Study of the seismic activity of the Almalik-Angren industrial zone based on lineament analysis

Abstract: In this work, an automated lineament analysis was carried out to search for earthquake precursors for the territory of the Almalyk-Angren industrial zone in Uzbekistan. The seven events with a magnitude of about 3 were selected for analysis. The Landsat 8 satellite images were processed using the automated lineament detection method in the LEFA software. The processing steps included detecting line elements in raster images, calculating the characteristics of the spatial distribution of line elements, and combining collinear linear elements into lineaments. The analyses of the cyclicality of precursors before and after earthquakes were based on the study of the distribution of the lineament trend in the study area using rose diagrams and lineament density maps. The results showed a change in the dynamics of the lineament structure. The statistics of the number of lineaments showed that their increase begins almost 20 days before the event, reaches its maximum about 1 – 2 days before the earthquake, decreases starting from 14 days after the earthquake, and has a minimum value of 1 – 2 months. The main trends observed in the lineament map showed the dominant trend in NS, WE, NW-SE directions.


ML interpretability: Simple isn't easy

Abstract: The interpretability of ML models is important, but it is not clear what it amounts to. So far, most philosophers have discussed the lack of interpretability of black-box models such as neural networks, and methods such as explainable AI that aim to make these models more transparent. The goal of this paper is to clarify the nature of interpretability by focussing on the other end of the "interpretability spectrum". The reasons why some models, linear models and decision trees, are highly interpretable will be examined, and also how more general models, MARS and GAM, retain some degree of interpretability. It is found that while there is heterogeneity in how we gain interpretability, what interpretability is in particular cases can be explicated in a clear manner.


Susceptibility of AutoML mortality prediction algorithms to model drift caused by the COVID pandemic

Abstract: Abstract Background Concept drift and covariate shift lead to a degradation of machine learning (ML) models. The objective of our study was to characterize sudden data drift as caused by the COVID pandemic. Furthermore, we investigated the suitability of certain methods in model training to prevent model degradation caused by data drift. Methods We trained different ML models with the H2O AutoML method on a dataset comprising 102,666 cases of surgical patients collected in the years 2014–2019 to predict postoperative mortality using preoperatively available data. Models applied were Generalized Linear Model with regularization, Default Random Forest, Gradient Boosting Machine, eXtreme Gradient Boosting, Deep Learning and Stacked Ensembles comprising all base models. Further, we modified the original models by applying three different methods when training on the original pre-pandemic dataset: (1) we weighted older data weaker, (2) used only the most recent data for model training and (3) performed a z-transformation of the numerical input parameters. Afterwards, we tested model performance on a pre-pandemic and an in-pandemic data set not used in the training process, and analysed common features. Results The models produced showed excellent areas under receiver-operating characteristic and acceptable precision-recall curves when tested on a dataset from January-March 2020, but significant degradation when tested on a dataset collected in the first wave of the COVID pandemic from April-May 2020. When comparing the probability distributions of the input parameters, significant differences between pre-pandemic and in-pandemic data were found. The endpoint of our models, in-hospital mortality after surgery, did not differ significantly between pre- and in-pandemic data and was about 1% in each case. However, the models varied considerably in the composition of their input parameters. None of our applied modifications prevented a loss of performance, although very different models emerged from it, using a large variety of parameters. Conclusions Our results show that none of our tested easy-to-implement measures in model training can prevent deterioration in the case of sudden external events. Therefore, we conclude that, in the presence of concept drift and covariate shift, close monitoring and critical review of model predictions are necessary.


AE-Qdrop: Towards Accurate and Efficient Low-Bit Post-Training Quantization for A Convolutional Neural Network

Abstract: Blockwise reconstruction with adaptive rounding helps achieve acceptable 4-bit post-training quantization accuracy. However, adaptive rounding is time intensive, and the optimization space of weight elements is constrained to a binary set, thus limiting the performance of quantized models. The optimality of block-wise reconstruction requires that subsequent network blocks remain unquantized. To address this, we propose a two-stage post-training quantization scheme, AE-Qdrop, encompassing block-wise reconstruction and global fine-tuning. In the block-wise reconstruction stage, a progressive optimization strategy is introduced as a replacement for adaptive rounding, enhancing both quantization accuracy and efficiency. Additionally, the integration of randomly weighted quantized activation helps mitigate the risk of overfitting. In the global fine-tuning stage, the weights of each quantized network block are corrected simultaneously through logit matching and feature matching. Experiments in image classification and object detection tasks validate that AE-Qdrop achieves high precision and efficient quantization. For the 2-bit MobileNetV2, AE-Qdrop outperforms Qdrop in quantization accuracy by 6.26%, and its quantization efficiency is fivefold higher.


SCDetect: A SeisComP Module for Real-Time Waveform Cross-Correlation-Based Earthquake Detection

Abstract: Abstract Enhanced earthquake catalogs based on waveform cross correlation (template matching) have become routine when studying regional or sequence specific seismicity. Currently, there is no existing open-source cross-correlation software that is designed to be fully integrated in real-time operations of seismic networks. To fill this gap, we introduce SCDetect, a software that implements real-time earthquake detection based on waveform cross correlation in the time domain. SCDetect with the extension module scdetect-cc is an open-source SeisComP package written in C++. scdetect-cc can be used to process both archived waveform data (playback mode), and real-time data. In the real-time application, waveforms are fetched through one of the SeisComP RecordStream interfaces, and its output (picks, origin times, amplitudes, and magnitudes) are sent to the SeisComP messaging system. The new origins are associated either with existing events detected by other pick-based SeisComP modules, or create new events. Optionally, the hypocenter location can be refined by downstream application of existing SeisComP modules. scdetect-cc offers two magnitude estimation methods that are based on the amplitudes of the template earthquakes and the new detections. In the real-time application, scdetect-cc can be scaled to handle thousands of templates without overloading the application or becoming too latent, unable to keep up with the data flow. In the playback mode, we applied scdetect-cc to three recent earthquake sequences occurring in Switzerland and surrounding regions between 2019 and 2020. Two scenarios are tested to simulate its performance in real time. The first scenario cross correlates the signals of the nearest station, whereas the second requires four stations. In both cases, we successfully detected most of the cataloged events and added hundreds of new detections. Overall, scdetect-cc is a computationally efficient and highly customizable tool to detect earthquakes for regional networks that implement the SeisComP system for earthquake monitoring.


On the different regimes of stochastic gradient descent

Abstract: Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>B</mml:mi> </mml:math> , and the step size or learning rate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>η</mml:mi> </mml:math> . For small <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>B</mml:mi> </mml:math> and large <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>η</mml:mi> </mml:math> , SGD corresponds to a stochastic evolution of the parameters, whose noise amplitude is governed by the “temperature” <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mi>T</mml:mi> <mml:mo>≡</mml:mo> <mml:mi>η</mml:mi> <mml:mo stretchy="false">/</mml:mo> <mml:mi>B</mml:mi> </mml:mrow> </mml:math> . Yet this description is observed to break down for sufficiently large batches <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mi>B</mml:mi> <mml:mo>≥</mml:mo> <mml:msup> <mml:mi>B</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:mrow> </mml:math> , or simplifies to gradient descent (GD) when the temperature is sufficiently small. Understanding where these cross-overs take place remains a central challenge. Here, we resolve these questions for a teacher-student perceptron classification model and show empirically that our key predictions still apply to deep networks. Specifically, we obtain a phase diagram in the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>B</mml:mi> </mml:math> - <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>η</mml:mi> </mml:math> plane that separates three dynamical phases: i) a noise-dominated SGD governed by temperature, ii) a large-first-step-dominated SGD and iii) GD. These different phases also correspond to different regimes of generalization error. Remarkably, our analysis reveals that the batch size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msup> <mml:mi>B</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> separating regimes (i) and (ii) scale with the size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>P</mml:mi> </mml:math> of the training set, with an exponent that characterizes the hardness of the classification problem.


Statistical learning of motor preparation.

Abstract: Statistical learning, the process of extracting regularities from the environment, is one of the most fundamental abilities playing an essential role in almost all aspects of human cognition. Previous studies have shown that attentional selection is biased toward locations that are likely to contain a target and away from locations that are likely to contain a distractor. The current study investigated whether participants can also learn to extract that a specific motor response is more likely when the target is presented at specific locations within the visual field. To that end, the additional singleton paradigm was adapted such that when the singleton target was presented at one specific location, one response (e.g., right index finger) was more likely than the other (e.g., right middle finger) and the reverse was true for another location. The results show that participants learned to extract that a particular motor response is more likely when the singleton target (which was unrelated to the response) was presented at a specific location within the visual field. The results also suggest that it is the location of the target and not its shape that is associated with the biased response. This learning cannot be considered as being top-down or conscious as participants showed little, if any, awareness of the response biases present. The results are discussed in terms of the event coding theory. The study increases the scope of statistical learning and shows how individuals adapt automatically, without much awareness, to the regularities present in the environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


A decentralized and security-enhanced professional title evaluation system in universities under mobile Internet of Things

Abstract:
Abstract
 Digital online application is a commonly used method for professional title evaluation in colleges and universities. With the rapid development of the mobile Internet of Things, the fastest way for users to access the evaluation system is through mobile devices. However, this also poses security challenges to the traditional professional title evaluation system. Therefore, this paper proposes a decentralized and security-enhanced digital title evaluation system in mobile IoT. Since the system uses blockchain to implement a decentralized review process, the transparency and fairness of the review process are guaranteed. At the same time, the review data and reviewee information in the system are encrypted and stored in the distributed network to ensure the security and non-tamperability of the data. All review records are recorded on the blockchain, and anyone can view and verify the legitimacy of the review results. Additionally, the system incorporates security enhancement mechanisms such as identity verification, smart contracts, and auditing functions to improve the credibility of the evaluation process and prevent cheating under mobile IoT. Experiments have shown that this system has important application value in the field of professional title review in universities, which can improve review efficiency, reduce human intervention, and provide strong support for the credibility of review results.


Artificial intelligence’s impact on breast cancer pathology: a literature review

Abstract: Abstract This review discusses the profound impact of artificial intelligence (AI) on breast cancer (BC) diagnosis and management within the field of pathology. It examines the various applications of AI across diverse aspects of BC pathology, highlighting key findings from multiple studies. Integrating AI into routine pathology practice stands to improve diagnostic accuracy, thereby contributing to reducing avoidable errors. Additionally, AI has excelled in identifying invasive breast tumors and lymph node metastasis through its capacity to process large whole-slide images adeptly. Adaptive sampling techniques and powerful convolutional neural networks mark these achievements. The evaluation of hormonal status, which is imperative for BC treatment choices, has also been enhanced by AI quantitative analysis, aiding interobserver concordance and reliability. Breast cancer grading and mitotic count evaluation also benefit from AI intervention. AI-based frameworks effectively classify breast carcinomas, even for moderately graded cases that traditional methods struggle with. Moreover, AI-assisted mitotic figures quantification surpasses manual counting in precision and sensitivity, fostering improved prognosis. The assessment of tumor-infiltrating lymphocytes in triple-negative breast cancer using AI yields insights into patient survival prognosis. Furthermore, AI-powered predictions of neoadjuvant chemotherapy response demonstrate potential for streamlining treatment strategies. Addressing limitations, such as preanalytical variables, annotation demands, and differentiation challenges, is pivotal for realizing AI’s full potential in BC pathology. Despite the existing hurdles, AI’s multifaceted contributions to BC pathology hold great promise, providing enhanced accuracy, efficiency, and standardization. Continued research and innovation are crucial for overcoming obstacles and fully harnessing AI’s transformative capabilities in breast cancer diagnosis and assessment. Graphical Abstract


Multi-Label Contrastive Learning for Abstract Visual Reasoning

Abstract: For a long time, the ability to solve abstract reasoning tasks was considered one of the hallmarks of human intelligence. Recent advances in the application of deep learning (DL) methods led to surpassing human abstract reasoning performance, specifically in the most popular type of such problems-Raven's progressive matrices (RPMs). While the efficacy of DL systems is indeed impressive, the way they approach the RPMs is very different from that of humans. State-of-the-art systems solving RPMs rely on massive pattern-based training and sometimes on exploiting biases in the dataset, whereas humans concentrate on the identification of the rules/concepts underlying the RPM to be solved. Motivated by this cognitive difference, this work aims at combining DL with the human way of solving RPMs. Specifically, we cast the problem of solving RPMs into a multilabel classification framework where each RPM is viewed as a multilabel data point, with labels determined by the set of abstract rules underlying the RPM. For efficient training of the system, we present a generalization of the noise contrastive estimation algorithm to the case of multilabel samples and a new sparse rule encoding scheme for RPMs. The proposed approach is evaluated on the two most popular benchmark datasets [I-RAVEN and procedurally generated matrices (PGM)] and on both of them demonstrate an advantage over the state-of-the-art results.


Unsupervised Feature Selection With Flexible Optimal Graph

Abstract: In the unsupervised feature selection method based on spectral analysis, constructing a similarity matrix is a very important part. In existing methods, the linear low-dimensional projection used in the process of constructing the similarity matrix is too hard, it is very challenging to construct a reliable similarity matrix. To this end, we propose a method to construct a flexible optimal graph. Based on this, we propose an unsupervised feature selection method named unsupervised feature selection with flexible optimal graph and l2,1 -norm regularization (FOG-R). Unlike other methods that use linear projection to approximate the low-dimensional manifold of the original data when constructing a similarity matrix, FOG-R can learn a flexible optimal graph, and by combining flexible optimal graph learning and feature selection into a unified framework to get an adaptive similarity matrix. In addition, an iterative algorithm with a strict convergence proof is proposed to solve FOG-R. l2,1 -norm regularization will introduce an additional regularization parameter, which will cause parameter-tuning trouble. Therefore, we propose another unsupervised feature selection method, that is, unsupervised feature selection with a flexible optimal graph and l2,0 -norm constraint (FOG-C), which can avoid tuning additional parameters and obtain a more sparse projection matrix. Most critically, we propose an effective iterative algorithm that can solve FOG-C globally with strict convergence proof. Comparative experiments conducted on 12 public datasets show that FOG-R and FOG-C perform better than the other nine state-of-the-art unsupervised feature selection algorithms.


Refining Graph Structure for Incomplete Multi-View Clustering

Abstract: As a challenging problem, incomplete multi-view clustering (MVC) has drawn much attention in recent years. Most of the existing methods contain the feature recovering step inevitably to obtain the clustering result of incomplete multi-view datasets. The extra target of recovering the missing feature in the original data space or common subspace is difficult for unsupervised clustering tasks and could accumulate mistakes during the optimization. Moreover, the biased error is not taken into consideration in the previous graph-based methods. The biased error represents the unexpected change of incomplete graph structure, such as the increase in the intra-class relation density and the missing local graph structure of boundary instances. It would mislead those graph-based methods and degrade their final performance. In order to overcome these drawbacks, we propose a new graph-based method named Graph Structure Refining for Incomplete MVC (GSRIMC). GSRIMC avoids recovering feature steps and just fully explores the existing subgraphs of each view to produce superior clustering results. To handle the biased error, the biased error separation is the core step of GSRIMC. In detail, GSRIMC first extracts basic information from the precomputed subgraph of each view and then separates refined graph structure from biased error with the help of tensor nuclear norm. Besides, cross-view graph learning is proposed to capture the missing local graph structure and complete the refined graph structure based on the complementary principle. Extensive experiments show that our method achieves better performance than other state-of-the-art baselines.


An Effective Video Transformer With Synchronized Spatiotemporal and Spatial Self-Attention for Action Recognition

Abstract: Convolutional neural networks (CNNs) have come to dominate vision-based deep neural network structures in both image and video models over the past decade. However, convolution-free vision Transformers (ViTs) have recently outperformed CNN-based models in image recognition. Despite this progress, building and designing video Transformers have not yet obtained the same attention in research as image-based Transformers. While there have been attempts to build video Transformers by adapting image-based Transformers for video understanding, these Transformers still lack efficiency due to the large gap between CNN-based models and Transformers regarding the number of parameters and the training settings. In this work, we propose three techniques to improve video understanding with video Transformers. First, to derive better spatiotemporal feature representation, we propose a new spatiotemporal attention scheme, termed synchronized spatiotemporal and spatial attention (SSTSA), which derives the spatiotemporal features with temporal and spatial multiheaded self-attention (MSA) modules. It also preserves the best spatial attention by another spatial self-attention module in parallel, thereby resulting in an effective Transformer encoder. Second, a motion spotlighting module is proposed to embed the short-term motion of the consecutive input frames to the regular RGB input, which is then processed with a single-stream video Transformer. Third, a simple intraclass frame interlacing method of the input clips is proposed that serves as an effective video augmentation method. Finally, our proposed techniques have been evaluated and validated with a set of extensive experiments in this study. Our video Transformer outperforms its previous counterparts on two well-known datasets, Kinetics400 and Something-Something-v2.


Toward Zero-Trust Security for the Metaverse

Abstract: By focusing on immersive interaction among users, the burgeoning Metaverse can be viewed as a natural extension of existing social media. Similar to traditional online social networks, there are numerous security and privacy issues in the Metaverse (e.g., attacks on user authentication and impersonation). In this paper, we develop a holistic research agenda for zero-trust user authentication in social virtual reality (VR), an early prototype of the Metaverse. Our proposed research includes four concrete steps: investigating biometrics-based authentication that is suitable for continuously authenticating VR users, leveraging federated learning (FL) for protecting user privacy in biometric data, improving the accuracy of continuous VR authentication with multimodal data, and boosting the usability of zero-trust security with adaptive VR authentication. Our preliminary study demonstrates that conventional FL algorithms are not well suited for biometrics-based authentication of VR users, leading to an accuracy of less than 10%. We discuss the root cause of this problem, the associated open challenges, and several future directions for realizing our research vision.


DRBM-ClustNet: A Deep Restricted Boltzmann–Kohonen Architecture for Data Clustering

Abstract: A Bayesian deep restricted Boltzmann-Kohonen architecture for data clustering termed deep restricted Boltzmann machine (DRBM)-ClustNet is proposed. This core-clustering engine consists of a DRBM for processing unlabeled data by creating new features that are uncorrelated and have large variance with each other. Next, the number of clusters is predicted using the Bayesian information criterion (BIC), followed by a Kohonen network (KN)-based clustering layer. The processing of unlabeled data is done in three stages for efficient clustering of the nonlinearly separable datasets. In the first stage, DRBM performs nonlinear feature extraction by capturing the highly complex data representation by projecting the feature vectors of d dimensions into n dimensions. Most clustering algorithms require the number of clusters to be decided a priori; hence, here, to automate the number of clusters in the second stage, we use BIC. In the third stage, the number of clusters derived from BIC forms the input for the KN, which performs clustering of the feature-extracted data obtained from the DRBM. This method overcomes the general disadvantages of clustering algorithms, such as the prior specification of the number of clusters, convergence to local optima, and poor clustering accuracy on nonlinear datasets. In this research, we use two synthetic datasets, 15 benchmark datasets from the UCI Machine Learning repository, and four image datasets to analyze the DRBM-ClustNet. The proposed framework is evaluated based on clustering accuracy and ranked against other state-of-the-art clustering methods. The obtained results demonstrate that the DRBM-ClustNet outperforms state-of-the-art clustering algorithms.


Guidance Through Surrogate: Toward a Generic Diagnostic Attack

Abstract: Adversarial training (AT) is an effective approach to making deep neural networks robust against adversarial attacks. Recently, different AT defenses are proposed that not only maintain a high clean accuracy but also show significant robustness against popular and well-studied adversarial attacks, such as projected gradient descent (PGD). High adversarial robustness can also arise if an attack fails to find adversarial gradient directions, a phenomenon known as "gradient masking." In this work, we analyze the effect of label smoothing on AT as one of the potential causes of gradient masking. We then develop a guided mechanism to avoid local minima during attack optimization, leading to a novel attack dubbed guided projected gradient attack (G-PGA). Our attack approach is based on a "match and deceive" loss that finds optimal adversarial directions through guidance from a surrogate model. Our modified attack does not require random restarts a large number of attack iterations or a search for optimal step size. Furthermore, our proposed G-PGA is generic, thus it can be combined with an ensemble attack strategy as we demonstrate in the case of auto-attack, leading to efficiency and convergence speed improvements. More than an effective attack, G-PGA can be used as a diagnostic tool to reveal elusive robustness due to gradient masking in adversarial defenses.


Efficient <i>N:M</i> Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design

Abstract: Sparse training is one of the promising techniques to reduce the computational cost of DNNs while retaining high accuracy. In particular, N:M fine-grained structured sparsity, where only N out of consecutive M elements can be nonzero, has attracted attention due to its hardware-friendly pattern and capability of achieving a high sparse ratio. However, the potential to accelerate N:M sparse DNN training has not been fully exploited, and there is a lack of efficient hardware supporting N:M sparse training. To tackle these challenges, this paper presents a computation-efficient training scheme for N:M sparse DNNs using algorithm, architecture, and dataflow co-design. At the algorithm level, a bidirectional weight pruning method, dubbed BDWP, is proposed to leverage the N:M sparsity of weights during both forward and backward passes of DNN training, which can significantly reduce the computational cost while maintaining model accuracy. At the architecture level, a sparse accelerator for DNN training, namely SAT, is developed to neatly support both the regular dense operations and the computation-efficient N:M sparse operations. At the dataflow level, multiple optimization methods ranging from interleave mapping, pre-generation of N:M sparse weights, and offline scheduling, are proposed to boost the computational efficiency of SAT. Finally, the effectiveness of our training scheme is evaluated on a Xilinx VCU1525 FPGA card using various DNN models and datasets. Experimental results show the SAT accelerator with the BDWP sparse training method under 2:8 sparse ratio achieves an average speedup of 1.75x over that with the dense training, accompanied by a negligible accuracy loss of 0.56% on average. Furthermore, our proposed training scheme significantly improves the training throughput by 2.97~25.22x and the energy efficiency by 1.36~3.58x over prior FPGA-based accelerators.


Latent variable modeling with applications to education assessment and NSF-REU projects for engineering students

Abstract: Abstract The latent variable modeling technique in education assessment is illustrated using problem solving examples in first semester physics for engineering and technology students. The regression modeling of data in the Newton second law laboratory class has equivalence to an analysis with the internal force serving as the latent variable. A path diagram would contain the internal force as a latent variable which manifest its influence on the observed datasets, namely, acceleration and force. Similarly, the pre-score and post-score data collected for education assessment can be studied with latent variable modeling to broaden the scope beyond regression which requires an independent variable. The numerical and flexibility advantages of latent variable modeling versus regression modeling in education assessment are presented. Free LISREL student version working details and free confirmatory factor analysis Microsoft Excel software from university faculty are presented. The adaptation of Queensborough Community College Physics faculty education research into Queensborough NSF-REU projects for engineering students is discussed.


Interpolation-Based Contrastive Learning for Few-Label Semi-Supervised Learning

Abstract: Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels. In the existing literature, consistency regularization-based methods, which force the perturbed samples to have similar predictions with the original ones have attracted much attention for their promising accuracy. However, we observe that the performance of such methods decreases drastically when the labels get extremely limited, e.g., 2 or 3 labels for each category. Our empirical study finds that the main problem lies with the drift of semantic information in the procedure of data augmentation. The problem can be alleviated when enough supervision is provided. However, when little guidance is available, the incorrect regularization would mislead the network and undermine the performance of the algorithm. To tackle the problem, we: 1) propose an interpolation-based method to construct more reliable positive sample pairs and 2) design a novel contrastive loss to guide the embedding of the learned network to change linearly between samples so as to improve the discriminative capability of the network by enlarging the margin decision boundaries. Since no destructive regularization is introduced, the performance of our proposed algorithm is largely improved. Specifically, the proposed algorithm outperforms the second best algorithm (Comatch) with 5.3% by achieving 88.73% classification accuracy when only two labels are available for each class on the CIFAR-10 dataset. Moreover, we further prove the generality of the proposed method by improving the performance of the existing state-of-the-art algorithms considerably with our proposed strategy. The corresponding code is available at https://github.com/xihongyang1999/ICL_SSL.


Maneuvering Target State Estimation in Range-Squared Coordinate

Abstract: In tracking applications without angle measurements, multiple sensors are usually applied. Accurate range-Doppler (R-D) estimates with a single sensor play an important role in clutter eliminating and date fusion in multi-sensor target tracking. In this paper, maneuvering models are first established in the range-squared (RS) coordinate, then the R-D estimation of a maneuvering target is investigated. According to the Cartesian nearly constant acceleration and constant turn models, pseudostate vectors are defined in the RS coordinate and linear time-evolution equations of pseudostate are derived to describe the corresponding maneuvers. The motion models are incorporated into the converted measurement Kalman filter to extract R-D estimates from converted measurements. Corresponding to the models, the filter initialization approach, which utilizes the prior information on motion model and kinematic parameters of target, is developed. Based on the model set in the RS coordinate and the interacting multiple model (IMM) method, a recursive estimator is developed to produce pseudostate estimates of a maneuvering target. Final R-D estimates are provided through the conversion adopting the scaled unscented transformation outside the estimation recursions to avoid the propagation of nonlinearity approximation errors. Numerical experiments in different scenarios demonstrate that the maneuvering models accurately describe the time-evolution of pseudostate and the IMM estimator achieves superior estimation accuracy and consistency thanks to the exploitation of linear motion models.


Mitigating Confounding Bias in Practical Recommender Systems With Partially Inaccessible Exposure Status

Abstract: To improve user experience, recommender systems have been widely used on many online platforms. In these systems, recommendation models are typically learned from positive/negative feedback that are collected automatically. Notably, recommender systems are a little different from general supervised learning tasks. In recommender systems, there are some factors (e.g. previous recommendation models or operation strategies of a online platform) that determine which items can be exposed to each individual user. Normally, the previous exposure results are not only relevant to the instances' features (i.e., user or item), but also affect their feedback ratings, thus leading to confounding bias in the recommendation models. To mitigate this bias, researchers have already provided a variety of strategies. However, there are still two issues that are underappreciated: 1) previous debiased RS approaches cannot effectively capture recommendation-specific, exposure-specific and their common knowledge simultaneously; 2) the true exposure results of the user-item pairs are partially inaccessible, so there would be some noises if we use their observability to approximate it as existing approaches. Motivated by this, we develop a novel debiasing recommendation approach. More specifically, we first propose a mutual information-based counterfactual learning framework based on the causal relationship among the instance features, exposure status, and ratings. This framework can 1) capture recommendation-specific, exposure-specific and their common knowledge by explicitly modeling the relationship among the causal factors, and 2) achieve robustness towards partially inaccessible exposure results by a pairwise learning strategy. Under such a framework, we implement an optimizable loss function with theoretical analysis. By minimizing this loss, we expect to obtain an unbiased recommendation model that reflects the users' real interests. Meanwhile, we also prove that our loss function has robustness towards the partial inaccessibility of the exposure status. Finally, extensive experiments on public datasets manifest the superiority of our proposed method in boosting the recommendation performance.


Bayesian multi-level modelling for predicting single and double feature visual search

Abstract: Performance in visual search tasks is frequently summarised by "search slopes" - the additional cost in reaction time for each additional distractor. While search tasks with a shallow search slopes are termed efficient (pop-out, parallel, feature), there is no clear dichotomy between efficient and inefficient (serial, conjunction) search. Indeed, a range of search slopes are observed in empirical data. The Target Contrast Signal (TCS) Theory is a rare example of quantitative model that attempts to predict search slopes for efficient visual search. One study using the TCS framework has shown that the search slope in a double-feature search (where the target differs in both colour and shape from the distractors) can be estimated from the slopes of the associated single-feature searches. This estimation is done using a contrast combination model, and a collinear contrast integration model was shown to outperform other options. In our work, we extend TCS to a Bayesian multi-level framework. We investigate modelling using normal and shifted-lognormal distributions, and show that the latter allows for a better fit to previously published data. We run a new fully within-subjects experiment to attempt to replicate the key original findings, and show that overall, TCS does a good job of predicting the data. However, we do not replicate the finding that the collinear combination model outperforms the other contrast combination models, instead finding that it may be difficult to conclusively distinguish between them.


Assessment of deep learning-based auto-contouring on interobserver consistency in target volume and organs-at-risk delineation for breast cancer: Implications for RTQA program in a multi-institutional study

Abstract: To quantify interobserver variation (IOV) in target volume and organs-at-risk (OAR) contouring across 31 institutions in breast cancer cases and to explore the clinical utility of deep learning (DL)-based auto-contouring in reducing potential IOV.In phase 1, two breast cancer cases were randomly selected and distributed to multiple institutions for contouring six clinical target volumes (CTVs) and eight OAR. In Phase 2, auto-contour sets were generated using a previously published DL Breast segmentation model and were made available for all participants. The difference in IOV of submitted contours in phases 1 and 2 was investigated quantitatively using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). The qualitative analysis involved using contour heat maps to visualize the extent and location of these variations and the required modification.Over 800 pairwise comparisons were analysed for each structure in each case. Quantitative phase 2 metrics showed significant improvement in the mean DSC (from 0.69 to 0.77) and HD (from 34.9 to 17.9 mm). Quantitative analysis showed increased interobserver agreement in phase 2, specifically for CTV structures (5-19 %), leading to fewer manual adjustments. Underlying IOV differences causes were reported using a questionnaire and hierarchical clustering analysis based on the volume of CTVs.DL-based auto-contours improved the contour agreement for OARs and CTVs significantly, both qualitatively and quantitatively, suggesting its potential role in minimizing radiation therapy protocol deviation.


SLAPP: Subgraph-level attention-based performance prediction for deep learning models

Abstract: The intricacy of the Deep Learning (DL) landscape, brimming with a variety of models, applications, and platforms, poses considerable challenges for the optimal design, optimization, or selection of suitable DL models. One promising avenue to address this challenge is the development of accurate performance prediction methods. However, existing methods reveal critical limitations. Operator-level methods, proficient at predicting the performance of individual operators, often neglect broader graph features, which results in inaccuracies in full network performance predictions. On the contrary, graph-level methods excel in overall network prediction by leveraging these graph features but lack the ability to predict the performance of individual operators. To bridge these gaps, we propose SLAPP, a novel subgraph-level performance prediction method. Central to SLAPP is an innovative variant of Graph Neural Networks (GNNs) that we developed, named the Edge Aware Graph Attention Network (EAGAT). This specially designed GNN enables superior encoding of both node and edge features. Through this approach, SLAPP effectively captures both graph and operator features, thereby providing precise performance predictions for individual operators and entire networks. Moreover, we introduce a mixed loss design with dynamic weight adjustment to reconcile the predictive accuracy between individual operators and entire networks. In our experimental evaluation, SLAPP consistently outperforms traditional approaches in prediction accuracy, including the ability to handle unseen models effectively. Moreover, when compared to existing research, our method demonstrates a superior predictive performance across multiple DL models.


Bayesian uncertainty quantification for anaerobic digestion models

Abstract: Uncertainty quantification is critical for ensuring adequate predictive power of computational models used in biology. Focusing on two anaerobic digestion models, this article introduces a novel generalized Bayesian procedure, called VarBUQ, ensuring a correct tradeoff between flexibility and computational cost. A benchmark against three existing methods (Fisher's information, bootstrapping and Beale's criteria) was conducted using synthetic data. This Bayesian procedure offered a good compromise between fitting ability and confidence estimation, while the other methods proved to be repeatedly overconfident. The method's performances notably benefitted from inductive bias brought by the prior distribution, although it requires careful construction. This article advocates for more systematic consideration of uncertainty for anaerobic digestion models and showcases a new, computationally efficient Bayesian method. To facilitate future implementations, a Python package called 'aduq' is made available.


A deep learning model for Alzheimer’s disease diagnosis based on patient clinical records

Abstract: Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem in older people. The creation of classification models based on AD risk factors using Deep Learning is a promising tool to minimize the impact of under-diagnosis.To develop a Deep Learning model that uses clinical data from patients with dementia to classify whether they have AD.A Deep Learning model to identify AD in clinical records is proposed. In addition, several rebalancing methods have been used to preprocess the dataset and several studies have been carried out to tune up the model.Model has been tested against other well-established machine learning techniques, having better results than these in terms of AUC with alpha less than 0.05.The developed Neural Network Model has a good performance and can be an accurate assisting tool for AD diagnosis.


Natural Language Processing: Chances and Challenges in Dentistry

Abstract: : Natural language processing (NLP) is an intersection between Computer Science and Linguistic which aims to enable machines to process and understand human language. We here summarized applications and limitations of NLP in dentistry. Narrative review. NLP has evolved increasingly fast. For the dental domain, relevant NLP applications are text classification (e.g., symptom classification) and natural language generation and understanding (e.g., clinical chatbots assisting professionals in office work and patient communication). Analyzing large quantities of text will allow understanding diseases and their trajectories and support a more precise and personalized care. Speech recognition systems may serve as virtual assistants and facilitate automated documentation. However, to date, NLP has rarely been applied in dentistry. Existing research focuses mainly on rule-based solutions for narrow tasks. Technologies such as Recurrent Neural Networks and Transformers have been shown to surpass the language processing capabilities of such rule-based solutions in many fields, but are data-hungry (i.e., rely on large amounts of training data), which limits their application in the dental domain at present. Technologies such as federated or transfer learning or data sharing concepts may allow to overcome this limitation, while challenges in terms of explainability, reproducibility, generalizability and evaluation of NLP in dentistry remain to be resolved for enabling approval of such technologies in medical devices and services. NLP will become a cornerstone of a number of applications in dentistry. The community is called to action to improve the current limitations and foster reliable, high-quality dental NLP. NLP for text classification (e.g., dental symptom classification) and language generation and understanding (e.g., clinical chatbots, speech recognition) will support administrative tasks in dentistry, provide deeper insights for clinicians and support research and education.


A hybrid facial features extraction-based classification framework for typhlotic people

Abstract: Facial features play a vital role in the real-time cloud-based applications. Since, most of the conventional models are difficult to detect heterogeneous facial features due to high computational memory and time for the internet of things (IoT) based video surveillance mechanisms. Video based facial features identification and extraction include a large number of candidates features which are difficult to detect the contextual similarity of the facial key points due to noise and computational memory. In order to resolve these issues, a hybrid multiple features extraction measures are implemented on the real-time video dataset to extract key points using the cloud-based classifier. In this work, a hybrid classifier is used to classify the key facial points in the cloud computing environment. Experimental results show that the proposed hybrid multiple feature extraction-based frameworks have better computational efficiency in terms of error rate, recall, precision, and accuracy than the conventional models.


Ultrasonic sensor decision-making algorithm for mobile robot motion in maze environment

Abstract: An autonomous mobile robot is one that can move from one location to another without the intervention of a human. A maze environment is a complex environment since it contains many obstacles and the major problem is moving through it. To avoid obstacles while moving through the maze, the mobile robot must be designed with an algorithm. This work proposes a decision-making system with an ultrasonic sensor to allow the developed autonomous mobile robot to avoid obstacles in any maze setting through its movements. The maze was designed with a size of 100×200 cm2 for the case study. Due to the height dimension of the barrier (20 cm) and the height dimension of the mobile robot including the ultrasonic sensor (20 cm), a distance of 20 cm was taken between the wall (obstacle) and the sensor. The result distance between the (wood wall) object and the sensor indicates that it is a reasonable distance chosen for the mobile robot to move and turn with flexibility as it travels through the maze environment from 0 cm to 300 cm. This mobile robot path took 1 minute to finish at a speed of 5 cm/sec, indicating that it is a quick algorithm as compared with related work.


A deep learning based architecture for malaria parasite detection

Abstract: During last decade, medical imaging has attracted great deal of research interests. Deep learning applications has revolutionized medical image analysis and diseases diagnosis. Convolutional neural networks (CNNs)-a class of deep learning-have been widely used for classification and feature extraction, and they revealed good performance for various imaging applications. However, despite the advances in medicine, malaria remains among the world’s deadliest diseases. Only in 2020, malaria recorded 241 million clinical episodes, and 627,000 deaths. The disease is examined visually through a microscope, which depends on the pathologists experience and skills and results may vary in different laboratories. This paper proposes an efficient CNN architecture that could be used in diagnosing of malaria disease. By processing on 27,558 red blood smear cell images with balanced samples of parasitized and unparasitized cells on a publicly available malaria dataset from the National Institute of Health, the proposed model achieves high accuracy rate with 99.8%, 98.2, and 97.7% for training, validation and testing sets. Furthermore, the statistical results approve that the proposed model is outperforming the state-of-the-art models.


Implementing and developing multi-stage cryptography technique for low-cost long-range communication system

Abstract: The requirement for a secure emergency communication system has become imperative in tandem with the industrial revolution. Additionally, the development of technology has led to increasingly robust penetration techniques that pose a threat to communication system security, leaving data vulnerable to unwanted third parties. This paper introduces a novel, powerful security approach that ensures a secure emergency communication system. Moreover, this research focuses on several cryptographic techniques among various symmetric and asymmetric ciphers, including advanced encryption standards, substitution, and transposition. The article presents an affordable and secure communication system that can transmit data over long distances with low power consumption using long-range technology. This system features a unique function that transmits updated locations, directing rescuers to the designated location.


High capacity double precision image steganography based on chaotic maps

Abstract: Steganography is the process of hiding confidential information within non-secret multimedia such that the 3rd party cannot distinguish if there is a secret message in it or not. Whereas cryptography is the technique of using mathematical concepts to convert information into unreadable codes via a key. This paper will propose two approaches, lossless and lossy image steganography. Both of them will use cryptography and steganography based on three different chaotic maps to ensure information security. In the cryptography part, two chaotic maps will be used to encrypt the secret information, while in the steganography section, one chaotic map is used to embed the message. The secret information will be concealed in the least significant bits (LSBs) of the double-precision image’s pixels. The double precision image is a high-quality image and can be represented in 64 bits per pixel for grayscale images, leading to a very high redundant bit. Simulation results show a high embedding capacity of 60.938% and 400% for lossless and lossy approaches respectively with a peak signal to noise ratio (PSNR) reach of 69.964 dB. Furthermore, this system is extremely secure due to the use of 3 chaotic maps with key space 2448.


Machine learning prediction for academic misconduct prediction: an analysis of binary classification metrics

Abstract: Academic misconduct is unethical behavior in academic work. To sustain integrity culture and mitigating unethical conducts among higher education institutions community, the academic misconduct detection must be done at an earlier stage. Thus, this study attempted to provide a new empirical contribution with the analysis of binary classification performances metrics to describe the ability of machine learning in predicting academic misconduct. Four machine learning algorithms have been used namely generalized linear model (GLM), logistic regression (LR), decision tree (DT), and random forest (RF). Beside performances comparison, this paper presents the analysis of academic misconduct factors that were constructed based on demography and fraud triangle theory (FTT). The findings showed that all the four machine learning algorithms have obtained good ability in the prediction models with the accuracy at above 80% and below 20% of the classification errors. Rationalization from the FTT attributes has shown as the most important factor in GLM, LR, and DT. In RF, opportunity of FTT attributes have become the most important. Compared to FTT attributes, demography attributes were not providing much benefits to all the machine learning models but remain applicable at very low weight correlations.


IndoPolicyStats: sentiment analyzer for public policy issues

Abstract: The government requires some vaccination for public health. This has led to a debate in recent years, especially during the Covid-19 pandemic. This research aims to analyze the two sentiments of the public regarding the vaccination policy. This would be helpful to ensure the acceptance of the government campaign about vaccination. The data used was text data obtained from Twitter when Indonesia was facing the second wave of the Covid-19 pandemic. The data were pre-processed by removing noise data, case folding, stemming, and tokenizing. Then, the data were classified with random forest, Naïve Bayes, and XGBoost. The results showed that all classifiers exhibit satisfying performance but XGBoost performs slightly better in accuracy value. This method can be deployed to be an automatic sentiment analyzer to help the government understand public feedback about its policies. This would be given by proper pre-processing and enough datasets.


Dataset of user evaluations of prototypicality, aesthetics, usability and trustworthiness of homepages of banking, e-commerce and university websites

Abstract: The dataset contains full-page screenshots of homepages of commercial banking (N = 1033), online shopping (N = 1064), and university (N = 1059) websites, as well as the raw and aggregated user ratings of webpage design prototypicality, visual aesthetics, perceived usability and trustworthiness, and user demographic information. Design prototypicality was measured with three items, including typicality, exemplar goodness, and family resemblance, whereas the other design dimensions were measured with a single item each. Amazon Mechanical Turk crowdworkers (N = 3319 rating sessions) provided their demographic data and rated the homepages online. The demographic data have been anonymized, with generated unique participant IDs replacing MTurk crowdworker IDs. The screenshots are identified with generated IDs to provide partial anonymization for the websites, limiting their potential misuse outside design-related or user experience-related academic research. The raw rating data contain all collected ratings, whereas the aggregated data contain the per-webpage, per-dimension ratings derived solely from the ratings of study-compliant crowdworkers. The non-compliance among crowdworkers was detected based on several indicators, including rate-rerate consistency, seen-unseen webpage recognition, free-form feedback analyses, demographic data analyses, and other indicators. Future research could utilize the dataset either in user studies that require full-page webpages as stimuli, e.g., studies on the determinants of first impression, user preference, and user experience, or in computational research on web design, including computational aesthetics, as this type of research requires a large number of user-rated webpages, which this dataset provides.


Dual-correlate optimized coarse-fine strategy for monocular laparoscopic videos feature matching via multilevel sequential coupling feature descriptor

Abstract: Feature matching of monocular laparoscopic videos is crucial for visualization enhancement in computer-assisted surgery, and the keys to conducting high-quality matches are accurate homography estimation, relative pose estimation, as well as sufficient matches and fast calculation. However, limited by various monocular laparoscopic imaging characteristics such as highlight noises, motion blur, texture interference and illumination variation, most exiting feature matching methods face the challenges of producing high-quality matches efficiently and sufficiently. To overcome these limitations, this paper presents a novel sequential coupling feature descriptor to extract and express multilevel feature maps efficiently, and a dual-correlate optimized coarse-fine strategy to establish dense matches in coarse level and adjust pixel-wise matches in fine level. Firstly, a novel sequential coupling swin transformer layer is designed in feature descriptor to learn and extract multilevel feature representations richly without increasing complexity. Then, a dual-correlate optimized coarse-fine strategy is proposed to match coarse feature sequences under low resolution, and the correlated fine feature sequences is optimized to refine pixel-wise matches based on coarse matching priors. Finally, the sequential coupling feature descriptor and dual-correlate optimization are merged into the Sequential Coupling Dual-Correlate Network (SeCo DC-Net) to produce high-quality matches. The evaluation is conducted on two public laparoscopic datasets: Scared and EndoSLAM, and the experimental results show the proposed network outperforms state-of-the-art methods in homography estimation, relative pose estimation, reprojection error, matching pairs number and inference runtime. The source code is publicly available at https://github.com/Iheckzza/FeatureMatching.


How word semantics and phonology affect handwriting of Alzheimer’s patients: A machine learning based analysis

Abstract: Using kinematic properties of handwriting to support the diagnosis of neurodegenerative disease is a real challenge: non-invasive detection techniques combined with machine learning approaches promise big steps forward in this research field. In literature, the tasks proposed focused on different cognitive skills to elicitate handwriting movements. In particular, the meaning and phonology of words to copy can compromise writing fluency. In this paper, we investigated how word semantics and phonology affect the handwriting of people affected by Alzheimer's disease. To this aim, we used the data from six handwriting tasks, each requiring copying a word belonging to one of the following categories: regular (have a predictable phoneme-grapheme correspondence, e.g., cat), non-regular (have atypical phoneme-grapheme correspondence, e.g., laugh), and non-word (non-meaningful pronounceable letter strings that conform to phoneme-grapheme conversion rules). We analyzed the data using a machine learning approach by implementing four well-known and widely-used classifiers and feature selection. The experimental results showed that the feature selection allowed us to derive a different set of highly distinctive features for each word type. Furthermore, non-regular words needed, on average, more features but achieved excellent classification performance: the best result was obtained on a non-regular, reaching an accuracy close to 90%.


Health-aware food recommendation system with dual attention in heterogeneous graphs

Abstract: Recommender systems (RS) have been increasingly applied to food and health. However, challenges still remain, including the effective incorporation of heterogeneous information and the discovery of meaningful relationships among entities in the context of food and health recommendations. To address these challenges, we propose a novel framework, the Health-aware Food Recommendation System with Dual Attention in Heterogeneous Graphs (HFRS-DA), for unsupervised representation learning on heterogeneous graph-structured data. HFRS-DA utilizes an attention technique to reconstruct node features and edges and employs a dual hierarchical attention mechanism for enhanced unsupervised learning of attributed graph representations. HFRS-DA addresses the challenge of effectively leveraging the heterogeneous information in the graph and discovering meaningful semantic relationships between entities. The framework analyzes recipe components and their neighbours in the heterogeneous graph and can discover popular and healthy recipes, thereby promoting healthy eating habits. We compare HFRS-DA using the Allrecipes dataset and find that it outperforms all the related methods from the literature. Our study demonstrates that HFRS-DA enhances the unsupervised learning of attributed graph representations, which is important in scenarios where labelled data is scarce or unavailable. HFRS-DA can generate node embeddings for unused data effectively, enabling both inductive and transductive learning.


Learning with centered reproducing kernels

Abstract: Kernel-based learning algorithms have been extensively studied over the past two decades for their successful applications in scientific research and industrial problem-solving. In classical kernel methods, such as kernel ridge regression and support vector machines, an unregularized offset term naturally appears. While its importance can be defended in some situations, it is arguable in others. However, it is commonly agreed that the offset term introduces essential challenges to the optimization and theoretical analysis of the algorithms. In this paper, we demonstrate that Kernel Ridge Regression (KRR) with an offset is closely connected to regularization schemes involving centered reproducing kernels. With the aid of this connection and the theory of centered reproducing kernels, we will establish generalization error bounds for KRR with an offset. These bounds indicate that the algorithm can achieve minimax optimal rates.


Accurate weight coefficient estimation of multi-camera light field PIV through backpropagation neural network

Abstract: Volumetric velocity measurement through multi-camera light field particle image velocimetry (LF-PIV) requires an accurate estimation of the weight coefficient (WC) of three-dimensional (3D) tracer particle distribution reconstruction. To achieve that, this study proposes a calibration method based on a backpropagation neural network (BP-NN) for the WC estimation of the multi-camera LF-PIV. The BP-NN model establishes a mapping relationship between the spatial voxels and pixels of the multi-cameras. The proposed method is compared with the direct ray tracing (DRT) method and it shows that the proposed method provides an accurate estimation of the WC. It also does not depend on the prior knowledge of angle separations of the multi-cameras as is required for the DRT method. The proposed method is initially evaluated by conducting synthetic tests of ring vortex field reconstruction and further verified by conducting experiments on a low-swirl injector (LSI) flow. Results show that the root mean square error of the ring vortex displacement field can be reduced from 0.71 voxels to 0.35 voxels by the proposed method. The relative errors of LSI flow axial and radial velocity components are smaller than 10%. Therefore, it demonstrates that the 3D flow velocity can be measured accurately by the multi-camera LF-PIV by incorporating the proposed BP-NN calibration method.


Thickness and surface estimation of thin porous media based on 3D image data

Abstract: Abstract Measuring the thickness of thin porous materials provides valuable insights into their structure, properties, and performance, including key properties such as porosity and permeability, and is highly beneficial for a range of industrial applications, particularly for ensuring effective quality control processes. A novel approach for estimating the thickness of porous media and their surfaces is proposed based on voxel sets of 3D images, such as 3D scans and segmented scan data. Initially, the solid volume fraction (SVF) is computed for each voxel layer perpendicular to the through direction. Then, fitting functions consisting of piecewise linear segments are chosen to ensure an accurate representation of the layer data. Each function is associated with various thickness regions of the medium, including the medium itself and its surface. An optimization problem is then solved to find the best-fitting function based on the squared area between the SVF and the fitting function. The thickness of the medium and its surfaces is determined based on the identified optimal fit. This robust, reliable, and fast approach aims to provide not only a non-intrusive method for thickness estimation of porous media represented by voxel sets but also a precise alternative to existing methodologies.


Multi-image optical information hiding algorithm based on Fourier transform without hidden key transmission

Abstract: Existing optical information hiding algorithms for multiple images require generating hidden keys for embedded images, resulting in the transmission of numerous keys. This challenge undermines the usage of these algorithms in low-quality networks. To address this issue and enhance transmission efficiency, we present a multi-image optical information hiding algorithm based on Fourier transformation principles, which is employed to generate hidden frequency maps and carrier frequency maps. Specific low-frequency information zones are extracted within these hidden frequency maps. A chaotic system integrates a phase mask, modulated with the low-frequency regions, positioned in the carrier frequency map’s high-frequency sector. The final stego image is obtained by subjecting the carrier frequency map to inverse Fourier transformation. Experimental analysis shows that concealing three images takes only 0.0089 s, with extraction requiring 0.0658 s. Post-extraction PSNR values for hidden images exceed 32 dB. Robustness and anti-attack experiments were done to prove the security of this algorithm. The compared experiments between the proposed method and other state-of-the-art algorithms affirm the algorithm’s attributes of simplicity, ease of implementation, robust security, and high efficiency. Importantly, the restoration process eliminates the necessity of transmitting hidden keys, reducing network burdens and enhancing both concealment and extraction efficiencies significantly.


Trace element geochemistry of high-temperature geothermal waters in the Yunnan-Tibet geothermal province, Southwest China

Abstract: To investigate the origins of the trace elements and related geochemical processes, seven representative geothermal systems in southern Tibet and one in western Yunnan were selected. The geothermal waters were divided into acidic and neutral-to-alkaline in magmatic geothermal systems and neutral-to-alkaline in non-magmatic geothermal systems. Acidic geothermal waters exhibit low concentrations of most trace elements. In the neutral-to-alkaline geothermal waters in magmatic geothermal systems, the composition of most trace elements is determined by the water–rock interactions, along with the solubility of secondary minerals, with magmatic fluids contributing to other trace elements (e.g., B, As, Sb, Mo and W). As for the geothermal waters of non-magmatic geothermal systems, trace elements are primarily derived from carbonate reservoir rock dissolution. Trace elements of magmatic origin are more abundant in the Tibetan geothermal waters than in those of Yunnan, which results from the varied chemical composition of their underlying magma chambers. Our findings indicate that the trace element geochemistry has the potential to identify the genesis of geothermal systems.


Multiuser medical image encryption algorithm using phase-only CGH in the gyrator domain

Abstract: In this paper, a multiuser medical image encryption algorithm is proposed. The proposed algorithm utilizes polar decomposition, which enables multiuser features in the proposed algorithm. A computer-generated hologram (CGH) improves the security of the proposed algorithm in the gyrator domain. The phase-only CGH-based multiuser algorithm offers advantages such as storing a large amount of information in a compact space, resistance to counterfeiting, and enhanced security. The proposed method is validated with various statistical metrics, such as information entropy, mean squared error, correlation coefficient, histogram, and mesh plots. Results confirm that the proposed algorithm is secure and robust against potential attacks, such as plaintext attacks, iterative attacks, and contamination attacks. The proposed method has a large keyspace, which makes it very difficult to be breached in real-time with existing computational power.


Light field camera calibration and point spread function calculation based on differentiable ray tracing

Abstract: The imaging process of the light field (LF) camera with a micro-lens array (MLA) may suffer from multiple aberrations. It is thus difficult to precisely calibrate the intrinsic hardware parameters and calculate the corresponding point spread function (PSF). To build an aberration-aware solution with better generalization, we propose an end-to-end imaging model based on the differentiable ray tracing. The input end is the point source location, and the output end is the rendered LF image, namely, PSF. Specially, a projection method is incorporated into the imaging model, eliminating the huge memory overhead induced by a large array of periodic elements. Taking captured PSF images as the ground truth, the LF camera is calibrated with the genetic algorithm initially and then the gradient-based optimization. This method is promising to be used in various LF camera applications, especially in challenging imaging conditions with severe aberrations.


Exploring EFL learners’ acceptance and cognitive absorption at VR-Based language learning: A survey and experimental study

Abstract:
Abstract
 This study aimed to explore the applicability of VR-based language learning in an EFL context. An online survey was conducted to understand the structural relationship between EFL learners' cognitive absorption, behavioral intention to use VR for English learning, and perceptions regarding the sense of immersion created by VR. The hedonic motivation system adoption model (HMSAM) was adopted, and 230 valid responses were retrieved for statistical analyses. The results showed that most constructs of HMSAM, namely, perceived ease of use, perceived usefulness, curiosity, joy, control, behavioral intention to use, and immersion, were significantly associated with other constructs. VR's immersion had a positive significant influence on the behavioral intention of EFL learners to engage in VR-based language learning. It was revealed that curiosity was not a significant predictor for immersion. Moreover, a within-subject neurophysiological experiment was conducted with 33 EFL learners who experienced both VR-based and non-VR-based settings to examine the influence of VR technologies on their cognitive absorption and learning outcomes. Results demonstrated that VR did increase the participants' cognitive absorption; furthermore, participants had better retention about the learned contents in VR-based setting. The findings have practical and theoretical implications based on the findings of the survey and experiment.


Time-aware forecasting of search volume categories and actual purchase

Abstract: The new e-commerce field has attracted businesses of all sizes, retailers, and individuals. Consequently, there is an ongoing necessity for applications that can offer predictions on trending products and optimal selling time. This research suggests aiding businesses in forecasting demand for various product categories by employing data mining algorithms on multivariate time series data. To ensure the most recent information, real-time data was gathered through APIs to build the first block in this research. While search volume was derived from the Keywords Everywhere tool, Amazon's search volume was derived from the Helium 10 tool and external features about actual purchased data. The harvested raw datasets went through multiple processes to generate the dataset and were validated. The models XGBoost, Linear Regression, Random Forest, long-short-term memory, and K-nearest neighbor were employed to predict the trends, and the performance is demonstrated using evaluation metrics, namely Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). Overall, Linear Regression outperformed, especially at a correlation coefficient of 0.9, with R2 = 90.688, MAE = 0.038, MSE = 0.003, and RMSE = 0.057. KNN outperformed on correlation coefficient of 0.7, R2 = 85.129, MAE = 0.045, MSE = 0.005, and RMSE = 0.068. XGBoost produced the best results with a correlation coefficient of 0.9, yielding R2 = 85.89, MAE = 0.042, MSE = 0.004, and RMSE = 0.062. Random Forest, on the other hand, achieves peak metrics with a correlation coefficient of 0.6, R2 = 84.854, MAE = 0.041, MSE = 0.004, and RMSE = 0.066.


Efficient and accurate personalized product recommendations through frequent item set mining fusion algorithm

Abstract: In the realm of personalized product recommendation, addressing the challenges of sparse data and "cold start" has been the primary focus. However, filtering invalid information amidst the overwhelming data on e-commerce platforms remains an underexplored issue. This paper proposes a fusion recommendation algorithm based on frequent item set mining to tackle this problem by compressing the commodity data set and identifying the frequent commodity set. The algorithm not only improves time efficiency by reducing the number of candidate frequent item sets but also generates more accurate recommendations by calculating user-commodity interest rankings and recommending similar products. We first present the existing problems in fusion recommendation algorithms based on frequent item set mining, such as redundant rules, low recommendation accuracy, and the inability to explore deep connections between users and products. Next, we introduce our proposed algorithm, which involves filtering the commodity data set, calculating user-commodity interest rankings, and defining similar product recommendation rules. The algorithm's effectiveness is demonstrated by its ability to adapt to users' dynamic preferences and capture their changing interests in real-time. A comparative analysis using our algorithm and other data mining algorithms reveals a reduction in the number of frequent commodity data sets and weighted frequent item sets, leading to decreased algorithm operation time. This research contributes to the development of more efficient and accurate personalized product recommendation algorithms, enhancing user experience on e-commerce platforms.


The Formal Solutions of Diophantine Equation ag = bx + c

Abstract: We develop a novel method to completely solve the 3-term partial exponential Diophantine equation that represents a generalization of the standard discrete logarithm problem. Our method not only reveals the internal structure of the equation's solution and yields a numerical algorithm to solve it systematically, but also provides an alternative approach to the discrete logarithm problem.


Real-time binocular visual localization system based on the improved BGNet stereo matching framework

Abstract: Binocular vision technology is widely used to acquire three-dimensional information of images because of its low cost. In recent years, the use of deep learning for stereo matching has shown promising results in improving the measurement stability of binocular vision systems, but the real-time performance in high-precision networks is typically poor. Therefore, this study constructed a deep-learning-based stereo matching binocular vision system based on the BGLGA-Net, which combines the advantages of past networks. Experiments showed that the ability to detect the edges of foreground objects was enhanced. The network was used to build a system on the Xavier NX. The measurement accuracy and stability were better than those of traditional algorithms.


CMBEE: A constraint-based multi-task learning framework for biomedical event extraction

Abstract: Event extraction plays a crucial role in natural language processing. However, in the biomedical domain, the presence of nested events adds complexity to event extraction compared to single events, and these events usually have strong semantic relationships and constraints. Previous approaches ignored the binding connections between these complex nested events. This study aims to develop a unified framework based on event constraint information that jointly extract biomedical event triggers and arguments and enhance the performance of nested biomedical event extraction. We propose a multi-task learning framework based on constraint information called CMBEE for the task of biomedical event extraction. The N-tuple form of event patterns is used to represent the constrained information, which is integrated into role detection and event type classification tasks. The framework use attention mechanism and gating mechanism to explore the fusion of multiple tuple information, as well as local and global constrained information fusion methods to dig further into the connections between events. Experimental results demonstrate that our proposed method achieves the highest F1 score on a multilevel event extraction biomedical (MLEE) corpus and performs favorably on the biomedical natural language processing shared task 2013 Genia event corpus (GE 13). The experimental results indicate that modeling event patterns and constraints for multi-event extraction tasks is effective for complex biomedical event extraction. The fusion strategy proposed in this study, which incorporates different constraint information, helps to better express semantic information.


Decision support framework for IT project manager recruitment

Abstract:
Abstract
 Information technology project managers (IT PM) have a critical influence on IT project success while attracting and selecting the right IT PM is challenging. We followed a four-level research design and firstly developed a taxonomy as an input for a cluster analysis to identify patterns in IT PM job advertisements. Based hereon, we developed a decision support framework for IT PM recruitment. We evaluated our findings in an online survey. We identified multiple design elements for IT PM job advertisements within five perspectives and deduced five IT PM archetypes. The decision support framework uses five questions to assist IT PM recruitment. We expand the knowledge base and consider not only IT PM requirements but also benefits. Our decision support framework is the first to holistically support IT PM recruitment, supports recruitment managers in structuring job interviews, identifies potential matches between applicants and recruiters, and assists in the final selection.


Loom Pedals: Retooling Jacquard Weaving for Improvisational Design Workflows

Abstract: We present the Loom Pedals, an open-source hardware/software interface for enhancing a weaver's ability to create on-the-fly, improvised designs in Jacquard weaving. Learning from traditional handweaving and our own weaving experiences, we describe our process of designing, implementing, and using the prototype Loom Pedals system with a TC2 Digital Jacquard loom. The Loom Pedals include a set of modular, reconfigurable foot pedals which can be mapped to parametric Operations that generate and transform digital woven designs. Our novel interface integrates design and loom control, providing a customizable workflow for playful, improvisational Jacquard weaving. We conducted a formative evaluation of the prototype through autobiographical methods and collaboratively developed future Loom Pedals features. We contribute our prototype, design process, and conceptual reflections on weaving as a human-machine dialog between a weaver, the loom, and many other agents.


Addressing machine learning challenges with microcomputing and federated learning

Abstract: In this issue of The Lancet Digital Health, Andrew A S Soltan and colleagues1Soltan AAS Thakur A Yang J et al.A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.Lancet Digital Health. 2024; 6: e93-104Summary Full Text Full Text PDF Google Scholar address a major challenge of artificial intelligence (AI) development through the use of microcomputing and federated learning. Access to diverse training data, rather than computing power, is the limiting factor for many machine learning problems, which Soltan and colleagues overcome using a full-stack pipeline on a preconfigured and inexpensive microcomputer that enabled federated learning across four hospital sites for COVID-19 prediction. Concerns that machine learning predictions are biased because of limitations of the available data mean that many of the transformational changes promised by artificial intelligence have not yet been realised. Machine learning models trained with insufficient or unrepresentative data have the potential to cause patient harm as a result of inaccurate predictions.2Wiens J Saria S Sendak M et al.Do no harm: a roadmap for responsible machine learning for health care.Nat Med. 2019; 25: 1337-1340Crossref PubMed Scopus (370) Google Scholar Data availability and diversity can be improved through federated learning to improve model robustness. Soltan and colleagues1Soltan AAS Thakur A Yang J et al.A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.Lancet Digital Health. 2024; 6: e93-104Summary Full Text Full Text PDF Google Scholar used federated learning to address data needs by federating four sites to increase patient recruitment numbers and improve model training, while simultaneously preserving local data control and patient privacy. Their full-stack pipeline achieved the largest secondary-care federated learning study to date in terms of patient numbers, using data from 130 941 patients (1772 of whom were COVID-19-positive) for training and 32 986 patients (3549 COVID-19-positive) for evaluation. Soltan and colleagues1Soltan AAS Thakur A Yang J et al.A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.Lancet Digital Health. 2024; 6: e93-104Summary Full Text Full Text PDF Google Scholar deployed federated learning at four hospital groups, in Oxford, Birmingham, Bedfordshire, and Portsmouth. Federated learning improved the area under the receiver operating characteristic curve (AUROC) from 0·574 [95% CI 0·560–0·589] to 0·872 [0·862–0·882] at Oxford and from 0·622 [0·608–0·637] to 0·876 [0·865–0·886] at Portsmouth—a substantial improvement through multi-site training. The final federated learning model showed robustness during model evaluation, with a high AUROC of 0·917 [0·893–0·942] at Bedfordshire. In addition to addressing the large amount of data required for machine learning, model training requires the use of advanced computing architectures that incorporate relevant hardware and software.3Hu Y, Liu Y, Liu Z. A survey on convolutional neural network accelerators: GPU, FPGA and ASIC. 2022 14th International Conference on Computer Research and Development; Jan 7–9, 2022 (pp 100–07).Google Scholar Machine learning frequently involves expensive computer hardware using a Compute Unified Device Architecture (CUDA)-enabled graphics processing unit for highly parallelised computing to optimise training time. The model developed by Soltan and colleagues1Soltan AAS Thakur A Yang J et al.A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.Lancet Digital Health. 2024; 6: e93-104Summary Full Text Full Text PDF Google Scholar runs on a microcomputer, the Raspberry Pi, which is inexpensive and can be shipped to additional sites. An inexpensive microcomputer can also easily be physically destroyed at the end of a clinical project for patient privacy and security, which is occasionally a requirement for clinical studies, such as in this work. The algorithm used by Soltan and colleagues1Soltan AAS Thakur A Yang J et al.A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.Lancet Digital Health. 2024; 6: e93-104Summary Full Text Full Text PDF Google Scholar analysed data that are routinely collected within 1 h of a patient arriving at the hospital: vital signs, full blood count, liver function tests, urea and electrolytes, and C-reactive protein concentrations. They concluded that the most impactful features for model prediction were the oxygen delivery device for the logistic regression model and the eosinophil count for the deep neural network model, similar to results obtained in clinical studies that did not involve AI.4Lindsley AW Schwartz JT Rothenberg ME Eosinophil responses during COVID-19 infections and coronavirus vaccination.J Allergy Clin Immunol. 2020; 146: 1-7Summary Full Text Full Text PDF PubMed Scopus (224) Google Scholar This finding suggests that such machine learning methods could be useful for biomarker refinement to optimise clinical trials. Federated learning removes the requirement for identifiable patient data to leave a site for multi-site model training, alleviating a number of ethical, security, and privacy concerns. Because these concerns—in addition to data and model licensing considerations—cannot be entirely removed through federated learning, the number or rate of new studies that can be conducted with machine learning and federated learning is still limited. These concerns are a probable reason why additional sites were not incorporated to match the numbers used in several other federated learning studies.5Dayan I Roth HR Zhong A et al.Federated learning for predicting clinical outcomes in patients with COVID-19.Nat Med. 2021; 27: 1735-1743Crossref PubMed Scopus (246) Google Scholar, 6Bai X Wang H Ma L et al.Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.Nat Mach Intell. 2021; 3: 1081-1089Crossref Scopus (22) Google Scholar Further work incorporating additional sites would more fully realise the potential of the method developed by Soltan and colleagues.1Soltan AAS Thakur A Yang J et al.A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.Lancet Digital Health. 2024; 6: e93-104Summary Full Text Full Text PDF Google Scholar The microcomputing approach used here has two major drawbacks. Raspberry Pi microcomputers can have security vulnerabilities that require consideration when using sensitive data.7Sainz-Raso J Martin S Diaz G Castro M Security vulnerabilities in Raspberry Pi—analysis of the system weaknesses.IEEE Consum Electron Mag. 2019; 8: 47-52Crossref Scopus (8) Google Scholar Soltan and colleagues1Soltan AAS Thakur A Yang J et al.A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.Lancet Digital Health. 2024; 6: e93-104Summary Full Text Full Text PDF Google Scholar address security through a firewall and a pre-agreed port. Health-care data, such as imaging5Dayan I Roth HR Zhong A et al.Federated learning for predicting clinical outcomes in patients with COVID-19.Nat Med. 2021; 27: 1735-1743Crossref PubMed Scopus (246) Google Scholar, 6Bai X Wang H Ma L et al.Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.Nat Mach Intell. 2021; 3: 1081-1089Crossref Scopus (22) Google Scholar or genomics8Stephens ZD Lee SY Faghri F et al.Big data: astronomical or genomical?.PLoS Biol. 2015; 13e1002195 Crossref PubMed Scopus (870) Google Scholar data, can easily overwhelm the computing cores and memory capacity of a Raspberry Pi, making microcomputing impractical; however, high-end computing architecture was not necessary for achieving the results in this work. With these considerations in mind, the work of Soltan and colleagues1Soltan AAS Thakur A Yang J et al.A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.Lancet Digital Health. 2024; 6: e93-104Summary Full Text Full Text PDF Google Scholar enabled multi-site machine learning to improve model training, which will help to achieve transformational changes in health-care. Full-stack microcomputing solutions can reach hospitals that otherwise do not conduct dedicated research yet hold patient information that is important for improving disease prediction. This inexpensive solution can enable rapid deployment and eliminates the need for specialist technical expertise, which is advantageous considering that hospitals vary in their data infrastructure.9Hertzum M Ellingsen G The implementation of an electronic health record: comparing preparations for Epic in Norway with experiences from the UK and Denmark.Int J Med Inform. 2019; 129: 312-317Crossref Scopus (27) Google Scholar As the code is provided online, researchers can fully detail the installation and setup processes involved in the work of Soltan and colleagues1Soltan AAS Thakur A Yang J et al.A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.Lancet Digital Health. 2024; 6: e93-104Summary Full Text Full Text PDF Google Scholar to replicate or develop it further. This illustration of microdevice federated learning helps to pave the way for further applications of this technology, such as with wearables that collect physiological data.10Wang W Li X Qiu X Zhang X Brusic V Zhao J A privacy preserving framework for federated learning in smart healthcare systems.Inf Process Manage. 2023; 60103167 Google Scholar In summary, federated learning, microdevices, and open-source code, as used by Soltan and colleagues,1Soltan AAS Thakur A Yang J et al.A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.Lancet Digital Health. 2024; 6: e93-104Summary Full Text Full Text PDF Google Scholar are welcomed for developing clinical studies with federated multi-site model training for the purpose of improving patient outcomes. I receive support from the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014), the Myrovlytis Trust (MT22_15_Marciniak), Cancer Research UK (A25922), and Addenbrooke's Charitable Trust, and have industrial partners including Rhino Health, the Asher Orion Group, GE Healthcare, GlaxoSmithKline, and AstraZeneca. I have recently held funding from the European Union's Horizon 2020 research and innovation programme (grant agreement no 761214). I am a former president of the Postdoctoral Society of Cambridge and held a committee role in the UK Research Staff Association. I do not receive personal fees nor benefits that could be considered a conflict of interest. The views expressed are my own and not those of the funders and partners. A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitalsWe developed an embedded system for federated learning, using microcomputing to optimise for ease of deployment. We deployed full-stack federated learning across four UK hospital groups to develop a COVID-19 screening test without centralising patient data. Federation improved model performance, and the resultant global models were generalisable. Full-stack federated learning could enable hospitals to contribute to AI development at low cost and without specialist technical expertise at each site. Full-Text PDF Open Access


Discrete residual diffusion model for high-resolution prostate MRI synthesis

Abstract: Objective.High-resolution magnetic resonance imaging (HR MRI) is an effective tool for diagnosing PCa, but it requires patients to remain immobile for extended periods, increasing chances of image distortion due to motion. One solution is to utilize super-resolution (SR) techniques to process low-resolution (LR) images and create a higher-resolution version. However, existing medical SR models suffer from issues such as excessive smoothness and mode collapse. In this paper, we propose a novel generative model avoiding the problems of existing models, called discrete residual diffusion model (DR-DM).Approach.First, the forward process of DR-DM gradually disrupts the input via a fixed Markov chain, producing a sequence of latent variables with increasing noise. The backward process learns the conditional transit distribution and gradually match the target data distribution. By optimizing a variant of the variational lower bound, training diffusion models effectively address the issue of mode collapse. Second, to focus DR-DM on recovering high-frequency details, we synthesize residual images instead of synthesizing HR MRI directly. The residual image represents the difference between the HR and LR up-sampled MR image, and we convert residual image into discrete image tokens with a shorter sequence length by a vector quantized variational autoencoder (VQ-VAE), which reduced the computational complexity. Third, transformer architecture is integrated to model the relationship between LR MRI and residual image, which can capture the long-range dependencies between LR MRI and the synthesized imaging and improve the fidelity of reconstructed images.Main results.Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image super-resolution tasks and compared to five state-of-the-art methods.Significance.Our experiments on the Prostate-Diagnosis and PROSTATEx datasets demonstrate that the DR-DM model significantly improves the signal-to-noise ratio of MRI for prostate cancer, resulting in greater clarity and improved diagnostic accuracy for patients.


High-performance reconstruction method combining total variation with a video denoiser for compressed ultrafast imaging

Abstract: Compressed ultrafast photography (CUP) is a novel two-dimensional (2D) imaging technique to capture ultrafast dynamic scenes. Effective image reconstruction is essential in CUP systems. However, existing reconstruction algorithms mostly rely on image priors and complex parameter spaces. Therefore, in general, they are time-consuming and result in poor imaging quality, which limits their practical applications. In this paper, we propose a novel reconstruction algorithm, to the best of our knowledge, named plug-in-plug-fast deep video denoising net-total variation (PnP-TV-FastDVDnet), which exploits an image's spatial features and correlation features in the temporal dimension. Therefore, it offers higher-quality images than those in previously reported methods. First, we built a forward mathematical model of the CUP, and the closed-form solution of the three suboptimization problems was derived according to plug-in and plug-out frames. Secondly, we used an advanced video denoising algorithm based on a neural network named FastDVDnet to solve the denoising problem. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are improved on actual CUP data compared with traditional algorithms. On benchmark and real CUP datasets, the proposed method shows the comparable visual results while reducing the running time by 96% over state-of-the-art algorithms.


Super-resolution reconstruction of underwater polarized images with a fused attention mechanism

Abstract: The polarization imaging technique leverages the disparity between target and background polarization information to mitigate the impact of backward scattered light, thereby enhancing image quality. However, the imaging model of this method exhibits limitations in extracting inter-image features, resulting in less-than-optimal outcomes in turbid underwater environments. In recent years, machine learning methodologies, particularly neural networks, have gained traction. These networks, renowned for their superior fitting capabilities, can effectively extract information from multiple images. The incorporation of an attention mechanism significantly augments the capacity of neural networks to extract inter-image correlation attributes, thereby mitigating the constraints of polarization imaging methods to a certain degree. To enhance the efficacy of polarization imaging in complex underwater environments, this paper introduces a super-resolution network with an integrated attention mechanism, termed as SRGAN-DP. This network is a fusion of an enhanced SRGAN network and the high-performance deep pyramidal split attention (DPSA) module, also proposed in this paper. SRGAN-DP is employed to perform high-resolution reconstruction of the underwater polarimetric image dataset, constructed specifically for this study. A comparative analysis with existing algorithms demonstrates that our proposed algorithm not only produces superior images but also exhibits robust performance in real-world environments.