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International Conference on Semantic computing

作者： Sarthak Tiwari Ajay Bansal School of Computing Informatics Decision Systems Engineering Arizona State University Mesa Arizona

ISBN: (数字)9781728188997

ISBN: (纸本)9781728189000

Smart home assistants are becoming a norm due to their ease-of-use. They employ spoken language as an interface, facilitating easy interaction with their users. Even with their obvious advantages, natural-language based interfaces are not prevalent outside the domain of home assistants. It is hard to adopt them for computer-controlled systems due to the numerous complexities involved with their implementation in varying fields. The main challenge is the grounding of natural language base terms into the underlying system's primitives. The existing systems that do use natural language interfaces are specific to one problem domain only. This paper presents a domain-agnostic framework that creates natural language interfaces for computer-controlled systems that have been developed by creating a customizable mapping between the language constructs and the system primitives. The framework employs ontologies built using OWL (Web Ontology Language) for knowledge representation and machine learning models for language processing tasks.

关键词： Natural languages OWL Semantics Smart homes Machine learning Ontologies Task analysis

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A decision support tool for calculating waste collection needs

A decision support tool for calculating waste collection nee...

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IISE Annual Conference and Expo 2021

作者： Kassem, Zeyad Gudivada, Venkata Saisrikar Escobedo, Adolfo R. Campbell, William F. School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Rob and Melani Walton Sustainability Solutions Service Arizona State University TempeAZ United States

ISBN: (纸本)9781713838470

We introduce a case study of a sustainability driven project involving a utility company based in Arizona. The company operates out of 22 facilities, which produce municipal solid waste hauled regularly by a waste hauling company. To increase the utilization of waste bins, we followed conceptually the Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) methodology. A field experiment was designed to measure the utilization of waste bins across four facilities, from which potential cost savings were identified. From these measurements, we created a decision support tool that provides detailed analyses on the utilization of waste bins and what-if analyses for experimenting with different bin configurations in terms of bin sizes, total number of bins, and pick-up schedules. The decision support tool has been able to detect potential annual savings close to 50% of current annual costs by increasing utilization across four facilities, and it could lead to higher savings when it is implemented to other facilities. Moreover, by reducing the number of waste hauler pick-ups, the tool would potentially lead to reductions in GHG emissions. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

关键词： Municipal solid waste

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Quantifying compliance with COVID-19 mitigation policies in the US: A mathematical modeling study

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Infectious Disease Modelling 2021年第1期6卷 503-513页

作者： Nao Yamamoto Bohan Jiang Haiyan Wang School of Human Evolution and Social Change Arizona State UniversityTempeAZ85287USA School of Computing Informaticsand Decision Systems EngineeringArizona State UniversityTempeAZ85287USA School of Mathematical and Natural Sciences Arizona State UniversityPhoenixAZ85069USA

The outbreak of COVID-19 disrupts the life of many people in the *** response to this global pandemic,various institutions across the globe had soon issued their prevention *** in the US had also implemented social distancing ***,those policies,which were designed to slow the spread of COVID-19,and its compliance,have varied across the states,which led to spatial and temporal heterogeneity in COVID-19 *** paper aims to propose a spatio-temporal model for quantifying compliance with the US COVID-19 mitigation policies at a regional *** achieve this goal,a specific partial differential equation(PDE)is developed and validated with shortterm *** proposed model describes the combined effects of transboundary spread among state clusters in the US and human mobilities on the transmission of *** model can help inform policymakers as they decide how to react to future outbreaks.

关键词： COVID-19 Partial differential equation Validation Social distancing Google Community Mobility Reports

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DeepCrashTest: Turning Dashcam Videos into Virtual Crash Tests for Automated Driving systems

DeepCrashTest: Turning Dashcam Videos into Virtual Crash Tes...

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2020 IEEE International Conference on Robotics and Automation, ICRA 2020

作者： Bashetty, Sai Krishna Ben Amor, Heni Fainekos, Georgios Arizona State University School of Computing Informatics and Decision Systems Engineering TempeAZ85281 United States

ISBN: (纸本)9781728173955

The goal of this paper is to generate simulations with real-world collision scenarios for training and testing autonomous vehicles. We use numerous dashcam crash videos uploaded on the internet to extract valuable collision data and recreate the crash scenarios in a simulator. We tackle the problem of extracting 3D vehicle trajectories from videos recorded by an unknown and uncalibrated monocular camera source using a modular approach. A working architecture and demonstration videos along with the open-source implementation are provided with the paper. © 2020 IEEE.

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Learn and Link: Learning Critical Regions for Efficient Planning

Learn and Link: Learning Critical Regions for Efficient Plan...

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2020 IEEE International Conference on Robotics and Automation, ICRA 2020

作者： Molina, Daniel Kumar, Kislay Srivastava, Siddharth Arizona State University School of Computing Informatics and Decision Systems Engineering TempeAR85281 United States

ISBN: (纸本)9781728173955

This paper presents a new approach to learning for motion planning (MP) where critical regions of an environment are learned from a given set of motion plans and used to improve performance on new environments and problem instances. We introduce a new suite of sampling-based motion planners, Learn and Link. Our planners leverage critical regions to overcome the limitations of uniform sampling, while still maintaining guarantees of correctness inherent to sampling-based algorithms. We also show that convolutional neural networks (CNNs) can be used to identify critical regions for motion planning problems. We evaluate Learn and Link against planners from the Open Motion Planning Library (OMPL) using an extensive suite of experiments on challenging motion planning problems. We show that our approach requires far less planning time than existing sampling-based planners. © 2020 IEEE.

关键词： Motion planning

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- D3WA+ - A case study of XAIP in a model acquisition task for dialogue planning 30

- D3WA+ - A case study of XAIP in a model acquisition task f...

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30th International Conference on Automated Planning and Scheduling, ICAPS 2020

作者： Sreedharan, Sarath Chakraborti, Tathagata Muise, Christian Khazaeni, Yasaman Kambhampati, Subbarao School of Computing Informatics and Decision Systems Engineering Arizona State University United States IBM Research AI School of Computing Queen's University

Recently, the D3WA system was proposed as a paradigm shift in how complex goal-oriented dialogue agents can be specified by taking a declarative view of design. However, it turns out actual users of the system have a hard time evolving their mental model and grasping the imperative consequences of declarative design. In this paper, we adopt ideas from existing works in the field of Explainable AI Planning (XAIP) to provide guidance to the dialogue designer during the model acquisition process. We will highlight in the course of this discussion how the setting presents unique challenges to the XAIP setting, including having to deal with the user persona of a domain modeler rather than the end-user of the system, and consequently having to deal with the unsolvability of models in addition to explaining generated plans. Quickview http://***/d3wa-xaip. Copyright © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Improving batch normalization with skewness reduction for deep neural networks 25

Improving batch normalization with skewness reduction for de...

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25th International Conference on Pattern Recognition, ICPR 2020

作者： Kevin Ding, Pak Lun Martin, Sarah Li, Baoxin School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

ISBN: (纸本)9781728188089

Batch Normalization (BN) is a well-known technique used in training deep neural networks. The main idea behind batch normalization is to normalize the features of the layers (i.e., transforming them to have a mean equal to zero and a variance equal to one). Such a procedure encourages the optimization landscape of the loss function to be smoother, and improves the learning of the networks for both speed and performance. In this paper, we demonstrate that the performance of the network can be improved, if the distributions of the features of the output in the same layer are similar. As normalizing based on mean and variance does not necessarily make the features to have the same distribution, we propose a new normalization scheme: Batch Normalization with Skewness Reduction (BNSR). Comparing with other normalization approaches, BNSR transforms not just only the mean and variance, but also the skewness of the data. By tackling this property of a distribution, we are able to make the output distributions of the layers to be further similar. The nonlinearity of BNSR may further improve the expressiveness of the underlying network. Comparisons with other normalization schemes are tested on the CIFAR-100 and ImageNet datasets. Experimental results show that the proposed approach can outperform other state-of-the-arts that are not equipped with BNSR. © 2020 IEEE

关键词： Deep neural networks

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Game-Theoretic Analysis on CBDC Adoption 1st

Game-Theoretic Analysis on CBDC Adoption

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1st BenchCouncil International Federated Intelligent computing and Block Chain Conferences, FICC 2020

作者： Mou, Chenqi Tsai, Wei-Tek Jiang, Xiaofang Yang, Dong LMIB – School of Mathematics and Systems Science Beihang University Beijing100191 China SKLSDE – School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States

ISBN: (纸本)9789811611599

As an important blockchain application, CBDC (Central Bank Digital Currency) has received significant worldwide attention as it can restructure financial market, affect national currency policies, and introduce new regulation policies and mechanisms. It is widely predicted that CBDC will introduce numerous digital currency competitions in various aspects of the global financial market, and winners will lead the next wave of digital currency market. This paper applies the game theory to study the competitions between different countries, in particular to analyze whether they should adopt the CBDC program. We propose two game-theoretic models for CBDC adoption, both analyzing whether to adopt the CBDC program via the Nash equilibrium. Both game-theoretic models draw the same conclusion that each country should adopt the CBDC program regardless of the choices of other counties. In other words, current currency leaders should adopt CBDC because it may lose the premier status, and other countries should adopt CBDC otherwise they risk of getting even further behind in the digital economy. According to our game-theoretic models, the current market leader who has 90% of market shares may lose about 19.2% shares if it is not the first mover. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Game theory

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SPESC-Translator: Towards Automatically Smart Legal Contract Conversion for Blockchain-based Auction Services

SPESC-Translator: Towards Automatically Smart Legal Contract...

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2021 IEEE World Congress on Services, SERVICES 2021

作者： Chen, E. Qin, Bohan Zhu, Yan Song, Weijing Wang, Shengdian Chu, Cheng-Chung William Yau, Stephen S. University Of Science And Technology Beijing School Of Computer And Communication Engineering Beijing China Tunghai University Computer Science Department Taichung City Taiwan Arizona State University School Of Computing Informatics And Decision Systems Engineering Tempe United States

ISBN: (纸本)9781665427197

Smart contract is a set of digital executable protocols intended to make contractual clauses partially or fully self-executing, self-enforcing, or both. As the second-generation blockchain technology, smart contracts have greatly enriched the functional expression of blockchain to make application development more convenient. All related data of smart contracts, including program codes, intermediate states, and executed results, will be stored in blockchain to ensure that these data are not tampered with. Also, the consensus protocols of blockchain verify the correctness of the running process by executing the smart contract with the same input at all nodes. Therefore, the blockchain's security mechanism with tamper-proof and traceable makes it possible for smart contracts to be recognized by law. © 2021 IEEE.

关键词： Smart contract

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ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data

arXiv

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arXiv 2022年

作者： Biehler, Michael Yan, Hao Shi, Jianjun Georgia Institute of Technology AtlantaGA30332 United States The School of Computing Informatics & Decision Systems Engineering Arizona State University TempeAZ85281 United States

Unstructured point clouds with varying sizes are increasingly acquired in a variety of environments through laser triangulation or Light Detection and Ranging (LiDAR). Predicting a scalar response based on unstructured point clouds is a common problem that arises in a wide variety of applications. The current literature relies on several pre-processing steps such as structured subsampling and feature extraction to analyze the point cloud data. Those techniques lead to quantization artifacts and do not consider the relationship between the regression response and the point cloud during pre-processing. Therefore, we propose a general and holistic "Bayesian Nonlinear Tensor Learning and Modeler" (ANTLER) to model the relationship of unstructured, varying-size point cloud data with a scalar or multivariate response. The proposed ANTLER simultaneously optimizes a nonlinear tensor dimensionality reduction and a nonlinear regression model with a 3D point cloud input and a scalar or multivariate response. ANTLER has the ability to consider the complex data representation, high-dimensionality, and inconsistent size of the 3D point cloud data. © 2022, CC BY-NC-ND.

关键词： Tensors

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