Reviewerly/exHarmony huggingface dataset:
Gaussian mixture model based phase prior learning for video motion estimation

Optic measurement nowadays shows more prominent features over the traditional sensor-based measurement techniques as it does not need sensor installment and can provide simultaneous measurements with very high spatial resolution. Phase-based video processing has been widely used in optic measurement for capturing small motions. It relies on the assumptions that the variance of noise has to be much lower than the pure signal and small motions are encoded in the phase shift of an individual pixel's color which fundamentally restricts its usefulness. To tackle these issues, this paper proposes a novel perspective of phase estimation by utilizing the image priors on phase patches. The patch group based Gaussian Mixture Model (PG-GMM) learning algorithm is used to learn the nonlocal self-similarity (NSS) prior from training images. Then the phase information is modeled by learned patch group priors, and further optimized using the steps of gaussian component selection and weighted sparse coding. The proposed method is validated in both magnification and modal analysis applications in the example analysis. Compared to the phase-based method, the proposed method achieves high-quality magnifications on the real video signal, with fewer artifacts and better anti-noise performance. Clearer time-domain motion estimates of video components can also be extracted by the proposed method.
Gutzwiller wave function on a quantum computer using a discrete Hubbard-Stratonovich transformation

We propose a quantum-classical hybrid scheme for implementing the nonunitary Gutzwiller factor using a discrete Hubbard-Stratonovich transformation, which allows us to express the Gutzwiller factor as a linear combination of unitary operators involving only single-qubit rotations, at the cost of the sum over the auxiliary fields. To perform the sum over the auxiliary fields, we introduce two approaches that have complementary features. The first approach employs a linear-combination-of-unitaries circuit, which enables one to probabilistically prepare the Gutzwiller wave function on a quantum computer, while the second approach uses importance sampling to estimate observables stochastically, similar to a quantum Monte Carlo method in classical computation. The proposed scheme is demonstrated with numerical simulations for the half-filled Fermi-Hubbard model. Furthermore, we perform quantum simulations using a real quantum device, demonstrating that the proposed scheme can reproduce the exact ground-state energy of the two-site Fermi-Hubbard model within error bars.
UAV Imagery for Automatic Multi-Element Recognition and Detection of Road Traffic Elements

Road traffic elements comprise an important part of roads and represent the main content involved in the construction of a basic traffic geographic information database, which is particularly important for the development of basic traffic geographic information. However, the following problems still exist for the extraction of traffic elements: insufficient data, complex scenarios, small targets, and incomplete element information. Therefore, a set of road traffic multielement remote sensing image datasets obtained by unmanned aerial vehicles (UAVs) is produced, and an improved YOLOv4 network algorithm combined with an attention mechanism is proposed to automatically recognize and detect multiple elements of road traffic in UAV imagery. First, the scale range of different objects in the datasets is counted, and then the size of the candidate box is obtained by the k-means clustering method. Second, mosaic data augmentation technology is used to increase the number of trained road traffic multielement datasets. Then, by integrating the efficient channel attention (ECA) mechanism into the two effective feature layers extracted from the YOLOv4 backbone network and the upsampling results, the network focuses on the feature information and then trains the datasets. At the same time, the complete intersection over union (CIoU) loss function is used to consider the geometric relationship between the object and the test object, to solve the overlapping problem of the juxtaposed dense test element anchor boxes, and to reduce the rate of missed detection. Finally, the mean average precision (mAP) is calculated to evaluate the experimental effect. The experimental results show that the mAP value of the proposed method is 90.45%, which is 15.80% better than the average accuracy of the original YOLOv4 network. The average detection accuracy of zebra crossings, bus stations, and roadside parking spaces is improved by 12.52%, 22.82%, and 12.09%, respectively. The comparison experiments and ablation experiments proved that the proposed method can realize the automatic recognition and detection of multiple elements of road traffic, and provide a new solution for constructing a basic traffic geographic information database.
Restaurants and robots: public preferences for robot food and beverage services

Purpose The hospitality industry in developed countries is under pressure due to labor shortages and it is likely more food and beverage operations will have to be automated in the future. This research investigates the public’s perceptions of the use of robots in food and beverage operations to learn about how the public perceives automation in food and beverage. Design/methodology/approach Data were collected from a survey disseminated online in 12 languages, resulting in a sample of 1,579 respondents. The data were analyzed using factor analysis and OLS regressions. Findings The data also reveal that generally positive attitudes toward the use of robots in tourism and hospitality is a strong indicator of positive attitudes toward the use of robots in an F&amp;B setting. The data also illustrate that the public’s perception of appropriateness of the use of robots in F&amp;B operations is positively related to robots’ perceived reliability, functionality and advantages compared to human employees. Research limitations/implications The implications illustrate that the public seems to be generally accepting robots in food and beverage operations, even considering the public’s understanding and acceptance of the limitations of such technologies. Practical implications The research suggests that a critical element in terms of incorporating automation into future food and beverage operations is encouraging consumers to have generally positive attitudes toward the use of robots in hospitality and tourism industries. Originality/value This survey is based upon the data gathered in multiple countries to learn about how individuals perceive the use of robots in food and beverage operations, illustrating the attitudes that will assist or hinder the automation of this service industry.
Low-light image enhancement based on deep learning: a survey

Images taken under low light or dim backlight conditions usually have insufficient brightness, low contrast, and poor visual quality of the image, which leads to increased difficulty in computer vision and human recognition of images. Therefore, low illumination enhancement is very important in computer vision applications. We mainly provide an overview of existing deep learning enhancement algorithms in the low-light field. First, a brief overview of the traditional enhancement algorithms used in early low-light images is given. Then, according to the neural network structure used in deep learning and its learning algorithm, the enhancement methods are introduced. In addition, the datasets and common performance indicators used in the deep learning enhancement technology are introduced. Finally, the problems and future development of the deep learning enhancement method for low-light images are described.
VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance

Generating and editing images from open domain text prompts is a challenging task that heretofore has required expensive and specially trained models. We demonstrate a novel methodology for both tasks which is capable of producing images of high visual quality from text prompts of significant semantic complexity without any training by using a multimodal encoder to guide image generations. We demonstrate on a variety of tasks how using CLIP [37] to guide VQGAN [11] produces higher visual quality outputs than prior, less flexible approaches like DALL-E [38], GLIDE [33] and Open-Edit [24], despite not being trained for the tasks presented. Our code is available in a public repository.
Dual Networks Based 3D Multi-Person Pose Estimation From Monocular Video

Monocular 3D human pose estimation has made progress in recent years. Most of the methods focus on single persons, which estimate the poses in the person-centric coordinates, i.e., the coordinates based on the center of the target person. Hence, these methods are inapplicable for multi-person 3D pose estimation, where the absolute coordinates (e.g., the camera coordinates) are required. Moreover, multi-person pose estimation is more challenging than single pose estimation, due to inter-person occlusion and close human interactions. Existing top-down multi-person methods rely on human detection (i.e., top-down approach), and thus suffer from the detection errors and cannot produce reliable pose estimation in multi-person scenes. Meanwhile, existing bottom-up methods that do not use human detection are not affected by detection errors, but since they process all persons in a scene at once, they are prone to errors, particularly for persons in small scales. To address all these challenges, we propose the integration of top-down and bottom-up approaches to exploit their strengths. Our top-down network estimates human joints from all persons instead of one in an image patch, making it robust to possible erroneous bounding boxes. Our bottom-up network incorporates human-detection based normalized heatmaps, allowing the network to be more robust in handling scale variations. Finally, the estimated 3D poses from the top-down and bottom-up networks are fed into our integration network for final 3D poses. To address the common gaps between training and testing data, we do optimization during the test time, by refining the estimated 3D human poses using high-order temporal constraint, re-projection loss, and bone length regularizations. Our evaluations demonstrate the effectiveness of the proposed method. Code and models are available: https://github.com/3dpose/3D-Multi-Person-Pose.
Face.evoLVe: A cross-platform library for high-performance face analytics

In this paper, we develop face.evoLVe -- a comprehensive library that collects and implements a wide range of popular deep learning-based methods for face recognition. First of all, face.evoLVe is composed of key components that cover the full process of face analytics, including face alignment, data processing, various backbones, losses, and alternatives with bags of tricks for improving performance. Later, face.evoLVe supports multi-GPU training on top of different deep learning platforms, such as PyTorch and PaddlePaddle, which facilitates researchers to work on both large-scale datasets with millions of images and low-shot counterparts with limited well-annotated data. More importantly, along with face.evoLVe, images before & after alignment in the common benchmark datasets are released with source codes and trained models provided. All these efforts lower the technical burdens in reproducing the existing methods for comparison, while users of our library could focus on developing advanced approaches more efficiently. Last but not least, face.evoLVe is well designed and vibrantly evolving, so that new face recognition approaches can be easily plugged into our framework. Note that we have used face.evoLVe to participate in a number of face recognition competitions and secured the first place. The version that supports PyTorch is publicly available at https://github.com/ZhaoJ9014/face.evoLVe.PyTorch and the PaddlePaddle version is available at https://github.com/ZhaoJ9014/face.evoLVe.PyTorch/tree/master/paddle. Face.evoLVe has been widely used for face analytics, receiving 2.4K stars and 622 forks.
BPI-MVQA: a bi-branch model for medical visual question answering

Abstract Background Visual question answering in medical domain (VQA-Med) exhibits great potential for enhancing confidence in diagnosing diseases and helping patients better understand their medical conditions. One of the challenges in VQA-Med is how to better understand and combine the semantic features of medical images (e.g., X-rays, Magnetic Resonance Imaging(MRI)) and answer the corresponding questions accurately in unlabeled medical datasets. Method We propose a novel Bi-branched model based on Parallel networks and Image retrieval for Medical Visual Question Answering (BPI-MVQA). The first branch of BPI-MVQA is a transformer structure based on a parallel network to achieve complementary advantages in image sequence feature and spatial feature extraction, and multi-modal features are implicitly fused by using the multi-head self-attention mechanism. The second branch is retrieving the similarity of image features generated by the VGG16 network to obtain similar text descriptions as labels. Result The BPI-MVQA model achieves state-of-the-art results on three VQA-Med datasets, and the main metric scores exceed the best results so far by 0.2 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>%</mml:mo></mml:math> , 1.4 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>%</mml:mo></mml:math> , and 1.1 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>%</mml:mo></mml:math> . Conclusion The evaluation results support the effectiveness of the BPI-MVQA model in VQA-Med. The design of the bi-branch structure helps the model answer different types of visual questions. The parallel network allows for multi-angle image feature extraction, a unique feature extraction method that helps the model better understand the semantic information of the image and achieve greater accuracy in the multi-classification of VQA-Med. In addition, image retrieval helps the model answer irregular, open-ended type questions from the perspective of understanding the information provided by images. The comparison of our method with state-of-the-art methods on three datasets also shows that our method can bring substantial improvement to the VQA-Med system.
C to checked C by 3c

Owing to the continued use of C (and C++), spatial safety violations (e.g., buffer overflows) still constitute one of today's most dangerous and prevalent security vulnerabilities. To combat these violations, Checked C extends C with bounds-enforced checked pointer types. Checked C is essentially a gradually typed spatially safe C - checked pointers are backwards-binary compatible with legacy pointers, and the language allows them to be added piecemeal, rather than necessarily all at once, so that safety retrofitting can be incremental. This paper presents a semi-automated process for porting a legacy C program to Checked C. The process centers on 3C, a static analysis-based annotation tool. 3C employs two novel static analysis algorithms - typ3c and boun3c - to annotate legacy pointers as checked pointers, and to infer array bounds annotations for pointers that need them. 3C performs a root cause analysis to direct a human developer to code that should be refactored; once done, 3C can be re-run to infer further annotations (and updated root causes). Experiments on 11 programs totaling 319KLoC show 3C to be effective at inferring checked pointer types, and experience with previously and newly ported code finds 3C works well when combined with human-driven refactoring.
All-Optical Nonlinear Activation Function Based on Germanium Silicon Hybrid Asymmetric Coupler

Nonlinear activation functions are crucial for optical neural networks (ONNs) to achieve more various functions. However, the current nonlinear functions suffer from some dilemma, including high power consumption, high loss, and limited bandwidth. Here, we propose and demonstrate an all-optical implementation of a nonlinear activation function based on germanium silicon hybrid integration. The principle lies in the intrinsic absorption and the carrier-induced refractive index change of germanium in C -band. It has a large operating bandwidth and a response frequency of 70 MHz, with a loss of 4.28 dB and a threshold power of 5.1 mW. Adopting it to the MNIST handwriting data set classification, it shows an improvement in accuracy from 91.6% to 96.8%. This proves that our scheme has great potential for advanced ONN applications.
A Comprehensive Review of Quantum Random Number Generators: Concepts, Classification and the Origin of Randomness

Random numbers are central to cryptography and various other tasks. The intrinsic probabilistic nature of quantum mechanics has allowed us to construct a large number of quantum random number generators (QRNGs) that are distinct from the traditional true number generators. This article provides a review of the existing QRNGs with a focus on their various possible features (e.g., device independence, semi-device independence) that are not achievable in the classical world. It also discusses the origin, applicability, and other facets of randomness. Specifically, the origin of randomness is explored from the perspective of a set of hierarchical axioms for quantum mechanics, implying that succeeding axioms can be regarded as a superstructure constructed on top of a structure built by the preceding axioms. The axioms considered are: (Q1) incompatibility and uncertainty; (Q2) contextuality; (Q3) entanglement; (Q4) nonlocality and (Q5) indistinguishability of identical particles. Relevant toy generalized probability theories (GPTs) are introduced, and it is shown that the origin of random numbers in different types of QRNGs known today are associated with different layers of nonclassical theories and all of them do not require all the features of quantum mechanics. Further, classification of the available QRNGs has been done and the technological challenges associated with each class are critically analyzed. Commercially available QRNGs are also compared.
Using virtual reality in lumbar puncture training improves students learning experience

Lumbar puncture (LP) is a commonly performed medical procedure in a wide range of indications. Virtual reality (VR) provides a stimulating, safe and efficient learning environment. We report the design and the evaluation of a three dimensions (3D) video for LP training.We recorded a stereoscopic 180-degrees 3D video from two LPs performed in clinical settings in Fernand Widal Lariboisière University Hospital, Paris, France. The video was administered to third-year medical students as well as to a residents and attendings group during LP simulation-based training sessions.On 168 participants (108 novice third-year medical students, and 60 residents and attendings with prior LP experience), satisfaction after video exposure was high (rated 4.7 ± 0.6 on a 5-point scale). No significant discomfort was reported (comfort score graded 4.5 ± 0.8 on 5). LP-naive students displayed higher satisfaction and perceived benefit than users with prior LP experience (overall, P < 0.05). Trainees evaluated favorably the 3D feature and supported the development of similar tutorials for other medical procedures (respectively, 3.9 ± 1.1 and 4.4 ± 0.9 on 5).We report our experience with a 3D video for LP training. VR support could increase knowledge retention and skill acquisition in association to LP simulation training.
RoPGen

Source code authorship attribution is an important problem often encountered in applications such as software forensics, bug fixing, and software quality analysis. Recent studies show that current source code authorship attribution methods can be compromised by attackers exploiting adversarial examples and coding style manipulation. This calls for robust solutions to the problem of code authorship attribution. In this paper, we initiate the study on making Deep Learning (DL)-based code authorship attribution robust. We propose an innovative framework called <u>Ro</u>bust coding style <u>P</u>atterns <u>Gen</u>eration (RoPGen), which essentially learns authors' unique coding style patterns that are hard for attackers to manipulate or imitate. The key idea is to combine data augmentation and gradient augmentation at the adversarial training phase. This effectively increases the diversity of training examples, generates meaningful perturbations to gradients of deep neural networks, and learns diversified representations of coding styles. We evaluate the effectiveness of RoPGen using four datasets of programs written in C, C++, and Java. Experimental results show that RoPGen can significantly improve the robustness of DL-based code authorship attribution, by respectively reducing 22.8% and 41.0% of the success rate of targeted and untargeted attacks on average.
The effect of digital review credibility on Jordanian online purchase intention

Recently, the credibility of digital reviews has played an essential role in the shopper's buying behaviors and decisions. Since there is a dearth of experimental research about the shoppers' credibility evaluation regarding digital reviews, this study aimed to investigate the factors that affect digital review credibility and its influence on buying choices among Jordanian consumers. With the help of elaboration likelihood theory, a research model has been established that experimentally test it through structural equation modelling from the data gathered from 246 users of the digital review website Amazon. The study's findings suggest factors that consist of the argument quality, like accuracy, completeness and quantity of digital reviews, and the peripheral cues, such as reviewer expertise, rating of goods or services, and website reputation. However, both significantly influence digital review credibility. Thus, they positively impact the buying decisions of shoppers.
Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading

Abstract In recent years, Bitcoin has received substantial attention as potentially high-earning investment. However, its volatile price movement exhibits great financial risks. Therefore, how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers. However, empirical works in the Bitcoin forecasting and trading support systems are at an early stage. To fill this void, this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market. Two primary steps are involved in our methodology framework, namely, data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price. Results demonstrate that the proposed model outperforms other benchmark models, including econometric models, machine-learning models, and deep-learning models. Furthermore, the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation. The robustness of the model is verified through multiple forecasting periods and testing intervals.