Reviewerly/exHarmony huggingface dataset:
Design and Construction of Intelligent Digital English Teaching Mode Based on Multimodal Low-Resource Language Information Processing

With the development of information technology, intelligent digital education is gradually emerging. At the same time, with the closer economic and trade relations between regions, the demand for English talents is becoming more and more urgent. Applying the concept and method of intelligent digital education to English Teaching (ET) is of great significance for improving students’ learning efficiency and academic level, and enhancing students’ English application ability and cross-cultural communication ability. However, due to the limitations of the traditional teaching model, such as the single and outdated model, the low interest of students in learning, and the lack of emphasis on cultivating students’ English practical ability, it can not effectively meet the requirements of improving students' learning effect.Therefore, this paper studies the intelligent digital ET mode based on multimodal low-resource language information processing, proposes the construction method of intelligent digital ET mode, and conducts experimental research on it. The research showed that the positive emotional input level index of Group X students was 8.47% higher than that of Group T students; the efficiency index of Group X students was 5.02% higher than that of Group T students; the autonomous learning ability and English learning level of Group X students were higher than those of Group T; the students in Group X had higher recognition of ET mode than those in Group T. The intelligent digital ET mode can effectively improve the learning effect.
Job satisfaction and work engagement among resort employees in Batangas province: Basis for human resource development plan

This study examines Batangas resort employees' job satisfaction and engagement during the pandemic and proposes a human resource development plan to make accredited resorts more competitive and resilient.A descriptive quantitative study using a questionnaire was employed to survey 303 Batangas resort workers.The Mann-Whitney U test and Kruskal Wallis test tested hypotheses, whereas descriptive statistics described respondents' demographics.The study found that respondents are satisfied with the compensation, promotion, benefits, recognition and rewards, training and development, and career development opportunities of their jobs.They are also cognitively, emotionally, and physically engaged in their work.Employee satisfaction and engagement vary by duration of employment.Physical work involvement differs by age and sex.Job satisfaction increases employee engagement, according to the study.Job satisfaction and employee engagement are positively correlated.A human resource development plan was proposed to enhance resort employee job satisfaction and engagement.This research was limited to the six variables of job satisfaction which are compensation, promotion, benefits, recognition and rewards, training and development, and career development opportunities and the three dimensions of work engagement.Google Form was used in the gathering of data as the study was conducted during the pandemic.
Content Moderation on End-to-End Encrypted Systems: A Legal Analysis

End-to-end encrypted online platforms are increasingly common in the digital ecosystem, found both in dedicated apps like Signal and widely adopted platforms like Android Messages. Though such encryption protects privacy and advances human rights, the law enforcement community and others have raised criticisms that end-to-end encryption shields bad behavior, preventing the platforms or government authorities from intercepting and responding to criminal activity, child exploitation, malware scams, and disinformation campaigns. At a time when major Internet platforms are under scrutiny for content moderation practices, the question of whether end-to-end encryption interferes with effective content moderation is of serious concern.Computer science researchers have responded to this challenge with a suite of technologies that enable content moderation on end-to-end encrypted platforms. Are these new technologies legal? This Article analyzes these new technologies in light of several major federal communication privacy regimes: the Wiretap Act, the Stored Communications Act, and the Communications Assistance for Law Enforcement Act.While generally we find that these content moderation technologies would pass muster under these statutes, the answers are not as clear-cut as one might hope. The advanced cryptographic techniques that these new content moderation strategies employ raise multiple unsettled questions of law under the communication privacy regimes considered. This legal uncertainty arises not because of the ambiguous ethical nature of the technologies themselves, but because the decades-old statutes failed to accommodate, or indeed contemplate, the innovations in cryptography that enable content moderation to coexist with encryption. To the extent that platforms are limited in their ability to moderate end-to-end encrypted content, then, those limits may arise not from the technology but from the law.
The Effect of Inequality on Poverty and Severity of Poverty in SSA: The Role of Financial Development Institutions

The present study investigates the incidence of financial institutions' dynamics of depth and access in the effect of income inequality on poverty and the severity of poverty in 42 Sub-Saharan African countries from 1980 to 2019. The Gini index is used to measure income inequality while poverty is measured as the poverty headcount ratio, and the severity of poverty is generated as the squared of the poverty gap index. An interactive quantile regression approach is used as an empirical strategy. Income inequality unconditionally increases poverty dynamics while the financial institutions' depth and access mitigate the adverse effects of income inequality on poverty dynamics. Financial institutions’ policy thresholds or minimum financial institutions levels needed to completely dampen the adverse effects of income inequality on poverty dynamics are provided. The findings are contingent on existing levels of poverty, poverty measurement and proxies for financial institutions. Policy implications are discussed.
Performance Comparison of Pre-processing Techniques for Image Denoising

The main objective of this survey is to compare different nonlinear filtering techniques for denoising and enhancing digital images for multiple noise environments. In this Survey, the various noise conditions are studied and some efficient nonlinear filters are designed to suppress bipolar fixed-valued impulse noise quite effectively. Efforts have been made to develop some noise removal techniques
Performance management algorithm of financial shared service center based on Internet of Things public cloud privacy protection

The Internet of Things (IoT) refers to the use of various communication technologies to achieve the interconnection of everything in cyberspace, and to achieve smart home and intelligent transportation, thus generating unprecedented amounts of data. In the financial sharing center, all businesses can extract effective data from these massive databases for analysis, and use data analysis tools to collect business, financial, human, process, knowledge and social data. At present, various types of IT (Internet Technology) systems have been widely used in financial sharing centers. However, a large number of sensitive data have also been generated. In order to protect these sensitive data, there is a high requirement for the personal information of IT system operation and financial sharing center personnel. In order to protect user data privacy, the optimal and most effective use of IT systems is an important issue that must be considered in privacy management. At present, there are many algorithms to protect data and privacy, but the effect is not ideal. Considering the balance between privacy issues, this paper proposed a K-means clustering algorithm based on IoT public cloud privacy protection technology to analyze the performance management of financial sharing center. The research results showed that before the improvement, the average number of employees who were dissatisfied with the post training ability and information platform construction ability of the financial sharing center was 57.9 and 57.8% respectively, more than half of them. After the improvement of IoT based public cloud privacy protection, the average number of employees dissatisfied with the post training ability and information platform construction ability of the financial sharing center was 5 and 3.9%, far less than the data prior to the improvement. It showed that IoT public cloud privacy protection was conducive to the performance management of the financial sharing center, and the relationship between the two was positive.
„OMNINO“ ‑ A COMPREHENSIVE DEVELOPMENT IN EXTRACURRICULAR ACTIVITIES OF UNIVERSITY STUDENTS

Developing the all-round competences of university students by applying the principle of credit recognition for the implementation of relevant extracurricular activities (EA), case studies and recommendations for practice (e.g. in the Service Learning programme, in teacher-oriented programmes and training in the preparation of future teachers, as well as in non-formal education in parachurch organisations in the case of students of theology, etc.).The starting point is Directive No. 11/2021 on the recognition of ECTS credits for extra-curricular activities at Matej Bel University (MBU) and its implementation in practice.The paper is an output of the funded project VEGA 1/0409/21 (National Agency of the Slovak Republic).The theoretical basis is the required learning outcomes of university students in selected fields of study and their expected competences.These outcomes are compared with learning outcomes in selected educational extra-curricular activities implemented outside the university of which they are students.This is a set of case studies of selected educational programs that are not courses in higher education but have the potential to provide specifically beneficial learning outcomes for college students whose completion may also earn them credit for extra-curricular activities.We conclude with recommendations for practice regarding the comparison of learning outcomes in parallel curricular and extra-curricular activities that are expected to yield the development of transversal competencies for college students in selected fields of study.
Technology adoption in autocratic economies: The role of fiscal capacity

Abstract What determines technology in autocratic regimes? In this paper, I assume that a rent‐extracting regime can adopt technology from the global technology frontier, tax‐paying citizens can oust the regime, and technology can decrease the ousting cost. I show that a lack of fiscal capacity can increase technology by preventing the ousting constraint from binding. Consistent with this prediction, tax collections and total factor productivity are negatively related in autocratic regimes. Extensions show that natural resources can divorce fiscal revenues from technology, which can encourage technology blocking. However, if technology adoption is socially costly, autocratic regimes can adopt too much technology.
Comparison of Data Models for Unsupervised Twitter Sentiment Analysis

"Identifying the sentiment of collected tweets has become a challenging and interesting task. In addition, mining and defining relevant features that can improve the quality of a classification system is crucial. The data modeling phase is fundamental for the whole process since it can reveal hidden information from the textual inputs. Two models are defined in the presented paper, considering Twitter-specific concepts: a hashtagbased representation and a text-based one. These models will be compared and integrated into an unsupervised system that determines groups of tweets based on sentiment labels (positive and negative). Moreover, wordembedding techniques (TF-IDF and frequency vectors) are used to convert the representations into a numeric input needed for the clustering methods. The experimental results show good values for Silhouette and Davies-Bouldin measures in the unsupervised environment. A detailed investigation is presented considering several items (dataset, clustering method, data representation, or word embeddings) for checking the best setup for increasing the quality of detecting the sentiment from Twitter’s messages. The analysis and conclusions show that the first results can be considered for more complex experiments. Keywords: Sentiment Analysis, Twitter, Data Representation, Hashtags, Clustering. "
Tracking and Data Association Based on Reinforcement Learning

Currently, most multi-target data association methods require the assumption that the target motion model is known, but this assumption is clearly not valid in a real environment. In the case of an unknown system model, the influence of environmental clutter and sensor detection errors on the association results should be considered, as well as the occurrence of strong target maneuvers and the sudden appearance of new targets during the association process. To address these problems, this paper designs a target tracking and data association algorithm based on reinforcement learning. First, this algorithm combines the dynamic exploration capability of reinforcement learning and the long-time memory function of LSTM network to design a policy network that predicts the probability of associating a point with its various possible source targets. Then, the Bayesian network and the multi-order least squares curve fitting method are combined to predict the location of target, and the results are fed into the Bayesian recursive function to obtain the reward. Simultaneously, some corresponding mechanisms are proposed for possible problems that interfere with the association process. Finally, the simulation experimental results show that this algorithm associates the results with higher accuracy compared to other algorithms when faced with the above problem.
Unlocking the Potential of Blockchain Technology in the Textile and Fashion Industry

The textile and fashion industry is on the brink of a major disruption, and blockchain technology (BT) presents a promising solution that could transform the industry by facilitating supply chain transparency, traceability, and sustainability. This article explores the potential of BT in the textile and fashion industry, with a focus on its current applications and potential impact. Using case studies and analyzing all announced blockchain projects from January 2017 to January 2023, we examine the diversity of blockchain applications across different aspects of the textile and fashion industry, including smart contracts and payment processing, supply chain tracking, sustainability applications, and customer engagement. The findings suggest an increasing number of companies are adopting BT, and that BT has the potential to revolutionize the T and F industry by creating a more transparent and efficient supply chain, reducing fraud and counterfeiting, and increasing customer confidence in products. We also identified the challenges and difficulties that may arise during the implementation of BT. This article contributes to the literature on BT in the textile and fashion industry, providing critical insights into its potential impact.
Phishing Node Detection in Ethereum Transaction Network Using Graph Convolutional Networks

As the use of digital currencies, such as cryptocurrencies, increases in popularity, phishing scams and other cybercriminal activities on blockchain platforms (e.g., Ethereum) have also risen. Current methods of detecting phishing in Ethereum focus mainly on the transaction features and local network structure. However, these methods fail to account for the complexity of interactions between edges and the handling of large graphs. Additionally, these methods face significant issues due to the limited number of positive labels available. Given this, we propose a scheme that we refer to as the Bagging Multiedge Graph Convolutional Network to detect phishing scams on Ethereum. First, we extract the features from transactions and transform the complex Ethereum transaction network into three simple inter-node graphs. Then, we use graph convolution to generate node embeddings that leverage the global structural information of the inter-node graphs. Further, we apply the bagging strategy to overcome the issues of data imbalance and the Positive Unlabeled (PU) problem in transaction data. Finally, to evaluate our approach’s effectiveness, we conduct experiments using actual transaction data. The results demonstrate that our Bagging Multiedge Graph Convolutional Network (0.877 AUC) outperforms all of the baseline classification methods in detecting phishing scams on Ethereum.
Estimating Territory Risk Relativity Using Generalized Linear Mixed Models and Fuzzy C-Means Clustering

Territory risk analysis has played an important role in auto insurance rate regulation. It aims to design rating territories from a set of basic rating units so that their respective risk relativities can be estimated to reflect the regional risk of insurance. In this work, spatially constrained clustering is first applied to insurance loss data to form such regions, using the forward sortation area (FSA) as a basic rating unit. The groupings of FSA by spatially constrained clustering reduce the insurance rate heterogeneity caused by smaller risk exposures. Furthermore, the generalized linear mixed model (GLMM) is proposed to derive the risk relativities of clusters and each FSA. In addition, as an alternative approach, fuzzy C-Means clustering is proposed to derive the risk relativity of FSA, and the obtained results are compared to the ones from GLMM. The spatially constrained clustering and risk relativity estimation help to retrieve a set of territory risk benchmarks used in rate filings within the regulation process. It also provides guidance for auto insurance companies on rate making.
Computational Analysis of Chemical Reactions Using a Variational Quantum Eigensolver Algorithm without Specifying Spin Multiplicity

The analysis of a chemical reaction along the ground-state potential energy surface in conjunction with an unknown spin state is challenging because electronic states must be separately computed several times using different spin multiplicities to find the lowest energy state. However, in principle, the ground state could be obtained with just a single calculation using a quantum computer without specifying the spin multiplicity in advance. In the present work, ground-state potential energy curves for PtCO were calculated as a proof-of-concept using a variational quantum eigensolver (VQE) algorithm. This system exhibits a singlet-triplet crossover as a consequence of the interaction between Pt and CO. VQE calculations using a statevector simulator were found to converge to a singlet state in the bonding region, while a triplet state was obtained at the dissociation limit. Calculations performed using an actual quantum device provided potential energies within ±2 kcal/mol of the simulated energies after error mitigation techniques were adopted. The spin multiplicities in the bonding and dissociation regions could be clearly distinguished even in the case of a small number of shots. The results of this study suggest that quantum computing can be a powerful tool for the analysis of the chemical reactions of systems for which the spin multiplicity of the ground state and variations in this parameter are not known in advance.
Geochemical Distribution of Platinum Metals, Gold and Silver in Intrusive Rocks of the Norilsk Region

The Norilsk ore district is one of the world leaders in the production of platinum metals. Long-term research focused on the detection of sulfide platinum-copper-nickel ores contributed to the accumulation of a large volume of scientific material on the geology and mineralization of the Norilsk area. Despite this, the issue of the composition of the initial melt for ore-bearing intrusive complexes and its degree of enrichment with noble metals remains open. Intrusive rocks of the Norilsk region are rarely analyzed for their ratio of noble metals. However, the analysis and comparison of geochemical parameters of different types of intrusions allows us to draw important conclusions not only about the composition of the initial magmas of ore-bearing complexes, but also about the formation conditions of the intrusions. This study demonstrates the distribution of platinum metals, gold and silver in the main petrographic differentiates of the Kharaelakh, Talnakh, Vologochan intrusions and Kruglogorsk-type intrusion. The regularities and variations of the distribution of metals depend on the host rocks. There are two series of rocks in the inner structure of the ore-bearing intrusions: 1. Picritic and taxitic gabbro-dolerites enriched in PGE-Au-Ag mineralization which forms disseminated ores at intrusion bottoms (ore-bearing rocks). 2. Olivine-, olivine-bearing, olivine-free gabbro-dolerites and leucogabbro with poor sulfide mineralization at the upper part of the intrusions (ore-free rocks). There is a distinct correlation between PGE, Cu, S and to a lesser extent correlation with Ni in the first rock group, which is a characteristic of sulfide PGE-Cu-Ni deposits. In the second group, correlations are also revealed, but the correlation coefficients are lower. The main element controlling the distribution of platinum metals is copper. The taxitic gabbro-dolerites of the Talnakh intrusion are the most enriched by noble metals. According to noble metal patterns the rocks of the Kharaelakh intrusion show the highest degree of melting of the initial mantle material during the formation of parental magmas chambers. Despite some differences, the geochemical features of the studied rocks indicate the similar characteristics of the accumulation of gold, silver and platinum metals in the intrusions of the Talnakh, Kruglogorsk and Zubovsk types, which allow suggesting the close conditions for the formation of ore mineralization of these intrusions.
Monocular Depth Estimation for 3D Map Construction at Underground Parking Structures

Converting the actual scenes into three-dimensional models has inevitably become one of the fundamental requirements in autonomous driving. At present, the main obstacle to large-scale deployment is the high-cost lidar for environment sensing. Monocular depth estimation aims to predict the scene depth and construct a 3D map via merely a monocular camera. In this paper, we add geometric consistency constraints to address the non-Lambertian surface problems in depth estimation. We also utilize the imaging principles and conversion rules to produce a 3D scene model from multiple images. We built a prototype and conduct extensive experiments in a corridor and an underground parking structure, and the results show the effectiveness for indoor location-based services.