Reviewerly/exHarmony huggingface dataset:
A novel fault diagnosis method for aircraft actuator based on ensemble model

The actuator of aircraft is the direct executor of flight control signal. When it fails, the aircraft will lose control and even crash. However, due to the low frequency of fault occurrence and the lack of real fault data, the amount of health data and fault data is seriously imbalanced. The research on fault diagnosis of aircraft actuator under imbalanced data has practical engineering significance. Inspired by the ensemble model for solving the problem of imbalanced data classification, this paper proposes an innovative ensemble model called ensemble deep auto-encoder based extreme learning machine (DELM-AE). DELM-AE is a deep network constructed by multi-layer extreme learning machine based auto-encoder, which has the advantages of strong feature mining ability, high accuracy and fast speed. Firstly, the fault simulation model of flight control actuator is established, and then residual analysis and feature extraction are carried out on the data. Finally, compared with other common shallow model (extreme learning machine, support vector machine, back propagation neural network), ensemble model (random forest) and deep networks, the advantages of the proposed method in accuracy, processing speed, robustness and ability to deal with imbalanced data are proved.
A Black-Box Attack Model for Visually-Aware Recommender Systems

Due to the advances in deep learning, visually-aware recommender systems (RS) have recently attracted increased research interest. Such systems combine collaborative signals with images, usually represented as feature vectors outputted by pre-trained image models. Since item catalogs can be huge, recommendation service providers often rely on images that are supplied by the item providers. In this work, we show that relying on such external sources can make an RS vulnerable to attacks, where the goal of the attacker is to unfairly promote certain pushed items. Specifically, we demonstrate how a new visual attack model can effectively influence the item scores and rankings in a black-box approach, i.e., without knowing the parameters of the model. The main underlying idea is to systematically create small human-imperceptible perturbations of the pushed item image and to devise appropriate gradient approximation methods to incrementally raise the pushed item's score. Experimental evaluations on two datasets show that the novel attack model is effective even when the contribution of the visual features to the overall performance of the recommender system is modest.
Application of Big Data with Fintech in Financial Services

Computing technologies perform a vital role in the transformation of contemporary financial services. The emergence of financial technology (fintech) in recent years has transformed different facets of the global financial industries, thus changing the way financial personnel lives, think, and work. The fintech similarly had developed numerous technologies to advance the industry. These developments focused on the advancement of financial segments through the use of big data, cloud computing, the Internet of Things, artificial intelligence, and blockchain, for better transaction and security. Big data has seen a substantial adjustment in approaches to gather, demonstrate, and appraise information. How to use the data to develop new business models has attracted considerable attention, and recent technological advances have allowed us to produce data much faster than ever. Big data analytics is the modeling of big data to discover useful information and patterns, thereby communicating the same to support decision making and suggest conclusions. Big data can be processed, accumulated, and accumulated in the financial industries through rapidly evolving fields of big data analytics, thus enabling the understanding, rationalization, and use of big data for different purposes. Therefore, this chapter discusses the concept of big data for guaranteeing better expansion and research toward fintech. The chapter likewise will examine how large-volume financial rights data can greatly help clarify and elucidate fintech patterns. The chapter will also make an effort to convey various benefits that financial industries can make by using fintech with big data technology. The hope of using fintech with big data in financial services will have a great impact on the quality of financial transactions that can be delivered to financial sectors across socioeconomic and geographic boundaries.
Deep Learning for Anomaly Detection: Challenges, Methods, and Opportunities

In this tutorial we aim to present a comprehensive survey of the advances in deep learning techniques specifically designed for anomaly detection (deep anomaly detection for short). Deep learning has gained tremendous success in transforming many data mining and machine learning tasks, but popular deep learning techniques are inapplicable to anomaly detection due to some unique characteristics of anomalies, e.g., rarity, heterogeneity, boundless nature, and prohibitively high cost of collecting large-scale anomaly data. Through this tutorial, audiences would gain a systematic overview of this area, learn the key intuitions, objective functions, underlying assumptions, advantages and disadvantages of different categories of state-of-the-art deep anomaly detection methods, and recognize its broad real-world applicability in diverse domains. We also discuss what challenges the current deep anomaly detection methods can address and envision this area from multiple different perspectives. Any audience who may be interested in deep learning, anomaly/outlier/novelty detection, out-of-distribution detection, representation learning with limited labeled data, and self-supervised representation learning would find it very helpful in attending this tutorial. Researchers and practitioners in finance, cybersecurity, healthcare would also find the tutorial helpful in practice.
Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision

Convolutional Neural Networks (CNNs) have proved very accurate in multiple computer vision image classification tasks that required visual inspection in the past (e.g., object recognition, face detection, etc.). Motivated by these astonishing results, researchers have also started using CNNs to cope with image forensic problems (e.g., camera model identification, tampering detection, etc.). However, in computer vision, image classification methods typically rely on visual cues easily detectable by human eyes. Conversely, forensic solutions rely on almost invisible traces that are often very subtle and lie in the fine details of the image under analysis. For this reason, training a CNN to solve a forensic task requires some special care, as common processing operations (e.g., resampling, compression, etc.) can strongly hinder forensic traces. In this work, we focus on the effect that JPEG has on CNN training considering different computer vision and forensic image classification problems. Specifically, we consider the issues that rise from JPEG compression and misalignment of the JPEG grid. We show that it is necessary to consider these effects when generating a training dataset in order to properly train a forensic detector not losing generalization capability, whereas it is almost possible to ignore these effects for computer vision tasks.
Eigenfrequency optimisation of free violin plates

We discuss how the modal response of violin plates changes as their shape varies. Starting with an accurate 3D scan of the top plate of a historic violin, we develop a parametric model that controls a smooth shaping of the interior of the plate, while guaranteeing that the boundary is the same as the original violin. This allows us to generate a family of violin tops whose shape can be smoothly controlled through various parameters that are meaningful to a violin maker: from the thickness in different areas of the top to the location, angle, and dimensions of the bass bar. We show that the interplay between the different parameters affects the eigenmodes of the plate frequencies in a nonlinear fashion. We also show that, depending on the parameters, the ratio between the fifth and the second eigenfrequencies can be set to match that used by celebrated violin makers of the Cremonese school. As the parameterisation that we define can be readily understood by violin makers, we believe that our findings can have a relevant impact on the violin making community, as they show how to steer geometric modifications of the violin to balance the eigenfrequencies of the free plates.
A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks

It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years. However, EAs suffer from the curse of dimensionality and are inefficient in training deep NNs (DNNs). By inheriting the advantages of both the gradient-based approaches and EAs, this article proposes a gradient-guided evolutionary approach to train DNNs. The proposed approach suggests a novel genetic operator to optimize the weights in the search space, where the search direction is determined by the gradient of weights. Moreover, the network sparsity is considered in the proposed approach, which highly reduces the network complexity and alleviates overfitting. Experimental results on single-layer NNs, deep-layer NNs, recurrent NNs, and convolutional NNs (CNNs) demonstrate the effectiveness of the proposed approach. In short, this work not only introduces a novel approach for training DNNs but also enhances the performance of EAs in solving large-scale optimization problems.
The practical ethics of bias reduction in machine translation: why domain adaptation is better than data debiasing

Abstract This article probes the practical ethical implications of AI system design by reconsidering the important topic of bias in the datasets used to train autonomous intelligent systems. The discussion draws on recent work concerning behaviour-guiding technologies, and it adopts a cautious form of technological utopianism by assuming it is potentially beneficial for society at large if AI systems are designed to be comparatively free from the biases that characterise human behaviour. However, the argument presented here critiques the common well-intentioned requirement that, in order to achieve this, all such datasets must be debiased prior to training. By focusing specifically on gender-bias in Neural Machine Translation (NMT) systems, three automated strategies for the removal of bias are considered – downsampling, upsampling, and counterfactual augmentation – and it is shown that systems trained on datasets debiased using these approaches all achieve general translation performance that is much worse than a baseline system. In addition, most of them also achieve worse performance in relation to metrics that quantify the degree of gender bias in the system outputs. By contrast, it is shown that the technique of domain adaptation can be effectively deployed to debias existing NMT systems after they have been fully trained. This enables them to produce translations that are quantitatively far less biased when analysed using gender-based metrics, but which also achieve state-of-the-art general performance. It is hoped that the discussion presented here will reinvigorate ongoing debates about how and why bias can be most effectively reduced in state-of-the-art AI systems.
Ethical perspectives in sharing digital data for public health surveillance before and shortly after the onset of the Covid-19 pandemic

Data from digital technologies are increasingly integrated in public health research. In April of 2020, we interviewed a subset of participants (N = 25) who completed a survey approximately one month earlier (just prior to the declaration of the COVID-19 pandemic in the United States). Using the survey, we contacted and interviewed participants who had expressed their willingness or unwillingness to share digital data (e.g., from contact tracing apps) for use in public health. We followed a directed content analysis approach for the analysis of the interview data. Among participants who had reported being unwilling to share data, concerns about privacy, confidentiality, and the purpose of the research were cited. During the interviews, 76.9% of the participants who had who had previously indicated that they were unwilling to share their data, expressed willingness to share data in order to assist with COVID-19 prevention. Our results contribute to our understanding of people’s perspectives on sharing personal data and of the way their perspectives can vary as a function of potential uses of their personal information (e.g., prevention of COVID-19).
An Ensemble Model using CNNs on Different Domains for ALASKA2 Image Steganalysis

We present our third place solution for the ALASKA2 Image Steganalysis competition. We develop detectors using convolutional neural networks (CNNs) on both the spatial domain and the frequency domain of the discrete cosine transform used in JPEG compression. Our CNN detectors use state-of-the-art architectures in image classification tasks. We adjust the architecture to better capture the features of steganography methods in the frequency domain. We build an ensemble model of these CNNs, in which both spatial and frequency domain models contribute to performance. In this paper, we describe those models in detail and explain how the techniques used in them improve accuracy through experiments.
IdentityDP: Differential private identification protection for face images

Because of the explosive growth of face photos as well as their widespread dissemination and easy accessibility in social media, the security and privacy of personal identity information become an unprecedented challenge. Meanwhile, the convenience brought by advanced identity-agnostic computer vision technologies is attractive. Therefore, it is important to use face images while taking careful consideration in protecting people’s identities. Given a face image, face de-identification, also known as face anonymization, refers to generating another image with similar appearance and the same background, while the real identity is hidden. Although extensive efforts have been made, existing face de-identification techniques are either insufficient in photo-reality or incapable of well-balancing privacy and utility. In this paper, we focus on tackling these challenges to improve face de-identification. We propose IdentityDP, a face anonymization framework that combines a data-driven deep neural network with a differential privacy (DP) mechanism. This framework encompasses three stages: facial representations disentanglement, ∊-IdentityDP perturbation and image reconstruction. Our model can effectively obfuscate the identity-related information of faces, preserve significant visual similarity, and generate high-quality images that can be used for identity-agnostic computer vision tasks, such as detection, tracking, etc. Different from the previous methods, we can adjust the balance of privacy and utility through the privacy budget according to practical demands and provide a diversity of results without pre-annotations. Extensive experiments demonstrate the effectiveness and generalization ability of our proposed anonymization framework.
Structural vibration measurement with multiple synchronous cameras

This paper presents a simulation and experimental study on the measurement with multiple, i.e. more than 2, synchronous cameras of the flexural deflection shape of a beam excited by a transverse force at the fundamental resonance frequency, which gives a good estimate of the first flexural natural mode shape. The first part of the paper presents the simplified optical model employed to simulate how the accuracy of the measurements varies with respect to: a) the distance of the cameras from the structure; b) the opening angle between pairs of cameras, which is analysed with respect to the azimuthal and elevation spherical coordinates, c) the resolution of cameras and d) the number of cameras. The second part of the paper provides experimental results taken on a beam rig and camera setup assembled using off-the-shelf devices. The study shows that the accuracy of the measurements significantly rises as the setup is equipped with an increasingly larger number of cameras, which should be arranged close to the vibrating structure with large opening angles between pairs of cameras characterized by large azimuthal and small elevation spherical coordinates. For instance, the average error of the measured flexural deflection shape falls from 15% to 3% when a setup with cameras arranged along a circumference oriented parallel the beam surface, with a small offset from the beam, passes from 2 cameras to 12 cameras.
Towards Multi-Modal Perception-Based Navigation: A Deep Reinforcement Learning Method

In this letter, we present a novel navigation system of unmanned ground vehicle (UGV) for local path planning based on deep reinforcement learning. The navigation system decouples perception from control and takes advantage of multi-modal perception for a reliable online interaction with the surrounding environment of the UGV, which enables a direct policy learning for generating flexible actions to avoid collisions with obstacles in the navigation. By replacing the raw RGB images with their semantic segmentation maps as the input and applying a multi-modal fusion scheme, our system trained only in simulation can handle real-world scenes containing dynamic obstacles such as vehicles and pedestrians. We also introduce a modal separation learning to accelerate the training and further boost the performance. Extensive experiments demonstrate that our method closes the gap between simulated and real environments, exhibiting the superiority over state-of-the-art approaches. Please refer to https://vsislab.github.io/mmpbnv1/ for the supplementary video demonstration of UGV navigation in both simulated and real-world environments.
The Catenary Robot: Design and Control of a Cable Propelled by Two Quadrotors

Transporting objects using aerial robots has been widely studied in the literature. Still, those approaches always assume that the connection between the quadrotor and the load is made in a previous stage. However, that previous stage usually requires human intervention, and autonomous procedures to locate and attach the object are not considered. Additionally, most of the approaches assume cables as rigid links, but manipulating cables requires considering the state when the cables are hanging. In this work, we design and control a catenary robot. Our robot is able to transport hook-shaped objects in the environment. The robotic system is composed of two quadrotors attached to the two ends of a cable. By defining the catenary curve with five degrees of freedom, position in 3-D, orientation in z-axis, and span, we can drive the two quadrotors to track a given trajectory. We validate our approach with simulations and real robots. We present four different scenarios of experiments. Our numerical solution is computationally fast and can be executed in real-time.
Fall Detection System-Based Posture-Recognition for Indoor Environments

The majority of the senior population lives alone at home. Falls can cause serious injuries, such as fractures or head injuries. These injuries can be an obstacle for a person to move around and normally practice his daily activities. Some of these injuries can lead to a risk of death if not handled urgently. In this paper, we propose a fall detection system for elderly people based on their postures. The postures are recognized from the human silhouette which is an advantage to preserve the privacy of the elderly. The effectiveness of our approach is demonstrated on two well-known datasets for human posture classification and three public datasets for fall detection, using a Support-Vector Machine (SVM) classifier. The experimental results show that our method can not only achieves a high fall detection rate but also a low false detection.
DROID: Minimizing the Reality Gap Using Single-Shot Human Demonstration

Reinforcement learning (RL) has demonstrated great success in the past several years. However, most of the scenarios focus on simulated environments. One of the main challenges of transferring the policy learned in a simulated environment to real world, is the discrepancy between the dynamics of the two environments. In prior works, Domain Randomization (DR) has been used to address the reality gap for both robotic locomotion and manipulation tasks. In this letter, we propose Domain Randomization Optimization IDentification (DROID), a novel framework to exploit single-shot human demonstration for identifying the simulator's distribution of dynamics parameters, and apply it to training a policy on a door opening task. Our results show that the proposed framework can identify the difference in dynamics between the simulated and the real worlds, and thus improve policy transfer by optimizing the simulator's randomization ranges. We further illustrate that based on these same identified parameters, our method can generalize the learned policy to different but related tasks.
FaceHop: A Light-Weight Low-Resolution Face Gender Classification Method

A light-weight low-resolution face gender classification method, called FaceHop, is proposed in this research. We have witnessed rapid progress in face gender classification accuracy due to the adoption of deep learning (DL) technology. Yet, DL-based systems are not suitable for resource-constrained environments with limited networking and computing. FaceHop offers an interpretable non-parametric machine learning solution. It has desired characteristics such as a small model size, a small training data amount, low training complexity, and low-resolution input images. FaceHop is developed with the successive subspace learning (SSL) principle and built upon the foundation of PixelHop++. The effectiveness of the FaceHop method is demonstrated by experiments. For gray-scale face images of resolution \(32 \times 32\) in the LFW and the CMU Multi-PIE datasets, FaceHop achieves correct gender classification rates of 94.63% and 95.12% with model sizes of 16.9K and 17.6K parameters, respectively. It outperforms LeNet-5 in classification accuracy while LeNet-5 has a model size of 75.8K parameters.