Reviewerly/exHarmony huggingface dataset:
A Mechanical Equipment Fault Diagnosis Model Based on TSK Fuzzy Broad Learning System

In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model—the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model’s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model—TSK-BLS, achieved significant improvements.
Heritage and Repeat Photography: Techniques, Management, Applications, and Publications

Repeat photography, the process of locating the camera position of an old photo and taking a new photo from this position, is utilized to protect our natural heritage, by measuring landscape changes between old and new image, and to conserve and recover our architectural and archaeological heritage, by observing changes between images taken before, during, and after conservation efforts or destructive events. Most importantly, repeat photography—in short, rephotography—is connecting us to our heritage on all levels from personal to global: while rephotographing, we connect with the old image and its creator and—when viewing and comparing the old image and the rephotograph—we connect with the tangible, intangible, and natural heritage shown in the images by observing changes and continuities. In this paper, we review publications and applications employing rephotography. We discuss the organization, retrieval, and presentation of rephotographic compilations, their generation by different image registration techniques, and their conservation as rephotographic heritage. Further, we describe challenges in rephotography, discuss how they differ from standard challenges in photography, computer vision, and image registration and present both analog and digital solutions and tools used in practice by rephotographers to address said challenges.
Basketball Action Recognition Method of Deep Neural Network Based on Dynamic Residual Attention Mechanism

Aiming at the problem that the features extracted from the original C3D (Convolutional 3D) convolutional neural network(C3D) were insufficient, and it was difficult to focus on keyframes, which led to the low accuracy of basketball players’ action recognition; hence, a basketball action recognition method of deep neural network based on dynamic residual attention mechanism was proposed. Firstly, the traditional C3D is improved to a dynamic residual convolution network to extract sufficient feature information. Secondly, the extracted feature information is selected by the improved attention mechanism to obtain the key video frames. Finally, the algorithm is compared with the traditional C3D in order to demonstrate the advance and applicability of the algorithm. Experimental results show that this method can effectively recognize basketball posture, and the average accuracy of posture recognition is more than 97%.
Horizon of cryptocurrency before vs during COVID-19

Investment cannot be separated from the level of return and risk inherent in assets. Today, investment instruments are not only stocks, currencies, bonds, deposits, savings and others. The beginning of Bitcoin’s emergence as a pioneer of Cryptocurrency was in 2009. Crypto assets are emerging rapidly and are accompanied by an increase in the number of transactions each period. The growth in the market capitalization value of crypto assets has also grown significantly. During COVID-19, many investments, such as stocks, experienced a decline due to market uncertainty. The results of this study prove that with the existence of COVID-19, the crypto market is not affected. Crypto is an attraction characterized by a high degree of fluctuation, and there is no limit to transactions in the open market 24 hours to trade. The Cryptocurrency market is currently a market that can provide short-term benefits to risk-taking investors, while the market in other investment instruments is declining. 78% of the value capitalization of the top 200 cryptocurrencies is represented by the top 9 cryptos used as samples in this study. So that if there is a decrease in these 9 cryptos, it will also have an impact on the overall capitalization value of crypto in the market. The future development of Cryptocurrencies will no longer be digital assets traded with many speculators who can control prices, it can even be digital money that can be used worldwide without any transaction fees and is controlled on a blockchain system.
Automatic collaborative water surface coverage and cleaning strategy of UAV and USVs

As the problem of surface garbage pollution becomes more serious, it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods. Due to lightness, unmanned aerial vehicles (UAVs) can traverse the entire water surface in a short time through their flight field of view. In addition, unmanned surface vessels (USVs) can provide battery replacement and pick up garbage. In this paper, we innovatively establish a system framework for the collaboration between UAV and USVs, and develop an automatic water cleaning strategy. First, on the basis of the partition principle, we propose a collaborative coverage path algorithm based on UAV off-site takeoff and landing to achieve global inspection. Second, we design a task scheduling and assignment algorithm for USVs to balance the garbage loads based on the particle swarm optimization algorithm. Finally, based on the swarm intelligence algorithm, we also design an autonomous obstacle avoidance path planning algorithm for USVs to realize autonomous navigation and collaborative cleaning. The system can simultaneously perform inspection and clearance tasks under certain constraints. The simulation results show that the proposed algorithms have higher generality and flexibility while effectively improving computational efficiency and reducing actual cleaning costs compared with other schemes.
Attentive statement fraud detection: Distinguishing multimodal financial data with fine-grained attention

Financial statement fraud caused by listed companies directly jeopardizes the reliability the financial reporting process. Leveraging multimodal information for financial statement fraud detection (FSFD) has recently become of great interest to academic research and industrial applications. Unfortunately, the predictive ability of multimodal information in FSFD remains largely underexplored, particularly the fusion ambiguity embedded in and among multi-modalities. In this study, we propose a novel attention-based multimodal deep learning method, named RCMA, toward an accurate FSFD. RCMA synthesizes a fine-grained attention mechanism including three innovative attention modules, i.e., ratio-aware attention, chapter-aware attention, and modality-aware attention mechanism. The first two attention mechanisms help to liberate the extraordinary predictive power of the financial modality and the textual modality on FSFD, respectively. Moreover, the proposed modality-aware attention mechanism enables better coordination between the two modalities. Furthermore, to ensure effective learning on the attention-based multimodal embedding, we design a novel loss function named Focal and Consistency Loss, or FCL. It considers class-imbalance and modality-consistency simultaneously, to specialize the optimization of FSFD. The experimental results on the real-world dataset show that the proposed RCMA on FSFD task outperformed the state-of-the-art benchmarks. Furthermore, interpretation analysis visualizes the attention weights of different ratio groups, chapters, and modalities from RCMA, and illustrates how these interpretations influence stakeholders' decision process for FSFD.
Metric to evaluate distribution shift from behavioral cloning for fuel-optimal landing policies

The development of robust fuel-optimal feedback controllers for the pinpoint landing problem has application to a variety of aerospace vehicles including lunar and planetary landers. Imitation learning has been used for a variety of low-fidelity lander formulations to train a policy based on numerically generated open-loop optimal trajectories. The simplest technique, Behavioral Cloning, can suffer from distribution shift, where the distribution of training data is different than the distribution of states seen in rollout. This paper presents a new metric to measure this shift, and a comprehensive study is performed on a variety of lander formulations and policy classes. It is found that shift increases with dimensionality of and the level of coupling in equations of motion, and that the non-parametric policy yields more shift than the parametric one.
Market efficiency of the cryptocurrencies: Some new evidence based on price–volume relationship

Abstract Cryptocurrencies have emerged as an important investment avenue in the past few years. Investors are increasingly interested in these currencies amid surging financial returns. In this context, understanding market efficiency of cryptocurrency has become very crucial for investors and academicians. The price–volume framework is a popular approach in financial economics to understand the market efficiency of stocks in the stock markets. Therefore, this article examines the market efficiency of cryptocurrencies through price–volume framework to understand whether crypto market is predictable. Towards this objective, data on both return and trading volume (TV) of the top eight cryptocurrencies are used for the period 8 August 2015–20 October 2022. As an empirical method, both linear and non‐linear causality models are used to validate the hypothesis. Our results confirm that TV cannot predict the cryptocurrencies' return, thereby validating the market efficiency hypothesis. Furthermore, we divide the sample according to the structural break period. The result from the post‐break period analysis also confirms the presence of market efficiency in the recent period for all currencies, barring XRP, XMR and DASH.
Differentially Private Crowdsourcing With the Public and Private Blockchain

As a result of the rapid development of Internet of Things (IoT) systems, an increasing number of academics are focusing on finding new applications for IoT systems. For IoT systems, crowdsourcing is a prevalent practise. Due to the large number of deployed devices in IoT networks, more research is still required on the privacy and trust issues that arise when utilizing crowdsourcing. As a result of the characteristics of social computing, the crowdsourcing network poses issues in terms of confidentiality and reliability. To consolidate and create this industry, we have built a differentially private crowdsourcing system that integrates public and private blockchains to address the privacy and trust issues of conventional crowdsourcing systems. Our proposed solution enables varying levels of privacy protection to protect the user’s identity and location. Moreover, the installation of blockchain networks might potentially ensure the data’s integrity. In the conclusion of this article, the possibility of deploying a crowdsourcing system with blockchain in IoE networks is examined.
Improvement Object Detection Algorithm Based on YoloV5 with BottleneckCSP

Detecting objects using deep learning technology has the advantage of getting good accuracy. The accuracy obtained depends on the processing time of using deep learning technology. One object detection algorithm is called You Only Look Once (YOLO), which currently has its fifth version or Yolov5. This paper proposes the real-time object detection algorithm with a video dataset recorded on the highway using Yolov5. The increase of YOLOv5 started by adding augmentation data mosaic by the size of 480x480. We practiced the YOLOV5 - BottleNeckCSP model to detect objects and then got the object information divided into six classes. The results of using mosaic data augmentation are mAP@0.5 of 0.984, mAP@0.5-0.95 of 0.696 by the precision value of 0.95, and a recall value of 0.98. Our research framework can be applied effectively to improve the performance of object detection algorithms.
CenterLoc3D: monocular 3D vehicle localization network for roadside surveillance cameras

Abstract Monocular 3D vehicle localization is an important task for vehicle behaviour analysis, traffic flow parameter estimation and autonomous driving in Intelligent Transportation System (ITS) and Cooperative Vehicle Infrastructure System (CVIS), which is usually achieved by monocular 3D vehicle detection. However, monocular cameras cannot obtain depth information directly due to the inherent imaging mechanism, resulting in more challenging monocular 3D tasks. Currently, most of the monocular 3D vehicle detection methods still rely on 2D detectors and additional geometric constraint modules to recover 3D vehicle information, which reduces the efficiency. At the same time, most of the research is based on datasets of onboard scenes, instead of roadside perspective, which is limited in large-scale 3D perception. Therefore, we focus on 3D vehicle detection without 2D detectors in roadside scenes. We propose a 3D vehicle localization network CenterLoc3D for roadside monocular cameras, which directly predicts centroid and eight vertexes in image space, and the dimension of 3D bounding boxes without 2D detectors. To improve the precision of 3D vehicle localization, we propose a multi-scale weighted-fusion module and a loss with spatial constraints embedded in CenterLoc3D. Firstly, the transformation matrix between 2D image space and 3D world space is solved by camera calibration. Secondly, vehicle type, centroid, eight vertexes, and the dimension of 3D vehicle bounding boxes are obtained by CenterLoc3D. Finally, centroid in 3D world space can be obtained by camera calibration and CenterLoc3D for 3D vehicle localization. To the best of our knowledge, this is the first application of 3D vehicle localization for roadside monocular cameras. Hence, we also propose a benchmark for this application including a dataset (SVLD-3D), an annotation tool (LabelImg-3D), and evaluation metrics. Through experimental validation, the proposed method achieves high accuracy with $$A{P_{3D}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:msub> <mml:mi>P</mml:mi> <mml:mrow> <mml:mn>3</mml:mn> <mml:mi>D</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> of 51.30%, average 3D localization precision of 98%, average 3D dimension precision of 85% and real-time performance with FPS of 41.18.
Semantic segmentation for remote sensing images based on an AD-HRNet model

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Elemental Abundances of Moon Samples Based on Statistical Distributions of Analytical Data

The successful return of Chang’E-5 (CE5) samples urges the hot topic of the study of the Moon in geochemistry. The elemental data of the analyzed moon samples reported in the literature were collected to determine the elemental abundances in moon samples. Based on 2365 analytical records of moon samples from ten missions of Apollo, Luna, and CE5, elemental abundances of 11 major oxides including Cr2O3, 50 trace elements including Ti, P, Mn, Cr, and 15 rare earth elements (REEs) including Y are derived based on statistical distributions of normal, log-normal, and additive log-ratio transformation, respectively. According to the value of 13.5% CaO content, moon samples are classified into two types, as low-Ca and high-Ca samples, whose elemental abundances are also calculated respectively based on the methods used in the total moon samples. With respect to the mid-ocean ridge basalt (MORB) of the Earth, moon samples (including the Moon, low-Ca, and high-Ca samples) are rich in Cr, REEs, Th, U, Pb, Zr, Hf, Cs, Ba, W, and Be and poor in Na, V, Cu, and Zn in terms of their concentrations, and are enriched in Cr and depleted in Na, K, Rb, P, V, Cu, Zn in spider diagrams. The CE5 sample is a low-Ca type of moon sample and is clearly rich in Ti, Fe, Mn, P, Sc, REEs, Th, U, Nb, Ta, Zr, Hf, Sr, Ba, W, and Be and poor in Mg, Al, Cr, and Ni in terms of their concentrations relative to the moon or the low-Ca samples. If compared with the moon sample, the CE5 sample is also clearly rich in K, REE, and P.
Light-Weight Deep Learning Techniques with Advanced Processing for Real-Time Hand Gesture Recognition

In the discipline of hand gesture and dynamic sign language recognition, deep learning approaches with high computational complexity and a wide range of parameters have been an extremely remarkable success. However, the implementation of sign language recognition applications for mobile phones with restricted storage and computing capacities is usually greatly constrained by those limited resources. In light of this situation, we suggest lightweight deep neural networks with advanced processing for real-time dynamic sign language recognition (DSLR). This paper presents a DSLR application to minimize the gap between hearing-impaired communities and regular society. The DSLR application was developed using two robust deep learning models, the GRU and the 1D CNN, combined with the MediaPipe framework. In this paper, the authors implement advanced processes to solve most of the DSLR problems, especially in real-time detection, e.g., differences in depth and location. The solution method consists of three main parts. First, the input dataset is preprocessed with our algorithm to standardize the number of frames. Then, the MediaPipe framework extracts hands and poses landmarks (features) to detect and locate them. Finally, the features of the models are passed after processing the unification of the depth and location of the body to recognize the DSL accurately. To accomplish this, the authors built a new American video-based sign dataset and named it DSL-46. DSL-46 contains 46 daily used signs that were presented with all the needed details and properties for recording the new dataset. The results of the experiments show that the presented solution method can recognize dynamic signs extremely fast and accurately, even in real-time detection. The DSLR reaches an accuracy of 98.8%, 99.84%, and 88.40% on the DSL-46, LSA64, and LIBRAS-BSL datasets, respectively.
Generating Transferable Adversarial Examples for Speech Classification

Despite the success of deep neural networks, the existence of adversarial attacks has revealed the vulnerability of neural networks in terms of security. Adversarial attacks add subtle noise to the original example, resulting in a false prediction. Although adversarial attacks have been mainly studied in the image domain, a recent line of research has discovered that speech classification systems are also exposed to adversarial attacks. By adding inaudible noise, an adversary can deceive speech classification systems and cause fatal issues in various applications, such as speaker identification and command recognition tasks. However, research on the transferability of audio adversarial examples is still limited. Thus, in this study, we first investigate the transferability of audio adversarial examples with different structures and conditions. Through extensive experiments, we discover that the transferability of audio adversarial examples is related to their noise sensitivity. Based on the analyses, we present a new adversarial attack called noise injected attack that generates highly transferable audio adversarial examples by injecting additive noise during the gradient ascent process. Our experimental results demonstrate that the proposed method outperforms other adversarial attacks in terms of transferability.
A Fuzzy AHP-based approach for prioritization of cost overhead factors in agile software development

Agile software development (ASD) is a widely-used practical approach for effective project management that intends to satisfy the client’s needs through continuous testing and frequent delivery. Accurate cost estimation plays a significant role in the success of ASD since the project’s success and failure greatly depend on the hidden cost factors. However, to the best of our knowledge, the current state-of-the-art lacks in considering, validating and prioritizing the critical cost factors helpful in improving the cost estimation accuracy during the ASD process. Thus, there is a need to provide a recent view of the critical factors impacting the cost of agile-based projects. This article provides an up-to-date view of the targeted research context. To accomplish this, we propose a quantitative framework that effectively prioritizes the identified cost factors grounded on the 4Ps (i.e., people, project, process, and product) standard. For identification and validation of the cost overhead factors, we perform a systematic literature review and empirical study in the ASD context. Likewise, the current study classifies and prioritizes the validated factors using a multi-criterion decision making Fuzzy-Analytic Hierarchy Process technique, which effectively rectifies the subjectivity and handles the uncertainty among the identified factors. The implementation results provide a list of prioritized cost overhead factors that would assist agile practitioners during the cost estimation process in the ASD context.