Reviewerly/exHarmony huggingface dataset:
Assuring Safety-Critical Machine Learning-Enabled Systems: Challenges and Promise

afety-critical systems, such as automobiles and medical devices, are systems whose failure could result in loss of life, significant property damage, and damage to the environment.The grave consequences of failure have compelled industry and regulatory authorities to adopt conservative design approaches and exhaustive verification and validation (V&V) procedures to prevent mishaps.In addition, strict licensing requirements are often placed on human operators of many safety-critical systems.Ultracritical systems, such as civil transport aircraft and nuclear power plants, are safety-critical systems that society deems should never suffer a catastrophic failure during their operating lifetime and whose development is subject to particularly strict regulatory constraints and rigorous operator training requirements.In practice, the V&V of avionics and other ultracritical software systems
Real-time semantic segmentation in traffic scene using Cross Stage Partial-based encoder–decoder network

Real-time semantic segmentation in traffic scenes plays an essential part in autonomous driving. The encoder–decoder-based network architecture can well combine the context information and detailed information required for the semantic segmentation task. Achieving a good balance between inference speed and accuracy is a crucial challenge, as considerable real-time semantic segmentation models process information in real-time at the expense of accuracy degradation. This paper presents an encoder–decoder network model based on Cross Stage Partial (CSP) block for real-time semantic segmentation in traffic scenes. Integrating the CSP block can not only lessen the computational overhead but also enhance the feature extraction ability of the network. In addition, we append the Fast Spatial Pyramid Pooling module to the backbone of the network, which can aggregate global information at a low computational cost. On NVIDIA RTX 3090, the middle model of our method can achieve a mean intersection over union (mIOU) of 80.8% at 64.3 frames per second (FPS) on the Cityscapes test set and an mIOU of 81.3% at 105.3 FPS on the CamVid Test Set. The large model of our method can realize an mIOU of 81.5% at 48.4 FPS on the test set of Cityscapes. Our source code is available at https://github.com/zhouliguo/cspsg.
LoLi-IEA: low-light image enhancement algorithm

Low-light image enhancement has posed a significant challenge in recent years due to non-uniform luminance in real-world images. Color restoration, luminance mapping, and estimation of the curve and levels are some techniques used by the algorithms to enhance the image. However, real-world images often have non-uniform luminance, requiring local enhancement in certain areas rather than global enhancement. In order to tackle this issue, this paper introduces a novel methodology based on deep learning, employing two convolutional network architectures. The first one classifies the brightness level of the input image, while the second one enhances the brightness level based on information obtained through the first architecture. To train this model, two commonly used datasets in state-of-the-art research are used: the LOL (Low-Light) and Synthetic Low-light. Both datasets contain low-light and ground-truth image pairs, which makes it possible to make a proper estimate between non-uniform and uniform luminosity. The proposed algorithm is applied over resized images from the UHD-LOL4k dataset, with a performance evaluation through the metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Evaluator (NIQE), and Blind /Unreferenced Image Spatial Quality Evaluator (BRISQUE). According to the results, the proposed method outperforms algorithms with more complex architectures in the literature. The double convolutional architecture emphasizes local enhancement in real-world scenes and global enhancement in images with very low luminosity. Overall, this paper presents a significant contribution to low-light image enhancement offering an effective solution to the challenges posed by non-uniform luminance in real-world images.
Inter-sphere consistency-based calibration for camera–projector pairs

We propose an inter-sphere consistency-based method for camera–projector pairs (CPPs) calibration. Previous sphere-based calibration works have only considered independent constraints from each sphere, and thus require at least three spheres for calibration. In this paper, it is shown that additional constraints can be extracted from consistency between estimated parameters from different spheres, which we term the inter-sphere consistency (ISC). Such ISC constraints facilitate more flexible and easy-to-use calibration using only two spheres, which has been considered a challenging ill-posed problem and never been well addressed before. Extensive experimental results have been provided for the validation.
Robust line segment mismatch removal using point-pair representation and Gaussian-uniform mixture formulation

Line segment matching (LSM) plays an important role in image matching, while it is always a challenging problem due to line fracture and high geometric complexity. In this paper, we propose a line segment mismatch removal to address the sensitivity to segment length and the fracture problem. The mismatch removal removes the massive false matches obtained by the descriptors like LBD. Specifically, we suggest calculating the endpoint error in LSM rather than the direction error in considering the error band and segment length. We propose a point-pair representation, and transform the LSM problem to be an intuitive point-pair alignment problem with a mixture model. The point-pair representation is more robust on the segment length than the linear equation representation used in RANSAC, which significantly preserves short line segments. Then, based on the point-pair representation, we propose a novel Directed Endpoint Drift method to solve the inherent fracture problem of line segments by allowing the endpoints to move along the line to complement the fracture. Compared with the recent learning-based method, the proposed method nearly doubles the number of correct matches on the remote sensing dataset. Especially, the recall is improved by 10% to 30% for both the short segments and the slightly fractured segments, compared with the popular RANSAC and MAGSAC++ methods. The code is available at https://github.com/shenliang16/DEpD.
Attention-guided and fine-grained feature extraction from face images for gaze estimation

Research on appearance gaze estimation based on deep learning has achieved considerable results. However, a lack of information in the extracted local blocks reduces the precision of gaze estimation since CNNs do not prioritize the information within the key picture blocks for face image estimation. In this research, fine-grained visual information is extracted from local blocks using a Transformer in Transformer (TNT) model that emphasizes the interactions between local blocks. First, TNT-based gaze estimation models (GTiT-Pure and GTiT-Hybrid) are established with the TNT model to capture both coarse- and fine-grained visual information about the face, which are then combined to provide a feature representation for gaze regression. Then, the performance of the two models are evaluated on four gaze estimation datasets. Experimental results demonstrate that the pre-trained GTiT-Pure has less gaze estimation error than the majority of CNNs and Transformer models, and that the GTiT-Hybrid model performs the best on the EyeDiap and RT-Gene datasets. The GTiT model which combines both coarse- and fine-grained gaze features in face images, can be used to further explore the advantages of the Transformer model in gaze feature extraction.
Representing uncertainty through sentiment and stance visualizations: A survey

Visual analytics combines automated analysis techniques with interactive visualizations for effective understanding, reasoning, and decision-making on complex data. However, accurately classifying sentiments and stances in sentiment analysis remains challenging due to ambiguity and individual differences. This survey examines 35 papers published between 2016 and 2022, identifying unaddressed sources of friction that contribute to a gap between individual sentiment, processed data, and visual representation. We explore the impact of visualizations on data perception, analyze existing techniques, and investigate the many facets of uncertainty in sentiment and stance visualizations. We also discuss the evaluation methods used and present opportunities for future research. Our work addresses a gap in previous surveys by focusing on uncertainty and the visualization of sentiment and stance, providing valuable insights for researchers in graphical models, computational methods, and information visualization.
Single-pixel optical-flow-based experimental modal analysis

Modal analysis using structural responses identified from high-speed cameras is a challenging task. The problem is that the measured displacements are relatively small (typically deep in the sub-pixel range) and submerged in noise due to the low dynamic range of the camera sensor. A typical approach to determine structural responses from high-speed camera data is the digital image correlation (DIC) method, a general, computationally intensive method for identifying displacements. Without knowing the assumptions of the modal analysis, DIC identifies the displacement in the time domain by minimising the difference between two consecutive regions of interest (ROIs). Optical flow is a method based on the change in intensity in a given pixel due to the change in reflection from a moving surface. The displacement is identified from the change in intensity and the spatial gradient of the intensity of the surface. For small, sub-pixel movements, the relationship between intensity change and displacement is linear, which opens up the possibility of performing the modal analysis directly on the pixel intensity measured by the camera. This research applies the recently introduced Morlet-wave modal method and introduces an experimental modal analysis based on a single pixel with optical flow directly from the pixel intensities and the spatial gradient of the intensity. Furthermore, it is shown that the natural frequencies and damping ratios do not require the spatial gradient. The introduced method was successfully applied to the experimental test case where a pixel-based, full-field modal analysis was performed. The influence of averaging the results from multiple pixels in the modal domain is investigated. Modal identification is compared with the results obtained from the displacements identified with a digital image correlation (DIC) method. The introduced direct pixel-based modal analysis provides a robust and numerically efficient way to a full-field modal analysis.
Sequence2Self: Self-supervised image sequence denoising of pixel-level spray breakup morphology

Optical imaging of fast and transient phenomena such as the turbulent breakup of liquid sprays exhibit low signal-to-noise ratios due to the limited illumination intensity relative to the short exposure time. Image denoising is required to facilitate physical studies over these data but is challenging due to the absence of clean ground-truths and the stringency of the denoising task (e.g., strong and complex noise, limited resolution, preserving physical fidelity), preventing supervised and existing un-/self-supervised deep learning methods. To this end, Sequence2Self (Seq2S) is proposed, an extension of Self2Self (S2S) to image sequences that leverages both the signal’s spatial and temporal correlation. Seq2S is demonstrated on time-resolved x-ray phase contrast imaging of liquid jet fuel sprays in a gas turbine combustor, which possesses all of challenges detailed above. Experiments are conducted across four fuels with different breakup morphology using various state-of-the-art methods. Overall, many of the methods failed and Seq2S was most successful: (1) Accurate spray structures were reconstructed with consistent evolution across frames void of artifacts. (2) The performance was robust, invariant to the hyperparameter choice. (3) Computational time is short and can be made eligible for real-time denoising. In particular, the images denoised by Seq2S showed spray droplet diameter distributions with near-zero Kullback–Leibler divergence (0.01 ± 0.01) to a cleaner reference, whereas the second best method yielded 0.06 ± 0.03. This suggests that Seq2S can be reliably used prior to subsequent quantitative spray analyses as it retains (if not, improves) the statistical physical properties of the data.
Spatial displacement tracking of vibrating structure using multiple feature points assisted binocular visual reconstruction

Current vision-based displacement measurement methods have limitations of requiring manual target and parameter adjustment, along with significant user involvement to achieve the desired results. This paper proposes an advanced and comprehensive vision system based on ZED cameras for full-field three-dimensional (3D) vibration displacement measurement of civil structures. Compared to existing binocular systems consisting of multiple monoculars, ZED binoculars offer greater flexibility on instrument setup and data acquisition. A novel keypoint detection and matching algorithm based on deep learning is used to achieve target-free measurement, which is capable of capturing feature points with high precision. The performance of the proposed vision-based approach is evaluated through experimental studies on a six-story scaled frame structure. Statistical analysis of the comparative results in sinusoidal cases demonstrates that the displacement responses obtained from the proposed vision-based approach show consistent results in compliance with displacement sensor measurements. System identification of the target structure is achieved through a seismic case and the error analysis is also concluded in detail.
Convolutional neural networks applied to microtomy: Identifying the trimming-end cutting routine on paraffin-embedded tissue blocks

In the field of histopathology, the microtomy procedure yields thin sections of tissue embedded in paraffin blocks, then to be further processed for diagnostic purposes. Within microtomy, trimming is an initial but critical process in which the excess paraffin covering the tissue of interest is removed by continuous cutting routines, until the tissue is suitably exposed and ready for sectioning. Trimming is currently a time-consuming process that is manually held by technicians. In this paper, we present a method to automatize this process, by analyzing tissue block surface images resulting from each cyclic cutting routine. Two types of Convolutional Neural Networks (CNNs) were fine-tuned: one for binary segmentation, the other for multi-class classification tasks, by exploring and optimizing lightweight architectures to provide fast analytical results on cost-effective edge computing. Two sequential online conditions followed the CNNs to output the current stage of the block surface and rule if the trimming-end cutting routine was reached. We compared the results obtained through our method with the ones obtained by three skilled technicians processing 75 tissue blocks. The proposed method identified the trimming-end cutting routine approximately as accurate as the technicians did, yielding up to 90% of trimmed blocks of optimal quality and evidencing the potential of this tool in future automated trimming instruments. We deployed our method to an Edge TPU hardware accelerator to showcase its capability to provide immediate and objective results at every microtomy station applied with an ad hoc hardware, potentially guaranteeing a throughput 50% higher than manual trimming.
Color enhancement of brand trademark image based on smooth filtering algorithm

Current color enhancement methods will excessively eliminate noise in image processing, resulting in the loss of color information and details in the image, affecting the effect of color enhancement. In order to improve the defects of the above color enhancement methods, a brand image color enhancement method based on smooth filtering algorithm is studied. After the histogram is used to equalize the image, the color of the image is segmented to obtain the color information. Based on Gaussian filter, a joint bilateral filter is designed to smooth the image. Deep learning convolutional network is constructed to realize image color enhancement by encoding and decoding. The simulation results show that the CNI value of the enhanced image is closer to 1, the color quality of the image is better, and the color enhancement effect of the method is better.
Study on Image Caption Generation Model with Improved Attention Mechanism

The image caption generation task uses computer vision technology to automatically complete the image target recognition, and then uses the machine translation model to express the content of the image into a natural language, which combines the research directions of the two fields of computer vision and natural language processing. In recent years, the image caption generation model is based on Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), but this model suffers from inaccurate visual feature selection and slow training speed. In response to this problem, this paper proposes an image caption generation model based on improved attention mechanism and long short-term memory (LSTM) network. The model uses Vgg19 and Resnet101 in the convolutional neural network as feature extraction networks, and introduces improved the attention mechanism improves the evaluation index of image caption generation compared with the previous attention model. This article uses the public data sets Flickr8K and Flickr8k_cn (Flickr8K Chinese data set marked by native Chinese speakers) for training, and uses a variety of evaluation indicators (BLEU, ROUGE_L, CIDEr, METEOR) to verify the model. The experimental results show that, compared with the traditional attention mechanism, the improved attention mechanism image caption generation model in this paper has a significant improvement in accuracy, and it is better than the traditional model in the four evaluation indicators.
Runtime Evolution of Bitcoin's Consensus Rules

The runtime evolution of a system concerns the ability to make changes during runtime without disrupting the service. Blockchain systems need to provide continuous service and integrity. Similar challenges have been observed in centrally controlled distributed systems or mobile applications that handle runtime evolution, mainly by supporting compatible changes or running different versions concurrently. However, these solutions are not applicable in the case of blockchains, and thus, new solutions are required. This study investigates Bitcoin consensus evolution by analysing over a decade of data from Bitcoin's development channels using Strauss’ grounded theory approach and root cause analysis. The results show nine deployment features which form nine deployment techniques and ten lessons learned. Our results illustrate how different deployment techniques fit different contexts and pose different levels of consensus failure risks. Furthermore, we provide guidelines for risk minimisation during consensus rule deployment for blockchain in general and Bitcoin in particular.
Optimization of a probabilistic quantum search algorithm with <i>a priori</i> information

A quantum computer encodes information in quantum states and runs quantum algorithms to surpass the classical counterparts by exploiting quantum superposition and quantum correlation. Grover's quantum search algorithm is a typical quantum algorithm that proves the superiority of quantum computing over classical computing. It has a quadratic reduction in the query complexity of a database search and is known to be optimal when no a priori information about the elements of the database is provided. In this work we consider a probabilistic Grover search algorithm allowing nonzero probability of failure for a database with a general a priori probability distribution of the elements and minimize the number of oracle calls by optimizing the initial state of the quantum system and the reflection axis of the diffusion operator. The initial state and the reflection axis are allowed to not coincide and thus the quantum search algorithm rotates the quantum system in a three-dimensional subspace spanned by the initial state, the reflection axis, and the search target state in general. The number of oracle calls is minimized by a variational method, and formal results are obtained with the assumption of low failure probability. The results show that for a nonuniform a priori distribution of the database elements, the number of oracle calls can be significantly reduced given a small decrease in the success probability of the quantum search algorithm, leading to a lower average query complexity to find the solution of the search problem. The results are applied to a simple but nontrivial database model with two-value a priori probabilities to show the power of the optimized quantum search algorithm. The paper concludes with a discussion about the generalization to higher-order results that allows for a larger failure probability for the quantum search algorithm.
Let Me Help You: Improving the Novice Experience of High-Performance Keyboard Layouts with Visual Clues

Since the advent of smartphones, work tasks shift progressively towards mobile phones. The main hurdle of this progression is the substantially slower input speed of smartphones in comparison with physical keyboards. This input speed could be improved by using keyboard layouts optimized for use with smartphones. As these keyboard layouts are not commonly used, switching to them results in a poor novice experience with an initially decreased text input speed. Previous investigations confirmed that this poor novice experience can be attenuated by using visual clues accentuating the most probable keys. This gives rise to the question of which visual impression leads to the best performance improvement. Therefore, this article evaluates the performance of six visual clues with different visual impressions via a user study conducted with 28 participants. The results showed that the visual clue with a pop-up animation and the visual clue with an increased font size performed the best with a 4% and 3%, respectively, typing performance improvement. Nonetheless, the questionnaire and personal preferences part of the user study showed that a 4% performance gain is not enough to crown one individual visual clue as the best visual clue to be used with every individual. This leads to the conclusion that functionality to choose one personal preference is more important than focusing on one specific best clue.