Reviewerly/exHarmony huggingface dataset:
Dynamic linkages among transparency, income inequality and economic growth in developing countries: Evidence from panel vector autoregressive (PVAR) model

This study examines the dynamic linkage among transparency, financial development, income inequality and economic growth of 68 developing countries from 2000 to 2016, using a Panel Vector Autoregressive (PVAR) model. Two transparency indicators namely: (i) World Governance Indicators (WGI) and (ii) Global Index of Information Transparency and Accountability (GIITA). The empirical results indicate that transparency index from WGI is positively significant determinant of economic growth, while the GIITA transparency index is positive but insignificant. Both transparency indicators, however, have no effect on income inequality. In terms of Granger causality, there is a uni-directional causal effect running from transparency to economic growth. The analysis also incorporated financial development in the model and this variable is positive and significant Granger causes economic growth but not income inequality. The variance decomposition and impulse response functions results are consistent with the panel causality results.
Convolutional Neural Network-Based Computer-Assisted Diagnosis of Hashimoto’s Thyroiditis on Ultrasound

This study investigates the efficiency of deep learning models in the automated diagnosis of Hashimoto's thyroiditis (HT) using real-world ultrasound data from ultrasound examinations by computer-assisted diagnosis (CAD) with artificial intelligence.We retrospectively collected ultrasound images from patients with and without HT from 2 hospitals in China between September 2008 and February 2018. Images were divided into a training set (80%) and a validation set (20%). We ensembled 9 convolutional neural networks (CNNs) as the final model (CAD-HT) for HT classification. The model's diagnostic performance was validated and compared to 2 hospital validation sets. We also compared the accuracy of CAD-HT against seniors/junior radiologists. Subgroup analysis of CAD-HT performance for different thyroid hormone levels (hyperthyroidism, hypothyroidism, and euthyroidism) was also evaluated.39 280 ultrasound images from 21 118 patients were included in this study. The accuracy, sensitivity, and specificity of the HT-CAD model were 0.892, 0.890, and 0.895, respectively. HT-CAD performance between 2 hospitals was not significantly different. The HT-CAD model achieved a higher performance (P < 0.001) when compared to senior radiologists, with a nearly 9% accuracy improvement. HT-CAD had almost similar accuracy (range 0.87-0.894) for the 3 subgroups based on thyroid hormone level.The HT-CAD strategy based on CNN significantly improved the radiologists' diagnostic accuracy of HT. Our model demonstrates good performance and robustness in different hospitals and for different thyroid hormone levels.
Improved dynamic adaptive ant colony optimization algorithm to solve pipe routing design

With the purpose of finding a satisfactory pipe path between the starting point and target point, pipe routing design (PRD) has been applied in many industry fields. The research of two-dimensional PRD is the foundation of solving complex RPD problems, and has widely applications in factory layout, facilities installation, and so on. The ant colony optimization (ACO) algorithm is one of the most widely used approaches to solve PRD. However, the traditional ACO has drawbacks such as slow convergence speed, easy to fall into local optimum and low efficiency. In this study, an improved dynamic adaptive ACO (IDAACO) is proposed. The IDAACO includes four novel mechanisms which are the heuristic strategy with direction information, adaptive pseudorandom transfer strategy, improved local pheromone updating mechanism and improved global pheromone updating mechanism. Then, a series of experiments are carried out to verify the effectiveness of the four proposed mechanisms included by IDAACO. Subsequently, the IDAACO is compared with several existing approaches for solving PRD, and the experimental results confirm the advantages of IDAACO in terms of the practicality and high-efficiency. Finally, the IDAACO is used to solve the PRD problem for semi-submersible production platform in oil and gas industry.
Soft Person Reidentification Network Pruning via Blockwise Adjacent Filter Decaying

Deep learning has shown significant successes in person reidentification (re-id) tasks. However, most existing works focus on discriminative feature learning and impose complex neural networks, suffering from low inference efficiency. In fact, feature extraction time is also crucial for real-world applications and lightweight models are needed. Prevailing pruning methods usually pay attention to compact classification models. However, these methods are suboptimal for compacting re-id models, which usually produce continuous features and are sensitive to network pruning. The key point of pruning re-id models is how to retain the original filter distribution in continuous features as much as possible. In this work, we propose a blockwise adjacent filter decaying method to fill this gap. Specifically, given a trained model, we first evaluate the redundancy of filters based on the adjacency relationships to preserve the original filter distribution. Second, previous layerwise pruning methods ignore that discriminative information is enhanced block-by-block. Therefore, we propose a blockwise filter pruning strategy to better utilize the block relations in the pretrained model. Third, we propose a novel filter decaying policy to progressively reduce the scale of redundant filters. Different from conventional soft filter pruning that directly sets the filter values as zeros, the proposed filter decaying can keep the pretrained knowledge as much as possible. We evaluate our method on three popular person reidentification datasets, that is: 1) Market-1501; 2) DukeMTMC-reID; and 3) MSMT17_V1. The proposed method shows superior performance to the existing state-of-the-art pruning methods. After pruning over 91.9% parameters on DukeMTMC-reID, the Rank-1 accuracy only drops 3.7%, demonstrating its effectiveness for compacting person reidentification.
Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

With the technological advancements of the modern era, the easy availability of image editing tools has dramatically minimized the costs, expense, and expertise needed to exploit and perpetuate persuasive visual tampering. With the aid of reputable online platforms such as Facebook, Twitter, and Instagram, manipulated images are distributed worldwide. Users of online platforms may be unaware of the existence and spread of forged images. Such images have a significant impact on society and have the potential to mislead decision-making processes in areas like health care, sports, crime investigation, and so on. In addition, altered images can be used to propagate misleading information which interferes with democratic processes (e.g., elections and government legislation) and crisis situations (e.g., pandemics and natural disasters). Therefore, there is a pressing need for effective methods for the detection and identification of forgeries. Various techniques are currently employed for the identification and detection of these forgeries. Traditional techniques depend on handcrafted or shallow-learning features. In traditional techniques, selecting features from images can be a challenging task, as the researcher has to decide which features are important and which are not. Also, if the number of features to be extracted is quite large, feature extraction using these techniques can become time-consuming and tedious. Deep learning networks have recently shown remarkable performance in extracting complicated statistical characteristics from large input size data, and these techniques efficiently learn underlying hierarchical representations. However, the deep learning networks for handling these forgeries are expensive in terms of the high number of parameters, storage, and computational cost. This research work presents Mask R-CNN with MobileNet, a lightweight model, to detect and identify copy move and image splicing forgeries. We have performed a comparative analysis of the proposed work with ResNet-101 on seven different standard datasets. Our lightweight model outperforms on COVERAGE and MICCF2000 datasets for copy move and on COLUMBIA dataset for image splicing. This research work also provides a forged percentage score for a region in an image.
An Efficient Privacy-Preserving Public Auditing Protocol for Cloud-Based Medical Storage System

The booming Internet of Things makes smart healthcare a reality, while cloud-based medical storage systems solve the problems of large-scale storage and real-time access of medical data. The integrity of medical data outsourced in cloud-based medical storage systems has become crucial since only complete data can make a correct diagnosis, and public auditing protocol is a key technique to solve this problem. To guarantee the integrity of medical data and reduce the burden of the data owner, we propose an efficient privacy-preserving public auditing protocol for the cloud-based medical storage systems, which supports the functions of batch auditing and dynamic update of data. Detailed security analysis shows that our protocol is secure under the defined security model. In addition, we have conducted extensive performance evaluations, and the results indicate that our protocol not only remarkably reduces the computational costs of both the data owner and the third-party auditor (TPA), but also significantly improves the communication efficiency between the TPA and the cloud server. Specifically, compared with other related work, the computational cost of the TPA in our protocol is negligible and the data owner saves more than 2/3 of computational cost. In addition, as the number of challenged blocks increases, our protocol saves nearly 90% of communication overhead between the TPA and the cloud server.
Deep neural network-based relation extraction: an overview

Knowledge is a formal way of understanding the world, providing human-level cognition and intelligence for the next-generation artificial intelligence (AI). An effective way to automatically acquire this important knowledge, called Relation Extraction (RE), plays a vital role in Natural Language Processing (NLP). To date, there are amount of studies for RE in previous works, among which these technologies based on deep neural networks (DNNs) have become the mainstream direction of this research. In particular, the supervised and distant supervision methods based on DNNs are the most popular and reliable solutions for RE, whose various evolutions on structure and settings have affected this task. Understanding the model structure and related settings will give the researchers a deep insight into RE. However, little research has been done on them. Hence, this paper starts from these two points and carries out analysis around the mainstream research routes, supervised and distant supervision. Meanwhile, we classify all related works according to the evolution of model structure to facilitate the analysis. Finally, we discuss some challenges of RE and give out our conclusion.
A Systematic Review on Oral Interactions in Robot-Assisted Language Learning

Although educational robots are known for their capability to support language learning, how actual interaction processes lead to positive learning outcomes has not been sufficiently examined. To explore the instructional design and the interaction effects of robot-assisted language learning (RALL) on learner performance, this study systematically reviewed twenty-two empirical studies published between 2010 and 2020. Through an inclusion/exclusion procedure, general research characteristics such as the context, target language, and research design were identified. Further analysis on oral interaction design, including language teaching methods, interactive learning tasks, interaction processes, interactive agents, and interaction effects showed that the communicative or storytelling approach served as the dominant methods complemented by total physical response and audiolingual methods in RALL oral interactions. The review provides insights on how educational robots can facilitate oral interactions in language classrooms, as well as how such learning tasks can be designed to effectively utilize robotic affordances to fulfill functions that used to be provided by human teachers alone. Future research directions point to a focus on meaning-based communication and intelligibility in oral production among language learners in RALL.
A novel phishing detection system using binary modified equilibrium optimizer for feature selection

The Digital era faces security concerns due to the explosive growth of cyber-attacks, such as phishing attacks, man-in-the-middle attacks, and many more. Phishing attackers deceive users by legitimate counterfeit websites that tend users to provide confidential information on a phishing website. This paper proposes a new phishing detection system that uses Binary Modified Equilibrium Optimizer (BMEO) with a proposed AV-shape transfer function (AV-BMEO) and k-nearest neighbor classifier. AV-BMEO having high exploration and exploitation abilities is used for the feature selection and classifier's hyperparameter optimization. The AV-shape transfer function is designed based on opposition-based learning to enhance the proposed algorithm's exploration capabilities. The statistical validation proves the better performance of AV-BMEO compared to seventeen algorithms in terms of accuracy and number of selected features on eighteen datasets. Further validation shows an improved prediction of phishing websites with the optimum number of features compared to several state-of-art techniques on phishing datasets.
Predictive Analytics for Recognizing Human Activities Using Residual Network and Fine-Tuning

Human Action Recognition (HAR) is a rapidly growing study area in computer vision due to its wide applicability. Because of their varied appearance and the broad range of stances that they can assume, detecting individuals in images is a difficult undertaking. Due to its superior performance over existing machine learning methods and high universality over raw inputs, deep learning is now widely used in a range of study fields. For many visual recognition tasks, the depth of representations is critical. For better model robustness and performance, more complex features can represent using deep neural networks but the training of these model are hard due to vanishing gradients problem. The use of skip connections in residual networks (ResNet) helps to address this problem and easy to learn identity function by residual block. So, ResNet overcomes the performance degradation issue with deep networks. This paper proposes an intelligent human action recognition system using residual learning-based framework “ResNet-50” with transfer learning which can automatically recognize daily human activities. The proposed work presents extensive empirical evidence demonstrating that residual networks are simpler to optimize and can gain accuracy from significantly higher depth. The experiments are performed using the UTKinect Action-3D public dataset of human daily activities. According to the experimental results, the proposed system outperforms other state-of-the-art methods and recorded high recognition accuracy of 98.25% with a 0.11 loss score in 200 epochs.
An approach to capturing and reusing tacit design knowledge using relational learning for knowledge graphs

Tacit design knowledge plays an important role in the process of product design and is a valuable knowledge asset for enterprises. In terms of the characteristics of tacit rational design knowledge, this paper puts forward a scientific hypothesis and approach on capturing and reusing tacit rational design knowledge. The presented approach represents the observable design result facts of products using design knowledge graphs. A design issue-solving oriented knowledge graph model is presented, where directed relation edges represent design issues, and nodes stand for design solutions. When a new design solutions requirement needs to be searched, tacit design knowledge can be reused by relational learning for the constructed design knowledge graphs. In relational learning, the design knowledge graph is converted into a three-order tensor, where two modes are solution nodes, and the third mode holds the issue relations. Then, a tensor factorization approach is employed to calculate the latent features between design solutions for an issue relation. As a result, a score vector to represent the existence of issue-solution relations can be obtained. By sorting the scores in descending order, we may select the solution node with the highest score as the design solution to be searched. Finally, a stamping die design case study is provided. The case study shows that the proposed approach is feasible, and effective, and has better flexibility, scalability and efficiency than CBR methods.
Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model

With social media’s dominating role in the socio-political landscape, several existing and new forms of racism took place on social media. Racism has emerged on social media in different forms, both hidden and open, hidden with the use of memes and open as the racist remarks using fake identities to incite hatred, violence, and social instability. Although often associated with ethnicity, racism is now thriving based on color, origin, language, cultures, and most importantly religion. Social media opinions and remarks provocating racial differences have been regarded as a serious threat to social, political, and cultural stability and have threatened the peace of different countries. Consequently, social media being the leading source of racist opinions dissemination should be monitored and racism remarks should be detected and blocked timely. This study aims at detecting Tweets that contain racist text by performing the sentiment analysis of Tweets. Owing to the superior performance of deep learning, a stacked ensemble deep learning model is assembled by combining gated recurrent unit (GRU), convolutional neural networks (CNN), and recurrent neural networks RNN, called, Gated Convolutional Recurrent- Neural Networks (GCR-NN). GRU is on the top in the GCR-NN model to extract the suitable and prominent features from raw text, CNN extracts important features for RNN to make accurate predictions. Obviously, several experiments are conducted to investigate and analyze the performance of the proposed GCR-NN within the scope of machine learning and deep learning models indicating the superior performance of GCR-NN with increased 0.98 accuracy. The proposed GCR-NN model can detect 97% of the tweets that contain racist comments.
Medical blockchain: Data sharing and privacy preserving of EHR based on smart contract

No doubt that medical data sharing is very crucial but important in realizing cross-hospital diagnosis and improving research development. In traditional mechanisms, there always exists a tradeoff between correctness of medical data and patient privacy. Here we are going to introduce the concept of blockchain and smart contract to build up an electronic medical record sharing mechanism: medical blockchain. Specifically, we have implemented the medical record sharing through cryptography design. Simulations have demonstrated that the correctness of medical data and privacy of patient can be guaranteed through the adoption of blockchain, while the integrity of a specific patient can be achieved via the smart contract control. Furthermore, the proposed medical blockchain can resist the potential Internet attack simulated by the formal verification. Thus, no leakage of patient identity occurs, and the tradeoff could be eliminated effectively without the modification of current hospital devices according to the simulation results.
Fault-tolerant scheduling and data placement for scientific workflow processing in geo-distributed clouds

Scientific workflow processing in geo-distributed cloud is crucial for the scheduling of large-scale tasks and the massive data placement among tasks. However, the task execution time and energy consumption of data transmission are two urgent issues when a scientific workflow is processed in the different geo-distributed data centers. Aiming at the data placement problem, this paper proposes a Lagrangian relaxation method. This method considers the workload balance, storage capacity, data dependency, transmission bandwidth, and transmission cost to obtain the minimum time of data transmission time. Aiming at the task scheduling problems, a fault-tolerant scheduling strategy is proposed. The strategy optimizes the task scheduling mechanism by considering the task execution time and energy consumption. Finally, the performance of the proposed methods is evaluated via extensive experiments In terms of the data placement, the experiment results imply that the data transmission time of the proposed relaxation algorithm can averagely achieve up to 14.61%, 38.03%, and 39.57% reduction of over that of ILP-FDP algorithm, GA-DPSO algorithm, and GPDP algorithm, respectively. As for the fault-tolerant scheduling, the energy consumption of the TSPT algorithm is the lowest. Compared with the MTS algorithm, EODS algorithm and EWTS algorithm, the average gains of the proposed algorithm are 15.33%, 16.65%, and 28.96%, respectively. Compared with the benchmark algorithms, the task execution time of the TSPT algorithm can averagely reduce up to 12.78,18.85 and 25.65, respectively.
A comparison of satellite cloud motion vectors techniques to forecast intra-day hourly solar global horizontal irradiation

Solar forecasting provides valuable information for grid management. Satellite-based forecasting tools account for the short-term intra-day time horizons, typically outperforming numerical weather predictions up to 4–5 h ahead. The method consist of three separated stages, namely, cloud motion estimation, motion extrapolation and satellite-to-irradiation conversion. In this work we compare different satellite-based proposals for hourly irradiation forecast up to 5 h ahead using a 2-years data set. The widely-used Lorenz’s block matching technique and four optical flow (OF) algorithms are assessed, both at image and irradiation levels. All the methods are locally optimized to obtain their peak performance. It is found that the OF algorithm which combines an L1 data penalty term on the optical flow equation with total variation regularization (TVL1) outperforms the rest. Different image extrapolation approaches and spatial smoothing are also tested. It is found that changing the extrapolation technique does not have much impact in the overall performance and that important gains can be obtained by optimally smoothing the predicted images previous to solar irradiation conversion. By doing this, all methods outperform the exigent convex persistence benchmark, achieving positive forecasting skills. The tests are performed using GOES-East satellite images of south-east South America, and the methods’ optimal parameters are given.