Reviewerly/exHarmony huggingface dataset:
Translator's Style Through Lexical Bundles: A Corpus-Driven Analysis of Two English Translations of Hongloumeng

Based on a corpus-driven analysis of two translated versions of Hongloumeng (one by David Hawkes and the other by Xianyi Yang and Gladys Yang) in parallel corpora, this article investigates the use of lexical bundles in an attempt to trace the stylistic features and differences in the translations produced by the respective translators. The Hongloumeng corpus is developed at the sentence level to facilitate co-occurrence of the source texts and the two corresponding translations. For this purpose, the three-word and four-word lexical bundles were first extracted and then analyzed with respect to the functional classification proposed by Biber et al. ( 2004 ). The results of the study show that Hawkes' translation is embedded with a greater number and variety of lexical bundles than the one by the Yang couple. The study also identified the differences between the two versions which can be traced back to the deployment of different translation strategies of the translators, appearing in turn to be influenced by the language backgrounds of the translators, the translation skopos and settings, and the social, political, and ideological milieu in which the translations were produced.
Data-driven inference modeling based on an on-line Wang-Mendel fuzzy approach

To address the modeling of continuous production process with dynamic and nonlinear characteristics, an on-line Wang-Mendel fuzzy inference model is proposed in this paper, which extracts the fuzzy rules from the raw data without prior knowledge. Considering the state of the industrial system changes dynamically online, typical data samples with support degrees are used to describe the sample distribution characteristics in each fuzzy region. With respect to online dynamic learning process, a self-evolutionary strategy with adaptive memory factors is proposed, and the fuzzy rule structures are updated gradually by a stochastic gradient descent method. For on-line updating, a loss function is constructed by considering the inference errors and the previous rule structure, in order to achieve continuous learning without forgetting the knowledge learned before. In the reasoning process, a sparse fuzzy reasoning approach is designed for extrapolating the knowledge of the fuzzy regions without sample data. To verify the effectiveness of the proposed method, chaotic time series data with noises and industrial practical data coming from a steel plant are employed for experimental analyses. The experimental results show that, the proposed method is capable of describing a variety of dynamic features and exhibiting high accuracy for the industrial data.
An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition

With accumulation of data and development of artificial intelligence, human activity recognition attracts lots of attention from researchers. Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. However, these algorithms have their own limitations and their prediction accuracy still has space to improve. In this study, we focus on K-nearest neighbors (KNN) and solve its limitations. Firstly, kernel method is employed in model KNN, which transforms the input features to be the high-dimensional features. The proposed model KNN with kernel (K-KNN) improves the accuracy of classification. Secondly, a novel reduced kernel method is proposed and used in model K-KNN, which is named as Reduced Kernel KNN (RK-KNN). It reduces the processing time and enhances the classification performance. Moreover, this study proposes an approach of defining number of K neighbors, which reduces the parameter dependency problem. Based on the experimental works, the proposed RK-KNN obtains the best performance in benchmarks and human activity datasets compared with other models. It has super classification ability in human activity recognition. The accuracy of human activity data is 91.60% for HAPT and 92.67% for Smartphone, respectively. Averagely, compared with the conventional KNN, the proposed model RK-KNN increases the accuracy by 1.82% and decreases standard deviation by 0.27. The small gap of processing time between KNN and RK-KNN in all datasets is only 1.26 seconds.
Global Groundwater: Source, Scarcity, Sustainability, Security, and Solutions

GroundwaterVolume 59, Issue 4 p. 479-480 Book Review/ Global Groundwater: Source, Scarcity, Sustainability, Security, and Solutions W. Todd Jarvis, Corresponding Author todd.jarvis@oregonstate.edu Institute for Water and Watersheds, Oregon State University, Corvallis, OR, 97331 USASearch for more papers by this author W. Todd Jarvis, Corresponding Author todd.jarvis@oregonstate.edu Institute for Water and Watersheds, Oregon State University, Corvallis, OR, 97331 USASearch for more papers by this author First published: 03 June 2021 https://doi.org/10.1111/gwat.13113Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume59, Issue4July/August 2021Pages 479-480 RelatedInformation
WITHDRAWN: Music Composition Feasibility using a Quality Classification Model based on Artificial Intelligence

There are several explanations for building music systems, including computer-generated settings, playing musical instruments, reflecting on the art of sound recordings, and others. The main challenge in music composition identifies the student's time, and it shows dissatisfaction with their lack of basic musical skills. The music compositions help develop a student's greater stronger vocabulary to determine that they are rising musicians. Therefore, this paper suggests the Music Composition and Melody Generation Adjustable (MCMGA) structure develop adaptive music. MCMGA's main objective is to build music in real-time that reflects various moods that can accomplish through a single combination of the chord-sequence generator on a cross graph. A search melody predictor and an emotional-expression concept have been analysed. Melody generation uses a novel technique for evolution that combines the Feasible Population Method (FPM) with vector optimization. FPM helps to explore a Pareto front of various solutions under vector optimization functions that are creatively equivalent. To test the program, two quantitative user studies are carried out. The first is based on music generation and the second on valence expression by adding dissonances. The study's outcome shows that every other part of the production and propagation increases the perceptual quality of the generated music and the relative effectiveness of the interpretation of reactivity through dissonance based on these parameters music experience 96.7%, usability and accessibility 98.4%, interaction analysis 99.5%, music teaching analysis 96.3%, music performance anxiety analysis 29.8% Stage fright analysis 22.5%.
A real-world application of Markov chain Monte Carlo method for Bayesian trajectory control of a robotic manipulator

Reinforcement learning methods are being applied to control problems in robotics domain. These algorithms are well suited for dealing with the continuous large scale state spaces in robotics field. Even though policy search methods related to stochastic gradient optimization algorithms have become a successful candidate for coping with challenging robotics and control problems in recent years, they may become unstable when abrupt variations occur in gradient computations. Moreover, they may end up with a locally optimal solution. To avoid these disadvantages, a Markov chain Monte Carlo (MCMC) algorithm for policy learning under the RL configuration is proposed. The policy space is explored in a non-contiguous manner such that higher reward regions have a higher probability of being visited. The proposed algorithm is applied in a risk-sensitive setting where the reward structure is multiplicative. Our method has the advantages of being model-free and gradient-free, as well as being suitable for real-world implementation. The merits of the proposed algorithm are shown with experimental evaluations on a 2-Degree of Freedom robot arm. The experiments demonstrate that it can perform a thorough policy space search while maintaining adequate control performance and can learn a complex trajectory control task within a small finite number of iteration steps.
COVID-19 and MRI Image Denoising Using Wavelet Transform and Basic Filtering

Eliminating noise from digital image is considered as an essential problem in image processing, for example in medical images as the noise present in the image will degrade the image quality which cannot be further classified with high accuracy.Hence, it is required to provide the images without any noises for better classification.Since the selection of denoising method to generate the quality image depends on the type of noise present in the image, it is suggested to find the best method to eliminate the noise as far as possible by preserving the edges.This paper presents the various denoising techniques such as BayesShrink and VisuShrink wavelet transform, non-local mean filter and median filter to remove the noises added through Gaussian, Speckle and salt and pepper noise in the COVID 19 chest x-ray images and the MRI images.The experiment shows that wavelet transform method performance is good for removing Gaussian noise and median filter performance is good to remove speckle and salt and pepper noise.The results are measured through visually and through the quality measure using Peak Signal to Noise Ratio (PSNR).
CPDS: A Cross-Blockchain Based Privacy-Preserving Data Sharing for Electronic Health Records

Electronic Health Records (EHRs) is an important form of medical data. Sharing health data is considered a key way to improve the quality of health care services and reduce the cost of health care. However, nowadays, many people will create electronic health records in different hospitals. In the process of cross-hospital diagnosis, due to the decentralized storage of electronic medical record in various hospitals, the electronic health records cannot be shared. This phenomenon will cause obstacles to doctors' diagnosis, impede data sharing and endanger patient privacy. To solve these problems, we propose an EHRs privacy protection data sharing scheme based on blockchain. In this scheme, the original EHRs are securely stored in the cloud. The generated index is stored on the hospital private blockchain. Visitors can obtain the index of electronic health records through the blockchain. It's convenient to view the patient's electronical health records and diagnose the patient's condition in detail. In this way, the risk of medical data leakage can be greatly reduced. The advantages in the blockchain ensure that EHRs cannot be arbitrarily modified. Meanwhile, the original data is stored in the cloud, while the blockchain only stores indexes, which solves the problem of limited storage space in the blockchain. According to the current medical situation, we put forward Wanchain as a service (WaaS), and propose a cross-blockchain based privacy-preserving data sharing for electronic health records. Wanchain is used to achieve cross-blockchain secure access to EHRs data when patients visit different hospitals. In addition, our scheme ensures that patients can delete EHRs data effectively and freely. The safety and effectiveness of the system are proved through security analysis and performance evaluation.
QR-BASED INVENTORY MANAGEMENT SYSTEM (QR-IMS) OF PASSENGER LUGGAGE USING WEBSITE

This research is an attempt to optimize web content in the m-flight environment. It provides the design and implementation of a travel booking system based on a paperless mobile network that can be delivered via QR codes. This article implements a web page that can be accessed through a mobile device browser. The site will provide travel schedules for all company flights and an online reservation system that will allow customers to purchase tickets electronically with a valid debit/credit card and have access to web applications via the Internet. In this project, QR codes are used to monitor and update passengers' luggage in this system. The airport department can easily obtain passenger information using QR codes. At the same time, at the airport, separate QR codes were assigned to store products, indicating that the QR codes can be used to easily track the available locations and owner of products.
Artificial Intelligence and Ethics in Portfolio Management

This work in progress aims to explore ethical dilemmas connected to the use of Artificial Intelligence (AI) in financial portfolio management, and their managerial implications. In old school quantitative investing, portfolio allocation decisions are typically based on a well-defined investment strategy. Financial portfolio managers devise and apply investment strategies to maximize expected returns for customers' portfolios. The introduction of AI-enhanced algorithms enables smart machines to automatically revise and update investment strategies, learning from the past. AI itself might produce significant effects on the gains and losses of the portfolio management strategies, raising ethical dilemmas connected with human versus machine responsibility, accountability, and risk. From the managerial point of view, a new dimension of performance measuring, competence evaluation and incentive allocation is required for managing AI software developers in this area. To explore such dilemmas, empirical evidence is drawn here from MDOTM, an innovative and successful young enterprise developing AI-driven investment strategies for financial markets.
Optimization of Human Resource File Information Decision Support System Based on Cloud Computing

With the rapid development of science and technology era, human resources and knowledge resources have become an important part of the development of enterprises. Therefore, it is very necessary to establish human resources data pool and carry out data mining based on it, so as to extract high quality and high quantity information to provide support for managers’ decision-making. In this study, the human resource archive information decision support system (DSS) is developed for various management and decision-making works by taking advantage of the characteristics of cloud computing, such as large scale, high reliability, versatility, and high expansibility. Based on the analysis of “cloud computing” advantages in resources integration and sharing and so on, on the basis of this system is designed by using the basis of the data acquisition layer, support layer of network services, cloud computing support layer, data standardization conversion layer, system application layer, system layer, decision support layer and so on 7 layer architecture, discusses the features and functions of each layer structure, the working mode and working mode of the Decision Support System (DSS) are introduced in detail. The system makes up for the defects of the traditional archive management, such as the lack of data resources, the inability to realize the isomorphism, and standardized processing of the data from multiple data sources.
Identity Expression—the Case of ‘Sakawa’ Boys in Ghana

Abstract Internet fraud remains a problem in Ghana and thus attracts the attention of teachers, researchers, civil society organisations, the state and policymakers. Existing studies on Internet fraud focused on the reasons, combat strategies, cyber spiritualism, the impact of Internet fraud on individuals and the country, and the inadequate legal frameworks for handling such cybercrimes. Despite efforts by the government and other interest groups in fighting the menace, the phenomenon continues to increase among youth in Ghana. Applying Paul Willis’ theory of ethnographic imagination, this study examined how Internet scammers— Sakawa boys—in the northern region of Ghana use their bodies and other cultural materials to express their identity and make meaning. The study reveals that Sakawa boys express their identities and make meaning using language—slang and jargon; conspicuous consumption of material goods; ostentatious lifestyle; techno-religiosity; and gender cyber-fraud collaboration. Thus, it offers a basal understanding of emic dimension of relationship between children and youth in cybercrime, unsuspected victims, and preventive measures. The study also gives theoretical contributions to research in understanding the broader socio-cultural milieu of children and youth in crimes and possible practical measures towards containment .
Hybrid Systems Verification with Isabelle/HOL: Simpler Syntax, Better Models, Faster Proofs

We extend a semantic verification framework for hybrid systems with the Isabelle/HOL proof assistant by an algebraic model for hybrid program stores, a shallow expression model for hybrid programs and their correctness specifications, and domain-specific deductive and calculational support. The new store model yields clean separations and dynamic local views of variables, e.g. discrete/continuous, mutable/immutable, program/logical, and enhanced ways of manipulating them using combinators, projections and framing. This leads to more local inference rules, procedures and tactics for reasoning with invariant sets, certifying solutions of hybrid specifications or calculating derivatives with increased proof automation and scalability. The new expression model provides more user-friendly syntax, better control of name spaces and interfaces connecting the framework with real-world modelling languages.
Monster aesthetics as an expression of decolonizing the design body

Institutionalized design education aims at training the human body to become a design body, a subject capable of designing according to aesthetic canons. In colonized territories, the modern canon predominates over indigenous, vernacular and other forms of expression. Manichaeism, utilitarianism, universalism, methodologism and various modern values are inculcated in the design body as if it did not have any. The colonization of design bodies makes young designers believe that once they learn what good design is, they need to save others from bad design. This research reports on a series of democratic design experiments held in a Brazilian university that questioned these values while decolonizing the design body. Comparing the works of design produced in the experiment with some works of art from the Neoconcrete movement, we recognize a characteristic form of expression we call monster aesthetics : a positive affirmation of otherness and collectivity that challenges colonialists’ standards of beauty and goodness.
Improving current interest with item and review sequential patterns for sequential recommendation

Sequential recommendation (SR) aims to recommend items based on user information and behavior sequences. Almost all the existing works for SR construct short-term preference and long-term preference only based on the user–item interactions or the reviews rather than considering the two types of information simultaneously. In fact, interaction items and reviews commonly reflect the user’s semantic information, and play significant roles in modeling the user preference. In this paper, we propose a novel model named Parallel Item sequential pattern and Review Sequential Pattern (PIRSP) for the sequential recommendation. Specifically, first, PIRSP learns two levels of sequential patterns from item and review information, respectively: (1) item sequential pattern, which uses a gated recurrent unit with an item-attention mechanism to model history behavior sequences; (2) review sequential pattern, which takes a convolution neural network with a target-attention mechanism for modeling associated reviews of interaction items. Then, we introduce a fusion gating mechanism for selectively combining the two sequential patterns to learn the short-term preference. Second, we employ a convolution neural network with aspect information to learn the long-term preference. Finally, we utilize a linear fusion on the long-term preference and short-term preference for modeling user preference and making final recommendation. The experimental results indicate that our model outperforms other state-of-the-art methods on the Amazon dataset. Our analysis of PIRSP’s recommendation process shows the positive effect of the two types of information and fusion gating mechanism on the performance of sequential recommendation.
HGK-GNN

While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture heterogeneous structures and attributes of an underlying graph. Furthermore, though many Heterogeneous GNN (HGNN) variants have been proposed and have achieved state-of-the-art results, there are limited theoretical understandings of their properties. To this end, we introduce graph kernel to HGNNs and develop a Heterogeneous Graph Kernel-based Graph Neural Networks (HGK-GNN). Specifically, we incorporate the Mahalanobis distance (MD) to build a Heterogeneous Graph Kernel (HGK), and incorporating it into deep neural architectures, thus leveraging a heterogeneous GNN with a heterogeneous aggregation scheme. Also, we mathematically bridge HGK-GNN to metapath-based HGNNs, which are the most popular and effective variants of HGNNs. We theoretically analyze HGK-GNN with the indispensable Encoder and Aggregator component in metapath-based HGNNs, through which we provide a theoretical perspective to understand the most popular HGNNs. To the best of our knowledge, we are the first to introduce HGK into the field of HGNNs, and mark a first step in the direction of theoretically understanding and analyzing HGNNs. Correspondingly, both graph and node classification experiments are leveraged to evaluate HGK-GNN, where HGK-GNN outperforms a wide range of baselines on six real-world datasets, endorsing the analysis.