Reviewerly/exHarmony huggingface dataset:
A machine-learning approach for long-term prediction of experimental cardiac action potential time series using an autoencoder and echo state networks

Computational modeling and experimental/clinical prediction of the complex signals during cardiac arrhythmias have the potential to lead to new approaches for prevention and treatment. Machine-learning (ML) and deep-learning approaches can be used for time-series forecasting and have recently been applied to cardiac electrophysiology. While the high spatiotemporal nonlinearity of cardiac electrical dynamics has hindered application of these approaches, the fact that cardiac voltage time series are not random suggests that reliable and efficient ML methods have the potential to predict future action potentials. This work introduces and evaluates an integrated architecture in which a long short-term memory autoencoder (AE) is integrated into the echo state network (ESN) framework. In this approach, the AE learns a compressed representation of the input nonlinear time series. Then, the trained encoder serves as a feature-extraction component, feeding the learned features into the recurrent ESN reservoir. The proposed AE-ESN approach is evaluated using synthetic and experimental voltage time series from cardiac cells, which exhibit nonlinear and chaotic behavior. Compared to the baseline and physics-informed ESN approaches, the AE-ESN yields mean absolute errors in predicted voltage 6-14 times smaller when forecasting approximately 20 future action potentials for the datasets considered. The AE-ESN also demonstrates less sensitivity to algorithmic parameter settings. Furthermore, the representation provided by the feature-extraction component removes the requirement in previous work for explicitly introducing external stimulus currents, which may not be easily extracted from real-world datasets, as additional time series, thereby making the AE-ESN easier to apply to clinical data.
Consolidating a common perspective on Technical Debt and its Management through a Tertiary Study

Technical Debt (TD) contextualizes the technical decisions on shortcuts and workarounds during software development, positively and negatively influencing software evolution. However, TD still seems to confound with any issue occurring during software development, impacting its proper understanding and management in software projects. To synthesize evidence regarding the conceptualization, characteristics, and management of TD in software projects. To undertake a tertiary study to strengthen the knowledge of TD using the principles of Grounded Theory to support qualitative analysis. Nineteen secondary studies provide evidence on TD and its management. They provided information regarding the TD's understanding (definitions and characteristics) and management (actions and technologies). Some causes, such as project constraints, technical decisions, and team members, promote different types of TD in software projects. The secondary studies also supported identifying the impacts of TD regarding project management, team members, the organization's business, and internal software quality. Besides helping identify TD challenges, such studies contributed to integrating a conjectured conceptual model of TD that can support future discussions and investigations regarding TD's understanding and management. The set of evidence regarding TD's understanding, actions, and technologies to manage TD can aid software practitioners in their software projects. However, it is observable an interpretation overload regarding its definition, inducing to classify any issue occurring during the software development as TD. Therefore, further discussions and investigations still represent essential steps towards consolidating a common perspective on TD and its management.
Optimal Dependence of Performance and Efficiency of Collaborative Filtering on Random Stratified Subsampling

Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering (CF) algorithms. The effect of this subsampling on the computing time and accuracy of CF is not fully understood, and clear guidelines for selecting optimal or even appropriate subsampling levels are not available. In this paper, we present a Density-based Random Stratified Subsampling using Clustering (DRSC) algorithm in which the desired Fraction of Users Dropped (FUD) and Fraction of Items Dropped (FID) are specified, and the overall density during subsampling is maintained. Subsequently, we develop simple models of the Training Time Improvement (TTI) and the Accuracy Loss (AL) as functions of FUD and FID, based on extensive simulations of seven standard CF algorithms as applied to various primary matrices from MovieLens, Yahoo Music Rating, and Amazon Automotive data. Simulations show that both TTI and a scaled AL are bi-linear in FID and FUD for all seven methods. The TTI linear regression of a CF method appears to be same for all datasets. Extensive simulations illustrate that TTI can be estimated reliably with FUD and FID only, but AL requires considering additional dataset characteristics. The derived models are then used to optimize the levels of subsampling addressing the tradeoff between TTI and AL. A simple sub-optimal approximation was found, in which the optimal AL is proportional to the optimal Training Time Reduction Factor (TTRF) for higher values of TTRF, and the optimal subsampling levels, like optimal FID/(1–FID), are proportional to the square root of TTRF.
Predicting Tumor Mutational Burden From Lung Adenocarcinoma Histopathological Images Using Deep Learning

Tumor mutation burden (TMB) is an important biomarker for tumor immunotherapy. It plays an important role in the clinical treatment process, but the gold standard measurement of TMB is based on whole exome sequencing (WES). WES cannot be done in most hospitals due to its high cost, long turnaround times and operational complexity. To seek out a better method to evaluate TMB, we divided the patients with lung adenocarcinoma (LUAD) in TCGA into two groups according to the TMB value, then analyzed the differences of clinical characteristics and gene expression between the two groups. We further explored the possibility of using histopathological images to predict TMB status, and developed a deep learning model to predict TMB based on histopathological images of LUAD. In the 5-fold cross-validation, the area under the receiver operating characteristic (ROC) curve (AUC) of the model was 0.64. This study showed that it is possible to use deep learning to predict genomic features from histopathological images, though the prediction accuracy was relatively low. The study opens up a new way to explore the relationship between genes and phenotypes.
Deep Multi-Similarity Hashing with semantic-aware preservation for multi-label image retrieval

Recently, many deep supervised hashing has been developed for multi-label image retrieval applications and has already achieved good effects. However, current methods quantify the similarities between image pairs by counting the number of shared labels roughly, totally ignoring the semantic relationships of image pairs. Besides, the sample imbalance of different classes could bias the training process toward majority categories, deteriorating the model performance. To solve the above-mentioned problems, we propose a novel deep supervised hashing framework, named Deep Multi-Similarity Hashing with semantic-aware preserving (DMSH), to generate binary codes with high-level semantic similarity. Specifically, to obtain distinguishing features, Convolutional Neural Networks (CNN) with correlation operators are introduced to capture discriminative semantic features of the input images, including auto-correlation characteristics and cross-correlation characteristics. By jointly exploiting label-level and semantic-level similarity, the semantic-aware similarity module is developed to quantify the high-level semantic similarity of image pairs, distinguishing the differences beyond categories. Furthermore, the multi-similarity loss is incorporated into deep hashing to collect informative pairs efficiently, which could alleviate the impact of imbalanced sample pairs during model training. Extensive comparison experiments on three benchmark datasets demonstrate that our DMSH achieves promising performance with respect to different evaluation metrics.
Optimizing the Deep Neural Networks by Layer-Wise Refined Pruning and the Acceleration on FPGA

To accelerate the practical applications of artificial intelligence, this paper proposes a high efficient layer-wise refined pruning method for deep neural networks at the software level and accelerates the inference process at the hardware level on a field-programmable gate array (FPGA). The refined pruning operation is based on the channel-wise importance indexes of each layer and the layer-wise input sparsity of convolutional layers. The method utilizes the characteristics of the native networks without introducing any extra workloads to the training phase. In addition, the operation is easy to be extended to various state-of-the-art deep neural networks. The effectiveness of the method is verified on ResNet architecture and VGG networks in terms of dataset CIFAR10, CIFAR100, and ImageNet100. Experimental results show that in terms of ResNet50 on CIFAR10 and ResNet101 on CIFAR100, more than 85% of parameters and Floating-Point Operations are pruned with only 0.35% and 0.40% accuracy loss, respectively. As for the VGG network, 87.05% of parameters and 75.78% of Floating-Point Operations are pruned with only 0.74% accuracy loss for VGG13BN on CIFAR10. Furthermore, we accelerate the networks at the hardware level on the FPGA platform by utilizing the tool Vitis AI. For two threads mode in FPGA, the throughput/fps of the pruned VGG13BN and ResNet101 achieves 151.99 fps and 124.31 fps, respectively, and the pruned networks achieve about 4.3× and 1.8× speed up for VGG13BN and ResNet101, respectively, compared with the original networks on FPGA.
Model averaging for identification of geochemical anomalies linked to mineralization

The complexity of geochemical patterns in the surficial media makes it necessary to consider the uncertainty in the process of identifying geochemical anomalies. The existence of various anomaly detectors that define geochemical anomaly under different assumptions, constitutes an important source of such uncertainty. In this study, the model averaging strategy was applied to combine the predictions of three different anomaly detectors, i.e., local singularity analysis, autoencoder network and one-class support vector machine, with the aim of accounting for this type of uncertainty. The receiver operating characteristic curve and area under the curve were used to evaluate the model performance. A case study of processing the stream sediment geochemical samples in Northwest Sichuan Province, China was presented to illustrate the procedure. The results indicate that: (1) model averaging can further improve the predictive performance with respect to the individual anomaly detector, which hence serves as an alternative approach to account for model uncertainty; (2) geochemical anomalies patterns identified by the model averaging approaches show strong correlations with known mineralization in the study area, and those associated with low uncertainty and favorable geological evidence (e.g., fault) provide valuable information for mineral exploration at a later stage.
Simplification in translated Chinese: An entropy-based approach

For a long time, translation researchers, particularly those working in corpus-based translation studies, have held the presumption that translated texts tend to be simpler in lexical and syntactical features than non-translated native texts. Such claims have led to the formulation of the simplification universal hypothesis in translation studies. However, this line of research which focuses predominantly on the investigation of individual linguistic features has failed to provide sufficient evidence to confirm the existence of the simplification universal. To a large extent, the lack of global quantitative indicators for evaluating the complexity level of the translated and non-translated texts has hindered progress in this field. The current study, using entropy as an indicator, analysed the linguistic complexity between translated and native Chinese from the information-theoretical perspective. Our research found that translational Chinese tends to be simpler than its non-translated counterpart at the lexical level based on unigram entropy, but not the syntactic level based on part-of-speech entropy. Our study has confirmed the use of entropy as a reliable measure for lexical and syntactic complexity in the field of translation studies.
Advanced beam shaping for laser materials processing based on diffractive neural networks

We propose a method based on neural network training algorithms for the design of diffractive neural networks - with the aim to perform advanced laser beam shaping in the NIR/VIS spectrum for laser materials processing. The method enables the efficient design of systems including multiple cascaded diffractive optical elements (DOEs) and allows the simultaneous optimization for complex (intensity and phase) target field distributions in multiple target planes. The multi-target boundary condition in the optimization method offers great potential for advanced laser beam shaping.
Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Artificial intelligence, particularly machine learning, carries high potential to automatically detect customers’ pain points, which is a particular concern the customer expresses that the company can address. However, unstructured data scattered across social media make detection a nontrivial task. Thus, to help firms gain deeper insights into customers’ pain points, the authors experiment with and evaluate the performance of various machine learning models to automatically detect pain points and pain point types for enhanced customer insights. The data consist of 4.2 million user-generated tweets targeting 20 global brands from five separate industries. Among the models they train, neural networks show the best performance at overall pain point detection, with an accuracy of 85% (F1 score = .80). The best model for detecting five specific pain points was RoBERTa 100 samples using SYNONYM augmentation. This study adds another foundational building block of machine learning research in marketing academia through the application and comparative evaluation of machine learning models for natural language–based content identification and classification. In addition, the authors suggest that firms use pain point profiling, a technique for applying subclasses to the identified pain point messages to gain a deeper understanding of their customers’ concerns.
Physics-informed neural network for polarimetric underwater imaging

Utilizing the polarization analysis in underwater imaging can effectively suppress the scattered light and help to restore target signals in turbid water. Neural network-based solutions can also boost the performance of polarimetric underwater imaging, while most of the existing networks are pure data driven which suffer from ignoring the physical mode. In this paper, we proposed an effective solution that informed the polarimetric physical model and constrains into the well-designed deep neural network. Especially compared with the conventional underwater imaging model, we mathematically transformed the two polarization-dependent parameters to a single parameter, making it easier for the network to converge to a better level. In addition, a polarization perceptual loss is designed and applied to the network to make full use of polarization information on the feature level rather than on the pixel level. Accordingly, the network was able to learn the polarization modulated parameter and to obtain clear de-scattered images. The experimental results verified that the combination of polarization model and neural network was beneficial to improve the image quality and outperformed other existing methods, even in a high turbidity condition.
Knowledge management process, infrastructure, and system quality as resilient strategies to respond to COVID‐19 pandemic challenges: Evidence from higher educational institutions in India

Abstract The COVID‐19 global pandemic has adversely affected educational institutions worldwide, resulting in frequent mandatory lockdowns and social distancing rules. After waiting for a brief period, most organizations, including educational institutions, moved their operations from traditional in‐class to virtual, web‐based teaching‐learning. As a result of unprecedented change, faculty, students, and administrators faced several challenges. However, how the educational institutions were coping with this challenging situation remained an important question. To address this, we present Knowledge Management Process (KMP), Knowledge Management System Infrastructure (KMSINF), and Knowledge Management System Quality (KMSQU) as resilient strategies to convert the challenges into opportunities. Using the KM processes and practices, a complex model is constructed to positively influence employee commitment, performance, and job satisfaction. A survey instrument was used to collect data from 747 faculty members from 14 higher educational institutions in the southern part of India. After testing the measurement properties using the Lisrel package of structural equation modeling, the complex model was tested using Hayes PROCESS macros. The results indicate (i) KMP is positively related to performance and employee commitment, (ii) employee commitment mediates the relationship between KMP and performance, (iii) KMSINF moderates the relationship between KMP and employee commitment, (iv) KMSQU moderates the moderated relationship between KMP and KMSINF in influencing the employee commitment, and (v) performance is positively related to job satisfaction. The results suggest that KM processes, system infrastructure, and system quality are effective, resilient strategies to bring educational institutions to normal functioning during the present global pandemic. The implications for KM, employee commitment, and job satisfaction are discussed.
Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation

In recent years, image segmentation techniques based on deep learning have achieved many applications in remote sensing, medical, and autonomous driving fields. In space exploration, the segmentation of spacecraft objects by monocular images can support space station on-orbit assembly tasks and space target position and attitude estimation tasks, which has essential research value and broad application prospects. However, there is no segmentation network designed for spacecraft targets. This paper proposes an end-to-end spacecraft image segmentation network using the semantic segmentation network DeepLabv3+ as the basic framework. We develop a multi-scale neural network based on sparse convolution. First, the feature extraction capability is improved by the dilated convolutional network. Second, we introduce the channel attention mechanism into the network to recalibrate the feature responses. Finally, we design a parallel atrous spatial pyramid pooling (ASPP) structure that enhances the contextual information of the network. To verify the effectiveness of the method, we built a spacecraft segmentation dataset on which we conduct experiments on the segmentation algorithm. The experimental results show that the encoder+ attention+ decoder structure proposed in this paper, which focuses on high-level and low-level features, can obtain clear and complete masks of spacecraft targets with high segmentation accuracy. Compared with DeepLabv3+, our method is a significant improvement. We also conduct an ablation study to research the effectiveness of our network framework.
Diverse image enhancer for complex underexposed image

The visual appearance of images changes in line with varying environmental light conditions. Adjusting the exposure of various distorted images is a highly complex process. Previous approaches have addressed this issue from different viewpoints and attained remarkable progress. However, they either failed to achieve visually pleasing results or were suitable for a single class of images (e.g., underexposed or nonuniform images). To fully consider the exposure in various distorted images, we proposed a diverse image enhancement model that improved the brightness and contrast, processed the colors, and eliminated the hazy effect. Accordingly, an input red green blue color image was transformed into a hue, saturation, value color image. The V component was inverted and enhanced using three steps. In the first step, the hyperbolic and statistical methods were applied, and then their results were combined using an adjusted logarithmic methodology. This method properly adjusted the high-contrast and low-contrast impact while preserving the vital image information. In the second step, the output of the first step was inverted back and fed into a complete optimization algorithm to estimate the illumination map. Then, the exposure ratio map was estimated using an illumination map, which was adjusted using the camera response function. In the third step, a nonlinear stretching function was introduced to control brightness and contrast. For instance, a lower value of α yielded maximum stretching, and a higher value of α eliminated haze in the image to a great extent. Finally, an empirical evaluation and comparison of the most recent state-of-the-art approaches on eight datasets revealed that the proposed model efficiently addressed the exposure in various degraded images.
Light-field image watermarking based on geranion polar harmonic Fourier moments

Light-field images provide a spatial and angular description of the light, and they can capture rich visual information in the natural world. In recent years, the research on light-field imaging has achieved many fruitful results. As more and more people are now focusing their attention to light-field imaging, the copyright protection of light-field images has become an urgent problem that needs to be addressed. Digital watermarking can effectively protect the copyright ownership of light-field images. Currently, there exists no light-field image watermarking scheme that can resist various geometric attacks. In this work, relying on geranion theory and polar harmonic Fourier moments (PHFMs), geranion polar harmonic Fourier moments (GPHFMs) are constructed and utilized for light-field image watermarking. The geranion represents a hypercomplex number containing one real part and thirty-one imaginary parts, and the imaginary parts of geranion can be used to encode multiple image color components while maintaining the correlation between each component. GPHFMs are stable image features, with good image reconstruction ability and stability. Essentially, the light-field image watermarking based on GPHFMs offers good imperceptibility and robustness, can resist various attacks, and effectively solves the problem of current watermarking schemes related to their inability of resisting geometric attacks. Furthermore, it is verified through experiments that the proposed scheme is more robust than the previously reported schemes.