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arXiv 2021年

作者： Díaz Ferreyra, Nicolás E. Hecking, Tobias Aïmeur, Esma Heisel, Maritta Hoppe, H. Ulrich University of Duisburg-Essen Department of Computer Science and Applied Cognitive Science Duisburg47057 Germany Institute for Software Technology Intelligent and Distributed Systems German Aerospace Center Köln51147 Germany Department of Computer Science and Operations Research University of Montréal Canada

Access-Control Lists (ACLs) (a.k.a. "friend lists") are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as "untrusted") to the emerging communities inside the user’s ego-network. However, unlike users’ access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination. Copyright © 2021, The Authors. All rights reserved.

关键词： Access control

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Multi-agent systems with memories under DoS attacks

Multi-agent systems with memories under DoS attacks

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International Conference on Control, Automation, robotics and Vision (ICARCV)

作者： Ricardo Almeida Ewa Girejko Luís Machado Agnieszka B. Malinowska Natália Martins Department of Mathematics Center for Research and Development in Mathematics and Applications University of Aveiro Portugal Faculty of Computer Science Bialystok University of Technology Białystok Poland Institute of Systems and Robotics - University of Coimbra Coimbra Portugal Department of Mathematics University of Trás-os-Montes e Alto Douro (UTAD) Vila Real Portugal

ISBN: (纸本)9781665476881

The paper is devoted to multi-agent systems with memories under denial-of-service (DoS) attacks. The memory is represented by the fractional derivative that appears in the dynamics of the considered systems. The problem that we aim to address is, under which conditions the system continues to be stable or is in consensus mode, despite DoS attacks. Solutions to these problems are proposed in the presented theorems. We strengthen the theoretical considerations with numerical simulations.

关键词： Linear systems Sufficient conditions Automation Denial-of-service attack Numerical simulation Stability analysis Robots

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Development of a VS ultrasound diagnostic system with image evaluation functions

Development of a VS ultrasound diagnostic system with image ...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Jiayi Zhou Norihiro Koizumi Yu Nishiyama Ryosuke Tsumura Hiroyuki Tsukihara Naoki Matsumoto Department of Mechanical and Intelligent Systems The University of Electro-Communications Tokyo Japan Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology Tokyo Japan Graduate School of Engineering The University of Tokyo Tokyo Japan Department of Gastroenterology and Hepatology Nihon University Tokyo Japan

ISBN: (纸本)9781665492331

An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross-section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Two different image processing methods (BiSeNet V2) were used to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50) in developed system.

关键词： Deep learning Visualization Ultrasonic imaging Image processing Medical services Probes Servomotors

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Synthesizing skeletal motion and physiological signals as a function of a virtual human’s actions and emotions

Synthesizing skeletal motion and physiological signals as a ...

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2021 SIAM International Conference on Data Mining, SDM 2021

作者： Banerjee, Bonny Kapourchali, Masoumeh Heidari Baruah, Murchana Deb, Mousumi Sakauye, Kenneth Olufsen, Mette Institute for Intelligent Systems Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Texas A&M AgriLife Research and Extension Center TempleTX76502 United States Department of Psychiatry University of Tennessee Health Science Center MemphisTN38163 United States Department of Mathematics North Carolina State University RaleighNC27695 United States

ISBN: (纸本)9781611976700

Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareable data from an individual as a function of his actions, interactions and emotions in a care facility or at home, with no risk of confidentiality breach or privacy invasion. In this paper, we develop for the first time a system consisting of computational models for synchronously synthesizing skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals as a function of an open-ended set of actions and emotions. Our experimental evaluations, involving user studies, benchmark datasets and comparison to findings in the literature, show that our models can generate skeletal motion and physiological signals with high fidelity. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data, and may be used as a training tool for ML practitioners and healthcare professionals. © 2021 by SIAM.

关键词： Actions Electrocardiogram Emotions Signal synthesis Skeletal motion Skin conductance

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A Spherical Harmonics-LSB-quantification Adaptive Watermarking Approach for 3D Multiresolution Meshes Security 19th

A Spherical Harmonics-LSB-quantification Adaptive Watermarki...

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19th International Conference on computer Analysis of Images and Patterns, CAIP 2021

作者： Sayahi, Ikbel Jallouli, Malika Mabrouk, Anouar Ben Amar, Chokri Ben Mahjoub, Mohamed Ali LATIS Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d’Ingénieurs de Sousse Sousse4023 Tunisia University of SFax Research Groups on Intelligent Machines Laboratory Private National Engineering School of Monastir Monastir Tunisia Department of Mathematics Higher Institute of Applied Mathematics and Computer Science University of Kairouan Kairouan3100 Tunisia Department of Mathematics Faculty of Science University of Tabuk Tabuk Saudi Arabia College of Computers and Information Technology Taif University Taif Saudi Arabia

ISBN: (纸本)9783030891305

Since the release of the first 3D watermarking algorithm in the last decade, 3D meshes applying such an algorithm have been growing up with a diversity of techniques during the embedding of information into meshes. The main objective is always to secure data shared by remote users. The originality of the present work is issued from the use of spherical harmonics to transform the mesh to the frequency domain to be watermarked, and the entropy concept to estimate the optimal order of the reconstruction. Our algorithm includes three parts. The first is the data pre-processing based on a combination of a signature generated by the well-known Secure Hash Algorithm, and a mesh description, subject to a convolutional error correcting code. The second step is the mesh treatment, consisting in extracting spherical harmonics coefficients to be watermarked. Finally, the third part consists in inserting data into coefficients using LSB and quantification method, and next to reconstruct the watermarked mesh. The experimentation of our approach has shown a high insertion rate, while maintaining the mesh quality. It is also robust against the most popular attacks. Our results showed that the present approach improves the existing works. © 2021, Springer Nature Switzerland AG.

关键词： Entropy

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Scalable marginal likelihood estimation for model selection in deep learning

arXiv

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arXiv 2021年

作者： Immer, Alexander Bauer, Matthias Fortuin, Vincent Rätsch, Gunnar Khan, Mohammad Emtiyaz Department of Computer Science ETH Zurich Switzerland Max Planck Institute for Intelligent Systems Germany University of Cambridge United Kingdom RIKEN Center for Advanced Intelligence Project Japan

Marginal-likelihood based model-selection, even though promising, is rarely used in deep learning due to estimation difficulties. Instead, most approaches rely on validation data, which may not be readily available. In this work, we present a scalable marginal-likelihood estimation method to select both hyperparameters and network architectures, based on the training data alone. Some hyperparameters can be estimated online during training, simplifying the procedure. Our marginal-likelihood estimate is based on Laplace’s method and Gauss-Newton approximations to the Hessian, and it outperforms cross-validation and manual-tuning on standard regression and image classification datasets, especially in terms of calibration and out-of-distribution detection. Our work shows that marginal likelihoods can improve generalization and be useful when validation data is unavailable (e.g., in nonstationary settings). Copyright © 2021, The Authors. All rights reserved.

关键词： Network architecture

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Environment Prediction from Sparse Samples for Robotic Information Gathering

Environment Prediction from Sparse Samples for Robotic Infor...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jeffrey A. Caley Geoffrey A. Hollinger Computer Science Department Pacific Lutheran University Tacoma Washington Collaborative Robotics and Intelligent Systems (CoRIS) Institute Oregon State University Corvallis Oregon

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Robots often require a model of their environment to make informed decisions. In unknown environments, the ability to infer the value of a data field from a limited number of samples is essential to many robotics applications. In this work, we propose a neural network architecture to model these spatially correlated data fields based on a limited number of spatially continuous samples. Additionally, we provide a method based on biased loss functions to suggest future areas of exploration to minimize reconstruction error. We run simulated robotic information gathering trials on both the MNIST hand written digits dataset and a Regional Ocean Modeling System (ROMS) ocean dataset for ocean monitoring. Our method outperforms Gaussian process regression in both environments for modeling the data field and action selection.

关键词： Robots Data models Oceans Neural networks Convolution Network architecture Logic gates

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Benchmarking unsupervised object representations for video sequences

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 8253-8313页

作者： Marissa A. Weis Kashyap Chitta Yash Sharma Wieland Brendel Matthias Bethge Andreas Geiger Alexander S. Ecker Institute of Computer Science University of Göttingen Germany and Institute for Theoretical Physics University of Tübingen Germany Department of Computer Science University of Tübingen Germany Institute for Theoretical Physics University of Tübingen Germany Institute for Theoretical Physics University of Tübingen Germany and Bernstein Center for Computational Neuroscience Tübingen Germany Department of Computer Science University of Tübingen Germany and Max Planck Institute for Intelligent Systems Tübingen Germany Institute of Computer Science University of Göttingen Germany and Campus Institute Data Science Göttingen Germany and Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Perceiving the world in terms of objects and tracking them through time is a crucial prerequisite for reasoning and scene understanding. Recently, several methods have been proposed for unsupervised learning of object-centric representations. However, since these models were evaluated on different downstream tasks, it remains unclear how they compare in terms of basic perceptual abilities such as detection, figure-ground segmentation and tracking of objects. To close this gap, we design a benchmark with four data sets of varying complexity and seven additional test sets featuring challenging tracking scenarios relevant for natural videos. Using this benchmark, we compare the perceptual abilities of four object-centric approaches: ViMON, a video-extension of MONet, based on recurrent spatial attention, OP3, which exploits clustering via spatial mixture models, as well as TBA and SCALOR, which use explicit factorization via spatial transformers. Our results suggest that the architectures with unconstrained latent representations learn more powerful representations in terms of object detection, segmentation and tracking than the spatial transformer based architectures. We also observe that none of the methods are able to gracefully handle the most challenging tracking scenarios despite their synthetic nature, suggesting that our benchmark may provide fruitful guidance towards learning more robust object-centric video representations.

关键词： unsupervised learning object-centric representations benchmark tracking segmentation

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DISCODVT: Generating long text with discourse-aware discrete variational transformer

arXiv

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arXiv 2021年

作者： Ji, Haozhe Huang, Minlie Department of Computer Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center Information Science and Technology Tsinghua University Beijing100084 China

Despite the recent advances in applying pretrained language models to generate high-quality texts, generating long passages that maintain long-range coherence is yet challenging for these models. In this paper, we propose DISCODVT, a discourse-aware discrete variational Transformer to tackle the incoherence issue. DISCODVT learns a discrete variable sequence that summarizes the global structure of the text and then applies it to guide the generation process at each decoding step. To further embed discourse-aware information into the discrete latent representations, we introduce an auxiliary objective to model the discourse relations within the text. We conduct extensive experiments on two open story generation datasets and demonstrate that the latent codes learn meaningful correspondence to the discourse structures that guide the model to generate long texts with better long-range coherence. © 2021, CC BY.

关键词：

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Detecting Worker Attention Lapses in Human-Robot Interaction: An Eye Tracking and Multimodal Sensing Study

arXiv

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arXiv 2023年

作者： Dai, Zhuangzhuang Park, Jinha Kaszowska, Aleksandra Li, Chen Dept. of Computer Science Engineering and Physical Science School of Engineering and Applied Science Aston University BirminghamB4 7ET United Kingdom SDU Robotics The Maersk Mc Kinney Moller Institute University of Southern Denmark Campusvej 55 Odense5230 Denmark the Department of Electronic Systems Aalborg University Fredrik Bajers Vej 7 B5-215 Aalborg9220 Denmark Department of Materials and Production Aalborg University Fibigerstræde 16 Aalborg9220 Denmark

The advent of industrial robotics and autonomous systems endow human-robot collaboration in a massive scale. However, current industrial robots are restrained in co-working with human in close proximity due to inability of interpreting human agents’ attention. Human attention study is non-trivial since it involves multiple aspects of the mind: perception, memory, problem solving, and consciousness. Human attention lapses are particularly problematic and potentially catastrophic in industrial workplace, from assembling electronics to operating machines. Attention is indeed complex and cannot be easily measured with single-modality sensors. Eye state, head pose, posture, and manifold environment stimulus could all play a part in attention lapses. To this end, we propose a pipeline to annotate multimodal dataset of human attention tracking, including eye tracking, fixation detection, third-person surveillance camera, and sound. We produce a pilot dataset containing two fully annotated phone assembly sequences in a realistic manufacturing environment. We evaluate existing fatigue and drowsiness prediction methods for attention lapse detection. Experimental results show that human attention lapses in production scenarios are more subtle and imperceptible than well-studied fatigue and drowsiness. © 2023, CC BY.

关键词： Eye tracking

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