Reviewerly/exHarmony huggingface dataset:
Research on Applications of Image Processing Technologies in Interior Design Information System

Due to the limitations of software technology and weak image processing technology, traditional interior design has limited scene reproduction ability and supports a small range of scenes. Therefore, this article proposes an interior design system based on image processing and virtual reality technology. The system hardware consists of an image processing module, a virtual simulation module, and a database support module, mainly responsible for processing image information. The system software mainly consists of program loading module, data storage and reading/writing module, bus transmission module, etc. After testing, the system's denoised image is smooth and clear, with obvious edge segmentation. After image rendering, the reflection and light effects are good, and the image processing time is short, which has a good indoor design effect.
Self-Supervised Temporal Graph Learning based on Multi-Head Self-Attention Weighted Neighborhood Sequences

Most current graph representation learning models require labeled samples for training, which may involve high costs in practice. Therefore, self-supervised learning has first been widely applied in static graph representation learning. By adding or removing operations on the adjacency matrix of static graphs to generate supervisory signals, it enables label-free learning of graph nodes. However, the majority of graphs in the real world are temporal graphs. Unlike static graphs, temporal graphs are usually organized through continuous time node interaction sequences, rather than adjacency matrices, making it difficult for self-supervised learning methods on static graphs to be directly migrated to temporal graphs. To address this challenge, we propose a novel self-supervised temporal graph learning approach, named Self-Supervised Temporal Graph Learning based on Neighborhood Sequences (SSHPD). Initially, we perform two types of masking operations on the neighborhood sequences of nodes, then model the neighborhood sequences of nodes using a Hawkes process. Specifically, in order to preserve the maximum neighborhood information of nodes during masking, we introduce a multi-head self-attention mechanism to calculate the attention weights between nodes, selecting those neighbor nodes with relatively small weights for masking. Additionally, we also utilize the multi-head self-attention mechanism to perform a weighted sum on the masked neighborhood sequences, thereby generating virtual nodes for each node and applying it to the Hawkes process, to maximize the extraction of node neighborhood information. We conducted experimental evaluations on three real-world datasets, the results of which demonstrate that, compared to the baseline methods, the embeddings learned by the SSHPD model exhibit superior performance in tasks such as node classification and network visualization.
Motion Recognition Model of Sports Video Based on Feature Extraction Algorithm

With the rapid development of computer technology, network technology and multimedia technology, multimedia data is increasing exponentially. As an important part of video multimedia data, its structure is complex and its data volume is huge. Especially from a monocular camera, it is a very meaningful work to realize the correct recognition of human movements in any visual angle. Video action recognition usually refers to the process of identifying human action categories from a video sequence. This technology is widely used in multimedia content analysis, human-computer interaction, intelligent real-time monitoring and other fields. It can be realized by feature extraction of video to generate feature vectors, and classification of feature vectors by classifier. To solve the above problems, this paper starts from the spatio-temporal correlation of video data, according to the mixed model structure of convolutional neural network (CNN) and long short term memory (LSTM), introduces the attention mechanism in semantic analysis, proposes a recursive machine network based on the attention mechanism, assigns different weights to each video frame, and improves the recognition effect in the captured video clips.
Variational Quantum Approach for the Multiple Traveling Salesman Problem Optimisation

The Multiple Traveling Salesman Problem (m-TSP) is a well-known combinatorial optimisation problem with diverse applications in various fields, including transportation, logistics, and network design. The m-TSP involves finding the optimal routes for multiple salesmen to visit a set of cities, minimising the total travel distance while ensuring that each city is visited exactly once. Traditional computational approaches struggle to efficiently solve large-scale instances of the m-TSP due to its inherent complexity. In recent years, quantum computing has emerged as a promising field for tackling combinatorial optimisation problems. In this study, we investigate the application of the Quantum Approximate Optimisation Algorithm (QAOA) to solve the m-TSP, leveraging the inherent parallelism and quantum computational power to potentially obtain high-quality solutions.
A Realistic AI Avatar Generation Method Combining GAN, Unity and Kinect Techniques

With the comprehensive progress of science and technology, virtual characters have begun to enter people’s vision. There is still a gap between virtual characters and real people. Hence, the study examined, digital virtual humans through GAN2Shape and Kinect 2.0 to obtain high-quality three-dimensional (3D) images of human faces. First, we captured and recognized facial expressions through Kinect. Second, we generated high-quality 3D images of the facial expressions through GAN2Shape and transferred the images to Unity 3D for front and back-end development to generate a facial model. Third, we recognized and captured motions through Kinect. Finally, we combined the facial model with the motions to form the virtual human model. Experimental results show that the virtual human model generated through this method has a clear face and a good effect in motion capture.
The impact of chatbot avatars on relationship quality in commercial scenarios

Recently, chatbots have been widely applied in commercial scenarios as supplements or replacements for traditional human customer service. When interacting with customers, chatbots often present themselves through avatars, playing a significant role in the interaction between businesses and customers. However, there is currently no conclusive evidence on which avatar type is most effective in improving relationship outcomes, especially in the case of conversation breakdown. Therefore, this study aims to explore the impact of different avatar types of chatbots in commercial settings on relationship quality, particularly in the case of conversation breakdown. Using a one factor between-subjects experimental design (avatars: human-like vs. animal vs. company logo), 505 valid data samples were collected on the Mturk platform to analyze the main effects of chatbot avatar types on relationship quality. The results of this study indicate that the avatar of chatbots has a significant impact on satisfaction and commitment, while its effect on trust is not significant. According to post-hoc comparisons, animal avatars had a significantly greater impact on satisfaction and commitment compared to human-like avatars and company logo avatars. This indicates that the use of animal avatars for chatbots can achieve higher levels of satisfaction and commitment than human-like avatars and company logo avatars. This study makes significant contributions to the fields of human-computer interaction theory, design concepts, and customer relationship theory by exploring the impact of different service agent types on customer relationship quality. Additionally, it provides valuable insights for businesses in deploying chatbots.
Research on Face Expression Complementary Recognition Based on DCGAN and VGG-16

Face expression is the key in face recognition, and after being occluded, the accuracy of the algorithm's judgment of expression will be reduced and cannot accurately convey the information. This thesis investigates face expression complementation and recognition by first artificially processing the dataset with dimming and occlusion, and then performing operations on the generator and discriminator of the Deep Convolutional Generative Adversarial Then, the generator and discriminator of Deep Convolutional Generative Adversarial Networks (DCGAN) algorithm are operated, including gradient zeroing, back propagation and parameter updating, to realize the face image complementation process. Based on this, the complemented dataset and the original data are classified and judged using the Visual Geometry Group (VGG-16) algorithm. Finally, it is concluded that the face after the complementation using DCGAN contains more emotional information and higher recognition accuracy than the pre-processing dataset, and the research method has good feedback and prospects for further research.
Using an $$A^*$$-based framework for decomposing combinatorial optimization problems to employ NISQ computers

Abstract Combinatorial optimization problems such as the traveling salesperson problem are ubiquitous in practical applications and notoriously difficult to solve optimally. Hence, many current endeavors focus on producing approximate solutions. The use of quantum computers could accelerate the generation of those approximate solutions or yield more exact approximations in comparable time. However, quantum computers are presently very limited in size and fidelity. In this work, we aim to address the issue of limited problem size by developing a scheme that decomposes a combinatorial optimization problem instance into arbitrarily small subinstances that can be solved on a quantum machine. This process utilizes A * as a foundation. Additionally, we present heuristics that reduce the runtime of the algorithm effectively, albeit at the cost of optimality. In experiments, we find that the heavy dependence of our approach on the choice of the heuristics used allows for a modifiable framework that can be adapted case by case instead of a concrete procedure.
An Attractive Attractor

AbstractWe explore a family of closed curves in the plane. Each member of the family is the limit of self-intersecting polygonal curves. Nonetheless, most members of the family are simple closed curves. One, the “attractive attractor,” surrounds an infinite set of disjoint regions. AcknowledgmentsWe are grateful to the referee for a careful reading and many helpful suggestions.Additional informationNotes on contributorsHayden Ruff Hayden Ruff (hr442@drexel.edu) is a pre-candidacy mathematics Ph.D. student enrolled at Drexel University in Philadelphia, PA. He completed a B.S. in mathematics and physics at the University of Michigan – Flint, where he collaborated with and received instruction from Dr. Schilling. His research interests include signal processing, psychoacoustics, and applications of mathematics to the study of musicality.Kenneth Schilling Kenneth Schilling (ksch@umich.edu) received his Ph.D. from the University of California, Berkeley in 1981. He is professor emeritus of mathematics from the University of Michigan-Flint. During his years on the faculty, he loved working with students and interactiing with colleagues and pretty much everything about university life except grading exams.
Use of artificial intelligence to identify patients to be assessed in a breast clinic on 2-week wait: a retrospective cohort study

The number of urgent referrals from primary care to specialist one stop breast clinics continues to rise beyond the capacity of the 2-week wait service. This study aims to use artificial intelligence (AI) to identify patients with new breast symptoms requiring a biopsy to identify those who should be prioritised for urgent breast clinic assessment.Data were collected retrospectively for patients attending one stop triple assessment breast clinic at Broomfield hospital between 1 June and 1 October 2021. PHP machine learning software was used to run AI on the data to identify patients who had a core biopsy in clinic.A total of 794 cases were referred to one stop breast clinic for new breast symptoms-37 male (4.6%) and 757 female (95.3%). The average age of the patients included was 43.2 years. Five hundred thirty-six patients (67.5%) presented with a breast lump, 180 (22.7%) with breast pain, 61 (7.7%) with changes to shape or skin and 13 (1.6%) with a lump identified by their general practitioner. The patients who had a biopsy were of increased age [52.8 (SD 17.9) vs. 44.1 (SD 16.8), P<0.001], and had previous mammogram [n=21, (31.8%) vs. n=148 (20.3%), P 0.03], previous benign breast disease [n=9 (13.6%) vs. n=23 (3.1%), P<0.001], and increased use of HRT [n=13 (19.7%) vs. n=53 (6.4%), P<0.001]. The sensitivity and specificity of AI with neural network algorithms were 84% and 90%, respectively.AI was very effective at predicting the presenting symptoms that are likely to result in biopsy and can therefore be used to identify patients who need to be seen urgently in breast clinic.
Initial State Encoding via Reverse Quantum Annealing and H-Gain Features

Quantum annealing is a specialized type of quantum computation that aims to use quantum fluctuations in order to obtain global minimum solutions of combinatorial optimization problems. Programmable D-Wave quantum annealers are available as cloud computing resources which allow users low level access to quantum annealing control features. In this paper, we are interested in improving the quality of the solutions returned by a quantum annealer by encoding an initial state into the annealing process. We explore two D-Wave features allowing one to encode such an initial state: the reverse annealing and the h-gain features. Reverse annealing (RA) aims to refine a known solution following an anneal path starting with a classical state representing a good solution, going backwards to a point where a transverse field is present, and then finishing the annealing process with a forward anneal. The h-gain (HG) feature allows one to put a time-dependent weighting scheme on linear ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$h$</tex-math></inline-formula> ) biases of the Hamiltonian, and we demonstrate that this feature likewise can be used to bias the annealing to start from an initial state. We also consider a hybrid method consisting of a backward phase resembling RA, and a forward phase using the HG initial state encoding. Importantly, we investigate the idea of iteratively applying RA and HG to a problem, with the goal of monotonically improving on an initial state that is not optimal. The HG encoding technique is evaluated on a variety of input problems including the edge-weighted Maximum Cut problem and the vertex-weighted Maximum Clique problem, demonstrating that the HG technique is a viable alternative to RA for some problems. We also investigate how the iterative procedures perform for both RA and HG initial state encoding on random whole-chip spin glasses with the native hardware connectivity of the D-Wave Chimera and Pegasus chips.
3DMAE: Joint SAR and Optical Representation Learning With Vertical Masking

The remote sensing community has shown increasingly interest in self-supervised learning for its ability to learn representations without labeled data. These representations can be easily adapted to downstream tasks through pre-training and fine-tuning. Recently, Masked Autoencoders (MAE) achieve better semantic representation by masking out a significant portion of the input image. However, the original design of MAE for RGB natural images may not be optimal for remote sensing (RS) images, which exhibit considerable variation between modalities like SAR and optical. To address this, we propose a 3D mask that enhances feature extraction along the vertical dimension. After fine-tuning, our 3DMAE model outperforms state-of-the-art contrastive and MAE-based models on BigEarthNet-MM classification and significantly reduces input data volume by at least 50% with the vertical mask, resulting in a more efficient model. Generalization experiments show a 5.9% F1-score improvement when applied to the SEN12MS dataset, which has diverse data distributions.
E2FIF: Push the Limit of Binarized Deep Imagery Super-Resolution Using End-to-End Full-Precision Information Flow

Binary neural network (BNN) provides a promising solution to deploy parameter-intensive deep single image super-resolution (SISR) models onto real devices with limited storage and computational resources. To achieve comparable performance with the full-precision counterpart, most existing BNNs for SISR mainly focus on compensating for the information loss incurred by binarizing weights and activations in the network through better approximations to the binarized convolution. In this study, we revisit the difference between BNNs and their full-precision counterparts and argue that the key to good generalization performance of BNNs lies on preserving a complete full-precision information flow along with an accurate gradient flow passing through each binarized convolution layer. Inspired by this, we propose to introduce a full-precision skip connection, or a variant thereof, over each binarized convolution layer across the entire network, which can increase the forward expressive capability and the accuracy of back-propagated gradient, thus enhancing the generalization performance. More importantly, such a scheme can be applied to any existing BNN backbones for SISR without introducing any additional computation cost. To validate the efficacy of the proposed approach, we evaluate it using four different backbones for SISR on four benchmark datasets and report obviously superior performance over existing BNNs and even some 4-bit competitors.
An Architecture for Control of Entanglement Generation Switches in Quantum Networks

Entanglement between quantum network nodes is often produced using intermediary devices-such as heralding stations-as a resource.When scaling quantum networks to many nodes, requiring a dedicated intermediary device for every pair of nodes introduces high costs.Here, we propose a cost-effective architecture to connect many quantum network nodes via a central quantum network hub called an entanglement generation switch (EGS).The EGS allows multiple quantum nodes to be connected at a fixed resource cost, by sharing the resources needed to make entanglement.We propose an algorithm called the rate control protocol, which moderates the level of competition for access to the hub's resources between sets of users.We proceed to prove a convergence theorem for rates yielded by the algorithm.To derive the algorithm we work in the framework of network utility maximization and make use of the theory of Lagrange multipliers and Lagrangian duality.Our EGS architecture lays the groundwork for developing control architectures compatible with other types of quantum network hubs as well as system models of greater complexity.
Emulation of Quantum Algorithms Using CMOS Analog Circuits

Quantum computers are regarded as the future of computing, as they are believed to be capable of solving extremely complex problems that are intractable on conventional digital computers. However, near-term quantum computers are prone to a plethora of noise sources that are difficult to mitigate, possibly limiting their scalability and precluding us from running any useful algorithms. Quantum emulation is an alternative approach that uses classical analog hardware to emulate the properties of superposition and entanglement, thereby mimicking quantum parallelism to attain similar speeds. By contrast, the use of classical digital hardware, such as field programmable gate arrays (FPGA), is less inefficient at emulating a quantum computer, as it does not take advantage of the fundamentally analog nature of quantum states. Consequently, this approach adds an inherent hardware overhead that also prevents scaling. In this work, an energy-efficient quantum emulator based on analog circuits realized in UMC 180-nm CMOS technology is proposed along with the design methodologies for a scalable computing architecture. A six-fold improvement in power consumption was observed over the FPGA-based approach for a ten-qubit emulation of Grover's search algorithm. The proposed emulator is also about 400 times faster than a Ryzen 5600x six-core processor performing a simulation of six-qubit Grover's search algorithm
GroupPrompter: A Prompting Method for Semantic Segmentation Based on SAM

The SAM shows remarkable generalization and transformable capabilities for category-agnostic segmentation. Although the semantics in latent space are explored slightly, more researches are working on instance segmentation. And it’s unclear how to design the appropriate prompts for semantic segmentation, which have large impact the performance. In this paper, by summarizing several existing methods for semantic segmentation with SAM, we proposed a learn prompt method for semantic segmentation based on the SAM model, incorporating the latent semantic features with prompt learning by a grouping approach, referred as GroupPrompter. This enables SAM to perform semantic segmentation with the automatic learned prompts. And the experimental results on ADE20K, Pascal Context and COCO-Stuff datasets validate the effectiveness of the proposed method.
Generative Adversarial Networks-Based Novel Approach for Fraud Detection for the European Cardholders 2013 Dataset

Credit card use poses a significant security issue on a global scale, with rule-based algorithms and traditional anomaly detection being two of the most often used methods. However, they are resource-intensive, time-consuming, and erroneous. Given fewer instances than legal payments, the dataset imbalance has become a serious issue. On the other hand, the generative technique is considered an effective way to rebalance the imbalanced class issue, as this technique balances both minority and majority classes before the training. In a more recent period, GAN is considered one of the most popular data generative techniques, as it is used in significant data settings. Hence, the research under study explores a classification system to detect fraudulent credit card transactions that are being trained using the European Cardholders 2013 dataset. It has 30 features, 28 of which are hidden due to sensitive information. Fraud activity accounts for less than 1% of the entire transaction volume of $284807. Additionally, GANs is a generative model based on game theory, in which a generator G and a discriminator D compete with one another. The generator’s goal is to make the discriminator uncertain. Distinguishing between instances from the generator and those from the original dataset is the discriminator’s goal, and we can increase classifiers’ discriminating strength by training GANs on a set of fraudulent credit card transactions. According to the outcome, our model outperformed the earlier experiments with an AUC score of 0.999. Additionally, it creates artificial data using GANs, enabling the production of a sizable volume of high-quality data. In terms of innovation and performance, this technique substantially improves over earlier research.