UK/US naval interoperability collaborative rersearch

This paper outlinees a collaborative program being carried out under an agreement between the US and the UK which started in January 2000, and is due to continue for four years. The research and is looking at the operational problems of coalition force interoperability initial from a naval perspective at the command and combat system level but then moving to a wider domain to cover both land and air participation. Details are given of why the research is necessary, the objectives and the approach being adopted. It then provides some information on the experiences gain from the initial trials which have been carried out during the first six months of this year

Autonomous rendezvous and docking: A commercial approach to on-orbit technology validation

The Space Automation and Robotics Center (SpARC), a NASA-sponsored Center for the Commercial Development of Space (CCDS), in conjunction with its corporate affiliates, is planning an on-orbit validation of autonomous rendezvous and docking (ARD) technology. The emphasis in this program is to utilize existing technology and commercially available components whenever possible. The primary subsystems that will be validated by this demonstration include GPS receivers for navigation, a video-based sensor for proximity operations, a fluid connector mechanism to demonstrate fluid resupply capability, and a compliant, single-point docking mechanism. The focus for this initial experiment will be expendable launch vehicle (ELV) based and will make use of two residual Commercial Experiment Transporter (COMET) service modules. The first COMET spacecraft will be launched in late 1992 and will serve as the target vehicle. The ARD demonstration will take place in late 1994, after the second COMET spacecraft has been launched. The service module from the second COMET will serve as the chase vehicle

The Clarens web services architecture

Clarens is a uniquely flexible web services infrastructure providing a unified access protocol to a diverse set of functions useful to the HEP community. It uses the standard HTTP protocol combined with application layer, certificate based authentication to provide single sign-on to individuals, organizations and hosts, with fine-grained access control to services, files and virtual organization (VO) management. This contribution describes the server functionality, while client applications are described in a subsequent talk.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 6 pages, LaTeX, 4 figures, PSN MONT00

Clarens Client and Server Applications

Several applications have been implemented with access via the Clarens web service infrastructure, including virtual organization management, JetMET physics data analysis using relational databases, and Storage Resource Broker (SRB) access. This functionality is accessible transparently from Python scripts, the Root analysis framework and from Java applications and browser applets.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 4 pages, LaTeX, no figures, PSN TUCT00

Top Management Team Attraction As A Strategic Asset: A Longitudinal Simulation Test Of The Resource Based View

The Resource Based View’s (RBV) main prescription is that strategic assets are sustainable sources of superior industry returns. In the current research, we examined the ability of top management team attraction (TMTA) to operate as a strategic asset and produce sustainable competitive advantage. We used a longitudinal study of 83 simulation teams functioning as top management teams of competing airlines to demonstrate that top management team attraction was positively associated with superior returns, and that this relationship increased over time. Our study benefits both theorists and managers. The key implication for theorists is that TMTA can positively impact firm performance over time, thereby providing strong support for the RBV. The key implication for managers is that taking steps to enhance TMTA and team dynamics can create competitive advantage for their firms

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN