Reviewerly/exHarmony huggingface dataset:
A 3D shape measurement method for high-reflective surface based on accurate adaptive fringe projection

Fringe projection profilometry (FPP) is a popular three-dimensional (3D) shape measurement technique with advantages of high-precision and non-contact. However, measuring high-reflective surface is still a challenging task for conventional FPP because image saturation leads to absolute phase errors and reconstruction errors. In this paper, a new adaptive fringe projection (AFP) method is proposed. Compared with existing AFP methods, we simplify the experimental process and get more accurate coordinate mapping. First, we built the projection intensity model for each projector pixel rather than camera pixel. Then, a uniform gray pattern was projected and two images were captured under high and low exposures respectively to calculate the quantitative low projection intensity. In coordinate mapping, sub-pixel coordinates were mapped from camera image onto projector image. They were filtered with absolute phase to deal with resolution difference between camera and projector and get accurate coordinate correspondence. Finally, adaptive fringe patterns were generated. Besides, we proposed a novel quantitative criterion to evaluate the effectiveness of AFP methods called point cloud integrality (PCI), which is calculated based on the number of 3D points of high-reflective areas. The experiments demonstrate that the proposed method increases both the measurement accuracy and PCI compared with conventional FPP and some existing AFP methods.
A regional-scale geochemical survey of stream sediment samples in Nappe zone, northern Tunisia: Implications for mineral exploration

The Nappe Zone (NZ) is a main major metallogenic zone in Tunisia, containing lead, zinc, iron and copper deposits. Complex geological, structural conditions and wide variety of ore deposit types could result in different geochemical patterns and geochemical anomalies. In this study, a regional geochemical data set containing 8307 stream sediment samples (<177 μm) analyzed for As, Ba, Co, Cr, Fe, Mn, Ni, Pb, Sb, Sr, V and Zn, was used to better understanding of the geochemical patterns and genesis of PbZn deposits. Due to compositional nature of geochemical data, a robust principal component analysis (RPCA) was used to identify the assemblages of elements related to mineralization. Geochemical anomalies for ore-forming elements and element assemblages were delineated based on median absolute deviation (MAD). The data were then visualized and analyzed using classical dot maps, which were gathered with geological units, tectonic, and database of the ore deposits of Tunisia using a geographic information system (GIS). RPCA, based on minimum covariance determinant (MCD) revealed that PbZn deposits in the Nappe zone belong to two genetic types: a Pb–Zn–Ba–Sr Mississippi-valley-type (MVT) hosted by carbonates (limestone and dolomites) and a Pb–As–Sb SEDEX type mainly hosted by Miocene shales. The geochemical anomalies delineated by the Median ± 2MAD method revealed that Pb–Zn–Ba–Sr mineralization displays zoning along the NE–SW-trending faults, while Pb–As–Sb mineralization displays zoning along the E–W-trending lineaments. The spatial associations of numerous anomalies with some regional structures suggest that this region can have good ore-potentiality. The results would not only be valuable to identify the ore-related geochemical associations and potential exploration targets, but also enhance the classification of mineralization in Nappe zone.
Multi-Scale Variational Graph AutoEncoder for Link Prediction

Link prediction has become a significant research problem in deep learning, and the graph-based autoencoder model is one of the most important methods to solve it. The existing graph-based autoencoder models only learn a single set of distributions, which cannot accurately represent the mixed distribution in real graph data. Meanwhile, existing learning models have been greatly restricted when the graph data has insufficient attribute information and inaccurate topology information. In this paper, we propose a novel graph embedding framework, termed multi-scale variational graph autoencoder (MSVGAE), which learns multiple sets of low-dimensional vectors of different dimensions through the graph encoder to represent the mixed probability distribution of the original graph data, and performs multiple sampling in each dimension. Furthermore, a self-supervised learning strategy (i.e., graph feature reconstruction auxiliary learning) is introduced to fully use the graph attribute information to help the graph structure learning. Experiment studies on real-world graphs demonstrate that the proposed model achieves state-of-the-art performance compared with other baseline methods in link prediction tasks. Besides, the robustness analysis shows that the proposed MSVGAE method has obvious advantages in the processes of graph data with insufficient attribute information and inaccurate topology information.
A Simple but Effective Bidirectional Framework for Relational Triple Extraction

Tagging based relational triple extraction methods are attracting growing research attention recently. However, most of these methods take a unidirectional extraction framework that first extracts all subjects and then extracts objects and relations simultaneously based on the subjects extracted. This framework has an obvious deficiency that it is too sensitive to the extraction results of subjects. To overcome this deficiency, we propose a bidirectional extraction framework based method that extracts triples based on the entity pairs extracted from two complementary directions. Concretely, we first extract all possible subject-object pairs from two paralleled directions. These two extraction directions are connected by a shared encoder component, thus the extraction features from one direction can flow to another direction and vice versa. By this way, the extractions of two directions can boost and complement each other. Next, we assign all possible relations for each entity pair by a biaffine model. During training, we observe that the share structure will lead to a convergence rate inconsistency issue which is harmful to performance. So we propose a share-aware learning mechanism to address it. We evaluate the proposed model on multiple benchmark datasets. Extensive experimental results show that the proposed model is very effective and it achieves state-of-the-art results on all of these datasets. Moreover, experiments show that both the proposed bidirectional extraction framework and the share-aware learning mechanism have good adaptability and can be used to improve the performance of other tagging based methods. The source code of our work is available at: https://github.com/neukg/BiRTE.
Ising machines with strong bilinear coupling

Networks of coupled parametric resonators (parametrons) hold promise for parallel computing architectures. En route to realizing complex networks, we report an experimental and theoretical analysis of two coupled parametrons. In contrast to previous studies, we explore the case of strong bilinear coupling between the parametrons, as well as the role of detuning. We show that the system can still operate as an Ising machine in this regime, even though careful calibration is necessary to ensure that the correct solution space is available. Apart from the formation of split normal modes, new states of mixed symmetry are generated. Furthermore, we predict that systems with $N>2$ parametrons will undergo multiple phase transitions before arriving at a regime that can be equivalent to the Ising problem.
Transfer learning with limited labeled data for fault diagnosis in nuclear power plants

Numerous achievements have been made in intelligent fault diagnosis of nuclear power plants, under the assumption that the labeled training data is sufficient and in the same distribution as the test data, which is always contrary to actual situations and limits their practical applications. Therefore, a novel transfer learning framework with diverse transfer strategies is proposed on basis of a pre-trained CNN model in this study, to deal with the problem of limited labeled data in target tasks. The pre-trained CNN is obtained by ample data in source task, then its weights are transferred to the target models and fine-tuned. The results show that with proper transfer strategy, the proposed transfer learning framework significantly boosts the diagnostic performances, compared with training a brand-new CNN model from scratch with insufficient labeled target data. Consequently, the feasibility and superiority of transfer learning in nuclear power plant fault diagnosis with limited labeled data are proven.
Targeting class imbalance problem using GAN

Imbalanced data categorization is inescapable, and it has an impact on the model’s classification problem, which can cause false results. The purpose of this paper is to propose a generative adversarial network (GAN) for restoring balance in imbalanced datasets. This is a challenge since the limited minority data may not be sufficient for GAN training. The proposed article overcomes this issue by adversarial training all available data of minority and majority classes. The data for the minority class is generated using the generative model, which learns all of the useful features from the majority class. The generator in the GAN generates realistic-looking minority class samples. To validate the given method’s classification performance, experiments are performed on a credit card fraud detection dataset. This paper uses a Generative Adversarial Network to give an appropriate solution for imbalanced data classification.
HFBSurv: hierarchical multimodal fusion with factorized bilinear models for cancer survival prediction

Cancer survival prediction can greatly assist clinicians in planning patient treatments and improving their life quality. Recent evidence suggests the fusion of multimodal data, such as genomic data and pathological images, is crucial for understanding cancer heterogeneity and enhancing survival prediction. As a powerful multimodal fusion technique, Kronecker product has shown its superiority in predicting survival. However, this technique introduces a large number of parameters that may lead to high computational cost and a risk of overfitting, thus limiting its applicability and improvement in performance. Another limitation of existing approaches using Kronecker product is that they only mine relations for one single time to learn multimodal representation and therefore face significant challenges in deeply mining rich information from multimodal data for accurate survival prediction.To address the above limitations, we present a novel hierarchical multimodal fusion approach named HFBSurv by employing factorized bilinear model to fuse genomic and image features step by step. Specifically, with a multiple fusion strategy HFBSurv decomposes the fusion problem into different levels and each of them integrates and passes information progressively from the low level to the high level, thus leading to the more specialized fusion procedure and expressive multimodal representation. In this hierarchical framework, both modality-specific and cross-modality attentional factorized bilinear modules are designed to not only capture and quantify complex relations from multimodal data, but also dramatically reduce computational complexity. Extensive experiments demonstrate that our method performs an effective hierarchical fusion of multimodal data and achieves consistently better performance than other methods for survival prediction.HFBSurv is freely available at https://github.com/Liruiqing-ustc/HFBSurv.Supplementary data are available at Bioinformatics online.
Efficient CityCam-to-Edge Cooperative Learning for Vehicle Counting in ITS

Vehicle counting is a fundamental component in Intelligent Transportation System (ITS) for city traffic management. Although a number of vehicle counting approaches have been proposed, their essential drawbacks limit the efficacy of vehicle counting in real applications. In this paper, we propose a CityCam-to-Edge cooperative learning framework by cooperating multiple city cameras with an edge server to count vehicles more efficiently. Our learning framework consists of a lightweight feature extraction scheme deployed on the city cameras and a vehicle counting model implemented on the edge server. We devise the lightweight feature extraction scheme by leveraging multiple convolutional layers with few kernels in the design of deep learning architecture to reduce the utilization of parameters for feature extraction, so that the city cameras’ memory consumption and the data transmission time can be greatly reduced. Moreover, we design two novel vehicle counting models, F2F-M and O2O-M, to improve the counting performance by exploiting the temporal correlation among videos captured from multiple city cameras in a frame-to-frame manner and a video-to-video manner, respectively. By combining the lightweight feature extraction scheme and the proposed vehicle counting models, we obtain two end-to-end vehicle counting models, Lite-F2F-M and Lite-O2O-M. Finally, via conducting extensive experiments, we demonstrate that Lite-F2F-M and Lite-O2O-M models outperform the state-of-the-art in terms of vehicle counting accuracy and time efficiency.
Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis

In this paper, we explore open-domain sketch-to-photo translation, which aims to synthesize a realistic photo from a freehand sketch with its class label, even if the sketches of that class are missing in the training data. It is challenging due to the lack of training supervision and the large geometric distortion between the freehand sketch and photo domains. To synthesize the absent freehand sketches from photos, we propose a framework that jointly learns sketch-to-photo and photo-to-sketch generation. However, the generator trained from fake sketches might lead to unsatisfying results when dealing with sketches of missing classes, due to the domain gap between synthesized sketches and real ones. To alleviate this issue, we further propose a simple yet effective open-domain sampling and optimization strategy to "fool" the generator into treating fake sketches as real ones. Our method takes advantage of the learned sketch-to-photo and photo-to-sketch mapping of in-domain data and generalizes it to the open-domain classes. We validate our method on the Scribble and SketchyCOCO datasets. Compared with the recent competing methods, our approach shows impressive results in synthesizing realistic color, texture, and maintaining the geometric composition for various categories of open-domain sketches.
A non-convex non-smooth bi-level parameter learning for impulse and Gaussian noise mixture removing

&lt;p style='text-indent:20px;'&gt;This paper introduce a novel optimization procedure to reduce mixture of Gaussian and impulse noise from images. This technique exploits a non-convex PDE-constrained characterized by a fractional-order operator. The used non-convex term facilitated the impulse component approximation controlled by a spatial parameter &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ \gamma $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. A non-convex and non-smooth bi-level optimization framework with a modified projected gradient algorithm is then proposed in order to learn the parameter &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \gamma $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. Denoising tests confirm that the non-convex term and learned parameter &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ \gamma $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; lead in general to an improved reconstruction when compared to results of convex norm and manual parameter &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ \lambda $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; choice.&lt;/p&gt;
Supervised Contrastive Learning for Generalizable and Explainable DeepFakes Detection

DeepFakes detection approaches have to be agnostic across generation type, quality, and appearance to provide a generalizable DeepFakes detector. Limited generalizability will hinder wide-scale deployment of detectors if they cannot handle unseen attacks in an open set scenario. We propose a generalizable detection model that can detect novel and unknown/unseen DeepFakes using a supervised contrastive (SupCon) loss. As DeepFakes can resemble the original image/video to a greater extent in terms of appearance and it becomes challenging to secern them, we propose to exploit the contrasts in the representation space to learn a generalizable detector. We further investigate the features learnt from our proposed approach for explainability. The analysis for explainability of the models advocates the need for fusion and motivated by this, we fuse the scores from the proposed SupCon model and the Xception network to exploit the variability from different architectures. The proposed model consistently performs better compared to the single model on both known data and unknown attacks consistently in a seen data setting and an unseen data setting, with generalizability and explainability as a basis. We obtain the highest accuracy of 78.74&#x0025; using proposed SupCon model and an accuracy of 83.99&#x0025; with proposed fusion in a true open-set evaluation scenario where the test class is unknown at the training phase. The paper also aligns with reproducible research by making the code available<sup>1</sup>.<sup>1</sup>https://github.com/xuyingzhongguo/deepfake_supcon
Universals in machine translation?

Abstract By examining and comparing the linguistic patterns in a self-built corpus of Chinese-English translations produced by WeChat Translate , the latest online machine translation app from the most popular social media platform (WeChat) in China, this study explores such questions as whether or not and to what extent simplification and normalization (hypothesized Translation Universals) exhibit themselves in these translations. The results show that, whereas simplification cannot be substantiated, the tendency of normalization to occur in the WeChat translations can be confirmed. The research finds that these results are caused by the operating mechanism of machine translation (MT) systems. Certain salient words tend to prime WeChat’s MT system to repetitively resort to typical language patterns, which leads to a significant overuse of lexical chunks. It is hoped that the present study can shed new light on the development of MT systems and encourage more corpus-based product-oriented research on MT.
Designing and evaluating a high interactive augmented reality system for programming learning

Programming plays a pivotal role in Science, Technology, Engineering, and Mathematics (STEM) education. However, programming concepts and syntax are extremely abstract. We developed an augmented reality (AR)-based learning system to make programming concepts tangible. Specifically, we adopted a high interactive AR mode that allows students to freely assemble puzzle cards to compare various 3D animated programming results. This study adopted a quasi-experimental design and recruited 98 fifth graders as participants. Three groups, named the high interactive AR, low interactive AR, and the traditional learning (control) groups, were formed. The results revealed that the AR-based instruction, especially the high interactive AR mode, helped the students improve their programming achievements, gain higher learning motivation, reduce their cognitive load, and acquire a high degree of technology acceptance. The interview data mainly showed that the students in the high interactive group attained more positive experiences of assembling puzzle cards to learn programming. Our study suggests that using AR technology alone did not facilitate learning motivation, although it did produce better programming achievements compared to the traditional learning. A high interactive design such as "puzzle cards" is vital for arousing higher motivation, which can in turn facilitate a more in-depth understanding of programming logic.
Analysing the importance of e-government in times of disruption: The case of public education in Rwanda during Covid-19 lockdown

The world has been in the grip of the Covid-19 pandemic since 2019. The Rwandan Government failed to establish e-government infrastructure to provide e-education during the lockdown (a.k.a Guma mu Rugo or Stay home), resulting in all children having to repeat the academic level they were in when the lockdown started in March 2020. The problems in Rwanda's education during Covid-19 are partly attributed to the government's ICT (a.k.a ikorana buhanga in Kinyarwanda) platform which does not prioritise e-education services, government's inability to provide infrastructures for e-government, and the lack of ICT equipment and skills. Research on the aspects of e-government and e-education in Rwanda is limited. Knowledge is scarce about what happened to public education in Rwanda during the lockdown. This study aimed to explore the effects of the lockdown on Rwanda's public education and how e-education could have helped provide public education. A desktop research method, involving collecting data from existing sources on e-government and the state of public education in Rwanda during Covid-19, was conducted. The results show that the lockdown has negatively affected public education because the country failed to provide e-education services. To address the impact of the Covid-19 lockdown and possible future lockdowns, the Government of Rwanda must establish an e-government infrastructure that provides e-education. It will also need to establish affordable infrastructure in rural areas where it does not exist.
Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm's performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms' performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.