Reviewerly/exHarmony huggingface dataset:
PRMS: Design and Development of Patients’ E-Healthcare Records Management System for Privacy Preservation in Third Party Cloud Platforms

In the current digital era, personal data storage on public platforms is a major cause of concern with severe security and privacy ramifications.This is true especially in e-health data management since patient's health data must be managed following a slew of established standards.The Cloud Service Providers (CSPs) primarily provide computing and storage resources.However, data security in the cloud is still a major concern.In several instances, Blockchain technology rescues the CSPs by providing the robust security to the underlying data by encrypting data using the unique and secret keys.Each network user in Blockchain has its own unique and secret keys linked directly to the transaction keys as a digital signature to protect the data.However, Blockchain technology suffers from the latency and throughput issues in high workload scenarios.To overcome e-healthcare records privacy issues in a third-party cloud, we designed a Patient's E-Healthcare Records Management System (PRMS) that focuses on latency and throughput.A comprehensive performance analysis of PRMS is carried out on different third-party clouds to validate its applicability.Moreover, the proposed PRMS system is compared with Blockchain platforms such as Hyperledger Fabric v0.6 and Etherium 1.5.8 against latency and throughput by adjusting the workload for each platform up to 10,000 transactions per second.The proposed PRMS is compared to the Secure and Robust Healthcare-Based Blockchain (SRHB) approach using Yahoo Cloud Serving Benchmark (YCSB) and small bank datasets.The experimental results indicate that deploying PRMS on Amazon Web Services decreases System Execution Time (SET) and the Average Delay (AD) time by 2.4%, 8.33%, and 25.15%, 15.26%, respectively.Additionally, deploying PRMS on the Google Cloud Platform decreases System Execution Time (SET) and Average Delay (AD) by 2.27%, 2.4%, and 2.72%, 4.73% AD, respectively.The experimental results confirm the superiority of the PRMS under the high workload scenario over SRHB and its applicability in cloud data centers.
AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

Artificial intelligence is changing the world, especially the interaction between machines and humans.Learning and interpreting natural languages and responding have paved the way for many technologies and applications.The amalgam of machine learning, deep learning, and natural language processing helped Conversational Artificial Intelligence (AI) to change the face of Human-Computer Interaction (HCI).A conversational agent is an excellent example of conversational AI, which imitates the natural language.This article presents a sweeping overview of conversational agents that includes different techniques such as pattern-based, machine learning, and deep learning used to implement conversational agents.It also discusses the panorama of different tasks in conversational agents.This study also focuses on how conversational agents can simulate human behavior by adding emotions, sentiments, and affect to the context.With the advancements in recent trends and the rise in deep learning models, the authors review the deep learning techniques and various publicly available datasets used in conversational agents.This article unearths the research gaps in conversational agents and gives insights into future directions.
Federated Learning and Its Role in the Privacy Preservation of IoT Devices

Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections. As researchers in the field promote ML configurations containing a large amount of private data, systems and infrastructure must be developed to improve the effectiveness of advanced learning systems. This study examines FL in-depth, focusing on application and system platforms, mechanisms, real-world applications, and process contexts. FL creates robust classifiers without requiring information disclosure, resulting in highly secure privacy policies and access control privileges. The article begins with an overview of FL. Then, we examine technical data in FL, enabling innovation, contracts, and software. Compared with other review articles, our goal is to provide a more comprehensive explanation of the best procedure systems and authentic FL software to enable scientists to create the best privacy preservation solutions for IoT devices. We also provide an overview of similar scientific papers and a detailed analysis of the significant difficulties encountered in recent publications. Furthermore, we investigate the benefits and drawbacks of FL and highlight comprehensive distribution scenarios to demonstrate how specific FL models could be implemented to achieve the desired results.
Exploratory hypothesis tests can be more compelling than confirmatory hypothesis tests

Preregistration has been proposed as a useful method for making a publicly verifiable distinction between confirmatory hypothesis tests, which involve planned tests of ante hoc hypotheses, and exploratory hypothesis tests, which involve unplanned tests of post hoc hypotheses.This distinction is thought to be important because it has been proposed that confirmatory hypothesis tests provide more compelling results (less uncertain, less tentative, less open to bias) than exploratory hypothesis tests.In this article, we challenge this proposition and argue that there are several advantages of exploratory hypothesis tests that can make their results more compelling than those of confirmatory hypothesis tests.We also consider some potential disadvantages of exploratory hypothesis tests and conclude that their advantages can outweigh the disadvantages.We conclude that exploratory hypothesis tests avoid researcher commitment and researcher prophecy biases, reduce the probability of data fraud, are more appropriate in the context of unplanned deviations, facilitate inference to the best explanation, and allow peer reviewers to make additional contributions at the data analysis stage.In contrast, confirmatory hypothesis tests may lead to an inappropriate level of confidence in research conclusions, less appropriate analyses in the context of unplanned deviations, and greater bias and errors in theoretical inferences.
A comprehensive bibliometric analysis of Apache Hadoop from 2008 to 2020

Purpose The purpose of this paper is to make an overview of 6,618 publications of Apache Hadoop from 2008 to 2020 in order to provide a conclusive and comprehensive analysis for researchers in this field, as well as a preliminary knowledge of Apache Hadoop for interested researchers. Design/methodology/approach This paper employs the bibliometric analysis and visual analysis approaches to systematically study and analyze publications about Apache Hadoop in the Web of Science database. This study aims to investigate the topic of Apache Hadoop by means of bibliometric analysis with the aid of visualization applications. Through the bibliometric analysis of the collected documents, this paper analyzes the main statistical characteristics and cooperation networks. Research themes, research hotspots and future development trends are also investigated through the keyword analysis. Findings The research on Apache Hadoop is still the top priority in the future, and how to improve the performance of Apache Hadoop in the era of big data is one of the research hotspots. Research limitations/implications This paper makes a comprehensive analysis of Apache Hadoop with methods of bibliometrics, and it is valuable for researchers can quickly grasp the hot topics in this area. Originality/value This paper draws the structural characteristics of the publications in this field and summarizes the research hotspots and trends in this field in recent years, aiming to understand the development status and trends in this field and inspire new ideas for researchers.
GAN-Siamese Network for Cross-Domain Vehicle Re-Identification in Intelligent Transport Systems

The vehicle re-identification (Re-ID) has become one of most important techniques for tracking vehicles in intelligent transport system. Vehicle Re-ID aims at matching identical vehicle images captured by different surveillance cameras. Recent vehicle Re-ID approaches explored deep learning-based features or distance metric learning methods for vehicle matching. However, most of the existing approaches focus on the vehicle Re-ID in the same domain, but ignore the challenging cross-domain problem, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , identifying the identical vehicles in different domains including the day-time and night-time domain. To tackle this problem, we propose a GAN-Siamese network structure for vehicle Re-ID. In this network structure, a generative adversarial network (GAN)-based domain transformer is employed to transform the domains of two input vehicle images to another domains, and then a four-branch Siamese network is designed to learn two distance metrics between the images in the two domains, respectively. Finally, the two distances are fused to measure the final similarity between the two input images for vehicle Re-ID. Experimental results demonstrate the proposed GAN-Siamese network structure achieves the state-of-the-art performances on four large-scale vehicle datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , VehicleID, VERI-Wild, VERI-Wild 2.0, and VeRi776.
Orthorectification of Fisheye Image under Equidistant Projection Model

The fisheye camera, with its large viewing angle, can acquire more spatial information in one shot and is widely used in many fields. However, a fisheye image contains large distortion, resulting in that many scholars have investigated its accuracy of orthorectification, i.e., generation of digital orthophoto map (DOM). This paper presents an orthorectification method, which first determines the transformation relationship between the fisheye image points and the perspective projection points according to the equidistant projection model, i.e., determines the spherical distortion of the fisheye image; then introduces the transformation relationship and the fisheye camera distortion model into the collinearity equation to derive the fisheye image orthorectification model. To verify the proposed method, high accuracy of the fisheye camera 3D calibration field is established to obtain the interior and exterior orientation parameters (IOPs/EOPs) and distortion parameters of the fisheye lens. Three experiments are used to verify the proposed orthorectification method. The root mean square errors (RMSEs) of the three DOMs are averagely 0.003 m, 0.29 m, and 0.61 m, respectively. The experimental results demonstrate that the proposed method is correct and effective.
Efficient and optimal penetration path planning for stealth unmanned aerial vehicle using minimal radar cross-section tactics and modified A-Star algorithm

Penetration path planning for stealth unmanned aerial vehicles (SUAVs) in the integrated air defense system (IADS) has been a hot research topic in recent years. The present study examines penetration path planning in different threat environments. Firstly, for the complex terrain and static radar threats, a modified A-Star algorithm containing the bidirectional sector expansion and variable step search strategy is proposed to elude static threats rapidly. Then, with regard to bandit threats, the minimal radar cross-section (RCS) tactics are presented to achieve path replanning. Furthermore, the combinatorial methodology of the minimum RCS tactics and the modified A-Star algorithm is applied to achieve the dynamic path planning for SUAV. The simulation results indicate that the modified A-Star algorithm and minimal RCS tactics can significantly reduce the probability of radar system, which has better superiority in calculation efficiency, path cost and safety. And the minimal RCS tactics have better real-time performance and are more convenient in dealing with dynamic threats, which enhances the survivability of SUAV and verifies the effectiveness of the proposed methodology.
Wavelets behind the scenes: Practical aspects, insights, and perspectives

Over the years, wavelet-based analyses have been responsible for remarkable achievements in physics and related sciences. Nevertheless, a deep inspection on wavelet-based strategies described in recent scientific papers, dissertations, and theses reveals that a significant number of authors, i.e., students and even researchers with a modest background on signal analysis, still misunderstand the fundamentals of wavelets. One classical source of confusion, for instance, involves two different but related approaches used to perform discrete-time wavelet transformations and their inverses: algebra- and filter-based. Although the latter is usually adopted in practice, the former reveals the beauty of multiresolution analysis over Heisenberg’s uncertainty principle, showing what really happens behind the scenes. Thus, based on the solid and easy-to-follow explanations provided in this smoothly written tutorial-review article, interested readers will definitively comprehend the different types of wavelet transforms and their specific applications, getting hands-on experience and insights on how to extract the most of their research by using that powerful tool. Because its mission is clarification, example wavelet-related applications are provided in this document to stimulate state-of-the-art research in a diversity of branches in physics.
Survey on the Applications of Blockchain in Agriculture

Dating back many millennia, agriculture is an ancient practice in the evolution of civilization. It was developed when humans thought about it and concluded that not everyone in the community was required to produce food. Instead, specialized labor, tools, and techniques could help people achieve surplus food for their community. Since then, agriculture has continuously evolved across the ages and has occupied a vital, synergistic position in the existence of humanity. The evolution of agriculture was based on a compulsion to feed the growing population, and, importantly, maintain the quality and traceability of food, prevent counterfeit products, and modernize and optimize yield. Recent trends and advancements in blockchain technology have some significant attributes that are ideal for agriculture. The invention and implementation of blockchain have caused a fair share of positive disruptions and evolutionary adoption in agriculture to modernize the domain. Blockchain has been adopted at various stages of the agriculture lifecycle for improved evolution. This work presents an intense survey of the literature on how blockchain has positively impacted and continues to influence various market verticals in agriculture, the challenges and the future.
Comparing the performance of machine learning algorithms using estimated accuracy

In this paper, we have worked on comparing various data mining algorithms using R tool and various comparison models. After comparison has been done, we have applied the best algorithm as per the result to make the prediction. In this paper, we worked on how to check algorithms on a dataset using R and find the most accurate algorithm model for our dataset. It cannot be easy to tell what algorithm to use on the dataset to get the best results. We don't know the best parameters to use for particular algorithms. Here we worked on the strategy of trial and error to choose the accurate algorithm. Data set is used to train models and a test option is used to evaluate the model. Test metrics are used for comparison. Worked on the various models for which model to choose, to configure them, and pre-process them using data. Applied various techniques for comparing the accuracy of constructed models. We have worked on some algorithms compared them and after choosing one algorithm we can improve the result of algorithms by tuning various algorithms parameters by combining or changing parameters. Once we find the best algorithm applied on the dataset for prediction and tried to improve it by changing various criteria. Here in this paper, we have worked on the decision tree and tried to find out the best-resulting model for prediction. This learning opportunities can be further used in affordable energy, agriculture, and environmentally sound technologies etc.
Customer preference identification from hotel online reviews: A neural network based fine-grained sentiment analysis

As a kind of user-generated information, online reviews contain customers’ preferences for different aspects of hotels, which not only influence customers’ booking decisions but also help hotel managers to improve service quality of hotels timely. The key of deriving customers’ preferences from hotels’ online reviews is to identify fine-grained sentiment towards hotel attributes. In general, fine-grained sentiment analysis involves multiple fundamental tasks such as sentiment element extraction, aspect-opinion pair (i.e., AOP) identification and sentiment orientation analysis. However, existing fine-grained sentiment analysis methods cannot efficiently identify AOPs, especially when dealing with Chinese reviews. To this end, we construct an improved convolutional neural network (i.e., CNN) model, which can comprehensively utilize unstructured features and structured features, to improve the performance of AOP identification. We further propose a refined fine-grained sentiment analysis methodology to calculate accurate customer sentiment intensity value for each evaluated aspect rather than positive or negative sentiment, integrated with aspect term clustering algorithm, to identify customers’ specific preferences for different hotel attributes. Finally, to illustrate the reasonability and advantages of the proposed methodology, we conduct an empirical study with hotels’ online reviews crawled from Ctrip.com. Empirical results indicate that our proposed method can indeed improve the performance of AOP identification, and can effectively identify customer preferences from hotels’ online reviews. Furthermore, we find that customers show different preferences for different hotel attributes, and these vary across the types of customers.
Federated Learning for Edge Computing: A Survey

New technologies bring opportunities to deploy AI and machine learning to the edge of the network, allowing edge devices to train simple models that can then be deployed in practice. Federated learning (FL) is a distributed machine learning technique to create a global model by learning from multiple decentralized edge clients. Although FL methods offer several advantages, including scalability and data privacy, they also introduce some risks and drawbacks in terms of computational complexity in the case of heterogeneous devices. Internet of Things (IoT) devices may have limited computing resources, poorer connection quality, or may use different operating systems. This paper provides an overview of the methods used in FL with a focus on edge devices with limited computational resources. This paper also presents FL frameworks that are currently popular and that provide communication between clients and servers. In this context, various topics are described, which include contributions and trends in the literature. This includes basic models and designs of system architecture, possibilities of application in practice, privacy and security, and resource management. Challenges related to the computational requirements of edge devices such as hardware heterogeneity, communication overload or limited resources of devices are discussed.
Short-Term Solar Power Forecasting via General Regression Neural Network with Grey Wolf Optimization

With the increasing awareness of environmental protection and the support of national policy, as well as the maturing of solar power generation technology, solar power generation has become one of the most promising renewable energies. However, due to changes in external factors such as season, time, weather, cloud cover, etc., solar radiation is uncertain, and it is difficult to predict energy output, even for the next hour. This inherent instability is a particularly difficult issue for the prediction of energy output in the effective operation of solar power systems. This paper proposes a grey wolf optimization (GWO)-based general regression neural network (GRNN), which is expected to provide more accurate predictions with shorter computational times. Therefore, a self-organizing map (SOM) is utilized to realize the weather clustering and the training of the GRNN with a GWO model. The performance of the proposed model is investigated using short-term and ultra-short-term forecasting in different seasons. It is very important to accurately predict the PV power output. Moreover, the numerical results demonstrate that the proposed approach can significantly enhance the prediction accuracy of PV systems.
Context-Aware Mixup for Domain Adaptive Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source domain to an unlabeled target domain. Existing UDA-based semantic segmentation approaches always reduce the domain shifts in pixel level, feature level, and output level. However, almost all of them largely neglect the contextual dependency, which is generally shared across different domains, leading to less-desired performance. In this paper, we propose a novel Context-Aware Mixup (CAMix) framework for domain adaptive semantic segmentation, which exploits this important clue of context-dependency as explicit prior knowledge in a fully end-to-end trainable manner for enhancing the adaptability toward the target domain. Firstly, we present a contextual mask generation strategy by leveraging the accumulated spatial distributions and prior contextual relationships. The generated contextual mask is critical in this work and will guide the context-aware domain mixup on three different levels. Besides, provided the context knowledge, we introduce a significance-reweighted consistency loss to penalize the inconsistency between the mixed student prediction and the mixed teacher prediction, which alleviates the negative transfer of the adaptation, e.g., early performance degradation. Extensive experiments and analysis demonstrate the effectiveness of our method against the state-of-the-art approaches on widely-used UDA benchmarks.