Artificial Intelligence and Robotics Commons^™

Pedestrian Detection Using Basic Polyline: A Geometric Framework For Pedestrian Detection, Liang Gongbo

Masters Theses & Specialist Projects

Pedestrian detection has been an active research area for computer vision in recently years. It has many applications that could improve our lives, such as video surveillance security, auto-driving assistance systems, etc. The approaches of pedestrian detection could be roughly categorized into two categories, shape-based approaches and appearance-based approaches. In the literature, most of approaches are appearance-based. Shape-based approaches are usually integrated with an appearance-based approach to speed up a detection process.

In this thesis, I propose a shape-based pedestrian detection framework using the geometric features of human to detect pedestrians. This framework includes three main steps. Give a static …

Learning In Vision And Robotics, Daniel P. Barrett

Open Access Dissertations

I present my work on learning from video and robotic input. This is an important problem, with numerous potential applications. The use of machine learning makes it possible to obtain models which can handle noise and variation without explicitly programming them. It also raises the possibility of robots which can interact more seamlessly with humans rather than only exhibiting hard-coded behaviors. I will present my work in two areas: video action recognition, and robot navigation. First, I present a video action recognition method which represents actions in video by sequences of retinotopic appearance and motion detectors, learns such models automatically …

Front Matter: Proceedings Of The Maics 2016 Conference, University Of Dayton

Content presented at the MAICS conference

Front matter contains:

• A list of program chairs and committee members
• Foreword to the proceedings by James P. Buckley, conference chair; Saverio Perugini, general chair

Editors: Phu H. Phung, University of Dayton; Ju Shen, University of Dayton; Michael Glass, Valparaiso University

Improving Structure Mcmc For Bayesian Networks Through Markov Blanket Resampling, Chengwei Su, Mark E. Borsuk

Dartmouth Scholarship

Algorithms for inferring the structure of Bayesian networks from data have become an increasingly popular method for uncovering the direct and indirect influences among variables in complex systems. A Bayesian approach to structure learning uses posterior probabilities to quantify the strength with which the data and prior knowledge jointly support each possible graph feature. Existing Markov Chain Monte Carlo (MCMC) algorithms for estimating these posterior probabilities are slow in mixing and convergence, especially for large networks. We present a novel Markov blanket resampling (MBR) scheme that intermittently reconstructs the Markov blanket of nodes, thus allowing the sampler to more effectively …

On The 3d Point Cloud For Human-Pose Estimation, Kai-Chi Chan

Open Access Dissertations

This thesis aims at investigating methodologies for estimating a human pose from a 3D point cloud that is captured by a static depth sensor. Human-pose estimation (HPE) is important for a range of applications, such as human-robot interaction, healthcare, surveillance, and so forth. Yet, HPE is challenging because of the uncertainty in sensor measurements and the complexity of human poses. In this research, we focus on addressing challenges related to two crucial components in the estimation process, namely, human-pose feature extraction and human-pose modeling.

In feature extraction, the main challenge involves reducing feature ambiguity. We propose a 3D-point-cloud feature called …

Patrol Scheduling In An Urban Rail Network, Hoong Chuin Lau, Zhi Yuan, Aldy Gunawan

Research Collection School Of Computing and Information Systems

This paper presents the problem of scheduling security teams to patrol a mass rapid transit rail network of a large urban city. The main objective of patrol scheduling is to deploy security teams to stations of the network at varying time periods subject to rostering as well as security-related constraints. We present several mathematical programming models for different variants of this problem. To generate randomized schedules on a regular basis, we propose injecting randomness by varying the start time and break time for each team as well as varying the visit frequency and visit time for each station according to …

Personal Credit Profiling Via Latent User Behavior Dimensions On Social Media, Guangming Guo, Feida Zhu, Enhong Chen, Le Wu, Qi Liu, Yingling Liu, Minghui Qiu

Research Collection School Of Computing and Information Systems

Consumer credit scoring and credit risk management have been the core research problem in financial industry for decades. In this paper, we target at inferring this particular user attribute called credit, i.e., whether a user is of the good credit class or not, from online social data. However, existing credit scoring methods, mainly relying on financial data, face severe challenges when tackling the heterogeneous social data. Moreover, social data only contains extremely weak signals about users’ credit label. To that end, we put forward a Latent User Behavior Dimension based Credit Model (LUBD-CM) to capture these small signals for personal …

Soft Robotic Grippers For Biological Sampling On Deep Reefs, Kevin C. Galloway, Kaitlyn P. Becker, Brennan Phillips, Jordan Kirby, Stephen Licht, Dan Tchernov, Robert J. Wood, David F. Gruber

Publications and Research

This article presents the development of an underwater gripper that utilizes soft robotics technology to delicately manipulate and sample fragile species on the deep reef. Existing solutions for deep sea robotic manipulation have historically been driven by the oil industry, resulting in destructive interactions with undersea life. Soft material robotics relies on compliant materials that are inherently impedance matched to natural environments and to soft or fragile organisms. We demonstrate design principles for soft robot end effectors, bench-top characterization of their grasping performance, and conclude by describing in situ testing at mesophotic depths. The result is the first use of …

Campus-Scale Mobile Crowd-Tasking: Deployment And Behavioral Insights, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Nikita Jaiman, Randy Tandriansiyah, Cen Chen, Hoong Chuin Lau, Deepthi Chander, Koustuv Dasgupta

Research Collection School Of Computing and Information Systems

Mobile crowd-tasking markets are growing at an unprecedented rate with increasing number of smartphone users. Such platforms differ from their online counterparts in that they demand physical mobility and can benefit from smartphone processors and sensors for verification purposes. Despite the importance of such mobile crowd-tasking markets, little is known about the labor supply dynamics and mobility patterns of the users. In this paper we design, develop and experiment with a realwporld mobile crowd-tasking platform, called TA$Ker. Our contributions are two-fold: (a) We develop TA$Ker, a system that allows us to empirically study the worker responses to push vs. pull …

Detection Of Bird Nests In Overhead Catenary System Images For High-Speed Rail, Xiao Wu, Ping Yuan, Qiang Peng, Chong-Wah Ngo, Jun-Yan He

Research Collection School Of Computing and Information Systems

The high-speed rail system provides a fast, reliable and comfortable means to transport large number of travelers over long distances. The existence of bird nests in overhead catenary system (OCS) can hazard to the safety of the high-speed rails, which will potentially result in long time delays and expensive damages. A vision-based intelligent inspection system capable of automatic detection of bird nests built on overhead catenary would avoid the damages and increase the reliability and punctuality, and therefore is attractive for a high-speed railway system. However, OCS images exhibit great variations with lighting changes, illumination conditions and complex backgrounds, which …

Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang

COBRA Preprint Series

Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …

Online Spatio-Temporal Matching In Stochastic And Dynamic Domains, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Spatio-temporal matching of services to customers online is a problem that arises on a large scale in many domains associated with shared transportation (ex: taxis, ride sharing, super shuttles, etc.) and delivery services (ex: food, equipment, clothing, home fuel, etc.). A key characteristic of these problems is that matching of services to customers in one round has a direct impact on the matching of services to customers in the next round. For instance, in the case of taxis, in the second round taxis can only pick up customers closer to the drop off point of the customer from the first …

A Proactive Sampling Approach To Project Scheduling Under Uncertainty, Pradeep Varakantham, Na Fu, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Uncertainty in activity durations is a key characteristic of many real world scheduling problems in manufacturing, logistics and project management. RCPSP/max with durational uncertainty is a general model that can be used to represent durational uncertainty in a wide variety of scheduling problems where there exist resource constraints. However, computing schedules or execution strategies for RCPSP/max with durational uncertainty is NP-hard and hence we focus on providing approximation methods in this paper. We pro- vide a principled approximation approach based on Sample Average Approximation (SAA) to compute proactive schedules for RCPSP/max with durational uncertainty. We further contribute an extension to …

Achieving Stable And Fair Profit Allocation With Minimum Subsidy In Collaborative Logistics, Lucas Agussurja, Hoong Chuin Lau, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

With the advent of e-commerce, logistics providers are faced with the challenge of handling fluctuating and sparsely distributed demand, which raises their operational costs significantly. As a result, horizontal cooperation are gaining momentum around the world. One of the major impediments, however, is the lack of stable and fair profit sharing mechanism. In this paper, we address this problem using the framework of computational cooperative games. We first present cooperative vehicle routing game as a model for collaborative logistics operations. Using the axioms of Shapley value as the conditions for fairness, we show that a stable, fair and budget balanced …

Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar

Research Collection School Of Computing and Information Systems

We present a new perspective on the classical shortest path routing (SPR) problem in graphs. We show that the SPR problem can be recast to that of probabilistic inference in a mixture of simple Bayesian networks. Maximizing the likelihood in this mixture becomes equivalent to solving the SPR problem. We develop the well known Expectation-Maximization (EM) algorithm for the SPR problem that maximizes the likelihood, and show that it does not get stuck in a locally optimal solution. Using the same probabilistic framework, we then address an NP-Hard network design problem where the goal is to repair a network of …

Robust Decision Making For Stochastic Network Design, Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon

Research Collection School Of Computing and Information Systems

We address the problem of robust decision making for stochastic network design. Our work is motivated by spatial conservation planning where the goal is to take management decisions within a fixed budget to maximize the expected spread of a population of species over a network of land parcels. Most previous work for this problem assumes that accurate estimates of different network parameters (edge activation probabilities, habitat suitability scores) are available, which is an unrealistic assumption. To address this shortcoming, we assume that network parameters are only partially known, specified via interval bounds. We then develop a decision making approach that …

Nlu Framework For Voice Enabling Non-Native Applications On Smart Devices, Soujanya Lanka, Deepika Panthania, Pooja Kushalappa, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Voice is a critical user interface on smart devices (wearables, phones, speakers, televisions) to access applications (or services) available on them. Unfortunately, only a few native applications (provided by the OS developer) are typically voice enabled in devices of today. Since, the utility of a smart device is determined more by the strength of external applications developed for the device, voice enabling non-native applications in a scalable, seamless manner within the device is a critical use case and is the focus of our work. We have developed a Natural Language Understanding (NLU) framework that uses templates supported by the application …

The Global Rock-Art Database Project Towards Machine Learning: Building A Collaborative Open Source Platform For Heritage Management From Information Structure To Information Visualization Using Australian Heritage Examples, Robert Haubt

Staff Scholarship - Australia & Dubai

This guest talk, presented at Lava Lab at the University of Hawaiʻi, explores the intersection of collaboration, data ontology, and information visualization in advancing machine learning within the Global Rock Art Database project. Drawing on insights from the project’s first four years, the talk emphasizes the critical need for cultural heritage preservation by systematically recording and structuring global rock art data in accessible and sustainable ways. This effort not only supports public education on rock art but also facilitates scholarly research.

Key discussions include advancements in data ontology using the CIDOC Conceptual Reference Model (CIDOC CRM) for semantic data management …

Artificially Intelligent Computer Assisted Language Learning System With Ai Student Component, Denee M. Mcclain

Capstone Research Projects

Intelligent Computer Assisted Language Learning (ICALL) systems follow an accepted format, which utilizes an artificially intelligent tutor. The systems allow the user to input a sentence in the target language and the AI tutor analyzes the sentence and provides error correction. This approach can be expensive, impractical, and inflexible. Inflexibility can result in a lower quality of learning for the users of these systems. Here I present an alternative format for ICALL systems that utilizes an artificially intelligent student. This alternative is cost effective and practical because it does not require extra development time to make the artificial intelligence an …

Techno-Apocalypse: Technology, Religion, And Ideology In Bryan Singer’S H+, Edward Brennan

Books/Book chapters

This essay critically analyses the digital series H+. In the near future, adults who can afford them, have replaced tablets and cell phones with nanotechnology implants. The H+ implant acts as a medical diagnostic and can overlay the user's senses with a computer interface. The apocalypse comes in the form of a computer virus which infects the H+ network and instantly kills one third of humanity. The series represents the anxiety and religiosity that surrounds the possible social consequences of digital technology. It also explores the tensions and intersections between technology and faith. This essay makes the case, however, that …