GaitSense: Towards Ubiquitous Gait-Based Human Identification with Wi-Fi

2022 ◽  
Vol 18 (1) ◽  
pp. 1-24
Author(s):  
Yi Zhang ◽  
Yue Zheng ◽  
Guidong Zhang ◽  
Kun Qian ◽  
Chen Qian ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Gait Recognition ◽  
Wearable Sensors ◽  
Human Identification ◽  
Training Data ◽  
Identification Accuracy ◽  
Identification System ◽  
Gait Patterns ◽  
Training Samples ◽  
Augmentation Techniques

Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present GaitSense , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitSense first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitSense reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques. GaitSense also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.

RF-Identity

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Chao Feng ◽  
Jie Xiong ◽  
Liqiong Chang ◽  
Fuwei Wang ◽  
Ju Wang ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Critical Role ◽  
Training Data ◽  
Identification Accuracy ◽  
Identification System ◽  
Frequency Bandwidth ◽  
Person Identification ◽  
Data Set ◽  
Rf Devices ◽  
Large Frequency

Person identification plays a critical role in a large range of applications. Recently, RF based person identification becomes a hot research topic due to the contact-free nature of RF sensing that is particularly appealing in current COVID-19 pandemic. However, existing systems still have multiple limitations: i) heavily rely on the gait patterns of users for identification; ii) require a large amount of data to train the model and also extensive retraining for new users and iii) require a large frequency bandwidth which is not available on most commodity RF devices for static person identification. This paper proposes RF-Identity, an RFID-based identification system to address the above limitations and the contribution is threefold. First, by integrating walking pattern features with unique body shape features (e.g., height), RF-Identity achieves a high accuracy in person identification. Second, RF-Identity develops a data augmentation scheme to expand the size of the training data set, thus reducing the human effort in data collection. Third, RF-Identity utilizes the tag diversity in spatial domain to identify static users without a need of large frequency bandwidth. Extensive experiments show an identification accuracy of 94.2% and 95.9% for 50 dynamic and static users, respectively.

Segmentation-driven Hierarchical RetinaNet for Detecting Protozoa in Micrograph

2019 ◽  
Vol 13 (03) ◽  
pp. 393-413
Author(s):  
Khoa Pho ◽  
Muhamad Kamal Mohammed Amin ◽  
Atsuo Yoshitaka
Keyword(s):  
Life Cycle ◽  
Data Augmentation ◽  
Expert Knowledge ◽  
Animal Health ◽  
Training Data ◽  
Life Cycle Stages ◽  
Detection And Identification ◽  
Training Samples ◽  
Augmentation Techniques ◽  
The Cost

Protozoa detection and identification play important roles in many practical domains such as parasitology, scientific research, biological treatment processes, and environmental quality evaluation. Traditional laboratory methods for protozoan identification are time-consuming and require expert knowledge and expensive equipment. Another approach is using micrographs to identify the species of protozoans that can save a lot of time and reduce the cost. However, the existing methods in this approach only identify the species when the protozoan are already segmented. These methods study features of shapes and sizes. In this work, we detect and identify the images of cysts and oocysts of various species such as: Giardia lamblia, Iodamoeba butschilii, Toxoplasma gondi, Cyclospora cayetanensis, Balantidium coli, Sarcocystis, Cystoisospora belli and Acanthamoeba, which have round shapes in common and affect human and animal health seriously. We propose Segmentation-driven Hierarchical RetinaNet to automatically detect, segment, and identify protozoans in their micrographs. By applying multiple techniques such as transfer learning, and data augmentation techniques, and dividing training samples into life-cycle stages of protozoans, we successfully overcome the lack of data issue in applying deep learning for this problem. Even though there are at most 5 samples per life-cycle category in the training data, our proposed method still achieves promising results and outperforms the original RetinaNet on our protozoa dataset.

scholarly journals A review: preprocessing techniques and data augmentation for sentiment analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Huu-Thanh Duong ◽  
Tram-Anh Nguyen-Thi
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Supervised Learning ◽  
Data Augmentation ◽  
Original Data ◽  
Training Data ◽  
Unseen Data ◽  
Augmentation Techniques ◽  
User Intervention

AbstractIn literature, the machine learning-based studies of sentiment analysis are usually supervised learning which must have pre-labeled datasets to be large enough in certain domains. Obviously, this task is tedious, expensive and time-consuming to build, and hard to handle unseen data. This paper has approached semi-supervised learning for Vietnamese sentiment analysis which has limited datasets. We have summarized many preprocessing techniques which were performed to clean and normalize data, negation handling, intensification handling to improve the performances. Moreover, data augmentation techniques, which generate new data from the original data to enrich training data without user intervention, have also been presented. In experiments, we have performed various aspects and obtained competitive results which may motivate the next propositions.

scholarly journals Insufficient Data Can Also Rock! Learning to Converse Using Smaller Data with Augmentation

2019 ◽  
Vol 33 ◽  
pp. 6698-6705
Author(s):  
Juntao Li ◽  
Lisong Qiu ◽  
Bo Tang ◽  
Dongmin Chen ◽  
Dongyan Zhao ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Network Models ◽  
Generative Models ◽  
Training Data ◽  
Insufficient Data ◽  
Dialogue System ◽  
Neural Network Models ◽  
Augmentation Techniques ◽  
Training Pair ◽  
Existing Data

Recent successes of open-domain dialogue generation mainly rely on the advances of deep neural networks. The effectiveness of deep neural network models depends on the amount of training data. As it is laboursome and expensive to acquire a huge amount of data in most scenarios, how to effectively utilize existing data is the crux of this issue. In this paper, we use data augmentation techniques to improve the performance of neural dialogue models on the condition of insufficient data. Specifically, we propose a novel generative model to augment existing data, where the conditional variational autoencoder (CVAE) is employed as the generator to output more training data with diversified expressions. To improve the correlation of each augmented training pair, we design a discriminator with adversarial training to supervise the augmentation process. Moreover, we thoroughly investigate various data augmentation schemes for neural dialogue system with generative models, both GAN and CVAE. Experimental results on two open corpora, Weibo and Twitter, demonstrate the superiority of our proposed data augmentation model.

scholarly journals OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes

2019 ◽  
Vol 5 (1) ◽  
pp. 239-244
Author(s):  
Jingrui Yu ◽  
Roman Seidel ◽  
Gangolf Hirtz
Keyword(s):  
Data Augmentation ◽  
Training Data ◽  
Fine Tuning ◽  
Single Shot ◽  
Augmentation Techniques ◽  
Indoor Scenes ◽  
One Step ◽  
Bounding Boxes ◽  
Omnidirectional Images ◽  
Fine Tune

AbstractWe propose a one-step person detector for topview omnidirectional indoor scenes based on convolutional neural networks (CNNs). While state of the art person detectors reach competitive results on perspective images, missing CNN architectures as well as training data that follows the distortion of omnidirectional images makes current approaches not applicable to our data. The method predicts bounding boxes of multiple persons directly in omnidirectional images without perspective transformation, which reduces overhead of pre- and post-processing and enables realtime performance. The basic idea is to utilize transfer learning to fine-tune CNNs trained on perspective images with data augmentation techniques for detection in omnidirectional images. We fine-tune two variants of Single Shot MultiBox detectors (SSDs). The first one uses Mobilenet v1 FPN as feature extractor (moSSD). The second one uses ResNet50 v1 FPN (resSSD). Both models are pre-trained on Microsoft Common Objects in Context (COCO) dataset. We fine-tune both models on PASCAL VOC07 and VOC12 datasets, specifically on class person. Random 90-degree rotation and random vertical flipping are used for data augmentation in addition to the methods proposed by original SSD. We reach an average precision (AP) of 67.3%with moSSD and 74.9%with resSSD on the evaluation dataset. To enhance the fine-tuning process, we add a subset of HDA Person dataset and a subset of PIROPO database and reduce the number of perspective images to PASCAL VOC07. The AP rises to 83.2% for moSSD and 86.3% for resSSD, respectively. The average inference speed is 28 ms per image for moSSD and 38 ms per image for resSSD using Nvidia Quadro P6000. Our method is applicable to other CNN-based object detectors and can potentially generalize for detecting other objects in omnidirectional images.

scholarly journals MIGAN: Malware Image Synthesis Using GANs

2019 ◽  
Vol 33 ◽  
pp. 10033-10034 ◽  
Author(s):  
Abhishek Singh ◽  
Debojyoti Dutta ◽  
Amit Saha
Keyword(s):  
Language Processing ◽  
Domain Knowledge ◽  
Data Augmentation ◽  
Image Synthesis ◽  
Substantial Improvement ◽  
Training Data ◽  
Malware Analysis ◽  
Training Procedure ◽  
Original Dataset ◽  
Augmentation Techniques

Majority of the advancement in Deep learning (DL) has occurred in domains such as computer vision, and natural language processing, where abundant training data is available. A major obstacle in leveraging DL techniques for malware analysis is the lack of sufficiently big, labeled datasets. In this paper, we take the first steps towards building a model which can synthesize labeled dataset of malware images using GAN. Such a model can be utilized to perform data augmentation for training a classifier. Furthermore, the model can be shared publicly for community to reap benefits of dataset without sharing the original dataset. First, we show the underlying idiosyncrasies of malware images and why existing data augmentation techniques as well as traditional GAN training fail to produce quality artificial samples. Next, we propose a new method for training GAN where we explicitly embed prior domain knowledge about the dataset into the training procedure. We show improvements in training stability and sample quality assessed on different metrics. Our experiments show substantial improvement on baselines and promise for using such a generative model for malware visualization systems.

Data set entity recognition based on distant supervision

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pengcheng Li ◽  
Qikai Liu ◽  
Qikai Cheng ◽  
Wei Lu
Keyword(s):  
Supervised Learning ◽  
Large Scale ◽  
Data Augmentation ◽  
Scientific Literature ◽  
Neural Model ◽  
Training Data ◽  
Entity Recognition ◽  
Data Set ◽  
Content Type ◽  
Augmentation Techniques

Purpose This paper aims to identify data set entities in scientific literature. To address poor recognition caused by a lack of training corpora in existing studies, a distant supervised learning-based approach is proposed to identify data set entities automatically from large-scale scientific literature in an open domain. Design/methodology/approach Firstly, the authors use a dictionary combined with a bootstrapping strategy to create a labelled corpus to apply supervised learning. Secondly, a bidirectional encoder representation from transformers (BERT)-based neural model was applied to identify data set entities in the scientific literature automatically. Finally, two data augmentation techniques, entity replacement and entity masking, were introduced to enhance the model generalisability and improve the recognition of data set entities. Findings In the absence of training data, the proposed method can effectively identify data set entities in large-scale scientific papers. The BERT-based vectorised representation and data augmentation techniques enable significant improvements in the generality and robustness of named entity recognition models, especially in long-tailed data set entity recognition. Originality/value This paper provides a practical research method for automatically recognising data set entities in scientific literature. To the best of the authors’ knowledge, this is the first attempt to apply distant learning to the study of data set entity recognition. The authors introduce a robust vectorised representation and two data augmentation strategies (entity replacement and entity masking) to address the problem inherent in distant supervised learning methods, which the existing research has mostly ignored. The experimental results demonstrate that our approach effectively improves the recognition of data set entities, especially long-tailed data set entities.

scholarly journals Data Augmentation and Feature Selection for Automatic Model Recommendation in Computational Physics

2021 ◽  
Vol 26 (1) ◽  
pp. 17
Author(s):  
Thomas Daniel ◽  
Fabien Casenave ◽  
Nissrine Akkari ◽  
David Ryckelynck
Keyword(s):  
Feature Selection ◽  
Data Augmentation ◽  
Classification Problem ◽  
Computational Physics ◽  
Training Data ◽  
Multilayer Perceptrons ◽  
Augmentation Techniques ◽  
Physical Fields ◽  
Classification Tasks ◽  
Selection Of

Classification algorithms have recently found applications in computational physics for the selection of numerical methods or models adapted to the environment and the state of the physical system. For such classification tasks, labeled training data come from numerical simulations and generally correspond to physical fields discretized on a mesh. Three challenging difficulties arise: the lack of training data, their high dimensionality, and the non-applicability of common data augmentation techniques to physics data. This article introduces two algorithms to address these issues: one for dimensionality reduction via feature selection, and one for data augmentation. These algorithms are combined with a wide variety of classifiers for their evaluation. When combined with a stacking ensemble made of six multilayer perceptrons and a ridge logistic regression, they enable reaching an accuracy of 90% on our classification problem for nonlinear structural mechanics.

scholarly journals Augmentasi Data Pengenalan Citra Mobil Menggunakan Pendekatan Random Crop, Rotate, dan Mixup

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Joseph Sanjaya ◽  
Mewati Ayub
Keyword(s):  
Data Augmentation ◽  
Learning System ◽  
Training Data ◽  
Two Dimensional ◽  
Deep Convolutional Neural Networks ◽  
Image Detection ◽  
Geometric Transformations ◽  
Car Model ◽  
Augmentation Techniques ◽  
Significant Generalization

Deep convolutional neural networks (CNNs) have achieved remarkable results in two-dimensional (2D) image detection tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. In this paper, a Deep Learning system for accurate car model detection is proposed using the ResNet-152 network with a fully convolutional architecture. It is demonstrated that significant generalization gains in the learning process are attained by randomly generating augmented training data using several geometric transformations and pixel-wise changes, such as image cropping and image rotation. We evaluated data augmentation techniques by comparison with competitive data augmentation techniques such as mixup. Data augmented ResNet models achieve better results for accuracy metrics than baseline ResNet models with accuracy 82.6714% on Stanford Cars Dataset.

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