown that cyber-attacks have caused a lot of damage to organisations, governments, and individuals around the world. Although developments are constantly occurring in the computer security field, cyber-attacks still cause damage as they are developed and evolved by hackers. This research looked at some industrial challenges in the intrusion detection area. The research identified two main challenges; the first one is that signature-based intrusion detection systems such as SNORT lack the capability of detecting attacks with new signatures without human intervention. The other challenge is related to multi-stage attack detection, it has been found that signature-based is not efficient in this area. The novelty in this research is presented through developing methodologies tackling the mentioned challenges. The first challenge was handled by developing a multi-layer classification methodology. The first layer is based on decision tree, while the second layer is a hybrid module that uses two data mining techniques; neural network, and fuzzy logic. The second layer will try to detect new attacks in case the first one fails to detect. This system detects attacks with new signatures, and then updates the SNORT signature holder automatically, without any human intervention. The obtained results have shown that a high detection rate has been obtained with attacks having new signatures. However, it has been found that the false positive rate needs to be lowered. The second challenge was approached by evaluating IP information using fuzzy logic. This approach looks at the identity of participants in the traffic, rather than the sequence and contents of the traffic. The results have shown that this approach can help in predicting attacks at very early stages in some scenarios. However, it has been found that combining this approach with a different approach that looks at the sequence and contents of the traffic, such as event- correlation, will achieve a better performance than each approach individually

Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning

Offline reinforcement learning (RL) is a promising direction that allows RL agents to pre-train on large datasets, avoiding the recurrence of expensive data collection. To advance the field, it is crucial to generate large-scale datasets. Compositional RL is particularly appealing for generating such large datasets, since 1) it permits creating many tasks from few components, 2) the task structure may enable trained agents to solve new tasks by combining relevant learned components, and 3) the compositional dimensions provide a notion of task relatedness. This paper provides four offline RL datasets for simulated robotic manipulation created using the 256 tasks from CompoSuite [Mendez et al., 2022a]. Each dataset is collected from an agent with a different degree of performance, and consists of 256 million transitions. We provide training and evaluation settings for assessing an agent's ability to learn compositional task policies. Our benchmarking experiments on each setting show that current offline RL methods can learn the training tasks to some extent and that compositional methods significantly outperform non-compositional methods. However, current methods are still unable to extract the tasks' compositional structure to generalize to unseen tasks, showing a need for further research in offline compositional RL

Research Recognition Evening

The Department of National Security Affairs recognizes Professor David Yost; Associate Professor Dan Moran is recognized for outstanding research; Associate Professor J. Bret Michael is recognized for outstanding research achievement in 2002; Department of Information Science recognizes Associate Professor William Kemple; Associate Professor Alexander Bordetsky is recognized for outstanding research achievement in the Department of Information Science; Associate Professor Robert F. Dell is recognized for outstanding research achievement in 2002 in the Department of Operations Research; the Department of Aeronautics and Astronautics recognizes Research Assistant Professor Christopher Brophy; Research Assistant Professor Jose Sinibaldi is recognized by the Department of Aeronautics and Astronautics; the Department of Electrical and Computer Engineering recognizes Professor Charles W. Therrien; Associate Professor Fariba Fahroo is recognized for outstanding research achievement in 2002 by the Department of Applied Mathematics; Distinguished Professor Turgut Sarpkaya is recognized in 2001 for outstanding research achievement in the Department of Mechanical Engineering; the Department of Mechanical Engineering recognizes Professor Terry McNelley;the Department of Meteorology proudly recognizes Professor Kenneth Davidson and Research Associate Paul Frederickson; Associate Professor Gamani Karunasiri of the Department of Physics is recognized for his 2001 research accomplishments; the Department of Physics recognizes two outstanding researchers. Associate Professor Richard Christopher Olsen; Assistant Professor Ryan Umstattd is recognized for his year 2002 research accomplishments in the highly-DoD- relevant area of high-power microwave (HPM) weapons research; the Space Systems Academic Group recognizes Dr. Alan Ross; the Space Systems Academic Group recognizes Professor Don Walters; the Department of Systems Engineering recognizes Senior Lecturer Robert C. Harney; Professor Larry Jones (Graduate School of Business and Public Policy) is recognized for his dedicated work in the area of government and public sector reforms worldwide; the MOVES Institute recognizes Associate Professor Don Brutzman; Dr. I. Michael Ross (Associate Professor in the Department of Aeronautics and Astronautics) is the fourteenth recipient of the “Menneken Award”

The Impact of Automated Cognitive Assistants on Situational Awareness in the Brigade Combat Team

This research investigated the impact of automated cognitive assistants, specifically, the Personalized Assistant that Learns (PAL), on situational awareness, efficiency and effectiveness of decision making in the brigade combat team. PAL was recently commissioned by Defense Advanced Research Projects Agency (DARPA) to enhance decision making with the Command Post of the Future (CPOF). This is the first study to investigate PAL's effectiveness. Previous literature has indicated that automated cognitive assistants can reduce cognitive load and improve the efficiency and effectiveness of decision-making. This is consistent with constructivist theories that assume that relegating repetitive tasks to an assistant allows decision makers to focus on the most critical issues. This is particularly true in those conditions where the environment is in continuous flux and the decision makers must remain cognizant of changing situations. To investigate PAL'S influence on situational awareness, two groups of military officers comprising a convenience sample were placed into two groups representing brigade combat teams. Before tests were administered, each team was trained on the cognitive assistants and given a hands-on examination to measure competency in PAL and CPOF. All subjects participated in one trial with PAL-enhanced CPOF and one trial with CPOF alone. Self-assessments of situational awareness were administered which included sub-scales on: task management, information management, decision support, and appreciation of the environment, visualization and trust. Speed and quality of decision-making were also measured. Repeated measures analysis of variance was used to compare PAL and CPOF only on situational awareness. In the repeated measures ANOVA, the overall difference on self-report of situational awareness approachedthe .05 level with PAL (M = 1.85, SD = 0.46) and CPOF (M = 2.06, SD = 0.57; F(1,10) = 4.61, p = .057), with the lower score indicating higher approval. There was a significant difference on the decision support category of situational awareness in the second trial using both PAL and CPOF (M = 2.21, SD = 0.59; rated higher than the first trial (M = 2.53, SD = 0.49; F(1,10) = 5.06, p = .048). The following differences were not significant but the means all favored PAL over CPOF: quality of decision making products PAL (M = 2.89, SD = 0.75); CPOF (M = 2.53, SD = 0.83), speed of submission in minutes PAL (M = 9:13, SD = 3:15); CPOF (M = 10:00, SD = 5:53), and Situational Awareness quizzes PAL (M = 67.03, SD = 7.15); CPOF (M = 59.24, SD = 8.23). While comparisons of PAL and CPOF were not significant, results indicate that the PAL automated cognitive assistant has promise in improving the situational awareness and efficiency of military leaders in complex decision making. The findings demonstrate that as military officers grow more accustomed to using these analytical systems, both PAL and CPOF, they rate their support in decision making higher. This initial study of PAL was conducted with a convenience sample of 12 military officers. Further studies are warranted to investigate the benefits of automated cognitive assistant on an array of factors that influence decision-making across conditions and audiences

Coalition Battle Management Language (C-BML) Study Group Final Report

Interoperability across Modeling and Simulation (M&S) and Command and Control (C2) systems continues to be a significant problem for today\u27s warfighters. M&S is well-established in military training, but it can be a valuable asset for planning and mission rehearsal if M&S and C2 systems were able to exchange information, plans, and orders more effectively. To better support the warfighter with M&S based capabilities, an open standards-based framework is needed that establishes operational and technical coherence between C2 and M&S systems

Air Force Institute of Technology Research Report 2000

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures