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Dive into the research topics where David Sidoti is active.

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Featured researches published by David Sidoti.


ieee aerospace conference | 2016

Proactive decision support for dynamic assignment and routing of Unmanned Aerial Systems

Bala Kishore Nadella; Gopi Vinod Avvari; Avnish Kumar; Manisha Mishra; David Sidoti; Krishna R. Pattipati; Ciara Sibley; Joseph Coyne; Samuel S. Monfort

Unmanned Aerial System (UAS) missions are executed by teams of operators with highly specialized training and roles; however, the task demands on each operator are highly variable, often resulting in uneven workloads among operators and sometimes in mishaps. Therefore, there is a need to develop anticipative and effective decision support algorithms that permit the evaluation of courses of action (COAs), while assuring that operators are attending to the right task at the right time and that task demands do not exceed the operators cognitive capabilities in dynamic multi-mission environments. Motivated by the need to assist UAS operators in efficiently managing their workloads, this paper develops algorithms for the dynamic scheduling of UAS tasks by providing efficient COA recommendations in an unobtrusive manner. The dynamic scheduling of a set of UASs to search for targets with varying rewards is an NP-hard problem. We model this problem as an extension to the open vehicle routing problem (OVRP). Extensions to OVRP include risk propensity of human decision making, task deadlines, and multiple vehicle types. UAS operators would benefit greatly from the COA recommendations and the algorithms proposed in this paper by (a) enhancing rapid planning and re-planning capabilities; (b) proactive allocation of UASs, while balancing operator workloads; and (c) adapting plans as new targets of opportunity appear or information is updated about a target and/or UAS. The proposed algorithms are embedded in the Supervisory Control Operations User Testbed (SCOUTTM), an experimental paradigm developed by the Naval Research Laboratory-Washington DC.


systems man and cybernetics | 2014

Optimization-Based Decision Support Software for a Team-in-the-Loop Experiment: Multilevel Asset Allocation

Xu Han; Manisha Mishra; Suvasri Mandal; Huy N. Bui; Diego Fernando Martinez Ayala; David Sidoti; Krishna R. Pattipati; David L. Kleinman

Motivated by the Navys interest in decision support tools that augment planning activities within a maritime operations center (MOC), we have developed a multilevel resource allocation model that is capable of interacting with human planners to dynamically allocate hierarchically-organized assets to process interdependent tasks in order to accomplish mission objectives. The planning problem is formulated as a mixed-integer nonlinear programming (MINLP) problem of minimizing the overall difference between the human-specified desired task accuracy performance criteria and the expected performance outcomes, the latter being based on how well the assigned resources match the required resources, subject to a number of real-world planning constraints. To solve the resulting large-scale MINLP problem, we propose two methods: 1) a Lagrangian relaxation method that solves the multilevel asset allocation problem with a measure of sub-optimality in terms of an approximate duality gap and 2) a dynamic list planning heuristic algorithm that provides high-quality sub-optimal solutions rapidly (less than 10 s for the scenarios considered here). Finally, we verify our methods using realistic MOC planning scenarios, provide a comparative evaluation of the performance measures of the two proposed methods, and investigate the value of information via human-in-the-loop experiments.


ieee international conference on technologies for homeland security | 2015

Context-based models to overcome operational challenges in maritime security

Diego Fernando Martinez Ayala; David Sidoti; Manisha Mishra; Xu Han; Krishna R. Pattipati

Piracy and smuggling are major international problems which not only threaten maritime security but also affect the global economy. Even though NATO and international forces have been relentlessly fighting maritime crime in East Africa (Gulf of Aden), the problems still persist and maritime crime has moved to West Africa (Gulf of Guinea). In the same vein, the Joint Interagency Task Force-South (JIATF-S) has had substantial operational success, controlling the vast geographical spread of smugglers in the East Pacific and Caribbean Sea. Due to limited number of maritime assets available, every resource needs to be efficiently allocated, both in time and space. This poses a great operational challenge requiring the integration and fusion of disparate information relevant to the mission and dynamic allocation of resources under uncertainty. Operational planning and execution for counter-smuggling and counter-piracy operations involve surveillance (to search, detect, track and identify potential threats) and interdiction operations (to intercept, investigate and potentially apprehend the suspects) in a dynamic and uncertain mission environment. In this paper, we present context-based models for counter-smuggling and counter-piracy missions, where the smuggling and piracy activities are represented in the form of color coded heat maps built using Intelligence information (INTEL), meteorological and oceanographic (METOC) information and other mission-specific attributes (sensor observations, target types and their behavior, etc.); these maps are interpreted as probability of activity (PoA) surfaces. These PoA surfaces form an input to the decision support module discussed in this paper.


systems man and cybernetics | 2017

A Multiobjective Path-Planning Algorithm With Time Windows for Asset Routing in a Dynamic Weather-Impacted Environment

David Sidoti; Gopi Vinod Avvari; Manisha Mishra; Lingyi Zhang; Bala Kishore Nadella; James E. Peak; James A. Hansen; Krishna R. Pattipati

This paper presents a mixed-initiative tool for multiobjective planning and asset routing (TMPLAR) in dynamic and uncertain environments. TMPLAR is built upon multiobjective dynamic programming algorithms to route assets in a timely fashion, while considering fuel efficiency, voyage time, distance, and adherence to real world constraints (asset vehicle limits, navigator-specified deadlines, etc.). TMPLAR has the potential to be applied in a variety of contexts, including ship, helicopter, or unmanned aerial vehicle routing. The tool provides recommended schedules, consisting of waypoints, associated arrival and departure times, asset speed and bearing, that are optimized with respect to several objectives. The ship navigation is exacerbated by the need to address multiple conflicting objectives, spatial and temporal uncertainty associated with the weather, multiple constraints on asset operation, and the added capability of waiting at a waypoint with the intent to avoid bad weather, conduct opportunistic training drills, or both. The key algorithmic contribution is a multiobjective shortest path algorithm for networks with stochastic nonconvex edge costs and the following problem features: 1) time windows on nodes; 2) ability to choose vessel speed to next node subject to (minimum and/or maximum) speed constraints; 3) ability to select the power plant configuration at each node; and 4) ability to wait at a node. The algorithm is demonstrated on six real world routing scenarios by comparing its performance against an existing operational routing algorithm.


Archive | 2017

Online Anomaly Detection in Big Data: The First Line of Defense Against Intruders

Balakumar Balasingam; Pujitha Mannaru; David Sidoti; Krishna R. Pattipati; Peter Willett

We live in a world of abundance of information, but lack the ability to fully benefit from it, as succinctly described by John Naisbitt in his 1982 book, “we are drowning in information, but starved for knowledge”. The information, collected by various sensors and humans, is corrupted by noise, ambiguity and distortions and suffers from the data deluge problem. Combining the noisy, ambiguous and distorted information that comes from a variety of sources scattered around the globe in order to synthesize accurate and actionable knowledge is a challenging problem. To make things even more complex, there are intentionally developed intrusive mechanisms that aim to disturb accurate information fusion and knowledge extraction; these mechanisms include cyber attacks, cyber espionage and cyber crime, to name a few. Intrusion detection has become a major research focus over the past two decades and several intrusion detection approaches, such as rule-based, signature-based and computer intelligence based approaches were developed. Out of these, computational intelligence based anomaly detection mechanisms show the ability to handle hitherto unknown intrusions and attacks. However, these approaches suffer from two different issues: (i) they are not designed to detect similar attacks on a large number of devices, and (ii) they are not designed for quickest detection. In this chapter, we describe an approach that helps to scale-up existing computational intelligence approaches to implement quickest anomaly detection in millions of devices at the same time.


IEEE Aerospace and Electronic Systems Magazine | 2016

Why context and context-driven decision support matters

David Sidoti

On October 5, 2015, El Faro, a cargo ship heading to San Juan, Puerto Rico, foundered in the Atlantic Ocean off the Bahamas due to Category 4 Hurricane Joaquin. Thirty-three crew members remain missing. The captain cut south of the storm, due to the rare occurrence of such storm systems traveling south, but ended up sailing straight into the high winds and seas in the area, a situation that had a high probability of occurrence given the forecast. This event and its outcome demonstrate the importance of using context in weather routing. Context is assumed to mean any information that can be used to characterize the situation of a person, place, physical or computational object considered relevant to providing a clear picture of the dynamics of the problem.


computational intelligence and security | 2015

Dynamic asset allocation for counter-smuggling operations under disconnected, intermittent and low-bandwidth environment

Gopi Vinod Avvari; David Sidoti; Manisha Mishra; Lingyi Zhang; Bala Kishore Nadella; Krishna R. Pattipati; James A. Hansen

Counter-smuggling operations constitute a high priority national security mission since drug-trafficking not only involves many criminals, but can also be a source of financing for many illicit activities such as narco-terrorism and arms trafficking. The counter-smuggling mission involves surveillance operations (to search, detect, track and identify potential threats) and interdiction operations (to intercept, investigate and potentially apprehend suspects). Potential smuggling activity is represented in the form of color-coded heat maps built using intelligence and meteorological and oceanographic information, which are interpreted in the form of probability of activity (PoA) surfaces. The PoA surfaces constitute the “sufficient statistics” for the asset allocation and scheduling processes. However, in the case of disconnected, intermittent, and low-bandwidth environments, the problem of allocating resources becomes very challenging as PoA information is unavailable or is not up to date. In this paper, we propose to utilize flow (historic PoA)-based surfaces, which provide cues on where the smugglers may have traversed in the past. Using the flow surfaces, we allocate the surveillance and interdiction assets to best thwart potential smuggling activities. We further evaluate the quality of our solution in terms of the number of targets interdicted and the amount of contraband seized.


systems, man and cybernetics | 2014

Decision support software for Anti-Submarine warfare mission planning within a dynamic environmental context

Manisha Mishra; Woosun An; Xu Han; David Sidoti; Diego Fernando Martinez Ayala; Krishna R. Pattipati

Anti-Submarine Warfare (ASW) involves effective allocation and path planning of ASW platforms to search for, detect, classify, track and prosecute hostile submarines within an evolving environment. As the environmental context evolves rapidly, continuously collected Meteorological and Oceanographic (METOC) data is used for assessing the impact of the current and forecasted environment on individual sensors and weapon platforms, as well as on tactics in the form of performance surfaces, which is presented to the commanders in making go/no-go decisions. However, due to the overwhelming amount of METOC information, it is very challenging for the commanders to interpret and analyze the data for generating plans or evaluating courses of action in a timely manner. In this paper, motivated by the need to assist ASW commanders in making proactive decisions in an evolving environmental context, we present a decision support tool for modeling and incorporating the appropriate METOC information from multiple sources and further utilizing it to determine the search regions and optimal trajectories to search for and track the enemy submarines in a timely manner.


ieee/sice international symposium on system integration | 2014

Decision support information integration platform for context-driven interdiction operations in counter-smuggling missions

David Sidoti; Diego Fernando Martinez Ayala; Sravanth Sankavaram; Xu Han; Manisha Mishra; Woosun An; David L. Kellmeyer; James A. Hansen; Krishna R. Pattipati

Context-driven decision making is at the top of the Navys agenda of important concepts to be embedded in future proactive decision support systems for command decision making. There are manifold challenges associated with relaying contextual data in a timely manner to the decision maker. In the counter-smuggling domain, for example, although high value information is accessible, it is dispersed across databases and the decision making team. There are numerous research challenges in integrating this information in an efficient manner to effectively present viable courses of action to a decision making team. In this paper, we propose a decision support tool for counter-smuggling missions modeled as a stochastic control problem of dynamically managing assets to maximize the probability of detecting and interdicting maritime illicit trafficking operations. We additionally propose a method to explain the algorithm behavior to the human decision maker and provide them with interactive controls to develop “what-if” solutions or to constrain solutions to a desired path.


IEEE Transactions on Systems, Man, and Cybernetics | 2017

Context-Aware Dynamic Asset Allocation for Maritime Interdiction Operations

David Sidoti; Xu Han; Lingyi Zhang; Gopi Vinod Avvari; Diego Fernando Martinez Ayala; Manisha Mishra; Muni Sravanth Sankavaram; David L. Kellmeyer; James A. Hansen; Krishna R. Pattipati

This paper validates two approximate dynamic programming approaches on a maritime interdiction problem involving the allocation of multiple heterogeneous assets over a large area of responsibility to interdict multiple drug smugglers using heterogeneous types of transportation on the sea with varying contraband weights. The asset allocation is based on a probability of activity surface, which represents spatio-temporal target activity obtained by integrating intelligence data on drug smuggler whereabouts/waypoints for contraband transportation, behavior models, and meteorological and oceanographic information. We validate the proposed architectural and algorithmic concepts via several realistic mission scenarios. We conduct sensitivity analyses to quantify the robustness and proactivity of our approach, as well as to measure the value of information used in the allocation process. The contributions of this paper have been transitioned to and are currently being tested by Joint Interagency Task Force—South, an organization tasked with providing the initial line of defense against drug trafficking in the East Pacific and Caribbean Oceans.

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Manisha Mishra

University of Connecticut

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Xu Han

University of Connecticut

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James A. Hansen

United States Naval Research Laboratory

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Woosun An

University of Connecticut

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Lingyi Zhang

University of Connecticut

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