Reviewerly/exHarmony huggingface dataset:
Trade Openness, Institutions, and Inclusive Growth in Sub-Saharan Africa

This study assesses the moderating role of institutions in the trade openness and inclusive growth nexus in Sub-Saharan Africa (SSA). Based on the System General Method of Moment estimation technique applied to data from 39 SSA countries from 1996 to 2017, the results offer support for the assertion that institutions strengthen the positive relationship between trade openness and inclusive growth in SSA. Economies in SSA should put in policies to strengthen their institutions to improve the positive link between trade openness and inclusive growth.
Identification of Anemia and Its Severity Level in a Peripheral Blood Smear Using 3-Tier Deep Neural Network

The automatic detection of blood cell elements for identifying morphological deformities is still a challenging research domain. It has a pivotal role in cognition and detecting the severity level of disease. Using a simple microscope, manual disease detection, and morphological disorders in blood cells is mostly time-consuming and erroneous. Due to the overlapped structure of RBCs, pathologists face challenges in differentiating between normal and abnormal cell shape and size precisely. Currently, convolutional neural network-based algorithms are effective tools for addressing this issue. Existing techniques fail to provide effective anemia detection, and severity level prediction due to RBCs’ dense and overlapped structure, non-availability of standard datasets related to blood diseases, and severity level detection techniques. This work proposed a three tier deep convolutional fused network (3-TierDCFNet) to extract optimum morphological features and identify anemic images to predict the severity of anemia. The proposed model comprises two modules: Module-I classifies the input image into two classes, i.e., Healthy and Anemic, while Module-II detects the anemia severity level and categorizes it into Mild or Chronic. After each tier’s training, a validation function is employed to reduce the inappropriate feature selection. To authenticate the proposed model for healthy, anemic RBC classification and anemia severity level detection, a state-of-the-art anemic and healthy RBC dataset was developed in collaboration with Shaukat Khanum Hospital and Research Center (SKMCH&amp;RC), Pakistan. To evaluate the proposed model, the training, validation, and test accuracies were computed along with recall, F1-Score, and specificity. The global results reveal that the proposed model achieved 91.37%, 88.85%, and 86.06% training, validation, and test accuracies with 98.95%, 98.12%, and 98.12% recall F1-Score and specificity, respectively.
Finite-time convergent zeroing neural network for solving time-varying algebraic Riccati equations

Various forms of the algebraic Riccati equation (ARE) have been widely used to investigate the stability of nonlinear systems in the control field. In this paper, the time-varying ARE (TV-ARE) and linear time-varying (LTV) systems stabilization problems are investigated by employing the zeroing neural networks (ZNNs). In order to solve the TV-ARE problem, two models are developed, the ZNNTV-ARE model which follows the principles of the original ZNN method, and the FTZNNTV-ARE model which follows the finite-time ZNN (FTZNN) dynamical evolution. In addition, two hybrid ZNN models are proposed for the LTV systems stabilization, which combines the ZNNTV-ARE and FTZNNTV-ARE design rules. Note that instead of the infinite exponential convergence specific to the ZNNTV-ARE design, the structure of the proposed FTZNNTV-ARE dynamic is based on a new evolution formula which is able to converge to a theoretical solution in finite time. Furthermore, we are only interested in real symmetric solutions of TV-ARE, so the ZNNTV-ARE and FTZNNTV-ARE models are designed to produce such solutions. Numerical findings, one of which includes an application to LTV systems stabilization, confirm the effectiveness of the introduced dynamical evolutions.
AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications

Production high-performance computing (HPC) systems are adopting and integrating GPUs into their design to accommodate artificial intelligence (AI), machine learning, and data visualization workloads. To aid with the design and operations of new and existing GPU-based large-scale systems, we provide a detailed characterization of system operations, job characteristics, user behavior, and trends on a contemporary GPU-accelerated production HPC system. Our insights indicate that the pre-mature phases in modern AI workflow take up significant GPU hours while underutilizing GPUs, which opens up the opportunity for a multi-tier system. Finally, we provide various potential recommendations and areas for future investment for system architects, operators, and users.
Density Map-based vehicle counting in remote sensing images with limited resolution

Observing traffic flow is of great significance to contemporary urban management. Overhead images, as represented by remote sensing images, provide a major source of information about traffic flow. However, the spatial resolutions of most common high-resolution remote sensing images are often limited to 0.5 m and even below, which makes it unrealistic to count vehicles by means of widely used object detection methods. Therefore, to explore the potential of remote sensing data for studying global urban development and management, this paper introduces a density map-based vehicle counting method for remote sensing imagery with limited resolution. Density map-based models regard the vehicle counting task as estimating the density of vehicle targets in terms of pixel values. We propose an improved CNN-based network, called Congested Scene Recognition Network Minus (CSRNet—), that generates a density map of vehicles from the input remote sensing imagery. A new dataset, RSVC2021, which was generated from the public DOTA and ITCVD datasets, is also introduced for network training and testing. A benchmark on the RSVC2021 dataset is accordingly established and CSRNet— is selected as the baseline model for subsequent experiments. A set of GF-2 time series images with a resolution of 1 m taken before, during and after the COVID-19 epidemic lockdown covering Wuhan city are applied for real-world application testing. The testing results on both the RSVC2021 dataset and real satellite images confirm that, in terms of both the counting values and the visualized density maps, the proposed method achieves good performance and exhibits considerable application potential in this task. The generating codes of RSVC2021 dataset will be publicly available at https://github.com/YinongGuo/RSVC2021-Dataset.
Semantic consistency learning on manifold for source data-free unsupervised domain adaptation

Recently, source data-free unsupervised domain adaptation (SFUDA) attracts increasing attention. Current work shows that the geometry of the target data is helpful to solving this challenging problem. However, these methods define the geometric structures in Euclidean space. The geometry cannot completely draw the semantic relationship between the target data distributed on a manifold. This article proposed a new SFUDA method, semantic consistency learning on manifold (SCLM), to address this problem. Firstly, we generated pseudo-labels for the target data using a new clustering method, EntMomClustering, that enhanced k-means clustering by fusing the entropy momentum. Secondly, we constructed semantic neighbor topology (SNT) to capture complete geometric information on the manifold. Specifically, in SNT, the global neighbor was detected by a developed collaborative representation-based manifold projection, while the local neighbors were obtained by similarity comparison. Thirdly, we performed a semantic consistency learning on SNT to drive a new kind of deep clustering where SNT was taken as the basic clustering unit. To ensure SNT move as entirety, in the developed objective, the entropy regulator was constructed based on a semantic mixture fused on SNT, while the self-supervised regulator encouraged similar classification on SNT. Experiments on three benchmark datasets show that our method achieves state-of-the-art results. The code is available on https://github.com/tntek/SCLM.
Effects of Resistance Barriers to Service Robots on Alternative Attractiveness and Intention to Use

This study examined the effect of resistance barriers on alternative attractiveness of service robots and intention to use to explain the effect of innovation resistance against such robots on customer behavior. An online survey was conducted with South Korean customers who had used hotel rooms in the past year. This study tested its hypotheses with partial least squares–structural equation modeling. The results indicate that usage barriers related to customers’ familiarity with use and tradition as well as image barriers caused by psychological instability and decreased alternative attractiveness. However, the value and risk barriers based on technological level had no significant effect on alternative attractiveness. The results show that alternative attractiveness increases intention to use; however, alternative attractiveness affected by resistance barriers decreases intention to use. This study broadens the research scope by applying the innovation resistance theory to prior service robot literature with pro-innovation bias. Hospitality managers can refer to these results to design service environments that can lower the resistance barrier against using service robots.
Software defect prediction via optimal trained convolutional neural network

By creating an effective prediction model, SDP helps to find the possible problems in recent components of software earlier. The model's effectiveness was harmed by characteristics that were irrelevant or redundant. This article will present a new SDP (Software Defect Prediction) framework with several stages. Firstly, the input data proceeded through a preprocessing stage. Statistical features (variance, mean), raw features, higher-order statistical features, as well as suggested correlations are obtained from the preprocessed data. Weighted average & continuous probability distribution are among the statistical features of mean, whereas discrete random variables are among the statistical features of variance. Furthermore, to choose the necessary characteristics, improved PCA (Principle Component Analysis) is employed. Next, the chosen features are used to predict defects using an improved CNN (Convolutional Neural Network). The CNN weights are tuned optimally by a proposed hybrid SALO (Seagull Adopted Ant Lion Optimization) model for making the detection highly exact and precise. To analyze the performance of this work, certain performance measures have been used.
How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing

Abstract Deep learning (DL) models for natural language processing (NLP) tasks often handle private data, demanding protection against breaches and disclosures. Data protection laws, such as the European Union’s General Data Protection Regulation (GDPR), thereby enforce the need for privacy. Although many privacy-preserving NLP methods have been proposed in recent years, no categories to organize them have been introduced yet, making it hard to follow the progress of the literature. To close this gap, this article systematically reviews over sixty DL methods for privacy-preserving NLP published between 2016 and 2020, covering theoretical foundations, privacy-enhancing technologies, and analysis of their suitability for real-world scenarios. First, we introduce a novel taxonomy for classifying the existing methods into three categories: data safeguarding methods, trusted methods, and verification methods. Second, we present an extensive summary of privacy threats, datasets for applications, and metrics for privacy evaluation. Third, throughout the review, we describe privacy issues in the NLP pipeline in a holistic view. Further, we discuss open challenges in privacy-preserving NLP regarding data traceability, computation overhead, dataset size, the prevalence of human biases in embeddings, and the privacy-utility tradeoff. Finally, this review presents future research directions to guide successive research and development of privacy-preserving NLP models.
The Bibliometric Analysis on Finite Mixture Model

A finite mixture model is well-known in statistics due to its versatility and is being actively researched. This paper reviews finite mixture model literature via bibliometric analysis, focusing on the trend and link between finite mixture model studies. The bibliometric analysis consists of four main phases; formulating research questions, locating research, research selection, evaluation, and analyzing and synthesizing selected papers. There are 667 journal articles extracted from the Web of Science (WoS) database from publications within 1988 to 2020. The Biblioshiny with R packages and VOSViewer were used as analytical tools. The findings show that there is an increasing trend of annual publication on the finite mixture model study. The results also outline key journals and the highest cited articles. Network analysis was also conducted and explored in scientific cooperation in the finite mixture model study. This study proposed a research agenda in the finite mixture model by identifying its current state and population trends.
Cybersecurity in the food and beverage industry: A reference framework

Given the tendency of food companies to adopt digital technologies throughout the food supply chain and the importance assumed by the security and reliability of these systems in Industry 4.0 applications, we carried out a systematic literature review of cybersecurity issues in food and beverage industry. The research methodology was defined based on the PRISMA guidelines, and a reference framework was created that leveraged a thematic analysis of the following content categories: definitions, focus on the food and beverage industry, characterization of cybersecurity, and management of cybersecurity risks. This analysis allowed us to compare the advancement in cybersecurity in the food and beverage industry with those proposed in the literature for the Industry 4.0 paradigm. This comparison is useful, since although the Industry 4.0 paradigm finds a wide range of applications in such domain (e.g., Agriculture 4.0), the structural characteristics of this sector, its supply chain and products create a specific context and situations that may require ad hoc cybersecurity strategies and solutions. The body of knowledge on the food and beverage industry resulted mature in terms of definitions, the target supply chain, industrial assets, and cyber-threats, but requires future research effort in terms of practical analysis that can fill the emerging gaps in relation to risks, countermeasures, vulnerabilities, solutions and guidelines. The results of our review guided discussions, implications and definition of future research routes.
Architectural Attack Propagation Analysis for Identifying Confidentiality Issues

Exchanging data between different systems enables us to build new smart services and digitise various areas of our daily life. This digitalisation leads to more efficient usage of resources, and an increased monetary value. However, the connection of different systems also increases the number of potential vulnerabilities. The vulnerabilities on their own might be harmless, but attackers could build attack paths based on the combination of different vulnerabilities. Additionally, attackers might exploit existing access control policies to further propagate through the system. For analysing this dependency between vulnerabilities and access control policies, we extended an architecture description language (ADL) to model access control policies and specify vulnerabilities. We developed an attack propagation analysis operating on the extended ADL, which can help to determine confidentiality violations in a system. We evaluated our approach by analysing the accuracy and the effort compared to a manual analysis using different scenarios in three case studies. The results indicate that our analysis is capable of identifying attack paths and reducing the effort compared to manual detection.
Vulnerability Detection via Multimodal Learning: Datasets and Analysis

A vulnerability is a weakness that can be exploited by an attacker, e.g., performing unauthorized actions within a computer system. For example, privilege escalation is a type of vulnerability in software, which can be used to gain elevated access to resources that are normally protected from an application or user. However, most applications contain vulnerabilities, some are fixed over time by patches, but many are discovered only after exploitation, which results in steep costs. Furthermore, program analysis tools are generally quite difficult to use. Security analysts still do manual investigation on software, i.e., using static analysis tools on machine code or source code to find bugs. Multimodal learning has been widely used in image processing, but is rarely seen in software security. We introduce a new dataset for multimodal deep learning, MVDSC-C (Multisource for Vulnerability Detection in Source Code - C/C++). Our preliminary results show that combined modalities perform better than single modalities.
Alleviating the attribute conditional independence and I.I.D. assumptions of averaged one-dependence estimator by double weighting

Learning Bayesian network classifiers (BNCs) from data is NP-hard. Of numerous BNCs, averaged one-dependence estimator (AODE) performs extremely well against more sophisticated newcomers, and its trade-off between bias and variance can be attributed to the independence assumption and i.i.d. assumption, which respectively address the issues of structure complexity and data complexity. To alleviate these assumptions and improve AODE, we propose to apply double weighting, including attribute weighting and model weighting, to finely tune the estimates of conditional probability based on generative learning and joint probability based on discriminative learning, respectively. Instance weighting is introduced to define the information-theoretic metrics for identifying the variation in probability distributions for different data points. This highly scalable learning approach can establish a decision boundary that is specifically tailored to each instance. Our extensive experimental evaluation on 34 datasets from the UCI machine learning repository shows that, attribute weighting and model weighting are complementary although they can work separately. The proposed AODE applying double weighting schema, called DWAODE, is a competitive alternative to other weighting approaches. The experimental results show that DWAODE demonstrates significant advantage in terms of zero–one loss, bias–variance decomposition, RMSE (root mean squared error), Friedman and Nemenyi tests.
vol2Brain: A New Online Pipeline for Whole Brain MRI Analysis

Automatic and reliable quantitative tools for MR brain image analysis are a very valuable resource for both clinical and research environments. In the past few years, this field has experienced many advances with successful techniques based on label fusion and more recently deep learning. However, few of them have been specifically designed to provide a dense anatomical labeling at the multiscale level and to deal with brain anatomical alterations such as white matter lesions (WML). In this work, we present a fully automatic pipeline (vol2Brain) for whole brain segmentation and analysis, which densely labels (N > 100) the brain while being robust to the presence of WML. This new pipeline is an evolution of our previous volBrain pipeline that extends significantly the number of regions that can be analyzed. Our proposed method is based on a fast and multiscale multi-atlas label fusion technology with systematic error correction able to provide accurate volumetric information in a few minutes. We have deployed our new pipeline within our platform volBrain (www.volbrain.upv.es), which has been already demonstrated to be an efficient and effective way to share our technology with the users worldwide.
Restoration of speckle noise corrupted SAR images using regularization by denoising

Speckle noise removal is a well-established problem in synthetic aperture radar (SAR) image processing. Among different methods focused on the reconstruction of SAR images, variational models have achieved state-of-the-art performance. In this paper, a Rayleigh based speckle reduction algorithm is developed using the variational framework. The forward model is combined with recently proposed regularization by denoising (RED) prior. However, RED has been proposed in literature for the additive noise model. Multiplicative noise in SAR images prevents the direct application of RED to variational models. Hence, logarithm transformation is applied to change the multiplicative noise model to additive model, and the forward model from Rayleigh to Fisher–Tippett distribution. The resulting optimization problem is solved using the alternating direction method of multipliers. Further, the proof of the convergence analysis is carried out for the above framework. Simulations convey that the proposed method has better despeckling performance compared to that of state-of-the-art methods.
Transductive distribution calibration for few-shot learning

Few-shot image classification aims at learning a model from previous experiences that can be rapidly adapted to classify images of new classes with a few labeled examples. The learned model is easy to overfit since the distributions of new classes formed by a small number of samples are severely biased. Recently, Distribution Calibration (DC) tackles this problem by transferring the Gaussian statistics of seen classes with sufficient samples to calibrate the distributions of new classes. In this paper, we first take a closer look at the calibration mechanism from the source class distribution to the new class distribution in DC and propose a simplified version using averaged mean and covariance of all base classes as source statistics for all new classes. We further extend the simplified DC to the transductive setting. We extract the Gaussian statistics of unlabeled query samples to calibrate the distributions of new classes. We augment the labeled samples by sampling from the calibrated distributions to train a more accurate task-specific classifier. Our method can be readily applied on top of any existing pre-trained feature extractor and classifier without extra learnable parameters. Extensive experiments on several few-shot learning benchmarks demonstrate the effectiveness of our method. We provide visualizations to show that new classes are better separated under our calibrated distributions.