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Online explanation generation for human-robot teaming

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arXiv 2019年

作者： Zakershahrak, Mehrdad Gong, Ze Sadassivam, Nikhillesh Zhang, Yu School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States

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 explain its behavior is one of the key requirements of explainable agency. Prior work on explanation generation focuses on supporting the rationale behind the robot's decision (or behavior). These approaches, however, fail to consider the mental workload needed to understand the received explanation. In other words, the human teammate is expected to understand any explanation provided no matter how much information is presented. In this work, we argue that explanations, especially ones 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 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 properties. The new explanation generation methods are based on a model reconciliation setting introduced in our prior work. We evaluate our methods both with human subjects in a standard planning competition (IPC) domain, using NASA Task Load Index (TLX), as well as in simulation with ten different problems across two IPC domains. Results strongly suggest that our methods not only generate explanations that are perceived as less cognitively demanding and much preferred over the baselines but also are computationally efficient. Copyright © 2019, The Authors. All rights reserved.

关键词： NASA

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Models genesis

arXiv 2020年

作者： Zhou, Zongwei Sodha, Vatsal Pang, Jiaxuan Gotway, Michael B. Liang, Jianming Department of Biomedical Informatics Arizona State University ScottsdaleAZ85259 United States School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Department of Radiology Mayo Clinic ScottsdaleAZ85259 United States

Transfer learning from natural image to medical image has been established as one of the most practical paradigms in deep learning for medical image analysis. To fit this paradigm, however, 3D imaging tasks in the most prominent imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D, losing rich 3D anatomical information, thereby inevitably compromising its performance. To overcome this limitation, we have built a set of models, called Generic Autodidactic Models, nicknamed Models Genesis, because they are created ex nihilo (with no manual labeling), self-taught (learnt by self-supervision), and generic (served as source models for generating application-specific target models). Our extensive experiments demonstrate that our Models Genesis significantly outperform learning from scratch in all five target 3D applications covering both segmentation and classification. More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D/2.5D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of Models Genesis for 3D medical imaging. This performance is attributed to our unified self-supervised learning framework, built on a simple yet powerful observation: the sophisticated and recurrent anatomy in medical images can serve as strong yet free supervision signals for deep models to learn common anatomical representation automatically via self-supervision. As open science, all codes and pre-trained Models Genesis are available at https://***/MrGiovanni/ModelsGenesis. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Anytime integrated task and motion policies for stochastic environments

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arXiv 2019年

作者： Shah, Naman Kumar, Kislay Kamojjhala, Pranav Vasudevan, Deepak Kala Srivastava, Siddharth School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States

In order to solve complex, long-horizon tasks, intelligent robots need to be able 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 are often unexecutable in practice. These problems are aggravated in more realistic situations with stochastic dynamics, where the robot needs to reason about, and plan for multiple possible 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 with branching structure encoding agent behaviors for various possible 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. Copyright © 2019, The Authors. All rights reserved.

关键词： Motion planning

An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics

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Healthcare Analytics 2022年 2卷 100096-100096页

作者： Ukwandu, Ogechukwu Hindy, Hanan Ukwandu, Elochukwu Department of Computer Communication and Information Systems School of Engineering and Built Environment Glasgow Caledonian University United Kingdom Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo 11566 Egypt Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Cardiff CF5 2YB United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

Timely and rapid diagnoses are core to informing on optimum interventions that curb the spread of COVID-19. The use of medical images such as chest X-rays and CTs has been advocated to supplement the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) test, which in turn has stimulated the application of deep learning techniques in the development of automated systems for the detection of infections. decision support systems relax the challenges inherent to the physical examination of images, which is both time consuming and requires interpretation by highly trained clinicians. A review of relevant reported studies to date shows that most deep learning algorithms utilised approaches are not amenable to implementation on resource-constrained devices. Given the rate of infections is increasing, rapid, trusted diagnoses are a central tool in the management of the spread, mandating a need for a low-cost and mobile point-of-care detection systems, especially for middle- and low-income nations. The paper presents the development and evaluation of the performance of lightweight deep learning technique for the detection of COVID-19 using the MobileNetV2 model. Results demonstrate that the performance of the lightweight deep learning model is competitive with respect to heavyweight models but delivers a significant increase in the efficiency of deployment, notably in the lowering of the cost and memory requirements of computing resources. © 2022 The Author(s)

关键词： COVID-19 detection Diagnostic analytics Lightweight deep learning techniques Machine learning Point-of-care Resource-constrained devices

HUNTER: AI based holistic resource management for sustainable cloud computing

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arXiv 2021年

作者： Tuli, Shreshth Gill, Sukhpal Singh Xu, Minxian Garraghan, Peter Bahsoon, Rami Prof, Scharam Dustdar Sakellariou, Rizos Rana, Omer Buyya, Rajkumar Casale, Giuliano Jennings, Nicholas R. Department of Computing Imperial College London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China School of Computing and Communications Lancaster University United Kingdom School of Computer Science University of Birmingham United Kingdom Distributed Systems Group Vienna University of Technology Austria Department of Computer Science University of Manchester United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively. © 2021, CC BY.

关键词： Energy efficiency

Universality, criticality and complexity of information propagation in social media

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arXiv 2021年

作者： Notarmuzi, Daniele Castellano, Claudio Flammini, Alessandro Mazzilli, Dario Radicchi, Filippo Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Via dei Taurini 19 RomaI-00185 Italy Centro Fermi Via Panisperna 89 A Roma Italy

Information avalanches in social media are typically studied in a similar fashion as avalanches of neuronal activity in the brain. Whereas a large body of literature reveals substantial agreement about the existence of a unique process characterizing neuronal activity across organisms, the dynamics of information in online social media is far less understood. Statistical laws of information avalanches are found in previous studies to be not robust across systems, and radically different processes are used to represent plausible driving mechanisms for information propagation. Here, we analyze almost 1 billion time-stamped events collected from a multitude of online platforms – including Telegram, Twitter and Weibo – over observation windows longer than 10 years to show that the propagation of information in social media is a universal and critical process. Universality arises from the observation of identical macroscopic patterns across platforms, irrespective of the details of the specific system at hand. Critical behavior is deduced from the power-law distributions, and corresponding hyperscaling relations, characterizing size and duration of avalanches of information. Neuronal activity may be modeled as a simple contagion process, where only a single exposure to activity may be sufficient for its diffusion. On the contrary, statistical testing on our data indicates that a mixture of simple and complex contagion, where involvement of an individual requires exposure from multiple acquaintances, characterizes the propagation of information in social media. We show that the complexity of the process is correlated with the semantic content of the information that is propagated. Conversational topics about music, movies and TV shows tend to propagate as simple contagion processes, whereas controversial discussions on political/societal themes obey the rules of complex contagion. © 2021, CC BY.

关键词： Information dissemination

Predicting future opioid incidences today

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arXiv 2019年

作者： Choudhuri, Sandipan Basu, Kaustav Thomas, Kevin Sen, Arunabha NetXT Lab School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ

According to the Center of Disease Control (CDC), the Opioid epidemic has claimed more than 72,000 lives in the US in 2017 alone. In spite of various efforts at the local, state and federal level, the impact of the epidemic is becoming progressively worse, as evidenced by the fact that the number of Opioid related deaths increased by 12.5% between 2016 and 2017. Predictive analytics can play an important role in combating the epidemic by providing decision making tools to stakeholders at multiple levels - from health care professionals to policy makers to first responders. Generating Opioid incidence heat maps from past data, aid these stakeholders to visualize the profound impact of the Opioid epidemic. Such post-fact creation of the heat map provides only retrospective information, and as a result, may not be as useful for preventive action in the current or future time-frames. In this paper, we present a novel deep neural architecture, which learns subtle spatio-temporal variations in Opioid incidences data and accurately predicts future heat maps. We evaluated the efficacy of our model on two open source datasets-(i) The Cincinnati Heroin Overdose dataset, and (ii) Connecticut Drug Related Death Dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Corrigendum to “Consistency analysis of the Simplified Refined Instrumental Variable method for Continuous-time systems” [Automatica 113 (2020) 108767]

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Automatica 2022年 136卷

作者： Siqi Pan Rodrigo A. González James S. Welsh Cristian R. Rojas School of Electrical Engineering and Computing University of Newcastle Callaghan NSW 2308 Australia Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm 10044 Sweden

Debates About EdTech in a Time of Pandemics Should Include Youth's Voices

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Postdigital science and education 2021年第3期3卷 990-1007页

作者： Velislava Hillman João Pedro Martins Emmanuel C Ogu London School of Economics and Political Science London WC2A 2AE UK. Centre for Informatics and Systems University of Coimbra Pólo Il-Pinhal de Marrocos 3030-290 Coimbra Portugal. Department of Information Technology School of Computing & Engineering Sciences Babcock University Ilishan-Remo Ogun State Nigeria.

The EdTech debate continues to gather attention among academia, digital rights and privacy advocates, and the general public. While much ink is spilled by EdTech critics, with best interest for students facing a datafied and platformised education at heart, youth's voice is rarely present. Much is written about what EdTech means for education processes and what they might do to students or for them than is said in their own words. With the pandemic having kept children and youth at a computer distance from each other and society for over a year, what has been happening in their lives during this time should be heard - in their own words. This article follows on two emergencies gripping education globally as highlighted during the 2020 United Nations Internet Governance Forum (IGF), emphasised by two IGF youth representatives. On one hand, the pandemic crisis has exacerbated digital inequalities worldwide; on the other, the 'overdigitisation' of education has increased the risks of dataveillance and privacy loss. These two extremes intersect in their techno-deterministic drive, which risks side-lining students' perspectives. To counter this risk, this paper brings these youths' perspectives and reflections over the dichotomy of 'none' versus 'too much' EdTech and what 'works' and does not in their learning in a time of pandemic.

关键词： Covid-19 Digital divide Digital surveillance EdTech Postdigital Youth