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IEEE Visualization Conference (IEEE VIS)

作者： Olson, Matthew L. Nguyen, Thuy-Vy Dixit, Gaurav Ratzlaff, Neale Wong, Weng-Keen Kahng, Minsuk Oregon State Univ Corvallis OR 97331 USA

ISBN: (纸本)9781665433358

Identifying covariate shift is crucial for making machine learning systems robust in the real world and for detecting training data biases that are not reflected in test data. However, detecting covariate shift is challenging, especially when the data consists of high-dimensional images, and when multiple types of localized covariate shift affect different subspaces of the data. Although automated techniques can be used to detect the existence of covariate shift, our goal is to help human users characterize the extent of covariate shift in large image datasets with interfaces that seamlessly integrate information obtained from the detection algorithms. In this paper, we design and evaluate a new visual interface that facilitates the comparison of the local distributions of training and test data. We conduct a quantitative user study on multi-attribute facial data to compare two different learned low-dimensional latent representations (pretrained ImageNet CNN vs. density ratio) and two user analytic workflows (nearest-neighbor vs. cluster-to-cluster). Our results indicate that the latent representation of our density ratio model, combined with a nearest-neighbor comparison, is the most effective at helping humans identify covariate shift.

关键词： Training Visualization Conferences Training data Data visualization Machine learning detection algorithms

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Database Matching Under Column Deletions

Database Matching Under Column Deletions

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IEEE International Symposium on Information Theory (ISIT)

作者： Bakirtas, Serhal Erkip, Elza NYU Dept Elect & Comp Engn New York NY 10003 USA

ISBN: (纸本)9781538682098

De-anonymizing user identities by matching various forms of user data available on the internet raises privacy concerns. A fundamental understanding of the privacy leakage in such scenarios requires a careful study of conditions under which correlated databases can be matched. Motivated by synchronization errors in lime indexed databases, in this work, matching of random databases under random column deletion is investigated. Adapting tools from information theory, in particular ones developed for the deletion channel, conditions for database matching in the absence and presence of deletion location information arc derived, showing that partial deletion information significantly increases the achievable database growth rate for successful matching. Furthermore, given a batch of correctly-matched rows, a deletion detection algorithm that provides partial deletion information is proposed and a lower bound on the algorithm's deletion detection probability in terms of the column size and the batch size is derived. The relationship between the database size and the batch size required to guarantee a given deletion detection probability using the proposed algorithm suggests that a batch size growing double-logarithmic with the row size is sufficient for a nonzero detection probability guarantee.

关键词： Data privacy Databases Tools Synchronization detection algorithms Information theory

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Evaluation of Communities from Exploratory Evolutionary Compression of Weighted Graphs

Evaluation of Communities from Exploratory Evolutionary Comp...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Rutkowski, Emilia Sargant, James Houghten, Sheridan Brown, Joseph Alexander Brock Univ Dept Comp Sci St Catharines ON Canada Innopolis Univ AI Games Dev Lab Innopolis Russia

ISBN: (纸本)9781728183923

Contact networks are used as a representation for the modeling of illness transmission. In this study, we represent not only the links of the transmissions but also utilize a weighted graph to represent the probability of transfer. These graphs can be large and complex when taking into account the number of contacts used in tracing. Compression of the graph allows for the development of community detection as well as providing a simpler graph. By examining the contact networks developed by an evolutionary algorithm for compression, it is discovered that the choice of fitness function and the appropriate weighting of edges leads to a different compressed graph, finding different connected communities;this is also true when compared to the communities identified by the Louvain community detection algorithm. This demonstrates the importance of considering weighting in contact networks, and suggests that in the future an understanding of the community structure should be utilized by public health officials.

关键词： Image edge detection Evolutionary computation Public healthcare detection algorithms

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Automatic segmentation for neonatal phonocardiogram 43

Automatic segmentation for neonatal phonocardiogram

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Gomez-Quintana, Sergi Shelevytsky, Ihor Shelevytska, Victoriya Popovici, Emanuel Temko, Andriy Univ Coll Cork Elect & Elect Engn Cork Ireland Kryvyi Rih Inst Econ Fac Informat Technol Kryvyi Rih Ukraine Dnipropetrovsk Med Acad Hlth Fac Postgrad Educ Dnepropetrovsk Ukraine

ISBN: (纸本)9781728111797

This work addresses the automatic segmentation of neonatal phonocardiogram (PCG) to be used in the artificial intelligence-assisted diagnosis of abnormal heart sounds. The proposed novel algorithm has a single free parameter - the maximum heart rate. The algorithm is compared with the baseline algorithm, which was developed for adult PCG segmentation. When evaluated on a large clinical dataset of neonatal PCG with a total duration of over 7h, an F1 score of 0.94 is achieved. The main features relevant for the segmentation of neonatal PCG are identified and discussed. The algorithm is able to increase the number of cardiac cycles by a factor of 5 compared to manual segmentation, potentially allowing to improve the performance of heart abnormality detection algorithms.

关键词： Heart rate Pediatrics Manuals Biology detection algorithms Phonocardiography

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A Benchmark for Spray from Nearby Cutting Vehicles

A Benchmark for Spray from Nearby Cutting Vehicles

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IEEE Intelligent Transportation Systems Conference (ITSC)

作者： Walz, Stefanie Bijelic, Mario Kraus, Florian Ritter, Werner Simon, Martin Doric, Igor Mercedes Benz AG Stuttgart Germany Princeton Univ Princeton NJ 08544 USA MESSRING Act Safety GmbH Gilching Germany

ISBN: (纸本)9781728191423

Current driver assistance systems and autonomous driving stacks are limited to well-defined environment conditions and geo fenced areas. To increase driving safety in adverse weather conditions, broadening the application spectrum of autonomous driving and driver assistance systems is necessary. In order to enable this development, reproducible benchmarking methods are required to quantify the expected distortions. In this publication, a testing methodology for disturbances from spray is presented. It introduces a novel lightweight and configurable spray setup alongside an evaluation scheme to assess the disturbances caused by spray. The analysis covers an automotive RGB camera and two different LiDAR systems, as well as downstream detection algorithms based on YOLOv3 and PV-RCNN. In a common scenario of a closely cutting vehicle, it is visible that the distortions are severely affecting the perception stack up to four seconds showing the necessity of benchmarking the influences of spray.

关键词： Laser radar Conferences Benchmark testing Distortion Safety detection algorithms Autonomous vehicles

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Survivalism: Systematic Analysis of Windows Malware Living-Off-The-Land 42

Survivalism: Systematic Analysis of Windows Malware Living-O...

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42nd IEEE Symposium on Security and Privacy (SP)

作者： Barr-Smith, Frederick Ugarte-Pedrero, Xabier Graziano, Mariano Spolaor, Riccardo Martinovic, Ivan Univ Oxford Oxford England Cisco Syst San Jose CA USA

ISBN: (纸本)9781728189345

As malware detection algorithms and methods become more sophisticated, malware authors adopt equally sophisticated evasion mechanisms to defeat them. Anecdotal evidence claims Living-Off-The-Land (LotL) techniques are one of the major evasion techniques used in many malware attacks. These techniques leverage binaries already present in the system to conduct malicious actions. We present the first large-scale systematic investigation of the use of these techniques by malware on Windows systems. In this paper, we analyse how common the use of these native system binaries is across several malware datasets, containing a total of 31,805,549 samples. We identify an average 9.41% prevalence. Our results show that the use of LotL techniques is prolific, particularly in Advanced Persistent Threat (APT) malware samples where the prevalence is 26.26%, over twice that of commodity malware. To illustrate the evasive potential of LotL techniques, we test the usage of LotL techniques against several fully patched Windows systems in a local sandboxed environment and show that there is a generalised detection gap in 10 of the most popular anti-virus products.

关键词： Privacy Systematics Malware Security detection algorithms

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A Review of the Commercially Available ECG detection and Transmission Systems-The Fuzzy Logic Approach in the Prevention of Sudden Cardiac Arrest

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MICROMACHINES 2021年第12期12卷 1489-1489页

作者： Lewandowski, Michal Natl Inst Cardiol Dept Arrhythmia 2 PL-04628 Warsaw Poland

Sudden cardiac death (SCD) constitutes a major clinical and public health problem, whose death burden is comparable to the current worldwide pandemic. This comprehensive review encompasses the following topics: available rescue systems, wearable electrocardiograms (ECG), detection and transmission technology, and a newly developed fuzzy logic algorithm (FA) for heart rhythm classification which is state-of-the art in the field of SCD prevention. Project "PROTECTOR", the Polish Rapid Transtelephonic ECG to Obtain Resuscitation for development of a rapid rescue system for patients at risk of sudden cardiac arrest (SCA), is presented. If a lethal arrhythmia is detected on the basis of FA, the system produces an alarm signal audible for bystanders and transmits the alarm message along with location to the emergency medical center. Phone guided resuscitation can be started immediately because an automated external defibrillator (AED) localization map is available. An automatic, very fast diagnosis is a unique feature of the PROTECTOR prototype. The rapid detection of SCA is based on a processor characterized by 100% sensitivity and 97.8% specificity (as measured in the pilot studies). An integrated circuit which implements FA has already been designed and a diagnosis is made within few seconds, which is extremely important in ischemic brain damage prophylaxis. This circuit could be implemented in smart implants (Sis).

关键词： sudden cardiac arrest sudden cardiac death out-of-hospital cardiac arrest ischemic brain damage prevention cardiopulmonary resuscitation electrocardiogram (ECG) fuzzy logic artificial intelligence detection algorithms

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Mixed Autonomous Supervision in Traffic Signal Control

Mixed Autonomous Supervision in Traffic Signal Control

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IEEE Intelligent Transportation Systems Conference (ITSC)

作者： Jayawardana, Vindula Landler, Anna Wu, Cathy MIT Lab Informat & Decis Syst Cambridge MA 02139 USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA MIT Dept Civil & Environm Engn Cambridge MA 02139 USA MIT Dept Civil & Environm Engn Lab Informat & Decis Syst Cambridge MA 02139 USA MIT Inst Data Syst & Soc Cambridge MA 02139 USA

ISBN: (纸本)9781728191423

Traffic signal control is a critical component for ensuring smooth traffic flows in city corridors. To this end, deep reinforcement learning (RL) agents have recently been proposed. These learned black-box policies may out-perform manually tuned policies on average, but they remain imperfect and come at a cost of how easy they are to interpret and supervise. These challenges hinder their adoption in real-world systems. To address the first challenge, this paper devises naturally interpretable decision tree policies that imitate expert deep neural network policies. To address supervision, we define a new formalization called Mixed Autonomous Supervision (MAS), which concerns integrating an imperfect policy into an existing supervision system. We propose a two-part supervision model with online automated supervision and offline human supervision to implement MAS. We present a novel blind spot detection algorithm for decision tree policies to encourage the safe transfer of control to an automated fail-safe policy (online supervision) and an interactive dashboard DTLight for offline human supervision. We show the decision tree policies are just as performant as the RL policies, and the proposed supervision model has a significant benefit in scenarios derived from real traffic situations.

关键词： Deep learning Costs Conferences Urban areas Reinforcement learning Decision trees detection algorithms

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Self-calibrated dense 3D sensor using multiple cross line-lasers based on light sectioning method and visual odometry

Self-calibrated dense 3D sensor using multiple cross line-la...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Nagamatsu, Genki Takamatsu, Jun Iwaguchi, Takafumi Thomas, Diego Kawasaki, Hiroshi Kyushu Univ Dept Adv Informat Technol Motooka 744 Japan Nara Inst Sci & Technol Grad Sch Informat Sci Ikoma Nara Japan

ISBN: (纸本)9781665417143

Among various 3D capturing systems, since the system with line lasers based on the light sectioning method is simple and accurate, it has widely attracted many developers and used for many purposes. In addition, there is no need to synchronize the camera and the laser and also the configuration of the camera and the lasers is flexible, and thus, the system can be used for extreme conditions, such as underwater. There are two open problems for the system. The first problem is a low density of the 3D shape obtained from a single image, i.e., just several curves. The second problem is the accuracy of line detection in the wild. In this paper, we propose a self-calibration method using visual odometry (VO) to bundle a large number of frames to increase the density to solve the first problem. We also propose a robust line detection algorithm using CNN to solve the second problem. Comparative experiments prove the effectiveness of our proposed method. In addition, the system was tested in the extreme condition for demonstration.

关键词： Three-dimensional displays Shape Lasers Cameras Multitasking Synchronization detection algorithms

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Disconnection-aware Attack detection in Networked Control Systems 21st

Disconnection-aware Attack Detection in Networked Control Sy...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Sasahara, Hampei Ishizaki, Takayuki Imura, Jun-ichi Sandberg, Henrik KTH Royal Inst Technol SE-10044 Stockholm Sweden Tokyo Inst Technol Tokyo 1528552 Japan

This study deals with security issues in dynamical networked control systems. The goal is to establish a unified framework of the attack detection stage, which includes the four processes of monitoring the system state, making a decision based on the monitored signal, disconnecting the corrupted subsystem, and operating the remaining system during restoration. This paper, in particular, considers a disconnection-aware attack detector design problem. Traditionally, observer-based attack detectors are designed based on the system model with a fixed network topology and cannot cope with a change of the topology caused by disconnection. The disconnection-aware design problem is mathematically formulated and a solution is proposed in this paper. A numerical example demonstrates the effectiveness of the proposed detector through an inverter-based voltage control system in a benchmark model. Copyright (C) 2020 The Authors.

关键词： detection algorithms distributed detection large-scale systems networks on-line security analysis system security

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