Conflict-driven Hybrid Observer-based Anomaly Detection

This paper presents an anomaly detection method using a hybrid observer -- which consists of a discrete state observer and a continuous state observer. We focus our attention on anomalies caused by intelligent attacks, which may bypass existing anomaly detection methods because neither the event sequence nor the observed residuals appear to be anomalous. Based on the relation between the continuous and discrete variables, we define three conflict types and give the conditions under which the detection of the anomalies is guaranteed. We call this method conflict-driven anomaly detection. The effectiveness of this method is demonstrated mathematically and illustrated on a Train-Gate (TG) system

The OBSERVER: an intelligent and automated video surveillance system

Comunicação apresentada na ICIAR, 3, Póvoa de Varzim, Portugal, 2006.In this work we present a new approach to learn, detect and predict unusual and abnormal behaviors of people, groups and vehicles in real-time. The proposed OBSERVER video surveillance system acquires images from a stationary color video camera and applies state-of-the-art algorithms to segment and track moving objects. The segmentation is based in a background subtraction algorithm with cast shadows, highlights and ghost’s detection and removal. To robustly track objects in the scene, a technique based on appearance models was used. The OBSERVER is capable of identifying three types of behaviors (normal, unusual and abnormal actions). This achievement was possible due to the novel N-ary tree classifier, which was successfully tested on synthetic data.Fundação para a Ciência e a Tecnologia (FCT)

An Intelligent Knowledge Management System from a Semantic Perspective

Abstract. Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

An Intelligent Knowledge Management System from a Semantic Perspective

Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

Dynamic Arrival Rate Estimation for Campus Mobility on Demand Network Graphs

Mobility On Demand (MOD) systems are revolutionizing transportation in urban settings by improving vehicle utilization and reducing parking congestion. A key factor in the success of an MOD system is the ability to measure and respond to real-time customer arrival data. Real time traffic arrival rate data is traditionally difficult to obtain due to the need to install fixed sensors throughout the MOD network. This paper presents a framework for measuring pedestrian traffic arrival rates using sensors onboard the vehicles that make up the MOD fleet. A novel distributed fusion algorithm is presented which combines onboard LIDAR and camera sensor measurements to detect trajectories of pedestrians with a 90% detection hit rate with 1.5 false positives per minute. A novel moving observer method is introduced to estimate pedestrian arrival rates from pedestrian trajectories collected from mobile sensors. The moving observer method is evaluated in both simulation and hardware and is shown to achieve arrival rate estimates comparable to those that would be obtained with multiple stationary sensors.Comment: Appears in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). http://ieeexplore.ieee.org/abstract/document/7759357

Seeing Government Purpose Through the Objective Observer\u27s Eyes: The Evolution-Intelligent Design Debates

In October, 2004, the Dover, Pennsylvania School District became the first in the nation to adopt a policy requiring students studying evolution to be told about the concept of intelligent design. Soon thereafter, parents filed a lawsuit challenging the policy as violating the Establishment Clause. But, Establishment Clause doctrine is one of the most splintered, incoherent areas of the Court’s jurisprudence—and even more so after the Court’s June 2005 McCreary County v. Kentucky decision. Read strictly, McCreary County imports the effects-endorsement “objective observer” into the government purpose inquiry. This subtle shift has significant ramifications: McCreary County changes the nature of the constitutional harm from an allegedly impermissible actual purpose to apparent purpose; results in an objective observer with a bizarrely expansive knowledge of issues of law but more limited knowledge of issues of fact; and requires de novo review throughout the circuits which previously were split on the proper standard of review for a government purpose inquiry. Analyzing McCreary County’s meaning in the context of the intelligent design debates shows that while McCreary County’s general focus on government purpose is helpful, the objective observer’s perspective on government purpose does more doctrinal harm than good. Thus, courts should return the reasonable observer to the effects-endorsement aspect of the Establishment Clause analysis