Reviewerly/exHarmony huggingface dataset:
Quantum control operations with fuzzy evolution trajectories based on polyharmonic magnetic fields

This manuscript surveys quantum operations under the influence of harmonic magnetic fields subject to time variations. The author scrutinises the dynamic interplay of these fields and canonical variables, leading to effects such as squeezing, looping effects, and parametric resonances. The time evolution of observables tethered to a time-dependent quadratic Hamiltonian is examined. The results of this inquiry offer contributions to the quantum control theory.
The Application of Case Teaching Method in Computer English Teaching under OBE Mode——Taking College English Blending Teaching of Hetao College as An Example

This article takes college English blending teaching in Hetao College as an example to analyze the concept of case teaching method under OBE mode. The author combines the application value of the case teaching method under the OBE mode and the current research on the current situation of computer English teaching to decompose the learning task. This article studies how to choose suitable teaching cases, how to guide students do well teamwork, how to improve the comprehensiveness of the assessment system, and how to reverse design the curriculum system. The author’s purpose is to enhance the effectiveness of college English classroom teaching and enhance students’ comprehensive learning ability.
Writing Error Analysis in Exposition Text of the EFL Junior High School Students

The objective of this research was to understand the writing error of eleventh-grade students of SMA Negeri 4 Samarinda. The descriptive qualitative method was used to examine the written exposition text made by 15 students. The data was collected from the participants’ writing on exposition text and analyzed based on the errors’ categorization. The result showed that there are 16 categories of errors found in the data. They were punctuation, missing word, capitalization, subject-verb agreement, pronoun agreement, spelling, singular or plural, unnecessary word, wrong word form, wrong word, wrong order, fragment, preposition, conjunction, article, and need-further support error. The error factors were intralingua transfer, interlingua transfer, and students’ lack of knowledge about writing.
Image and Point Data Fusion for Enhanced Discrimination of Ore and Waste in Mining

Sensor technologies provide relevant information on the key geological attributes in mining. The integration of data from multiple sources is advantageous in making use of the synergy among the outputs for the enhanced characterisation of materials. Sensors produce various types of data. Thus, the fusion of these data requires innovative data-driven strategies. In the present study, the fusion of image and point data is proposed, aiming for the enhanced classification of ore and waste materials in a polymetallic sulphide deposit at 3%, 5% and 7% cut-off grades. The image data were acquired in the visible-near infrared (VNIR) and short-wave infrared (SWIR) regions of the electromagnetic spectrum. The point data cover the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectral regions. A multi-step methodological approach was developed for the fusion of the image and point data at multiple levels using the supervised and unsupervised classification techniques. Several possible combinations of the data blocks were evaluated to select the optimal combinations in an optimised way. The obtained results indicate that the individual image and point techniques resulted in a successful classification of ore and waste materials. However, the classification performance greatly improved with the fusion of image and point data, where the K-means and support vector classification (SVC) models provided acceptable results. The proposed approach enables a significant reduction in data volume while maintaining the relevant information in the spectra. This is principally beneficial for the integration of data from high-throughput and large data volume sources. Thus, the effectiveness and practicality of the approach can permit the enhanced separation of ore and waste materials in operational mines.
Accounting Information System Courses: Developing a Hybrid Syllabus in the Era of Digitization

Technology is becoming a major pillar in many professions. It plays a critical role in developing and enhancing the quality and accuracy of tasks. Because the formal education system is the first place where future employees are educated, it is very important to ensure that educational institutions offer continuously updated technology-related courses that cover a wide range of new and emerging topics. This research has two objectives. First, it explores the validity of the claim that AIS education is of low quality in the Middle East, as stated in some studies. This is conducted through highlighting the status quo for AIS courses in Arab countries within the Middle East by focusing on a representative case study. This part of the work was conducted through a thorough exploration of the universities’ websites and by distributing and analyzing a survey for faculty members in a number of universities. Second, the research performs a deep analysis to develop new, up-to-date, aligned objectives, contents, resources, and assignments in order to create a new, comprehensive syllabus in the higher-education sector that will serve stakeholders from universities to accounting communities. The output of this research identified suitable learning objectives based on current literature and faculty responses. This was followed by designing new course content and assessment tools that are aligned with learner- and competency-centered approaches.
The Use of Microsoft Sway 365 in Teaching Reading Descriptive Text: A Response to Pandemic Situation

The aim of the study is to investigate the use of Microsoft Sway 365 in teaching reading descriptive text as a response in pandemic situation. The method used in the research was classroom action research. Subject of the research was 36 students from tenth grade of SMAN 1 Baros, Banten Province. Data collective techniques used in this recent study were test and questionnaire. The result of research shows that students achieve average scores 67.5 from reading test in pre-cycle. This result, of course, still lower than the ideal score 75. Since Sway Microsoft 365 is implemented, students’ scores are increased. This can be seen from the average score in cycle 1 is 77.5; and it increases into 81.5 in cycle 2. These results also matched to the questionnaire result which 78% of students agree if Microsoft Sway 365 can be used as an alternative online learning media for reading descriptive text, especially proposed for pandemic situation.
An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method

With the development of emerging intelligent traffic signal (I-SIG) system, congestion-involved security issues are drawing attentions of researchers and developers on the vulnerability introduced by connected vehicle technology, which empowers vehicles to communicate with the surrounding environment such as road-side infrastructure and traffic control units. A congestion attack to the controlled optimization of phases algorithm (COP) of I-SIG is recently revealed. Unfortunately, such analysis still lacks a timely visualized prediction on later congestion when launching an initial attack. In this paper, we argue that traffic image feature-based learning has available knowledge to reflect the relation between attack and caused congestion and propose a novel analysis framework based on cycle generative adversarial network (CycleGAN). Based on phase order, we first extract four-direction road images of one intersection and perform phase-based composition for generating new sample image of training. We then design a weighted L1 regularization loss that considers both last-vehicle attack and first-vehicle attack, to improve the training of CycleGAN with two generators and two discriminators. Experiments on simulated traffic flow data from VISSIM platform show the effectiveness of our approach.
TDAF: Top-Down Attention Framework for Vision Tasks

Human attention mechanisms often work in a top-down manner, yet it is not well explored in vision research. Here, we propose the Top-Down Attention Framework (TDAF) to capture top-down attentions, which can be easily adopted in most existing models. The designed Recursive Dual-Directional Nested Structure in it forms two sets of orthogonal paths, recursive and structural ones, where bottom-up spatial features and top-down attention features are extracted respectively. Such spatial and attention features are nested deeply, therefore, the proposed framework works in a mixed top-down and bottom-up manner. Empirical evidence shows that our TDAF can capture effective stratified attention information and boost performance. ResNet with TDAF achieves 2.0% improvements on ImageNet. For object detection, the performance is improved by 2.7% AP over FCOS. For pose estimation, TDAF improves the baseline by 1.6%. And for action recognition, the 3D-ResNet adopting TDAF achieves improvements of 1.7% accuracy.
Analysis of Students’ Vocabulary in Learning English

The purpose of this study was to analysis students’ vocabulary in learning English of Ninth grade at SMP Swasta Yayasan Pangeran Antasari, Medan in the academic year of 2018/2019. The researchers used descriptive qualitative method to describe and implementing factual condition and characteristic. There are 72 students that decided two classes at ninth grade as the population. But the researcher only took one class consists of 36 students as the sample. To collecting data, researchers use questionnaire and vocabulary comprehension test as procedure. In analyzing the data, the researchers identifying the difficulties of students’ vocabulary and cause difficulties by analyzing students answer sheets and their mistakes. There were 25 students (70%) incorrect answer in the questions. There were 6 students (16%) in modal, there were 8 students (23%) in simple past tense, There were 8 students ( 24 %) in simple present tense, 3 students (7%) troubled in simple continuous tense. Meaning. Students who gave incorrect answer in the questions were 29 students (82%). Others difficulties is translation being other types of the questions that examined in this research. it was found that there were 23 students (64%) who were unable to give the correct answer. Concluding the cause difficulties of students’ vocabulary in learning English are the first students difficulties in pronouncing the words. Grammatical form be the second cause because the written form is different from the spoken form in english. The last, students does not give their attention to study english more, knowing a words much more, and limitations their time to sources of information about new words vocabulary. The last, forgot the words easily.&#x0D; Keywords : Analysis Students’ Vocabulary, Difficulties, Cause
Precise Takagi-Sugeno fuzzy logic system for UAV longitudinal stability: an Industry 4.0 case study for aerospace

Industry 4.0 is making inroads even into the field of aerospace, which is extremely conservative due to the safety concerns involved with the introduction of new technology. We used fuzzy logic to create a very reliable simplification and reduce the complexity of the critical longitudinal stability equations for a flying wing drone. Our approximate method allowed us to have a very light calculation effort at the price of a negligible error in terms of the size and dynamics of the craft, thus reducing the work required by the telecommunication segment that manages the takeoff and landing manoeuvres
“We Will Build a Great Wall”: Domination, Criminalization, and Expatriation in Trump Campaign and Rally Speeches

Given the import and impact of political campaign promises, this study systematically analyzed Donald Trump’s campaign and rally speeches using a typology of verbal-textual hostility (V.T.H.) developed by Asquith (2013) from criminal hate incidents in the United Kingdom. Trump used all forms of V.T.H. previously identified by Asquith, except for sexualization, and new forms that may be specific to the political context. Analysis of speeches from 2015–2018 revealed that expatriation, criminalization, and domination were the most frequently used forms of V.T.H. deployed by Trump, which we consider in relation to the historical, social, and political context and consequences.
Analisis Survival pada Data Pasien Covid 19 di Kabupaten Jember

The confirmed number of positive Covid 19 cases in Indonesia until June 15th, 2020 was 38.227 people with 3.134 dead, case fatality rate 5,9%. Case fatality rate is the percentage of the number of dead people from all confirmed and reported positive cases . Particularly, in Jember Regency, the spreading of Covid 19 is still underway day by day with the increasing of number of patient so that appropriate preventive and treatment should be done precisely. The problem of the paper is the survival analysis of Covid 19 patient by using Kaplan Meier and Log Rank test method. The result of this paper, the result of the analysis using Kaplan Meier Curve method, patients with male sex have a chance of recovering faster compared with female patients and patients with age interval of 40-49 years have a chance of recovering faster than any other age intervals, meanwhile Log rank test did not provide significant results. So the Kaplan Meier Curve method is more appropriate to analyze Covid 19 patient data in Jember compared to the Log rank test.
The Use of Mobile Technology in Learning With Online and Offline Systems

Mobile phones can be functionalized in the process of learning using cellular devices and computing systems. The present study aims to (1) develop mobile learning applications and (2) find out student responses in the learning process using m-learning. This research used the 4D model (defining, designing, developing, and disseminating) written by Thiagarajan. Validation used a questionnaire to collect data, while effectiveness was revealed through student responses that took part in m-learning. The results showed 1) the application used in e-learning learning was very valid (&amp;gt; 85%), and 2) research subjects (two classes) provided very positive feedback (&amp;gt; 85%). It means that the mobile learning application developed is feasible to use because it has high validity and is able to increase students' positive responses. This study reveals that mobile learning applications can have a positive impact on student learning outcomes in the future.
Efficient subspace search in data streams

In the real world, data streams are ubiquitous -- think of network traffic or sensor data. Mining patterns, e.g., outliers or clusters, from such data must take place in real time. This is challenging because (1) streams often have high dimensionality, and (2) the data characteristics may change over time. Existing approaches tend to focus on only one aspect, either high dimensionality or the specifics of the streaming setting. For static data, a common approach to deal with high dimensionality -- known as subspace search -- extracts low-dimensional, `interesting' projections (subspaces), in which patterns are easier to find. In this paper, we address both Challenge (1) and (2) by generalising subspace search to data streams. Our approach, Streaming Greedy Maximum Random Deviation (SGMRD), monitors interesting subspaces in high-dimensional data streams. It leverages novel multivariate dependency estimators and monitoring techniques based on bandit theory. We show that the benefits of SGMRD are twofold: (i) It monitors subspaces efficiently, and (ii) this improves the results of downstream data mining tasks, such as outlier detection. Our experiments, performed against synthetic and real-world data, demonstrate that SGMRD outperforms its competitors by a large margin.
Correction to “Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis”

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 4</xref> was erroneously copied from the published article <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . The original figure was meant to be used as a reference to create a new image using our algorithm. The original <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 4</xref> is similar to that in <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . The correct <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 4</xref> is shown in the following illustration. In articles <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> and <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> , the same deep learning algorithms were adopted, and the same database was used resulting in the closely matched figures.
World-class university in the era of digitalization

The article examines the tendencies and prospects for the development of universities wishing to occupy leading positions in world and national rankings that appeared in connection with digitalization, which has the most noticeable impact in the field of education since 2020. In this regard, the most urgent is to consider the identification of the main factors by which universities are assessed by leading international and national ratings, evolutionary and meaningful models of creating a world-class university, vectors for the development of digital transformation in relation to higher education, as well as analysis of existing ones, internship at world-class universities in the field of digitalization. The presented results of the study allow to draw conclusions about the attitude of representatives of educational organizations (students and teachers) and the business community of a number of cities in Russia, Poland and Italy to the ongoing digital transformation of universities and the formation of new world-class universities.
On null 3-hypergraphs

Given a 3-uniform hypergraph H consisting of a set V of vertices, and T⊆V3 triples, a null labelling is an assignment of ±1 to the triples such that each vertex is contained in an equal number of triples labelled +1 and −1. Thus, the signed degree of each vertex is zero. A necessary condition for a null labelling is that the degree of every vertex of H is even. The Null Labelling Problem is to determine whether H has a null labelling. It is proved that this problem is NP-complete. Computer enumerations suggest that most hypergraphs which satisfy the necessary condition do have a null labelling. Some constructions are given which produce hypergraphs satisfying the necessary condition, but which do not have a null labelling. A self complementary 3-hypergraph with this property is also constructed.
Learning Sparse Filters in Deep Convolutional Neural Networks with a $$l_1/l_2$$ Pseudo-Norm

While deep neural networks (DNNs) have proven to be efficient for numerous tasks, they come at a high memory and computation cost, thus making them impractical on resource-limited devices. However, these networks are known to contain a large number of parameters. Recent research has shown that their structure can be more compact without compromising their performance.In this paper, we present a sparsity-inducing regularization term based on the ratio \(l_1/l_2\) pseudo-norm defined on the filter coefficients. By defining this pseudo-norm appropriately for the different filter kernels, and removing irrelevant filters, the number of kernels in each layer can be drastically reduced leading to very compact Deep Convolutional Neural Networks (DCNN) structures. Unlike numerous existing methods, our approach does not require an iterative retraining process and, using this regularization term, directly produces a sparse model during the training process. Furthermore, our approach is also much easier and simpler to implement than existing methods. Experimental results on MNIST and CIFAR-10 show that our approach significantly reduces the number of filters of classical models such as LeNet and VGG while reaching the same or even better accuracy than the baseline models. Moreover, the trade-off between the sparsity and the accuracy is compared to other loss regularization terms based on the l1 or l2 norm as well as the SSL [], NISP [] and GAL [] methods and shows that our approach is outperforming them.
Leveraging Stack4Things for Federated Learning in Intelligent Cyber Physical Systems

During the last decade, the Internet of Things acted as catalyst for the big data phenomenon. As result, modern edge devices can access a huge amount of data that can be exploited to build useful services. In such a context, artificial intelligence has a key role to develop intelligent systems (e.g., intelligent cyber physical systems) that create a connecting bridge with the physical world. However, as time goes by, machine and deep learning applications are becoming more complex, requiring increasing amounts of data and training time, which makes the use of centralized approaches unsuitable. Federated learning is an emerging paradigm which enables the cooperation of edge devices to learn a shared model (while keeping private their training data), thereby abating the training time. Although federated learning is a promising technique, its implementation is difficult and brings a lot of challenges. In this paper, we present an extension of Stack4Things, a cloud platform developed in our department; leveraging its functionalities, we enabled the deployment of federated learning on edge devices without caring their heterogeneity. Experimental results show a comparison with a centralized approach and demonstrate the effectiveness of the proposed approach in terms of both training time and model accuracy.
IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE

This paper introduces our systems for the first two subtasks of SemEval Task4: Commonsense Validation and Explanation.To clarify the intention for judgment and inject contrastive information for selection, we propose the input reconstruction strategy with prompt templates.Specifically, we formalize the subtasks into the multiple-choice question answering format and construct the input with the prompt templates, then, the final prediction of question answering is considered as the result of subtasks.Experimental results show that our approaches achieve significant performance compared with the baseline systems.Our approaches secure the third rank on both official test sets of the first two subtasks with an accuracy of 96.4 and an accuracy of 94.3 respectively.