Reviewerly/exHarmony huggingface dataset:
Perfect state transfer on bi-Cayley graphs over abelian groups

The study of perfect state transfer on graphs has attracted a great deal of attention during the past ten years because of its applications to quantum information processing and quantum computation. Perfect state transfer is understood to be a rare phenomenon. This paper establishes necessary and sufficient conditions for a bi-Cayley graph having a perfect state transfer over any given finite abelian group. As corollaries, many known and new results are obtained on Cayley graphs having perfect state transfer over abelian groups, (generalized) dihedral groups, semi-dihedral groups and generalized quaternion groups. Especially, we give an example of a connected non-normal Cayley graph over a dihedral group having perfect state transfer between two distinct vertices, which was thought impossible.
A novel compound fault diagnosis method for rolling bearings based on graph label manifold metric transfer

Abstract Metric transfer learning overcomes the problem of no domain distribution alienation in traditional sample transfer, and also improves the classification accuracy. However, some problems exist; for example, the target domain must have labelled samples without considering the manifold connection between data samples, and the algorithm is not robust under sparse samples. A compound fault diagnosis method for rolling bearings based on graph label manifold metric transfer is proposed. First, the pseudo-label of the target domain is acquired through a weighted graph constructed on the source domain with known fault labels. In order to ensure the accuracy of metrics in manifold space, we adopt the method of graph label propagation (LP) to gradually spread the label information. Then, the sample data are mapped to the low-dimensional manifold space by locality preserving projections (LPP), which gives higher weight to the source domain samples close to the target domain to solve the domain offset problem. At the same time, combined with the labelled samples of the target domain in LP, the manifold distance measure is learned to minimize the intra-class distance and maximize the class spacing, eliminate the overlap phenomenon in the domain and realize the distribution alienation between classes in the domain. After each iteration, unsupervised manifold metric transfer learning is realized by increasing the range of LP and gradually reducing the error of graph LP. Experiments show that the new unsupervised transfer learning method has better fault identification ability than the semi-supervised transfer learning method. In the case of sparse training samples, it still maintains a high diagnostic accuracy, and the highest fault recognition accuracy can reach more than 97% with good robustness.
Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning

Efficient and feasible anomaly detection scheme that could utilize data collected by supervisory-control-and-data-acquisition (SCADA) system is essential for wind turbines, which could greatly increase the amount of available condition monitoring data, and improve the power generation efficiency and reduce maintenance costs. While it is also very challenging as condition estimation with such massive field data is difficult to tackle, especially the SCADA data is not labeled in most cases. This work presents an unsupervised anomaly detection framework for wind turbines incorporating physical-statistical feature fusion and graph neural networks (GNNs), realizing dimensionality reduction, temporal dependence extraction, and latent nonlinear correlation capture of high-dimensional data. Firstly, graphical modeling for SCADA data of wind turbines is presented. Secondly, the physical-statistical feature fusion is implemented via local-global mutual information maximization. Finally, anomaly detection is realized with an energy-based method to learn patterns in the updated nodes' feature matrix. The results show that i) the features designed by physical information can reasonably represent equipment's state and reduce the information-redundancy, ii) the time-series based graph structure can effectively express the dataset structure information and extract temporal dependence, iii) and the anomaly detection model can fully use the physical-statistical information and local-global information, which outperforms comparison methods.
Real-Time Channel Mixing Net for Mobile Image Super-Resolution

Recently, deep learning based image super-resolution (SR) models show a strong performance thanks to the convolution neural network (CNN). However, these CNN-based models mostly need large memory and use a lot of power cost, which limits its use in mobile devices. To solve this problem, we propose a channel mixing Net (CDFM-Mobile) for mobile SR. The idea of the CDFM-Mobile is based on making channel mixing by using a pointwise convolution and deep features extraction by using 3 $$\times $$ 3 convolution. In addition, inspired by the prior work in the field, we used anchor-based residual learning and deep feature residual learning, which improved the performance. In addition, we used the quantization-aware training approach to optimize the model performance based on training at 8-bit quantize. Finally, we take part in MAI 2022 for mobile SR, and extensive results are conducted to show the model performance.
Bokeh-Loss GAN: Multi-stage Adversarial Training for Realistic Edge-Aware Bokeh

In this paper, we tackle the problem of monocular bokeh synthesis, where we attempt to render a shallow depth of field image from a single all-in-focus image. Unlike in DSLR cameras, this effect can not be captured directly in mobile cameras due to the physical constraints of the mobile aperture. We thus propose a network-based approach that is capable of rendering realistic monocular bokeh from single image inputs. To do this, we introduce three new edge-aware Bokeh Losses based on a predicted monocular depth map, that sharpens the foreground edges while blurring the background. This model is then finetuned using an adversarial loss to generate a realistic Bokeh effect. Experimental results show that our approach is capable of generating a pleasing, natural Bokeh effect with sharp edges while handling complicated scenes.
CAIR: Fast and Lightweight Multi-scale Color Attention Network for Instagram Filter Removal

Image restoration is an important and challenging task in computer vision. Reverting a filtered image to its original image is helpful in various computer vision tasks. We employ a nonlinear activation function free network (NAFNet) for a fast and lightweight model and add a color attention module that extracts useful color information for better accuracy. We propose an accurate, fast, lightweight network with multi-scale and color attention for Instagram filter removal (CAIR). Experiment results show that the proposed CAIR outperforms existing Instagram filter removal networks in fast and lightweight ways, about 11 $$\times $$ faster and 2.4 $$\times $$ lighter while exceeding 3.69 dB PSNR on IFFI dataset. CAIR can successfully remove the Instagram filter with high quality and restore color information in qualitative results. The source code and pretrained weights are available at https://github.com/hnvlab-syu/CAIR .
XCAT - Lightweight Quantized Single Image Super-Resolution Using Heterogeneous Group Convolutions and Cross Concatenation

We propose a lightweight, single-image super-resolution mobile device network named XCAT, and introduce Heterogeneous Group Convolution Blocks with Cross Concatenations (HXBlock). The heterogeneous split of the input channels to the group convolution blocks reduces the number of operations, and cross concatenation allows for information flow between the intermediate input tensors of cascaded HXBlocks. Cross concatenations inside HXBlocks can also avoid using more expensive operations like 1 $$\times $$ 1 convolutions. To further prevent expensive tensor copy operations, XCAT utilizes non-trainable convolution kernels to apply upsampling operations. Designed with integer quantization in mind, XCAT also utilizes several techniques in training, like intensity-based data augmentation. Integer quantized XCAT operates in real-time on Mali-G71 MP2 GPU with 320 ms, and on Synaptics Dolphin NPU with 30 ms (NCHW) and 8.8 ms (NHWC), suitable for real-time applications.
Adaptive Mask-Based Pyramid Network for Realistic Bokeh Rendering

Bokeh effect highlights an object (or any part of the image) while blurring the rest of the image, and creates a visually pleasant artistic effect. Due to the sensor-based limitations on mobile devices, machine learning (ML) based bokeh rendering has gained attention as a reliable alternative. In this paper, we focus on several improvements in ML-based bokeh rendering; i) on-device performance with high-resolution images, ii) ability to guide bokeh generation with user-editable masks and iii) ability to produce varying blur strength. To this end, we propose Adaptive Mask-based Pyramid Network (AMPN), which is formed of a Mask-Guided Bokeh Generator (MGBG) block and a Laplacian Pyramid Refinement (LPR) block. MGBG consists of two lightweight networks stacked to each other to generate the bokeh effect, and LPR refines and upsamples the output of MGBG to produce the high-resolution bokeh image. We achieve i) via our lightweight, mobile-friendly design choices, ii) via the stacked-network design of MGBG and the weakly-supervised mask prediction scheme and iii) via manually or automatically editing the intensity values of the mask that guide the bokeh generation. In addition to these features, our results show that AMPN produces competitive or better results compared to existing methods on the EBB! dataset, while being faster and smaller than the alternatives.
Quaternion fractional-order weighted generalized Laguerre–Fourier moments and moment invariants for color image analysis

Orthogonal moments based on Laguerre polynomials, especially the quaternion representation of fractional-order orthogonal moments have attracted interest in the imaging field. However, most of the existing quaternion fractional-order color orthogonal moments (QFr-COMs) based on Laguerre polynomials are constructed on Cartesian coordinate space and do not have direct rotation geometric invariance. Inspired by the idea of polynomial transformation, we present a weighted radial normalized fractional-order generalized Laguerre polynomials (WRNFr-GLPs) in polar coordinate space. On the basis, a new set of quaternion fractional-order weighted generalized Laguerre–Fourier moments (QFr-WGLFMs) is proposed and a fast and reliable computing method for geometric invariance of QFr-WGLFMs also is introduced in this paper. Theoretical analyses and experimental results showed that the proposed QFr-WGLFMs and moment invariants offer enhanced image reconstruction and pattern recognition compared with the existing quaternion fractional-order-based orthogonal moments and deep learning-based​ methods, especially under the conditions of image processing with noise and smooth filtering.
Efficient large-scale oblique image matching based on cascade hashing and match data scheduling

In this paper, we design an efficient large-scale oblique image matching method. First, to reduce the number of redundant transmissions of match data, we propose a novel three-level buffer data scheduling (TLBDS) algorithm that considers the adjacency between images for match data scheduling from disk to graphics memory. Second, we adopt the epipolar constraint to filter the initial candidate points of cascade hashing matching, thereby significantly increasing the robustness of matching feature points. Comprehensive experiments are conducted on three oblique image datasets to test the efficiency and effectiveness of the proposed method. The experimental results show that our method can complete a match pair within 2.50∼2.64 ms, which not only is much faster than two open benchmark pipelines (i.e., OpenMVG and COLMAP) by 20.4∼97.0 times but also have higher efficiency than two state-of-the-art commercial software (i.e., Agisoft Metashape and Pix4Dmapper) by 10.4∼50.0 times.
Dynamic dual graph networks for textbook question answering

Textbook Question Answering (TQA) is a complex task oriented to multi-modal context, which requires reasoning on a diagram and a long essay to get the correct answer. There are mainly two related issues for the task. First, diagrams are mostly abstract expressions of real world and some constituents with similar appearance may have different semantics, which makes it difficult to understand them effectively. Secondly, a long essay contains abundant and useful information for question answering, which shows that it is vital to extract the relevant information from the abundant text and then perform reasoning on it. To address the two issues, we propose a new model, Dynamic Dual Graph Networks (DDGNet), which performs question-guided multi-step reasoning on the dynamic directed Diagram Graph Network (DGN) for the diagram and Textual Graph Network (TGN) for the most related paragraph extracted from a long essay. Specifically, DGN combines text features with positional features of text boxes in the diagram as the node feature to avoid the ambiguity of visual features for the abstract constituents and help express explicit semantics. TGN uses the representation of each sentence in the most related paragraph as the node feature to learn the contextualized interaction of the useful information in the graph reasoning process. For the reasoning strategy, we propose a question-guided multi-step graph reasoning method to update both DGN and TGN dynamically under the question guidance in every step. Experimental results show that our proposed model outperforms the baselines on the TQA dataset. Moreover, extensive ablation studies are also conducted to analyze the effectiveness of our proposed model.
Operation of the Neural z-Vertex Track Trigger for Belle II in 2021 - a Hardware Perspective

Abstract To reduce the background the z -Vertex Track Trigger estimates the collision origin in the Belle II experiment using neural networks. The main part is a pre-trained multilayer perceptron. The task of this perceptron is to estimate the z -vertex of the collision to suppress background from outside the interaction point. For this, a low latency real-time FPGA implementation is needed. We present an overview of the architecture and the FPGA implementation of the neuronal network and the preprocessing. We also show the handling of missing input data through preprocessing with specially trained neuronal networks implemented in hardware. For this, we will show the results of the z -vertex estimation and the latency for the implementation in the Belle II trigger system. Major update for the preprocessing stage utilizing a 3D Hough transformation processing step is ongoing.
Detecting Unknown Vulnerabilities in Smart Contracts with Binary Classification Model Using Machine Learning

The emergence of smart contracts has led to the wider use of blockchain, which involves a large number of virtual currency transactions. Because smart contracts are inevitably written with some vulnerabilities, which makes them vulnerable to attacks that cause property damage, and existing detection techniques and static analysis methods mainly target known vulnerability detection. We design a machine learning-based unknown vulnerability detection scheme using opcode sequences. The scheme first obtains opcode sequences of the execution path of contract transactions in the Ethereum virtual machine (EVM) by replaying them in Ethereum. Then we use a combination of N-gram model and a vector weight penalty mechanism to extract opcode features. Finally, we validate the effectiveness of our scheme by three machine learning models, namely the K-Near Neighbor Algorithm (KNN), the Support Vector Machine (SVM), and the Logistic Regression (LR). The SVM model achieves an accuracy of 91.4% and F1-score of 75.3% for the detection of unknown vulnerabilities.
Running the Dual-PQC GAN on noisy simulators and real quantum hardware

In an earlier work, we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of a quantum GAN. We applied the model on a realistic High-Energy Physics (HEP) use case: the exact theoretical simulation of a calorimeter response with a reduced problem size. This paper explores the dual- PQC GAN for a more practical usage by testing its performance in the presence of different types of quantum noise, which are the major obstacles to overcome for successful deployment using near-term quantum devices. The results propose the possibility of running the model on current real hardware, but improvements are still required in some areas.
An open-source modular framework for quantum computing

In this proceedings we describe the current development status and recent technical achievements of Qibo, an open-source framework for quantum simulation. After a concise overview of the project goal, we introduce the modular layout for backend abstraction released in version 0.1.7. We discuss the advantages of each backend choice with particular emphasis on hardware accelerators for quantum state vector simulation. Finally, we summarize the primitives and models currently available.
Design and Experimental Study of a Novel Semi-Physical Unmanned-Aerial-Vehicle Simulation Platform for Optical-Flow-Based Navigation

In the process of unmanned aerial vehicle (UAV) visual-navigation-algorithm design and accuracy verification, the question of how to develop a high-precision and high-reliability semi-physical simulation platform has become a significant engineering problem. In this study, a new UAV semi-physical-simulation-platform architecture is proposed, which includes a six-degree-of-freedom mechanical structure, a real-time control system and real-time animation-simulation software. The mechanical structure can realistically simulate the flight attitude of a UAV in a three-dimensional space of 4 × 2 × 1.4 m. Based on the designed mechanical structure and its dynamics, the control system and the UAV real-time flight-animation simulation were designed. Compared with the conventional simulation system, this system enables real-time flight-attitude simulation in a real physical environment and simultaneous flight-attitude simulation in virtual-animation space. The test results show that the repeated positioning accuracy of the three-axis rotary table reaches 0.006°, the repeated positioning accuracy of the three-axis translation table reaches 0.033 mm, and the dynamic-positioning accuracy reaches 0.04° and 0.4 mm, which meets the simulation requirements of high-precision visual UAV navigation.
Grey-Box Fuzzing Based on Reinforcement Learning for XSS Vulnerabilities

Cross-site scripting (XSS) vulnerabilities are significant threats to web applications. The number of XSS vulnerabilities reported has increased annually for the past three years, posing a considerable challenge to web application maintainers. Black-box scanners are mainstream tools for security engineers to perform penetration testing and detect XSS vulnerabilities. Unfortunately, black-box scanners rely on crawlers to find input points of web applications and cannot guarantee all input points are tested. To this end, we propose a grey-box fuzzing method based on reinforcement learning, which can detect reflected and stored XSS vulnerabilities for Java web applications. We first use static analysis to identify potential input points from components (i.e., Java code, configuration files, and HTML files) of the Java web application. Then, an XSS vulnerability payload generation method is proposed, which is used together with the reinforcement learning model. We define the state, action, and reward functions of three reinforcement learning models for XSS vulnerability detection scenarios so that the fuzz loop can be performed automatically. To demonstrate the effectiveness of the proposed method, we compare it against four state-of-the-art web scanners. Experimental results show that our method finds all XSS vulnerabilities and has no false positives.
Modelling of Acid Mine Drainage in Open Pit Lakes Using Sentinel-2 Time-Series: A Case Study from Lusatia, Germany

Strong acid mine drainage (AMD) processes in the flooded, formerly open pits in the Lusatia area present an enormous environmental challenge for the rehabilitation of the post-mining landscape. Extensive and costly monitoring is required for optimal AMD management and remediation planning and control. Because of the large size of the area and the dimension of the problem, the regular sampling can only provide limited point data, which needs to be extrapolated to the entire area. Consequently, the search for effective approaches for extrapolating the point data to the area of all water bodies is essential for rehabilitation success monitoring and for understanding the dependencies between AMD and environmental factors such as land use, weather conditions, geology, and hydrogeology. The main aim of this study was to investigate the suitability of Sentinel-2 multispectral imagery and artificial neural networks (ANNs) for the quantitative mapping of acid mine drainage (AMD) constituents, such as dissolved iron, pH value, and sulfate in large water bodies, for an area of approximately 7220 km2 (the area of the pit lakes is about 185 km2). Correlations between different chemical water parameters were also investigated. An extensive water monitoring dataset was used to train artificial neural networks for the identification of dependencies between the multispectral remote sensing data and the water quality ground measurements. Respective relationships have been identified, especially for dissolved iron and pH. These trained ANNs have been used to produce water quality maps with high spatial (10 × 10 m) and temporal (any cloud-free period) resolution, which show the wide variability of water quality in the different parts of the mining region. Concrete sources of AMD can be identified using the water quality maps of single lakes, and the success of sanitation measures such as liming was visualized. The approach opens many doors for the optimization of both the monitoring program and sanitation technology.
Research on Pedestrian Detection Based on the Multi-Scale and Feature-Enhancement Model

Pedestrian detection represents one of the critical tasks of computer vision; however, detecting pedestrians can be compromised by problems such as the various scale of pedestrian features and cluttered background, which can easily cause a loss of accuracy. Therefore, we propose a pedestrian detection method based on the FCOS network. Firstly, we designed a feature enhancement module to ensure that effective high-level semantics are obtained while preserving the detailed features of pedestrians. Secondly, we defined a key-center region judgment to reduce the interference of background information on pedestrian feature extraction. By testing on the Caltech pedestrian dataset, the AP value is improved from 87.36% to 94.16%. The results of the comparison experiment illustrate that the model proposed in this paper can significantly increase the accuracy.