RCRN: Real-world Character Image Restoration Network via Skeleton Extraction

Constructing high-quality character image datasets is challenging because real-world images are often affected by image degradation. There are limitations when applying current image restoration methods to such real-world character images, since (i) the categories of noise in character images are different from those in general images; (ii) real-world character images usually contain more complex image degradation, e.g., mixed noise at different noise levels. To address these problems, we propose a real-world character restoration network (RCRN) to effectively restore degraded character images, where character skeleton information and scale-ensemble feature extraction are utilized to obtain better restoration performance. The proposed method consists of a skeleton extractor (SENet) and a character image restorer (CiRNet). SENet aims to preserve the structural consistency of the character and normalize complex noise. Then, CiRNet reconstructs clean images from degraded character images and their skeletons. Due to the lack of benchmarks for real-world character image restoration, we constructed a dataset containing 1,606 character images with real-world degradation to evaluate the validity of the proposed method. The experimental results demonstrate that RCRN outperforms state-of-the-art methods quantitatively and qualitatively.Comment: Accepted to ACM MM 202

Experimental Study of the Jet Engine Exhaust Flow Field of Aircraft and Blast Fences

A combined blast fence is introduced in this paper to improve the solid blast fences and louvered ones. Experiments of the jet engine exhaust flow (hereinafter jet flow for short) field and tests of three kinds of blast fences in two positions were carried out. The results show that the pressure and temperature at the centre of the jet flow decrease gradually as the flow moves farther away from the nozzle. The pressure falls fast with the maximum rate of 41.7%. The dynamic pressure 150 m away from the nozzle could reach 58.8 Pa, with a corresponding wind velocity of 10 m/s. The temperature affected range of 40°C is 113.5×20 m. The combined blast fence not only reduces the pressure of the flow in front of it but also solves the problems that the turbulence is too strong behind the solid blast fences and the pressure is too high behind the louvered blast fences. And the pressure behind combined blast fence is less than 10 Pa. The height of the fence is related to the distance from the jet nozzle. The nearer the fence is to the nozzle, the higher it is. When it is farther from the nozzle, its height can be lowered

CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising

Degraded images commonly exist in the general sources of character images, leading to unsatisfactory character recognition results. Existing methods have dedicated efforts to restoring degraded character images. However, the denoising results obtained by these methods do not appear to improve character recognition performance. This is mainly because current methods only focus on pixel-level information and ignore critical features of a character, such as its glyph, resulting in character-glyph damage during the denoising process. In this paper, we introduce a novel generic framework based on glyph fusion and attention mechanisms, i.e., CharFormer, for precisely recovering character images without changing their inherent glyphs. Unlike existing frameworks, CharFormer introduces a parallel target task for capturing additional information and injecting it into the image denoising backbone, which will maintain the consistency of character glyphs during character image denoising. Moreover, we utilize attention-based networks for global-local feature interaction, which will help to deal with blind denoising and enhance denoising performance. We compare CharFormer with state-of-the-art methods on multiple datasets. The experimental results show the superiority of CharFormer quantitatively and qualitatively.Comment: Accepted by ACM MM 202

UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation

Multi-object tracking (MOT) in video sequences remains a challenging task, especially in scenarios with significant camera movements. This is because targets can drift considerably on the image plane, leading to erroneous tracking outcomes. Addressing such challenges typically requires supplementary appearance cues or Camera Motion Compensation (CMC). While these strategies are effective, they also introduce a considerable computational burden, posing challenges for real-time MOT. In response to this, we introduce UCMCTrack, a novel motion model-based tracker robust to camera movements. Unlike conventional CMC that computes compensation parameters frame-by-frame, UCMCTrack consistently applies the same compensation parameters throughout a video sequence. It employs a Kalman filter on the ground plane and introduces the Mapped Mahalanobis Distance (MMD) as an alternative to the traditional Intersection over Union (IoU) distance measure. By leveraging projected probability distributions on the ground plane, our approach efficiently captures motion patterns and adeptly manages uncertainties introduced by homography projections. Remarkably, UCMCTrack, relying solely on motion cues, achieves state-of-the-art performance across a variety of challenging datasets, including MOT17, MOT20, DanceTrack and KITTI. More details and code are available at https://github.com/corfyi/UCMCTrackComment: Accepted to AAAI 202

Adversarial Samples on Android Malware Detection Systems for IoT Systems

Many IoT(Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a \textbf{t}esting framework for \textbf{l}earning-based \textbf{A}ndroid \textbf{m}alware \textbf{d}etection systems(TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android Application with a success rate of nearly 100\% and can perform black-box testing on the system

Research on floating body resistance characteristics of floating photovoltaic and analysis of influencing factors

The floating structure of floating photovoltaic can be attached by aquatic organisms, resulting in changes in the draft depth of the floating body, which can affect the resistance characteristics of the floating body at different water velocities. The analysis for the characteristics of flow field is the key to revealing the change law of resistance under different conditions. The k-ϵ turbulence model which has been verified by water channel experiment is used to research the influence of draft depths, velocities and number of floating bodies for the drag in the paper. The research results show that the draft depth has more influence on the drag of the single floating body than on the velocity of water flow. The main reason is that the separation of the boundary layer produces a larger separation bubble, which increases the pressure difference between the front and back surfaces of the floating body, leading to a larger entrainment range and reflux velocity in the wake. The high flow velocity will enlarge the influence of the draft depth on the drag. The shielding effect of the tandem floating bodies is reflected in the non-uniform fluctuation of velocity and pressure along the flow direction, which affects the wake development of the tandem floating bodies, resulting in the typical spatial characteristics of resistance at different positions. The increase of the number of tandem floating bodies will further expand the difference of flow field, which can affect the resistance distribution law. The research results can provide theoretical support for the stability design of floating photovoltaic

Route to high-energy density polymeric nitrogen t-N via He-N compounds.

Polymeric nitrogen, stabilized by compressing pure molecular nitrogen, has yet to be recovered to ambient conditions, precluding its application as a high-energy density material. Here we suggest a route for synthesis of a tetragonal polymeric nitrogen, denoted t-N, via He-N compounds at high pressures. Using first-principles calculations with structure searching, we predict a class of nitrides with stoichiometry HeN4 that are energetically stable (relative to a mixture of solid He and N2) above 8.5 GPa. At high pressure, HeN4 comprises a polymeric channel-like nitrogen framework filled with linearly arranged helium atoms. The nitrogen framework persists to ambient pressure on decompression after removal of helium, forming pure polymeric nitrogen, t-N. t-N is dynamically and mechanically stable at ambient pressure with an estimated energy density of ~11.31 kJ/g, marking it out as a remarkable high-energy density material. This expands the known polymeric forms of nitrogen and indicates a route to its synthesis