Reviewerly/exHarmony huggingface dataset:
Can You Hear Me <i>Now</i>? Sensitive Comparisons of Human and Machine Perception

The rise of machine-learning systems that process sensory input has brought with it a rise in comparisons between human and machine perception. But such comparisons face a challenge: Whereas machine perception of some stimulus can often be probed through direct and explicit measures, much of human perceptual knowledge is latent, incomplete, or unavailable for explicit report. Here, we explore how this asymmetry can cause such comparisons to misestimate the overlap in human and machine perception. As a case study, we consider human perception of adversarial speech - synthetic audio commands that are recognized as valid messages by automated speech-recognition systems but that human listeners reportedly hear as meaningless noise. In five experiments, we adapt task designs from the human psychophysics literature to show that even when subjects cannot freely transcribe such speech commands (the previous benchmark for human understanding), they can sometimes demonstrate other forms of understanding, including discriminating adversarial speech from closely matched nonspeech (Experiments 1 and 2), finishing common phrases begun in adversarial speech (Experiments 3 and 4), and solving simple math problems posed in adversarial speech (Experiment 5) - even for stimuli previously described as unintelligible to human listeners. We recommend the adoption of such "sensitive tests" when comparing human and machine perception, and we discuss the broader consequences of such approaches for assessing the overlap between systems.
Optimization algorithm for low‐light image enhancement based on Retinex theory

To improve the visual quality of low-light images and discover hidden details in images, an image enhancement algorithm is proposed, which is based on a fast and robust fuzzy C-means (FRFCM) clustering algorithm combined with Retinex theory. The algorithm is based on Retinex theory to solve the above problems as followings: Firstly, the initial illumination estimation image is constructed by max-RGB and segmented by FRFCM algorithm. Secondly, initial illumination estimation image and its segmented image linearly fused with a certain proportion is to obtain the optimized illumination estimation image, then is smoothed by guided filtering. Finally, reflected image is obtained by Retinex theory and the edge details of the image by equal ratio are enhanced, showing the enhanced image rich in detail texture. In order to verify the proposed algorithm, a large number of low-light image datasets are applied to test the proposed algorithm. And the effects of image enhancement of the algorithm and other existing enhanced algorithms are also compared. The experimental results show that the proposed algorithm performs well in both subjective and objective evaluation, especially the good ability to keep meticulous of details and colour retention.
Steep freeform measurement method based on a normal transverse differential confocal

A normal transverse laser differential confocal freeform measurement (NTDCFM) method was proposed to address the high-precision measurement difficulty of steep freeform surfaces with large variations in inclination, scattering, and reflectance. Using D-shaped diaphragm technology, the freeform surface under test (FSUT) axial variation transformed into a spot transverse movement on the detection focal plane. Meanwhile, a 2D position sensitive detector (PSD) was used to obtain the normal vector of the sampling points so that the measuring sensor's optical axis could track the FSUT normal direction. The focus tracking method extended the sensor measurement range. Theoretical analysis and experimental results showed that the axial resolution of the NTDCFM was better than 0.5 nm, the direction resolution of the normal vector was 0.1°, the maximum surface inclination could be measured up to 90°, the sensor range was 5 mm, and the measurement repeatability of the FSUT was better than 9 nm. It provides an effective new anti-inclination, anti-scattering, and anti-reflectivity method for accurately measuring steep freeform surfaces.
LRP‐based network pruning and policy distillation of robust and non‐robust DRL agents for embedded systems

Summary Reinforcement learning (RL) is an effective approach to developing control policies by maximizing the agent's reward. Deep reinforcement learning uses deep neural networks (DNNs) for function approximation in RL, and has achieved tremendous success in recent years. Large DNNs often incur significant memory size and computational overheads, which may impede their deployment into resource‐constrained embedded systems. For deployment of a trained RL agent on embedded systems, it is necessary to compress the policy network of the RL agent to improve its memory and computation efficiency. In this article, we perform model compression of the policy network of an RL agent by leveraging the relevance scores computed by layer‐wise relevance propagation (LRP), a technique for Explainable AI (XAI), to rank and prune the convolutional filters in the policy network, combined with fine‐tuning with policy distillation. Performance evaluation based on several Atari games indicates that our proposed approach is effective in reducing model size and inference time of RL agents. We also consider robust RL agents trained with RADIAL‐RL versus standard RL agents, and show that a robust RL agent can achieve better performance (higher average reward) after pruning than a standard RL agent for different attack strengths and pruning rates.
Lifelines for a drowning science ‐ improving findability and synthesis of hydrologic publications

Abstract Increasing publication numbers make it difficult to keep up with knowledge evolution in a science like hydrology. Here we give recommendations to authors and journals for writing future‐proof articles that contribute to knowledge accumulation and synthesis.
Natural Language Processing Techniques for Text Classification of Biomedical Documents: A Systematic Review

The classification of biomedical literature is engaged in a number of critical issues that physicians are expected to answer. In many cases, these issues are extremely difficult. This can be conducted for jobs such as diagnosis and treatment, as well as efficient representations of ideas such as medications, procedure codes, and patient visits, as well as in the quick search of a document or disease classification. Pathologies are being sought from clinical notes, among other sources. The goal of this systematic review is to analyze the literature on various problems of classification of medical texts of patients based on criteria such as: the quality of the evaluation metrics used, the different methods of machine learning applied, the different data sets, to highlight the best methods in this type of problem, and to identify the different challenges associated. The study covers the period from 1 January 2016 to 10 July 2022. We used multiple databases and archives of research articles, including Web Of Science, Scopus, MDPI, arXiv, IEEE, and ACM, to find 894 articles dealing with the subject of text classification, which we were able to filter using inclusion and exclusion criteria. Following a thorough review, we selected 33 articles dealing with biological text categorization issues. Following our investigation, we discovered two major issues linked to the methodology and data used for biomedical text classification. First, there is the data-centric challenge, followed by the data quality challenge.
Simple crack tip and stress intensity factor determination method for model I crack using digital image correlation

In the past three decades, quantitative characterization of cracks attracted much attention with the development of digital image correlation (DIC), especially for estimation of crack tip position (CTP) and stress intensity factor (SIF). Quantifying CTP and SIF is of great importance in the damage tolerance assessment of fracture-critical components and structures. In previous research, multivariate fitting is performed on the displacement field to determine CTP and SIF. However, high gradient deformation near the crack tip causes inaccurate results and an involving complex estimation process. In this paper, a simple method is proposed to determine the CTP and SIF based on the displacement field calculated by DIC. Instead of the full-field fitting method, two lines symmetrically distributed on both sides of the crack tip are selected to fit the CTP. Vertical displacement differences between two lines are fitted with the theoretical solutions of mode I crack. Compared with the actual position observed by the micrograph, the estimation error is less than 2 pixels (about 6 μm). Additionally, a similar method is proposed to estimate the SIF utilizing the CTP results. To avoid the bias errors caused by the plastic zone, two lines are selected in the elastic zone away from the crack tip. Vertical displacements on two lines are fitted with the theoretical solutions to obtain SIF. The fitting results of the proposed method are close to that of the full-field fitting method. Compared with the previous methods, the computation burden of the proposed method is greatly reduced while the accuracy is not affected.
Quantum attacks on two-round even-mansour

Even-Mansour is one of the most important constructions in symmetric cryptography, both from a theoretical and practical perspective. With the rapid development of quantum computing, the security of Even-Mansour construction in quantum setting needs to be considered. For one round Even-Mansour construction, it is well settled by classical and quantum attacks. While for the iterated scheme, the situation is much more complex. In this paper, we study the next case in line in detail and depth: quantum attacks against two rounds case. We first make an asymptotic comparison with existing classical and quantum attacks. Then we give concrete resource estimation for the proposed quantum attacks on round reduced LED cipher and AES 2 . The resource estimation allows to deduce the most efficient attacks based on the trade-off of the number of qubits and Toffoli depth.
Seeing Is Believing: Using Eye-Tracking Devices in Environmental Research

This article aims to provide methodological guidance for research that uses eye-tracking devices (ETDs) to study environment and behavior relationships.Vision is an important human sense through which people acquire a large amount of environmental information. ETDs are tools for detecting eye/gaze behaviors, facilitating better understanding about how people collect visual information and how such information is related to emotions and psychological states. However, there is a lack of guidance for the application of ETDs to environment and behavior studies.A literature review was conducted on articles reporting empirical studies that used ETDs. The data were extracted and compiled, including information such as research questions, research design, types of ETDs, variables measured, types of physical environment (or visual stimuli), stimuli durations, data analysis methods, and so on.Fifty articles were identified. The main research topics were related to urban and landscape environments, and architecture and interior spaces. Most of the research designs were experimental or quasi-experimental designs, with a few cross-sectional studies. The majority types of ETDs were screen-based ETDs, followed by mobile ETDs (glasses). Main variables were gaze fixations, fixation durations, and scan paths. Typical types of stimuli included images, videos, virtual reality, and real environments and/or objects.Guidance for eye-tracking research on environment and behavior was developed based on the literature review results, to provide direction for determining research questions, selecting appropriate research designs, establishing participant inclusion and/or excluding criteria, collecting and analyzing data, and interpreting research results.
Breaking down Polyblur: Fast Blind Correction of Small Anisotropic Blurs

Polyblur is a two stage blind deblurring technique for removing small-sized blurs, like small camera shake or the lens point-spread function, proposed in 2021 by Delbracio et al.First, the blur is modeled with a zero-mean anisotropic Gaussian kernel whose parameters are rapidly estimated from the oriented blurry image gradients.Second, a sharp estimate is obtained by applying an approximate deconvolution filter, which is designed as a polynomial function of the estimated blurring kernel.Since in practice true blurs are not exactly Gaussian filters, the residual blur is gradually removed by repeating this two-stage procedure.Because it relies only on simple image manipulations, Polyblur is a quick blind deblurring technique, running in a fraction of a second on a smartphone.In this presentation, we analyze its key ingredients, showcase several use cases on real images, and provide Numpy and Pytorch implementations.
Distributed extended object tracking information filter over sensor networks

Abstract This work aims to design a distributed extended object tracking system over a realistic network, where both the extent and kinematics are required to retain consensus within the entire network. To this end, we resort to the multiplicative error model (MEM) that allows the extent parameters of perpendicular axis‐symmetric objects to have individual uncertainty. To incorporate the MEM into the information filter (IF) style, we use the moment‐matching technique to derive two pair linear models with only additive noise. The separation is merely in a fashion, and the cross‐correlation between states is preserved as parameters in each other's model. As a result, the closed‐form expressions are transferred into an alternating iteration of two linear IFs. With the two models, a centralized IF is proposed wherein the measurements are converted into a summation of innovation parts. Later, under a sensor network with the communication nodes and sensor nodes, we present two distributed IFs through the consensus on information and consensus on measurement schemes, respectively. Moreover, we prove the estimation errors of the proposed filter are exponentially bounded in the mean square. The benefits are testified by numerical experiments in comparison to state‐of‐the‐art filters in literature.
Hyperspectral Image Denoising via Weighted Multidirectional Low-Rank Tensor Recovery

Recently, low-rank tensor recovery methods based on subspace representation have received increased attention in the field of hyperspectral image (HSI) denoising. Unfortunately, those methods usually analyze the prior structural information within different dimensions indiscriminately, ignoring the differences between modes, leaving substantial room for improvement. In this article, we first consider the low-rank properties in the subspace and prove that the structure correlation across the nonlocal self-similarity mode is much stronger than in the spatial sparsity and spectral correlation modes. On that basis, we introduce a new multidirectional low-rank regularization, in which each mode is assigned a different weight to characterize its contribution to estimating the tensor rank. After that, integrating the proposed regularization with the subspace-based tensor recovery framework, an optimization model for HSI mixed noise removal is developed. The proposed model can be addressed efficiently via the alternating minimization algorithm. Extensive experiments implemented with synthetic and real data demonstrate that the proposed method significantly outperforms other state-of-the-art HSI denoising methods, which clearly indicates the effectiveness of the proposed approach in HSI denoising.
Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

In this paper we propose an unsupervised feature extraction method to capture temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich latent vectors.Instead of simply treating the latent features of nearby frames as positive pairs and those of temporally-distant ones as negative pairs as in other CSS approaches, we explicitly disentangle each latent vector into a time-variant component and a time-invariant one.We then show that applying contrastive loss only to the time-variant features and encouraging a gradual transition on them between nearby and away frames while also reconstructing the input, extract rich temporal features, well-suited for human pose estimation.Our approach reduces error by about 50% compared to the standard CSS strategies, outperforms other unsupervised single-view methods and matches the performance of multi-view techniques.When 2D pose is available, our approach can extract even richer latent features and improve the 3D pose estimation accuracy, outperforming other state-of-the-art weakly supervised methods.
Grading of HCC Biopsy Images Using Nucleus and Texture Features

Hepatocellular carcinoma (HCC) is one of the most critical health problems in the world. For proper treatment, it is important to identify the grade of cancer morbidity from HCC biopsy image. The diagnostic work is not only time-consuming but also subjective. The same biopsy image may be diagnosed as of different grades by different doctors, due to lack of experience or difference in opinion. In this work, we proposed an automatic grading system with classification accuracy matching to an experienced doctor, to help augment the diagnosis process. First, we proposed a segmentation method to isolate all nucleus-like objects present in a biopsy image. Non-target objects (here the target is a single HCC nucleus) present in the biopsy image are isolated too in the segmentation process. To eliminate such non-target objects, we proposed clustering of segmented images and a novel method to filter out target objects. Next, we proposed a two track neural network, where input consists of 2 different images. It combines a single segmented nucleus and a random cropped texture patch of the biopsy image to which the nucleus belongs. At this classifier output, we grade the single nucleus. Finally, a majority voting method is used to identify the grade of the whole biopsy image. We achieved an accuracy of 99.03% for nucleus image grading and 99.66% accuracy for grading biopsy images.
Large scale K-means clustering using GPUs

Abstract The k -means algorithm is widely used for clustering, compressing, and summarizing vector data. We present a fast and memory-efficient GPU-based algorithm for exact k -means, Asynchronous Selective Batched K -means (ASB K -means). Unlike most GPU-based k -means algorithms that require loading the whole dataset onto the GPU for clustering, the amount of GPU memory required to run our algorithm can be chosen to be much smaller than the size of the whole dataset. Thus, our algorithm can cluster datasets whose size exceeds the available GPU memory. The algorithm works in a batched fashion and applies the triangle inequality in each k -means iteration to omit a data point if its membership assignment, i.e., the cluster it belongs to, remains unchanged, thus significantly reducing the number of data points that need to be transferred between the CPU’s RAM and the GPU’s global memory and enabling the algorithm to very efficiently process large datasets. Our algorithm can be substantially faster than a GPU-based implementation of standard k -means even in situations when application of the standard algorithm is feasible because the whole dataset fits into GPU memory. Experiments show that ASB K -means can run up to 15x times faster than a standard GPU-based implementation of k -means, and it also outperforms the GPU-based k -means implementation in NVIDIA’s open-source RAPIDS machine learning library on all the datasets used in our experiments.
Web information mining and semantic analysis in heterogeneous unstructured text data using enhanced latent Dirichlet allocation

Summary Information mining and semantic analysis have gained significant attention over recent years to obtain appropriate information from unstructured data. Several approaches have been introduced for web information mining. However, the expected accuracy is not reached by these approaches. Therefore, hybrid fuzzy clustering and enhanced latent Dirichlet allocation (ELDA) are proposed for the accuracy increment in this work. The information clustering process is performed using the hybrid fuzzy clustering algorithm called fuzzy‐C‐medoids optimized using improved whale algorithm. The clustering procedure entails grouping data points into more similar clusters than data points from other clusters. Finally, the context of the text is recognized by analyzing the semantic information with ELDA, which offers a suitable index for accurate and fast data extraction. PYTHON tool is used to develop the proposed web mining model, and the simulation analysis of the proposed model is carried out using the BibTex dataset and compared with baseline models. The performance of the proposed method is evaluated using purity, normalized mutual information, accuracy, and precision metrics. Also, it is compared with different existing algorithms. Thus, the analysis of the results proved that the proposed approach achieves better outcomes than the existing approaches.
Comparing BIM-Based XR and Traditional Design Process from Three Perspectives: Aesthetics, Gaze Tracking, and Perceived Usefulness

With technological development and industrial transformation, the architecture, engineering, and construction (AEC) industry, comprising architecture, engineering, and construction, has shifted from a traditional drawing-based design mode to a digital and computer-based mode. In recent years, the application of extended reality (XR) technology, including virtual reality (VR), augmented reality (AR), and mixed reality (MR) technology, emphasizes the immersive and interactive experiences between reality and virtuality, bringing breakthrough developments to architectural projects. This study proposes a new design process mode—the BIM-based XR system—and compares it with the traditional design process mode through an actual stadium design project. Three evaluation perspectives including aesthetics, gaze tracking, and perceived usefulness assessment are used to compare the differences between the two modes. The result showed that the use of the BIM-based XR system could bring users more immersive experience and aesthetic assessment preference, and perceived usefulness in design decision-making, communication, and spatial cognition. The gaze tracking result also revealed that the BIM-based XR system can implement the design process more efficient. It is expected that XR and BIM technologies can be effectively integrated to enhance the integrity of industrial applications and establish a new design collaboration mode for the AEC industry.