scholarly journals Creating explorable extended reality environments with semantic annotations

2020 ◽  
Author(s):  
Jakub Flotyński
Keyword(s):  
Domain Knowledge ◽  
Description Logics ◽  
Main Element ◽  
Arbitrary Domain ◽  
Aspect Oriented Programming ◽  
Domain Experts ◽  
Semantic Annotations ◽  
Knowledge Based ◽  
And Control ◽  
3D Content

Abstract The main element of extended reality (XR) environments is behavior-rich 3D content consisting of objects that act and interact with one another as well as with users. Such actions and interactions constitute the evolution of the content over time. Multiple application domains of XR, e.g., education, training, marketing, merchandising, and design, could benefit from the analysis of 3D content changes based on general or domain knowledge comprehensible to average users or domain experts. Such analysis can be intended, in particular, to monitor, comprehend, examine, and control XR environments as well as users’ skills, experience, interests and preferences, and XR objects’ features. However, it is difficult to achieve as long as XR environments are developed with methods and tools that focus on programming and 3D modeling rather than expressing domain knowledge accompanying content users and objects, and their behavior. The main contribution of this paper is an approach to creating explorable knowledge-based XR environments with semantic annotations. The approach combines description logics with aspect-oriented programming, which enables knowledge representation in an arbitrary domain as well as transformation of available environments with minimal users’ effort. We have implemented the approach using well-established development tools and exemplify it with an explorable immersive car showroom. The approach enables efficient creation of explorable XR environments and knowledge acquisition from XR.

Optimal Event Monitoring through Internet Mashup over Multivariate Time Series

2013 ◽  
Vol 5 (2) ◽  
pp. 46-69
Author(s):  
Chun-Kit Ngan ◽  
Alexander Brodsky
Keyword(s):  
Time Series ◽  
Domain Knowledge ◽  
Query Language ◽  
Multivariate Time Series ◽  
Global Constraints ◽  
Optimal Decision ◽  
University Campus ◽  
Domain Experts ◽  
Knowledge Based ◽  
Application Service

The authors propose a Web-Mashup Application Service Framework for Multivariate Time Series Analytics (MTSA) that supports the services of model definitions, querying, parameter learning, model evaluations, data monitoring, decision recommendations, and web portals. This framework maintains the advantage of combining the strengths of both the domain-knowledge-based and the formal-learning-based approaches and is designed for a more general class of problems over multivariate time series. More specifically, the authors identify a general-hybrid-based model, MTSA – Parameter Estimation, to solve this class of problems in which the objective function is maximized or minimized from the optimal decision parameters regardless of particular time points. This model also allows domain experts to include multiple types of constraints, e.g., global constraints and monitoring constraints. The authors further extend the MTSA data model and query language to support this class of problems for the services of learning, monitoring, and recommendation. At the end, the authors conduct an experimental case study for a university campus microgrid as a practical example to demonstrate our proposed framework, models, and language.

scholarly journals Effective DGA-Domain Detection and Classification with TextCNN and Additional Features

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1070
Author(s):  
Chanwoong Hwang ◽  
Hyosik Kim ◽  
Hooki Lee ◽  
Taejin Lee
Keyword(s):  
Security Policy ◽  
Domain Knowledge ◽  
Malicious Code ◽  
Ip Address ◽  
Advanced Persistent Threat ◽  
Knowledge Based ◽  
Proposed Model ◽  
Label Prediction ◽  
Additional Domain ◽  
And Control

Malicious codes, such as advanced persistent threat (APT) attacks, do not operate immediately after infecting the system, but after receiving commands from the attacker’s command and control (C&C) server. The system infected by the malicious code tries to communicate with the C&C server through the IP address or domain address of the C&C server. If the IP address or domain address is hard-coded inside the malicious code, it can analyze the malicious code to obtain the address and block access to the C&C server through security policy. In order to circumvent this address blocking technique, domain generation algorithms are included in the malware to dynamically generate domain addresses. The domain generation algorithm (DGA) generates domains randomly, so it is very difficult to identify and block malicious domains. Therefore, this paper effectively detects and classifies unknown DGA domains. We extract features that are effective for TextCNN-based label prediction, and add additional domain knowledge-based features to improve our model for detecting and classifying DGA-generated malicious domains. The proposed model achieved 99.19% accuracy for DGA classification and 88.77% accuracy for DGA class classification. We expect that the proposed model can be applied to effectively detect and block DGA-generated domains.

scholarly journals Tackling the DM Challenges with cDMN: A Tight Integration of DMN and Constraint Reasoning

2021 ◽  
pp. 1-24
Author(s):  
SIMON VANDEVELDE ◽  
BRAM AERTS ◽  
JOOST VENNEKENS
Keyword(s):  
Domain Knowledge ◽  
Decision Model ◽  
Formal Model ◽  
Decision Procedures ◽  
Knowledge Engineer ◽  
Domain Experts ◽  
Knowledge Based ◽  
Constraint Reasoning ◽  
Decision Logic ◽  
User Friendly

Abstract Knowledge-based AI typically depends on a knowledge engineer to construct a formal model of domain knowledge – but what if domain experts could do this themselves? This paper describes an extension to the Decision Model and Notation (DMN) standard, called Constraint Decision Model and Notation (cDMN). DMN is a user-friendly, table-based notation for decision logic, which allows domain experts to model simple decision procedures without the help of IT staff. cDMN aims to enlarge the expressiveness of DMN in order to model more complex domain knowledge, while retaining DMNs goal of being understandable by domain experts. We test cDMN by solving the most complex challenges posted on the DM Community website. We compare our own cDMN solutions to the solutions that have been submitted to the website and find that our approach is competitive. Moreover, cDMN is able to solve more challenges than any other approach.

Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples

1988 ◽  
Vol 3 (3) ◽  
pp. 183-210 ◽  
Author(s):  
B. Chandrasekaran
Keyword(s):  
Problem Solving ◽  
Domain Knowledge ◽  
Building Blocks ◽  
Knowledge Based Systems ◽  
Diagnostic Systems ◽  
Planning Systems ◽  
Knowledge Based ◽  
Generic Tasks ◽  
High Level ◽  
And Control

AbstractThe level of abstraction of much of the work in knowledge-based systems (the rule, frame, logic level) is too low to provide a rich enough vocabulary for knowledge and control. I provide an overview of a framework called the Generic Task approach that proposes that knowledge systems should be built out of building blocks, each of which is appropriate for a basic type of problem solving. Each generic task uses forms of knowledge and control strategies that are characteristic to it, and are in general conceptually closer to domain knowledge. This facilitates knowledge acquisition and can produce a more perspicuous explanation of problem solving. The relationship of the constructs at the generic task level to the rule-frame level is analogous to that between high-level programming languages and assembly languages in computer science. I describe a set of generic tasks that have been found particularly useful in constructing diagnostic, design and planning systems. In particular, I describe two tools, CSRL and DSPL, that are useful for building classification-based diagnostic systems and skeletal planning systems respectively, and a high level toolbox that is under construction called the Generic Task toolbox.

A blackboard / knowledge - based systems approach to manufacturing scheduling and control

1990 ◽  
Vol 19 (1-4) ◽  
pp. 72-76
Author(s):  
C. Parks ◽  
J. Subramanian ◽  
S. Srinivas ◽  
A. Waikar ◽  
G. Graves ◽  
...  
Keyword(s):  
Systems Approach ◽  
Knowledge Based Systems ◽  
Manufacturing Scheduling ◽  
Knowledge Based ◽  
And Control

Model-based diagnosis of the space shuttle main engine

1992 ◽  
Vol 6 (3) ◽  
pp. 131-148 ◽  
Author(s):  
Martin O. Hofmann ◽  
Thomas L. Cost ◽  
Michael Whitley
Keyword(s):  
Space Shuttle ◽  
Diagnostic System ◽  
End User ◽  
Reasoning Process ◽  
Domain Experts ◽  
Model Based ◽  
Knowledge Based ◽  
Modeling Analysis ◽  
Engine Design ◽  
Main Engine

The process of reviewing test data for anomalies after a firing of the Space Shuttle Main Engine (SSME) is a complex, time-consuming task. A project is under way to provide the team of SSME experts with a knowledge-based system to assist in the review and diagnosis task. A model-based approach was chosen because it can be adapted to changes in engine design, is easier to maintain, and can be explained more easily. A complex thermodynamic fluid system like the SSME introduces problems during modeling, analysis, and diagnosis which have as yet been insufficiently studied. We developed a qualitative constraint-based diagnostic system inspired by existing qualitative modeling and constraint-based reasoning methods which addresses these difficulties explicitly. Our approach combines various diagnostic paradigms seamlessly, such as the model-based and heuristic association-based paradigms, in order to better approximate the reasoning process of the domain experts. The end-user interface allows expert users to actively participate in the reasoning process, both by adding their own expertise and by guiding the diagnostic search performed by the system.

A Collaborative Approach for Minimal-Cost Monitor Deployment in Cloud Environment

2015 ◽  
Vol 25 (06) ◽  
pp. 935-960
Author(s):  
Yuan-Hsin Tung ◽  
Shian-Shyong Tseng ◽  
Wei-Tek Tsai
Keyword(s):  
Approximate Solutions ◽  
Relationship Matrix ◽  
Minimal Cost ◽  
Cloud Environment ◽  
Cloud Infrastructure ◽  
Domain Experts ◽  
Knowledge Based ◽  
Expert Opinions ◽  
Service Oriented ◽  
System Maintenance

Monitoring is widely applied in problem diagnosis, fault localization, and system maintenance. And since the cloud infrastructure is complex, the applications on the cloud are therefore complex, which makes monitoring in cloud more difficult. Rich monitors that contain composite and heterogeneous probes are often used in service-oriented system monitoring. These rich monitors often involve multiple entities, and the interpretation may require expert opinions from multiple domains. This paper proposes a knowledge-based collaborative monitoring approach to find out minimal cost monitor deployment in a cloud environment. The approach contains two main phases. In the knowledge acquisition phase, three acquisition tables, monitor-probe relationship matrix, cost of monitoring, and probe-problem dependence matrix, are generated according to diagnosis ontology and monitor ontology acquired from domain experts. And then based upon the three acquisition tables and three consensus building strategies, we formulate the problem of optimizing the cost of monitoring as an Integer Linear Programming (ILP) problem, which is NP-Complete. In the monitor deployment phase, the proposed algorithm applies two heuristic rules to address the problem. Three experiments are conducted to evaluate the performance of the proposed approach. The results from the experiments show that our approach is effective and produce quality approximate solutions in monitor deployment.

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