This paper describes the integration of semantic and episodic memory (EM) models and the benefits of such integration. Semantic memory (SM) is used as a foundation of knowledge and concept learning, and is needed for the operation of any cognitive system. EM retains personal experiences stored based on their significance-it is supported by the SM, and in return, it supports SM operations. Integrated declarative memories are critical for cognitive system development, yet very little research has been done to develop their computational models. We considered structural self-organization of both semantic and episodic memories with a symbolic representation of input events. Sequences of events are stored in EM and are used to build associations in SM. We demonstrated that integration of semantic and episodic memories improves the native operation of both types of memories. Experimental results are presented to illustrate how the two memories complement each other by improving recognition, prediction, and context-based generalization of individual memories.

This paper presents the development of a motivated learning (ML) agent with symbolic I/O. Our earlier work on the ML agent was enhanced, giving it autonomy for interaction with other agents. Specifically, we equipped the agent with drives and pains that establish its motivations to learn how to respond to desired and undesired events and create related abstract goals. The purpose of this paper is to explore the autonomous development of motivations and memory in agents within a simulated environment. The ML agent has been implemented in a virtual environment created within the NeoAxis game engine. Additionally, to illustrate the benefits of an ML-based agent, we compared the performance of our algorithm against various reinforcement learning (RL) algorithms in a dynamic test scenario, and demonstrated that our ML agent learns better than any of the tested RL agents.This paper presents the development of a motivated learning (ML) agent with symbolic I/O. Our earlier work on the ML agent was enhanced, giving it autonomy for interaction with other agents. Specifically, we equipped the agent with drives and pains that establish its motivations to learn how to respond to desired and undesired events and create related abstract goals. The purpose of this paper is to explore the autonomous development of motivations and memory in agents within a simulated environment. The ML agent has been implemented in a virtual environment created within the NeoAxis game engine. Additionally, to illustrate the benefits of an ML-based agent, we compared the performance of our algorithm against various reinforcement learning (RL) algorithms in a dynamic test scenario, and demonstrated that our ML agent learns better than any of the tested RL agents.

Various approaches have been developed for quantifying and displaying network traffic information for determining network status and in detecting anomalies. Although many of these methods are effective, they rely on the collection of long-term network statistics. Here, we present an approach that uses short-term observations of network features and their respective time averaged entropies. Acute changes are localized in network feature space using adaptive Wiener filtering and auto-regressive moving average modeling. The color-enhanced datagram is designed to allow a network engineer to quickly capture and visually comprehend at a glance the statistical characteristics of a network anomaly. First, average entropy for each feature is calculated for every second of observation. Then, the resultant short-term measurement is subjected to first- and second-order time averaging statistics. These measurements are the basis of a novel approach to anomaly estimation based on the well-known Fisher linear discriminant (FLD). Average port, high port, server ports, and peered ports are some of the network features used for stochastic clustering and filtering. We empirically determine that these network features obey Gaussian-like distributions. The proposed algorithm is tested on real-time network traffic data from Ohio University's main Internet connection. Experimentation has shown that the presented FLD-based scheme is accurate in identifying anomalies in network feature space, in localizing anomalies in network traffic flow, and in helping network engineers to prevent potential hazards. Furthermore, its performance is highly effective in providing a colorized visualization chart to network analysts in the presence of bursty network traffic.

Fast and efficient detection of anomalies is essential for maintaining a robust and secure network. This research presents a method of anomaly detection based on adaptive Wiener filtering of noise followed by ARMA modeling of network flow data. We dynamically calculate noise and traffic signal statistics using network-monitoring metrics for traffic features such as average port, high port, server ports, and peered ports. The underlying approach is tested on near-real-time Internet traffic in the wide-area network (WAN) of Ohio University. The average port feature is determined to be the most informative measure in the estimation process. High port, server ports, and peered ports are used for confirmation of the anomaly detection result. We empirically determine that most of the network features obey Gaussian-like distributions. Experiments reveal that the method is highly effective in predicting anomalies in network traffic flow and preventing any hazard that they may cause.

This paper describes a non-linear IR (infra-red) scene prediction method for range video surveillance and navigation. A Gabor-filter bank is selected as a primary detector for any changes in a given IR range image sequence. The detected ROI (region of interest) involving arbitrary motion is fed to a non-linear Kalman filter for predicting the next scene in time-varying 3D IR video. Potential applications of this research are mainly in indoor/outdoor heat-change based range measurement, synthetic IR scene generation, rescue missions, and autonomous navigation. Experimental results reported herein show that non-linear Kalman filtering-based scene prediction can perform more accurately than linear estimation of future frames in range and intensity driven sensing. The low least mean square error (LMSE), on the average of about 2% using a bank of 8 Gabor filters, also proves the reliability of the IR scene estimator (or predictor) developed in this work.

This paper presents a Kalman filtering approach to predicting 3-D video infrared (IR) scenes as a CMOS multi-coordinate axis sensory-camera mounted on a mobile vehicle moves forward in a controlled environment. Potential applications of this research can be found in indoor/outdoor heat-change based range measurement, synthetic IR scene generation, rescue missions, and autonomous navigation. Experimental results reported herein dictate that linear Kalman filtering based scene prediction accurately estimates future frames in range and intensity sensing. The low least mean square error (LMSE), on the average of 1%, proves the reliability of the approach to IR scene prediction. Currently, the proposed method is devised for piece-wise linear motion of the sensory system as it navigates in hallway or corridor.

Several methods exist for detecting Linux kernel module (LKM) rootkits, most of which rely on a priori system-specific knowledge. We propose an alternative detection technique that only requires knowledge of the distribution of system call addresses in an uninfected system. Our technique relies on outlier analysis, a statistical technique that compares the distribution of system call addresses in a suspect system to that in a known uninfected system. Experimental results indicate that it is possible to detect LKM rootkits with a high degree of confidence.

RADARSAT-2 will be the next Canadian commercial Earth observation SAR satellite. The construction of the spacecraft and the development of the ground segment are progressing well. This paper presents an overview of the mission, some featured innovations, imaging capabilities and data distribution aspects.

Rapid and effective design of new drugs to combat new strains of antibiotic resistant organisms, more effectively treat chronic conditions, and provide other life sustaining treatment is a key challenge for the medical industry. Current drug design methodologies can take several years just in the initial chemical evaluation stages before compounds can be created for animal and human testing. This paper presents some recent research results in a new parallel machine learning approach that can expedite the drug design cycle. An inductive logic programming search has been reformulated and parallelized to run on an eight node Beowulf cluster. Initial testing with several data sets indicate almost linear speedup using the cluster.

This paper presents initial results from the evaluation of a new virtual reality, force feedback device for measurement of global hand function. In particular, this paper describes the software developed to better support the collection, transformation and display of hand data from the device hardware, and it also describes the initial testing of the device using the improved software interface. The raw vector information provided by the device is of only limited use in understanding the hand motion being measured. By transforming and incorporating the vector data into an articulated 3-D structure using the Open Graphics Library standard, a crude but useable graphical model of the hand and its movements has been developed. Several options are available for manipulation of the model, including camera positioning, lighting, emphasis effects, and digit segment rotation estimates. Studies with the device revealed several hardware constraints that prevent comprehensive data analysis and force feedback. However, the software was shown to be an effective way to quantify and visualize the movements of the human hand. Future work will include retrieval and display of movement rates and forces, as well as a more realistic display of the hand.

Cable harnesses are used in various equipment including automobiles, airplanes, locomotives and computers. In the Cable Harness Design and Assembly System, cables are designed at a CAD workstation and the cable harness is built by a remote robot. This paper covers the bundling subsystem of the Cable Harness Design and Assembly System. This involved both hardware and software development. The hardware consists of the interface of the bundling tool to the robot controller and the circuitry for driving an optical sensor located at the end of the bundling tool for monitoring the cable height. The software developed consists of several programs, for processing the information coming from the CAD database and driving the manipulator through the process. The bundling subsystem relies on feedback from the sensor at the manipulator end effector for adapting to minor errors and for temporary suspension of the process when non-recoverable errors occur.