Reviewerly/exHarmony huggingface dataset:
Mimic The Raw Domain: Accelerating Action Recognition in the Compressed Domain

Video understanding usually requires expensive computation that prohibits its deployment, yet videos contain significant spatiotemporal redundancy that can be exploited. In particular, operating directly on the motion vectors and residuals in the compressed video domain can significantly accelerate the compute, by not using the raw videos which demand colossal storage capacity. Existing methods approach this task as a multiple modalities problem. In this paper we are approaching the task in a completely different way; we are looking at the data from the compressed stream as a one unit clip and propose that the residual frames can replace the original RGB frames from the raw domain. Furthermore, we are using teacher-student method to aid the network in the compressed domain to mimic the teacher network in the raw domain. We show experiments on three leading datasets (HMDB51, UCF1, and Kinetics) that approach state-of-the-art accuracy on raw video data by using compressed data. Our model MFCD-Net outperforms prior methods in the compressed domain and more importantly, our model has 11X fewer parameters and 3X fewer Flops, dramatically improving the efficiency of video recognition inference. This approach enables applying neural networks exclusively in the compressed domain without compromising accuracy while accelerating performance.
DPLCF

Most existing recommender systems leverage users' complete original behavioral logs, which are collected from mobile devices and stored by the service provider and further fed into recommendation models. This may lead to a high risk of privacy leakage since the recommendation service provider may be trustless. Despite many research efforts on privacy-aware recommendation, the problem of building an effective recommender system completely preserving user privacy is still open.
Efficient High Cross-User Recognition Rate Ultrasonic Hand Gesture Recognition System

Hand gesture recognition is the key technique to realize free air hand gesture human-computer interaction, which is promising and attractive for a wide range of emerging ubiquitous applications. Ultrasonic-based solutions are lighting-condition independent and low-power and have little interference. A low-complexity 40 kHz ultrasonic-based hand gesture recognition system featuring a novel classification algorithm robust to temporal variations of motions is proposed in this paper. It employs one transmitter, three receivers, and highly digitalized front-end hardware. A coherent pulse train ultrasonic system with moving target detection capability is built to collect the RDM (range Doppler map) streams of gestures. During training and inference, the classifier algorithm aligns the sequential features extracted from the RDM of each frame with the template sequence of each class by dynamic time warping in advance. For each class, a two-class random forest that determines whether the testing sample belongs to the class according to the aligned features is trained. Then, the prediction is made by comparing the probabilities of the testing sample belonging to different classes. Experiments show that the proposed classifier trained based on 6 people has a better leave-one-subject-out validation accuracy than the competitors. It can identify 8 3D gestures with 93.9% accuracy in 37 ms on a PC. Its model size is 700 Kbytes. It is a better choice for the user-independent embedded applications.
Mitosis detection in breast cancer histopathology images using hybrid feature space

Breast Cancer grading is a challenging task as regards image analysis, which is normally based on mitosis count rate. The mitotic count provides an estimate of aggressiveness of the tumor. The detection of mitosis is a challenging task because in a frame of slides at X40 magnification, there are hundreds of nuclei containing few mitotic nuclei. However, manual counting of mitosis by pathologists is a difficult and time intensive job, moreover conventional method rely mainly on the shape, color, and/or texture features as well as pathologist experience. The objective of this study is to accept the atypaia-2014 mitosis detection challenge, automate the process of mitosis detection and a proposal of a hybrid feature space that provides better discrimination of mitotic and non-mitotic nuclei by combining color features with morphological and texture features. To exploit color channels, they were first selected, and then normalized and cumulative histograms were computed in wavelet domain. A detailed analysis presented on these features in different color channels of respective color spaces using Random Forest (RF) and Support Vector Machine (SVM) classifiers. The proposed hybrid feature space when used with SVM classifier achieved a detection rate of 78.88% and F-measure of 72.07%. Our results, especially high detection rate, indicate that proposed hybrid feature space model contains discriminant information for mitotic nuclei, being therefore a very capable are for exploration to improve the quality of the diagnostic assistance in histopathology.
Autonomous Target Docking of Nonholonomic Mobile Robots Using Relative Pose Measurements

This article investigates the target docking problem for nonholonomic mobile robots using relative position and orientation measurements of the target. Considering the fact that target docking operations generally happen in unstructured environments where obstacles are left lying around, the whole docking process is divided into two phases, namely the approaching phase and the autonomous docking phase. In the approaching phase, the robot navigates in the operational environment until it reaches a switching region around the target, which triggers the autonomous docking phase. To account for the noise-corrupted and intermittent target observations, an extended Kalman filter-based relative pose estimation algorithm is designed to estimate the position and orientation of the target in the local reference frame of the robot. To achieve a perfect docking operation in the autonomous docking phase, which requires a zero-impact angle and a zero-lateral offset at the final engagement stage, a practical solution is proposed where motion planning and tracking control of the robot are incorporated into a unified scheme. Unlike most of the existing docking approaches in the literature which only account for static targets, our proposed strategy addresses the moving target docking problem as well. Real robot experiments have been performed to demonstrate the effectiveness of the proposed method.
Automatic Digitization of Engineering Diagrams using Deep Learning and Graph Search

A Piping and Instrumentation Diagram (P&ID) is a type of engineering diagram that uses symbols, text, and lines to represent the components and flow of an industrial process. Although used universally across industries such as manufacturing and oil & gas, P&IDs are usually trapped in image files with limited metadata, making their contents unsearchable and siloed from operational or enterprise systems. In order to extract the information contained in these diagrams, we propose a pipeline for automatically digitizing P&IDs. Our pipeline combines a series of computer vision techniques to detect symbols in a diagram, match symbols with associated text, and detect connections between symbols through lines. For the symbol detection task, we train a Convolutional Neural Network to classify certain common symbols with over 90% precision and recall. To detect connections between symbols, we use a graph search approach to traverse a diagram through its lines and discover interconnected symbols. By transforming unstructured diagrams into structured information, our pipeline enables applications such as diagram search, equipment-to-sensor mapping, and asset hierarchy creation. When integrated with operational and enterprise data, the extracted asset hierarchy serves as the foundation for a facility-wide digital twin, enabling advanced applications such as machine learning-based predictive maintenance.
Enriched Learning Experience using Augmented Reality Framework in Engineering Education

In the current day scenario, teaching takes place either on the blackboard or on a power point presentation projected on the wall. Traditional methods of teaching are going to become a thing of the past. Instead, imagine students focusing their mobile screens in the middle of the classroom and they will be all viewing holographic 3D objects surfacing from a table and the teacher explaining the visuals. This is going to be a reality in the near future and this technological innovation will transform the teaching-learning process. The objective of this paper is to propose a framework for using Augmented Reality in mobile phones for engineering education. Use of mobile devices in education provide a new educational paradigm, called M-Learning, It offers many opportunities for students to work on their creativity, while at same time it becomes an element of motivation and collaboration. This paper proposes the modern way of AR technology in engineering education, a handy tool to make teaching and learning effective and also proposes a experimental setup to convert a topic to an Android App.
GAS-GCN: Gated Action-Specific Graph Convolutional Networks for Skeleton-Based Action Recognition

Skeleton-based action recognition has achieved great advances with the development of graph convolutional networks (GCNs). Many existing GCNs-based models only use the fixed hand-crafted adjacency matrix to describe the connections between human body joints. This omits the important implicit connections between joints, which contain discriminative information for different actions. In this paper, we propose an action-specific graph convolutional module, which is able to extract the implicit connections and properly balance them for each action. In addition, to filter out the useless and redundant information in the temporal dimension, we propose a simple yet effective operation named gated temporal convolution. These two major novelties ensure the superiority of our proposed method, as demonstrated on three large-scale public datasets: NTU-RGB + D, Kinetics, and NTU-RGB + D 120, and also shown in the detailed ablation studies.
Syncretic-NMS: A Merging Non-Maximum Suppression Algorithm for Instance Segmentation

Instance segmentation is typically based on an object detection framework. Semantic segmentation is conducted on the bounding boxes that are returned by detectors. NMS (non-maximum suppression) is a common post-processing operation in instance segmentation and object detection tasks. It is typically used after bounding box regression to eliminate redundant bounding boxes. The evaluation criteria for object detection require that the bounding box be as close as possible to the ground truth, but they do not emphasize the integrity of the included object. However, sometimes the bounding boxes cannot contain the complete objects, and the parts beyond the bounding boxes cannot be correctly predicted in the subsequent semantic segmentation. To solve this problem, we propose the Syncretic-NMS algorithm. The algorithm takes traditional NMS as the first step and processes the bounding boxes obtained by traditional NMS, judges the neighboring bounding boxes of each bounding box, and combines the neighboring boxes that are strongly correlated with the corresponding bounding boxes. The coordinates of the merged box are the four coordinate extremes of the bounding box and the highly relevant neighboring box. The neighboring box with strong correlation is merged with the corresponding bounding box. Based on an analysis of the influences of corresponding factors, the criteria for correlation judgment are specified. Experimental results on the MS COCO dataset demonstrate that Syncretic-NMS can steadily increase the accuracy of instance segmentation, while experimental results on the Cityscapes dataset prove that the algorithm can adapt to application scenario changes. The computational complexity of Syncretic-NMS is the same as that of traditional NMS. Syncretic-NMS is easy to implement, requires no additional training, and can be easily integrated into the available instance segmentation framework.
Coextensive space: virtual reality and the developing relationship between the body, the digital and physical space

Virtual Reality (VR) has traditionally required external sensors placed around a designated play space. In contrast, more recent wired and wireless systems, such as the Oculus Rift S (released in March 2019) and the Oculus Quest (released in May 2019) use cameras located on the outside of these devices to monitor their physical position. Users can now mark out a physical space that is then digitally tracked within their display. Once a play space has been established, users are alerted if they come close to breaching this boundary by the visual inclusion of a grid. Should this threshold be breached, the headset display shifts to an image of the surrounding concrete environment. We contend that physical space is increasingly being incorporated into the digital space of VR in a manner that meaningfully differs from older systems. We build our argument in the following way. First, the article explores how theories surrounding VR have implicated only a limited relationship with physical space. Second, the article introduces the concept of coextensive space as a way of understanding the developing relationship between the physical, digital and concrete reality enacted by current VR systems.
Learn to cycle: Time-consistent feature discovery for action recognition

Generalizing over temporal variations is a prerequisite for effective action recognition in videos. Despite significant advances in deep neural networks, it remains a challenge to focus on short-term discriminative motions in relation to the overall performance of an action. We address this challenge by allowing some flexibility in discovering relevant spatio-temporal features. We introduce Squeeze and Recursion Temporal Gates (SRTG), an approach that favors inputs with similar activations with potential temporal variations. We implement this idea with a novel CNN block that uses an LSTM to encapsulate feature dynamics, in conjunction with a temporal gate that is responsible for evaluating the consistency of the discovered dynamics and the modeled features. We show consistent improvement when using SRTG blocks, with only a minimal increase in the number of GFLOPs. On Kinetics-700, we perform on par with current state-of-the-art models, and outperform these on HACS, Moments in Time, UCF-101 and HMDB-51.1
Multi-risk assessment of heat waves under intensifying climate change using Bayesian Networks

Continuous climate change has increased the intensity and frequency of abnormal weather, temperature fluctuations, and natural disasters, along with their subsequent damages. The temperature rise is related to the increased number of days with heat waves and tropical nights, which generate problems in many different areas. The risks from heat waves appear in multiple forms, such as disease severity and energy shortages. To respond effectively to such complex risks, it is necessary to analyze where and what risks occur. Recently, Bayesian Network methodology has been applied in various fields, and the causality and probability of occurrence of complex events can be evaluated. Particularly, it is suitable for fields, such as climate change and disaster because of its consideration to uncertainty. Therefore, in this study, multi-risk networks were built, and a multi-risk assessment using Bayesian Network was performed considering statistical probabilities. In addition, this study examined the change in future risks by reflecting the climate scenarios proposed by Intergovernmental Panel on Climate Change. The disease risks related to heat waves have increased with time, particularly in the areas adjacent to the coast, centering on the southeast side of South Korea. As for energy shortage, the risk increased in the region around Daegu, a city known for its high temperatures as well as in the Seoul metropolitan areas. Through this study, it is possible to improve the methodology for evaluating multi-risks, and the analyzed results will be able to provide support for effective responses to risks caused by heat waves in Korea.
Multiscale modeling of human cerebrovasculature: A hybrid approach using image-based geometry and a mathematical algorithm

The cerebral vasculature has a complex and hierarchical network, ranging from vessels of a few millimeters to superficial cortical vessels with diameters of a few hundred micrometers, and to the microvasculature (arteriole/venule) and capillary beds in the cortex. In standard imaging techniques, it is difficult to segment all vessels in the network, especially in the case of the human brain. This study proposes a hybrid modeling approach that determines these networks by explicitly segmenting the large vessels from medical images and employing a novel vascular generation algorithm. The framework enables vasculatures to be generated at coarse and fine scales for individual arteries and veins with vascular subregions, following the personalized anatomy of the brain and macroscale vasculatures. In this study, the vascular structures of superficial cortical (pial) vessels before they penetrate the cortex are modeled as a mesoscale vasculature. The validity of the present approach is demonstrated through comparisons with partially observed data from existing measurements of the vessel distributions on the brain surface, pathway fractal features, and vascular territories of the major cerebral arteries. Additionally, this validation provides some biological insights: (i) vascular pathways may form to ensure a reasonable supply of blood to the local surface area; (ii) fractal features of vascular pathways are not sensitive to overall and local brain geometries; and (iii) whole pathways connecting the upstream and downstream entire-scale cerebral circulation are highly dependent on the local curvature of the cerebral sulci.
Data augmentation method for improving the accuracy of human pose estimation with cropped images

Neural networks have improved the accuracy of human pose estimation from a single RGB image. However, such estimation remains difficult, especially when the human body is only partially visible due to a limited field of view of the camera or occlusions. In this paper, we introduce a data augmentation method called body-cropping augmentation (BCA), which generalizes the dataset for effective training in human pose estimation. This technique includes the policies of data generation and the training strategy using the augmented data. The experiments with the COCO val2017 dataset with ground-truth bounding boxes show BCA consistently enhances accuracies of state-of-the-art neural networks by an average of 1.08% without any modification to the network architecture. Moreover, the proposed BCA technique effectively reduces the false negatives of localizing keypoints, especially in an input image with a few visible keypoints.
Ensembles of feature selectors for dealing with class-imbalanced datasets: A proposal and comparative study

Feature selection is an important task in many machine learning and data mining problems. Due to the increasing size of datasets, the removal of redundant, erroneous or noisy features is frequently an initial step. In many common applications, datasets have imbalanced class distribution. Although feature selection suffers in the presence of an uneven distribution of samples among the classes, there are few methods specifically designed for this situation. We propose a new approach based on the use of feature selection boosting to address this problem. Ensembles of feature selectors using both, standard and specifically designed for class-imbalanced datasets methods, are constructed using boosting algorithms,. These ensembles are used to perform feature selection in class-imbalanced datasets. The combination of different rounds of feature selectors over different samples with an adaptive distribution of the instances achieves an improved performance when compared with standard feature selection methods in class-imbalanced datasets. A comprehensive set of experiments that employ 18 different ensemble methods, 7 different feature selection methods and 140 class-imbalanced datasets demonstrates the efficiency of the ensemble approach in terms of reduction ability and classification performance. Further study of the proposed method shows its robustness in the presence of class label noise.
DeGAN: Mixed noise removal via generative adversarial networks

Restoration of images corrupted by mixed noise (e.g., additive white Gaussian noise and impulse noise) is very difficult due to the complexity of the mixed noise distribution. Various mixed noise removal models involve the preprocessing based on outlier detection. However, the performance of these models largely depends on the accuracy of pixel location detection of outliers, and artifacts and missing image details are prone to occur when the mixture noise is strong. In this paper, a new denoising model based on generative adversarial network (DeGAN) is proposed to remove mixed noise in images. The proposed model combines generator, discriminator, and feature extractor networks. Through the mutual game between the generator and discriminator networks combined with additional training from the feature extractor network, the generator network implements a direct mapping from the noisy image domain to the noise-free image domain. In addition, we design a new joint loss function to incorporate information from image features and human visual perception into the mixed noise elimination task, which further improves the image quality and the visual effect. Abundant experiments show that the performance of our model is better than the state-of-the-art mixed noise removal methods in three different types of mixed noise scenarios, and the joint loss function does improve the denoising performance.
P-MEC: Polynomial Congruence-Based Multimedia Encryption Technique Over Cloud

In this multimedia era, the security of multimedia content over the Cloud becomes a crucial research area. There are several modes of communication available for transmitting the information over the Cloud from one place to another place around the globe. Such communication networks are open to all where anyone can access the data very easily. Therefore, the security of multimedia data over the Cloud becomes a major challenge. This study highlights better protection of multimedia content over Cloud in comparison to the existing approaches, where the information cannot be unwrapped in a reasonable time. The Strength of RSA, ElGamal, Diffie Hellman, and many more Cryptographic algorithms depend on the concept of congruence. As of now, the solution for linear and quadratic congruence had made its use limited, but this study had changed it by introducing the solution for Cubic and Polynomial congruence. The polynomial congruence concept has been used to encrypt the multimedia content. Based on the qualitative and quantitative experimental analysis, it can be concluded that the P-MEC model stands firm compared with the state-of-the-art models. The security analysis and experimental results display that the proposed scheme has strong robustness against statistical attack, vulnerability attack, and other comprehensive attacks.