Reviewerly/exHarmony huggingface dataset:
Fraud recognition in Digital Transactions by using SMOTE algorithm

Digital transactions make our lives easier. At the same time, every human is facing fraud issues by using Digital Transactions like credit cards with the growing number of transactions. Many intruders try to steal credit card details using various internet sources and cheat credit card holders. The intruders play unique tricks to cheat users, like sending trustworthy messages and emails. An enhanced fraud detection technique has become necessary to keep users sustainable to overcome the problem. An Ensemble model is constructed in this study utilizing the SMOTE algorithm to detect fraudulent transactions and alert users. The model performance is evaluated by using Machine Learning Models like KNN, Logistic Regression, and SMOTE. Among these, the SMOTE algorithm has the highest accuracy in detecting fraud.
Moods and Modality in Virtual Communication: The Study of Multimodal Analysis in Teaching Practice

The focus of the study was on moods and modality in virtual communication. The purposes of the research were to analyze the moods and modalities used by the lectures for virtual teaching to evaluate the manner of the moods and modalities realized in virtual teaching and to investigate reasons underlying the use of moods and modalities in virtual teaching. The sources of data for this research were verbal and nonverbal language from the interactions of virtual teaching. The data were utterances and gestures produced by the lecturers. The research design was qualitative research. The data were collected through recorded videos, observations, interviews, and questionnaires. The techniques of analyzing the data were applying interactive data analysis: data condensation, data display, and drawing and verification conclusion. It was found that the lectures used various kinds of moods. The declarative mood was dominantly used. In terms of modality, both modalization and modulation types of modality were used. The value of the modality selected was low, medium, and high. The manner of the mood system was realized by both marked and unmarked ones. The modalities were realized through the integration of verbal and verbal modalities. The modalities were used to manage the classroom and create a pleasant and understandable teaching and learning atmosphere. The result of this study gives a new insight into the function of language in teaching through online mode.
MORAL MOTIVATION FOR PROFESSIONAL ACTIVITY OF UNIVERSITY TEACHERS

Introduction. The paper presents an analysis of the factors, conditions, and specific features that affect the effectiveness of the motivation of teachers. The problems of moral motivation of professional activity of teachers of higher educational institutions are considered. Research methods. The analysis of the questionnaire-based job profile diagram has revealed the fundamental motives that determine the effectiveness of stimulating teachers in professional activity. The study relies on the analysis of methodological literature, pedagogical observation, and experiment. Results of the research. The findings indicate that the moral incentives (attitude towards work as a personally significant value in terms of satisfaction from the work performed, inner comfort, moral and psychological climate in the teaching team, and others) are significant for 35.4 % of teachers. Conclusion. The study has determined the types of moral incentives and ranked them according to their importance for teachers. Various non-material incentives for the work of the teachers of higher educational institutions have been shown to depend on the age of the teacher and work experience.
Neural Field Convolutions by Repeated Differentiation

Neural fields are evolving towards a general-purpose continuous representation for visual computing. Yet, despite their numerous appealing properties, they are hardly amenable to signal processing. As a remedy, we present a method to perform general continuous convolutions with general continuous signals such as neural fields. Observing that piecewise polynomial kernels reduce to a sparse set of Dirac deltas after repeated differentiation, we leverage convolution identities and train a repeated integral field to efficiently execute large-scale convolutions. We demonstrate our approach on a variety of data modalities and spatially-varying kernels.
Efficient Multimodal Sampling via Tempered Distribution Flow

Sampling from high-dimensional distributions is a fundamental problem in statistical research and practice. However, great challenges emerge when the target density function is unnormalized and contains isolated modes. We tackle this difficulty by fitting an invertible transformation mapping, called a transport map, between a reference probability measure and the target distribution, so that sampling from the target distribution can be achieved by pushing forward a reference sample through the transport map. We theoretically analyze the limitations of existing transport-based sampling methods using the Wasserstein gradient flow theory, and propose a new method called TemperFlow that addresses the multimodality issue. TemperFlow adaptively learns a sequence of tempered distributions to progressively approach the target distribution, and we prove that it overcomes the limitations of existing methods. Various experiments demonstrate the superior performance of this novel sampler compared to traditional methods, and we show its applications in modern deep learning tasks such as image generation. The programming code for the numerical experiments is available in the supplementary material.
Lexical and grammatical errors analysis on Indonesian-English translation of EFL students in Indonesia

This current study aimed to find out lexical and grammatical errors in Indonesian to English translation texts made by Indonesian EFL learners in a private senior high school in Lampung. A qualitative case study design was adopted in this research. Documentation and literature review were used as the research instruments. To obtain the data, the researchers used 15 simple sentences and a short text in Indonesian. A total of 25 students participated as the sample in this research. The results suggested that there was a total of 402 errors made by the students and the students made more grammatical errors than lexical errors. Regardless of the discrepancy of students’ CEFR level, the most frequent errors found in students’ translation were omissions (24.13%), confusion of sense relations (18.16%), misformations (11.44%), and distortions (9.95%). After identifying the errors, the researcher also attempted to describe the possible sources of errors to fill the gap in previous studies. Based on the analysis, the possible factors influencing the errors were transfer of phonological system, transfer of lexico-semantic errors, transfer of morphological elements, transfer of grammatical elements, transfer of stylistic and cultural elements in interlingual errors and communication strategy-based errors and learning strategy-based errors in intralingual errors.
Improving the evaluation of change requests using past cases

As one of the leading causes of project failures, requirements changes are inevitable in any software project. Hence, we propose an intelligent approach to facilitate the risk analysis of a change request by providing information about past cases found in similar change requests, the solutions adopted, and a support tool. The proposed approach uses case-based reasoning to retrieve previous cases similar to the current case. This approach also uses association rules to analyze patterns in the dataset and calculate the probability of risks associated with change requests. We prepared a case study to validate the proposal by analyzing the most frequent challenges in change management and considering how it can solve or minimize such problems. Results show that the proposed approach successfully assists decision-making, predicts potential risks, and suggests coherent solutions to the user. We have developed a support tool to evaluate this approach in practice with experts and obtained four different outcomes. Only a small set of cases failed to provide relevant results to the user. The use of case-based reasoning and association rules has proven to be advantageous in change management despite validity threats associated with the small number of test cases and experts involved.
Fine-grained Retail Goods Recognition based on Deep Learning

Nowadays, much effort has been devoted to image recognition technology, while the application of image recognition technology to the self-service checkout system of retail goods is still a subject with little research and great application value. For the problems of low efficiency and high cost of manual checkout in supermarkets, this study proposes to apply deep learning to commodity picture recognition. Due to the small difference between classes and the large difference within classes of commodity images, commodity image recognition is modeled as a fine-grained commodity image classification task, and the NTS-Net model is used to classify commodity images. The algorithm automatically extracts the partial features of commodity images and classifies them by combining partial features with the features of the whole commodity images. Training is conducted in a self-built commodity dataset, and tests the recognition effect of the model on commodity images from different angles and environments. This study show that the algorithm can effectively and accurately identify commodities, and the model has a good performance in identifying the side of commodities and the pictures of commodities with low brightness.
Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation

Temporal graphs exhibit dynamic interactions between nodes over continuous time, whose topologies evolve with time elapsing. The whole temporal neighborhood of nodes reveals the varying preferences of nodes. However, previous works usually generate dynamic representation with limited neighbors for simplicity, which results in both inferior performance and high latency of online inference. Therefore, in this paper, we propose a novel method of temporal graph convolution with the whole neighborhood, namely Temporal Aggregation and Propagation Graph Neural Networks (TAP-GNN). Specifically, we first analyze the computational complexity of the dynamic representation problem by unfolding the temporal graph in a message-passing paradigm. The expensive complexity motivates us to design the AP (aggregation and propagation) block, which significantly reduces the repeated computation of historical neighbors. The final TAP-GNN supports online inference in the graph stream scenario, which incorporates the temporal information into node embeddings with a temporal activation function and a projection layer besides several AP blocks. Experimental results on various real-life temporal networks show that our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
The Impact of Longitudinal <scp>Data‐Completeness</scp> of Electronic Health Record Data on the Prediction Performance of Clinical Risk Scores

The impact of electronic health record (EHR) discontinuity (i.e., receiving care outside of a given EHR system) on EHR‐based risk prediction is unknown. We aimed to assess the impact of EHR‐continuity on the performance of clinical risk scores. The study cohort consisted of patients aged ≥ 65 years with ≥ 1 EHR encounter in the 2 networks in Massachusetts (MA; 2007/1/1–2017/12/31, internal training and validation dataset), and one network in North Carolina (NC; 2007/1/1–2016/12/31, external validation dataset) that were linked with Medicare claims data. Risk scores were calculated using EHR data alone vs. linked EHR‐claims data (not subject to misclassification due to EHR‐discontinuity): (i) combined comorbidity score (CCS), (ii) claim‐based frailty score (CFI), (iii) CHAD 2 DS 2 ‐VASc, and (iv) Hypertension, Abnormal renal/liver function, Stroke, Bleeding, Labile, Elderly, and Drugs (HAS‐BLED). We assessed the performance of CCS and CFI predicting death, CHAD 2 DS 2 ‐VASc predicting ischemic stroke, and HAS‐BLED predicting bleeding by area under receiver operating characteristic curve (AUROC), stratified by quartiles of predicted EHR‐continuity (Q1‐4). There were 319,740 patients in the MA systems and 125,380 in the NC system. In the external validation dataset, AUROC for EHR‐based CCS predicting 1‐year risk of death was 0.583 in Q1 (lowest) EHR‐continuity group, which increased to 0.739 in Q4 (highest) EHR‐continuity group. The corresponding improvement in AUROC was 0.539 to 0.647 for CFI, 0.556 to 0.637 for CHAD 2 DS 2 ‐VASc, and 0.517 to 0.556 for HAS‐BLED. The AUROC in Q4 EHR‐continuity group based on EHR alone approximates that based on EHR‐claims data. The prediction performance of four clinical risk scores was substantially worse in patients with lower vs. high EHR‐continuity.
HOTGP - Higher-Order Typed Genetic Programming

Program synthesis is the process of generating a computer program following a set of specifications, which can be a high-level description of the problem and/or a set of input-output examples. The synthesis can be modeled as a search problem in which the search space is the set of all the programs valid under a grammar. As the search space is vast, brute force is usually not viable and search heuristics, such as genetic programming, also have difficulty navigating it without any guidance. In this paper we present HOTGP, a new genetic programming algorithm that synthesizes pure, typed, and functional programs. HOTGP leverages the knowledge provided by the rich data-types associated with the specification and the built-in grammar to constrain the search space and improve the performance of the synthesis. The grammar is based on Haskell's standard base library (the synthesized code can be directly compiled using any standard Haskell compiler) and includes support for higher-order functions, $\lambda$-functions, and parametric polymorphism. Experimental results show that, when compared to $6$ state-of-the-art algorithms using a standard set of benchmarks, HOTGP is competitive and capable of synthesizing the correct programs more frequently than any other of the evaluated algorithms.
A Metrological Study on the Spatial Narrative of the Qishu Genre: Take A Dream of Red Mansions and Water Margin as Examples

The Qishu genre is Andrew H. Plaks’s collective name for the four great classic Chinese novels, The Golden Lotus, and The Scholars. The reason is that these works represent a mature narrative style and a unique aesthetic structure of Ancient Chinese Novels. The Qishu genre is influenced by the concept of the “coexistence of fiction and reality” in Chinese historical narrative and the “non-narrative” of Chinese mythology, showing the characteristics of “spatialization”. The previous works on the narrative space of the Qishu genre are mainly focused on qualitative research, and lack objective evidence to support conclusions. Therefore, in this paper, we take A Dream of Red Mansions and Water Margin as examples to analyze spatial narrative from the perspective of quantitative analysis. We use statistical analysis and visualization methods and combine them with the relevant theories of spatial narratology. Then we analyze and discuss from three perspectives: the narrative function of space, the performance of narrative rhythm in space, and the role of narrative time in the spatial narrative. This paper tries to provide a new research perspective for the spatial narrative research of the Qishu genre.
A Study on the Design and Development of a Korean Ayurveda Mind-Body Balance Education Program from the perspective of Holistic Education

The purpose of this study is to design and develop a mind-body balance education program using the Korean Ayurveda education and training system from the perspective of holistic education. For this purpose, first of all, through the review of preceding studies, the holistic educational perspective of the Korean Ayurveda education and training system and the concept and approach of mind-body balance are analyzed, and after examining the mind-body balance education method, the Korean Ayurveda mind-body balance education program from the perspective of holistic education suggested. The significance of this mind-body balance education program is as follows. First, it provides an integrated perspective on mind-body balance education. It provides an integrated approach in the cognitive, emotional, and physical domains of mind-body healing and mind-body balance, and provides ways to strengthen subjective self-healing capabilities. Second, it is a systematically structured mind-body balance education program. In accordance with the structure of the Korean Ayurveda education and training system, the educational goals and contents leading to self-understanding, me and you (interpersonal relationships), collective understanding (social self), integrated mind-body balance, and happy life design are designed in a spiral structure that repeats, deepens, and expands. Third, it is a learner-participatory educational program designed centering on learners. Experiential learning and narrative educational approaches are used as the main teaching and learning methods. Fourth, it is meaningful as a lifelong education program.
Research on Deduplication Technology Based on B+ Tree Index and Hash Index

With the continuous development of information technology and improve data has become decided to enterprise survival and the development of one of the key factors. However, with the increasing amount of data information, the storage and backup of massive data bring great pressure to the enterprise data center. Statistics show that there is a lot of duplicate data between the growing mass data, so the deduplication technology is put forward. As a new data compression technology, deduplication still faces many problems and challenges. In this paper, the repeated detection rate and performance metadata size of deduplication system are studied, and two solutions based on B+ tree Index and Hash Index are proposed, and their respective advantages and disadvantages are quantitatively evaluated and analyzed.
CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems?

The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most successful methods for solving black-box continuous optimization problems. One practically useful aspect of the CMA-ES is that it can be used without hyperparameter tuning. However, the hyperparameter settings still have a considerable impact, especially for difficult tasks such as solving multimodal or noisy problems. In this study, we investigate whether the CMA-ES with default population size can solve multimodal and noisy problems. To perform this investigation, we develop a novel learning rate adaptation mechanism for the CMA-ES, such that the learning rate is adapted so as to maintain a constant signal-to-noise ratio. We investigate the behavior of the CMA-ES with the proposed learning rate adaptation mechanism through numerical experiments, and compare the results with those obtained for the CMA-ES with a fixed learning rate. The results demonstrate that, when the proposed learning rate adaptation is used, the CMA-ES with default population size works well on multimodal and/or noisy problems, without the need for extremely expensive learning rate tuning.
Chapter 3: Supervised Learning

PreviousNext No AccessMachine Learning for Science and Engineering Volume I: FundamentalsChapter 3: Supervised Learninghttps://doi.org/10.1190/1.9781560803898.ch3 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InReddit Abstract This chapter on supervised learning covers a wide variety of algorithms. Linear regression is solved with direct methods and, alternatively, is viewed as an optimization problem and in relation to the covariance of the data set. This allows the introduction of the notion of least-squares errors, regularization, and cost functions. Minimizing cost functions with the gradient descent method is central in ML. Huge data sizes and the large number of possible parameters in optimization methods prevent the use of classical algorithms such as Gaussian elimination, or matrix decomposition (e.g., LU, Cholesky, QR, SVD, and others). Hence, we put much effort into explaining the gradient method from different perspectives. We include illustrations and algorithms on the subject of steepest descent (SD) and linear conjugate gradient (CG) methods. Permalink: https://doi.org/10.1190/1.9781560803898.ch3FiguresReferencesRelatedDetails Mar 2023Pages: 408ISBN (print):978-1-56080-388-1ISBN (online):978-1-56080-389-8 publication data© 2023 Copyright © 2023 Society of Exploration Geophysicists. All rights reserved. This book or parts hereof may not be reproduced in any form without written permission from the publisher.Publisher:Society of Exploration Geophysicists History CITATION INFORMATION (2023), "Chapter 3: Supervised Learning," Course Notes Series : 87-296. https://doi.org/10.1190/1.9781560803898.ch3 Plain-Language Summary PDF DownloadLoading ...
Impact of Vulnerability Assesment and Penetration Testing (VAPT) on Operating System Security

Vulnerability Assessment and Penetration Testing (VAPT) is an important component of an organization's overall security strategy. VAPT helps identify security vulnerabilities in a computer system, network, or web application, allowing organizations to take corrective measures to address these vulnerabilities and prevent potential security breaches. By conducting regular VAPT, organizations can improve their security posture and reduce the likelihood of successful attacks. In this paper Metasploit was used to show importance of regular vulnerability assessment of critical systems in order to discover vulnerabilities before attacker do it and exploit them. The authors showed Metasploit beside its usage to conduct a vulnerability assessment, it can be utilized by attackers to harm systems. VAPT is not a one-time event, but rather a ongoing process. As new vulnerabilities are discovered and new threats emerge, organizations need to regularly assess their systems to ensure they are protected.