Reviewerly/exHarmony huggingface dataset:
Cryptocurrency and Data Privacy in Human Resource Management

Cryptocurrency and data privacy are two important considerations in human resource management. Cryptocurrency, such as Bitcoin, can be used to pay employees, provide incentives, and manage payroll, but it also poses risks such as price volatility and regulatory issues. Data privacy, on the other hand, is the protection of personal information from unauthorized access and use. The use of blockchain technology, which is the underlying technology behind many cryptocurrencies, can enhance data privacy in human resource management by providing a secure and tamper-proof record of all transactions. However, it’s important to comply with all applicable laws and regulations related to the use of cryptocurrency and data privacy in human resource management.
Adaptive Particle Filtering With Variational Bayesian and Its Application for INS/GPS Integrated Navigation

This article considers the unknown measurement noise covariance problem in the nonlinear situation of a navigation system. Aiming at the contaminated global position system (GPS) signals and the outlier environment, there are many variational Bayesian (VB)-based Gaussian approximation methods in the integrated navigation system (INS). However, the integrated navigation is nonlinear, especially for the inaccurate initial state provided by the initial alignment stage. The VB method is first incorporated into cubature particle filter (PF), of which the proposal distribution is set as cubature Kalman filter (CKF) to provide the accuracy and stable estimation for the application of navigation system. Then, the Kullback–Leibler distance (KLD) resampling method is merged into the VB-based cubature PF to supply sufficient particles that enhance the stability of the proposed filter. The numerical simulation demonstrates that the proposed filter does better at presenting accurate and stable estimation than the VB-based CKF, and the experiments verify the effectiveness of the proposed filter for the application of INS.
Read-All-in-Once (RAiO): Multi-Layer Contextual Architecture for Long-Text Machine Reading Comprehension

Machine reading comprehension (MRC) is a cutting-edge technology in natural language processing (NLP), which focus on teaching machines to read and understand the meaning of texts based on the emergence of large-scale datasets and neural network models. Recently, with the successful development of pre-trained transformer models (e.g., BERT), MRC has advanced significantly, surpassing human parity in several public datasets and being applied in various NLP tasks (e.g., QA systems). Nevertheless, long document MRC is still a remain challenge since the transformer-based models are limited by the input length. For instance, several well-known pre-trained language models such as BERT and RoBERTa are limited by 512 tokens. This study aims to provide a new simple approach for long document MRC. Specifically, recent state-of-the-art models follow the architecture with two crucial stages for reading long texts in order to enable local and global context representations. In this study, we present a new architecture that is able to enrich the global information of the context with one stage by exploiting the interaction of different levels of semantic units of the context (i.e., sentence and word level). Therefore, we name the proposed model as RAiO (Read-All-in-Once) approach. For the experiment, we evaluate RAiO on two benchmark long document MRC datasets such as NewsQA and NLQuAD. Accordingly, the experiment shows promising results of the proposed approach compared with strong baselines in this research field.
A Task-Oriented General-Purpose Distributed Computing System Based on CLTS Scheduling Algorithm

Most current research on commonly used distributed computing frameworks neglects the difficulty of learning these frameworks. These commonly used distributed computing frameworks are designed for professionals, and users must understand them well to use them for calculation tasks. In order to solve the challenging balance problem between versatility and user learning difficulty, a new distributed computing system is proposed. The design of this system is based on the idea of divide and conquer in distributed computing. The paper uses Netty, Zookeeper, Hadoop Distributed File System (HDFS) and other tools to implement the system functions. Firstly, the paper analyzes the research status of distributed computing frameworks at home and abroad; Then, according to the idea of divide and conquer, the paper uses Netty to implement the basic architecture of the system, uses Zookeeper and Netty to implement relevant distributed mechanisms, uses HDFS as the distributed storage, designs and implements a Task-Oriented General-Purpose (TOGP) distributed computing system, TOGP; Finally, the paper intensely studies the task scheduling problem of the system, proposes a scheduling algorithm, Node Processing Capacity and Distributed Lock Task Scheduling (CLTS) algorithm. The system scalability test proves that the TOGP has good horizontal scalability. In the comparative experiment, the processing capacity of the TOGP using the round robin is about 16% higher than that of Hadoop, which proves that the TOGP is available and efficient. A series of experiments prove that the CLTS scheduling algorithm can effectively adapt to the heterogeneous cluster environment and efficiently schedule tasks.
Zero-Shot Hyperspectral Image Denoising Using Self-Completion With 3D Random Patterned Masks

Hyperspectral images (HSIs) have higher spectral resolution than RGB images and are used in various tasks. However, HSIs are prone to degradation due to noise generated during imaging, making it difficult to obtain non-degraded images. Additionally, supervised learning, which relies on pairs of degraded and non-degraded images, is often challenging to apply to HSI restoration because of the high cost of imaging and the need to prepare large amounts of data. To overcome these limitations, recent advances in self-supervised learning have led to the development of learning-based image restoration methods that do not require non-degraded images. However, these methods have limitations, including low accuracy and the need to estimate the noise distribution. In this paper, we propose a zero-shot HSI deep denoising method based on self-supervised image restoration. The proposed method achieves zero-shot recovery by repeatedly predicting blind-spots in 3D blocks during the learning process. Notably, our method does not require training or clean images, nor does it rely on noise distribution information. Numerical experiments and ablation studies confirmed that the restoration accuracy of the proposed method is comparable to or better than that of conventional zero-shot methods.
Exploiting Field Dependencies for Learning on Categorical Data

Traditional approaches for learning on categorical data underexploit the dependencies between columns (a.k.a. fields) in a dataset because they rely on the embedding of data points driven alone by the classification/regression loss. In contrast, we propose a novel method for learning on categorical data with the goal of exploiting dependencies between fields. Instead of modelling statistics of features globally (i.e., by the covariance matrix of features), we learn a global field dependency matrix that captures dependencies between fields and then we refine the global field dependency matrix at the instance-wise level with different weights (so-called local dependency modelling) w.r.t. each field to improve the modelling of the field dependencies. Our algorithm exploits the meta-learning paradigm, i.e., the dependency matrices are refined in the inner loop of the meta-learning algorithm without the use of labels, whereas the outer loop intertwines the updates of the embedding matrix (the matrix performing projection) and global dependency matrix in a supervised fashion (with the use of labels). Our method is simple yet it outperforms several state-of-the-art methods on six popular dataset benchmarks. Detailed ablation studies provide additional insights into our method.
Multiobservation-Based Closely Spaced Extended Target Angle Estimation Method

Highly accurate target angle estimation is essential for target tracking. In closely spaced scenarios, when multiple targets are close to each other, their measurement points will be located in the same range-azimuth cell. In particular, under wideband radar conditions, the target is expanded into multiple scattering points. When multiple extended targets are located close to each other in space, there may be multiple target scattering points falling within the same range-azimuth cell. In this case, there is a large bias in the estimated angle of each extended target, which affects the accuracy of the estimated angle of the closely spaced extended target. To solve this problem, this article proposes a closely spaced extended target angle estimation method under multiple observations. The proposed method utilizes the principle of invariance of extended target scattering characteristics between adjacent pulses to predict the scattering of extended target echoes and employs maximum likelihood estimation (MLE) to estimate the extended target angle. The effectiveness and feasibility of the proposed method are evaluated through experimental simulation.
Body Weight Estimation Using Virtual Anthropometric Measurements From a Single Image

Direct estimation of body weight through noncontact methods is crucial for applications such as health monitoring, surveillance, and robot-assisted casualty rescue. Existing methods for body weight estimation from images are often affected by various factors, such as camera distance, human orientation, and body posture, etc., which ignore the fact that bodies typically inhabit 3D space. To address the problem, we propose an approach for estimating body weight based on virtual anthropometric measurements and deep features instead of estimating by reasoning pixels. Specifically, we develop a three-branch framework that includes face feature extraction, body feature extraction as well as deep feature extraction, and maps all features with a regressor. The developed method adds 3D shape reconstruction to explicitly reason about virtual anthropometric measurements. To enable this, our model is trained to robustly compute anthropometric measurements in various orientations and postures. Furthermore, we evaluate our method on a public dataset and Image-VM-BMI, a new dataset of 4740 images, including BMI labels and virtual anthropometric measurement labels with paired 3D reconstruction. Extensive experimental results demonstrate that the proposed method outperforms pixel-based analysis approaches on BMI estimation.
Adversarial Continual Learning to Transfer Self-Supervised Speech Representations for Voice Pathology Detection

In recent years, voice pathology detection (VPD) has received considerable attention because of the increasing risk of voice problems. Several methods, such as support vector machine and convolutional neural network-based models, achieve good VPD performance. To further improve the performance, we use a self-supervised pretrained model as feature representation instead of explicit speech features. When the pretrained model is fine-tuned for VPD, an overfitting problem occurs due to a domain shift from conversation speech to the VPD task. To mitigate this problem, we propose an adversarial task adaptive pretraining (A-TAPT) approach by incorporating adversarial regularization during the continual learning process. Experiments on VPD using the Saarbrucken Voice Database show that the proposed A-TAPT improves the unweighted average recall (UAR) by an absolute increase of 12.36% and 15.38% compared with SVM and ResNet50, respectively. It is also shown that the proposed A-TAPT achieves a UAR that is 2.77% higher than that of conventional TAPT learning.
Publicly Verifiable Homomorphic Secret Sharing for Polynomial Evaluation

There are two main security concerns in outsourcing computations. One is how to protect the privacy of the outsourced data, and the other is how to ensure the correctness of the outsourced computations. Homomorphic secret sharing (HSS) schemes allow a client to store a set of private data on two servers and then offload a computation on the data to servers. Such schemes ensure that each individual server learns no information about the data. While HSS schemes that allow the client to use a secret key to verify the correctness of the computation results exist and relieve both security concerns, the current literature lacks a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">publicly verifiable</i> HSS scheme for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">polynomial evaluations</i> . In this paper, we consider a two-server publicly verifiable HSS (PVHSS) model, where any third party can use a public key to perform verifications. We propose both a basic construction and an improved construction of PVHSS for evaluating polynomials. Our PVHSS ensures that no single server is able to learn any information about the outsourced data or persuade the verifier to accept a wrong result. We also implement the proposed scheme. For polynomials of degree ≤20, our experiments show that: (1) the proposed PVHSS is 2×-144× faster than the existing non-verifiable or privately verifiable HSS on the server-side; (2) the proposed PVHSS is friendly to resource-restricted clients and takes less than 27ms to reconstruct and verify the results.
DIY Your EasyNAS for Vision: Convolution Operation Merging, Map Channel Reducing, and Search Space to Supernet Conversion Tooling

Despite its popularity as a one-shot Neural Architecture Search (NAS) approach, the applicability of differentiable architecture search (DARTS) on complex vision tasks is still limited by the high computation and memory costs incurred by the over-parameterized supernet. We propose a new architecture search method called EasyNAS, whose memory and computational efficiency is achieved via our devised operator merging technique which shares and merges the weights of candidate convolution operations into a single convolution, and a dynamic channel refinement strategy. We also introduce a configurable search space-to-supernet conversion tool, leveraging the concept of atomic search components, to enable its application from classification to more complex vision tasks: detection and semantic segmentation. In classification, EasyNAS achieves state-of-the-art performance on the NAS-Bench-201 benchmark, attaining an impressive 76.2% accuracy on ImageNet. For detection, it achieves a mean average precision (mAP) of 40.1 with 120 frames per second (FPS) on MS-COCO test-dev. Additionally, we transfer the discovered architecture to the rotation detection task, where EasyNAS achieves a remarkable 77.05 mAP 50 on the DOTA-v1.0 test set, using only 21.1 M parameters. In semantic segmentation, it achieves a competitive mean intersection over union (mIoU) of 72.6% at 173 FPS on Cityscape, after searching for only 0.7 GPU-day.
Graph Learning from Signals with Smoothness Superimposed by Regressors

There is an increasing interest in processing data described by graph structures resulting in graph signal processing (GSP) and graph neural networks (GNN). One of the fundamental problems in GSP is graph learning, which uncovers the network topology from the signals measured at vertices. However, most existing approaches to graph learning merely look at the functional mapping from the smooth signals to the graph without considering signals' regressors. This paper proposes a novel algorithm (GLReg) for graph learning from smooth signals on the network and other regressors, considering the graph signals' smoothness and their relationship with other regressors. The theoretical derivation explains the proposed algorithm GLReg, and experimental tests on synthetic and real-world graphs show the effectiveness of our algorithm. The results of our study provide new insight into the graph learning model, and can be widely applied in the analysis of geographical, biomedical, and social networks.
Blockchain-Based Solution for Patient Health Records Sharing by Adopting Private Decentralized Storage

The recent Covid-19 pandemic serves as a great trial of the flexibility and the competency of patient health record sharing among healthcare providers, which resulted in an immense disruption to the healthcare sectors. This public turning point disaster has plunged in a rapid alteration of the patient health records sharing (PHRS) structure to respond to the demands and furnish proper patient care. In addition, the downside effects of the Covid-19 outbreak modified the healthcare system permanently. As a result, more patients demand control, security, and a seamless experience when they desire to get a hold of their health records, increasing rapidly. However, the issues merge from the lack of interoperability among the healthcare sectors and providers. However, the added burden of cyber-attacks on an already overwhelmed system is to inquire about a swift solution. In this chapter, the developed system is leveraging the benefits and aspects of Blockchain, smart contracts, and the private IPFS file system to safeguard patients’ complete control over their health records and stored them in their private IPFS. Then, patients can share their data with authorized providers. The assessed security and privacy features show promising results in supplying time savings in sharing patient health records during the Covid-19 pandemic, improving confidentiality, and less disrupting the interaction between the patient and the provider. Furthermore, the chapter proves that our smart contracts code is security vulnerabilities and bugs free.
A Blockchain-Based Secure Framework for Homomorphic AI in IoHT for Tackling COVID-19 Pandemic

Pandemics like COVID-19 have posed new challenges in the healthcare sector around the world. Artificial Intelligence (AI) and the Internet of Healthcare Things (IoHT) have been widely used to combat the recent pandemic and provide assistance to the population. Security concerns in international cooperation impede the advancement of AI in IoHT services. Arguably, the confidentiality of data, the technology edge of AI providers, integrity, and availability of service are the primary concerns. To this end, in this chapter, we have proposed a blockchain-based secure framework for homomorphic AI in IoHT networks to handle security concerns. In this framework, we focus on AI as the core computation of IoHT networks. The proposed framework is based on blockchain technology and distributed storage to ensure integrity and availability in a decentralized network. The proposed framework uses homomorphic encryption to enable privacy-preserving computation, mainly AI services. Moreover, the security analysis of this framework shows that it is formally provable to meet the defined security requirements. We present a use case for diagnosing and monitoring COVID-19 during a pandemic in order to evaluate the proposed framework.
Mechanical Malfunction Detection in Vehicle Using Artificial Intelligence

Acoustics is well renowned for contributing significantly to research. It does a promising job of providing a solution for several research-based problems. It includes the investigation of mechanical waves in gases, liquids, and solids. Discussion topics regarding sound, vibration, infrasound, and ultrasound are included. The applications can be used in all the main areas of contemporary society. The noise and audio control industries are where acoustics is most useful. The fundamental idea is that while acoustics can be used to a variety of situations, it can also be utilised to address issues with accident detection in VANET. The primary goal of this study is to present an auditory signal processing-based approach to automobile accident detection.
Robust Self-training with Label Refinement for Noisy Domain Adaptation

Unsupervised Domain Adaptation (UDA) intends to achieve excellent results by transferring knowledge from labeled source domains to unlabeled target domains in which the data or label distribution changes. Previous UDA methods have acquired great success when labels in source domain are pure. However, even the acquisition of a large scare clean labels in source domain needs plenty of cost as well. In the presence of label noise in source domain, the traditional UDA methods will be seriously degraded as they do not take appropriate measures against the label noise. In this paper, we propose an approach named Robust Self-training with Label Refinement (RSLR) to address the above issue. RSLR adopts the self-training framework by maintaining two Labeling Networks (LNets) on the source domain, which are used to provide confident pseudo-labeled to target samples, and a Target-specific Network (TNet) trained by the pseudo-labeled samples. To combat the effect of label noise, LNets progressively distinguish and refine the mislabeled source samples. RSLR leads to significant improvements over the state-of-the-art methods on extensive benchmark datasets.
Off-Line and Real-Time Estimators for Monocular Position-Based Visual Servoing

Object tracking is a task employed in many applications; in this article, we discussed about using a vision camera to detect fiducial markers and obtain a reference frame. Due to the 2D nature of the marker, their corners can be used to estimate the camera pose. The evolution of the points will be used to transform the initial position of the camera and obtain the path described by the robot end-effector. Finally, we show a comparison of the classic Kalman filter for real-time estimation against the UFIR Filter to minimize the errors due to the vision system. Also, we test offline estimators and discuss the principal characteristics of the two presented schemas. The proposed estimators are tested in hardware with a 6DOF robot Nachi MZ04, the pose computed through ArUco markers will be compared against the pose obtained with a T265 Intel Realsense camera.
Age Estimation and Gender Recognition Based on Canny-MobileNetV2

Face age recognition and gender recognition can provide a qualitative description of humans and are of great research value. At the same time, age recognition and gender recognition are more challenging than normal face recognition because they are influenced by factors such as face contour, skin colour and the position of the five senses. In view of this, this paper proposes the Canny-MobileNetV2 network model to recognize age groups (juvenile, youth, middle age, and elderly) and to classify gender (male and female). Firstly, the feature extraction method is improved by using the MobileNetV2 model as the feature extraction backbone network, and the face structure map obtained by Canny edge detection is fed into the model together with the source map to extract more relevant features, and then feature fusion is performed with different weights to obtain fused features; secondly, combining the function that the cross-entropy loss function can increase the inter-class distance and the function that the central loss function can reduce the union loss function is proposed to improve the classification accuracy of the model by combining the function that the cross-entropy loss function can increase the distance between classes and the function that the central loss function can reduce the distance within classes. To verify the performance of the Canny-MobileNetV2 model, experimental validation was conducted on the Adience dataset, and the recognition accuracy of age and gender reached 85.10% and 92.96% respectively, both of which were higher than that of the MobileNetV2 model.