Reviewerly/exHarmony huggingface dataset:
Seamless Texture Optimization for RGB-D Reconstruction

Restoring high-fidelity textures for 3D reconstructed models are an increasing demand in AR/VR, cultural heritage protection, entertainment, and other relevant fields. Due to geometric errors and camera pose drifting, existing texture mapping algorithms are either plagued by blurring and ghosting or suffer from undesirable visual seams. In this paper, we propose a novel tri-directional similarity texture synthesis method to eliminate the texture inconsistency in RGB-D 3D reconstruction and generate visually realistic texture mapping results. In addition to RGB color information, we incorporate a novel color image texture detail layer serving as an additional context to improve the effectiveness and robustness of the proposed method. First, we select an optimal texture image for each triangle face of the reconstructed model to avoid texture blurring and ghosting. During the selection procedure, the texture details are weighted to avoid generating texture chart partitions across high-frequency areas. Then, we optimize the camera pose of each texture image to align with the reconstructed 3D shape. Next, we propose a tri-directional similarity function to resynthesize the image context within the boundary stripe of texture charts, which can significantly diminish the occurrence of texture seams. Finally, we introduce a global color harmonization method to address the color inconsistency between texture images captured from different viewpoints. The experimental results demonstrate that the proposed method outperforms state-of-the-art texture mapping methods and effectively overcomes texture tearing, blurring, and ghosting artifacts.
Traffic Refinery

Network management often relies on machine learning to make predictions about performance and security from network traffic. Often, the representation of the traffic is as important as the choice of the model. The features that the model relies on, and the representation of those features, ultimately determine model accuracy, as well as where and whether the model can be deployed in practice. Thus, the design and evaluation of these models ultimately requires understanding not only model accuracy but also the systems costs associated with deploying the model in an operational network. Towards this goal, this paper develops a new framework and system that enables a joint evaluation of both the conventional notions of machine learning performance (e.g., model accuracy) and the systems-level costs of different representations of network traffic. We highlight these two dimensions for two practical network management tasks, video streaming quality inference and malware detection, to demonstrate the importance of exploring different representations to find the appropriate operating point. We demonstrate the benefit of exploring a range of representations of network traffic and present Traffic Refinery, a proof-of-concept implementation that both monitors network traffic at 10~Gbps and transforms traffic in real time to produce a variety of feature representations for machine learning. Traffic Refinery both highlights this design space and makes it possible to explore different representations for learning, balancing systems costs related to feature extraction and model training against model accuracy.
Blockchain-based approach to supply chain modeling in a smart farming system

Blockchain is a technology that, integrated into intelligent systems, can solve many of the tasks and challenges in intelligent agriculture, such as monitoring and tracking the supply chain, financial management, establishing critical data, including those received by the IoT, and others. Launched in March 2021, the National Research Program “Intelligent Plant Breeding” sets the task to develop models for efficient blockchains for intelligent agriculture in Bulgaria. The report presents a model for the development of a supply chain based on blockchain technologies, which includes the following as separate channels: the BG Gene Bank, farmers-curators, farmers-producers, food producers, a trade network of stores, logistics, and transport, financial and insurance institutions, distributors, government regulators, etc.
Self-error-rejecting multipartite entanglement purification for electron systems assisted by quantum-dot spins in optical microcavities

We present a self-error-rejecting multipartite entanglement purification protocol (MEPP) for N -electron-spin entangled states, resorting to the single-side cavity-spin-coupling system. Our MEPP has a high efficiency containing two steps. One is to obtain high-fidelity N -electron-spin entangled systems with error-heralded parity-check devices (PCDs) in the same parity-mode outcome of three electron-spin pairs, as well as M -electron-spin entangled subsystems (2 ≤ M &lt; N ) in the different parity-mode outcomes of those. The other is to regain the N -electron-spin entangled systems from M -electron-spin entangled states utilizing entanglement link. Moreover, the quantum circuits of PCDs make our MEPP works faithfully, due to the practical photon-scattering deviations from the finite side leakage of the microcavity, and the limited coupling between a quantum dot and a cavity mode, converted into a failed detection in a heralded way.
Visualizing Standardized Model-Based Design and Inspection Data in Augmented Reality

Abstract Augmented reality (AR) has already helped manufacturers realize value across a variety of domains, including assistance in maintenance, process monitoring, and product assembly. However, coordinating traditional engineering data representations into AR systems without loss of context and information remains a challenge. A major barrier is the lack of interoperability between manufacturing-specific data models and AR-capable data representations. In response, we present a pipeline for porting standards-based design and inspection data into an AR scene. As a result, product manufacturing information with three-dimensional (3D) model data and corresponding inspection results are successfully overlaid onto a physical part. We demonstrate our pipeline by interacting with annotated parts while continuously tracking their pose and orientation. We then validate the pipeline by testing against six fully toleranced design models, accompanied by idealized inspection results. Our work (1) provides insight on how to address fundamental issues related to interoperability between domain-specific models and AR systems and (2) establishes an open software pipeline from which others can implement and further develop.
A multi-source information fusion method for ship target recognition based on Bayesian inference and evidence theory

The ship target recognition (STR) is greatly related to the battlefield situation awareness, which has recently gained prominence in the military domains. With the diversification and complexity of military missions, ship targets are mostly performed in the form of formations. Therefore, using the formation information to improve the accuracy of the ship target type recognition is worth studying. To effectively identify ship target type, we in this paper jointly consider the ship dynamic, formation, and feature information to propose a STR method based on Bayesian inference and evidence theory. Specifically, we first calculate the ship position distance matrix and the directional distance matrix with the Dynamic Time Warping (DTW) and the difference-vector algorithm taken into account. Then, we use the two distance matrices to obtain the ship formation information at different distance thresholds by the hierarchical clustering method, based on which we can infer the ship type. Thirdly, formation information and other attribute information are as nodes of the Bayesian Network (BN) to infer the ship type. Afterward, we can convert the recognition results at different thresholds into body of evidences (BOEs) as multiple information sources. Finally, we fuse the BOEs to get the final recognition. The proposed method is verified in simulation battle scenario in this paper. The simulation results demonstrate that the proposed method achieves performance superiority as compared with other ship recognition methods in terms of recognition accuracy.
Subspace clustering via adaptive least square regression with smooth affinities

It has been proved that subspace clustering algorithms with weighted norm regularizers usually outperform their special cases with non-weighted regularizers. However, it is difficult to design suitable weighted regularizers for different types of subspace clustering algorithms. In this paper, we firstly provide an adaptive weighted norm regularizer construction strategy by using the recently proposed adaptive graph construction method. Secondly, we give a proposition that the affinities obtained by the adaptive graph construction method should be changed smoothly on the original data manifold. Hence, we devise a graph-constraint for the obtained affinity matrix. Thirdly, by integrating the above two techniques and least square regression (LSR) algorithm together, we design a new subspace clustering algorithm, called adaptive least square regression with smooth affinities (ALSR). For solving ALSR problem, we present an optimization algorithm whose computation burden and convergence are analyzed. Finally, plentiful experiments conducted on different types databases show the superiorities of ALSR.
Rigid motion‐resolved prediction using deep learning for real‐time parallel‐transmission pulse design

Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx B1+ distributions following within-slice motion, which can then be used for tailored pTx pulse redesign.Using simulated data, conditional generative adversarial networks were trained to predict B1+ distributions in the head following a displacement. Predictions were made for two virtual body models that were not included in training. Predicted maps were compared with ground-truth (simulated, following motion) B1 maps. Tailored pTx pulses were designed using B1 maps at the original position (simulated, no motion) and evaluated using simulated B1 maps at displaced position (ground-truth maps) to quantify motion-related excitation error. A second pulse was designed using predicted maps (also evaluated on ground-truth maps) to investigate improvement offered by the proposed method.Predicted B1+ maps corresponded well with ground-truth maps. Error in predicted maps was lower than motion-related error in 99% and 67% of magnitude and phase evaluations, respectively. Worst-case flip-angle normalized RMS error due to motion (76% of target flip angle) was reduced by 59% when pulses were redesigned using predicted maps.We propose a framework for predicting B1+ maps online with deep neural networks. Predicted maps can then be used for real-time tailored pulse redesign, helping to overcome head motion-related error in pTx.
Emerging Technologies and Their Impact on the Future of the Tourism and Hospitality Industry

The evolution in technology has become now a strategic choice to develop every organization and its existence in the future. The tourism industry is not an exception. This study highlights the development of technologies and the impact of their integration in the field of tourism. Furthermore, it discusses their influence on the quality of the touristic products. This study focuses on how does the emerging technology can improve the tourism industry, and the most usable information systems that are used in this domain. The purposed model has been designed to investigate the effect of adopting the technology among tourism agencies. A sample of 72 tourism agencies in Jordan has been surveyed and discussed by using structural equation modeling. The results reveal that efficiency, productivity, profitability, effectiveness, and marketing are improved after employing new technologies. Therefore, it can be concluded that the integration of technology in tourism is unavoidable for the continued existence of service providers in the market.
Decision Support for Sharing Data using Differential Privacy

Owners of data may wish to share some statistics with others, but they may be worried of privacy of the underlying data. An effective solution to this problem is to employ provable privacy techniques, such as differential privacy, to add noise to the statistics before releasing them. This protection lowers the risk of sharing sensitive data with more or less trusted data sharing partners. Unfortunately, applying differential privacy in its mathematical form requires one to fix certain numeric parameters, which involves subtle computations and expert knowledge that the data owners may lack.In this paper, we first describe a differential privacy parameter selection procedure that minimizes what lay data owners need to know. Second, we describe a user visualization and workflow that makes this procedure available for lay data owners by helping them set the level of noise appropriately to achieve a tolerable risk level. Finally, we describe a user study in which human factors professionals who were native to differential privacy were briefly trained on the concept of using differential privacy for data sharing and then used the visualization to determine an appropriate level of noise.
Fine-Grained Unsupervised Temporal Action Segmentation and Distributed Representation for Skeleton-Based Human Motion Analysis

Understanding the fine-grained temporal structure of human actions and its semantic interpretation is beneficial to many real-world tasks, such as sports movements, rehabilitation exercises, and daily-life activities analysis. Current action segmentation methods mainly rely on deep neural networks to derive feature embedding of actions from motion data, while works on analyzing human actions in fine-granularity are still lacking due to the lack of clear and generic definitions of subactions and related datasets. On the other hand, the motion representations obtained in current action segmentation methods lack semantic or mathematical interpretability that can be used to evaluate action/subaction similarity in quantitative motion analysis. Toward the goal of fine-grained, interpretable, scalable, and efficient action segmentation, we propose a novel unsupervised action segmentation and distributed representation framework based on intuitive motion primitives defined on pose data. Metrics for comprehensive evaluation of the unsupervised fine-grained action segmentation task performance are proposed, and both public and self-constructed datasets are adopted in the experiments. The results show that the proposed method has good performance and generality across different subjects, datasets, and application scenarios.
RRT-Based Path Planning for Follow-the-Leader Motion of Hyper-Redundant Manipulators

Hyper-redundant manipulators with slender body and high dexterity are widely applied for operations in confined spaces. Among the motion planning methods for these operations, the follow-the-leader motion controller is generally developed to avoid the obstacles, while the path trajectories are usually given. In this paper, we present an autonomous motion planner with a specialized rapidly exploring random tree (Sp-RRT) approach for follow-the-leader motion of hyper-redundant manipulators. Starting from the target pose in the workspace, the exploring tree can expand to multiple entrances while guaranteeing the final pose of the manipulator’s end-effector. Meanwhile, the dexterity of hyper-redundant manipulators (even with different segments) can be utilized sufficiently with customized expanding parameters. Simulation results compared with existing methods are conducted to demonstrate the aforementioned characteristics and effectiveness. For further validation, we experimentally verify the development with our custom-built hyper-redundant manipulator to realize the generated path with follow-the-leader motion.
Reliable Template Matching for Image Detection in Vision Sensor Systems

Template matching is a simple image detection algorithm that can easily detect different types of objects just by changing the template without tedious training procedures. Despite these advantages, template matching is not currently widely used. This is because traditional template matching is not very reliable for images that differ from the template. The reliability of template matching can be improved by using additional information (depths for the template) available from the vision sensor system. Methods of obtaining the depth of a template using stereo vision or a few (two or more) template images or a short template video via mono vision are well known in the vision literature and have been commercialized. In this strategy, this paper proposes a template matching vision sensor system that can easily detect various types of objects without prior training. To this end, by using the additional information provided by the vision sensor system, we study a method to increase the reliability of template matching, even when there is a difference in the 3D direction and size between the template and the image. Template images obtained through the vision sensor provide a depth template. Using this depth template, it is possible to predict the change of the image according to the difference in the 3D direction and the size of the object. Using the predicted changes in these images, the template is calibrated close to the given image, and then template matching is performed. For ease of use, the algorithm is proposed as a closed form solution that avoids tedious recursion or training processes. For wider application and more accurate results, the proposed method considers the 3D direction and size difference in the perspective projection model and the general 3D rotation model.
Deep dual-domain semi-blind network for compressed image quality enhancement

Compressed image quality enhancement has attracted a large amount of attention in recent years. In general, the primary goal of compression is artifact reduction to produce a higher-quality output from a low-quality input. Information loss and compression artifacts are mostly due to quantization. The quantization matrix is determined by the compression quality factor (QF). However, there has thus far been little related research to estimate the compression quality factor for JPEG images. To address this issue, in this paper, we propose a deep dual-domain semi-blind network (D3SN) that combines compression quality factor detection and compressed image quality enhancement. Specifically, a quality factor detection (QFD) module is designed to capture contextual information of the space and frequency domains. Furthermore, we build a novel deep dual-domain compressed image quality enhancement network to remove the compression artifacts by using the prior in terms of both the discrete cosine transform (DCT) and pixel domains. Different from previous algorithms, our proposed approach can remove compression artifacts generated at different quality factors by inferring the image quality. Experimental results demonstrate the superiority of our deep dual-domain semi-blind network over state-of-the-art methods in terms of objective quality and visual results.
Metaheuristics on quantum computers: Inspiration, simulation and real execution

Quantum-inspired metaheuristics are solvers that incorporate principles inspired from quantum mechanics into classical-approximate algorithms using non-quantum machines. Due to the uniqueness of quantum principles, the inspiration of quantum phenomena and the way it is done in fundamentally different non-quantum systems rather than real or simulated quantum computers raise important questions about these algorithms’ design and the reproducibility of their results in real or simulated quantum devices. Thus, this work’s contribution stands in a first step towards answering those questions as an attempt to identify key findings in the existing literature that should be considered or adapted in order to build hybrid or fully-quantum algorithms that can be used in quantum machines. This is done by proposing and studying four inspired, simulated and real quantum cellular genetic algorithms that, as far as the authors’ knowledge, are the first quantum structured metaheuristics studied in the three quantum realms using a quantum simulator with 32 quantum bits and a real quantum machine employing 15 superconducting quantum bits. The users’ mobility management in cellular networks is taken as a validation problem using 13 real-world instances. The comparisons have been made against 6 diverse algorithms using 9 comparison metrics. Thorough statistical tests and parameters’ sensitivity analysis have been also conducted. The experiments allowed answering several questions, including how quantum hardware influences the studied-algorithms’ search process. They also enabled opening new perspectives in quantum metaheuristics’ design.
Adversarial Multi-task Learning-based Bug Fixing Time and Severity Prediction

Bug reports contain important information for software quality assurance. Conventionally, engineers complete bug report analysis tasks, which are extremely burdensome. Recently, researchers and companies have been working towards the automation of bug report analysis. Many machine learning and deep learning models are utilized for triage and prediction of bug report attributes such as bug fixing time and bug severity based on the description and comment text in bug reports. However, due to the rapid growth of data size in bug reporting systems, the prediction accuracy in single-task machine learning models is neither efficient nor effective. Multi-task learning (MTL) is a transfer learning scheme, which can train multiple related tasks together, reducing the training time while improving the overall performance. In our study, we utilize adversarial multi-task learning, which addresses the problem of contaminated shared feature space in common MTL models towards a purer shared feature space. Our adversarial convolutional neural network model (ADV-CNN) improved the validation accuracy of the bug fixing time prediction from 83.67% of a ST-CNN model to 89.25%.
Forward-reverse adaptive graph convolutional networks for skeleton-based action recognition

In the latest research based on skeleton data, the graph convolutional networks (GCN) based methods have achieved excellent performance on action recognition tasks. Existing GCN-based methods commonly adopt the strategy of fusing the data flow of joints and bones to obtain better results. However, these approaches ignore the reversibility of the skeleton data in the temporal dimension. Compared with the forward sequences input, which can achieve better results in certain actions through the end-to-end networks, the reverse skeleton data has excellent discrimination and richer information for some specific actions. In this work, we propose the novel forward-reverse adaptive graph convolutional networks (FR-AGCN) for skeleton-based action recognition. The sequences of joints and bones, as well as their reverse information, are modeled in the multi-stream networks at the same time. By extracting the features of forward and reverse deep information and performing multi-stream fusion, this strategy can significantly improve the recognition accuracy. Extensive experiments on two large-scale datasets NTU 60 & 120 show that the performance of our strategies has exciting advantages. On the latest dataset UAV-Human, the proposed FR-AGCN outperforms other state-of-the-art (SOTA) methods. Concretely, compared with 4s Shift-GCN, one of the most advanced models, FR-AGCN obtains significant improvements of +6.0% on CSv1 benchmark and +2.46% on CSv2 benchmark.