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Evaluating Fluency in Human-Robot Collaboration

IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2019年第3期49卷 209-218页

作者： Hoffman, Guy Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA

Collaborative fluency is the coordinated meshing of joint activities between members of a well-synchronized team. In recent years, researchers in human-robot collaboration have been developing robots to work alongside humans aiming not only at task efficiency, but also at human-robot fluency. As part of this effort, we have developed a number of metrics to evaluate the level of fluency in human-robot shared-location teamwork. While these metrics are being used in existing research, there has been no systematic discussion on how to measure fluency and how the commonly used metrics perform and compare. In this paper, we codify subjective and objective human-robot fluency metrics, provide an analytical model for four objective metrics, and assess their dynamics in a turn-taking framework. We also report on a user study linking objective and subjective fluency metrics and survey recent use of these metrics in the literature.

关键词： artificial intelligence computational and artificial intelligence cooperative systems human-robot interaction intelligent robots intelligent systems man-machine systems systems man cybernetics user interfaces

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Sparse Label Smoothing Regularization for Person Re-Identification

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IEEE ACCESS 2019年 7卷 27899-27910页

作者： Ainam, Jean-Paul Qin, Ke Liu, Guisong Luo, Guangchun Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China Adventist Cosendai Univ BP 04 Nanga Eboko Cameroon

Person re-identification (re-id) is a cross-camera retrieval task which establishes a correspondence between images of a person from multiple cameras. Deep learning methods have been successfully applied to this problem and have achieved impressive results. However, these methods require a large amount of labeled training data. Currently, the labeled datasets in person re-id are limited in their scale and manual acquisition of such large-scale datasets from surveillance cameras is a tedious and labor-intensive task. In this paper, we propose a framework that performs intelligent data augmentation and assigns the partial smoothing label to generated data. Our approachfirst exploits the clustering property of existing person re-id datasets to create groups of similar objects that model cross-view variations. Each group is then used to generate realistic images through adversarial training. Our aim is to emphasize the feature similarity between generated samples and the original samples. Finally, we assign a non-uniform label distribution to the generated samples and define a regularized loss function for training. The proposed approach tackles two problems 1) how to efficiently use the generated data and 2) how to address the over-smoothness problem found in current regularization methods. The extensive experiments on four large-scale datasets show that our regularization method significantly improves the re-id accuracy compared to existing methods.

关键词： computational and artificial intelligence artificial neural network feature extraction image retrieval

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Greedy Sensor Placement With Cost Constraints

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IEEE SENSORS JOURNAL 2019年第7期19卷 2642-2656页

作者： Clark, Emily Askham, Travis Brunton, Steven L. Kutz, J. Nathan Univ Washington Dept Phys Seattle WA 98195 USA Univ Washington Dept Appl Math Seattle WA 98195 USA Univ Washington Dept Mech Engn Seattle WA 98195 USA

The problem of optimally placing sensors under a cost constraint arises naturally in the design of industrial and commercial products, as well as in scientific experiments. We consider a relaxation of the full optimization formulation of this problem and then extend a well-established greedy algorithm for the optimal sensor placement problem without cost constraints. We demonstrate the effectiveness of this algorithm on the datasets related to facial recognition, climate science, and fluid mechanics. This algorithm is scalable and often identifies sparse sensors with near-optimal reconstruction performance, while dramatically reducing the overall cost of the sensors. We find that the cost-error landscape varies by application, with intuitive connections to the underlying physics. In addition, we include experiments for various pre-processing techniques and find that a popular technique based on the singular value decomposition is often suboptimal.

关键词： Sensor phenomena and characterization computational and artificial intelligence computation theory greedy algorithms data systems data processing data preprocessing

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Understanding Time-Based Trends in Stakeholders' Choice of Learning Activity Type Using Predictive Models

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IEEE ACCESS 2019年 7卷 3106-3121页

作者： Drlik, Martin Munk, Michal Constantine Philosopher Univ Nitra Dept Comp Sci Nitra 94974 Slovakia

The learning analytics communities, as well as most learning analytics research, have not frequently focused on time-based trends in the same virtual learning environment over different years of deployment, or on temporal trends in the selection of different activity types over a typical day. This paper contributes to this debate and provides a novel approach to learning analytics using a multinomial logit model for modeling the probabilities of students' choice of learning activities during the hours of the day over several academic years. An abstraction called activity is introduced, which categorizes individual student's log accesses to the virtual learning environment into more semantically meaningful categories. Consequently, the activity represents a sequence of semantically meaningful Web accesses related to a particular activity or task that a student of the virtual learning environment performs. This paper includes a comprehensive explanation of the model and an evaluation of the model. This paper introduces a case study, which shows that the multinomial logit model can give useful insight into the course schedule, as it shows what the peak times are for different types of activities. This paper also discusses the possible implications of the results in the context of virtual learning environment management and content improvement at the institutional level.

关键词： computational and artificial intelligence learning management systems predictive models Web mining

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Particle Swarm Optimization Based Support Vector Machine (P-SVM) for the Segmentation and Classification of Plants

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IEEE ACCESS 2019年 7卷 29374-29385页

作者： Kour, Vippon Preet Arora, Sakshi Shri Mata Vaishno Devi Univ Dept Comp Sci & Engn Katra 182320 India

With the rapid growth in urbanization and population, it has become an earnest task to nurture and grow plants that are both important in sustaining the nature and the living beings needs. In addition, there is a need for preserving the plants having global importance both economically and environmentally. Locating such species from the forest or shrubs having human involvement is a time consuming and costly task to perform. Therefore, in this paper, a novel method is presented for the segmentation and classification of the seven different plants, named Guava, Jamun, Mango, Grapes, Apple, Tomato, and Arjun, based on their leaf images. In the first phase, both real-time images and images from the crowdAI database are collected and preprocessed for noise removal, resizing, and contrast enhancement. Then, in the second phase, different features are extracted based on color and texture. The third phase includes the segmentation of images using a k-means algorithm. The fourth phase consists of the training of support vector machine, and finally, in the last phase, the testing is performed. Particle swarm optimization algorithm is used for selecting the best possible value of the initialization parameter in both the segmentation and classification processes. The proposed work achieves higher experimental results, such as sensitivity = 0.9581, specificity = 0.9676, and accuracy = 0.9759, for segmentation and classification accuracy = 95.23 when compared with other methods.

关键词： computational and artificial intelligence imaging image classification image segmentation particle swarm optimization support vector machine

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computationally Efficient Off-Line Joint Change Point Detection in Multiple Time Series

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2019年第1期67卷 149-163页

作者： Eriksson, Markus Olofsson, Tomas Uppsala Univ Dept Engn Sci Signals & Syst S-75237 Uppsala Sweden

In this paper, a computationally efficient algorithm for Bayesian joint change point detection (CPD) in multiple time series is presented. The data generation model includes a number of change configurations (CC), each affecting a unique subset of the time series, which introduces correlation between the positions of change points (CPs) in the monitored time series. The inference objective is to identify joint changes and the associated CC. The algorithm consists of two stages: First, a univariate CPD algorithm is applied separately to each of the involved time series. The outcomes of this step are maximum a posteriori (MAP) detected CPs and posterior distributions of CPs conditioned on the MAP CPs. These outcomes are used in combination to approximate the posterior for the CCs. In the second algorithm stage, dynamic programming is used to find the maxima of this approximate CC posterior. The algorithm is applied to synthetic data, and it is shown to be both significantly faster and more accurate compared to a previously proposed algorithm designed to solve similar problems. Also, the initial algorithm is extended with steps from the maximization-maximization algorithm, which allows the hyperparameters of the data generation model to be estimated jointly with the CCs, and we show that these estimates coincide with estimates obtained from a Markov chain Monte Carlo algorithm.

关键词： computational and artificial intelligence computer applications big data applications mathematics statistics time series analysis numerical analysis iterative methods iterative algorithms

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Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2019年第8期38卷 1971-1980页

作者： Man, Yunze Huang, Yangsibo Feng, Junyi Li, Xi Wu, Fei Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China

The segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions, and non-rigid geometrical features. To address these difficulties, we introduce a deep Q network (DQN) driven approach with deformable U-Net to accurately segment the pancreas by explicitly interacting with contextual information and extract anisotropic features from pancreas. The DQN-based model learns a context-adaptive localization policy to produce a visually tightened and precise localization bounding box of the pancreas. Furthermore, deformable U-Net captures geometry aware information of pancreas by learning geometrically deformable filters for feature extraction. The experiments on NIH dataset validate the effectiveness of the proposed framework in pancreas segmentation.

关键词： computational and artificial intelligence image processing image segmentation imaging tomography computed tomography pancreas segmentation DQN deformable U-net

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Re-training and parameter sharing with the Hash trick for compressing convolutional neural networks

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APPLIED SOFT COMPUTING 2020年第PartB期97卷 106783-106783页

作者： Gou, Xu Qing, Linbo Wang, Yi Xin, Mulin Wang, Xianmin Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Guangzhou Univ Inst Artificial Intelligence & Blockchain Guangzhou 510006 Peoples R China

As an ubiquitous technology for improving machine intelligence, deep learning has largely taken the dominant position among nowadays most advanced computer vision systems. To achieve superior performance on large-scale datasets, convolutional neural networks (CNNs) are often designed as complex models with millions of parameters. This limits the deployment of CNNs in embedded intelligent computer vision systems, such as intelligent robots that are resource-constrained with real-time computing requirement. This paper proposes a simple and effective model compression scheme to improve the real-time sensing of the surrounding objects. In the proposed framework, the Hash trick is first applied to a modified convolutional layer, and the compression of the convolutional layer is realized via weight sharing. Subsequently, the Hash index matrix is introduced to represent the Hash function, and its relaxation regularization is introduced into the fine-tuned loss function. Through the dynamic retraining of the index matrix, the Hash function can be updated. We evaluate our method using several state-of-the-art CNNs. Experimental results showed that the proposed method can reduce the number of parameters in AlexNet by 24x with no accuracy loss. In addition, the compressed VGG16 and ResNet50 can achieve a more than 60x increased speed, which is significant. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Model compression Machine learning Parameter learning and tuning computational and artificial intelligence

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Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval 21

Deep Aggregation of Regional Convolutional Activations for C...

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IEEE 21st International Workshop on Multimedia Signal Processing (MMSP)

作者： Schall, Konstantin Barthel, Kai Uwe Hezel, Nico Jung, Klaus HTW Berlin Visual Comp Grp Berlin Germany

ISBN: (纸本)9781728118178

One of the key challenges of deep learning based image retrieval remains in aggregating convolutional activations into one highly representative feature vector. Ideally, this descriptor should encode semantic, spatial and low level information. Even though off-the-shelf pre-trained neural networks can already produce good representations in combination with aggregation methods, appropriate fine tuning for the task of image retrieval has shown to significantly boost retrieval performance. In this paper we present a simple yet effective supervised aggregation method built on top of existing regional pooling approaches. In addition to the maximum activation of a given region, we calculate regional average activations of extracted feature maps. Subsequently, weights for each of the pooled feature vectors are learned to perform a weighted aggregation to a single feature vector. Furthermore, we apply our newly proposed NRA loss function for deep metric learning to fine tune the backbone neural network and to learn the aggregation weights. Our method achieves state-of-the-art results for the INRIA Holidays data set and competitive results for the Oxford Buildings and Paris data sets while reducing the training time significantly.

关键词： computational and artificial intelligence Multi-layer neural network Image retrieval Content-based retrieval Machine learning Feature extraction Machine learning algorithms Nearest neighbor searches Computer vision

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artificial intelligence in Operating System 2019

Artificial Intelligence in Operating System

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Proceedings of the 2019 3rd International Conference on Computer Science and artificial intelligence

作者： Abdul Waheed Sanaullah Haider Ali Farid Khan School of International Education Beijing Institute of Graphic Communication Beijing P.R. China School of Information and Communication Engineering Beijing Institute of Graphic Communication Beijing P.R. China

ISBN: (纸本)9781450376273

The rapid advancement in artificial intelligence, semiconductor and electronic technology, software engineering and programming techniques as well as hardware interfaces and communication has raised the demand for advancement in operating system. In this article we have studied the classical and current operating systems and have proposed the advanced components such as new user interfaces, advanced human-to-machine interfaces, communication, artificial intelligence and advanced hardware technology etc. which are necessary for next generation operating systems.

关键词： artificial intelligence and Intelligent Computing artificial intelligence computational and artificial intelligence computational Thinking Operating System Computer Vision

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