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Showing papers on "Class (philosophy) published in 2022"


MonographDOI
22 Nov 2022
TL;DR: The Working Class in Welfare Capitalism as mentioned in this paper examines the position of the working class in the Swedish pattern of welfare capitalism and compares it with other capitalist industrial countries and discusses the prospects for a development towards economic democracy.
Abstract: First published in 1978, The Working Class in Welfare Capitalism looks at the position of the working class in the Swedish pattern of welfare capitalism and compares it with other capitalist industrial countries. Beginning with an analysis of class, class conflict, power and social change in classical and modern social theory, Professor Korpi discusses the development of the Swedish labour movement and its strategies of class conflict. He focuses on the situation of the worker at the workplace and in the community, on the functioning of the labour union, on industrial conflict, and on the political views and standpoints of the workers. He also examines political developments in Sweden and discusses the prospects for a development towards economic democracy. A challenging and comprehensive study of Swedish social democracy in action, carried out by a Swede within a comparative frame of reference, the book presents an analysis which is of central relevance to all capitalist societies, especially when mass communist parties in Europe appear to be moving towards reformistic socialism. This book will be of interest to students of sociology, social class, economy and history.

203 citations


Journal ArticleDOI
TL;DR: In this paper , the authors proposed a system that incorporates virtual reality and metaverse methods into the classroom to compensate for the shortcomings of the existing remote models of practical education, and they developed an aircraft maintenance simulation and conducted an experiment comparing their system to a video training method.
Abstract: Due to the COVID-19 pandemic, there has been a shift from in-person to remote education, with most students taking classes via video meetings. This change inhibits active class participation from students. In particular, video education has limitations in replacing practical classes, which require both theoretical and empirical knowledge. In this study, we propose a system that incorporates virtual reality and metaverse methods into the classroom to compensate for the shortcomings of the existing remote models of practical education. Based on the proposed system, we developed an aircraft maintenance simulation and conducted an experiment comparing our system to a video training method. To measure educational effectiveness, knowledge acquisition, and retention tests were conducted and presence was investigated via survey responses. The results of the experiment show that the group using the proposed system scored higher than the video training group on both knowledge tests. As the responses given to the presence questionnaire confirmed a sense of spatial presence felt by the participants, the usability of the proposed system was judged to be appropriate.

71 citations


Journal ArticleDOI
TL;DR: A comparison of the MLCM results against statistics from the current techniques is presented to show the effectiveness in providing a concise and unambiguous understanding of a multi-label classifier behavior.
Abstract: Concise and unambiguous assessment of a machine learning algorithm is key to classifier design and performance improvement. In the multi-class classification task, where each instance can only be labeled as one class, the confusion matrix is a powerful tool for performance assessment by quantifying the classification overlap. However, in the multi-label classification task, where each instance can be labeled with more than one class, the confusion matrix is undefined. Performance assessment of the multi-label classifier is currently based on calculating performance averages, such as hamming loss, precision, recall, and F-score. While the current assessment techniques present a reasonable representation of each class and overall performance, their aggregate nature results in ambiguity when identifying false negative (FN) and false positive (FP) results. To address this gap, we define a method of creating the multi-label confusion matrix (MLCM) based on three proposed categories of multi-label problems. After establishing the shortcomings of current methods for identifying FN and FP, we demonstrate the usage of the MLCM with the classification of two publicly available multi-label data sets: i) a 12-lead ECG data set with nine classes, and ii) a movie poster data set with eighteen classes. A comparison of the MLCM results against statistics from the current techniques is presented to show the effectiveness in providing a concise and unambiguous understanding of a multi-label classifier behavior.

51 citations


Journal ArticleDOI
TL;DR: In this article , a tri-level attribute reduction framework is proposed to enrich three-way granular computing, and two approaches are suggested for constructing a specific reduct. But, the trilevel reducts are not unified by trilevel consistency.
Abstract: Attribute reduction serves as a pivotal topic of rough set theory for data analysis. The ideas of tri-level thinking from three-way decision can shed new light on three-level attribute reduction. Existing classification-specific and class-specific attribute reducts consider only macro-top and meso-middle levels. This paper introduces a micro-bottom level of object-specific reducts. The existing two types of reducts apply to the global classification with all objects and a local class with partial objects, respectively. The new type applies to an individual object. These three types of reducts constitute tri-level attribute reducts. Their development and hierarchy are worthy of systematical explorations. Firstly, object-specific reducts are defined by object consistency from dependency, and they improve both classification-specific and class-specific reducts. Secondly, tri-level reducts are unified by tri-level consistency. Hierarchical relationships between object-specific reducts and class-specific, classification-specific reducts are analyzed, and relevant connections of three-way classifications of attributes are given. Finally, tri-level reducts are systematically analyzed, and two approaches, i.e., the direct calculation and hierarchical transition, are suggested for constructing a specific reduct. We build a framework of tri-level thinking and analysis of attribute reduction to enrich three-way granular computing. Tri-level reducts lead to the sequential development and hierarchical deepening of attribute reduction, and their results profit intelligence processing and system reasoning.

46 citations


Journal ArticleDOI
TL;DR: In this paper, a tri-level attribute reduction framework is proposed to enrich three-way granular computing, and two approaches are proposed for constructing a specific reduct. But, the trilevel reducts are not unified by trilevel consistency.
Abstract: Attribute reduction serves as a pivotal topic of rough set theory for data analysis. The ideas of tri-level thinking from three-way decision can shed new light on three-level attribute reduction. Existing classification-specific and class-specific attribute reducts consider only macro-top and meso-middle levels. This paper introduces a micro-bottom level of object-specific reducts. The existing two types of reducts apply to the global classification with all objects and a local class with partial objects, respectively. The new type applies to an individual object. These three types of reducts constitute tri-level attribute reducts. Their development and hierarchy are worthy of systematical explorations. Firstly, object-specific reducts are defined by object consistency from dependency, and they improve both classification-specific and class-specific reducts. Secondly, tri-level reducts are unified by tri-level consistency. Hierarchical relationships between object-specific reducts and class-specific, classification-specific reducts are analyzed, and relevant connections of three-way classifications of attributes are given. Finally, tri-level reducts are systematically analyzed, and two approaches, i.e., the direct calculation and hierarchical transition, are suggested for constructing a specific reduct. We build a framework of tri-level thinking and analysis of attribute reduction to enrich three-way granular computing. Tri-level reducts lead to the sequential development and hierarchical deepening of attribute reduction, and their results profit intelligence processing and system reasoning.

46 citations


Journal ArticleDOI
13 Aug 2022
TL;DR: In this article , the authors used two rounds of classroom action research to investigate the use of hand puppet media in learning Arabic for the third-grade students of Islamic Elementary School Sungai Tarab in learning activities.
Abstract: Learning Arabic speaking skills depends on the teacher's teaching to his students by choosing learning media according to the needs and goals of a teacher to realize Arabic speaking skills to his students. The purpose of this research is the implementation of the learning activities of hand puppet media to improve the speaking skills of Sungai Tarab State Islamic Elementary students. Then, hand puppet media can improve the speaking skills of Sungai Tarab State Islamic Elementary students. This study uses two rounds of classroom action research. Each round consists of four stages: planning, implementation, observation and reflection. The research population was all students of Islamic Elementary School Sungai Tarab, while the sample was class Three Islamic Elementary School Sungai Tarab, totaling 35 students. Data collection techniques using interview, observation and documentation methods. At the same time, the analysis technique used in analyzing is data reduction, data presentation and conclusion drawing. The results of this study indicate that the use of hand puppet media in learning Arabic can improve the ability to speak Arabic for the third-grade students of Islamic Elementary School Sungai Tarab in learning activities.

44 citations


Journal ArticleDOI
TL;DR: In this paper , the authors presented a neural network capable of predicting precipitation at a high resolution up to 12 hours ahead of current state-of-the-art physics-based models.
Abstract: Abstract Existing weather forecasting models are based on physics and use supercomputers to evolve the atmosphere into the future. Better physics-based forecasts require improved atmospheric models, which can be difficult to discover and develop, or increasing the resolution underlying the simulation, which can be computationally prohibitive. An emerging class of weather models based on neural networks overcome these limitations by learning the required transformations from data instead of relying on hand-coded physics and by running efficiently in parallel. Here we present a neural network capable of predicting precipitation at a high resolution up to 12 h ahead. The model predicts raw precipitation targets and outperforms for up to 12 h of lead time state-of-the-art physics-based models currently operating in the Continental United States. The results represent a substantial step towards validating the new class of neural weather models.

43 citations


Journal ArticleDOI
TL;DR: A systematic review of FIMIX-PLS applications published in major business research journals provides an overview of the interdependencies between researchers’ choices and identifies potential problem areas.

42 citations



Journal ArticleDOI
TL;DR: A novel 14-layered deep convolutional neural network (14-DCNN) to detect plant leaf diseases using leaf images was proposed and the overall performance of the proposed DCNN model was better than the existing transfer learning approaches.
Abstract: In this research, we proposed a novel 14-layered deep convolutional neural network (14-DCNN) to detect plant leaf diseases using leaf images. A new dataset was created using various open datasets. Data augmentation techniques were used to balance the individual class sizes of the dataset. Three image augmentation techniques were used: basic image manipulation (BIM), deep convolutional generative adversarial network (DCGAN) and neural style transfer (NST). The dataset consists of 147,500 images of 58 different healthy and diseased plant leaf classes and one no-leaf class. The proposed DCNN model was trained in the multi-graphics processing units (MGPUs) environment for 1000 epochs. The random search with the coarse-to-fine searching technique was used to select the most suitable hyperparameter values to improve the training performance of the proposed DCNN model. On the 8850 test images, the proposed DCNN model achieved 99.9655% overall classification accuracy, 99.7999% weighted average precision, 99.7966% weighted average recall, and 99.7968% weighted average F1 score. Additionally, the overall performance of the proposed DCNN model was better than the existing transfer learning approaches.

38 citations


Journal ArticleDOI
TL;DR: In this paper , a systematic review of FIMIX-PLS applications is presented, which provides an overview of the interdependencies between researchers' choices and identifies potential problem areas.
Abstract: With the increasing prominence of partial least squares structural equation modeling (PLS-SEM) in business research, the use of latent class analyses for identifying and treating unobserved heterogeneity has also gained momentum. Researchers have introduced various latent class approaches in a PLS-SEM context, of which finite mixture PLS (FIMIX-PLS) plays a central role due to its ability to identify heterogeneity and indicate a suitable number of segments to extract from the data. However, applying FIMIX-PLS requires researchers to make several choices that, if incorrect, could lead to wrong results and false conclusions. Addressing this concern, we present the results of a systematic review of FIMIX-PLS applications published in major business research journals. Our review provides an overview of the interdependencies between researchers’ choices and identifies potential problem areas. Based on our results, we offer concrete guidance on how to prevent common pitfalls when using FIMIX-PLS, and identify future research areas.

Journal ArticleDOI
TL;DR: In this article , a systematic survey of fine-grained image analysis is presented, where the authors attempt to re-define and broaden the field of FGIA by consolidating two fundamental finegrained research areas.
Abstract: Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas – fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community.

Proceedings ArticleDOI
31 Mar 2022
TL;DR: It is shown that an adversary who can poison a training dataset can cause models trained on this dataset to leak significant private details of training points belonging to other parties, casting doubts on the relevance of cryptographic privacy guarantees in multiparty computation protocols for machine learning, if parties can arbitrarily select their share of training data.
Abstract: We introduce a new class of attacks on machine learning models. We show that an adversary who can poison a training dataset can cause models trained on this dataset to leak significant private details of training points belonging to other parties. Our active inference attacks connect two independent lines of work targeting the integrity and privacy of machine learning training data. Our attacks are effective across membership inference, attribute inference, and data extraction. For example, our targeted attacks can poison <0.1% of the training dataset to boost the performance of inference attacks by 1 to 2 orders of magnitude. Further, an adversary who controls a significant fraction of the training data (e.g., 50%) can launch untargeted attacks that enable 8× more precise inference on all other users' otherwise-private data points. Our results cast doubts on the relevance of cryptographic privacy guarantees in multiparty computation protocols for machine learning, if parties can arbitrarily select their share of training data.

Proceedings ArticleDOI
01 Jun 2022
TL;DR: In this paper , a coupled knowledge distillation (DKDISTILLER) method is proposed for logit distillation, which enables TCKD and NCKD to play their roles more efficiently and flexibly.
Abstract: State-of-the-art distillation methods are mainly based on distilling deep features from intermediate layers, while the significance of logit distillation is greatly overlooked. To provide a novel viewpoint to study logit distillation, we re-formulate the classical KD loss into two parts, i.e., target class knowledge distillation (TCKD) and non-target class knowledge distillation (NCKD). We empirically investigate and prove the effects of the two parts: TCKD transfers knowledge concerning the “difficulty” of training samples, while NCKD is the prominent reason why logit distillation works. More importantly, we reveal that the classical KD loss is a coupled formulation, which (1) suppresses the effectiveness of NCKD and (2) limits the flexibility to balance these two parts. To address these issues, we present Decoupled Knowledge Distillation (DKD), enabling TCKD and NCKD to play their roles more efficiently and flexibly. Compared with complex feature-based methods, our DKD achieves comparable or even better results and has better training efficiency on CIFAR-100, ImageNet, and MS-COCO datasets for image classification and object detection tasks. This paper proves the great potential of logit distillation, and we hope it will be helpful for future research. The code is available at https://github.com/megviiresearch/mdistiller.

Journal ArticleDOI
TL;DR: In this article , a class rebalancing strategy based on a class-balanced dynamically weighted loss function where weights are assigned based on the class frequency and predicted probability of ground-truth class is proposed.
Abstract: Imbalanced class distribution is an inherent problem in many real-world classification tasks where the minority class is the class of interest. Many conventional statistical and machine learning classification algorithms are subject to frequency bias, and learning discriminating boundaries between the minority and majority classes could be challenging. To address the class distribution imbalance in deep learning, we propose a class rebalancing strategy based on a class-balanced dynamically weighted loss function where weights are assigned based on the class frequency and predicted probability of ground-truth class. The ability of dynamic weighting scheme to self-adapt its weights depending on the prediction scores allows the model to adjust for instances with varying levels of difficulty resulting in gradient updates driven by hard minority class samples. We further show that the proposed loss function is classification calibrated. Experiments conducted on highly imbalanced data across different applications of cyber intrusion detection (CICIDS2017 data set) and medical imaging (ISIC2019 data set) show robust generalization. Theoretical results supported by superior empirical performance provide justification for the validity of the proposed dynamically weighted balanced (DWB) loss function.

Journal ArticleDOI
TL;DR: In this paper , a new infinite class of gravitational observables in asymptotically anti-de Sitter space living on codimension-one slices of the geometry, the most famous of which is the volume of the maximal slice, is presented.
Abstract: We present a new infinite class of gravitational observables in asymptotically anti-de Sitter space living on codimension-one slices of the geometry, the most famous of which is the volume of the maximal slice. We show that these observables display universal features for the thermofield-double state: they grow linearly in time at late times and reproduce the switchback effect in shock wave geometries. We argue that any member of this class of observables is an equally viable candidate as the extremal volume for a gravitational dual of complexity.

Journal ArticleDOI
TL;DR: In this article , the performance of all EfficientNet variants on this imbalanced multiclass classification task using metrics such as Precision, Recall, Accuracy, F1 Score, and Confusion Matrices was evaluated.

Journal ArticleDOI
TL;DR: In this article , the leader-following consensus problem for nonlinear multiagent systems with Lipschitz dynamics is studied. But the authors focus on the leader and all edges.
Abstract: Considering that there are many systems with limited network bandwidth in practice, this article studies the leader-following consensus problem for a class of nonlinear multiagent systems (MASs). The purpose of this article is to reduce unnecessary information transmission between any pair of adjacent agents including the leader in the MASs through intermittent communication. The novel event-triggered and asynchronous edge-event triggered mechanisms are designed for the leader and all edges, respectively. The static and dynamic consensus protocols under these mechanisms are proposed to address the leader-following consensus problem for MASs with Lipschitz dynamics, and the systems will not exhibit Zeno behavior under these two control schemes. Note that the dynamic consensus protocol does not rely on any global values of MASs, it is a fully distributed way. Finally, a practice simulation example is introduced to illustrate the theoretical results obtained.

Journal ArticleDOI
TL;DR: In this paper , the authors provide an overview of the factors that contribute to the effectiveness of the flipped classroom and how these factors can be stimulated by structuring the learning process and focusing the teacher training on competencies and learning-and teaching approaches.
Abstract: In a flipped classroom, students acquire knowledge before class and deepen and apply this knowledge during class. This way, lower-order learning goals are achieved before class and higher-order skills are reached during class. This study aims to provide an overview of the factors that contribute to the effectiveness of the flipped classroom and how these factors can be stimulated. The effectiveness of the flipped classroom is conceptualized in this study as test scores, the achievement of higher learning goals, and student perceptions.A state-of-the-art review was conducted. The databases MEDLINE, PsycINFO, PubMed, Web of Science, and Scopus were consulted. The timeframe is 2016 till 2020. The studies were qualitatively analyzed according to the grounded theory method.After screening the studies based on the inclusion-and exclusion criteria, 88 studies were included in this review. The qualitative analysis of these studies revealed six main factors that affect the effectiveness of the flipped classroom: student characteristics, teacher characteristics, implementation, task characteristics, out-of-class activities, and in-class activities. Mediating factors are, amongst other factors, the learner's level of self-regulated learning, teacher's role and motivation, assessment approach, and guidance during self-study by means of prompts or feedback. These factors can be positively stimulated by structuring the learning process and focusing the teacher training on competencies and learning-and teaching approaches that are essential for the flipped classroom.This paper provides insight into the factors that contribute to the effectiveness of the flipped classroom and how these factors could be stimulated. In order to stimulate the effectiveness of the flipped classroom, the positively and negatively affecting factors and mediating factors should be taken into account in the design of the flipped classroom. The interventions mentioned in this paper could also be used to enhance the effectiveness.

Journal ArticleDOI
TL;DR: In this paper , an efficient evolutionary algorithm is used to handle imbalanced dataset and feature selection process is also enhanced with the required functionalities, the proposed methodology effectively balances the dataset with less number of features.
Abstract: Today’s datasets are usually very large with many features and making analysis on such datasets is really a tedious task. Especially when performing classification, selecting attributes that are salient for the process is a brainstorming task. It is more difficult when there are many class labels for the target class attribute and hence many researchers have introduced methods to select features for performing classification on multi-class attributes. The process becomes more tedious when the attribute values are imbalanced for which researchers have contributed many methods. But, there is no sufficient research to handle extreme imbalance and feature selection together and hence this paper aims to bridge this gap. Here Particle Swarm Optimization (PSO), an efficient evolutionary algorithm is used to handle imbalanced dataset and feature selection process is also enhanced with the required functionalities. First, Multi-objective Particle Swarm Optimization is used to transform the imbalanced datasets into balanced one and then another version of Multi-objective Particle Swarm Optimization is used to select the significant features. The proposed methodology is applied on eight multi-class extremely imbalanced datasets and the experimental results are found to be better than other existing methods in terms of classification accuracy, G mean, F measure. The results validated by using Friedman test also confirm that the proposed methodology effectively balances the dataset with less number of features than other methods.

Journal ArticleDOI
TL;DR: In this article , the impact of various multi-class imbalanced difficulty factors on the performance of three popular classifiers was investigated and the results demonstrated a strong influence of the class overlapping with the extent of its impact related to the types of overlapped classes.
Abstract: Multi-class imbalanced classification is more difficult and less frequently studied than its binary counterpart. Moreover, research on the causes of the difficulty of multi-class imbalanced data is quite limited and insufficient. Therefore, we experimentally study the impact of various multi-class imbalanced difficulty factors on the performance of three popular classifiers. The results demonstrated a strong influence of the class overlapping with the extent of its impact related to the types of overlapped classes. In particular, overlapping between minority and majority classes was more difficult than the others. The type of the class size configuration turned out to be another important factor, highlighting the special role of the configurations with classes of intermediate sizes. The obtained results could support studying the nature of the multi-class imbalanced data as well as the development of new methods for improving classifiers. • Difficulty factors in multi-class imbalanced data are experimentally studied. • A strong influence of class overlapping related to types of class inter-relation. • High impact of the class size configuration (multi-majority — most difficult). • The gradual configurations with classes of intermediate sizes play special roles.

Journal ArticleDOI
TL;DR: In this article , the authors considered a class of fractional order Volterra integro-differential equations of first kind where the fractional derivative is considered in the Caputo sense.

Journal ArticleDOI
TL;DR: In this article , the authors proposed the General Performance Score (GPS) , a methodological approach to build performance metrics for binary and multi-class classification problems, which combines a set of individual metrics, penalising low values in any of them.
Abstract: Abstract Several performance metrics are currently available to evaluate the performance of Machine Learning (ML) models in classification problems. ML models are usually assessed using a single measure because it facilitates the comparison between several models. However, there is no silver bullet since each performance metric emphasizes a different aspect of the classification. Thus, the choice depends on the particular requirements and characteristics of the problem. An additional problem arises in multi-class classification problems, since most of the well-known metrics are only directly applicable to binary classification problems. In this paper, we propose the General Performance Score (GPS) , a methodological approach to build performance metrics for binary and multi-class classification problems. The basic idea behind GPS is to combine a set of individual metrics, penalising low values in any of them. Thus, users can combine several performance metrics that are relevant in the particular problem based on their preferences obtaining a conservative combination. Different GPS -based performance metrics are compared with alternatives in classification problems using real and simulated datasets. The metrics built using the proposed method improve the stability and explainability of the usual performance metrics. Finally, the GPS brings benefits in both new research lines and practical usage, where performance metrics tailored for each particular problem are considered.

Journal ArticleDOI
TL;DR: Zhang et al. as discussed by the authors investigated the application of a class-weighted algorithm combined with conventional machine learning model (logistic regression (LR)) and ensemble machine learning models (LightGBM and random forest (RF)) to the landslide susceptibility evaluation.

Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper investigated the application of a class-weighted algorithm combined with conventional machine learning model (logistic regression (LR)) and ensemble machine learning models (LightGBM and random forest (RF)) to the landslide susceptibility evaluation.

Journal ArticleDOI
01 Sep 2022-Optik
TL;DR: In this paper , a class of (3 + 1)-dimensional hyperbolic nonlinear Schrödinger equation soliton solutions with optical features is presented. But the spectral properties of these solutions are unknown.

Journal ArticleDOI
TL;DR: In this article , the authors discuss classes of exact and perturbative spherically symmetric solutions in f(T,B)-gravity, and present general methods and strategies, like generalized Bianchi identities, to find spheically solutions in modified teleparallel theories of gravity.
Abstract: Abstract Spherically symmetric solutions of theories of gravity built one fundamental class of solutions to describe compact objects like black holes and stars. Moreover, they serve as starting point for the search of more realistic axially symmetric solutions which are capable to describe rotating compact objects. Theories of gravity that do not possess spherically symmetric solutions which meet all observational constraints are easily falsified. In this article, we discuss classes of exact and perturbative spherically symmetric solutions in f(T,B)-gravity. The perturbative solutions add to the ones which have already been found in the literature, while the exact solutions are presented here for the first time. Moreover, we present general methods and strategies, like generalized Bianchi identities, to find spherically solutions in modified teleparallel theories of gravity.


Journal ArticleDOI
TL;DR: 3D-HyperGAMO as discussed by the authors uses generative adversarial minority oversampling to generate more samples for minority classes at training time, using the existing samples of that class.
Abstract: Recently, convolutional neural networks (CNNs) have exhibited commendable performance for hyperspectral image (HSI) classification. Generally, an important number of samples are needed for each class to properly train CNNs. However, existing HSI data sets suffer from a significant class imbalance problem, where many classes do not have enough samples to characterize the spectral information. The performance of existing CNN models is biased toward the majority classes, which possess more samples for the training. This article addresses this issue of imbalanced data in HSI classification. In particular, a new 3D-HyperGAMO model is proposed, which uses generative adversarial minority oversampling. The proposed 3D-HyperGAMO automatically generates more samples for minority classes at training time, using the existing samples of that class. The samples are generated in the form of a 3-D hyperspectral patch. A different classifier from the generator and the discriminator is used in the 3D-HyperGAMO model, which is trained using both original and generated samples to determine the classes of newly generated samples to which they actually belong. The generated data are combined classwise with the original training data set to learn the network parameters of the class. Finally, the trained 3-D classifier network validates the performance of the model using the test set. Four benchmark HSI data sets, namely, Indian Pines (IP), Kennedy Space Center (KSC), University of Pavia (UP), and Botswana (BW), have been considered in our experiments. The proposed model shows outstanding data generation ability during the training, which significantly improves the classification performance over the considered data sets. The source code is available publicly at https://github.com/mhaut/3D-HyperGAMO .

Journal ArticleDOI
TL;DR: In this article , the authors examined the interpersonal competence of student communication in the Business English study program and found that interpersonal competence refers to the ability of individuals to collaborate and communicate in groups, both verbally and nonverbally.
Abstract: The research examines the interpersonal competence of student communication in the Business English study program. Interpersonal competence refers to the ability of individuals to collaborate and communicate in groups, both verbally and nonverbally. People with effective interpersonal skills will be sensitive to the feelings and emotions of others around them. This ability is a way to mea- sure the quality of interpersonal communication, which includes knowledge of the rules of nonverbal communication, such as physical contact and intimacy, knowledge of interaction by context, attention to the person to whom to com- municate, and attention to the amount. This is evidenced by the test results data of class A students of the Class of 2020, the average score obtained in the listening skill aspect is 53.56, the emotional intelligence aspect is 57.65, and verbal communication is 45. 47, communication in groups is 53.27, and the average score of students totalling 45 is 53.11. There are eleven students who are at a level below average, this happens because of several factors. Then the average score in class B class of 2020, in the aspect of listening skills is 53.35, emotional intelligence is 57.67, verbal communication is 47.47, and communi- cation in the group is 53.28 while the total average number of res throughout is 53.44. There were 21 students who were in the below-average category out of a total of 43. Based on the data obtained, it shows that the scores obtained by class A and B Year 2020 are not too significantly different, this happens because the students experience the same difficulties