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Strategic information revelation in crowdsourcing systems without verification

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arXiv 2021年

作者： Huang, Chao Yu, Haoran Huang, Jianwei Berry, Randall A. Department of Information Engineering Chinese University of Hong Kong Hong Kong School of Computer Science Beijing Institute of Technology China School of Science and Engineering Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Department of Electrical and Computer Engineering Northwestern University United States

We study a crowdsourcing problem where the platform aims to incentivize distributed workers to provide high-quality and truthful solutions without the ability to verify the solutions. While most prior work assumes that the platform and workers have symmetric information, we study an asymmetric information scenario where the platform has informational advantages. Specifically, the platform knows more information regarding workers' average solution accuracy, and can strategically reveal such information to workers. Workers will utilize the announced information to determine the likelihood that they obtain a reward if exerting effort on the task. We study two types of workers: (1) naive workers who fully trust the announcement, and (2) strategic workers who update prior belief based on the announcement. For naive workers, we show that the platform should always announce a high average accuracy to maximize its payoff. However, this is not always optimal for strategic workers, as it may reduce the credibility of the platform's announcement and hence reduce the platform's payoff. Interestingly, the platform may have an incentive to even announce an average accuracy lower than the actual value when facing strategic workers. Another counter-intuitive result is that the platform's payoff may decrease in the number of high-accuracy workers. Copyright © 2021, The Authors. All rights reserved.

关键词： Crowdsourcing

Deep Learning for Brain Age Estimation: A Systematic Review

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arXiv 2022年

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Ser, Javier Del Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Assam Silchar788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

An Appraisal in to the Effects of Partial Shading on an Urban Photovoltaic System Using the Internet of Things

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An Appraisal in to the Effects of Partial Shading on an Urba...

Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Lakshmana Phaneendra Maguluri S Farook Saravanan R Ashutosh Dixit K.P. Aishwarya JoshuvaArockia Dhanraj Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electrical and Electronics Engineering School of Engineering Mohan Babu University Tirupathi Andhra Pradesh India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Electrical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Information Technology Panimalar Engineering College Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

Partial shadowing of a solar array is known to decrease the array's output power. Unfortunately, it is not always possible to calculate the exact degree of decrease in energy output from the darkened region alone. In this study, the process of partial PV shadowing on multiple PV cells by using IoT wired in series and/or parallel, with and without bypass diodes, is elucidated. A layperson interested in learning how a certain shading geometry affects a PV system may benefit from this study, which is provided in clear language. Commercial 100 W panel and 10 kW PV array data are used to show the study.

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AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image Segmentation

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arXiv 2023年

作者： Lin, Jiacheng Chen, Jiajun Yang, Kailun Roitberg, Alina Li, Siyu Li, Zhiyong Li, Shutao The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Artificial Intelligence The University of Stuttgart Stuttgart70569 Germany The College of Electrical and Information Engineering The Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Hunan University Changsha410082 China

Interactive Image Segmentation (IIS) has emerged as a promising technique for decreasing annotation time. Substantial progress has been made in pre- and post-processing for IIS, but the critical issue of interaction ambiguity, notably hindering segmentation quality, has been under-researched. To address this, we introduce ADAPTIVECLICK – a click-aware transformer incorporating an adaptive focal loss that tackles annotation inconsistencies with tools for mask- and pixel-level ambiguity resolution. To the best of our knowledge, AdaptiveClick is the first transformer-based, mask-adaptive segmentation framework for IIS. The key ingredient of our method is the Click-Aware Mask-adaptive transformer Decoder (CAMD), which enhances the interaction between click and image features. Additionally, AdaptiveClick enables pixel-adaptive differentiation of hard and easy samples in the decision space, independent of their varying distributions. This is primarily achieved by optimizing a generalized Adaptive Focal Loss (AFL) with a theoretical guarantee, where two adaptive coefficients control the ratio of gradient values for hard and easy pixels. Our analysis reveals that the commonly used Focal and BCE losses can be considered special cases of the proposed AFL. With a plain ViT backbone, extensive experimental results on nine datasets demonstrate the superiority of AdaptiveClick compared to state-of-the-art methods. The source code is publicly available at https://***/lab206/AdaptiveClick. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

Characterizing multidimensional capacitive servoing for physical human-robot interaction

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arXiv 2021年

作者： Erickson, Zackory Clever, Henry M. Gangaram, Vamsee Xing, B.S. Eliot Turk, Greg Liu, C. Karen Kemp, Charles C. The Robotics Institute Carnegie Mellon University PittsburghPA United States The Healthcare Robotics Lab Georgia Institute of Technology AtlantaGA United States The School of Interactive Computing Georgia Institute of Technology AtlantaGA United States The Department of Computer Science Stanford University StanfordCA United States

Towards the goal of robots performing robust and intelligent physical interactions with people, it is crucial that robots are able to accurately sense the human body, follow trajectories around the body, and track human motion. This study introduces a capacitive servoing control scheme that allows a robot to sense and navigate around human limbs during close physical interactions. Capacitive servoing leverages temporal measurements from a multi-electrode capacitive sensor array mounted on a robot's end effector to estimate the relative position and orientation (pose) of a nearby human limb. Capacitive servoing then uses these human pose estimates from a data-driven pose estimator within a feedback control loop in order to maneuver the robot's end effector around the surface of a human limb. We provide a design overview of capacitive sensors for human-robot interaction and then investigate the performance and generalization of capacitive servoing through an experiment with 12 human participants. The results indicate that multidimensional capacitive servoing enables a robot's end effector to move proximally or distally along human limbs while adapting to human pose. Using a cross-validation experiment, results further show that capacitive servoing generalizes well across people with different body size. Copyright © 2021, The Authors. All rights reserved.

关键词： Capacitive sensors

An Intelligent Deterministic Scheduling Method for Ultra-Low Latency Communication in Edge Enabled Industrial Internet of Things

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arXiv 2022年

作者： Lu, Yinzhi Yang, Liu Yang, Simon X. Hua, Qiaozhi Sangaiah, Arun Kumar Guo, Tan Yu, Keping The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China The Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada The Computer school Hubei University of Arts and Science Xiangyang441000 China The National Yunlin University of Science and Technology Taiwan The Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan The RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, Time Sensitive Network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then a non-collision theory based deterministic scheduling (NDS) method is proposed to achieve ultra-low latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultra-low latency services and guarantee efficient bandwidth utilization. © 2022, CC BY.

关键词： Scheduling

Unified regularity measures for sample-wise learning and generalization

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arXiv 2021年

作者： Zhang, Chi Ma, Xiaoning Liu, Yu Wang, Le Su, Yuanqi Liu, Yuehu Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China College of Artificial Intelligence Xi’an Jiaotong University Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China

Fundamental machine learning theory shows that different samples contribute unequally both in learning and testing processes. Contemporary studies on DNN imply that such sample difference is rooted on the distribution of intrinsic pattern information, namely sample regularity. Motivated by the recent discovery on network memorization and generalization, we proposed a pair of sample regularity measures for both processes with a formulation-consistent representation. Specifically, cumulative binary training/generalizing loss (CBTL/CBGL), the cumulative number of correct classifications of the training/testing sample within training stage, is proposed to quantize the stability in memorization-generalization process;while forgetting/mal-generalizing events, i.e., the mis-classification of previously learned or generalized sample, are utilized to represent the uncertainty of sample regularity with respect to optimization dynamics. Experiments validated the effectiveness and robustness of the proposed approaches for mini-batch SGD optimization. Further applications on training/testing sample selection show the proposed measures sharing the unified computing procedure could benefit for both tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

ARUSH:Automated Runtime Universal Scanner for Hybrid Cloud computing

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ARUSH:Automated Runtime Universal Scanner for Hybrid Cloud c...

2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Luthra, Jatin Choudhary, Arjun Sharma, Abhishek Jayakody, Dushantha Nalin K. The LNM Institute of Information Technology Jaipur India Sardar Patel University of Police Security and Criminal Justice Centre for Cyber Security Jodhpur India Sri Lanka Technological Campus Centre for Telecommunication Research School of Engineering Padukka Sri Lanka School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Sciences and Technologies Department Universidade Aut onoma de Lisboa Lisboa1169-023 Portugal Autonoma TechLab Lisboa1169-023 Portugal

ISBN: (纸本)9781665423908

With the shift of leading companies to the cloud, so comes the risk of data leaks and breaches because of many possible misconfigurations in the setup of various cloud computing environments. Though many solutions have come up, each is platform-specific, and none of them targets multiple or hybrid cloud platforms all together. This work proposes one such solution where three major public cloud platforms are supported, and the main focus is on the storage services provided by them. Since there is no such open-source solution that supports multiple cloud platforms, the program in this work is supposed to provide such a solution. Each cloud platform provides native SDK libraries for issuing API calls to the platform. These API calls aim to query the currently employed configuration for the storage services at the very least if not change. The solution can combine all the libraries into a single driver program. The program can facilitate security management of storage accounts across multiple as well hybrid cloud services and opens possibilities for automation and routine security and audit checks. © 2021 IEEE.

关键词： Libraries

Unlimited neighborhood interaction for heterogeneous trajectory prediction

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arXiv 2021年

作者： Zheng, Fang Wang, Le Zhou, Sanping Tang, Wei Niu, Zhenxing Zheng, Nanning Hua, Gang School of Software Engineering Xi'An Jiaotong University China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University China University of Illinois at Chicago United States School of Computer Science and Technology Xidian University China Wormpex AI Research

Understanding complex social interactions among agents is a key challenge for trajectory prediction. Most existing methods consider the interactions between pairwise traffic agents or in a local area, while the nature of interactions is unlimited, involving an uncertain number of agents and non-local areas simultaneously. Besides, they treat heterogeneous traffic agents the same, namely those among agents of different categories, while neglecting people's diverse reaction patterns toward traffic agents in different categories. To address these problems, we propose a simple yet effective Unlimited Neighborhood Interaction Network (UNIN), which predicts trajectories of heterogeneous agents in multiple categories. Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously, which is adaptive to any number of agents and any range of interaction area. Meanwhile, a hierarchical graph attention module is proposed to obtain category-to-category interaction and agent-to-agent interaction. Finally, parameters of a Gaussian Mixture Model are estimated for generating the future trajectories. Extensive experimental results on benchmark datasets demonstrate a significant performance improvement of our method over the state-of-the-art methods. © 2021, CC BY-NC-ND.

关键词： Trajectories