Army-NASA aircrew/aircraft integration program (A3I) software detailed design document, phase 3

The capabilities and design approach of the MIDAS (Man-machine Integration Design and Analysis System) computer-aided engineering (CAE) workstation under development by the Army-NASA Aircrew/Aircraft Integration Program is detailed. This workstation uses graphic, symbolic, and numeric prototyping tools and human performance models as part of an integrated design/analysis environment for crewstation human engineering. Developed incrementally, the requirements and design for Phase 3 (Dec. 1987 to Jun. 1989) are described. Software tools/models developed or significantly modified during this phase included: an interactive 3-D graphic cockpit design editor; multiple-perspective graphic views to observe simulation scenarios; symbolic methods to model the mission decomposition, equipment functions, pilot tasking and loading, as well as control the simulation; a 3-D dynamic anthropometric model; an intermachine communications package; and a training assessment component. These components were successfully used during Phase 3 to demonstrate the complex interactions and human engineering findings involved with a proposed cockpit communications design change in a simulated AH-64A Apache helicopter/mission that maps to empirical data from a similar study and AH-1 Cobra flight test

TrusNet: Peer-to-Peer Cryptographic Authentication

Originally, the Internet was meant as a general purpose communication protocol, transferring primarily text documents between interested parties. Over time, documents expanded to include pictures, videos and even web pages. Increasingly, the Internet is being used to transfer a new kind of data which it was never designed for. In most ways, this new data type fits in naturally to the Internet, taking advantage of the near limit-less expanse of the protocol. Hardware protocols, unlike previous data types, provide a unique set security problem. Much like financial data, hardware protocols extended across the Internet must be protected with authentication. Currently, systems which do authenticate do so through a central server, utilizing a similar authentication model to the HTTPS protocol. This hierarchical model is often at odds with the needs of hardware protocols, particularly in ad-hoc networks where peer-to-peer communication is prioritized over a hierarchical model. Our project attempts to implement a peer-to-peer cryptographic authentication protocol to be used to protect hardware protocols extending over the Internet. The TrusNet project uses public-key cryptography to authenticate nodes on a distributed network, with each node locally managing a record of the public keys of nodes which it has encountered. These keys are used to secure data transmission between nodes and to authenticate the identities of nodes. TrusNet is designed to be used on multiple different types of network interfaces, but currently only has explicit hooks for Internet Protocol connections. As of June 2016, TrusNet has successfully achieved a basic authentication and communication protocol on Windows 7, OSX, Linux 14 and the Intel Edison. TrusNet uses RC-4 as its stream cipher and RSA as its public-key algorithm, although both of these are easily configurable. Along with the library, TrusNet also enables the building of a unit testing suite, a simple UI application designed to visualize the basics of the system and a build with hooks into the I/O pins of the Intel Edison allowing for a basic demonstration of the system

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

A knowledge-based approach for the extraction of machining features from solid models

Computer understanding of machining features such as holes and pockets is essential for bridging the communication gap between Computer Aided Design and Computer Aided Manufacture. This thesis describes a prototype machining feature extraction system that is implemented by integrating the VAX-OPS5 rule-based artificial intelligence environment with the PADL-2 solid modeller. Specification of original stock and finished part geometry within the solid modeller is followed by determination of the nominal surface boundary of the corresponding cavity volume model by means of Boolean subtraction and boundary evaluation. The boundary model of the cavity volume is managed by using winged-edge and frame-based data structures. Machining features are extracted using two methods : (1) automatic feature recognition, and (2) machine learning of features for subsequent recognition. [Continues.

Encoding techniques for complex information structures in connectionist systems

Two general information encoding techniques called relative position encoding and pattern similarity association are presented. They are claimed to be a convenient basis for the connectionist implementation of complex, short term information processing of the sort needed in common sense reasoning, semantic/pragmatic interpretation of natural language utterances, and other types of high level cognitive processing. The relationships of the techniques to other connectionist information-structuring methods, and also to methods used in computers, are discussed in detail. The rich inter-relationships of these other connectionist and computer methods are also clarified. The particular, simple forms are discussed that the relative position encoding and pattern similarity association techniques take in the author's own connectionist system, called Conposit, in order to clarify some issues and to provide evidence that the techniques are indeed useful in practice

Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks

Large knowledge graphs often grow to store temporal facts that model the dynamic relations or interactions of entities along the timeline. Since such temporal knowledge graphs often suffer from incompleteness, it is important to develop time-aware representation learning models that help to infer the missing temporal facts. While the temporal facts are typically evolving, it is observed that many facts often show a repeated pattern along the timeline, such as economic crises and diplomatic activities. This observation indicates that a model could potentially learn much from the known facts appeared in history. To this end, we propose a new representation learning model for temporal knowledge graphs, namely CyGNet, based on a novel timeaware copy-generation mechanism. CyGNet is not only able to predict future facts from the whole entity vocabulary, but also capable of identifying facts with repetition and accordingly predicting such future facts with reference to the known facts in the past. We evaluate the proposed method on the knowledge graph completion task using five benchmark datasets. Extensive experiments demonstrate the effectiveness of CyGNet for predicting future facts with repetition as well as de novo fact prediction.Comment: AAAI 2021; Updated in accordance with camera read