Reviewerly/exHarmony huggingface dataset:
Further predictive value of lymphovascular invasion explored via supervised deep learning for lymph node metastases in breast cancer

Lymphovascular invasion, specifically lymph-blood vessel invasion (LBVI), is a risk factor for metastases in breast invasive ductal carcinoma (IDC) and is routinely screened using hematoxylin-eosin histopathological images. However, routine reports only describe whether LBVI is present and does not provide other potential prognostic information of LBVI. This study aims to evaluate the clinical significance of LBVI in 685 IDC cases and explore the added predictive value of LBVI on lymph node metastases (LNM) via supervised deep learning (DL), an expert-experience embedded knowledge transfer learning (EEKT) model in 40 LBVI-positive cases signed by the routine report. Multivariate logistic regression and propensity score matching analysis demonstrated that LBVI (OR 4.203, 95% CI 2.809-6.290, P < 0.001) was a significant risk factor for LNM. Then, the EEKT model trained on 5780 image patches automatically segmented LBVI with a patch-wise Dice similarity coefficient of 0.930 in the test set and output counts, location, and morphometric features of the LBVIs. Some morphometric features were beneficial for further stratification within the 40 LBVI-positive cases. The results showed that LBVI in cases with LNM had a higher short-to-long side ratio of the minimum rectangle (MR) (0.686 vs. 0.480, P = 0.001), LBVI-to-MR area ratio (0.774 vs. 0.702, P = 0.002), and solidity (0.983 vs. 0.934, P = 0.029) compared to LBVI in cases without LNM. The results highlight the potential of DL to assist pathologists in quantifying LBVI and, more importantly, in exploring added prognostic information from LBVI.
Predicting anisotropic parameters of strata by deep multiple triangular kernel extreme learning machine optimized by flower pollination algorithm

Stratigraphy in the crust is widely anisotropic. Anisotropic parameters play an important role from inversion and migration to stratigraphic interpretation and reservoir characterization. At present, under conventional geophysical methods, whether logging or seismic, do not directly measure anisotropic parameters. That is, it is difficult to obtain anisotropic parameters. However, there is a certain correlation between anisotropy parameters and other kinds of logging data, so that anisotropy parameters can be calculated from other logging curves. In view of the complexity of this relationship, a machine learning approach can be used. So, we propose a deep multiple triangular kernel extreme learning machine optimized by the flower pollination algorithm (FPA-D-MK-ELM), which is used to predict the anisotropy parameters of the strata. The accuracy and stability of the FPA-D-MK-ELM algorithm are verified by comparing the algorithm before and after optimization.
Algebraic formulae for solving systems of max-min inverse fuzzy relational equations

This article is intended to solve the systems of inverse fuzzy relational equations with max–min composition, which is beneficial for solving the well-known problems of fuzzy abductive/backward reasoning. Almost all the existing methods are based on numerical algorithms, and either cannot find the globally optimal solutions or consume significant computational cost to acquire the most accurate result. Due to these drawbacks, the existing methods seem not to render the retroduction applying inverse fuzzy relation popular in many real-time expert systems such as intelligent diagnosis. It is well known that the system of inverse fuzzy relational equations is not always consistent. In this case, according to the preselected weights, we employ weighted L1 norm distances to define a variety of best approximate solutions. Then we show that there exist very straightforward algebraic formulae for finding the best approximate solutions. The proposed approach not only finds the globally optimal solutions, but also has the advantage of being computationally very simple and efficient, and hence it has a lot of potentials to perform real-time abductive reasoning in many expert systems.
A detection method for hybrid attacks in recommender systems

To defend recommender systems, some methods have been proposed to detect model-generative shilling attacks and group shilling attacks respectively. Unfortunately, these two categories of attacks are often mixed together to carry out actual attacks. Without the additional knowledge about attack categories, traditional detection methods are likely to be trapped in the poor performance under hybrids of model-generative shilling attacks and group shilling attacks. To simultaneously detect these hybrid attacks, we put forward a detector based on the graph convolutional networks (GCN). Firstly, we extract five user features from the item popularity sequence and rating values to characterize both model-generative shilling profiles and group shilling profiles. And we define the users’ distance to construct the user graph. Secondly, we develop a two-stage scheme for detecting shilling profiles based on user features and the user graph. In particular, we propose a cluster-based method to partially label user nodes, and then these labeled samples are fed into a GCN-based detector to training the model to identify the other tangled shilling profiles. In the GCN-based model, we present a weighted loss function with R-drop regularization to solve the over-fitting problem and the imbalanced classification problem for the specific detection task. Finally, we make extensive experiments on three datasets to evaluate the proposed detector. Experiential results demonstrate the efficacy of our method when detecting the hybrids of model-generative shilling attacks and group shilling attacks.
The development fit index of digital currency electronic payment between China and the one belt one road countries

Central bank digital currency is one of the most important financial innovations in the global economy. However, the understanding of its cross-border application is insufficient. This study analyses the driving and supporting factors that affect the application of China's digital currency electronic payment system in One Belt One Road countries. Using country-level data for 2019, this study proposes a systematic development fit index, consisting of six first-level indicators and 41 proxy variables. The index value shows that fit is determined by bilateral investment, bilateral trade, level of financial development, geopolitics, economic foundation and infrastructure development. Among the sample One Belt One Road countries (average index value 56.39), Malaysia (83.98), Singapore (80.5), Thailand (78.76), Russia (76.25) and the United Arab Emirates (75.48) have the most potential to participate in the new monetary system launched by China. The results provide practical implications for Chinese and OBOR governments in making related strategies.
An attention-based hybrid deep learning approach for bengali video captioning

Video captioning is an automated process of captioning a video by understanding the content within it. Although numerous studies have been performed on video captioning in English, the field of video captioning in Bengali remains nearly unexplored. Therefore, this research aims at generating Bengali captions that plausibly describe the gist of a specific video as well as identifying the best performing model for Bengali video captioning. To accomplish this, several sequence-to-sequence models – LSTM, BiLSTM, and GRU are implemented that takes the video frame features as input, extracted through different CNN models – VGG-19, Inceptionv3, and ResNet50v2, and provides a corresponding textual description as output. Moreover, the Attention mechanism is incorporated with these models as a first-ever attempt in Bengali video captioning. In this study, a novel Bengali video captioning dataset is constructed from Microsoft Research Video Description Corpus (MSVD) dataset (an English video captioning dataset) through utilizing a deep learning-based translator and manual post-editing efforts. Finally, the model's performance is evaluated in terms of popular performance evaluation metrics - BLEU, METEOR, and ROUGE. The proposed attention-based hybrid model outperforms the existing models in terms of these evaluation metrics, establishing a new benchmark for Bengali video captioning.
Multiple metric learning via local metric fusion

Adaptive distance metric learning based on the characteristics of data can significantly improve the learner’s performance. Due to the limitations of single metric learning for heterogeneous data, multiple local metric learning has become an essential representative tool to describe local properties of data. Most existing multiple metric learning algorithms need to perform metric learning on a pre-obtained instance division. However, the number of clusters in the pre-obtained division affects the effectiveness of metric learning. To tackle this problem, we propose a Multiple Metric Learning via Local Metric Fusion (MML-LMF) framework, which unifies local metric learning and fusion of similar local metrics into one metric and adaptively determines the number of local metrics. As an application of the MML-LMF framework to pairwise constraints, we devise a MML-LMF algorithm by constructing a concrete optimization model and acquiring a closed-form solution to the model. The experimental results on several benchmarks, person re-identification, and face verification datasets show that the performance of the proposed algorithm is superior to that of the existing state-of-the-art global and multiple metric learning algorithms.
Detecting suspicious transactions in a virtual-currency-enabled online social network

Online social networks (OSN) have started to integrate financial capabilities such as the usage of virtual currency. In OSNs with such capabilities, user accounts can also be used as financial accounts to manage virtual currency. Attackers are highly motivated to compromise user accounts and engage them in transactions to "steal" virtual currency. Such attacks represent a real and ongoing threat against a massive number of users in global social networks such as Tencent QQ. Aiming to address this emerging security concern, we have proposed an effective system, namely VC-Guard, to detect transactions that are likely initiated by attackers (i.e., suspicious transactions). Our system features the design and integration of multi-faceted features of each transaction. We have performed extensive experiments based on real-world data collected from Tencent QQ, a global leading OSN that has a virtual-currency-enabled ecosystem. Experimental results have demonstrated that our system has accomplished a high detection rate of 98.76% at a very low false positive rate of 1.24%.
MCRS-SAE: multi-criteria recommender system based on sparse autoencoder

Due to a large amount of information in today’s world, recommender systems help users reach the things that are most similar to their tastes. Compared to traditional recommender systems (RSs), Multi-criteria recommender systems, consider several minor criteria instead of a general criterion, which causes predictions to be more similar to the target user’s preferences. However, high dimension and sparsity of data are always one of the main problems that reduce the quality of predictions. In recent years, it has also been observed that the use of deep learning techniques in recommender systems has increased the quality of predictions. In addition, some criteria prioritize the user when selecting an item. In this paper, we propose a multi-criteria recommender system using the sparse autoencoder (MCRS_SAE) to improve the problem of the sparsity of data and also use the radial basis function kernel (RBF kernel) to determine the weight of the criteria. Experiments on the Yahoo! Movies and TripAdvisor multi-criteria datasets show that our proposed method of presenting personal predictions has a better performance than other presented methods.
New Financial Ratios Based on the Compositional Data Methodology

Due to their type of mathematical construction, the use of standard financial ratios in studies analysing the financial health of a group of firms leads to a series of statistical problems that can invalidate the results obtained. These problems are originated by the asymmetry of financial ratios. The present article justifies the use of a new methodology using compositional data (CoDa) to analyse the financial statements of a sector, improving analyses using conventional ratios since the new methodology enables statistical techniques to be applied without encountering any serious drawbacks such as skewness and outliers, and without the results depending on the arbitrary choice as to which of the accounting figures is the numerator of the ratio and which is the denominator. An example with data of the wine sector is provided. The results show that when using CoDa, outliers and skewness are much reduced and results are invariant to numerator and denominator permutation.
Approximation of functions from Korobov spaces by deep convolutional neural networks

Abstract The efficiency of deep convolutional neural networks (DCNNs) has been demonstrated empirically in many practical applications. In this paper, we establish a theory for approximating functions from Korobov spaces by DCNNs. It verifies rigorously the efficiency of DCNNs in approximating functions of many variables with some variable structures and their abilities in overcoming the curse of dimensionality.
Integration of blockchain with emerging technologies in AEC industry: Merits and challenges

Abstract While in the last decade, digital tools and technologies have made their way into the construction industry, digital growth is still slow and incremental. The challenge is the consequential need for a collaborative and trustworthy environment with cooperation and concurrence among stakeholders. Blockchain technology holds promise and potential in providing this environment, infusing transparency and security in the mix. This study is thus motivated to explore the emerging information and communication technologies (ICT) in the architecture, engineering, and construction (AEC) industry and their integration with blockchain technology. Greater amounts of real-time digital information and automated systems with the security and transparency of blockchain could be a game-changer in planning and executing construction projects. It will affect not just the decision-makers at the top managerial level but will assist individuals down the line on-field to make decisions based on real-time data available to them.
WiperNet: A Lightweight Multi-Weather Restoration Network for Enhanced Surveillance

Adherent raindrops, rainstreaks and snow severely degrade the perceptual quality of an image, eventually affecting the performance of several computer vision based applications which are applied in outdoor scenarios, e.g., traffic monitoring, autonomous driving, etc. Due to the complex appearance properties, removal of such degradations from an image is a challenging task. Working towards mitigating this problem, in this paper, a lightweight network named as WiperNet is proposed which tackles the problem of raindrops, rain streaks and snow removal present in an image. The WiperNet makes use of the proposed Dual Restoration (DR) mechanism, where the input features are processed twice through the network. In the network, Multi-scale Context Aware Residual Block (MCARB) is proposed for integrating contextual information from various scales. Also, Adaptive Varying Receptive Fusion Block (AVRFB) is proposed for adaptively fusing the information acquired through different dilation rates. Finally, we propose a Feature Refinement Stream which makes use of multiple kernel sizes of convolution filters and spatio-channel attention blocks for focusing on relevant information for effective removal of the degradations while using the coarse outputs of the features from the initial layers of the network. Substantial experiments and ablation study scrutinize that the proposed lightweight WiperNet outperforms the existing state-of-the-art methods for raindrop, rain streak and snow removal. The code is provided at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/AshutoshKulkarni4998/WiperNet</uri> .
Evaluation of the modified AHP-VIKOR for mapping and ranking copper mineralized areas, a case study from the Kerman metallogenic belt, SE Iran

Abstract Exploration and prioritization of potentially mineralized areas by integrating different geoscientific datasets help to manage the time and costs of an exploration project. In this study, copper exploration and prioritization of explored areas were done on the Kerman copper belt, which was formed due to the subduction of the Arabian plate under the Iranian plate. After processing geoscientific layers (geology, hydrothermal alteration zones, stream sediment geochemistry, and airborne geophysics), the analytic hierarchy process (AHP) method was used to compute the weights of geoscientific layers by a decision-making group consisting of ten local experts in copper exploration. The weight of evidence (WofE) method was used to compare and assess the fairness of the AHP weights. The AHP weights were used for the multi-criteria optimization and compromise solution (VIKOR) method to integrate the geoscientific layers and create an AHP-VIKOR copper mineralization potential map. The “ v ” parameter of the VIKOR method was modified by replacing the concept of mathematical expectation value with the manual assignment. The real value-area (RV-A) fractal method was used to select optimum threshold values for different AHP-VIKOR copper mineralization potential classes. The accuracy of the classified AHP-VIKOR copper mineralized potential map was assessed by creating a confusion matrix and using existing mineralization locations. The average pixel values of the copper mineralization class were extracted and sorted to rank the very high potential areas. The AHP-VIKOR ranking results were compared with recorded ore grade and ore tonnage information of each copper deposition. The results demonstrated that the AHP ranks are roughly similar to the WofE ranks, except for lithology. An accuracy of 84% for classified copper mineralization potential map with the RV-A fractal method was obtained. The used expectation value for the “ v ” parameter of the VIKOR method showed that the decision-making group’s opinion was more important than the individual opinions ( v = 0.6). The AHP-VIKOR method identified mineralization areas, especially porphyry deposits. Also, the outcome of the AHP-VIKOR ranking showed that the currently active mines, especially porphyry deposits, have the highest priority for mining. This study showed that the AHP-VIKOR approach helps explore and rank favorable areas with high copper ore grade and tonnage characteristics.
Mesh-free technique for enhancement of the lung CT image

Lung cancer is abnormal cell disease and a leading cause of mortality for both men and women according to Global cancer statistics 2020 (GLOBACON). Pulmonary nodules appear to be one of the first signs of lung cancer which needs to be recognized for early diagnosis. CT images are distorted due to various noises and poor resolution. Image pre-processing plays an important role to get rid of noises and improve image quality. Enhancement of lung CT image facilitates to determine the sign of early stage of lung cancer in terms of tumor size, malignancy, rounded hematoma. A partial differential equation based heat diffusion equation is used in this work for enhancement of lung CT-scan images. Mesh-less based radial basis function technique is used to solve the heat equation. Proposed work employed on randomly selected sample images of two lung dataset named as The Cancer Genome Atlas Lung Adenocarcinoma(TCGA-LUAD) and QIN. The performance of the proposed lung enhancement technique is quantified by various parameters namely, Structure Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Contract Improvement Index (CII). The observational investigations demonstrated that the proposed technique is computationally more efficient as compared to FDM, Median, and CLAHE.
Automatic segmentation of ultrasound images using SegNet and local Nakagami distribution fitting model

Automatic and accurate segmentation of ultrasound (US) images plays a vital role in computer-aided diagnosis of many diseases. However, multiple degradations within US images, such as speckle noise, intensity inhomogeneity, shadows, low contrast, and low signal-to-noise ratio, often cover up the details of imaging tissues and blur the edges of lesions, resulting in limited accuracy for existing segmentation algorithms. To tackle these issues, a coarse-to-fine segmentation method combining deep learning with an active contour model (ACM) is proposed in this paper. At the first stage, a SegNet is trained and employed to predict the segmentation mask because it not only has a superior boundary delineation capability, but also has high efficiency in terms of memory and computational time during inference. Due to the multiple degradations of US images, the unavailability of large-scale US image datasets, and the loss of spatial information during downsampling, the SegNet may only infer the coarse object boundaries, especially for the tissue with blurred edges. At the second stage, the segmentation mask of the SegNet is refined to locate the accurate object boundaries by a novel ACM termed the local Nakagami distribution fitting (LNDF) model, which takes advantage of discrepancies between Nakagami distributions of different tissues around the boundaries within US images. Experimental results on two US image datasets demonstrate that, compared with some state-of-the-art segmentation methods, our proposed method achieves the highest segmentation accuracy at the cost of acceptable running time, and thereby adapts to the automatic segmentation of US images in clinical applications.
A Cross-platform Metaverse Data Management System

The current extended reality(XR) community is developing rapidly, and is promoting the development of the metaverse industry. Each XR community could be deemed as a sub-metaverse. This has attracted a group of internet giants and individual developers to generate their own forms of metaverse products. While there is not such a system that is able to achieve the goal of crossing all these metaverse, which appear to be contradictory to the original concept of the metaverse: relating every virtual world to a universe.To handle this problem, this paper proposes a cross-platform metaverse data management system(CMDMS), which is committed to allowing users to employ their own profile and space across different metaverse platforms.
A Comparative Analysis of LSTM and BILSTM Network-Based Methods in PV Power Prediction

With the widely deployment of PV power installations, the intermittent operation property comes from PV power cause various impacts to electric grid. It is thus necessary to seek effective prediction technologies in order to well manage the PV power generation. Recently, many deep learning-based methods are introduced in renewable energy predictions due to their outstanding performance. To follow this technical trend, this paper gives a comparative analysis in using LSTM and BILSTM network-based methods for PV power generation prediction. Data collected from a 2.28 MW campus PV site are used for testing and validating of introduced prediction methods. Result shows that BILSTM presents the better performance than traditional LSTM in PV power generation prediction.