Reviewerly/exHarmony huggingface dataset:
ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification

Acute Lymphoblastic Leukemia (ALL) is cancer in which bone marrow overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every year in the United States in both adults and children, accounting for around 25% of pediatric cancers, and the trend continues to rise. With the advancements of AI and big data analytics, early diagnosis of ALL can be used to aid the clinical decisions of physicians and radiologists. This research proposes a deep neural network-based (ALNett) model that employs depth-wise convolution with different dilation rates to classify microscopic white blood cell images. Specifically, the cluster layers encompass convolution and max-pooling followed by a normalization process that provides enriched structural and contextual details to extract robust local and global features from the microscopic images for the accurate prediction of ALL. The performance of the model was compared with various pre-trained models, including VGG16, ResNet-50, GoogleNet, and AlexNet, based on precision, recall, accuracy, F1 score, loss accuracy, and receiver operating characteristic (ROC) curves. Experimental results showed that the proposed ALNett model yielded the highest classification accuracy of 91.13% and an F1 score of 0.96 with less computational complexity. ALNett demonstrated promising ALL categorization and outperformed the other pre-trained models.
GCNet: Grid-like context-aware network for RGB-thermal semantic segmentation

Semantic segmentation methods can achieve satisfactory performance under poor lighting conditions by exploiting the complementary cues in RGB and thermal images. However, most methods employ straightforward fusion strategies, which may insufficiently explore complementary information and ignore cross-level information propagation, except spatial information. Further, high-level contextual information may be inadequately enhanced owing to the use of simple perceptive modules. To address these limitations, we introduce a grid-like context-aware network (GCNet) for the semantic segmentation of RGB-thermal images. We use a hybrid fusion module to integrate the complementary information across modalities while considering the propagation of fusion cues by incorporating previously fused features. Considering the significance of contextual cues in semantic segmentation, a grid-like context-aware module is designed to capture rich contextual information. A three-branch discriminator is constructed to evaluate the generated prediction maps and improve the performances of the parsing results. Experiments were performed on two RGB-thermal datasets, and the results show that the proposed network achieves state-of-the-art performance.
Improving the accuracy of predicting the performance of solar collectors through clustering analysis with artificial neural network models

Accurate prediction of collector performance is important for optimal planning of solar thermal systems. Here, a novel prediction method combining clustering of data with artificial neural network (ANN) model is presented. A novel all-glass straight-through tube solar collector is employed as reference solar technology. In the present approach, experimental collector performance data was first collected during different weather conditions (sunny, cloudy, rainy days) subject to a clustering analysis to screen out outlier samples. The data was then used to train and verify the neural network models. For the ANN, the Back Propagation (BP) and Convolutional Neural Network (CNN) models were used. For predicting the performance (thermal efficiency) of the solar collector, solar radiation intensity, ambient temperature, wind speed, fluid flow rate, and fluid inlet temperature were used as the input parameters in the model. The prediction accuracy of the neural network models after full-data-screening were superior to that of the pre-screening and partial-screening models. The CNN model provided somewhat better efficiency predictions than the BP model. The R2, RMSE and MAE of the CNN model prediction in sunny conditions with full-screening was 0.9693, 0.0039 and 0.0030, respectively. The average MAPE of the BP and CNN models for all three weather types dropped by 30.7% and 13.8%, respectively, when applying data pre-screening and partial-screening only. The accuracy of the ANN collector prediction model can thus be improved through data clustering, which provides an effective method for performance prediction of solar collectors.
RDEN: Residual Distillation Enhanced Network-Guided Lightweight Synthesized View Quality Enhancement for 3D-HEVC

In the three-dimensional video system, the depth image-based rendering is a key technique for generating synthesized views, which provides audiences with depth perception and interactivity. However, the inaccuracy of depth information leads to geometrical rendering position errors, and the compression distortion of texture and depth videos degrades the quality of the synthesized views. Although existing quality enhancement methods can eliminate the distortions in the synthesized views, their huge computational complexity hinders their applications in real-time multimedia systems. To this end, a residual distillation enhanced network (RDEN)-guided lightweight synthesized view quality enhancement (SVQE) method is proposed to minimize holes and compression distortions in the synthesized views while reducing the model complexity. First, a rethinking on the deep-learning-based SVQE methods is performed. Then, a feature distillation attention block is proposed to effectively reduce the distortions in the synthesized views and make the model fulfill more real-time tasks, which is a lightweight and flexible feature extraction block using an information distillation mechanism and a lightweight multi-scale spatial attention mechanism. Third, a residual feature fusion block is proposed to improve the enhancement performance by using the feature fusion mechanism, which efficiently improves the feature extraction capability without introducing any additional parameters. Experimental results prove that the proposed RDEN efficiently improves the SVQE performance while consuming few computational complexities compared with the state-of-the-art SVQE methods.
A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory

Sparse representation of real-life images is a very effective approach in imaging applications, such as denoising. In recent years, with the growth of computing power, data-driven strategies exploiting the redundancy within patches extracted from one or several images to increase sparsity have become more prominent. This paper presents a novel image denoising algorithm exploiting such an image-dependent basis inspired by the quantum many-body theory. Based on patch analysis, the similarity measures in a local image neighborhood are formalized through a term akin to interaction in quantum mechanics that can efficiently preserve the local structures of real images. The versatile nature of this adaptive basis extends the scope of its application to image-independent or image-dependent noise scenarios without any adjustment. We carry out a rigorous comparison with contemporary methods to demonstrate the denoising capability of the proposed algorithm regardless of the image characteristics, noise statistics and intensity. We illustrate the properties of the hyperparameters and their respective effects on the denoising performance, together with automated rules of selecting their values close to the optimal one in experimental setups with ground truth not available. Finally, we show the ability of our approach to deal with practical images denoising problems such as medical ultrasound image despeckling applications.
Autonomous retrosynthesis of gold nanoparticles <i>via</i> spectral shape matching

A Riemannian metric for spectral data is introduced to efficiently compute shape matching distances for material retrosynthesis in high-throughput autonomous laboratories.
Optimally adaptive Bayesian spectral density estimation for stationary and nonstationary processes

This article improves on existing methods to estimate the spectral density of stationary and nonstationary time series assuming a Gaussian process prior. By optimising an appropriate eigendecomposition using a smoothing spline covariance structure, our method more appropriately models data with both simple and complex periodic structure. We further justify the utility of this optimal eigendecomposition by investigating the performance of alternative covariance functions other than smoothing splines. We show that the optimal eigendecomposition provides a material improvement, while the other covariance functions under examination do not, all performing comparatively well as the smoothing spline. During our computational investigation, we introduce new validation metrics for the spectral density estimate, inspired from the physical sciences. We validate our models in an extensive simulation study and demonstrate superior performance with real data.
Dynamic Nonlinear Behavior of Ionic Liquid-Based Reservoir Computing Devices

Herein, a physical reservoir device that uses faradaic currents generated by redox reactions of metal ions in ionic liquids was developed. Synthetic time-series data consisting of randomly arranged binary number sequences ("1" and "0") were applied as isosceles-triangular voltage pulses with positive and negative voltage heights, respectively, and the effects of the faradaic current on short-term memory and parity-check task accuracies were verified. The current signal for the first half of the triangular voltage-pulse period, which contained a much higher faradaic current component compared to that of the second half of the triangular voltage-pulse period, enabled higher short-term memory task accuracy. Furthermore, when parity-check tasks were performed using a faradaic current generated by asymmetric triangular voltage-pulse levels of 1 and 0, the parity-check task accuracy was approximately eight times higher than that of the symmetric triangular voltage pulse in terms of the correlation coefficient between the output signal and target data. These results demonstrate the advantage of the faradaic current on both the short-term memory characteristics and nonlinear conversion capabilities and are expected to provide guidance for designing and controlling various physical reservoir devices that utilize electrochemical reactions.
Convolutional kernel aggregated domain adaptation for intelligent fault diagnosis with label noise

Unsupervised domain adaptation for intelligent fault diagnosis requires a well-annotated source domain to transfer knowledge to an unlabeled target domain, but the ubiquitous source label noise in realistic scenarios remains largely neglected. Recent efforts following adversarial domain adaptation attempt to learn with label noise conditioned on the classifier predictions. However, an essential flaw in the classifier capacity introduces improper adjustments to the loss function. Moreover, they treat domain-specific and domain-invariant representations as a whole, which threats the effectiveness of learning invariant representations. To address these issues, a Convolutional Kernel Aggregated Domain Adaptation (CKADA) strategy is proposed for fault knowledge transfer. Specifically, a convolutional kernel aggregated layer with domain-mixed attention weights is first designed to harness the diverse learning capacities of multiple kernels. Then, by extending such a layer to the classifier, a classification bridge layer is presented to ensure reliable predictions, based on which the side effects of label noise are further relieved through selecting and reusing source samples. Meanwhile, an additional discrimination bridge layer is constructed, which collaborates with the classification bridge layer to assist adversarial domain adaptation. Extensive experiments on three rolling bearing datasets with various types of noisy transfer tasks demonstrate the effectiveness and robustness of CKADA.
How to Attack and Generate Honeywords

Honeywords are decoy passwords associated with each user account to timely detect password leakage. The key issue lies in how to generate honeywords that are hard to be differentiated from real passwords. This security mechanism was first introduced by Juels and Rivest at CCS’13, and has been covered by hundreds of media and adopted in dozens of research domains. Existing research deals with honeywords primarily in an ad hoc manner, and it is challenging to develop a secure honeyword-generation method and well evaluate (attack) it. In this work, we tackle this problem in a principled approach. We first propose four theoretic models for characterizing the attacker $\mathcal{A}$’s best distinguishing strategies, with each model based on a different combination of information available to $\mathcal{A}$ (e.g., public datasets, the victim’s personal information and registration order). These theories guide us to design effective experiments with real-world password datasets to evaluate the goodness (flatness) of a given honeyword-generation method.Armed with the four best attacking theories, we develop the corresponding honeyword-generation method for each type of attackers, by using various representative probabilistic password guessing models. Through a series of exploratory investigations, we show the use of these password models is not straightforward, but requires creative and significant efforts. Both empirical experiments and user-study results demonstrate that our methods significantly outperform prior art. Besides, we manage to resolve several previously unexplored challenges that arise in the practical deployment of a honeyword method. We believe this work pushes the honeyword research towards statistical rigor.
DCA-IoMT: Knowledge-Graph-Embedding-Enhanced Deep Collaborative Alert Recommendation Against COVID-19

Filtration to optimal exactness is mandatory since the options inundate the online world. Knowledge graph embedding is extraordinarily contributing to the recommendations, but the existing knowledge graph (KG)-based recommendation methods only exploit the correlations among the preferences and stand-alone entities, without bonding the cocurricular features and tendencies of the context. Additionally, the integration of the location-based current data of coronavirus disease 2019 (COVID-19) into the KG is necessary for the recommendation of region-aware precautionary alerts to the concerned people—an essential application of the current and future Internet of Medical Things. Therefore, in this article, we propose a novel deep collaborative alert recommendation (DCA) approach to cope with the situation. Particularly, DCA collects current online data about COVID-19, purifies, and transforms them to the KG. Furthermore, it independently encapsulates the cocurricular features and tendencies of the context in the embedding space and encodes them to the independent hidden factors via a graph neural network. The bi-end hidden factors are computed via matrix factorization to infer the potential connections. Moreover, a relevance estimator and a cross transistor are configured to enhance the generalization capability of the model. Experiments on two real-world datasets are performed to evaluate the effectiveness of DCA. Results and analysis show that the proposed approach has outperformed the baseline methods with fine improvements in providing the required recommendations.
Blockchain and AI-Empowered Healthcare Insurance Fraud Detection: an Analysis, Architecture, and Future Prospects

Nowadays, health insurance has become an essential part of people&#x2019;s lives as the number of health issues increases. Healthcare emergencies can be troublesome for people who can&#x2019;t afford huge expenses. Health insurance helps people cover healthcare services expenses in case of a medical emergency and provides financial backup against indebtedness risk. Health insurance and its several benefits can face many security, privacy, and fraud issues. For the past few years, fraud has been a sensitive issue in the health insurance domain as it incurs high losses for individuals, private firms, and governments. So, it is essential for national authorities and private firms to develop systems to detect fraudulent cases and payments. A high volume of health insurance data in electronic form is generated, which is highly sensitive and attracts malicious users. Motivated by these facts, we present a systematic survey for Artificial Intelligence (AI) and blockchain-enabled secure health insurance fraud detection in this paper. This paper presents a taxonomy of various security issues in health insurance. We proposed a blockchain and AI-based secure and intelligent system to detect health insurance fraud. Then a case study related to health insurance fraud is presented. Finally, the open issues and research challenges in implementing the blockchain and an AI-empowered health insurance fraud detection system is presented.
Recommendation Systems: An Insight Into Current Development and Future Research Challenges

Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets.
An empirical evaluation of sampling methods for the classification of imbalanced data

In numerous classification problems, class distribution is not balanced. For example, positive examples are rare in the fields of disease diagnosis and credit card fraud detection. General machine learning methods are known to be suboptimal for such imbalanced classification. One popular solution is to balance training data by oversampling the underrepresented (or undersampling the overrepresented) classes before applying machine learning algorithms. However, despite its popularity, the effectiveness of sampling has not been rigorously and comprehensively evaluated. This study assessed combinations of seven sampling methods and eight machine learning classifiers (56 varieties in total) using 31 datasets with varying degrees of imbalance. We used the areas under the precision-recall curve (AUPRC) and receiver operating characteristics curve (AUROC) as the performance measures. The AUPRC is known to be more informative for imbalanced classification than the AUROC. We observed that sampling significantly changed the performance of the classifier (paired t-tests P < 0.05) only for few cases (12.2% in AUPRC and 10.0% in AUROC). Surprisingly, sampling was more likely to reduce rather than improve the classification performance. Moreover, the adverse effects of sampling were more pronounced in AUPRC than in AUROC. Among the sampling methods, undersampling performed worse than others. Also, sampling was more effective for improving linear classifiers. Most importantly, we did not need sampling to obtain the optimal classifier for most of the 31 datasets. In addition, we found two interesting examples in which sampling significantly reduced AUPRC while significantly improving AUROC (paired t-tests P < 0.05). In conclusion, the applicability of sampling is limited because it could be ineffective or even harmful. Furthermore, the choice of the performance measure is crucial for decision making. Our results provide valuable insights into the effect and characteristics of sampling for imbalanced classification.
Validation of portable tablets for transplant pathology diagnosis according to the College of American Pathologists’ Guidelines

Despite increased use of digital pathology, its application in the transplantation setting remains limited. One of the restraints is related to concerns that this technology is inadequate for supporting diagnostic work. In this study, we sought to establish non inferiority of whole slide imaging (WSI) to light microscopy (LM) for intraoperative transplantation diagnosis using inexpensive portable devices. A validation study was conducted according to updated guidelines from the College of American Pathologists (CAP) utilizing 80 intraoperative transplantation cases. Two pathologists reviewed glass slides with LM and digital slides on two different tablets after a washout period of 4 weeks. Diagnostic concordance and intra-observer agreement were recorded. A total of 45 (56%) cases were suitable for rendering transplant diagnoses and 35 (44%) for assessing cancer risk. Intra-observer agreement was 95.1% for organ suitability and 100% for cancer risk. There were no major discordances that could affect patient transplant management. Digital evaluation of intraoperative transplant specimens using tablets to view whole slide images was non-inferior to LM for primary diagnosis. This suggests that after validating WSI these digital tools can be safely used for remote intraoperative transplantation diagnostic work.
Accuracy and reliability of forensic handwriting comparisons

Forensic handwriting examination involves the comparison of writing samples by forensic document examiners (FDEs) to determine whether or not they were written by the same person. Here we report the results of a large-scale study conducted to assess the accuracy and reliability of handwriting comparison conclusions. Eighty-six practicing FDEs each conducted up to 100 handwriting comparisons, resulting in 7,196 conclusions on 180 distinct comparison sets, using a five-level conclusion scale. Erroneous "written by" conclusions (false positives) were reached in 3.1% of the nonmated comparisons, while 1.1% of the mated comparisons yielded erroneous "not written by" conclusions (false negatives). False positive rates were markedly higher for nonmated samples written by twins (8.7%) compared to nontwins (2.5%). Notable associations between training and performance were observed: FDEs with less than 2 y of formal training generally had higher error rates, but they also had higher true positive and true negative rates because they tended to provide more definitive conclusions; FDEs with at least 2 y of formal training were less likely to make definitive conclusions, but those definitive conclusions they made were more likely to be correct (higher positive predictive and negative predictive values). We did not observe any association between writing style (cursive vs. printing) and rates of errors or incorrect conclusions. This report also provides details on the repeatability and reproducibility of conclusions, and reports how conclusions are affected by the quantity of writing and the similarity of content.