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Improving access to housing and supportive services for runaway and homeless youth: Reducing vulnerability to human trafficking in New York City

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A I I E Transactions

A I I E Transactions 2024年第3期56卷

作者： Yaren Bilge Kaya Kayse Lee Maass Geri L. Dimas Renata Konrad Andrew C. Trapp Meredith Dank a Mechanical and Industrial Engineering Northeastern University Boston MA USA b Data Science Program Worcester Polytechnic Institute Worcester MA USA c WPI Business School Worcester Polytechnic Institute Worcester MA USA b Data Science Program Worcester Polytechnic Institute Worcester MA USAc WPI Business School Worcester Polytechnic Institute Worcester MA USA d New York University New York City NY USA

Recent estimates indicate that there are over a million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services such as physical and mental healthcare is an effective response to the vulnerability of RHY towards being trafficked, the number of youth experiencing homelessness exceeds the capacity of available housing resources in most US communities. We undertake a RHY-informed, systematic, and data-driven approach to project the collective capacity required by service providers to adequately meet the needs of RHY in New York City, including those most at risk of being trafficked. Our approach involves an integer linear programming model that extends the multiple multidimensional knapsack problem and is informed by partnerships with key stakeholders. The mathematical model allows for time-dependent allocation and capacity expansion, while incorporating stochastic youth arrivals and length of stays, services provided in a periodic fashion, and service delivery time windows. Our RHY and service provider-centered approach is an important step toward meeting the actual, rather than presumed, survival needs of vulnerable youth.

关键词： Human trafficking homelessness capacity expansion mixed-integer linear programming homeless shelters

DISCRETE EVENT SIMULATION TO EVALUATE SHELTER CAPACITY EXPANSION OPTIONS FOR LGBTQ+ HOMELESS YOUTH

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arXiv 2022年

作者： Kaya, Yaren Bilge Mantell, Sophia Lee Maass, Kayse Konrad, Renata Trapp, Andrew C. Dimas, Geri L. Dank, Meredith Mechanical and Industrial Engineering Northeastern University 360 Huntington Ave BostonMA02115 United States School of Business Worcester Polytechnic Institute 100 Institute Rd WorcesterMA01609 United States Data Science Program Worcester Polytechnic Institute 100 Institute Rd WorcesterMA01609 United States Marron Institute of Urban Management New York University 60 5th Ave 2nd floor New YorkNY10011 United States

The New York City (NYC) youth shelter system provides housing, counseling, and other support services to runaway and homeless youth and young adults (RHY). These resources reduce RHY's vulnerability to human trafficking, yet most shelters are unable to meet demand. This paper presents a Discrete Event Simulation (DES) model of a crisis-emergency and drop-in center for LGBTQ+ youth in NYC, which aims to analyze the current operations and test potential capacity expansion interventions. The model uses data from publicly available resources and interviews with service providers and key stakeholders. The simulated shelter has 66 crisis-emergency beds, offers five different support services, and serves on average 1,399 LGBTQ+ RHY per year. The capacity expansion interventions examined in this paper are adding crisis-emergency beds and psychiatric therapists. This application of DES serves as a tool to communicate with policymakers, funders, and service providers-potentially having a strong humanitarian impact. © 2022, CC BY-NC-SA.

关键词： Discrete event simulation

Reinforcement Learning Adversarial Swarm Dynamics

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Reinforcement Learning Adversarial Swarm Dynamics

IEEE Southeastcon

作者： Davis S. Catherman Cory Neville Joshua Bloom Samuel S. White Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Computer Science Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9781728168616

ISBN: (纸本)9781728168623

Swarm robotics is a growing field that explores the implementation potential for multi-agent robotic systems completing tasks in a decentralized manor. Reinforcement Learning is a sub-field of Machine Learning that uses a feedback reward system to optimize the control laws on a given system. The Reinforcement Learning Adversarial Swarm Dynamics project will implement reinforcement learning into a simple game executed by adversarial homogeneous swarms for exploration into the feasibility and optimality of reinforcement learning in swarm robotic systems.

关键词： Swarm robotics Reinforcement learning Games Task analysis Robots

A receding-horizon MDP approach for performance evaluation of moving target defense in networks

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arXiv 2020年

作者： Qian, Zhentian Fu, Jie Zhu, Quanyan Robotics Engineering Program Dept. of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

In this paper, we study the problem of assessing the effectiveness of a proactive defense-by-detection policy with a network-based moving target defense. We model the network system using a probabilistic attack graph-a graphical security model. Given a network system with a proactive defense strategy, an intelligent attacker needs to repeatedly perform reconnaissance to learn about the locations of intrusion detection systems and re-plan optimally to reach the target while avoiding detection. To compute the attacker's strategy for security evaluation, we develop a receding-horizon planning algorithm in a risk-sensitive Markov decision process with a time-varying reward function. Finally, we implement both defense and attack strategies in a synthetic network and analyze how the frequency of network randomization and the number of detection systems can influence the success rate of the attacker. This study provides insights for designing proactive defense strategies against online and multi-stage attacks carried out by a resourceful attacker. Copyright © 2020, The Authors. All rights reserved.

关键词： Network security

SwarmMesh: A Distributed Data Structure for Cooperative Multi-Robot Applications

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SwarmMesh: A Distributed Data Structure for Cooperative Mult...

IEEE International Conference on robotics and Automation (ICRA)

作者： Nathalie Majcherczyk Carlo Pinciroli Robotics Engineering Worcester Polytechnic Institute MA USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

We present an approach to the distributed storage of data across a swarm of mobile robots that forms a shared global memory. We assume that external storage infrastructure is absent, and that each robot is capable of devoting a quota of memory and bandwidth to distributed storage. Our approach is motivated by the insight that in many applications data is collected at the periphery of a swarm topology, but the periphery also happens to be the most dangerous location for storing data, especially in exploration missions. Our approach is designed to promote data storage in the locations in the swarm that best suit a specific feature of interest in the data, while accounting for the constantly changing topology due to individual motion. We analyze two possible features of interest: the data type and the data item position in the environment. We assess the performance of our approach in a large set of simulated experiments. The evaluation shows that our approach is capable of storing quantities of data that exceed the memory of individual robots, while maintaining near-perfect data retention in high-load conditions.

关键词： Robots Data structures Peer-to-peer computing Overlay networks Routing Distributed databases Topology

Improving Human Performance Using Mixed Granularity of Control in Multi-Human Multi-Robot Interaction

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Improving Human Performance Using Mixed Granularity of Contr...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jayam Patel Carlo Pinciroli Robotics Engineering Worcester Polytechnic Institute MA USA

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

Due to the potentially large number of units involved, the interaction with a multi-robot system is likely to exceed the limits of the span of apprehension of any individual human operator. In previous work, we studied how this issue can be tackled by interacting with the robots in two modalities - environment-oriented and robot-oriented. In this paper, we study how this concept can be applied to the case in which multiple human operators perform supervisory control on a multirobot system. While the presence of extra operators suggests that more complex tasks could be accomplished, little research exists on how this could be achieved efficiently. In particular, one challenge arises - the out-of-the-loop performance problem caused by a lack of engagement in the task, awareness of its state, and trust in the system and in the other operators. Through a user study involving 28 human operators and 8 real robots, we study how the concept of mixed granularity in multi-human multi-robot interaction affects user engagement, awareness, and trust while balancing the workload between multiple operators.

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Qualitative planning in imperfect information games with active sensing and reactive sensor attacks: Cost of unawareness

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arXiv 2021年

作者： Kulkarni, Abhishek N. Han, Shuo Leslie, Nandi O. Kamhoua, Charles A. Fu, Jie The Robotics Engineering Program Dept. of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Electrical and Computer Engineering University of Illinois ChicagoIL60607 United States Raytheon Technologies U.S. Army Research Laboratory

We consider the probabilistic planning problem where the agent (called Player 1, or P1) can jointly plan the control actions and sensor queries in a sensor network and an attacker (called player 2, or P2) can carry out attacks on the sensors. We model such an adversarial interaction using a formal model-a reachability game with partially controllable observation functions. The main contribution of this paper is to assess the cost of P1's unawareness: Suppose P1 misinterprets the sensor failures as probabilistic node failures due to unreliable network communication, and P2 is aware of P1's misinterpretation in addition to her partial observability. Then, from which states can P2 carry out sensor attacks to ensure, with probability one, that P1 will not be able to complete her reachability task even though, due to misinterpretation, P1 believes that she can almost-surely achieve her task. We develop an algorithm to solve the almost-sure winning sensor-attack strategy given P1's observation-based strategy. Our attack analysis could be used for attack detection in wireless communication networks and the design of provably secured attack-aware sensor allocation in decision-theoretic models for cyber-physical systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Formal methods

Collaborative Suturing: A Reinforcement Learning Approach to Automate Hand-off Task in Suturing for Surgical Robots

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Collaborative Suturing: A Reinforcement Learning Approach to...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Vignesh Manoj Varier Dhruv Kool Rajamani Nathaniel Goldfarb Farid Tavakkolmoghaddam Adnan Munawar Gregory S Fischer Robotics Engineering Department Worcester Polytechnic Institute MA USA

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

Over the past decade, Robot-Assisted Surgeries (RAS), have become more prevalent in facilitating successful operations. Of the various types of RAS, the domain of collaborative surgery has gained traction in medical research. Prominent examples include providing haptic feedback to sense tissue consistency, and automating sub-tasks during surgery such as cutting or needle hand-off - pulling and reorienting the needle after insertion during suturing. By fragmenting suturing into automated and manual tasks the surgeon could essentially control the process with one hand and also circumvent workspace restrictions imposed by the control interface present at the surgeon's side during the operation. This paper presents an exploration of a discrete reinforcement learning-based approach to automate the needle hand-off task. Users were asked to perform a simple running suture using the da Vinci Research Kit. The user trajectory was learnt by generating a sparse reward function and deriving an optimal policy using Q-learning. Trajectories obtained from three learnt policies were compared to the user defined trajectory. The results showed a root-mean-square error of [0.0044mm, 0.0027mm, 0.0020mm] in ℝ 3 . Additional trajectories from varying initial positions were produced from a single policy to simulate repeated passes of the hand-off task.

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Distributed data storage and fusion for collective perception in resource-limited mobile robot swarms

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arXiv 2020年

作者： Majcherczyk, Nathalie Nallathambi, Daniel Jeswin Antonelli, Tim Pinciroli, Carlo Robotics Engineering Worcester Polytechnic Institute MA United States

In this paper, we propose an approach to the distributed storage and fusion of data for collective perception in resource-limited robot swarms. We demonstrate our approach in a distributed semantic classification scenario. We consider a team of mobile robots, in which each robot runs a pre-trained classifier of known accuracy to annotate objects in the environment. We provide two main contributions: (i) a decentralized, shared data structure for efficient storage and retrieval of the semantic annotations, specifically designed for low-resource mobile robots;and (ii) a voting-based, decentralized algorithm to reduce the variance of the calculated annotations in presence of imperfect classification. We discuss theory and implementation of both contributions, and perform an extensive set of realistic simulated experiments to evaluate the performance of our approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots