Reviewerly/exHarmony huggingface dataset:
Exploring zero-shot and joint training cross-lingual strategies for aspect-based sentiment analysis based on contextualized multilingual language models

Aspect-based sentiment analysis (ABSA) has attracted many researchers' attention in recent years.However, the lack of benchmark datasets for specific languages is a common challenge because of the prohibitive cost of manual annotation.The zero-shot cross-lingual strategy can be applied to solve this gap in research.Moreover, previous works mainly focus on improving the performance of supervised ABSA with pre-trained languages.Therefore, there are few to no systematic comparisons of the benefits of multilingual models in zero-shot and joint training cross-lingual for the ABSA task.In this paper, we focus on the zero-shot and joint training cross-lingual transfer task for the ABSA.We fine-tune the latest pre-trained multilingual language models on the source language, and then it is directly predicted in the target language.For the joint learning scenario, the models are trained on the combination of multiple source languages.Our experimental results show that (1) fine-tuning multilingual models achieve promising performances in the zero-shot crosslingual scenario; (2) fine-tuning models on the combination training data of multiple source languages outperforms monolingual data in the joint training scenario.Furthermore, the experimental results indicated that choosing other languages instead of English as the source language can give promising results in the low-resource languages scenario.
Bad Employees: Examining Deviant Security Behaviors

Control balance theory (CBT) is a control-based theory of deviant behavior When imbalances in control between an individual and their environment exist, deviant behavior can occur. However, sufficient violation motivation and the facilitating conditions must exist. Past research has shown that technology itself often provides the means for deviant behavior among organizational employees. Presently, research on employee deviance using technology is underdeveloped in the field of Information Systems. Further, no work has been done to explore CBT in the context of Information Systems Security Policy violations, which are often a form of technology-facilitated deviant behavior. This study examines, for the first time, Tittle’s full set of proposed constructs, including fear, in the context of technology-based employee security deviance. This research uses CBT to demonstrate the importance of understanding the interplay between technology and employee deviance and the role of organizations in preventing it.
Spark-based deep classifier framework for imbalanced data classification

Imbalanced data are those datasets that have an uneven distribution of observations across the target class; one class label has a very high number of observations while the other has a very low number. The data imbalance occurs when the data are biased towards a certain class and exclude the minority class, which has major significance in the accomplishment of the classifier. The data imbalance problem provides a consequence of poor predictive performance and to avoid this, multiple researchers are engaged in developing multiclass classification techniques for handling imbalance issues. In this research, the data imbalance problem is conquered using Synthetic Minority Oversampling technique (SMOTE) technique that equally distributes the data. The multiclass classification is boosted by employing the Spark architecture and the classification is executed utilising the Lupusbug-deep Convolutional Neural Network (Lupusbug optimisation-deep CNN) classifier that effectively classifies the data through finding the optimal weights of the classifier. Importantly, incremental learning is initiated in this research based on the error function and the selection criterion that facilitates the classifier remodelling and improves the tendency to deal with the incremental data. The proposed method attains 94.348% accuracy, 93.510% precision and 96.121% recall, which was quite efficient and outperforms the existing methods.
Part of speech tagging of grammatical features related to L2 Chinese development: A case analysis of Stanza in the L2 writing context

Grammatical complexity has received extensive attention in second language acquisition. Although computational tools have been developed to analyze grammatical complexity, most relevant studies investigated this construct in the context of English as a second language. In response to an increasing number of L2 Chinese learners, it is important to extend the investigation of grammatical complexity in L2 Chinese. To promote relevant research, we evaluated the new computational tool, Stanza, on its accuracy of part-of-speech tagging for L2 Chinese writing. We particularly focused on eight grammatical features closely related to L2 Chinese development. Then, we reported the precisions, recalls, and F-scores for the individual grammatical features and offered a qualitative analysis of systematic tagging errors. In terms of the precision, three features have high rates, over 90% (i.e., ba and bei markers, classifiers, -de as noun modifier marker). For recall, four features have high rates, over 90% (i.e., aspect markers, ba and bei markers, classifiers, -de as noun modifier marker). Overall, based on the F-scores, Stanza has a good tagging performance on ba and bei markers, classifiers, and -de as a noun modifier marker. This evaluation provides research implications for scholars who plan to use this computational tool to study L2 Chinese development in second language acquisition or applied linguistics in general.
Mobile Money Operations: Policies for Managing Cash and Digital Currency Inventories in the Developing World

Problem definition: Mobile money systems—platforms built and managed by mobile network platform operators (MMPOs) to allow money to be stored as digital currency—connect millions of poor and “unbanked” people to the formal financial system. Unfortunately, low service levels because of the suboptimal management of cash and digital currency (e-float) inventory impede the development of these ecosystems. Accordingly, we seek to answer the question of how agents should manage inventories of cash and e-float. Academic/practical relevance: This paper extends inventory theory to the mobile money context, unique in that sales of cash generate inventory of e-float and vice versa. In doing so, we address a key pain point for an emerging sector that improves lives at the base of the pyramid. Methodology: We develop an analytical heuristic to determine initial stocking levels for cash and e-float and analyze its performance on simulated and actual data. Results: By partnering with an MMPO, we tested the performance of the heuristic inventory policy with data from more than 35 million transactions. The heuristic captured 99.9998% of the optimal profit on simulated data and, on actual data, we found that following the recommendations could increase agents’ profits by an average of 15.4%. Managerial implications: We develop a pragmatic inventory policy that performs nearly optimally. We also analyze under which conditions the performance deteriorates and examine heterogeneity among agents with respect to the heuristic’s impact on their performance. Thus, we equip MMPOs with guidance as to whom to target and how. By contributing to service level and profit improvements, this work can make mobile money a more effective financial inclusion tool in the developing world as well as improve the livelihoods of agents. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1175 .
TAM GAN: Tamil Text to Naturalistic Image Synthesis Using Conventional Deep Adversarial Networks

Text-to-image synthesis has advanced recently as a prospective area for improvement in computer vision applications. The image synthesis model follows significant neural network architectures such as Generative Adversarial Networks (GANs). The flourishing text-to-image generation approaches can nominally reflect the meaning of the text in generated images. Still, they need the prospect of providing the necessary details and eloquent object features. Intelligent systems are trained in text-to-image synthesis applications for various languages. However, their contribution to regional languages is yet to be explored. Autoencoders prompt the synthesis of images, but they result in blurriness, which results in clear output and essential features of the picture. Based on textual descriptions, The GAN model is capable of producing realistic images of a high quality that can be used in various applications, like fashion design, photo editing, computer-aided design, and educational platforms. The proposed method uses two-stage processing to create a language model using a BERT model called TAM-BERT and an existing MuRIL BERT, followed by image synthesis using a GAN. The work was conducted using the Oxford-102 dataset, and the model's efficiency was evaluated using the F1-Score measure.
A credible and adjustable load resource trading system based on blockchain networks

In recent years, the residents’ demands for power supply is increasing. The load resource trading system responds to the demands through intelligent scheduling, which can effectively relieve the severe power load pressure. The load resource trading system is a type of nonlinear system because the trading price is adjustable with user’s credit, instead of being linear to the power trading volume. The adjustable load resource trading is faced with the challenges of large demands, strong user autonomy, and secure and tamper-proof transaction data. The blockchain technology has been widely concerned by industrial and academic domains due to its decentralization, strong encryption of account information, and traceability of transaction behaviour. In this paper, we propose a credible and adjustable load resource trading framework based on blockchain, which uses blockchain to achieve credible grid load resource trading. Firstly, we propose a two-layer blockchain architecture based on the alliance chain. The main chain maintains all the data of the system, and the station-area nodes constitute the alliance chain. We design a distributed trading processing mechanism based on hybrid consensus and sharding technologies, which promotes the speed of cross-station transaction consensus. Next, we propose a two-level bidding model, which determines the trading price of load resources based on the maximum benefit of users and the lowest cost of grid companies. The results of extensive experiments shows that our proposed framework can achieve the promising result.
Machine Learning–Based Identification of Lithic Microdebitage

ABSTRACT Archaeologists tend to produce slow data that is contextually rich but often difficult to generalize. An example is the analysis of lithic microdebitage, or knapping debris, that is smaller than 6.3 mm (0.25 in.). So far, scholars have relied on manual approaches that are prone to intra- and interobserver errors. In the following, we present a machine learning–based alternative together with experimental archaeology and dynamic image analysis. We use a dynamic image particle analyzer to measure each particle in experimentally produced lithic microdebitage ( N = 5,299) as well as an archaeological soil sample ( N = 73,313). We have developed four machine learning models based on Naïve Bayes, glmnet (generalized linear regression), random forest, and XGBoost (“Extreme Gradient Boost[ing]”) algorithms. Hyperparameter tuning optimized each model. A random forest model performed best with a sensitivity of 83.5%. It misclassified only 28 or 0.9% of lithic microdebitage. XGBoost models reached a sensitivity of 67.3%, whereas Naïve Bayes and glmnet models stayed below 50%. Except for glmnet models, transparency proved to be the most critical variable to distinguish microdebitage. Our approach objectifies and standardizes microdebitage analysis. Machine learning allows studying much larger sample sizes. Algorithms differ, though, and a random forest model offers the best performance so far.
DDRCN: Deep Deterministic Policy Gradient Recommendation Framework Fused with Deep Cross Networks

As an essential branch of artificial intelligence, recommendation systems have gradually penetrated people’s daily lives. It is the active recommendation of goods or services of potential interest to users based on their preferences. Many recommendation methods have been proposed in both industry and academia. However, there are some limitations of previous recommendation methods: (1) Most of them do not consider the cross-correlation between data. (2) Many treat the recommendation process as a one-time act and do not consider the continuity of the recommendation system. To overcome these limitations, we propose a recommendation framework based on deep reinforcement learning techniques, known as DDRCN: a deep deterministic policy gradient recommendation framework incorporating deep cross networks. We use a Deep network and a Cross network to fit the cross relationships between the data, to obtain a representation of the user interaction data. The Actor-Critic network is designed to simulate the continuous interaction behavior of users through a greedy strategy. A deep deterministic policy gradient network is also used to train the recommendation model. Finally, we conduct experiments with two publicly available datasets and find that our proposed recommendation framework outperforms the baseline approach in the recall and ranking phases of recommendations.
Bitcoin Price Prediction and NFT Generator Based on Sentiment Analysis

Twitter sentiment has been found to be useful in predicting whether the price of Bitcoin will rise or fall will climb or decline. Modelling market activity and hence emotion in the Bitcoin ecosystem gives insight into Bitcoin price forecasts. We take into account not just the emotion retrieved not just from tweets, but also from the quantity of tweets. With the goal of optimising time window within which expressed emotion becomes a credible predictor of price change, we provide data from research that examined the link among both sentiment and future price at various temporal granularities. We demonstrate in this study that not only can price direction be anticipated, but also the magnitude of price movement with same accuracy, and this is the study's major scientific contribution. Non-Fungible Token (NFT) has gained international interest in recent years as a blockchain-based application. The most prevalent kind of NFT that can be stored on many blockchains is digital art. We did studies on CryptoPunks, the most popular collection on the NFT market, in examine and depict each and every major ethical challenges. We investigated ethical concerns from three perspectives: design, trade transactions, and relevant Twitter topics. Using Python libraries, a Twitter crawler, and sentiment analysis tools, we scraped data from Twitter and performed the analysis and prediction on bitcoin and NFTs.
An Automatic Music-Driven Folk Dance Movements Generation Method Based on Sequence-To-Sequence Network

Music-driven automatic dance movement generation has become a hot research topic in the field of computer vision and internet of things in the recent past. To address the problems of increasing loss of Chinese folk dance culture, high cost of manual choreography methods and requirements for professional background, this paper proposes an automatic generation method for folk dance movements. Firstly, the proposed method collects paired folk music and dance videos to construct a synchronized folk music–dance dataset, extracting music and dance features using a feature extraction tool and a multi-scale fusion high-resolution network, respectively. Afterward, a sequence-to-sequence network model is constructed and then trained based on music features and dance features to synthesize rhythmically matched dance sequences for new music clips. Finally, an easy-to-use and effective automatic folk dance choreography method is implemented. Experimental data show that the proposed method performs well in automatic folk dance generation and the generated dances have folk characteristics and match the rhythm of the given music.
Applying the Fuzzy BWM to Determine the Cryptocurrency Trading System under Uncertain Decision Process

The crypto and digital assets ecosystems have attracted investment, regulators, and speculators to their environment. As the blockchain-based framework can reduce transaction costs, generate distributed trust, and enable decentralized platforms, it has become a potential new base for decentralized business models. Previous studies have highlighted the advantages and drawbacks of each platform, such as interest rates, cost concerns, transparency issues, hacking issues, and hazards. Consequently, it is challenging for investors to evaluate the cryptocurrency trading system which determines the optimum exchanges and crucial aspects. Therefore, in order to rank the optimal digital token trading system, this paper develops an evaluation architecture to determine the various token trading systems. The developed architecture integrates fuzzy theory and the best-worst method (BWM) into the decision-making process to assess decision behaviors regarding preference for digital token trading systems in investors in Taiwan. First, this work establishes the views and parameters by modifying the Delphi method based on a literature review and survey. Second, the fuzzy-BWM is applied to obtain the fuzzy weights of the views and parameters. Then, defuzzification and BWM are used to rank the optimal alternatives of the digital token trading systems for investors. The results indicate that the optimal digital token trading system is the decentralized platform, and the critical parameters are gas fees, interest rates, and the mechanism of savings under fuzzy uncertain scenarios. This means that when considering the uncertain and ambiguous characteristics of the expert decision process in digital token trading systems, the evaluation is decentralized and the gas fees are the most important parameter in the digital token investment platform. Academically, the fuzzy BWM-based decision-making architecture can provide corporations and investors with valuable guidance to rank the optimal digital token trading systems based on fuzzy uncertain scenarios. Commercially, the proposed architecture could provide corporations and investors with a useful model to measure the optimal digital token trading system.
The World Was Their Parish: Evangelistic Work of the Single Female Missionaries from the Methodist Episcopal Church, South, to Korea, 1887–1940

The Woman’s Foreign Missionary Society (WFMS) (1897–1909) and the Woman’s Missionary Council (WMC) (1910–1940) of the Methodist Episcopal Church, South (MECS) worked in Korea from 1897 to 1940. Their work used a distinctive mission philosophy, hermeneutics, and implementation of strategies in their encounters with Korean women. Over the course of their years in Korea, Southern Methodist missionary women initiated the Great Korea Revival, established the first social evangelistic centers, educated the first indigenous female church historian, and ordained women for the first time in Korea. This article argues that, even though the missionary activities of the single female missionaries occurred in the context of “Christian civilization” as a mission theory, their holistic Wesleyan missiology departed from the colonial theory of mission as civilization. The first section of the article offers background information regarding the single female missionaries to help understand them. What motivated these females to venture in foreign lands with the Gospel? What was their preparation? The second section presents the religious, cultural, social, and political background of Korea during the time the missionaries arrived. The third section describes and analyzes the evangelistic and social ministries of the female missionaries in the nascent Korean mission. The final section describes and analyzes the appropriation and reinterpretation of the Bible and Christianity by Korean women, especially the work of Korean Bible women and Methodist female Christians in the quest for independence from Japanese control in the Independence Movement of 1919.
Potential Field Control of a Redundant Nonholonomic Mobile Manipulator with Corridor-Constrained Base Motion

This work proposes a solution for redundant nonholonomic mobile manipulator control with corridor constraints on base motion. The proposed control strategy applies an artificial potential field for base navigation to achieve joint control with desired trajectory tracking of the end effector. The overall kinematic model is created by describing the nonholonomic mobile platform and the kinematics of the manipulator. The objective function used consists of a primary control task that optimizes the joint variables to achieve the desired pose or trajectory of the end effector and a secondary control task that optimizes the joint variables for the base to support the arm and stay within the corridor. As a last priority, an additional optimization is introduced to optimize the maneuverability index. The proposed baseline navigation has global convergence without local minima and is computationally efficient. This is achieved by an optimal grid-based search on a coarse discrete grid and a bilinear interpolation to obtain a continuous potential function and its gradient. The performance of the proposed control algorithm is illustrated by several simulations of a mobile manipulator model derived for a Pal Tiago mobile base and an Emiko Franka Panda robotic manipulator.
Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence

In recent years, there has been an increased effort to digitise whole-slide images of cancer tissue. This effort has opened up a range of new avenues for the application of deep learning in oncology. One such avenue is virtual staining, where a deep learning model is tasked with reproducing the appearance of stained tissue sections, conditioned on a different, often times less expensive, input stain. However, data to train such models in a supervised manner where the input and output stains are aligned on the same tissue sections are scarce. In this work, we introduce a dataset of ten whole-slide images of clear cell renal cell carcinoma tissue sections counterstained with Hoechst 33342, CD3, and CD8 using multiple immunofluorescence. We also provide a set of over 600,000 patches of size 256 × 256 pixels extracted from these images together with cell segmentation masks in a format amenable to training deep learning models. It is our hope that this dataset will be used to further the development of deep learning methods for digital pathology by serving as a dataset for comparing and benchmarking virtual staining models.
An Image Denoising Method for Arc-Scanning SAR for Airport Runway Foreign Object Debris Detection

Arc-scanning synthetic aperture radar (AS-SAR) is an emerging technical means for detecting foreign object debris (FOD). Most FOD are small and appear as weak targets with a low signal-to-noise ratio (SNR) in AS-SAR images. Therefore, image noise is a fundamental challenge in detecting FOD on airport runways that leads to many false alarms. A weak scattering denoising method is proposed to aim at the noise caused by speckle and rough surface scattering. To enhance FOD detection, a transformation parameter concept is offered and adopted, which has different characteristics for the target and background. This paper estimates the transformation parameter through logarithms, normalization, and morphological erosion and optimizes them with edge-preserving filtering. The results show that despeckling and runway scattering suppression can be simultaneously implemented, and that field experiments validate the performance of this method.