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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Chandralekha Yenugunti Stephen S. Yau School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe USA

ISBN: (数字)9781728166094

ISBN: (纸本)9781728166100

Large organizations have multiple networks that are subject to attacks which can be detected by Intrusion Detection systems. Collaborative Intrusion Detection systems (CIDS) are used for efficient detection of distributed attacks in large networks by having a global view of the attacks in the networks. However, CIDS are vulnerable to various attacks, which compromise some of the nodes of CIDS. The major challenge caused by these attacks on CIDS is due to insider attacks. These insider attacks decrease the mutual trust among the nodes in CIDS, which is required for sharing critical and sensitive alert data. The compromised nodes will further decrease the accuracy of CIDS by generating false positives and false negatives of the traffic classifications. In this paper, an approach based on trust score system is presented to identify and suspend the compromised nodes in CIDS to improve the trust among the nodes for collaboration. This approach is implemented on a private blockchain because private blockchain provides the features to satisfy the accountability, integrity and privacy requirements of CIDS.

关键词： Privacy Intrusion detection Collaboration Blockchain Organizations Big Data

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Ensemble Learning on Deep Neural Networks for Image Caption Generation

Ensemble Learning on Deep Neural Networks for Image Caption ...

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International Conference on Semantic computing

作者： Harshitha Katpally Ajay Bansal School of Computing Informatics and Decision Systems Engineering Arizona State University Mesa AZ

ISBN: (数字)9781728163321

ISBN: (纸本)9781728163338

Capturing the information in an image into a natural language sentence is considered a difficult problem to be solved by computers. Image captioning involves not just detecting objects from images but understanding the interactions between the objects to be translated into relevant captions. So, expertise in the field of computer vision paired with natural language processing is crucial for this purpose. The sequence to sequence modelling strategy of deep neural networks is the traditional approach to generate a sequential list of words that are combined to represent the image. But these models suffer from the problem of high variance by not being able to generalize well on the training data. The main focus of this paper is to reduce the variance factor that will help in generating better captions. To achieve this, Ensemble Learning techniques have been explored, which have the reputation of solving the high variance problem that occurs in machine learning algorithms. Three different ensemble techniques namely, k-fold ensemble, bootstrap aggregation ensemble and boosting ensemble have been evaluated in our work. For each of these techniques, three output combination approaches have been analyzed. Extensive experiments have been conducted on the Flickr8k dataset which has a collection of 8000 images and 5 different captions for every image. The bleu score performance metric, which is considered to be the standard for evaluating natural language processing (NLP) problems, is used to evaluate the predictions. Based on this metric, the analysis shows that ensemble learning performs significantly better and generates more meaningful captions compared to any of the individual models used.

关键词： Feature extraction Predictive models Training Computer architecture Computational modeling Machine learning

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Online Explanation Generation for Planning Tasks in Human-Robot Teaming

Online Explanation Generation for Planning Tasks in Human-Ro...

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2020 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Mehrdad Zakershahrak Ze Gong Nikhillesh Sadassivam Yu Zhang School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

As AI becomes an integral part of our lives, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative. For a robotic teammate, the ability to generate explanations to justify its behavior is one of the key requirements of explainable agency. Prior work on explanation generation has been focused on supporting the rationale behind the robot's decision or behavior. These approaches, however, fail to consider the mental demand for understanding the received explanation. In other words, the human teammate is expected to understand an explanation no matter how much information is presented. In this work, we argue that explanations, especially those of a complex nature, should be made in an online fashion during the execution, which helps spread out the information to be explained and thus reduce the mental workload of humans in highly cognitive demanding tasks. However, a challenge here is that the different parts of an explanation may be dependent on each other, which must be taken into account when generating online explanations. To this end, a general formulation of online explanation generation is presented with three variations satisfying different "online" properties. The new explanation generation methods are based on a model reconciliation setting introduced in our prior work. We evaluated our methods both with human subjects in a simulated rover domain, using NASA Task Load Index (TLX), and synthetically with ten different problems across two standard IPC domains. Results strongly suggest that our methods generate explanations that are perceived as less cognitively demanding and much preferred over the baselines and are computationally efficient.

关键词： NASA Planning Task analysis Artificial intelligence Standards Intelligent robots Load modeling

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Achieving Multi-Tasking Robots in Multi-Robot Tasks

arXiv

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arXiv 2020年

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

One simplifying assumption made in distributed robot systems is that the robots are single-tasking: each robot operates on a single task at any time. While such a sanguine assumption is innocent to make in situations with sufficient resources so that the robots can operate independently, it becomes impractical when they must share their capabilities. In this paper, we consider multi-tasking robots with multirobot tasks. Given a set of tasks, each achievable by a coalition of robots, our approach allows the coalitions to overlap and task synergies to be exploited by reasoning about the physical constraints that can be synergistically satisfied for achieving the tasks. The key contribution of this work is a general and flexible framework to achieve this ability for multi-robot systems in resource-constrained situations to extend their capabilities. The proposed approach is built on the information invariant theory, which specifies the interactions between information requirements. In our work, we map physical constraints to information requirements, thereby allowing task synergies to be identified via the information invariant framework. We show that our algorithm is sound and complete under a problem setting with multi-tasking robots. Simulation results show its effectiveness under resource-constrained situations and in handling challenging situations in a multi-UAV simulator. Copyright © 2020, The Authors. All rights reserved.

关键词： Industrial robots

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ASU at TextGraphs 2019 shared task: Explanation regeneration using language models and iterative re-ranking 13

ASU at TextGraphs 2019 shared task: Explanation regeneration...

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13th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2019, in conjunction with the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019

作者： Banerjee, Pratyay School of Computing Informatics and Decision Systems Engineering Arizona State University United States

ISBN: (纸本)9781950737864

In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large graphs. Our approach consists of modeling the explanation regeneration task as a learning to rank problem, for which we use state-of-theart language models and explore dataset preparation techniques. We utilize an iterative reranking based approach to further improve the rankings. Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3% on the test set. © 2019 EMNLP-IJCNLP 2019 - Graph-Based Methods for Natural Language Processing - Proceedings of the 13th Workshop. All rights reserved.

关键词： Iterative methods

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Automatic Discovery of Motion Patterns that Improve Learning Rate in Communication-Limited Multi-Robot systems

Automatic Discovery of Motion Patterns that Improve Learning...

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Taeyeong Choi Theodore P. Pavlic School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA School of Computing Informatics and Decision Systems Engineering the School of Sustainability the School of Life Sciences Arizona State University Tempe AZ USA

ISBN: (数字)9781728164229

ISBN: (纸本)9781728164236

Learning in robotic systems is largely constrained by the quality of the training data available to a robot learner. Robots may have to make multiple, repeated expensive excursions to gather this data or have humans in the loop to perform demonstrations to ensure reliable performance. The cost can be much higher when a robot embedded within a multi-robot system must learn from the complex aggregate of the many robots that surround it and may react to the learner's motions. In our previous work [1], [2], we considered the problem of Remote Teammate Localization (ReTLo), where a single robot in a team uses passive observations of a nearby neighbor to accurately infer the position of robots outside of its sensory range even when robot-to-robot communication is not allowed in the system. We demonstrated a communication-free approach to show that the rearmost robot can use motion information of a single robot within its sensory range to predict the positions of all robots in the convoy. Here, we expand on that work with Selective Random Sampling (SRS), a framework that improves the ReTLo learning process by enabling the learner to actively deviate from its trajectory in ways that are likely to lead to better training samples and consequently gain accurate localization ability with fewer observations. By adding diversity to the learner's motion, SRS simultaneously improves the learner's predictions of all other teammates and thus can achieve similar performance as prior methods with less data.

关键词： Robot multi-robot systems Robot System patterning of movement learning rate RSS gene Surround sound SAVANNAH RIVER SITE

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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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IEEE International Conference on Robotics and Automation (ICRA)

作者： Sai Krishna Bashetty Heni Ben Amor Georgios Fainekos School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

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.

关键词： Three-dimensional displays Trajectory Cameras Videos Tracking Vehicle crash testing Data mining

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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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IEEE International Conference on Robotics and Automation (ICRA)

作者： Daniel Molina Kislay Kumar Siddharth Srivastava School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe Arizona 85281

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

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.

关键词： Planning Probabilistic logic Robots Density measurement Task analysis Buildings Convolutional neural networks

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A Blockchain-based Testing Approach for Collaborative Software Development

A Blockchain-based Testing Approach for Collaborative Softwa...

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IEEE International Conference on Blockchain (Blockchain)

作者： Stephen S. Yau Jinal S. Patel School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA

ISBN: (数字)9780738104959

ISBN: (纸本)9781665423229

Development of large-scale and complex software systems requires multiple teams, including software development teams, domain experts, user representatives, and other project stakeholders, to work collaboratively to achieve software development goals. These teams rely on the use of agreed software development processes, knowledge management tools, and communication channels collaboratively in the software development project. Software testing is an important and complicated process due to reasons such as difficulties in achieving testing goals with the given time constraint, absence of efficient data sharing policies, vague testing acceptance criteria at various levels of testing, and lack of trusted coordination among the teams involved in software testing. The efficiency of the software testing relies on efficient, reliable, and trusted information sharing among these teams. Existing approaches to software testing for collaborative software development use centralized or decentralize tools for software testing, knowledge management, and communication channels. Existing approaches have the limitations of centralized authority, a single point of failure/compromise, lack of automatic requirement compliance checking and transparency in information sharing, and lack of unified data sharing policy, and reliable knowledge management repositories for sharing and storing past software testing artifacts and data. In this paper, a software testing approach for collaborative software development using private blockchain is presented, and the desirable properties of private blockchain, such as distributed data management, tamper-resistance, auditability and automatic requirement compliance checking, are incorporated to greatly improve the quality of software testing for collaborative software development.

关键词： Software testing Collaborative software Blockchain Tools Software Knowledge management Software reliability

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Anytime Integrated Task and Motion Policies for Stochastic Environments

Anytime Integrated Task and Motion Policies for Stochastic E...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Naman Shah Deepak Kala Vasudevan Kislay Kumar Pranav Kamojjhala Siddharth Srivastava School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstract models are typically lossy and plans or policies computed using them can be unexecutable. These problems are exacerbated in stochastic situations where the robot needs to reason about, and plan for multiple contingencies. We present a new approach for integrated task and motion planning in stochastic settings. In contrast to prior work in this direction, we show that our approach can effectively compute integrated task and motion policies whose branching structures encoding agent behaviors handling multiple execution-time contingencies. We prove that our algorithm is probabilistically complete and can compute feasible solution policies in an anytime fashion so that the probability of encountering an unresolved contingency decreases over time. Empirical results on a set of challenging problems show the utility and scope of our methods.

关键词： Robots Planning Task analysis Computational modeling Collision avoidance Stochastic processes Trajectory

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