Reviewerly/exHarmony huggingface dataset:
Quantum pattern recognition on real quantum processing units

Abstract One of the most promising applications of quantum computing is the processing of graphical data like images. Here, we investigate the possibility of realizing a quantum pattern recognition protocol based on swap test, and use the IBMQ noisy intermediate-scale quantum (NISQ) devices to verify the idea. We find that with a two-qubit protocol, swap test can efficiently detect the similarity between two patterns with good fidelity, though for three or more qubits, the noise in the real devices becomes detrimental. To mitigate this noise effect, we resort to destructive swap test, which shows an improved performance for three-qubit states. Due to limited cloud access to larger IBMQ processors, we take a segment-wise approach to apply the destructive swap test on higher dimensional images. In this case, we define an average overlap measure which shows faithfulness to distinguish between two very different or very similar patterns when run on real IBMQ processors. As test images, we use binary images with simple patterns, grayscale MNIST numbers and fashion MNIST images, as well as binary images of human blood vessel obtained from magnetic resonance imaging (MRI). We also present an experimental set up for applying destructive swap test using the nitrogen vacancy (NVs) center in diamond. Our experimental data show high fidelity for single qubit states. Lastly, we propose a protocol inspired from quantum associative memory, which works in an analogous way to supervised learning for performing quantum pattern recognition using destructive swap test.
Gesture Detection and Recognition Based on Object Detection in Complex Background

In practical human–computer interaction, a hand gesture recognition method based on improved YOLOv5 is proposed to address the problem of low recognition accuracy and slow speed with complex backgrounds. By replacing the CSP1_x module in the YOLOv5 backbone network with an efficient layer aggregation network, a richer combination of gradient paths can be obtained to improve the network’s learning and expressive capabilities and enhance recognition speed. The CBAM attention mechanism is introduced to filtering gesture features in channel and spatial dimensions, reducing various types of interference in complex background gesture images and enhancing the network’s robustness against complex backgrounds. Experimental verification was conducted on two complex background gesture datasets, EgoHands and TinyHGR, with recognition accuracies of mAP0.5:0.95 at 75.6% and 66.8%, respectively, and a recognition speed of 64 FPS for 640 × 640 input images. The results show that the proposed method can recognize gestures quickly and accurately with complex backgrounds, and has higher recognition accuracy and stronger robustness compared to YOLOv5l, YOLOv7, and other comparative algorithms.
Arbitrary-Oriented Ellipse Detector for Ship Detection in Remote Sensing Images

At present, in arbitrary-oriented object detection, the angular periodicity problem of rotated bounding box described by angle causes an object to have different numerical representations, which leads to uncertainty of rotated bounding box regression.To eliminate the angular periodicity problem, in this paper, we propose a novel and simple ellipse parameters representation method for arbitrary-oriented object, which hides the angle of object in the focal vector of ellipse to avoid direct angle prediction.Moreover, the proposed representation method can enable the arbitrary-oriented object to have only one numerical representation, which is beneficial to alleviate the uncertainty of bounding box regression.In order to adapt the proposed ellipse parameters representation method, we adopt 2D Gaussian distribution label assign for coarse samples selection, then the KLD loss and SimOTA are used to refine the coarse samples to obtain the best positive samples.We extend the YOLOX with medium parameters as an oriented ship detector according to the proposed ellipse parameters representation method, and conduct the experiments on HRSC2016, RSDD-SAR and RHRSID to demonstrate the effectiveness of the proposed method.The experimental results show that the proposed representation method achieves impressive results compared with state-of-the-art arbitrary-oriented object detection methods.
Metallogeny of the Xiaotongjiapuzi gold deposit, Liaodong Peninsula (Eastern China): Perspective from sulfide trace element geochemistry and sulfur isotopes

Most gold ores have variable sulfur isotopic and trace elemental compositions at micro-/nano-scale, which makes the understanding of the gold formation process more challenging. The advancement of in-situ analytical techniques offer opportunity to gain insight into gold mineralization processes and ore-material source. The Xiaotongjiapuzi is a representative intrusion-related gold deposit in the Qingchengzi orefield of the Liaodong Peninsula, Eastern China. Alteration and gold mineralization comprise four stages: (I) quartz-pyrite-Au, (II) quartz-pyrite-biotite-Au, (III) Au-barren quartz-pyrite, and (IV) post-ore quartz-calcite. Detailed scanning electron microscope (SEM) backscattered electron (BSE) imaging, electron probe microanalysis (EPMA), and laser ablation inductively coupled plasma mass spectrometry (LA–ICP–MS) elemental and LA–multi-collector (MC)–ICP–MS sulfur isotope analyses were conducted to reveal the ore-material source and mineralization process. Three texturally distinct auriferous pyrite generations were identified: Py1 and Py2 (in both stage I and II) and Py3 (in stage II and III). Py1 is porous and weakly fragmented, poorly crystallized, and overgrown by Py2. Py2 is commonly intergrown with arsenopyrite and marcasite, whilst Py3 overgrew around Py2 or replace it. Gold exists mainly as solid solution (Au+) in the pyrite lattice and nanoparticles (Au0), as well as (rare) micron-scale inclusions along the Py2-Py3 interface. Py2 has the highest gold content (mean 47 ppm, max 344 ppm), especially when intergrown with marcasite (Mrc1). The δ34S values of pyrite (5.71–15.31‰, n = 18) and marcasite (-19.59 to 7.45‰. n = 6) are the highest and lowest, respectively, and the values increase steadily from Py1 to Py3 (Py1: 5.71–7.54‰ → Py2: 7.44–9.55‰ → Py2 + Mrc1: 12.88–13.92‰ → Py3: 15.17–15.31‰). The Paleoproterozoic Dashiqiao and Gaixian formation host rocks contain large amount of sulfate minerals (e.g., barite and gypsum), and the coeval global sulfate has heavy sulfur isotope enrichment, suggesting that sulfate reduction may have provided H2S-rich fluids [Au(HS)2- and/or Au(HS)0] for the Mesozoic mineralization. For the multistage gold enrichment at Xiaotongjiapuzi, we suggest that the Paleoproterozoic sedimentary and metamorphic rocks may have formed the initial sulfur reservoir, which was then reactivated by Mesozoic magmatic-hydrothermal events.
OGMN: Occlusion-guided multi-task network for object detection in UAV images

Occlusion between objects is one of the overlooked challenges for object detection in UAV images. Due to the variable altitude and angle of UAVs, occlusion in UAV images happens more frequently than that in natural scenes. Compared to occlusion in natural scene images, occlusion in UAV images happens with feature confusion problem and local aggregation characteristic. And we found that extracting or localizing occlusion between objects is beneficial for the detector to address this challenge. According to this finding, the occlusion localization task is introduced, which together with the object detection task constitutes our occlusion-guided multi-task network (OGMN). The OGMN contains the localization of occlusion and two occlusion-guided multi-task interactions. In detail, an occlusion estimation module (OEM) is proposed to precisely localize occlusion. Then the OGMN utilizes the occlusion localization results to implement occlusion-guided detection with two multi-task interactions. One interaction for the guide is between two task decoders to address the feature confusion problem, and an occlusion decoupling head (ODH) is proposed to replace the general detection head. Another interaction for guide is designed in the detection process according to local aggregation characteristic, and a two-phase progressive refinement process (TPP) is proposed to optimize the detection process. Extensive experiments demonstrate the effectiveness of our OGMN on the Visdrone and UAVDT datasets. In particular, our OGMN achieves 35.0% mAP on the Visdrone dataset and outperforms the baseline by 5.3%. And our OGMN provides a new insight for accurate occlusion localization and achieves competitive detection performance.
PW-360IQA: Perceptually-Weighted Multichannel CNN for Blind 360-Degree Image Quality Assessment

Image quality assessment of 360-degree images is still in its early stages, especially when it comes to solutions that rely on machine learning. There are many challenges to be addressed related to training strategies and model architecture. In this paper, we propose a perceptually weighted multichannel convolutional neural network (CNN) using a weight-sharing strategy for 360-degree IQA (PW-360IQA). Our approach involves extracting visually important viewports based on several visual scan-path predictions, which are then fed to a multichannel CNN using DenseNet-121 as the backbone. In addition, we account for users' exploration behavior and human visual system (HVS) properties by using information regarding visual trajectory and distortion probability maps. The inter-observer variability is integrated by leveraging different visual scan-paths to enrich the training data. PW-360IQA is designed to learn the local quality of each viewport and its contribution to the overall quality. We validate our model on two publicly available datasets, CVIQ and OIQA, and demonstrate that it performs robustly. Furthermore, the adopted strategy considerably decreases the complexity when compared to the state-of-the-art, enabling the model to attain comparable, if not better, results while requiring less computational complexity.
Geometry-Consistent Adversarial Registration Model for Unsupervised Multi-Modal Medical Image Registration

Deformable multi-modal medical image registration aligns the anatomical structures of different modalities to the same coordinate system through a spatial transformation. Due to the difficulties of collecting ground-truth registration labels, existing methods often adopt the unsupervised multi-modal image registration setting. However, it is hard to design satisfactory metrics to measure the similarity of multi-modal images, which heavily limits the multi-modal registration performance. Moreover, due to the contrast difference of the same organ in multi-modal images, it is difficult to extract and fuse the representations of different modal images. To address the above issues, we propose a novel unsupervised multi-modal adversarial registration framework that takes advantage of image-to-image translation to translate the medical image from one modality to another. In this way, we are able to use the well-defined uni-modal metrics to better train the models. Inside our framework, we propose two improvements to promote accurate registration. First, to avoid the translation network learning spatial deformation, we propose a geometry-consistent training scheme to encourage the translation network to learn the modality mapping solely. Second, we propose a novel semi-shared multi-scale registration network that extracts features of multi-modal images effectively and predicts multi-scale registration fields in an coarse-to-fine manner to accurately register the large deformation area. Extensive experiments on brain and pelvic datasets demonstrate the superiority of the proposed method over existing methods, revealing our framework has great potential in clinical application.
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Named Entity Recognition

Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Most existing prototype-based sequence labeling models tend to memorize entity mentions which would be easily confused by close prototypes. In this paper, we proposed a Prototypical Semantic Decoupling method via joint Contrastive learning (PSDC) for few-shot NER. Specifically, we decouple class-specific prototypes and contextual semantic prototypes by two masking strategies to lead the model to focus on two different semantic information for inference. Besides, we further introduce joint contrastive learning objectives to better integrate two kinds of decoupling information and prevent semantic collapse. Experimental results on two few-shot NER benchmarks demonstrate that PSDC consistently outperforms the previous SOTA methods in terms of overall performance. Extensive analysis further validates the effectiveness and generalization of PSDC.
Semi-Supervised Medical Image Segmentation With Voxel Stability and Reliability Constraints

Semi-supervised learning is becoming an effective solution in medical image segmentation because annotations are costly and tedious to acquire. Methods based on the teacher-student model use consistency regularization and uncertainty estimation and have shown good potential in dealing with limited annotated data. Nevertheless, the existing teacher-student model is seriously limited by the exponential moving average algorithm, which leads to the optimization trap. Moreover, the classic uncertainty estimation method calculates the global uncertainty for images but does not consider local region-level uncertainty, which is unsuitable for medical images with blurry regions. In this article, the Voxel Stability and Reliability Constraint (VSRC) model is proposed to address these issues. Specifically, the Voxel Stability Constraint (VSC) strategy is introduced to optimize parameters and exchange effective knowledge between two independent initialized models, which can break through the performance bottleneck and avoid model collapse. Moreover, a new uncertainty estimation strategy, the Voxel Reliability Constraint (VRC), is proposed for use in our semi-supervised model to consider the uncertainty at the local region level. We further extend our model to auxiliary tasks and propose a task-level consistency regularization with uncertainty estimation. Extensive experiments on two 3D medical image datasets demonstrate that our method outperforms other state-of-the-art semi-supervised medical image segmentation methods under limited supervision.
Improving the robustness of analog deep neural networks through a Bayes-optimized noise injection approach

Abstract Analog deep neural networks (DNNs) provide a promising solution, especially for deployment on resource-limited platforms, for example in mobile settings. However, the practicability of analog DNNs has been limited by their instability due to multi-factor reasons from manufacturing, thermal noise, etc. Here, we present a theoretically guaranteed noise injection approach to improve the robustness of analog DNNs without any hardware modifications or sacrifice of accuracy, which proves that within a certain range of parameter perturbations, the prediction results would not change. Experimental results demonstrate that our algorithmic framework can outperform state-of-the-art methods on tasks including image classification, object detection, and large-scale point cloud object detection in autonomous driving by a factor of 10 to 100. Together, our results may serve as a way to ensure the robustness of analog deep neural network systems, especially for safety-critical applications.
A Population-Based Hybrid Approach for Hyperparameter Optimization of Neural Networks

In recent years, large amounts of data have been generated, and computer power has kept growing. This scenario has led to a resurgence in the interest in artificial neural networks. One of the main challenges in training effective neural network models is finding the right combination of hyperparameters to be used. Indeed, the choice of an adequate approach to search the hyperparameter space directly influences the accuracy of the resulting neural network model. Common approaches for hyperparameter optimization are Grid Search, Random Search, and Bayesian Optimization. There are also population-based methods such as CMA-ES. In this paper, we present HBRKGA, a new population-based approach for hyperparameter optimization. HBRKGA is a hybrid approach that combines the Biased Random Key Genetic Algorithm with a Random Walk technique to search the hyperparameter space efficiently. Several computational experiments on eight different datasets were performed to assess the effectiveness of the proposed approach. Results showed that HBRKGA could find hyperparameter configurations that outperformed (in terms of predictive quality) the baseline methods in six out of eight datasets while showing a reasonable execution time.
Federated vs Local vs Central Deep Learning of Tooth Segmentation on Panoramic Radiographs

Federated Learning (FL) enables collaborative training of artificial intelligence (AI) models from multiple data sources without directly sharing data. Due to the large amount of sensitive data in dentistry, FL may be particularly relevant for oral and dental research and applications. This study, for the first time, employed FL for a dental task, automated tooth segmentation on panoramic radiographs. We employed a dataset of 4,177 panoramic radiographs collected from nine different centers (n = 143 to n = 1881 per center) across the globe and used FL to train a machine learning model for tooth segmentation. FL performance was compared against Local Learning (LL), i.e., training models on isolated data from each center (assuming data sharing not to be an option). Further, the performance gap to Central Learning (CL), i.e., training on centrally pooled data (based on data sharing agreements) was quantified. Generalizability of models was evaluated on a pooled test dataset from all centers. For 8 out of 9 centers, FL outperformed LL with statistical significance (p<0.05); only the center providing the largest amount of data FL did not have such an advantage. For generalizability, FL outperformed LL across all centers. CL surpassed both FL and LL for performance and generalizability. If data pooling (for CL) is not feasible, FL is shown to be a useful alternative to train performant and, more importantly, generalizable deep learning models in dentistry, where data protection barriers are high. This study proves the validity and utility of FL in the field of dentistry, which encourages researchers to adopt this method to improve the generalizability of dental AI models and ease their transition to the clinical environment.
Automated Restaurant Management System

Abstract: The Restaurant Management System will provide the user a new experience in the restaurant. It will allow a manager and an owner to store the data and transactions made by customer. The System will allow user to order the food by scanning QR code placed on the table. It will manage the employee master and stock details and also produce the bill receipt a per the customer convenience(i.e. via Whatsapp, Email, SMS). It will manage the workload of chefs also. That will be very beneficial for the restaurant to give more better service to their customer.
Efficient reversible data hiding via two layers of double-peak embedding

Reversible data hiding continues to attract significant attention in recent years. In particular, an increasing number of authors focus on the higher significant bit (HSB) plane of an image which can yield more redundant space. On the other hand, the lower significant bit planes are often ignored for embedding in existing schemes due to their harm to the embedding rate. This paper proposes an efficient reversible data hiding scheme via a double-peak two-layer embedding (DTLE) strategy with prediction error expansion. The higher six-bit planes of the image are assigned as the HSB plane, and double prediction error peaks are applied in either embedding layer. This makes fuller use of the redundancy space of images compared with the one error peak strategy. Moreover, we carry out the median-edge detector pre-processing for complex images to reduce the size of the auxiliary information. A series of experimental results show that our DTLE approach makes better performance on the scenarios of high capacity embedding and achieves up to 83% higher embedding rate on real-world datasets, BOSSbase, BOWS2 and UCID, than the state-of-the-art approaches while guaranteeing better image quality.
Adaptive Particle Grey Wolf Optimizer with Deep Learning-based Sentiment Analysis on Online Product Reviews

The increasing use of e-commerce websites and social networks is continually generating an immense amount of data in various forms, such as text, images or sounds, videos, etc. Sentiment analysis (SA) in online product reviews is a method of identifying the overall sentiment of customers about a specific product or service. This study used Natural Language Processing (NLP) and Machine Learning (ML) algorithms to identify and extract opinions and emotions expressed in text. Online reviews are often written in informal language, slang, and dialects, making it difficult for ML models to accurately classify sentiments. In addition, the use of misspelled words or incorrect grammar can further complicate the analysis. The recent developments of Deep Learning (DL) models can be used for the accurate classification of sentiments. This paper presents an Adaptive Particle Grey Wolf Optimizer with Deep Learning Based Sentiment Analysis (APGWO-DLSA) method to accurately classify sentiments in product reviews. Initially, data pre-processing was performed to improve the quality of the product reviews using the word2vec embedding process. For sentiment classification, the proposed method used a Deep Belief Network (DBN) model. Finally, the hyperparameter tuning of the DBN was performed using the APGWO algorithm. An extensive experimental analysis demonstrated the improved results of APGWO-DLSA over other methods, showing a maximum accuracy of 94.77% and 85.31% on the Cell Phones And Accessories (CPAA) and Amazon Products (AP) datasets.
Concert experiences in virtual reality environments

Abstract Spurred by recent advances in digital technologies, virtual concerts have become established modes for event attendance and represent a rapidly growing segment of the music industry. Yet, up to now, general experience of virtual concert attendees remains largely underexplored. Here, we focus on a subcategory in this domain: music concerts in virtual reality (VR). Our approach is situated within the theoretical framework of embodied music cognition and entailed investigation through a survey study. Responses of seventy-four VR concert attendees were collected, consisting of demographics, motivations, experiences, and future perspectives. In contrast to previous research, which generally identified social connectedness as a main motivator for concert attendance, our sample regarded it as one of the least important incentives. On the other hand, in line with previous studies, ‘seeing specific artists perform’ and ‘uniqueness of the experience’, were pivotal. The latter was mostly fueled by the possibility to experience/interact with visuals and environments considered as unconceivable in the real world. Furthermore, 70% of our sample regarded VR concerts as ‘the future of the music industry’, mainly relating to the accessibility of such events. Positive evaluations of VR concert experiences, as well as future perspectives regarding the medium, were significantly influenced by the level of experienced immersivity. To our knowledge, this is the first study to provide such an account.