Asymptotically-Optimal Topological Nearest-Neighbor Filtering,
2020
University of Richmond
Asymptotically-Optimal Topological Nearest-Neighbor Filtering, Read Sandström, Jory Denny, Nancy M. Amato
Department of Math & Statistics Faculty Publications
Nearest-neighbor finding is a major bottleneck for sampling-based motion planning algorithms. The cost of finding nearest neighbors grows with the size of the roadmap, leading to a significant computational bottleneck for problems which require many configurations to find a solution. In this work, we develop a method of mapping configurations of a jointed robot to neighborhoods in the workspace that supports fast search for configurations in nearby neighborhoods. This expedites nearest-neighbor search by locating a small set of the most likely candidates for connecting to the query with a local plan. We show that this filtering technique can preserve asymptotically-optimal …
Modular Neural Networks For Low-Power Image Classification On Embedded Devices,
2020
Purdue University
Modular Neural Networks For Low-Power Image Classification On Embedded Devices, Abhinav Goel, Sara Aghajanzadeh, Caleb Tung, Shuo-Han Chen, George K. Thiruvathukal, Yung-Hisang Lu
Computer Science: Faculty Publications and Other Works
Embedded devices are generally small, battery-powered computers with limited hardware resources. It is difficult to run deep neural networks (DNNs) on these devices, because DNNs perform millions of operations and consume significant amounts of energy. Prior research has shown that a considerable number of a DNN’s memory accesses and computation are redundant when performing tasks like image classification. To reduce this redundancy and thereby reduce the energy consumption of DNNs, we introduce the Modular Neural Network Tree architecture. Instead of using one large DNN for the classifier, this architecture uses multiple smaller DNNs (called modules) to progressively classify images …
Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools,
2020
Old Dominion University
Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools, Murat Kuzlu, Umit Cali, Vinayak Sharma, Özgür Güler
Engineering Technology Faculty Publications
Over the last two decades, Artificial Intelligence (AI) approaches have been applied to various applications of the smart grid, such as demand response, predictive maintenance, and load forecasting. However, AI is still considered to be a ‘‘black-box’’ due to its lack of explainability and transparency, especially for something like solar photovoltaic (PV) forecasts that involves many parameters. Explainable Artificial Intelligence (XAI) has become an emerging research field in the smart grid domain since it addresses this gap and helps understand why the AI system made a forecast decision. This article presents several use cases of solar PV energy forecasting using …
Knowledge Enhanced Neural Fashion Trend Forecasting,
2020
Singapore Management University
Knowledge Enhanced Neural Fashion Trend Forecasting, Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Fashion trend forecasting is a crucial task for both academia and industry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real fashion trends. Towards insightful fashion trend forecasting, this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from Instagram with extracted time series fashion element records and user information. Furthermore, to effectively model the time series data of fashion elements with rather complex patterns, we propose …
Co-Design And Evaluation Of An Intelligent Decision Support System For Stroke Rehabilitation Assessment,
2020
Singapore Management University
Co-Design And Evaluation Of An Intelligent Decision Support System For Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Badia
Research Collection School Of Computing and Information Systems
Clinical decision support systems have the potential to improve work flows of experts in practice (e.g. therapist's evidence-based rehabilitation assessment). However, the adoption of these systems is challenging, and the gains of these systems have not fully demonstrated yet. In this paper, we identified the needs of therapists to assess patient's functional abilities (e.g. alternative perspectives with quantitative information on patient's exercise motions). As a result, we co-designed and developed an intelligent decision support system that automatically identifies salient features of assessment using reinforcement learning to assess the quality of motion and generate patient-specific analysis. We evaluated this system with …
We Mind Your Well-Being: Preventing Depression In Uncertain Social Networks By Sequential Interventions,
2020
Singapore Management University
We Mind Your Well-Being: Preventing Depression In Uncertain Social Networks By Sequential Interventions, Aye Phye Phye Aung, Xinrun Wang, Bo An, Xiaoli Li
Research Collection School Of Computing and Information Systems
Mental health has become a major concern according to WHO who estimates that more than 350 million people worldwide are affected by depression. Studies have shown that interventions and social support can reduce stress and depression. However, counselling centers do not have enough resources to provide counselling and social support to all the participants in their interest. This paper helps social support organizations (e.g., university counselling centers) sequentially select the participants for interventions. Unfortunately, previous works do not consider emotion propagation from other neighbours of the influencees and initial uncertainties of mental states and influence. Moreover, they fail to scale …
The Future Of Work Now: Ai-Driven Transaction Surveillance At Dbs Bank,
2020
Babson College
The Future Of Work Now: Ai-Driven Transaction Surveillance At Dbs Bank, Thomas H. Davenport, Steven M. Miller
Research Collection School Of Computing and Information Systems
One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon. Steve Miller of Singapore Management University and I co-authored the story.
The Future Of Work Now: Automl At 84.51°And Kroger,
2020
Babson College
The Future Of Work Now: Automl At 84.51°And Kroger, Thomas H. Davenport, Steven M. Miller
Research Collection School Of Computing and Information Systems
One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon.
Foodbot: A Goal-Oriented Just-In-Time Healthy Eating Interventions Chatbot,
2020
Singapore Management University
Foodbot: A Goal-Oriented Just-In-Time Healthy Eating Interventions Chatbot, Philips Kokoh Prasetyo, Palakorn Achananuparp, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Recent research has identified a few design flaws in popular mobile health (mHealth) applications for promoting healthy eating lifestyle, such as mobile food journals. These include tediousness of manual food logging, inadequate food database coverage, and a lack of healthy dietary goal setting. To address these issues, we present Foodbot, a chatbot-based mHealth application for goal-oriented just-in-time (JIT) healthy eating interventions. Powered by a large-scale food knowledge graph, Foodbot utilizes automatic speech recognition and mobile messaging interface to record food intake. Moreover, Foodbot allows users to set goals and guides their behavior toward the goals via JIT notification prompts, interactive …
Gesture Enhanced Comprehension Of Ambiguous Human-To-Robot Instructions,
2020
Singapore Management University
Gesture Enhanced Comprehension Of Ambiguous Human-To-Robot Instructions, Weerakoon Mudiyanselage Dulanga Kaveesha Weerakoon, Vigneshwaran Subbaraju, Nipuni Karumpulli, Minh Anh Tuan Tran, Qianli Xu, U-Xuan Tan, Joo Hwee Lim, Archan Misra
Research Collection School Of Computing and Information Systems
This work demonstrates the feasibility and benefits of using pointing gestures, a naturally-generated additional input modality, to improve the multi-modal comprehension accuracy of human instructions to robotic agents for collaborative tasks.We present M2Gestic, a system that combines neural-based text parsing with a novel knowledge-graph traversal mechanism, over a multi-modal input of vision, natural language text and pointing. Via multiple studies related to a benchmark table top manipulation task, we show that (a) M2Gestic can achieve close-to-human performance in reasoning over unambiguous verbal instructions, and (b) incorporating pointing input (even with its inherent location uncertainty) in M2Gestic results in a significant …
Reinforcement Learning For Zone Based Multiagent Pathfinding Under Uncertainty,
2020
Singapore Management University
Reinforcement Learning For Zone Based Multiagent Pathfinding Under Uncertainty, Jiajing Ling, Tarun Gupta, Akshat Kumar
Research Collection School Of Computing and Information Systems
We address the problem of multiple agents finding their paths from respective sources to destination nodes in a graph (also called MAPF). Most existing approaches assume that all agents move at fixed speed, and that a single node accommodates only a single agent. Motivated by the emerging applications of autonomous vehicles such as drone traffic management, we present zone-based path finding (or ZBPF) where agents move among zones, and agents' movements require uncertain travel time. Furthermore, each zone can accommodate multiple agents (as per its capacity). We also develop a simulator for ZBPF which provides a clean interface from the …
Online Traffic Signal Control Through Sample-Based Constrained Optimization,
2020
Johns Hopkins University
Online Traffic Signal Control Through Sample-Based Constrained Optimization, Srishti Dhamija, Alolika Gon, Pradeep Varakantham, William Yeoh
Research Collection School Of Computing and Information Systems
Traffic congestion reduces productivity of individuals by increasing time spent in traffic and also increases pollution. To reduce traffic congestion by better handling dynamic traffic patterns, recent work has focused on online traffic signal control. Typically, the objective in traffic signal control is to minimize expected delay over all vehicles given the uncertainty associated with the vehicle turn movements at intersections. In order to ensure responsiveness in decision making, a typical approach is to compute a schedule that minimizes the delay for the expected scenario of vehicle movements instead of minimizing expected delay over the feasible vehicle movement scenarios. Such …
Dual-Slam: A Framework For Robust Single Camera Navigation,
2020
Singapore Management University
Dual-Slam: A Framework For Robust Single Camera Navigation, Huajian Huang, Wen-Yan Lin, Siying Liu, Dong Zhang, Sai-Kit Yeung
Research Collection School Of Computing and Information Systems
SLAM (Simultaneous Localization And Mapping) seeks to provide a moving agent with real-time self-localization. To achieve real-time speed, SLAM incrementally propagates position estimates. This makes SLAM fast but also makes it vulnerable to local pose estimation failures. As local pose estimation is ill-conditioned, local pose estimation failures happen regularly, making the overall SLAM system brittle. This paper attempts to correct this problem. We note that while local pose estimation is ill-conditioned, pose estimation over longer sequences is well-conditioned. Thus, local pose estimation errors eventually manifest themselves as mapping inconsistencies. When this occurs, we save the current map and activate two …
Chess As A Testing Grounds For The Oracle Approach To Ai Safety,
2020
Smith College
Chess As A Testing Grounds For The Oracle Approach To Ai Safety, James D. Miller, Roman Yampolskiy, Olle Häggström, Stuart Armstrong
Faculty and Staff Scholarship
To reduce the danger of powerful super-intelligent AIs, we might make the first such AIs oracles that can only send and receive messages. This paper proposes a possibly practical means of using machine learning to create two classes of narrow AI oracles that would provide chess advice: those aligned with the player's interest, and those that want the player to lose and give deceptively bad advice. The player would be uncertain which type of oracle it was interacting with. As the oracles would be vastly more intelligent than the player in the domain of chess, experience with these oracles might …
Trainable Structure Tensors For Autonomous Baggage Threat Detection Under Extreme Occlusion,
2020
Center for Cyber-Physical Systems (C2PS), Khalifa University, United Arab Emirates
Trainable Structure Tensors For Autonomous Baggage Threat Detection Under Extreme Occlusion, Taimur Hassan, Samet Akçay, Mohammed Bennamoun, Salman Khan, Naoufel Werghi
Computer Vision Faculty Publications
Detecting baggage threats is one of the most difficult tasks, even for expert officers. Many researchers have developed computer-aided screening systems to recognize these threats from the baggage X-ray scans. However, all of these frameworks are limited in identifying the contraband items under extreme occlusion. This paper presents a novel instance segmentation framework that utilizes trainable structure tensors to highlight the contours of the occluded and cluttered contraband items (by scanning multiple predominant orientations), while simultaneously suppressing the irrelevant baggage content. The proposed framework has been extensively tested on four publicly available X-ray datasets where it outperforms the state-of-the-art frameworks …
The Expansion From System Simulation To Domain Simulation,
2020
1. College of Systems Engineering, National University of Defense Technology, Changsha 410073, China; ;2. The PLA's 31002 Unit, Beijing 100094, China;
The Expansion From System Simulation To Domain Simulation, Xiaogang Qiu, Yazhou Chen, Zhang Peng
Journal of System Simulation
Abstract: With the generalization of simulation application and complex system simulation becoming the focus, domain simulation is becoming more and more important. In 1995 United States Department of Defense (DoD) presented the master plan of modeling and simulation, which gives birth to the basic idea of domain simulation. The advances in information technology such as network technology and cloud computing help domain simulation to access to use. Taking the change of simulation research object as the basis, the development of simulation research from the aspects of model researches, simulation tools and simulation applications are summarized. The goal of domain simulation …
Review On Agv Scheduling Optimization,
2020
1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China; ;
Review On Agv Scheduling Optimization, Jianlin Fu, Hengzhi Zhang, Zhang Jian, Liangkui Jiang
Journal of System Simulation
Abstract: AGV scheduling plays an important role in improving the efficiency and reducing manufacturing cost, but it is also a very complex combinatorial optimization procedure. AGV scheduling optimization is divided into three types, AGV static scheduling, AGV dynamic scheduling and AGV simultaneous scheduling with other resources scheduling. Various methods are summarized and listed, including traditional analysis method, modeling and simulation method, intelligent optimization algorithm and hybrid optimization method, and the advantages and disadvantages of each method are also analyzed. The deficiencies of AGV scheduling research are pointed out and the research directions for future are presented.
Research On Recursive Variable Sampling Period Scheduling Algorithm Based On Two-Parameter Priority,
2020
College of Electrical and Information Engineering Dalian Jiaotong University, Dalian 116028, China;
Research On Recursive Variable Sampling Period Scheduling Algorithm Based On Two-Parameter Priority, Weiguo Shi, Xu Chao, Wang Xun, Xiangtai Wang
Journal of System Simulation
Abstract: A recursive variable sampling periodic dynamic scheduling algorithm based on two-parameter priority is proposed for the resource-constrained network control system. The network demand degree and network urgency double parameter of the control loop are calculated, and the two-parameter weight is established by the two-square root mapping function which is created by the absolute value of the control loop error to realize the scheduling of the sensor priority. The comprehensive analysis of the control loop performance recursively adjusts the sampling period of each control loop, and completes the scheduling adjustment under the premise of ensuring the stability of the …
Finite Element Method Evaluation On Chest Blunt Injury By Rubber Projectile,
2020
College of Equipment Management and Support, Engineering University of People's Armed Police, Xi'an 710086, China;
Finite Element Method Evaluation On Chest Blunt Injury By Rubber Projectile, Wang Song, Renjun Zhan, Xiongyi Duan
Journal of System Simulation
Abstract: In order to reveal the non-lethal injury mechanism of rubber projectile and improve its safety service level, the 18.4 mm rubber projectile finite element model and the improved chest blunt injury assessment model are constructed and verified. The virtual impact test experiments under different loads are carried out, and the stress-time response and deformation-time response data are obtained. The results show that the time to reach the peak stress (less than 0.2 ms) is much shorter than the time to reach the maximum deformation variable (4~5 ms), which means the damage is not caused by the impact itself …
Simulation Analysis On Penetration To Aircraft Carrier Of Image Homing Rocket,
2020
Artillery and Air Defense Academy of PLA, Hefei 230031, China;
Simulation Analysis On Penetration To Aircraft Carrier Of Image Homing Rocket, Shidong Fang, Chen Dong, Quanli Ning, Zhang Jie, Li Yong
Journal of System Simulation
Abstract: The effect of image homing rocket projectile penetrating aircraft carrier is the important basis for its operation. According to the characteristics of projectile and aircraft carrier, the geometric equivalent model and simulation model are established, and the penetration process is simulated by LS-DYNA. Simulation results are verified by the failure form and penetration limit velocity, and show the correctness of the model. Using the established models, the penetration processes are simulated under the different conditions. The overall force on the projectile is analyzed according to the extreme acceleration value of projectile. Based on the analysis of some collected key …
