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International Joint Conference on Neural Networks

作者： Antonio Sze-To H. R. Tizhoosh Andrew K. C. Wong Systems Design Engineering University of Waterloo Ontario Canada N2L 3G1 KIMIA Lab University of Waterloo Ontario Canada N2L 3G1

ISBN: (纸本)9781509006212

A Content-Based Image Retrieval (CBIR) system which identifies similar medical images based on a query image can assist clinicians for more accurate diagnosis. The recent CBIR research trend favors the construction and use of binary codes to represent images. Deep architectures could learn the non-linear relationship among image pixels adaptively, allowing the automatic learning of high-level features from raw pixels. However, most of them require class labels, which are expensive to obtain, particularly for medical images. The methods which do not need class labels utilize a deep autoencoder for binary hashing, but the code construction involves a specific training algorithm and an ad-hoc regularization technique. In this study, we explored using a deep de-noising autoencoder (DDA), with a new unsupervised training scheme using only backpropagation and dropout, to hash images into binary codes. We conducted experiments on more than 14,000 x-ray images. By using class labels only for evaluating the retrieval results, we constructed a 16-bit DDA and a 512-bit DDA independently. Comparing to other unsupervised methods, we succeeded to obtain the lowest total error by using the 512-bit codes for retrieval via exhaustive search, and speed up 9.27 times with the use of the 16-bit codes while keeping a comparable total error. We found that our new training scheme could reduce the total retrieval error significantly by 21.9%. To further boost the image retrieval performance, we developed Radon Autoencoder Barcode (RABC) which are learned from the Radon projections of images using a de-noising autoencoder. Experimental results demonstrated its superior performance in retrieval when it was combined with DDA binary codes.

关键词： Binary codes Training Noise reduction X-ray imaging Image retrieval Medical diagnostic imaging

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Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning

arXiv

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arXiv 2021年

作者： Gomrokchi, Maziar Amin, Susan Aboutalebi, Hossein Wong, Alexander Precup, Doina Department of Computer Science McGill University Mila Canada Cheriton School of Computer Science University of Waterloo VIP Lab Canada Department of Systems Design Engineering University of Waterloo VIP Lab DarwinAI Canada Department of Computer Science McGill University Mila DeepMind Canada

While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms to membership inference attacks. In such attacking systems, the adversary targets the set of collected input data on which the deep reinforcement learning algorithm has been trained. To address this gap, we propose an adversarial attack framework designed for testing the vulnerability of a state-of-the-art deep reinforcement learning algorithm to a membership inference attack. In particular, we design a series of experiments to investigate the impact of temporal correlation, which naturally exists in reinforcement learning training data, on the probability of information leakage. Moreover, we compare the performance of collective and individual membership attacks against the deep reinforcement learning algorithm. Experimental results show that the proposed adversarial attack framework is surprisingly effective at inferring data with an accuracy exceeding 84% in individual and 97% in collective modes in three different continuous control Mujoco tasks, which raises serious privacy concerns in this regard. Finally, we show that the learning state of the reinforcement learning algorithm influences the level of privacy breaches significantly. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Towards an Interaction Architecture for the iCub Robot: Social Gaze Space Model Adaptation for Social Interaction

Towards an Interaction Architecture for the iCub Robot: Soci...

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IEEE-RAS International Conference on Humanoid Robots

作者： Sahand Shaghaghi Pourya Aliasghari Bryan Tripp Britt Anderson Kerstin Dautenhahn Chrystopher L. Nehaniv Social & Intelligent Robotics Research Lab University of Waterloo 200 University Ave. W. Waterloo Ontario Canada Dept. of Systems Design Enginering University of Waterloo 200 University Ave. W. Waterloo Ontario Canada Dept. of Electrical & Computer Engineering University of Waterloo 200 University Ave. W. Waterloo Ontario Canada Centre for Theoretical Neuroscience University of Waterloo 200 University Ave. W. Waterloo Ontario Canada Dept. of Psychology University of Waterloo 200 University Ave. W. Waterloo Ontario Canada

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Gaze behaviour plays a crucial role in social interactions extending to Human-Robot Interactions. Humanoid robots equipped with anthropomorphic vision systems may be expected to employ natural gaze behaviour in interactions with humans. Here, we implemented a novel first-person human gaze state detection tool on the iCub robot and assessed its gaze detection accuracy. Using this tool, we then developed two social gaze interaction architectures for the iCub robot. The Social Gaze Space theory (SGS) of Jording and colleagues was the theoretical framework for building our first architecture, SGS Base (SGS-B). Our second architecture, SGS Interaction Architecture (SGS-IA), is the extended version of SGS Base that lets the robot take on different higher-level goals in an interaction, including being attentive to the interaction partner or attempting to direct their attention to an object. These architectures support dyadic social interactions involving initiating and responding to joint attention towards objects of interest, and are expected to allow the robot to interact in a more human-like manner in social interactions. The gaze detection accuracy of the iCub robot equipped with our choice of algorithms exceeded that of previously implemented methods. System validation trials confirmed that the robot detects the social gaze states correctly in the majority of the instances, allowing for behavioural control according to the Social Gaze Space theory. Validation tests presented in this article demonstrate the functionality of the SGS Interaction Architecture and also highlight the differences in personal interaction dynamics when the humanoid robot has no particular goals as opposed to when it has interactional goals appropriate to a teaching scenario.

关键词： Accuracy Architecture Education Humanoid robots Human-robot interaction Modulation Aerospace electronics Visual systems System validation Standards

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Comparison of Wrist and Forearm EMG for Multi-day Biometric Authentication

Comparison of Wrist and Forearm EMG for Multi-day Biometric ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Tingting Fu Ashirbad Pradhan Jiayuan He Chaoming He Ning Jiang School of Mechanical Engineering Southwest Jiaotong University Chengdu China Department of Systems Design Engineering Engineering Bionics Lab Faculty of Engineering University of Waterloo Waterloo Canada National Clinical Research Center for Geriatrics West China Hospital Sichuan University China Med-X Center for Manufacturing Sichuan University Chengdu China

Recently, electromyography (EMG) has been established as a promising new biometric trait that provides a unique dual mode security: biometrics and knowledge. For authentication that is used daily and long-term by general consumers, the wrist is a suitable location, which could be easily integrated into the existing form of smartwatches and fitness trackers. However, current EMG-based biometrics still follow the historical path of powered prosthetics research, where EMG signals were usually recorded from forearm positions. Moreover, the robustness of EMG processing algorithms across multiple days is still an open problem that needs to be addressed before for long-term reliable use. This study intends to investigate the difference in authentication performance between wrist and forearm EMG signals, in a within-day and two cross-day analyses. Our open dataset (GRABMyo dataset) was used to examine this difference, which contains forearm and wrist EMG data collected from 43 participants over three different days with long separation (Days 1, 8, and 29). The results showed wrist EMG signals led to at least comparable with forearm EMG signals in within-day Equal-error rate (EER). In cross-day analysis, the EER of the wrist EMG signals was higher than that of forearm signals. In general, the low median EER (<0.1) of wrist EMG in cumulative cross-day analysis demonstrates the promise of using wrist EMG signals for authentication in long-term applications.

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Guest Editorial

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Intelligent Automation & Soft Computing 2000年第3期6卷 171-172页

作者： Mohamed Kamel[a] & Mohammad Jamshidi[b] [a] Pattern Analysis and Machine Intelligence Lab Department of Systems Design Engineering University of Waterloo Waterloo Ontario N2L 3G1 Canada [b] Nasa Center for Autonomous Control Engineering and Department of Electrical and Computer Engineering The University of New Mexico Albuquerque NM 87131 USA http://ace.tmm.edu

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Genetic Algorithms for Lens design: a Review

arXiv

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arXiv 2018年

作者： Höschel, Kaspar Lakshminarayanan, Vasudevan TU Wien Karlsplatz 13 Wien1040 Austria University of Waterloo Theoretical and Experimental Epistemology Lab School of Optometry & Vision Science Depts. of Physics ECE and Systems Design Engineering WaterlooONN2L 3G1 Canada

Genetic algorithms (GAs) have a long history of over four decades. GAs are adaptive heuristic search algorithms that provide solutions for optimization and search problems. The GA derives expression from the biological terminology of natural selection, crossover, and mutation. In fact, GAs simulate the processes of natural evolution. Due to their unique simplicity, GAs are applied to the search space to find optimal solutions to various problems in science and engineering. Using GAs for lens design was investigated mostly in the 1990s, but were not fully exploited. But in the past few years there have been a number of newer studies exploring the application of GAs or hybrid GAs in optical design. In this paper we discuss the basic ideas behind GAs and demonstrate their application in optical lens design. Copyright © 2018, The Authors. All rights reserved.

关键词： Genetic algorithms

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Simultaneous induction of SSMVEP and SMR using a gaiting video stimulus: A novel hybrid brain-computer interface

arXiv

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arXiv 2019年

作者： Zhang, Xin Xu, Guanghua Ravi, Aravind Pearce, Sarah Jiang, Ning The Department of Systems Design Engineering Engineering Bionics Lab University of Waterloo WaterlooONN2L3G1 Canada School of Mechanical Engineering Xi'an Jiaotong University Xi'an Shaanxi710049 China Cognixion Inc. TorontoONM5H 1K5 Canada

We proposed a novel visual stimulus for brain-computer interface. The stimulus is in the form gaiting sequence of a human. The hypothesis is that observing such a visual stimulus would simultaneously induce 1) steady-state motion visual evoked potential (SSMVEP) in the occipital area, similarly to an SSVEP stimulus;and 2) sensorimotor rhythm (SMR) in the primary sensorimotor area, because such action observation (AO) could activate the mirror neuron system. Canonical correlation analysis (CCA) was used to detect SSMVEP from occipital EEG, and event-related spectral perturbations (ERSP) were used to identify SMR in the EEG from the sensorimotor area. The results showed that the proposed visual gaiting stimulus-induced SSMVEP, with classification accuracies of 88.9 ± 12.0% in a four-class scenario. More importantly, it induced clear and sustained event-related desynchronization/synchronization (ERD/ERS) in the EEG from the sensorimotor area, while no ERD/ERS in the sensorimotor area could be observed when the other two SSVEP stimuli were used. Further, for participants with sufficiently clear SSMVEP pattern (classification accuracy > 85%), the ERD index values in mu-beta band induced by the proposed gaiting stimulus were statistically different from that of the other two types of stimulus. Therefore, a novel BCI based on the proposed stimulus has potential in neurorehabilitation applications because it simultaneously has the high accuracy of an SSMVEP (~90% accuracy in a four-class setup) and the ability to activate sensorimotor cortex. And such potential will be further explored in future studies. Copyright © 2019, The Authors. All rights reserved.

关键词： Brain computer interface

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Toward the universal quantum cloner limit for designing compact photonic CNOT gate

arXiv

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arXiv 2019年

作者： Gueddana, Amor Lakshminarayanan, Vasudevan Green & Smart Communication Systems Lab Gres'Com Engineering School of Communication of Tunis Sup'Com University of Carthage Ghazela Technopark Ariana2083 Tunisia Theoretical & Experimental Epistemology Lab TEEL School of Optometry and Vision Science University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada Department of Physics Department of Electrical and Computer Engineering Department of Systems Design Engineering University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada

We suggest a near deterministic compact model of a photonic CNOT gate based on a quantum dot trapped in a double sided optical microcavity and a universal cloner. Our design surpasses the cloner optimal limit of 5/6 and we show that it provides fidelity around 91 % in the weak coupling regime. Copyright © 2019, The Authors. All rights reserved.

关键词： Semiconductor quantum dots

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Can a universal quantum cloner be used to design an experimentally feasible near-deterministic CNOT gate?

arXiv

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arXiv 2018年

作者： Gueddana, Amor Gholami, Peyman Lakshminarayanan, Vasudevan Theoretical and Experimental Epistemology Lab TEEL School of Optometry and Vision Science University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada Green and Smart Communication Systems Lab Gres'Com Engineering School of Communication of Tunis Sup'Com University of Carthage Ghazela Technopark Ariana2083 Tunisia Department of Physics Department of Electrical and Computer Engineering Department of Systems Design Engineering University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada

We propose a non-deterministic CNOT gate based on a quantum cloner, a quantum switch based on all optical routing of single photon by single photon, a quantum-dot spin in a double-sided optical microcavity with two photonic qubits, delay lines and other linear optical photonic devices. Our CNOT provides a fidelity of 78% with directly useful outputs for a quantum computing circuit and requires no ancillary qubits or electron spin measurements. Copyright © 2018, The Authors. All rights reserved.

关键词： Particle beams

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Texture classification in extreme scale variations using GANet

arXiv

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arXiv 2018年

作者： Liu, Li Chen, Jie Zhao, Guoying Fieguth, Paul Chen, Xilin Pietikäinen, Matti Center for Machine Vision and Signal Analysis University of Oulu Oulu90014 Finland Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing100190 China

Research in texture recognition often concentrates on recognizing textures with intraclass variations such as illumination, rotation, viewpoint and small scale changes. In contrast, in real-world applications a change in scale can have a dramatic impact on texture appearance, to the point of changing completely from one texture category to another. As a result, texture variations due to changes in scale are amongst the hardest to handle. In this work we conduct the first study of classifying textures with extreme variations in scale. To address this issue, we first propose and then reduce scale proposals on the basis of dominant texture patterns. Motivated by the challenges posed by this problem, we propose a new GANet network where we use a Genetic Algorithm to change the units in the hidden layers during network training, in order to promote the learning of more informative semantic texture patterns. Finally, we adopt a FV-CNN (Fisher Vector pooling of a Convolutional Neural Network filter bank) feature encoder for global texture representation. Because extreme scale variations are not necessarily present in most standard texture databases, to support the proposed extreme-scale aspects of texture understanding we are developing a new dataset, the Extreme Scale Variation Textures (ESVaT), to test the performance of our framework. It is demonstrated that the proposed framework significantly outperforms gold-standard texture features by more than 10% on ESVaT. We also test the performance of our proposed approach on the KTHTIPS2b and OS datasets and a further dataset synthetically derived from Forrest, showing superior performance compared to the state of the art. Copyright © 2018, The Authors. All rights reserved.

关键词： Textures

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