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2021 Winter Simulation Conference, WSC 2021

作者： Fard, Mostafa D. Sarjoughian, Hessam S. Arizona Center for Integrative Modeling Simulation School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

ISBN: (纸本)9781665433112

Modeling the interactions between separate models contributes to building flexible hybrid simulation frameworks. In earlier work, an Algorithmic Interaction Model was proposed and developed to integrate the componentized WEAP and LEAP RESTful frameworks for modeling and simulating water and energy systems. However, this approach does not separate modeling and simulation protocol from each other. It also does not support flexible, structured model hierarchies. To overcome these limitations, the parallel DEVS formalism is used to develop an Interaction Model for use with the DEVS-Suite simulator. The resulting DEVS Interaction Model (DEVS-IM) is supported with a RESTful framework and MongoDB for storing the interaction models for the coupled water and energy models. The DEVS-IM, grounded in system theory and component-based modeling, offers strong support for the model reusability, flexibility, and maintainability traits essential for developing realistic simulations of coupled energy and water systems. © 2021 IEEE.

关键词： Reusability

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DECENTRALIZED ATTRIBUTION OF GENERATIVE MODELS 9

DECENTRALIZED ATTRIBUTION OF GENERATIVE MODELS

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9th International Conference on Learning Representations, ICLR 2021

作者： Kim, Changhoon Ren, Yi Yang, Yezhou School of Computing Informatics and Decision Systems Engineering Arizona State University United States School for Engineering of Matter Transport and Energy Arizona State University United States

Growing applications of generative models have led to new threats such as malicious personation and digital copyright infringement. One solution to these threats is model attribution, i.e., the identification of user-end models where the contents under question are generated from. Existing studies showed empirical feasibility of attribution through a centralized classifier trained on all user-end models. However, this approach is not scalable in reality as the number of models ever grows. Neither does it provide an attributability guarantee. To this end, this paper studies decentralized attribution, which relies on binary classifiers associated with each user-end model. Each binary classifier is parameterized by a user-specific key and distinguishes its associated model distribution from the authentic data distribution. We develop sufficient conditions of the keys that guarantee an attributability lower bound. Our method is validated on MNIST, CelebA, and FFHQ datasets. We also examine the trade-off between generation quality and robustness of attribution against adversarial post-processes. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Economic and social effects

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A Spark-Based Open Source Framework for Large-Scale Parallel Processing of Rich Text Documents 8

A Spark-Based Open Source Framework for Large-Scale Parallel...

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8th International Conference on Future Internet of Things and Cloud, FiCloud 2021

作者： Chen, Qiang Chen, Yinong Wu, Sheng Zhang, Zili College of Computer and Information Science Southwest University Chongqing China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States

ISBN: (纸本)9781665425742

The large amount of unstructured educational data is an important data source of educational big data analysis. However, there is a lack of efficient and simple distributed solutions to process these unstructured documents. To address this problem, this study proposes a Spark-based parallel processing framework of large-scale rich text documents (mainly as MS Word documents), which can seamlessly to extend the serial open-source algorithm to distributed computing algorithm to accomplish parallel processing of large-scale rich text documents. We focus on how to achieve non-intrusive deployment of clusters and how Python codes access to HDFS files. The experimental results show that by using 8 physical nodes (with 128 cores all together), the speedup reaches 48 times compared to the standalone Python-docx in the experiments of extracting 232 GB docx files. The experiments also analyze the Spark parameters and explore how to optimize the parameter settings. © 2021 IEEE.

关键词： Big data

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Partitioning and Gaussian Processes for Accelerating Sampling in Monte Carlo Tree Search for Continuous decisions

Partitioning and Gaussian Processes for Accelerating Samplin...

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2021 Winter Simulation Conference, WSC 2021

作者： Liu, Menghan Pedrielli, Giulia Cao, Yumeng Arizona State University School of Computing Informatics Decision Systems Engineering Tempe85281 United States Arizona State University School of Mathematical and Statistical Sciences Tempe85281 United States

ISBN: (纸本)9781665433112

We propose Part-MCTS for sampling continuous decisions at each stage of a Monte Carlo Tree Search algorithm. At each MCTS stage, Part-MCTS sequentially partitions the decision space and keeps a collection of Gaussian processes to describe the landscape of the objective function. A classification criteria based on the estimation of the minimum allows us to focus the attention on regions with better predicted behavior, reducing the evaluation effort elsewhere. Within each subregion, we can use any sampling distribution, and we propose to sample using Bayesian optimization. We compare our approach to KR-UCT (Yee et al. 2016) as state of the art competitor. Part-MCTS achieves better accuracy over a set of nonlinear test functions, and it has the ability to identify multiple promising solutions in a single run. This can be important when multiple solutions from a stage can be preserved and expanded at subsequent stages. © 2021 IEEE.

关键词： Gaussian noise (electronic)

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A tool for teaching web dependency analysis 36

A tool for teaching web dependency analysis

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36th Information systems Education Conference, ISECON 2020

作者： Gary, Kevin A. Gupta, Vasu Kalsi, Manit Singh School of Computing Informatics and Decision Systems Engineering Arizona State University United States

Since its inception, web application development has undergone constant evolution, and with it the evolution of the web application software developer. Developers are increasingly specialized in the frameworks and toolchains needed to provide highly functional modern web functionality. This creates a challenge for educators as they seek to teach the underlying fundamentals of web development and produce graduates versed in the latest technologies for their industry constituents. At Arizona State University, an upper-division software engineering course in web development seeks to address this dichotomy, specifically for front-end applications on the HTML5/Javascript/CSS stack. This paper reports on a study of student developers and the nature of syntactic defect injection in web application development. © 2020 Proceedings of the Information systems Education Conference, ISECON. All rights reserved.

关键词： Information use

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Enhancing Collective Estimates by Aggregating Cardinal and Ordinal Inputs 8th

Enhancing Collective Estimates by Aggregating Cardinal and O...

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8th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2020

作者： Kemmer, Ryan Yoo, Yeawon Escobedo, Adolfo R. Maciejewski, Ross School of Computing Informatics and Decision Systems Engineering Arizona State University United States

ISBN: (纸本)9781577358480

There are many factors that affect the quality of data received from crowdsourcing, including cognitive biases, varying levels of expertise, and varying subjective scales. This work investigates how the elicitation and integration of multiple modalities of input can enhance the quality of collective estimations. We create a crowd sourced experiment where participants are asked to estimate the number of dots within images in two ways: ordinal (ranking) and cardinal (numerical) estimates. We run our study with 300 participants and test how the efficiency of crowdsourced computation is affected when asking participants to provide ordinal and/or cardinal inputs and how the accuracy of the aggregated outcome is affected when using a variety of aggregation methods. First, we find that more accurate ordinal and cardinal estimations can be achieved by prompting participants to provide both cardinal and ordinal information. Second, we present how accurate collective numerical estimates can be achieved with significantly fewer people when aggregating individual preferences using optimization-based consensus aggregation models. Interestingly, we also find that aggregating cardinal information may yield more accurate ordinal estimates. © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Crowdsourcing

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Constructing flow graphs from procedural cybersecurity texts

arXiv

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arXiv 2021年

作者： Pal, Kuntal Kumar Kashihara, Kazuaki Banerjee, Pratyay Mishra, Swaroop Wang, Ruoyu Baral, Chitta School of Computing Informatics and Decision Systems Engineering Arizona State University

Following procedural texts written in natural languages is challenging. We must read the whole text to identify the relevant information or identify the instruction flows to complete a task, which is prone to failures. If such texts are structured, we can readily visualize instruction-flows, reason or infer a particular step, or even build automated systems to help novice agents achieve a goal. However, this structure recovery task is a challenge because of such texts' diverse nature. This paper proposes to identify relevant information from such texts and generate information flows between sentences. We built a large annotated procedural text dataset (CTFW) in the cybersecurity domain (3154 documents). This dataset contains valuable instructions regarding software vulnerability analysis experiences. We performed extensive experiments on CTFW with our LM-GNN model variants in multiple settings. To show the generalizability of both this task and our method, we also experimented with procedural texts from two other domains (Maintenance Manual and Cooking), which are substantially different from cybersecurity. Our experiments show that Graph Convolution Network with BERT sentence embeddings outperforms BERT in all three domains. © 2021, CC BY-NC-ND.

关键词： Automation

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Achieving Multitasking Robots in Multi-Robot Tasks

Achieving Multitasking Robots in Multi-Robot Tasks

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Winston Smith Yu Zhang School of Computing Informatics and Decision Systems Engineering Arizona State University

One simplifying assumption made in the existing and well-performing multi-robot systems is that the robots are single-tasking: each robot operates on a single task at any time. While this assumption is innocent to make in situations with sufficient resources such that robots can work independently, it becomes a restriction when they must share capabilities. In this paper, we consider multitasking robots with multi-robot tasks. Given a set of tasks, each achievable by a coalition of robots, our approach allows the coalitions to overlap by exploiting task synergies based on the physical constraints required to maintain these coalitions. The key contribution is a general and flexible framework that extends the current multi-robot systems to enable multitasking. The proposed approach is inspired by the information invariant theory, which orients around the equivalence of different information requirements. We map physical constraints to information requirements in our work, thereby allowing task synergies to be identified by reasoning about the relationships between such requirements. We show that our algorithm is sound and complete. Simulation results show its effectiveness under resource-constrained situations and in handling challenging scenarios in a realistic UAV simulator.

关键词： Simulation Conferences Multitasking Cognition Inference algorithms Multi-robot systems Object recognition

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Analyzing gaze behavior for text-embellished narrative visualizations under different task scenarios

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Visual informatics 2020年第3期4卷 41-50页

作者： Chris Bryan Aditi Mishra Hidekazu Shidara Kwan-Liu Ma School of Computing Informaticsand Decision Systems EngineeringArizona State UniversityUnited States Department of Computer Science University of CaliforniaDavisUnited States

We conduct an eye tracking study to investigate perception text-embellished narrative visualizations under different task *** stimuli are data visualizations embellished with text-based elements:annotations,captions,labels,and descriptive *** consider three common viewing tasks that occur when these types of graphics are viewed:(1)simple observation,(2)active search to answer a query,and(3)information memorization for later *** overarching goal is to understand,at a perceptual level,if and how task affects how these visualizations are interacted *** analyzing collected gaze data and conducting advanced semantic scanpath analysis,we find,at a high level,diverse patterns of gaze behavior:simple observation and information memorization lead to similar optical viewing strategies,while active search significantly diverges,both in regards to which areas of the visualization are focused upon and how often embellishments are interacted *** discuss study outcomes in the context of embellishing visualizations with text for various usage scenarios.

关键词： Eye tracking Perception User study

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TWE‐WSD: An effective topical word embedding based word sense disambiguation

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CAAI Transactions on Intelligence Technology 2021年第1期6卷 72-79页

作者： Lianyin Jia Jilin Tang Mengjuan Li Jinguo You Jiaman Ding Yinong Chen Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunmingChina Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunmingChina Library Yunnan Normal UniversityKunmingChina School of Computing Informaticsand Decision SystemsArizona State UniversityTempeArizonaUSA

Word embedding has been widely used in word sense disambiguation(WSD)and many other tasks in recent years for it can well represent the semantics of ***,the existing word embedding methods mostly represent each word as a single vector,without considering the homonymy and polysemy of the word;thus,their performances are *** order to address this problem,an effective topical word embedding(TWE)‐based WSD method,named TWE‐WSD,is proposed,which integrates Latent Dirichlet Allocation(LDA)and word *** of generating a single word vector(WV)for each word,TWE‐WSD generates a topical WV for each word under each *** integrating strategies are designed to obtain high quality contextual *** experiments on SemEval‐2013 and SemEval‐2015 for English all‐words tasks showed that TWE‐WSD outperforms other state‐of‐the‐art WSD methods,especially on nouns.

关键词： embedding Word WSD

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