Reviewerly/exHarmony huggingface dataset:
Augmented Reality in Science Laboratories: Investigating High School Students’ Navigation Patterns and Their Effects on Learning Performance

Augmented reality (AR) has the potential to fundamentally transform science education by making learning of abstract science ideas tangible and engaging. However, little is known about how students interacted with AR technologies and how these interactions may affect learning performance in science laboratories. This study examined high school students’ navigation patterns and science learning with a mobile AR technology, developed by the research team, in laboratory settings. The AR technology allows students to conduct hands-on laboratory experiments and interactively explore various science phenomena covering biology, chemistry, and physics concepts. In this study, seventy ninth-grade students carried out science laboratory experiments in pairs to learn thermodynamics. Our cluster analysis identified two groups of students, which differed significantly in navigation length and breadth. The two groups demonstrated unique navigation patterns that revealed students’ various ways of observing, describing, exploring, and evaluating science phenomena. These navigation patterns were associated with learning performance as measured by scores on lab reports. The results suggested the need for providing access to multiple representations and different types of interactions with these representations to support effective science learning as well as designing representations and connections between representations to cultivate scientific reasoning skills and nuanced understanding of scientific processes.
Assessing Individual VR Sickness Through Deep Feature Fusion of VR Video and Physiological Response

Recently, VR sickness assessment for VR videos is highly demanded in industry and research fields to address VR viewing safety issues. Especially, it is difficult to evaluate VR sickness of individuals due to individual differences. To achieve the challenging goal, we focus on deep feature fusion of sickness-related information. In this paper, we propose a novel deep learning-based assessment framework which estimates VR sickness of individual viewers with VR videos and corresponding physiological responses. We design the content stimulus guider imitating the phenomenon that humans feel VR sickness. The content stimulus guider extracts a deep stimulus feature from a VR video to reflect VR sickness caused by VR videos. In addition, we devise the physiological response guider to encode physiological responses that are acquired while humans experience VR videos. Each physiology sickness feature extractor (EEG, ECG, and GSR) in the physiological response guider is designed to suit their physiological characteristics. Extracted physiology sickness features are then fused into a deep physiology feature that comprehensively reflects individual deviations of VR sickness. Finally, the VR sickness predictor assesses individual VR sickness effectively with the fusion of the deep stimulus feature and the deep physiology feature. To validate the proposed method extensively, we built two benchmark datasets which contain 360-degree VR videos with physiological responses (EEG, ECG, and GSR) and SSQ scores. Experimental results show that the proposed method achieves meaningful correlations with human SSQ scores. Further, we validate the effectiveness of the proposed network designs by conducting analysis on feature fusion and visualization.
Improved Object Detection Algorithm of YOLOv3 Remote Sensing Image

Due to the low detection accuracy of YOLOv3 target detection method, this paper proposes an improved target detection method of YOLOv3 remote sensing image. Firstly, the feature extraction network DarkNet53 is strengthened to improve the ability of feature extraction; Secondly, the original Leaky ReLU activation function is replaced by the Mish activation function, thus improving the generalization of the method in this paper; Finally, the Learning rate and BatchSize parameters are modified to prevent overfitting. The remote sensing image datasets of RSOD and TGRS-HRRSD are used in this paper. The Average Precision (AP) results of the method on the RSOD datasets in this paper show that the mAP value is 5.33 percent higher than that of the previous YOLOV3 method. The log average miss-rate (LAMR) results show that the LAMR value is 0.1100 lower than that of the previous YOLOV3 method. The mAP results of the method on the TGRS-HRRSD datasets show that the MAP value is 1.29 percent higher than that of the previous YOLOV3 method, and the LAMR results show that the LAMR value is 0.0338 lower than that of the previous YOLOv3 method.
AdvRush: Searching for Adversarially Robust Neural Architectures

Deep neural networks continue to awe the world with their remarkable performance. Their predictions, however, are prone to be corrupted by adversarial examples that are imperceptible to humans. Current efforts to improve the robustness of neural networks against adversarial examples are focused on developing robust training methods, which update the weights of a neural network in a more robust direction. In this work, we take a step beyond training of the weight parameters and consider the problem of designing an adversarially robust neural architecture with high intrinsic robustness. We propose AdvRush, a novel adversarial robustness-aware neural architecture search algorithm, based upon a finding that independent of the training method, the intrinsic robustness of a neural network can be represented with the smoothness of its input loss landscape. Through a regularizer that favors a candidate architecture with a smoother input loss landscape, AdvRush successfully discovers an adversarially robust neural architecture. Along with a comprehensive theoretical motivation for AdvRush, we conduct an extensive amount of experiments to demonstrate the efficacy of AdvRush on various benchmark datasets. Notably, on CIFAR-10, AdvRush achieves 55.91% robust accuracy under FGSM attack after standard training and 50.04% robust accuracy under AutoAttack after 7-step PGD adversarial training.
Quantitative detection of cervical cancer based on time series information from smear images

Existing cervical cancer detection methods usually screen the samples based on separated cells. Cell misclassification leads to poor robustness and accuracy, quantitative analysis is missed. Global smear information and cell relationship are also not fully utilized. Aiming at the mentioned limitations, a cervical quantitative detection framework which combines the fine-tuned Long Short-Term Memory Fully Convolutional Network and Fuzzy Nonlinear Regression is proposed. Time series-based​ screening improves the detection performance. Deoxyribonucleic Acid (DNA) value is better expressed by the rectified method using soft computing. A cervical dataset containing 657 samples is used for training and validation, accuracy, sensitivity, and specificity of 98.3%, 98.1% and 97.9% are achieved with the time series features, providing an automatic and effective way for the computer-assisted screening of cervical cancer.
No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology. In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning 'quality-aware' features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of 'quality-aware' features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.
Recyclable Waste Classification Using Computer Vision And Deep Learning

Recycling solid waste is an important step to reduce harmful impact such as sanitary and health problems resulting from the over use of landfills. Yet, recycling requires the sorting of solid waste, which is complex and expensive. In an attempt to ease this process, our work proposes a Deep Learning approach using computer vision to automatically identify the type of waste and classify it into five main categories: plastic, metal, paper, cardboard and glass. Our conceptual system consists of an automated recycling bin which automatically opens the lid corresponding to the type of waste identified. This work focuses mainly on the Machine Learning algorithms which can be trained for efficient identification. Pre-existing images have been used to train a minimum of 12 variants of the Convolutional Neural Network (CNN) algorithm over three classifiers: Support Vector Machine (SVM), Sigmoid and SoftMax. Our results show that VGG19 with SoftMax classifier has an accuracy of around 88%.
Optimization of Business Process Execution in Services Architecture: A Systematic Literature Review

Web services have become a standard way to provide functions of information systems.The number of web services grows rapidly with the increasing popularity of microservices architecture.In consequence, many business processes are executed entirely through web services.Therefore, optimizing the performance of business process execution may bring many benefits.There are many optimization methods in this area.Our systematic literature review aims to introduce available methods to researchers interested in the optimization of business process execution.We queried four databases: ACM, IEEE Xplore, Science Direct, and Springer.Out of 12150 initially found papers, we have selected 128 for the review.We have grouped methods presented in those papers into three stages of business process optimization: Resource Allocation, Service Composition, and Service Scheduling.Service Composition attracts the largest group of researchers with a vast majority of 119 articles in it.Moreover, the most popular are genetic algorithms.In general, researchers mainly propose heuristic methods that optimize business processes during run-time.We see the potential for further exploration at both Resource Allocation and Service Scheduling stages.
A novel density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy

Abstract Recently the density peaks clustering algorithm (DPC) has received a lot of attention from researchers. The DPC algorithm is able to find cluster centers and complete clustering tasks quickly. It is also suitable for different kinds of clustering tasks. However, deciding the cutoff distance $${d}_{c}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>d</mml:mi> <mml:mi>c</mml:mi> </mml:msub> </mml:math> largely depends on human experience which greatly affects clustering results. In addition, the selection of cluster centers requires manual participation which affects the efficiency of the algorithm. In order to solve these problems, we propose a density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy (KNN-ADPC). A clusters merging strategy is proposed to automatically aggregate over-segmented clusters. Additionally, the K nearest neighbors are adopted to divide data points more reasonably. There is only one parameter in KNN-ADPC algorithm, and the clustering task can be conducted automatically without human involvement. The experiment results on artificial and real-world datasets prove higher accuracy of KNN-ADPC compared with DBSCAN, K-means++, DPC, and DPC-KNN.
Logical English meets legal English for swaps and derivatives

Abstract In this paper, we present an informal introduction to Logical English (LE) and illustrate its use to standardise the legal wording of the Automatic Early Termination (AET) clauses of International Swaps and Derivatives Association (ISDA) Agreements. LE can be viewed both as an alternative to conventional legal English for expressing legal documents, and as an alternative to conventional computer languages for automating legal documents. LE is a controlled natural language (CNL), which is designed both to be computer-executable and to be readable by English speakers without special training. The basic form of LE is syntactic sugar for logic programs, in which all sentences have the same standard form, either as rules of the form conclusion if conditions or as unconditional sentences of the form conclusion. However, LE extends normal logic programming by introducing features that are present in other computer languages and other logics. These features include typed variables signalled by common nouns, and existentially quantified variables in the conclusions of sentences signalled by indefinite articles. Although LE translates naturally into a logic programming language such as Prolog or ASP, it can also serve as a neutral standard, which can be compiled into other lower-level computer languages.
Gender Bias and Under-Representation in Natural Language Processing Across Human Languages

Natural Language Processing (NLP) systems are at the heart of many critical automated decision-making systems making crucial recommendations about our future world. However, these systems reflect a wide range of biases, from gender bias to a bias in which voices they represent. In this paper, a team including speakers of 9 languages - Chinese, Spanish, English, Arabic, German, French, Farsi, Urdu, and Wolof - reports and analyzes measurements of gender bias in the Wikipedia corpora for these 9 languages. In the process, we also document how our work exposes crucial gaps in the NLP-pipeline for many languages. Despite substantial investments in multilingual support, the modern NLP-pipeline still systematically and dramatically under-represents the majority of human voices in the NLP-guided decisions that are shaping our collective future. We develop extensions to profession-level and corpus-level gender bias metric calculations originally designed for English and apply them to 8 other languages, including languages like Spanish, Arabic, German, French and Urdu that have grammatically gendered nouns including different feminine, masculine and neuter profession words. We compare these gender bias measurements across the Wikipedia corpora in different languages as well as across some corpora of more traditional literature.
Topological Order, Quantum Codes, and Quantum Computation on Fractal Geometries

We investigate topological order on fractal geometries embedded in $n$ dimensions. In particular, we diagnose the existence of the topological order through the lens of quantum information and geometry, i.e., via its equivalence to a quantum error-correcting code with a macroscopic code distance or the presence of macroscopic systoles in systolic geometry. We first prove a no-go theorem that $\mathbb{Z}_N$ topological order cannot survive on any fractal embedded in 2D. For fractal lattice models embedded in 3D or higher spatial dimensions, $\mathbb{Z}_N$ topological order survives if the boundaries of the interior holes condense only loop or membrane excitations. Moreover, for a class of models containing only loop or membrane excitations, and are hence self-correcting on an $n$-dimensional manifold, we prove that topological order survives on a large class of fractal geometries independent of the type of hole boundaries. We further construct fault-tolerant logical gates using their connection to global and higher-form topological symmetries. In particular, we have discovered a logical CCZ gate corresponding to a global symmetry in a class of fractal codes embedded in 3D with Hausdorff dimension asymptotically approaching $D_H=2+\epsilon$ for arbitrarily small $\epsilon$, which hence only requires a space-overhead $\Omega(d^{2+\epsilon})$ with $d$ being the code distance. This in turn leads to the surprising discovery of certain exotic gapped boundaries that only condense the combination of loop excitations and gapped domain walls. We further obtain logical $\text{C}^{p}\text{Z}$ gates with $p\le n-1$ on fractal codes embedded in $n$D. In particular, for the logical $\text{C}^{n-1}\text{Z}$ in the $n^\text{th}$ level of Clifford hierarchy, we can reduce the space overhead to $\Omega(d^{n-1+\epsilon})$. Mathematically, our findings correspond to macroscopic relative systoles in fractals.
Weighted Ensemble of Deep Learning Models based on Comprehensive Learning Particle Swarm Optimization for Medical Image Segmentation

In recent years, deep learning has rapidly become a method of choice for segmentation of medical images. Deep neural architectures such as UNet and FPN have achieved high performances on many medical datasets. However, medical image analysis algorithms are required to be reliable, robust, and accurate for clinical applications which can be difficult to achieve for some single deep learning methods. In this study, we introduce an ensemble of classifiers for semantic segmentation of medical images. The ensemble of classifiers here is a set of various deep learning-based classifiers, aiming to achieve better performance than using a single classifier. We propose a weighted ensemble method in which the weighted sum of segmentation outputs by classifiers is used to choose the final segmentation decision. We use a swarm intelligence algorithm namely Comprehensive Learning Particle Swarm Optimization to optimize the combining weights. Dice coefficient, a popular performance metric for image segmentation, is used as the fitness criteria. Experiments conducted on some medical datasets of the CAMUS competition on cardiographic image segmentation show that our method achieves better results than both the constituent segmentation models and the reported model of the CAMUS competition.
Aspect-Level Sentiment Analysis Approach via BERT and Aspect Feature Location Model

With the rapid development of Internet social platforms, buyer shows (such as comment text) have become an important basis for consumers to understand products and purchase decisions. The early sentiment analysis methods were mainly text-level and sentence-level, which believed that a text had only one sentiment. This phenomenon will cover up the details, and it is difficult to reflect people’s fine-grained and comprehensive sentiments fully, leading to people’s wrong decisions. Obviously, aspect-level sentiment analysis can obtain a more comprehensive sentiment classification by mining the sentiment tendencies of different aspects in the comment text. However, the existing aspect-level sentiment analysis methods mainly focus on attention mechanism and recurrent neural network. They lack emotional sensitivity to the position of aspect words and tend to ignore long-term dependencies. In order to solve this problem, on the basis of Bidirectional Encoder Representations from Transformers (BERT), this paper proposes an effective aspect-level sentiment analysis approach (ALM-BERT) by constructing an aspect feature location model. Specifically, we use the pretrained BERT model first to mine more aspect-level auxiliary information from the comment context. Secondly, for the sake of learning the expression features of aspect words and the interactive information of aspect words’ context, we construct an aspect-based sentiment feature extraction method. Finally, we construct evaluation experiments on three benchmark datasets. The experimental results show that the aspect-level sentiment analysis performance of the ALM-BERT approach proposed in this paper is significantly better than other comparison methods.
Efficient Key Recovery for All HFE Signature Variants

The HFE cryptosystem is one of the most popular multi- variate schemes. Especially in the area of digital signatures, the HFEv- variant offers short signatures and high performance. Recently, an instance of the HFEv- signature scheme called GeMSS was selected as one of the alternative candidates for signature schemes in the third round of the NIST Post-Quantum Crypto (PQC) Standardization Project. In this paper, we propose a new key recovery attack on the HFEv- signature scheme. Our attack shows that both the Minus and the Vinegar modification do not enhance the security of the basic HFE scheme significantly. This shows that it is very difficult to build a secure and efficient signature scheme on the basis of HFE. In particular, we use our attack to show that the proposed parameters of the GeMSS scheme are not as secure as claimed.
Dual-Source Detection and Identification System Based on UAV Radio Frequency Signal

With the rapid development of the technology industry, low, slow, and small civilian Unmanned aerial vehicle (UAV) have been popularized, bringing fun and convenience to life and work. However, with the popularization of UAV, all kinds of illegal flying behaviors have appeared successively, posing a threat to people's life and national security. To solve this problem, this article proposes a method and designs a system to detect and identify the UAV through the radio frequency (RF) signal emitted by the UAV itself. First, the RF front-end scans and intercepts the radio signals in the target airspace, then analyzes and identifies them by algorithms, and then implements the algorithm by field-programmable gate array (FPGA). Among them, algorithm recognition includes UAV flight control signal (FCS) detection and recognition, UAV image transmission signal (ITS) blind detection, and accurate recognition. Through the algorithm recognition result, it can be judged whether there are UAVs in the protected area. Finally, the hierarchical experimental verification method is used to demonstrate that the system can identify the number, type, and frequency band of UAVs in urban complex electromagnetic environment by using dual source detection and recognition algorithm, which provides a good foundation for UAV management and control.
Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers

We consider the problem of generating a time-optimal quadrotor trajectory for highly maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the optimal trajectory is located on the boundary of the set of dynamically feasible trajectories. This boundary is hard to model as it involves limitations of the entire system, including complex aerodynamic and electromechanical phenomena, in agile high-speed flight. In this work, we propose a multi-fidelity Bayesian optimization framework that models the feasibility constraints based on analytical approximation, numerical simulation, and real-world flight experiments. By combining evaluations at different fidelities, trajectory time is optimized while the number of costly flight experiments is kept to a minimum. The algorithm is thoroughly evaluated for the trajectory generation problem in two different scenarios: (1) connecting predetermined waypoints; (2) planning in obstacle-rich environments. For each scenario, we conduct both simulation and real-world flight experiments at speeds up to 11 m/s. Resulting trajectories were found to be significantly faster than those obtained through minimum-snap trajectory planning.