Reviewerly/exHarmony huggingface dataset:
Noise-based enhancement for foveated rendering

Human visual sensitivity to spatial details declines towards the periphery. Novel image synthesis techniques, so-called foveated rendering, exploit this observation and reduce the spatial resolution of synthesized images for the periphery, avoiding the synthesis of high-spatial-frequency details that are costly to generate but not perceived by a viewer. However, contemporary techniques do not make a clear distinction between the range of spatial frequencies that must be reproduced and those that can be omitted. For a given eccentricity, there is a range of frequencies that are detectable but not resolvable. While the accurate reproduction of these frequencies is not required, an observer can detect their absence if completely omitted. We use this observation to improve the performance of existing foveated rendering techniques. We demonstrate that this specific range of frequencies can be efficiently replaced with procedural noise whose parameters are carefully tuned to image content and human perception. Consequently, these frequencies do not have to be synthesized during rendering, allowing more aggressive foveation, and they can be replaced by noise generated in a less expensive post-processing step, leading to improved performance of the rendering system. Our main contribution is a perceptually-inspired technique for deriving the parameters of the noise required for the enhancement and its calibration. The method operates on rendering output and runs at rates exceeding 200 FPS at 4K resolution, making it suitable for integration with real-time foveated rendering systems for VR and AR devices. We validate our results and compare them to the existing contrast enhancement technique in user experiments.
Facial hair tracking for high fidelity performance capture

Facial hair is a largely overlooked topic in facial performance capture. Most production pipelines in the entertainment industry do not have a way to automatically capture facial hair or track the skin underneath it. Thus, actors are asked to shave clean before face capture, which is very often undesirable. Capturing the geometry of individual facial hairs is very challenging, and their presence makes it harder to capture the deforming shape of the underlying skin surface. Some attempts have already been made at automating this task, but only for static faces with relatively sparse 3D hair reconstructions. In particular, current methods lack the temporal correspondence needed when capturing a sequence of video frames depicting facial performance. The problem of robustly tracking the skin underneath also remains unaddressed. In this paper, we propose the first multiview reconstruction pipeline that tracks both the dense 3D facial hair, as well as the underlying 3D skin for entire performances. Our method operates with standard setups for face photogrammetry, without requiring dense camera arrays. For a given capture subject, our algorithm first reconstructs a dense, high-quality neutral 3D facial hairstyle by registering sparser hair reconstructions over multiple frames that depict a neutral face under quasi-rigid motion. This custom-built, reference facial hairstyle is then tracked throughout a variety of changing facial expressions in a captured performance, and the result is used to constrain the tracking of the 3D skin surface underneath. We demonstrate the proposed capture pipeline on a variety of different facial hairstyles and lengths, ranging from sparse and short to dense full-beards.
Face deblurring using dual camera fusion on mobile phones

Motion blur of fast-moving subjects is a longstanding problem in photography and very common on mobile phones due to limited light collection efficiency, particularly in low-light conditions. While we have witnessed great progress in image deblurring in recent years, most methods require significant computational power and have limitations in processing high-resolution photos with severe local motions. To this end, we develop a novel face deblurring system based on the dual camera fusion technique for mobile phones. The system detects subject motion to dynamically enable a reference camera, e.g., ultrawide angle camera commonly available on recent premium phones, and captures an auxiliary photo with faster shutter settings. While the main shot is low noise but blurry (Figure 1(a)), the reference shot is sharp but noisy (Figure 1(b)). We learn ML models to align and fuse these two shots and output a clear photo without motion blur (Figure 1(c)). Our algorithm runs efficiently on Google Pixel 6, which takes 463 ms overhead per shot. Our experiments demonstrate the advantage and robustness of our system against alternative single-image, multi-frame, face-specific, and video deblurring algorithms as well as commercial products. To the best of our knowledge, our work is the first mobile solution for face motion deblurring that works reliably and robustly over thousands of images in diverse motion and lighting conditions.
Moving Target Detection Technology Based on UAV Vision

The detection of moving objects by machine vision is a hot research direction in recent years. It is widely used in military, medical, transportation, and agriculture. With the rapid development of UAV technology, as well as the high mobility of UAVs and the wide range of high-altitude vision, the target detection technology based on UAV vision is applied to traffic management such as vehicle tracking and detection of vehicle violations. The moving target detection technology in this study is based on the YOLOv3 algorithm. It implements moving vehicle tracking by means of Mean-Shift and Kalman filtering. In this paper, the Gaussian background difference technology is used to analyze the illegal behavior of the vehicle, and the color feature extraction technology is used to identify and locate the license plate, and the information of the illegal vehicle is entered into the database. The experiment compares the moving target detection of UAV vision and the traditional target detection in four aspects: recognition accuracy, recognition speed, manual time, and divergent results. The results show that the average accuracy rates of UAV vision-based moving target detection and traditional pattern recognition are 98.4% and 87.8%, respectively. The recognition speeds are 24.9 (vehicles/sec) and 10.6 (vehicles/sec), respectively. However, the artificial time and divergence results of moving target detection based on UAV vision are only 1/3 of the traditional mode. The moving target detection based on UAV vision has a better moving target detection ability.
Deep Learning-Based Recognition of Different Thyroid Cancer Categories Using Whole Frozen-Slide Images

Introduction: The pathological rare category of thyroid is a type of lesion with a low incidence rate and is easily misdiagnosed in clinical practice, which directly affects a patient's treatment decision. However, it has not been adequately investigated to recognize the rare, benign, and malignant categories of thyroid using the deep learning method and recommend the rare to pathologists. Methods: We present an empirical decision tree based on the binary classification results of the patch-based UNet model to predict rare categories and recommend annotated lesion areas to be rereviewed by pathologists. Results: Applying this framework to 1,374 whole-slide images (WSIs) of frozen sections from thyroid lesions, we obtained an area under a curve of 0.946 and 0.986 for the test datasets with and without WSIs, respectively, of rare types. However, the recognition error rate for the rare categories was significantly higher than that for the benign and malignant categories (p < 0.00001). For rare WSIs, the addition of the empirical decision tree obtained a recall rate and precision of 0.882 and 0.498, respectively; the rare types (only 33.4% of all WSIs) were further recommended to be rereviewed by pathologists. Additionally, we demonstrated that the performance of our framework was comparable to that of pathologists in clinical practice for the predicted benign and malignant sections. Conclusion: Our study provides a baseline for the recommendation of the uncertain predicted rare category to pathologists, offering potential feasibility for the improvement of pathologists' work efficiency.
Near optical coaxial phase measuring deflectometry for measuring structured specular surfaces

Phase measuring deflectometry (PMD) is an important technique for the form measurement of specular surfaces. However, the existing stereo-PMD techniques have noticeable weaknesses for structured specular surfaces measurement due to the optical axis of the imaging system must have a notable intersection angle with the optical axis of the display system according to the law of reflection. This leads to the imaging sensor and the fringe display screen must be located on the opposite sides of the normal of the surface under test (SUT), which results in large system volume and measurement shadows when measuring discontinuous specular surfaces. In this paper, we propose a novel near optical coaxial PMD (NCPMD) by utilizing a plate beamsplitter. With the assistance of plate beamsplitter, the optical axis of display screen can be configured much closer to the optical axis of the imaging system which makes the system more compact and has significantly reduced volume compared with the conventional PMD configuration. Moreover, imaging sensors in the proposed configuration can perpendicularly capture the SUT, which can drastically decrease measurement shadows caused by discontinuous structures on the SUT and increases measurement efficiency. A comparison between the proposed NCPMD and the conventional PDM is studied by measuring a specular step to show the advantage of the proposed configuration in reducing measurement error caused by structure shadows. A portable NCPMD prototype with stereo imaging sensors is developed and verified through experiments. Experimental results show the portable prototype has comparable measurement accuracy with the existing PMD techniques while has obviously advanced performances for portable and embedded form measurement, such as small system volume, and light weight.
Deep Controllable Backlight Dimming for HDR Displays

High dynamic range (HDR) displays with dual-panels are one type of displays that can provide HDR content. These are composed of a white backlight panel and a colour LCD panel. Local dimming algorithms are used to control the backlight panel in order to reproduce content with high dynamic range and contrast at a high fidelity. However, existing local dimming algorithms usually process low dynamic range (LDR) images, which are not suitable for processing HDR images. In addition, these methods use hand-crafted features to estimate the backlight values, which may not be suitable for many kind of images. In this work, a novel deep learning based local dimming method is proposed for rendering HDR images on dual-panel HDR displays. The method uses a Convolutional Neural Network (CNN) to directly predict backlight values, using as input the HDR image that is to be displayed. The model is designed and trained via a controllable power parameter that allows a user to trade off between power and quality. The proposed method is evaluated against seven other methods on a test set of 105 HDR images, using a variety of quantitative quality metrics. Results demonstrate improved display quality and better power consumption when using the proposed method compared to the best alternatives.
Scrum for safety: an agile methodology for safety-critical software systems

Abstract In the last years, agile methodologies are gaining substantial momentum, becoming increasingly popular in a broad plethora of industrial contexts. Unfortunately, many obstacles have been met while pursuing adoption in secure and safe systems, where different standards and operational constraints apply. In this paper, we propose a novel agile methodology for the development and innovation of safety-critical systems. In particular, we developed an extension of the well-known Scrum methodology and discussed the complete workflow. We finally validated the applicability of the proposed methodology over a real case study from the railway domain.
Identity authentication for edge devices based on zero‐trust architecture

Summary Device identity authentication is the first line of defense for edge computing security mechanisms. Many authentication schemes are often accompanied by high communication and computational overhead. In addition, due to the continuous enhancement of network virtualization and dynamics, the security requirements for logical boundaries of many enterprise information systems “cloudification,” and the huge data security challenges faced by enterprise core assets, all make the original "one‐time authentication, all the way" trust model no longer reliable. Therefore, the paper proposes a local identity authentication and roaming identity authentication protocol based on a zero‐trust architecture. First, we propose a revocable group signature scheme, the expiration time is bound to the key of each edge terminal device. According to this solution, since the identity authentication token generated by the expired key is invalid, it does not need to be included in the revocation list, which improves the efficiency of revocation checking. Compared with the current identity authentication protocol, this article not only builds a model based on the zero trust architecture, effectively solves the shortcomings of the network security protection architecture, but also considers the unforgeability of the expiration time, and realizes effective revocation and more efficient identity authentication.
Stereo deflectometry based automatic segmentation technique for measuring structured specular surfaces

Techniques have been studied to extend the measurement capability of stereo deflectometry from continuous specular surfaces only to complex structured specular surfaces. Segmentation is a key process in these techniques, which separates a measured structured surface into several continues segments with gradient and coordinate data to achieve high form measurement accuracy. However, it is a great challenge to achieve automatic, fast, and accurate segmentation since specular surfaces have no RGB (red, greed, and blue) texture to be used in the segmentation process. In this paper, we present an automatic segmentation technique based on the characteristics of gradient variation and 3D coordinate data of the measured structured specular surfaces. Different segmentation strategies are applied and discussed for the measured discontinuous specular surfaces and continuous non-differentiable specular surfaces. Compared with the existing segmentation methods in terms of stereo deflectometry, the proposed technique shows significantly advantages in automation, speed, and flexibility. Experimental results verified the effectiveness and accuracy of the proposed method in the measurement of structured specular surfaces.
Exploring Blockchains Cyber Security Techno- Regulatory Gap. An Application to Crypto-Asset Regulation in the EU

Blockchain technology has taken off rapidly in the last decade, as blockchain- based applications have expanded across sectors and acquired substantial global user support. This rise has been accelerated in the domain of cryptocurrencies, as one of blockchains’ most famous (or infamous) applications. This relatively novel type of a financial asset class with increasingly large market valuation has sparked the interest of regulators and law makers. As a means of fostering fintech innovation, the EU has stated its support for adoption and development of blockchain and cryptocurrencies in the European Economic Area. Nevertheless, cryptocurrencies lie at the top of a potentially dangerous feedback loop mediated by market valuation. Cyber-attacks targeting cryptocurrencies (and the underlying blockchain technology) have been on the rise, costing users and businesses millions of euros. From a European Union regulatory standpoint, high cyber security resilience is a precondition for sustainable innovation in an increasingly digitalized financial sector, where protecting users and businesses is a priority. In this paper, we discuss aspects of blockchains' technical vulnerabilities and related cyber-attacks in order to develop a deeper understanding of the extent and efficiency of possible regulatory remedies concerning crypto-assets in the EU. We present a regulatory overview of the emerging fields of cyber risk and blockchain in Europe and illustrate a techno- regulatory gap which requires further attention. We underline the difficulty of assigning traditional cyber regulatory measures due to certain technical characteristics related to blockchains. We maintain how the relationship between cyber law and technology may evolve in the near future, as decentralized technologies and the cyber risks that go with them, continue to develop rapidly. By providing an interdisciplinary perspective of cyber security in the blockchain domain, we aim to bridge the gap that exists between legal and technical research, supporting policy makers in their regulatory decisions concerning crypto-assets, decentralized technologies and associated cyber risks.
Knowledge Graph Recommendation Model Based on Adversarial Training

The recommendation model based on the knowledge graph (KG) alleviates the problem of data sparsity in the recommendation to a certain extent and further improves the accuracy, diversity, and interpretability of recommendations. Therefore, the knowledge graph recommendation model has become a major research topic, and the question of how to utilize the entity and relation information fully and effectively in KG has become the focus of research. This paper proposes a knowledge graph recommendation model based on adversarial training (ATKGRM), which can dynamically and adaptively adjust the knowledge graph aggregation weight based on adversarial training to learn the features of users and items more reasonably. First, the generator adopts a novel long- and short-term interest model to obtain user features and item features and generates a high-quality set of candidate items. Then, the discriminator discriminates candidate items by comparing the user’s scores of positive items, negative items, and candidate items. Finally, experimental studies on five real-world datasets with multiple knowledge graph recommendation models and multiple adversarial training recommendation models prove the effectiveness of our model.
An Edge‐Cloud Collaborative Cross‐Domain Identity‐Based Authentication Protocol with Privacy Protection

Edge-cloud collaborative application scenario is more complex, it involves collaborative operations among different security domains, frequently accessing and exiting application system of mobile terminals. A cross-domain identity authentication protocol based on privacy protection is proposed. The main advantages of the protocol are as follows. 1) Self-certified key generation algorithm: the public/private key pair of the mobile terminal is generated by the terminal members themselves. It avoids security risks caused by third-party key distribution and key escrow; 2) Cross-domain identity authentication: the alliance keys are calculated among edge servers through blockchain technology. Cross-domain identity authentication is realized through the signature authentication of the alliance domain. The cross-domain authentication process is simple and efficient; 3) Revocability of identity authentication: When the mobile terminal has logged off or exited the system, the legal identity of the terminal in the system will also become invalid immediately, so as to ensure the forward and backward security of accessing system resources. Under the hardness assumption of discrete logarithm problem and computational Diffie-Hellman problem, the security of the protocol is proven, and the efficiency of the protocol is verified.
Thin liquid film as an optical nonlinear-nonlocal medium and memory element in integrated optofluidic reservoir computer

Understanding light–matter interaction lies at the core of our ability to harness physical effects and to translate them into new capabilities realized in modern integrated photonics platforms. Here, we present the design and characterization of optofluidic components in an integrated photonics platform and computationally predict a series of physical effects that rely on thermocapillary-driven interaction between waveguide modes and topography changes of optically thin liquid dielectric film. Our results indicate that this coupling introduces substantial self-induced phase change and transmittance change in a single channel waveguide, transmittance through the Bragg grating waveguide, and nonlocal interaction between adjacent waveguides. We then employ the self-induced effects together with the inherent built-in finite relaxation time of the liquid film, to demonstrate that the light-driven deformation can serve as a reservoir computer capable of performing digital and analog tasks, where the gas–liquid interface operates both as a nonlinear actuator and as an optical memory element.
How does Heterophily Impact the Robustness of Graph Neural Networks?

We bridge two research directions on graph neural networks (GNNs), by formalizing the relation between heterophily of node labels (i.e., connected nodes tend to have dissimilar labels) and the robustness of GNNs to adversarial attacks. Our theoretical and empirical analyses show that for homophilous graph data, impactful structural attacks always lead to reduced homophily, while for heterophilous graph data the change in the homophily level depends on the node degrees. These insights have practical implications for defending against attacks on real-world graphs: we deduce that separate aggregators for ego- and neighbor-embeddings, a design principle which has been identified to significantly improve prediction for heterophilous graph data, can also offer increased robustness to GNNs. Our comprehensive experiments show that GNNs merely adopting this design achieve improved empirical and certifiable robustness compared to the best-performing unvaccinated model. Additionally, combining this design with explicit defense mechanisms against adversarial attacks leads to an improved robustness with up to 18.33% performance increase under attacks compared to the best-performing vaccinated model.
Anomaly detection of adversarial examples using class-conditional generative adversarial networks

Deep neural networks (DNNs) have been shown vulnerable to Test-Time Evasion attacks (TTEs, or adversarial examples), which, by making small changes to the input, alter the DNN’s decision. We propose an unsupervised attack detector for DNN classifiers based on class-conditional Generative Adversarial Networks (GANs). We model the distribution of clean data conditioned on the predicted class label by an Auxiliary Classifier GAN (AC-GAN). Given a test sample and its predicted class, three detection statistics are calculated based on the AC-GAN generator and discriminator. Experiments on image classification datasets under various TTE attacks show that our method outperforms previous detection methods. We also investigate the effectiveness of anomaly detection using different DNN layers (input features or internal-layer features) and demonstrate, as one might expect, that anomalies are harder to detect using features closer to the DNN’s output layer. Finally, our approach is also investigated for more general out-of-distribution detection.