Reviewerly/exHarmony huggingface dataset:
An interpretable decision-support systems for daily cryptocurrency trading

Cryptocurrencies, especially Bitcoin (BTC), have become an important commodity for both individual and corporate investors within the last decade. The limited supply, high volatility, and random price fluctuations have increased investors' interest in BTC, especially in daily trading. Although BTC has been yielding a high rate of returns, price fluctuations and constant speculations make the investors wary of unexpected price movements. Predictive modeling suffers from the complexity of the datasets (i.e., the high number of features employed to forecast BTC movements) as well as the black-box nature of most machine learning algorithms (which is especially problematic for corporate investors since they are obligated to disclose their investment decisions to their clients). Therefore, the main goal of the current study is to assist individual and corporate investors in making transparent and interpretable daily BTC trading decisions by developing a predictive analytics framework. To address the complexities posed by the datasets, a comprehensive tri-level feature selection approach is proposed. The selected features are then, fed into the Classification & Regression Tree (C&RT) to build a highly parsimonious, transparent, and interpretable prediction model. The resultant model was not only evaluated on the test (holdout) sample but was also tested on challenging time periods, including the first half of 2020 (the start of the pandemic era) to exhibit the viability and reliability of the proposed framework. Finally, a decision support tool is developed for the practical implementation of the model. The tool can be used by short-term investors not only due to its highly simplistic, transparent, and interpretable structure, but also its higher accuracy, sensitivity, and specificity results when compared to the extant literature.
Training optronic convolutional neural networks on an optical system through backpropagation algorithms

The development of optical neural networks greatly slows the urgent demand of searching for fast computing approaches to solve big data processing. However, most optical neural networks following electronic training and optical inferencing do not really take full advantage of optical computing to reduce computational burden. Take the extensively used optronic convolutional neural networks (OPCNN) as an example, the convolutional operations still require vast computational operations in training stages on the computer. To address this issue, this study proposes the in-situ training algorithm to train the networks directly in optics. We derive the backpropagation algorithms of OPCNN hence the complicated gradient calculation in backward propagating processes can be obtained through optical computing. Both forward propagation and backward propagation are all executed on the same optical system. Furthermore, we successfully realize the introduction of optical nonlinearity in networks through utilizing photorefractive crystal SBN:60 and we also derive the corresponding backpropagation algorithm. The numerical simulation results of classification performance on several datasets validates the feasibility of the proposed algorithms. Through in-situ training, the reduction in performance resulting from the inconsistency of the plantform between training and inferencing stages can be eliminated completely. For example, we demonstrate that by using the optical training approach, OPCNN is capable of gaining a strong robustness under several misalignmed situations, which enhances the practicability of OPCNN and greatly expands its application range.
Post-pandemic Education Strategy: Framework for Artificial Intelligence-Empowered Education in Engineering (AIEd-Eng) for Lifelong Learning

The unprecedented rate of technological advancements led by the artificial intelligence (AI) revolution is transforming teaching and learning. The rapidly dynamic specifics and the distinct nature of engineering education transcend the incorporation of core technical skills to accommodate other essential skills, including collaboration, creativity and innovation, critical thinking, and problem-solving skills. AI-empowered education (AIEd) systems offer customized and personalized experiences for different learning groups, teachers, and decision-makers, thus supporting lifelong learning. This study systematically analyzes the body of scientific literature on the field of AIEd in Engineering (AIEd-Eng), which has undergone significant developments over the last decades. Based on the results, the study proposes a framework and strategy for directing future research initiatives in AIEd-Eng. In addition, the study intends to assess the influence of AI on different educational processes, including instruction, learning, management, and decision-making practices, in engineering education.
MonoTrack: Shuttle trajectory reconstruction from monocular badminton video

Trajectory estimation is a fundamental component of racket sport analytics, as the trajectory contains information not only about the winning and losing of each point, but also how it was won or lost. In sports such as badminton, players benefit from knowing the full 3D trajectory, as the height of shuttlecock or ball provides valuable tactical information. Unfortunately, 3D reconstruction is a notoriously hard problem, and standard trajectory estimators can only track 2D pixel coordinates. In this work, we present the first complete end-to-end system for the extraction and segmentation of 3D shuttle trajectories from monocular badminton videos. Our system integrates badminton domain knowledge such as court dimension, shot placement, physical laws of motion, along with vision-based features such as player poses and shuttle tracking. We find that significant engineering efforts and model improvements are needed to make the overall system robust, and as a by-product of our work, improve state-of-the-art results on court recognition, 2D trajectory estimation, and hit recognition.
Two-dimensional semi-nonnegative matrix factorization for clustering

In this paper, we propose a new Semi-Nonnegative Matrix Factorization method for 2-dimensional (2D) data, named TS-NMF. It overcomes the drawback of existing methods that seriously damage the spatial information of the data by converting 2D data to vectors in a preprocessing step. In particular, projection matrices are sought under the guidance of building new data representations, such that the spatial information is retained and projections are enhanced by the goal of clustering, which helps construct optimal projection directions. Moreover, to exploit nonlinear structures of the data, manifold is constructed in the projected subspace, which is adaptively updated according to the projections and less afflicted with noise and outliers of the data and thus more representative in the projected space. Hence, seeking projections, building new data representations, and learning manifold are seamlessly integrated in a single model, which mutually enhance other and lead to a powerful data representation. Comprehensive experimental results verify the effectiveness of TS-NMF in comparison with several state-of-the-art algorithms, which suggests high potential of the proposed method for real world applications.
Effects of augmented reality application integration with computational thinking in geometry topics

Abstract In this article, researchers conducted a study that integrates Augmented Reality application with Computational Thinking into Geometry Topics. Three variables were measured, Computational Thinking, Visualisation Skills and Geometry Topic achievement. The study was implemented with 124 students in two different schools using quasi-experimental study design. The t-test analysis was performed to see the differences before and after the intervention between the control and treatment groups. Next, a MANCOVA was conducted to see the effectiveness of the intervention in more depth on Computational Thinking, Visualisation Skills and Geometry Topic achievement after controlling for existing knowledge factors. The results show that there is a positive effect of teaching methods using Augmented Reality applications with Computational Thinking for students in the improvement of Computational Thinking, Visualisation Skills and Geometry Topic achievement. Moreover, the results of the MANCOVA show that overall, teaching methods using Augmented Reality applications with Computational Thinking are factors in the improvement of Computational Thinking scores, Visualisation Skills and Geometry Topic achievement, which means the students’ existing knowledge did not affect the results of the three dependent variables. In conclusion, this study proposed that the Computational Thinking approach with Augmented Reality application can improve Computational Thinking, Visualisation Skills and Geometry Topic achievement among students.
Pull Request Decisions Explained: An Empirical Overview

Context:The pull-based development model is widely used in open source projects, leading to the emergence of trends in distributed software development.One aspect that has garnered significant attention concerning pull request decisions is the identification of explanatory factors.Objective: This study builds on a decade of research on pull request decisions and provides further insights.We empirically investigate how factors influence pull request decisions and the scenarios that change the influence of such factors.Method: We identify factors influencing pull request decisions on GitHub through a systematic literature review and infer them by mining archival data.We collect a total of 3,347,937 pull requests with 95 features from 11,230 diverse projects on GitHub.Using these data, we explore the relations among the factors and build mixed effects logistic regression models to empirically explain pull request decisions.Results: Our study shows that a small number of factors explain pull request decisions, with that concerning whether the integrator is the same as or different from the submitter being the most important factor.We also note that the influence of factors on pull request decisions change with a change in context; e.g., the area hotness of pull request is important only in the early stage of project development, however it becomes unimportant for pull request decisions as projects become mature.
A Deep Learning-Based Intelligent Receiver for Improving the Reliability of the MIMO Wireless Communication System

Multiple-input–multiple-output (MIMO) technology is one of the most widely used communication technologies. However, with the increasing number of antennas, the complexity of the MIMO wireless communication receiver becomes higher and higher. On the other hand, the complex communication channels also raise up a great challenge to the reliability of the communication receiver system. With the rapid development and wide application of deep learning, it has been applied in the field of communication to solve some problems that are difficult to solve by the traditional methods, and thereby, improves the reliability of communication systems. Inspired by this idea, this article reviewed the signal processing process of the MIMO receiver system from the perspective of system reliability. Based on deep learning, the signal processing modules of the receiver system are jointly optimized, which changes the information recovery process of the traditional receiver and proposes the intelligent receiver for MIMO communication. In order to verify the system reliability of the intelligent receiver, this article analyzes it from the aspects of antenna numbers and channel conditions. The influence of different implementation methods of the intelligent receiver on the system reliability is also analyzed. Simulation results show that the proposed intelligent receiver for the MIMO wireless communication can recover information with a lower bit error rate and higher reliability compared with the traditional receiver under different conditions and antenna configurations.
Health monitoring of a conveyor belt system using machine vision and real-time sensor data

To truly achieve prognostic health monitoring of complex manufacturing systems, real-time data from multiple sensors must be continuously monitored in order to have a system-wide view of the health of the machine and the effect of different operating conditions on system performance. Belt misalignment is a critical factor in the conveyor belt system traditionally assessed through mechanical means. This research successfully implements multiple sensors and communication protocols along with machine vision for monitoring the health of a conveyor belt system. Data is captured using industrial-grade sensors and conditioned in a PLC. This information is sent to a wireless radio frequency module that transmits wirelessly to a remote receiver paired to an IoT gateway. Images taken by a camera are simultaneously processed by a local computer running a machine vision algorithm used to establish if the conveyor belt is operating normally. Both the sensor data and the machine vision information are sent to the cloud for remote users to monitor the system's operating conditions and to detect potential failure prior to its occurrence. The system communicates using a set of Radio Frequency modules, Zumlink™, and the range of this system was tested and determined to be greater than 2.5 miles with obstructions typical in a manufacturing environment.
A Novel Hybrid Reversible-Zero Watermarking Scheme to Protect Medical Image

The verification of copyright and authenticity for medical images is critical in telemedical applications. Watermarking is a key technique for protecting medical images and can be mainly divided into three categories: region of interest (ROI) lossless watermarking, reversible watermarking and zero-watermarking. However, ROI lossless watermarking causes biases on diagnosis. Reversible watermarking can hardly provide a continuous verification function and may face verification disputes after image recovering. Zero-watermarking requires third-party storage which may cause additional security problems. To address these issues, a hybrid reversible-zero watermarking (HRZW) is proposed in this paper to effectively combine the complementary advantages of reversible watermarking and zero-watermarking. In our scheme, a novel hybrid structure is designed including a zero-watermarking component and a reversible watermarking component. In the first component, ownership share is generated by mapping nearest neighbor grayscale residual (NNGR) based features and watermark information. In the second component, the generated ownership share is embedded reversibly based on Slantlet Transform, Singular Value Decomposition and Quantization Index Modulation (SLT-SVD-QIM). Experimental results demonstrate that our proposed scheme not only yields remarkable watermarking imperceptibility, distinguishability and robustness, but also provides continuous verification function without any dispute or third-party storage, which outperforms existing watermarking schemes for medical images.
Quantum Federated Learning With Decentralized Data

Variational quantum algorithm (VQA) accesses the centralized data to train the model, and using distributed computing can significantly improve the training overhead; however, the data is privacy sensitive. In this paper, we propose communication-efficient learning of VQA from decentralized data, which is so-called quantum federated learning (QFL). Motivated by the classical federated learning algorithm, we improve data privacy by aggregating updates from local computation to share model parameters. Here, aiming to find approximate optima in the parameter landscape, we develop an extension of the conventional VQA. Finally, we deploy on the TensorFlow Quantum processor within variational quantum tensor networks classifiers, approximate quantum optimization for the Ising model, and variational quantum eigensolver for molecular hydrogen. Our algorithm demonstrates model accuracy from decentralized data, which have higher performance on near-term processors. Importantly, QFL may inspire new investigations in the field of secure quantum machine learning.
Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation

Unsupervised domain adaptive (UDA) image segmentation has recently gained increasing attention, aiming to improve the generalization capability for transferring knowledge from the source domain to the target domain. However, in high spatial resolution remote sensing image (RSI), the same category from different domains (e.g., urban and rural) can appear to be totally different with extremely inconsistent distributions, which heavily limits the UDA accuracy. To address this problem, in this article, we propose a novel deep covariance alignment (DCA) model for UDA RSI segmentation. The DCA can explicitly align category features to learn shared domain-invariant discriminative feature representations, which enhance the ability of model generalization. Specifically, a category feature pooling (CFP) module is first used to extract category features by combining coarse outputs and deep features. Then, we leverage a novel covariance regularization (CR) to enforce the intracategory features to be closer and the intercategory features to be further separate. Compared with the existing category alignment methods, our CR aims to regularize the correlation between different dimensions of the features, and thus performs more robustly when dealing with divergent category features of imbalanced and inconsistent distributions. Finally, we propose a stagewise procedure to train the DCA to alleviate error accumulation. Experiments on both rural-to-urban and urban-to-rural scenarios of the LoveDA dataset demonstrate the superiority of our proposed DCA over other state-of-the-art UDA segmentation methods. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Luffy03/DCA</uri> .
Phase-shifting algorithms with known and unknown phase shifts: comparison and hybrid

The phase-shifting interferometry has been intensively studied for more than half a century, and is still actively investigated and improved for more demanding precision measurement requirements. A proper phase-shifting algorithm (PSA) for phase extraction should consider various error sources including (i) the phase-shift errors, (ii) the intensity harmonics, (iii) the non-uniform phase-shift distributions and (iv) the random additive intensity noise. Consequently, a large pool of PSAs has been developed, including those with known phase shifts (abbreviated as kPSA) and those with unknown phase shifts (abbreviated as uPSA). While numerous evaluation works have been done for the kPSAs, there are very few for the uPSAs, making the overall picture of the PSAs unclear. Specifically, there is a lack of (i) fringe pattern parameters' restriction analysis for the uPSAs and (ii) performance comparison within the uPSAs and between the uPSAs and the kPSAs. Thus, for the first time, we comprehensively evaluated the pre-requisites and performance of four representative uPSAs, the advanced iterative algorithm, the general iterative algorithm (GIA), the algorithm based on the principal component analysis and the algorithm based on VU factorization, and then compare the uPSAs with twelve benchmarking kPSAs. From this comparison, the demand for proper selection of a kPSA, and the restriction and attractive performance of the uPSAs are clearly depicted. Due to the outstanding performance of the GIA, a hybrid kPSA-GIA is proposed to boost the performance of a kPSA and relieve the fringe density restriction of the GIA.
Privacy-Preserving Mutual Heterogeneous Signcryption Schemes Based on 5G Network Slicing

With the wide application of 5G network, much research has been carried out in recent years toward the 5G network slicing technology in order to save 5G network resources and satisfy the service requirements of different users. Different public key cryptosystems may be deployed in different 5G network slicings. Therefore, heterogeneous signcryption is required to achieve secure communications between different 5G network slices. The existing scheme is a one-to-one communication between 5G network slicings. To obtain one-to-many and many-to-one secure communications, two new privacy-preserving heterogeneous signcryption schemes have been proposed in this article based on 5G network slicings, which can accomplish mutual communications between the public key infrastructure (PKI) and the certificateless public-key cryptography. In this work, we present PKI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> CLC completely anonymous multireceiver signcryption (PMRCHS) and CLC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula> PKI heterogeneous aggregate signcryption (CPHAS) schemes based on the 5G Internet of Things (IoT) slicing and the 5G Internet of Vehicle (IoV) slicing. Under the random oracle model (ROM), the proposed schemes have proved to satisfy confidentiality and unforgeability under the computational Diffie–Hellman problem and discrete logarithm problem (DLP). Furthermore, we make comparisons of the proposed work with the existing works in terms of computational cost, communication cost, anonymity, and communication direction. The results show that the proposed schemes are more secure and effective.
RESPIRE: Reducing Spatial–Temporal Redundancy for Efficient Edge-Based Industrial Video Analytics

Video camera plays a growing important role in advancing industrial control towards a higher level of automation. Thus, video analytics become highly demanded, especially for low-latency and high-accuracy analytic results. Yet, the data volume produced by camera clusters is prohibitively high. In this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Respire</i> , a system that can remove redundant frames for reducing the cost of transmission and processing based on edge computing nodes, while maintaining useful frames for high analytic accuracy. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Respire</i> incorporates a new way for characterizing the spatial–temporal redundancy between frames. Then, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Respire</i> prioritizes the uploading of frames for redundancy reduction. As the search space of the entire collected frames is exponential for the set of frames containing the maximal information, we jointly consider offline and online pruning of frames and propose a heuristic algorithm to reduce the search space. Extensive real-world dataset-based experiments demonstrate that the proposed system can significantly reduce communication and computation costs, while providing sufficient information for guaranteed video analytic accuracy.