Reviewerly/exHarmony huggingface dataset:
Determination of optimal binary defocusing based on digital correlation for fringe projection profilometry

In fringe projection proﬁlometry (FPP), non-sinusoidal projected fringes due to the gamma effect of a projector will cause phase measurement errors. To solve this problem, a binary defocusing technique has been introduced in recent years, while it is difficult to determine an appropriate amount of defocus quantitatively with high sensitivity and high accuracy. In this paper, a novel approach based on digital correlation is proposed to determine an optimal amount of defocus, aiming to get sinusoidal fringes from a binary-structured fringe pattern with projector defocusing. Firstly, a window containing multiple fringes is selected from the captured binary defocusing fringe pattern projected onto a calibration plate or a flat part of the surface of the object being measured. Then calculate the correlation coefficient between the selected window and its second-order differential in the direction perpendicular to the fringe. Since the second-order differential of a sinusoidal wave remains still sinusoidal, the maximum correlation coefficient should correspond to the optimal sinusoidality of the binary defocusing fringe. Therefore, by finding the maximum correlation coefficient while defocusing the projector, the optimal binary defocusing level can be determined accordingly. One of the characteristics of the proposed method is that only one fringe window is required to judge the defocus level in practical measurement, which may speed up 3D measurement. In addition, the proposed method has no requirements for fringe pitch and is applicable to fringes of any spatial frequency. Both numerical simulations and experimental results have been demonstrated to exhibit the validity and high accuracy of the proposed method.
An overhead-free region-based JPEG framework for task-driven image compression

An increasing amount of captured images are streamed to a remote server or stored in a device for deep neural network (DNN) inference. In most cases, raw images are compressed with encoding algorithms such as JPEG to cope with resource limitations. However, the standard JPEG optimized for human visual systems may induce significant accuracy loss in DNN inference tasks. In addition, the standard JPEG compresses all regions in an image at the same quality level, while some areas may not contain valuable information for the target task. In this paper, we propose a target-driven JPEG compression framework that performs region-adaptive quantization of the DCT coefficients. The region-based quality map is generated from an end-to-end trainable neural network. In addition, we present a deep learning approach to remove the requirement of storing the overhead information induced by the region-based encoding process. Our framework can be easily implemented on devices with commonly used JPEG and also produce images that achieve a higher compression rate with minimum degradation of the classification accuracy. The codes are available at https://github.com/jshye/qf-map-generator.
Multiple-level thresholding for breast mass detection

Detection of breast mass plays a very important role in making the diagnosis of breast cancer. For faster detection of breast cancer caused by breast mass, we developed a novel and efficient patch-based breast mass detection system for mammography images. The proposed framework is comprised of three modules, including pre-processing, multiple-level breast tissue segmentation, and final breast mass detection. An improved Deeplabv3+ model for pectoral muscle removal is deployed in pre-processing. We then proposed a multiple-level thresholding segmentation method to segment breast mass and obtained the connected components (ConCs), where the corresponding image patch to each ConC is extracted for mass detection. In the final detection stage, each image patch is classified into breast mass and breast tissue background by trained deep learning models. The patches that are classified as breast mass are then taken as the candidates for breast mass. To reduce the false positive rate in the detection results, we applied the non-maximum suppression algorithm to combine the overlapped detection results. Once an image patch is considered a breast mass, the accurate detection result can then be retrieved from the corresponding ConC in the segmented images. Moreover, a coarse segmentation result can be simultaneously retrieved after detection. Compared to the state-of-the-art methods, the proposed method achieved comparable performance. On CBIS-DDSM, the proposed method achieved a detection sensitivity of 0.87 at 2.86 FPI (False Positive rate per Image), while the sensitivity reached 0.96 on INbreast with an FPI of only 1.29.
Quantum Teleportation Error Suppression Algorithm Based on Convolutional Neural Networks and Quantum Topological Semion Codes

Quantum error correction (QEC) is a key technique for building scalable quantum computers that can be used to mitigate the effects of errors on physical quantum bits. Since quantum states are more or less affected by noise, errors are inevitable. Traditional QEC codes face huge challenges. Therefore, designing an error suppression algorithm based on neural networks (NN) and quantum topological error correction (QTEC) codes is particularly important for quantum teleportation. In this paper, QTEC codes: semion codes—a greater than 2 dimensional (2D) error correction code based on the double semion model—are used to suppress errors during quantum teleportation, using a NN to build a decoder based on semion codes and to simulate the quantum information error suppression process and the suppression effect. The proposed convolutional neural network (CNN) decoder is suitable for small distance topological semion codes. The aim is to optimize the NN for better decoder performance while deriving the relationship between decoder performance and slope and pseudothreshold during training and calculate the thresholds for different noise areas when the code distances are the same, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi mathvariant="normal">t</mi> <mi mathvariant="normal">h</mi> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">h</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">d</mi> </mrow> </msub> <mo>=</mo> <mn>0.082</mn> </math> for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">a</mi> <mo>&lt;</mo> <mn>0.007</mn> <mtext> </mtext> <mi mathvariant="normal">d</mi> <mi>B</mi> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi mathvariant="normal">t</mi> <mi mathvariant="normal">h</mi> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">h</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">d</mi> </mrow> </msub> <mo>=</mo> <mn>0.096</mn> </math> for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">a</mi> <mo>&lt;</mo> <mn>0.01</mn> <mtext> </mtext> <mi mathvariant="normal">d</mi> <mi>B</mi> </math> . This paper demonstrates the ability of CNNs to suppress errors in quantum transmission information and the great potential of NNs in the field of quantum computing.
Optimal non-asymptotic analysis of the Ruppert–Polyak averaging stochastic algorithm

This paper is devoted to the non-asymptotic analysis of the Ruppert–Polyak averaging method introduced in Polyak and Juditsky (1992) and Ruppert (1988)[26] for the minimization of a smooth function f with a stochastic algorithm. We first establish a general non-asymptotic optimal bound: if θˆn is the position of the algorithm at step n, we prove that E|θˆn−argmin(f)|2⩽Tr(Σ⋆)n+Cd,fn−rβ,where Σ⋆ is the limiting covariance matrix of the CLT demonstrated in Polyak and Juditsky (1992) and Cd,fn−rβ is a new state-of-the-art second order term that translates the effect of the dimension. We also identify the optimal gain of the baseline SGD γn=γn−3/4, leading to a second-order term with r3/4=5/4. Second, we show that this result holds under some Kurdyka-Łojiasewicz-type condition (Kurdyka, 1988; Lojasiewicz, 1963) for function f, which is far more general than the standard uniformly strongly convex case. In particular, it makes it possible to handle some pathological examples such as on-line learning for logistic regression and recursive quantile estimation.
Character-level arabic text generation from sign language video using encoder–decoder model

Video to text conversion is a vital activity in the field of computer vision. In recent years, deep learning algorithms have dominated automatic text generation in English, but there are a few research works available for other languages. In this paper, we propose a novel encoding–decoding system that generates character-level Arabic sentences from isolated RGB videos of Moroccan sign language. The video sequence was encoded by a spatiotemporal feature extraction using pose estimation models, while the label text of the video is transmitted to a sequence of representative vectors. Both the features and the label vector are joined and treated by a decoder layer to derive a final prediction. We trained the proposed system on an isolated Moroccan Sign Language dataset (MoSLD), composed of RGB videos from 125 MoSL signs. The experimental results reveal that the proposed model attains the best performance under several evaluation metrics.
Site-Adaptation for Correcting Satellite-Derived Solar Irradiance: Performance Comparison between Various Regressive and Distribution Mapping Techniques for Application in Daejeon, South Korea

Satellite-derived solar irradiance is advantageous in solar resource assessment due to its high spatiotemporal availability, but its discrepancies to ground-observed values remain an issue for reliability. Site adaptation can be employed to correct these errors by using short-term high-quality ground-observed values. Recent studies have highlighted the benefits of the sequential procedure of a regressive and a distribution-mapping technique in comparison to their individual counterparts. In this paper, we attempted to improve the sequential procedure by using various distribution mapping techniques in addition to the previously proposed quantile mapping. We applied these site-adaptation techniques on the global horizontal irradiance (GHI) and direct normal irradiance (DNI) obtained from the UASIBS-KIER model in Daejeon, South Korea. The best technique, determined by a ranking methodology, can reduce the mean bias from −5.04% and 13.51% to −0.45% and −2.02% for GHI and DNI, respectively, and improve distribution similarity by 2.5 times and 4 times for GHI and DNI, respectively. Partial regression and residual plot analysis were attempted to examine our finding that the sequential procedure is better than individual techniques for GHI, whereas the opposite is true for DNI. This is an initial study to achieve generalized site-adaptation techniques for the UASIBS-KIER model output.
Hyde: The First Open-Source, Python-Based, Gpu-Accelerated Hyperspectral Denoising Package

As with any physical instrument, hyperspectral cameras induce different kinds of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial step for analyzing hyperspectral images (HSIs). Conventional computational methods rarely use GPUs to improve efficiency and are not fully open-source. Alternatively, deep learning-based methods are often open-source and use GPUs, but their training and utilization for real-world applications remain non-trivial for many researchers. Consequently, we propose HyDe: the first open-source, GPU-accelerated Python-based, hyperspectral image denoising toolbox, which aims to provide a large set of methods with an easy-to-use environment. HyDe includes a variety of methods ranging from low-rank wavelet-based methods to deep neural network (DNN) models. HyDe’s interface dramatically improves the interoperability of these methods and the performance of the underlying functions. In fact, these methods maintain similar HSI denoising performance to their original implementations while consuming nearly ten times less energy. Furthermore, we present a method for training DNNs for denoising HSIs which are not spatially related to the training dataset, i.e., training on ground-level HSIs for denoising HSIs with other perspectives including airborne, drone-borne, and space-borne. To utilize the trained DNNs, we show a sliding window method to effectively denoise HSIs which would otherwise require more than 40 GB. The package can be found at: https://github.com/Helmholtz-AI-Energy/HyDe.
The effect of modern technology app on the self-regulation skills of students with disabilities

BACKGROUND: Augmented reality (AR) is capable of facilitating constructive learning through the provision of a learner-centered environment, making for more flexible learning and providing a more distinct experience of learning. AR technology has a significant influence on the teaching approach provided to students with learning disabilities, particularly on their level of self-regulation, and this premise is what motivated the present study.MATERIALS AND METHODS: The research is a quasi-experimental study that adopted a pre- and post-test control group design, involving 24 students who have disabilities. The students were divided into AR group and control group, and data were then exposed to the descriptive statistics and paired sample t test.RESULTS: The results showed that AR technology has a significant effect on the enhancement of the self-regulation of students with learning disabilities.CONCLUSION: The study indicated a significant effect of AR app on the self-regulation skills of students. The study, therefore, encouraged decision academicians to implement technology approaches in the disability setting.
Reflection Equivariant Quantum Neural Networks for Enhanced Image Classification

Machine learning is among the most widely anticipated use cases for near-term quantum computers, however there remain significant theoretical and implementation challenges impeding its scale up. In particular, there is an emerging body of work which suggests that generic, data agnostic quantum machine learning (QML) architectures may suffer from severe trainability issues, with the gradient of typical variational parameters vanishing exponentially in the number of qubits. Additionally, the high expressibility of QML models can lead to overfitting on training data and poor generalisation performance. A promising strategy to combat both of these difficulties is to construct models which explicitly respect the symmetries inherent in their data, so-called geometric quantum machine learning (GQML). In this work, we utilise the techniques of GQML for the task of image classification, building new QML models which are equivariant with respect to reflections of the images. We find that these networks are capable of consistently and significantly outperforming generic ansatze on complicated real-world image datasets, bringing high-resolution image classification via quantum computers closer to reality. Our work highlights a potential pathway for the future development and implementation of powerful QML models which directly exploit the symmetries of data.
A multi-scenario text generation method based on meta reinforcement learning

Multi-scenario text generation is an essential task in natural language generation because of the multi-scene interlaced property of real-world problems. Traditional methods typically train the multi-scenario text generation models based on maximum likelihood estimation, which may suffer from the problem of exposure bias. Reinforcement learning (RL) based text generation methods could mitigate the exposure bias problem to some extent. However, the RL-based text generation methods are limited to the single-scenario tasks, which cannot be straightforwardly generalized to new scenario tasks. To address this problem, in this paper, we propose a multi-scenario text generation method based on meta RL (MetaRL-TG), which implements the method of model-agnostic meta-learning (MAML) in the framework of RL-based text generation. The proposed MetaRL-TG method first learns the initial parameters from multiple training tasks, then fine-tunes them in the target task. Thus, the proposed method is expected to efficiently achieve high-quality generated text in the new scenario. Finally, the effectiveness and generalization capability of the proposed method are demonstrated for eight scenarios through English test datasets.
Classroom Video Image Emotion Analysis Method for Online Teaching Quality Evaluation

Classroom emotion is an important dimension to evaluate teaching effect, and the application of image processing to online teaching emotion analysis has become an inevitable trend of development. Aiming at the problems of low accuracy of expression recognition, unclear emotion scheme for online teaching evaluation, and low applicability of expression recognition model in existing methods, this paper conducts a research on classroom video image emotion analysis method for online teaching quality evaluation. First, the classroom video image emotion analysis task is divided into facial expression recognition task and facial feature point location task, and multi-task learning is carried out to achieve real-time switching between the two tasks for different types of input. The tag attention mechanism is proposed to deeply mine the key areas of the face in the classroom video image, so as to maintain the compactness of the distribution of the center sample of the classroom video image and its neighborhood samples in the feature space. Finally, based on the expression activity of teachers and students in the online classroom, the online classroom teaching emotion is analyzed, and the online teaching quality is evaluated from the side. The experimental results verify the validity of the model.
Optimal tests of genuine multipartite nonlocality

Abstract We propose an optimal and efficient numerical test for witnessing genuine multipartite nonlocality based on a geometric approach. In particular, we consider two non-equivalent models of local hidden variables, namely the Svetlichny and the no-signaling bilocal models. While our knowledge concerning these models is well established for Bell-type scenarios involving two measurement settings per party, the general case based on an arbitrary number of settings is a considerably more challenging task and very little work has been done in this field. In this paper, we applied such general tests to detect and characterize genuine n -way nonlocal correlations for various states of three qubits and qutrits. Apart from the fundamental problem of characterizing genuine multipartite nonlocal correlations, the extension of the number of measurements beyond two is also of practical importance. As a measure of nonlocality, we use the probability of violation of local realism under randomly sampled observables, and the strength of nonlocality, described by the resistance to white noise admixture. In particular, we analyze to what extent the Bell-type scenario involving two measurement settings can be used to certify genuine n -way nonlocal correlations generated for more general models. In addition, we propose a simple procedure to detect such nonlocal correlations for randomly chosen settings with an efficiency of up to 100%. Due to its near-perfect efficiency, our method may open new possibilities in device-independent quantum cryptography applications where strong nonlocality between all partners is required.
A Study of the Effect of Hybrid English Teaching by Teachers Using MOOC Mode

In order to test the improvement effect of the Massive Open Online Courses (MOOC)-based hybrid English teaching mode on the English listening, speaking, reading, and writing skills of students, this study divided 200 college students into two groups. One group was the control group which was taught by the traditional teaching mode, while the other group was the experimental group which was taught by the MOOC-based hybrid teaching mode. The listening, speaking, reading, and writing abilities of the two groups were tested before and after teaching. Moreover, a questionnaire survey was carried out after teaching test. The results showed that the listening, speaking, reading, and writing abilities of the experimental group what was taught by the MOOC-based hybrid English teaching mode was effectively improved. The results of the questionnaire survey also demonstrated that the MOOC-based hybrid English teaching mode was more effective in stimulating students’ interest in learning.
Institutions – Economic Growth Nexus in Sub-Saharan Africa

This research explores the causal link between institutions and economic growth in Sub-Saharan Africa countries. System generalized method of moments (GMM) is applied to fit dynamic models for a panel of 43 countries in the region over thirteen-year period. Major findings include (1) per capita gross domestic product (GDP) is persistent in the region; (2) no significant cause-effect link between institution and economic growth of the countries; and (3) impact of institutions on economic growth appeared affected by control variables. The results show that institutions did not lead to economic growth in SSA countries. The finding adds to the already developing puzzle regarding whether institutions matter for economic development of nations. The study suggests further work in institution-growth studies for proper recognition of institutions role in explaining economic growth differences of countries. In this regard, current research identifies focus areas for future work, which includes considering institutions quality threshold and inclusion of indicators from informal institutions in addition to the formal ones.
Forecasting Solar Radiation Using Machine Learning Method: New Optimization Algorithm

This paper presents a new algorithm approach for global solar radiation (GSR) forecasting based on multiple machine learning techniques. The accuracy of forecasting models is essential to promote clean energy sustainability and optimize the energy performance of photovoltaic power systems. Specifically, in this work, we develop a new optimization algorithm method to forecast hourly global solar radiation (GSR), which includes Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network with Back Propagation algorithm (ANN), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and persistence model. In the training algorithm, the clearness index (Kt), top of atmosphere (TOA), maximum temperature (Tmax), the difference of temperature (∆T), temperature ratio (Tratio), and average temperature (TAverage) are used. To decide on the success of the optimization methodology, five computational statistical metrics are discussed in this work. By analyzing the optimization performance of the forecasted models, all five machine-learning algorithms exhibit excellent performance in the forecasting of Hourly GSR.