Human detection deep learning

  • R-GBD Person Tracking is a ROS framework for detecting and tracking people from Forked from JaideepBedarkar/Human-following-Bot-using-Deep-Learning   Sep 26, 2018 Recently, deep neural network models have started delivering on their . . U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. This technique involves feeding your model large volumes of data, but it requires less feature engineering than a linear regression model would. Learning & Training. Request PDF on ResearchGate | Human detection in laser range data using deep learning and 3-D objects | A novel and intelligent method for human detection using laser is presented in this paper. Deep Learning based Edge Detection in OpenCV: OpenCV has integrated a deep learning based edge detection technique in its new fancy DNN module. Sc in chemical engineering Emotion Detection from Text Using Deep Learning Katz et al. Esther Rietmann, Tech and Machine Learning at Tryolabs. Region Proposal Scalable Object Detection Using Deep Neural Networks [cvpr14] Deep learning for sensor-based human activity recognition. Microsoft Research. This repository contains implementations of YOLOv2 in Keras. Here's an introduction to the different techniques used in Human Pose Estimation based on Deep Learning. 4 over Python 3. To close the gap, we propose a new deep learning framework for person search. g. It successfully integrates a joint learning between foreground and  Human detection is essential step for establishing interactions between humans . Ouyang and Xiaogang Wang, “Joint Deep Learning for Pedestrian Detection,” IEEE ICCV 2013. 1 day ago · “Deep Learning, while very advanced, is also the most resource-intensive, needing thousands of times the amount of data and computation required for simpler techniques, but also providing human-level performance on an entirely new class of problems such as object detection, handwriting decoding and so on,” Adams says. The current state of art in human detection is RetinaNet. There are many resources for learning how to use Deep Learning to process imagery. A 2019 guide to Human Pose Estimation with Deep Learning by Sudharshan Chandra Babu 3 months ago 14 min read Human Pose estimation is an important problem that has enjoyed the attention of the Computer Vision community for the past few decades. A. Learn about the relationship between deep learning vs. Abstract. The object detection network is trained with our dataset and presents preliminary potential for deep learning based approaches in underwater applications. machine learning and how they fit into the category of artificial intelligence. By establishing automatic, mutual interac-tion among components, the deep model achieves a 9% re-duction in the average miss rate compared with the cur-rent best-performing pedestrian detection approaches on the largest Caltech benchmarkdataset. Feb 27, 2019 in computer vision, including human detection in video, human pose have also applied deep learning to human action recognition. Skin cancer, the most common human malignancy 1,2,3, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. A dermatologist uses a dermatoscope, a type of handheld microscope, to look at skin. loss in average miss rate (42%). Deep Learning based Human Detection ABOUT Following the improvements in deep neural networks, state-of-the-art networks have been proposed for human segmentation using point clouds captured by light detection and ranging. Use the neural network to identify various feature value (weight = x, height = y,etc) from neural network. While the developers have tested the framework on all sorts of object images – like kangaroo detection, self-driving car, red blood cell detection, etc. ) or parts. Oliveira hand a tool to its human counterpart must be able to detect where the hands are to  Dec 20, 2017 We just wanted to share our experience with deploying a custom Deep learned neural network model trained using Tensorflow, to detect  sults obtained by hand detector, we propose a deep learn- ing based skin face detection, hand gesture tracking, human computer in- teraction and  The closest work is probably on "pedestrian detection". But with deep-learning comes great responsibility. 1 Gun detection The rst and traditional sub-area in gun detection focuses on detecting con- One of the first advances in using deep learning for object detection was OverFeat from NYU published in 2013. 4 along with 10000 images of human bodies to train a Inception Faster-CNN DeepNet. Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. Deep learning learns through an artificial neural network that acts very much like a human brain and allows the machine to analyze data in a structure very much as humans do. However, it's critical to be able to use and automate machine-based object detection to solve real-world problems. Through the Face2Gene app, facial recognition software is being combined with machine learning to help clinicians diagnose rare diseases (in this case, from facial dysmorphic features). mix of object-oriented programming can sharpen your deep learning prototype. Caffe-based face detector can be   Oct 5, 2018 Convolutional neural networks (CNN) have enabled significant improvements With the deep VGG16 model, our pedestrian detection method achieves by significant intra-class variability since human tend to have greatly  This document describes the Human Presence Detection Machine Learning Neural Network reference design. due to the rise of machine learning and deep learning algorithms. And all three are part of the reason why AlphaGo trounced Lee Se-Dol. The project implements an R-CNN in order to detect the action sequence of a hand-gun being drawn. Use machine learning to train deep neural net to identify feature value. An Online Instance Match-ing (OIM) loss function is proposed to train the network ef- Object detection using Deep Learning : Part 7 A Brief History of Image Recognition and Object Detection Our story begins in 2001; the year an efficient algorithm for face detection was invented by Paul Viola and Michael Jones. Human detection API ( Deep Learning ) on SAP Cloud foundry This is our first step towards deploying a custom trained Deep Learning model on the SAP Cloud foundry and we are really enlightened to see that SAP Cloud Foundry is a great platform to deploy such applications. , a deep convolutional multi-layer neural network) and that you can use data fill in the missing parameters. Deep learning for automatic detection and classification of microaneurysms, hard and soft exudates, and hemorrhages for diabetic retinopathy diagnosis. However, very few resources exist to demonstrate how to process data from other sensors such as acoustic, seismic, radio, or radar. 5. Mucientes and Bugarı introduced pattern classifier for learning the spatial temporal Analysis and Machine Intelligence, IEEE Transactions on, 11(7), 674- 693. Human Object Detection in Forest with Deep Learning based on Drone's Vision. Introduction One such application is human activity recognition (HAR) using data collected from smartphone’s accelerometer. 2. Pedestrian detection has been an important problem for decades, given its relevance to a number of . We introduce DensePose-COCO, a large-scale ground-truth dataset with image-to-surface correspondences manually annotated on 50K COCO images. 3 applied human detection by analyzing . edu Abstract The aim of this project is to detect the fall of an individual in indoor environments by monitoring Wi-Fi Deep learning methods have achieved great successes in pedestrian detection, owing to its ability to learn dis- criminative features from raw pixels. In the domain of Artificial Intelligence, deep learning is part of a broader family of machine learning methods based on deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks that have been applied to fields including computer vision, medical image analysis, histopathological diagnosis, with Signal Detection Using Deep Learning. One is the ACF [6], and the other is Faster-RCNN (CNN) [27] with ResNet-50, which is equivalent to our framework but with- out the identification task. The dataset was augmented on the fly using random crop, flips, lightning processes. Deep learning techniques for state-of-the-art object detection systems are assessed in this paper. CNN was also utilized in driving assistance system to detect humans [ 15 ]. As a result we will be able to design products that will quickly and accurately detect objects within baggage and parcels, improving safety and reducing smuggling. 24, 2014 Emotion Detection from Text Using Deep Learning. Its applications range from healthcare to security (gait analysis for human identification, for instance). In this post, I like to investigate this dataset and at least propose a baseline method with deep learning. Deep learning algorithm does as well as dermatologists in identifying skin cancer. Real-time object detection with deep learning and OpenCV. Yonglong Tian1,3. In recent years, motion recognition based on RGB-D data has attracted much attention. Here, these limitations are addressed. W. This process may not scale well, especially in regions where the right expertise is hard to find. It is a next generation, fully autonomous, self-learning and intelligent "artificial neural network" system based on layered algorithms and raw data, with the highest threat detection and lowest false positive rates in the cyber security and machine learning market. Hand-crafting features in a specific application area require very good domain knowledge. “A human will Use of human participants. It has many applications, including human action recognition, human-computer interaction, animation, etc. Aug 20, 2018 To detect every person in the image, most answers to the questions above are datasets are too small to train deep learning models. PDF | Domain Name Service is a central part of Internet regular operation. Wang, "A Discriminative Deep Model for Pedestrian Detection with Occlusion Handling,“ CVPR 2012. We verify that our dataset suits deep learning model in underwater environ IRJET- Comparative Analysis of Video Processing Object Detection. Guest lecture for UW CSE 455. For a learning-from-demonstration task, one could take advantage of the high level description of human parts. complementary parts (components of human body) from a large part pool. Deep Learning — A Technique for Implementing Machine Learning Herding cats: Picking images of cats out of YouTube videos was one of the first breakthrough demonstrations of deep learning. Specifically, you learned: Activity recognition is the problem of predicting the movement of a person, often indoors, based on sensor data, such as an accelerometer in a smartphone. 1(a)). image transformations to simulate the human visual system by using the  Computer Vision: Is there any open source for human detection algorithm in . Deep learning allows techniques which understand the semantics and structure of the sentences. Identifying and addressing the limitations of deep learning software. Human pose estimation for care robots using deep learning. . Human Fall Detection in Indoor Environments Using Channel State Information of Wi-Fi Signals Sankalp Dayal, Hirokazu Narui, Paraskevas Deligiannis {sankalpd, hirokaz2, pdelig}@stanford. For machines, the task is much more difficult. Face detection is the first component to localize faces, after that comes facial landmark tracking, then AU classification and finally emotion classification. Deep learning software brings enormous potential when it comes to the efficiency and accuracy involved in various inspection processes, but it still has its limitations. For accurate detection, deep learning requires a lot of training data and it has so far been used only in high resolution images. From a technical perspective, so far we experimented with Face detection using the following deep learning approaches: Face detection using a segmentation based approach; Face detection using Region Proposal Networks (RPN) For both approaches, we observed clear improvements of deep learning over our previous algorithms, in which we used Deep Learning architecture: We used Tensorflow 1. Pose Estimation is a general problem in Computer Vision where we detect the position and orientation of an object. Today’s blog post is broken into two parts. Moreover, deep models only require a single frame to classify the optical flow patches as human or nonhuman (i. [1] Note that prepro- cessing, e. Another algorithmic approach from the early machine-learning crowd, artificial neural networks, came and mostly went over the decades. The overall proposed multistage architecture is fully differentiable and therefore can be trained in an end-to-end fashion using backpropagation. S denotes the step size, whereas D refers to the number of convolutional kernels or units per layer. (iv) The proposed deep models are robust against various activities, positions, orientations, viewpoints, cloth color, scale, and altitudes. Thus, malaria detection is definitely an intensive manual process which can perhaps be automated using deep learning which forms the basis of this article. In addition, we propose a simple pose estimation network. ages. ) is useful, Human motion recognition is one of the most important branches of human-centered research activities. ” Recurrent Neural Network Models Recurrent neural networks, or RNNs for short, are a type of neural network that was designed to learn from sequence data, such as sequences of observations over time, or a sequence of words in a sentence. There are several techniques proposed in the literature for HAR using machine learning (see ) The performance (accuracy) of such methods largely depends on good feature extraction methods. This reference design can be implemented on   Convolutional neural networks (CNNs, or ConvNets) are essential tools for deep learning, and are especially useful for image classification, object detection,  May 21, 2018 approach combines computer vision with neural networks to detect a human Keywords: computer vision, machine learning, human detection,  Aug 16, 2018 Human features as objects of study have been widely used in various machine learning applications — be it face detection, video surveillance  Sep 24, 2018 We wished to explore whether machine learning algorithms using Relative Synonymous Codon Usage frequency (RSCU) could improve the  Aug 13, 2018 Learn how to detect objects in single video frames from camera feeds with Keras, OpenCV, and ImageAI. , human detection). Deep learning Object Detection Using Deep Learning For a human, it's relatively simple to find an object, like a car or a face, in an image. k. With a human in the loop, these approaches fail to take advantage of a central advantage of deep learning, that is, the ability to learn from the sperm image data afresh, without the constraints Deep Learning is a subfield of Machine Learning. A CNN, a type of deep learning method, has recently become a very  Deep Learning for Human Part Discovery in Images. This trend started with AlexNet which won  Aug 3, 2018 It also compares the performance of different Object Detection models using Anyone familiar with Deep Learning would know that image classifiers time, for Humans, Traditional Computer Vision (CV) and Deep Learning. of cascade classifiers with the accuracy of deep neural networks. 1. The reason for its importance is the abundance of applications that can benefit from such a technology. 3 was officially released, it has highly improved deep neural networks (dnn) module. For example, in the problem of face pose estimation (a. Deep Learning emphasizes the kind of model you might want to use (e. Human Detection using Tensorflow Object Detection API TensorFlow ™ is an open-source API from Google , which is widely used for solving machine learning tasks that involve Deep Neural Networks . Histograms of oriented gradients for human detection. Abstract: In the past decade, various new and impressive applications have  Real-Time Human Detection for Aerial Captured Video Sequences via Deep On the other hand, the proposed feature learning approaches are cheaper and  Aug 31, 2018 2. Object detection, deep learning, and R-CNNs.  Connectable to the third-party network cameras. deep learning framework and propose a new deep network architecture1. Aug 27, 2018 You can take almost any industry and person detection will arise. e. Deep learning frameworks and services available for object detection are also enunciated. 5–7 Xu et al. As an example, for care at nursing homes and other such facilities, it is anticipated that robots will check the condition of the residents while patrolling the facility. CVPR,. Deep learning is a form of machine learning that is inspired by the structure of the human brain and is particularly effective in feature detection. His background is in chemical engineering with a B. They proposed a multi-scale sliding window algorithm using Convolutional Neural Networks (CNNs). It includes 404351 question pairs with a label column indicating if they are duplicate or not. The seminal VeryFast method [4] runs at 100 FPS but with even further loss in miss rate. So far we’ve been approaching the emotion recognition problem by explicitly dividing our system into different components and training a deep learning model for each one. The representation is based on the words’ arrangements and their interdependencies. , elbow, wrist, etc. Compared with the traditional method, the deep learning neural network has the human detection, temporal consistency, deep learning, thermal images  Deep Neural Networks (DNNs) have recently shown outstanding performance on image classification . Object detection is a computer technology related to computer vision and image processing On the other hand, deep learning techniques that are able to do end-to-end object "Histograms of oriented gradients for human detection" (PDF ). Compared with the traditional method, the deep learning neural network has the advantages of shorter computing time, higher accuracy and easier operation. Therefore, deep learning method has been widely used in object detection. deep learning/neural net techniques, this paper has: Ol-mos, Tabik, and Herrera investigate automatic gun detec-tion in surveillance videos, triggering an alarm if the gun is detected (Automatic Handgun Detection Alarm in Videos Using Deep Learning) [6]. person, bicycle, car, motorcycle, airplane, Nov 29, 2017 of interest (ROI). 14 . The ensemble was chosen to maximize complementary diversity between the models. Human Body Detection. Such classifications are essential for designing the detection techniques and algorithms. Collection of papers and other resources for object detection and tracking using deep learning. pytorch. If you do this successfully, you are succeeded in your task. Such importance has made it a common target of different malicious behaviors such as the application of Domain Generation Deep learning methods have achieved great successes in pedestrian detection, owing to its ability to learn dis-criminative features from raw pixels. They used a human engineered ensemble of Faster RCNN with Inception Resnet v2 and Resnet 101 architectures. Ouyang and X. 5 Deep Learning for People Detection. learning from demonstration and human robot handovers can also benefit from accurate human part segmentation. To address this ambiguity, this work jointly op- Machine learning rivals human skills in cancer detection April 22, 2016 Two announcements yesterday (April 21) suggest that deep learning algorithms rival human skills in detecting cancer from ultrasound images and in identifying cancer in pathology reports. Deep Learning Strong Parts for Pedestrian Detection. 2 A general human detection framework. For example, when Google DeepMind’s AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go earlier this year, the terms AI, machine learning, and deep learning were used in the media to describe how DeepMind won.  Up to 32-ch IP cameras can be connected. Oct 11, 2018 In short, these are powerful deep learning algorithms. With OpenCV you can perform face detection using pre-trained deep learning face detector model which is shipped with the library. However, to estimate human body pose, deep learning approach to predict a single class label per image has to be supported by a high resolution semantic segmentation output. Abstract: Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. Beside the proposed method, it includes some examples showing how to use Pandas, Gensim, Spacy and Keras. The algorithm for deep learning may represent the entire sentence. Deep learning, aka deep “Imagine you have an automated threat-detection system on the bridge of your ship and it picks up a small object on the horizon,” Mittu says. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. Human Pose Estimation is one of the main research areas in computer vision. For the full code you check Github. This article describes how deep learning solves scenarios, such as fraud detection, voice and facial recognition, sentiment analytics, and time series forecasting. The google object detection team were kind enough to hold a talk about how they won 1st place in COCO 2016. In this system, automatic feature learning via fast deep network cascades was used to perform human detection.  Deep learning-based analysis: re-recognition of the human body target in behavior detection to filter false alarm. Each part could be used as an explicit mapping between the human and joints of the robot for learning control actions. Computer scientists at Stanford have created an artificially intelligent diagnosis algorithm for skin cancer that matched the performance of board-certified dermatologists.   Feb 12, 2019 How Machine Learning Improves Image Detection. Object detection, deep learning, and R-CNNs Partly from Ross Girshick Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05. Dec 31, 2018 The main contributions of this paper are a novel and real-time Deep Learning person detection and a standardization of personal space, that  It involves the process of tracking an object which could be a person, ball or a car box detections in advance so tracker needs to be combined with a detector. Static Detection. iDS-9600NXI-I8/16S DeepInmind SERIES NVR. Here we apply deep learning for body part detection in low resolution cases. • Accurate human body detection: the Deep Learning technology dramatically increases the accuracy of intrusion and eliminates the influences from animal, shaking leaves and etc. Patient photos are analyzed using facial analysis and deep learning to detect phenotypes that correlate with rare genetic diseases. Instead of breaking it down into two separate tasks—pedestrian detection and person re-identification, we jointly handle both aspects in a single convolutional neural network. The rst addresses gun detection using clas-sical methods and the second focuses on improving the performance of object detection using deep CNNs. Deep Learning architecture: We used Tensorflow 1. Idiap is performing active research on human activity analysis from multiple sensors. In this blog post, we will introduce some basic methods for utilizing a Convolutional Neural Network (CNN) to process Radio Frequency (RF) signals. version;; a human detection algorithm dedicated to videos captured by static cameras. When OpenCV 3. Each object in the image , from a person to a kite, have been located and identified with  The primary contributor to this module was Aleksandr Rybnikov, and Rybnikov included accurate, deep learning face detector. " Moving into the future, Lee and his colleagues would like to explore how this method will work on data types beyond human speech and optical images, such as physical environment sensor data and 1 day ago · The study, involving almost 181,000 patients and published in The Lancet, is the first to use deep learning to identify patients with potentially undetected atrial fibrillation and had an overall its application to object detection and pose estimation. activity detection, as well as reduced resources and energy needs. , they have released the pretrained model for raccoon detection. This entry was posted in C++, Computer Vision, Image Processing, Machine Learning, Tutorials and tagged deep learning, human detection, openpose, Pose Estimationing, Sports analysis on May 17, 2018 by admin. A challenging task in the past was detection of faces and their features like eyes, nose, mouth and even deriving emotions from their shapes. View For example, in , it is shown that deep learning is able to successfully incorporate both RGB and motion features for the task of human body pose detection in video. Ross Girshick. YOLO is an ultra popular object detection framework for deep learning applications. Figure 2: The architecture of the baseline deep network for pedestrian detection. Transfer learning is a technique that optimizes a lot of this work by taking a fully trained model for a set of categories like ImageNet and retrains from the existing weights for new classes. Gabriel L. Zeng, W. Jointing training with periodical intermediate supervision replenishes gradients and guides the network to produce increasingly accurate belief maps Deep Learning and Human Brain Deep learning is an imitation of the human brain almost in the aspect of precision and speed. [2] proposed the SWAT system where they mapped the words and each scored each word according to multiple labels. IRJET Journal. Convolutional Neural Networks (CNN) is inspired from brain cortex. Please check their linked slides above. Video Input and Transmission. Deep learning is a tricky field to get acclimated with, that’s why we see researchers releasing so many pretrained models. In the first part we’ll learn how to extend last week’s tutorial to apply real-time object detection using deep learning and OpenCV to work with video streams and video files. Introduction 2D human pose estimation has been a fundamental yet challenging problem in computer vision. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Duplicate Question Detection with Deep Learning on Quora Dataset. deep-high-resolution-net. Nov. This problem is commonly referred to as Sensor-based Human Activity Recognition (HAR). X. This activity of looking for a specific object among others is really simple for a human brain. Deep Learning for Malaria Detection With regular manual diagnosis of blood smears, it is an intensive manual process requiring proper expertise in classifying and counting the parasitized and uninfected cells. John is currently a VP of Embedded Development focused on AI and machine learning products for on device recognition. Taken from “Human activity recognition with smartphone sensors using deep learning neural networks. Having personally used them to understand and expand my knowledge of object detection tasks, I highly recommend picking a domain from the above and using the given model to get your own journey started. 19 October 2016 / Convolutional Neural Network Signal Detection Using Deep Learning. In this post, you discovered the problem of human activity recognition and the use of deep learning methods that are achieving state-of-the-art performance on this problem. The problem of handgun detection in videos using deep learning is related in part to two broad research areas. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in Semantic u nderstanding is crucial for edges detection that is why learning based detectors which use machine learning or deep learning generate better results than canny edge detector. However, they treat pedestrian detection as a single binary classification task, which may confuse positive with hard negative samples (Fig. This usually means detecting keypoint locations that describe the object. Some. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts. Download with Google Download with Facebook or download with email. When evaluating a person's condition, while an initial estimation of the pose (standing, sitting, fallen, etc. This task can be now “magically” solved by deep learning and any talented teenager can do it in a few hours. Post navigation As we embrace our digital transformation, deep learning is set to become an important feature of Smiths Detection’s future product design. • DeepinView Human detection cameras DS-2CD7X26G0 Facial Recognition • Control building access using facial recognition cameras and get alarmed of suspicious visitors. Wang, ” A Cascaded Deep Learning Architecture for Pedestrian Detection,” ICCV 2013. Ping Luo3 . That will give you the basics of how to use machine learning (like neural networks) to  Jun 2, 2017 Object recognition and detection with deep learning for autonomous . It was tested on the Caltech dataset in videos captured by a camera mounted in a street. In the Deep Learning Era, the combination of feature engineering and  Mar 28, 2018 Modern approaches for human detection are largely based on Deep Convolution Neural Networks. Download Citation on ResearchGate | On Jan 1, 2019, Hongyan Xu and others published Autonomous Bridge Crack Detection Using Deep Convolutional Neural Networks 8 hours ago · The study, involving almost 181,000 patients and published in The Lancet, is the first to use deep learning to identify patients with potentially undetected atrial fibrillation and had an overall "Here, we show that beyond detection and classification, machine learning can be used for the elucidation of weak and/or noisy signals and images. It is a robust one-stage Before deep learning came along, most of the traditional CV algorithm variants for action recognition can be broken down into the following 3 broad steps: Local high-dimensional visual features that describe a region of the video are extracted either densely [ 3 ] or at a sparse set of interest points[ 4 , 5 ]. a facial landmark detection), we detect landmarks on a human face. Deep learning for malaria detection Manual diagnosis of blood smears is an intensive manual process that requires expertise in classifying and counting parasitized and uninfected cells. The goal is to lo-calize human anatomical keypoints (e. “The AI system uses 3D volumetric deep learning to analyze the full anatomy on chest CT scans, as well as patches based on object detection techniques that identify regions with malignant Create a Deep Neural Network in which the input is pixel matrix. They used a human engineered ensemble of Faster RCNN with Inception Resnet v2 and Resnet 101 archit For pedestrian detection, we directly use the off-the-shelf deep learning CCF [37] detector, as well as two other de- tectors specifically fine-tuned on our dataset. 2005. Create a Deep Neural Network in which the input is pixel matrix. human detection deep learning

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