Reviewerly/exHarmony huggingface dataset:
Ship Detection from Despeckled Satellite Images using Deep Learning

Synthetic Aperture Radar is a technique of forming fine resolution images from resolution restricted radar systems. SAR images are captured by a moving system such as an airplane or a satellite using the motion of a radar antenna. The most common application of SAR images is object detection. In this paper, a ship detection model has been proposed from satellite images using deep learning. In general, a SAR system generates speckled images, which can reduce the performance of a detection model. To reduce deleterious effects, various speckle filtering methods have been used by several researchers in the past. De-speckling is a technique which reduces noise (speckle) from the images. In this paper, two filters are used for despeckling the SAR images and the images are used for evaluation of the proposed Convolutional neural networks. The proposed CNN based deep learning model has been used to classify the object present in the images as ship or not-ship. A comparison has also been made between speckled dataset and despeckled dataset as well as for different epochs in the model to find the best results.
FusePose: IMU-Vision Sensor Fusion in Kinematic Space for Parametric Human Pose Estimation

Commercial motion-capture systems produce excell- ent in-studio reconstructions, but offer no comparable solution for acquisition in everyday environments. We present a system for acquiring motions almost anywhere. This wearable system gathers ultrasonic time-of-flight and inertial measurements with a set of inexpensive miniature sensors worn on the garment. After recording, the information is combined using an Extended Kalman Filter to reconstruct joint configurations of a body. Experimental results show that even motions that are traditionally difficult to acquire are recorded with ease within their natural settings. Although our prototype does not reliably recover the global transformation, we show that the resulting motions are visually similar to the original ones, and that the combined acoustic and intertial system reduces the drift commonly observed in purely inertial systems. Our final results suggest that this system could become a versatile input device for a variety of augmented-reality applications.
Double-Scale Convolutional Autoencoder and Extreme Learning Machine for Parameter Identification of DC Bus Capacitor in Power Electronic Transformer

Aluminum electrolytic capacitors (AECs) are utilized as the key components in power electronic transformers (PETs). The AEC degradation monitoring is crucial for the maintenance of PET. Degradation of AEC performance is often reflected by changes of capacitance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> ) and equivalent series resistance (ESR). Since <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> and ESR dominate the low- and mid-frequency impedance characteristics of the AEC, respectively, the features of the corresponding frequency bands of the respective signals need to be simultaneously extracted. However, the current studies on parameter identification of AECs have not focused on this issue. In this article, the double-scale convolutional autoencoder and extreme learning machine (DCAE-ELM) framework is proposed to identify <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> and ESR based on AEC voltage. Specifically, DCAE extracts the low- and mid-frequency features of AEC voltages with large- and small-scale convolutional kernels, respectively. Then ELM is employed to identify <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> and ESR based on the features extracted by DCAE. Moreover, the mathematical mechanisms between the gradients and reconstructed data of DCAE with data concatenated in columns (cDCAE) and rows (rDCAE) are analyzed. Validation results of both simulation and experimental data have verified the data reconfiguration performance of rDCAE and the parameter identification capability of the proposed DCAE-ELM framework.
Multi-site Forecasting of Energy Time Series with Spatio-Temporal Graph Neural Networks

Climate change has prompted the energy sector to shift its focus to renewable energy sources, which are environmentally friendly but less in terms of cost, complexity, and plants' management. It becomes critical to have a reliable method for estimating the output power of these systems, which are dispersed across the country and vary in kind and technology, and whose output power is mostly determined by meteorological factors. In this paper, we exploit the capability of modeling dynamic graph-like data of a specific type of graph neural network, spatio-temporal graph neural network, which can process spatial information about plants' distribution in a particular region as well as temporal data on individual plant power production. Plants in the same region can share information and make more accurate forecasts in this way. The suggested model was evaluated on two types of datasets: one with data gathered from real photovoltaic systems and the other with synthesized power time series reconstructed from data acquired by satellite detection. Our studies discovered how these systems can estimate the production outputs of photovoltaic stations simultaneously and with higher accuracy with respect to previous state-of-the-art models, performing effectively even in the absence of meteorological data.
Students’ Opinions on the Usage of Mobile Augmented Reality Application in Health Education

The use of mobile augmented reality technology has recently improved in education and training methods. Studies have shown that this new technology has a positive effect on education and students. This study aims to present a synthesis of the studies on mobile augmented reality in health education with a critical approach and to develop suggestions for future researchers. In addition, by presenting an application on the effect of the use of augmented reality in health education on the performance of students, it is aimed to investigate the students' perspectives on the use of this technology and its use as an educational tool in teaching-learning through students' perspectives. For this purpose, an anatomy application for anatomy lesson was designed with Unity 3B platform, Blender and After Effect, and 150 students studying at Vocational Department of Health Services were asked to experience the augmented reality application developed. Mobile augmented reality application was made available to students during the application process. as a result, “Student Views Questionnaire on the Use of Augmented Reality Application in Health Education” was applied. Frequency and percentage distributions were used to determine student views. The results obtained in the research show that students respond positively to the use of technology and they believe that the use of technology in the learning-teaching process will increase their performance.
Artificial Neural Network with Histogram Oriented Swerve Angle for Signature Verification

Offline signature verification is a challenging task, particular in distinguishing between genuine signatures and skilled forgery. How to distinguish features and how to calculate the similarity score are a key issue in signature verification. This paper proposes a comparison offline handwriting signature verification algorithms for solving skilled forgery problem by using Artificial Neural Network (ANN) with our proposed methodology as the Histogram Oriented Swerve Angle (HOSA). The main methodology were developed by the technique of skeletonization also known as the thinning process, which is an important step in the signature pre-processing. After preprocessing process, we use the histogram oriented swerve angle for determining the signature image feature extraction. The verification process is accessed with an artificial neural network (ANN) classifier. This paper applied ANN with a comparison 2 optimization methods between Stochastic Gradient Descent (SGD) and Levenberg-Marquardt (LM). The experiment was conducted with CEDAR datasets. The results showed that Skeletonization and Histogram Oriented Swerve Angle techniques can apply ANN with SGD and LM to distinguish between genuine signatures and skilled forgery signatures.
General Forgery Face Detection: Against Generative Adversarial Networks Using Knowledge Distillation

Generative adversarial networks (GANs) have been widely used for fake target face generation in film-making and journalism. What backfired is that GANs are being wildly misused to impersonate credible people and publish illegal, misleading, and confusing information to the public. To our dismay, however, the main problem with previous fake face detection methods is their inability to generalize to distinguish different GANs. To address this challenge, this paper begins by further analyzing the weaknesses of GAN-based generators. Our experiments revealed that the recent fake faces generated by GANs are still not robust enough because it does not consider enough pixels. Inspired by this finding, we design a novel convolutional neural network that uses frequency texture augmentation and knowledge distillation to enhance its global texture perception, effectively describe textures at different semantic levels in images, and improve robustness. Experimental results show that our model achieves better and more consistent performance in image processing or cross-domain settings, especially when images are subject to Gaussian noise.
Identity Management in IoT using Blockchain

Internet of Things(IoT) is a contemporary standard where RFID, WSN, GPRS, Bluetooth, LTE, and Zigbee of different wireless and wired technologies are used to interconnect between users, things, or smart objects through the Internet. Now IoT grabs remarkable attention from industrial, individual, and research communities for a significant influencing role in our daily lives. The fundamental structure of IoT is assembling data from different sensors and devices to analyze by software to form useful and actionable data. Due to different features like high connectivity, dynamic nature, sensing, and integration, IoT has many application areas. These application areas extent from transportation, public safety, waste management, home automation, healthcare, and smart infrastructure. Despite all the applications, the IoT is facing many security concerns such as authentication, key management, availability, privacy, and identity issues. Identity management is an essential aspect of an IoT ecosystem and is particularly important for controlling IoT information exchange. However, the bulk of genuine works uses centralized approaches that may result in the use of trusted third parties with a single-point failure and privacy issues. A blockchain consortium is a modern technology that delivers reliable and trusted IoT identity management solutions and storage platforms. In this paper, we focus on basic identity management problems in IoT and analyze the architecture and protocols to figure out solutions using blockchain technology to solve privacy, security, and scalability concerns in an IoT centralized system.
Lagrange Coded Federated Learning (L-CoFL) Model for Internet of Vehicles

In Internet-of-Vehicles (IoV), smart vehicles can efficiently process various sensing data through federated learning (FL) - a privacy-preserving distributed machine learning (ML) approach that allows collaborative development of the shared ML model without any data exchange. However, traditional FL approaches suffer from poor security against the system noise, e.g., due to low-quality trained data, wireless channel errors, and malicious vehicles generating erroneous results, which affects the accuracy of the developed ML model. To address this problem, we propose a novel FL model based on the concept of Lagrange coded computing (LCC) - a coded distributed computing (CDC) scheme that enables enhancing the system security. In particular, we design the first L-CoFL (Lagrange coded FL) model to improve the accuracy of FL computations in the presence of lowquality trained data and wireless channel errors, and guarantee the system security against malicious vehicles. We apply the proposed L-CoFL model to predict the traffic slowness in IoV and verify the superior performance of our model through extensive simulations.
Inductive Parallel Learning for Multiple Classification Problems

The default approach to the construction of pattern recognition models solving supervised learning problems is fitting the knowledge representation to the samples of a single problem, describing a single, static concept. In recent years, however, the transfer learning approach has been gaining popularity, consisting in training a model initially learned on a different, standard dataset to a specific problem. However, such solutions are mainly used for signal data, in applications typical of deep learning models. This paper proposes a new training procedure for tabular data, in which a serial ensemble of models based on neural networks is trained in parallel to recognize two different problems. In extensive experimental analysis, the approaches based on disjoint models, disjoint learning and - constituting the basic proposal of the work - inductive parallel learning of two problems were compared. The analysis carried out on synthetic and real-world problems shows that the proposed approach is a promising starting point for further research.
Secure data communication using DNA computing adaptable to wireless sensor network

Due to the proliferation of electronic devices such as mobile phones, tablets, laptops, desktops, e-drives, hard drives, etc., transferring information in the form of images through the Internet has become an essential practice in this digital era. Red, green, blue (RGB) images are used to store or share information, such as medical imaging for disease diagnosis, e-learning, online shopping, defense services, personal images, and much more. Adversaries are trying to steal this critical information from images for their gain or personal interest. Hence, we need to prevent an adversary from misusing this crucial information using encryption algorithms. This chapter aims to provide a new, secure, and fast encryption algorithm for RGB images in deoxyribonucleic acid (DNA)-encoded domain involving random sequences generated by arithmetic progression (AP) and logistic map associated with generalized Vigenère-type table suitable for wireless sensor network (WSN). The randomness of generated sequences is performed using the National Institute of Standards and Technology (NIST) statistical suite test, and the results are provided in Appendix. The proposed encryption algorithm provides a huge keyspace and is robust against brute-force, dictionary, side-channel, correlation, differential, cropping, noise, chosen cipher, and plain image attacks. The various security analysis is performed on standard test images to demonstrate the proposed algorithm's efficiency and security. Further, a comparison with other competing existing algorithms is shown. The analysis results conclude that the proposed algorithm can opt for practical application.
DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose Estimation

Due to the lack of diversity of datasets, the generalization ability of the pose estimator is poor. To solve this problem, we propose a pose augmentation solution via DH forward kinematics model, which we call DH-AUG. We observe that the previous work is all based on single-frame pose augmentation, if it is directly applied to video pose estimator, there will be several previously ignored problems: (i) angle ambiguity in bone rotation (multiple solutions); (ii) the generated skeleton video lacks movement continuity. To solve these problems, we propose a special generator based on DH forward kinematics model, which is called DH-generator. Extensive experiments demonstrate that DH-AUG can greatly increase the generalization ability of the video pose estimator. In addition, when applied to a single-frame 3D pose estimator, our method outperforms the previous best pose augmentation method. The source code has been released at https://github.com/hlz0606/DH-AUG-DH-Forward-Kinematics-Model-Driven-Augmentation-for-3D-Human-Pose-Estimation .
Enhanced Prediction Model for Human Activity Using an End-to-End Approach

Human activity recognition (HAR) based on ambient sensors aims to recognize a conducted activity. A large number of deep learning models (DLMs) for HAR have been proposed. In contrast, human activity prediction (HAP) aims to early predict an activity. Compared to HAR, the advantage of HAP is to prevent a person from being exposed to unexpected cases by early predicting the activity. However, few DLMs for HAP have been proposed. They predict the next activity via a nonend-to-end fashion, e.g., they take a sequence of consecutive activities where the activities were classified from the sensor information. Thus, the information that which sensors are activated is not used in the prediction. In this study, we propose an end-to-end HAP model to predict the next activity from a sequence of consecutive events. The model has an encoder, a classifier, and a regressor. The encoder gives an encoded vector by encoding events. The regressor learns temporal dependency from a sequence of encoded vectors to predict the next encoded vector. The classifier predicts the next activity using the next encoded vector. We use the Milan and Aruba data sets to study a prediction accuracy of the model. We compare our model with a nonend-to-end model based on long-term memory, taking a sequence of past activities. We show that our model achieves the better prediction accuracy than the nonend-to-end model by up to 4.73% and 7.39% for Milan and Aruba, respectively, meaning that the information related to events can be used in the prediction.
Multimodal lnteraction with Gaze and Controller Gesture

Dragging tasks are a common Human Computer Interaction (HCI) activity observed in VR. In conjunction with the growing availability of eye-tracking devices, the team proceeded to investigate the performance of eye tracking-based multimodal interactions for dragging tasks for VR applications. Thirty-one participants were recruited for the study which evaluated various combinations of input interactions involving eye tracking. The evaluation approach took into consideration assessment metrics defined within ISO 9241–9 to design and develop an immersive 3D dragging task testbed to facilitate the experiment. An additional categorical task was implemented and administered in tandem with the dragging task to investigate concurrent task performance. Based on experiment data, the VR handheld controller-only condition performed tasks fastest, achieved the highest throughput, accuracy and precision, and was the most preferred modality. Certain eye tracking-based interactions achieved similar speed to the VR handheld controller-only condition and were preferred by participants, albeit having displayed imprecisions in the dragging task. Based on the study findings and the interaction implementation approach, we recommend VR handheld controller as a primary modality for the immersive 3D dragging task.
An Adversarial Defense Scheme for Federated Learning Based on GAN

Federated learning is a new architecture that allows indirect use of decentralized data for machine learning purposes due to privacy protection, with a focus on individual privacy and security and an emphasis on data ownership. However, unprotected federated learning outcome models are as vulnerable to adversarial samples as single-unit models trained centrally on data, and the high-dimensional linearity of deep neural networks makes it possible to mislead the model by adding imperceptibly small perturbations to the samples, yielding false predictions with high confidence and enabling adversarial attacks. In the federation learning setting, the use of a generalized stand-alone adversarial defense approach results in slow convergence of the aggregated model and low adversarial accuracy, so we propose an efficiency-optimized federation learning adversarial defense scheme to ensure efficient convergence of the outcome model and significant adversarial defense, which consists of (1) implementing robust semi-supervised learning instead of adversarial training in the distributed training phase, and (2) applying robust semi-supervised learning to the aggregated end bridging the adversarial perturbation inference model, which is used for perturbation detection and perturbation sample prediction, i.e., screening out the adversarial samples for the global model and exempting it from the inference of the adversarial samples, so that the global inference model only needs to touch the pure samples, thus improving the prediction accuracy. It is experimentally demonstrated that the proposed scheme in this paper can significantly improve the model robustness against adversarial attacks with higher efficiency.
Cross-Domain Few-Shot Semantic Segmentation

Few-shot semantic segmentation aims at learning to segment a novel object class with only a few annotated examples. Most existing methods consider a setting where base classes are sampled from the same domain as the novel classes. However, in many applications, collecting sufficient training data for meta-learning is infeasible or impossible. In this paper, we extend few-shot semantic segmentation to a new task, called Cross-Domain Few-Shot Semantic Segmentation (CD-FSS), which aims to generalize the meta-knowledge from domains with sufficient training labels to low-resource domains. Moreover, a new benchmark for the CD-FSS task is established and characterized by a task difficulty measurement. We evaluate both representative few-shot segmentation methods and transfer learning based methods on the proposed benchmark and find that current few-shot segmentation methods fail to address CD-FSS. To tackle the challenging CD-FSS problem, we propose a novel Pyramid-Anchor-Transformation based few-shot segmentation network (PATNet), in which domain-specific features are transformed into domain-agnostic ones for downstream segmentation modules to fast adapt to unseen domains. Our model outperforms the state-of-the-art few-shot segmentation method in CD-FSS by 8.49% and 10.61% average accuracies in 1-shot and 5-shot, respectively. Code and datasets are available at https://github.com/slei109/PATNet .
VCFN-Video Computing Force Network

This paper aims to put forward the development of video computing force network (VCFN) through the analysis and discussion of the concept, state of art, system architecture, key technologies and application scenarios. It is hoped that it can provide reference and guidance for the industry in terms of technology, products and solutions related to VCFN.