Reviewerly/exHarmony huggingface dataset:
Educators’ and students’ perceptions of online distance education before and amid COVID-19: Key concerns and challenges

This paper aims to present Russian educators’ and students’ perceptions of online education before and during the pandemic. The author analyses the primary research data collected by the team of USUE authors via quantitate research methods, and secondary research data. Based on the empirical sociological data the author identifies the key challenges facing educators’ community – negative attitudes to online teaching and learning, lack of digital pedagogy skills and ineffective interaction practices. It is concluded that in the emergency situation teachers overall managed to fit rapid curriculum transformations. Yet, university leadership and educators should come up with solutions how to address the identified challenges in online distance teaching. These solutions can involve conducting in-depth research into digital pedagogy, disseminating knowledge about the specifics of online interactions and running professional development programs for the university teachers to enhance their performance in courses delivered online.
An Image Stitching Method for Airborne Wide-Swath HyperSpectral Imaging System Equipped with Multiple Imagers

The field of view (FOV) of pushbroom hyperspectral imager is limited by the compromise of the detector scale and requirements of spatial resolution. Combining imagers along the sampling direction effectively expands its FOV and improves the imaging efficiency. Due to the small overlapping area between the adjacent imagers, stitching the images using traditional methods need a large amount of ground control points (GCPs) or additional strips, which reduce the efficiency of both image acquisition and processing. This paper proposed a new method to precisely stitch images acquired from multiple pushbroom imagers. First, the relative orientation model was built based on the homonymy points to calculate the relative relationship between the adjacent imagers. Then rigorous geometric imaging model was adopted to generate a seamless stitching image. Simulation data was used to verify the accuracy of the method and to quantitatively analyze the effect of different error sources. Results show that the stitching accuracy is better than two pixels. Overall, this method provides a novel solution for stitching airborne multiple pushbroom images, to generate the seamless stitching image with wide FOV.
Simulation of English intelligent system based on CA-IAFSA algorithm and artificial intelligence

The traditional English teaching mode mostly relies on rote memorization of textbooks, but it lacks the training of oral expression skills and lacks intelligent guidance for students. Taking machine learning algorithm as the system algorithm, this paper combines the CA-IAFSA algorithm to construct an English intelligent system based on artificial intelligence. The system uses image recognition technology, introduces population pheromone and tribal pheromone, and adopts multiple ant colony planning and dual pheromone feedback strategies. Moreover, this paper improves the heuristic information search strategy, pheromone update strategy, and state transition probability of the basic ant colony algorithm. In addition, this paper proposes the MACDPA path planning algorithm to realize the intelligent analysis of English textbook images. Finally, after constructing the model, this paper conducts research and analysis on the performance of the model and uses controlled experimental methods and mathematical statistics to analyze the data. The research results show that the model constructed in this paper performs well in assisted teaching and intelligent translation and meets the expected requirements.
A multi-scale UAV image matching method applied to large-scale landslide reconstruction

&lt;abstract&gt; &lt;p&gt;Three-dimensional (3D) sparse reconstruction of landslide topography based on unmanned aerial vehicle (UAV) images has been widely used for landslide monitoring and geomorphological analysis. In order to solve the isolated island phenomenon caused by multi-scale image matching, which means that there is no connection between the images of different scales, we herein propose a method that selects UAV image pairs based on image retrieval. In this method, sparse reconstruction was obtained via the sequential structure-from-motion (SfM) pipeline. First, principal component analysis (PCA) was used to reduce high-dimensional features to low-dimensional features to improve the efficiency of retrieval vocabulary construction. Second, by calculating the query depth threshold and discarding the invalid image pairs, we improved the efficiency of image matching. Third, the connected network of the dataset was constructed based on the initial matching of image pairs. The lost multi-scale image pairs were identified and matched through the image query between the connection components, which further improved the integrity of image matching. Our experimental results show that, compared with the traditional image retrieval method, the efficiency of the proposed method is improved by 25.9%.&lt;/p&gt; &lt;/abstract&gt;
Tracking Keypoints from Consecutive Video Frames Using CNN Features for Space Applications

Hard time constraints in space missions bring in the problem of fast video processing for numerous autonomous tasks. Video processing involves the separation of distinct image frames, fetching image descriptors, applying different machine learning algorithms for object detection, obstacle avoidance, and many more tasks involved in the automatic maneuvering of a spacecraft. These tasks require the most informative descriptions of an image within the time constraints. Tracking these informative points from consecutive image frames is needed in flow estimation applications. Classical algorithms like SIFT and SURF are the milestones in the feature description development. But computational complexity and high time requirements force the critical missions to avoid these techniques to get adopted in real-time processing. Hence a time conservative and less complex pre-trained Convolutional Neural Network (CNN) model is chosen in this paper as a feature descriptor. 7-layer CNN model is designed and implemented with pre-trained VGG model parameters and then these CNN features are used to match the points of interests from consecutive image frames of a lunar descent video. The performance of the system is evaluated based on visual and empirical keypoints matching. The scores of matches between two consecutive images from the video using CNN features are then compared with state-of-the-art algorithms like SIFT and SURF. The results show that CNN features are more reliable and robust in case of time-critical video processing tasks for keypoint tracking applications of space missions.
Russian Higher Education as Influenced by COVID-19 Pandemic

The purpose of this article is to analyze trends in Russian higher education development during the coronavirus pandemic. The paper considers main problems faced by Russian universities in their students’ transition to distance learning. Particular attention is paid to the problem of the «digital inequality», which became urgent in the conditions of online teaching organization. It is shown that the «digital inequality» does not only mean differentiating students by their having necessary equipment to study remotely. The issues of «digital inequality» are being investigated at the levels of regions, universities, teachers and students. At the same time, the authors substantiate the thesis that it will not be possible to solve the problem of the dynamic development of the Russian higher education system within the framework of the current model of budget financing, and thus propose approaches to its change.
Research on automatic labeling of imbalanced texts of customer complaints based on text enhancement and layer-by-layer semantic matching

Abstract Due to its potential impact on business efficiency, automated customer complaint labeling and classification are of great importance for management decision making and business applications. The majority of the current research on automated labeling uses large and well-balanced datasets. However, customer complaint labels are hierarchical in structure, with many labels at the lowest hierarchy level. Relying on lower-level labels leads to small and imbalanced samples, thus rendering the current automatic labeling practices inapplicable to customer complaints. This article proposes an automatic labeling model incorporating the BERT and word2vec methods. The model is validated on electric utility customer complaint data. Within the model, the BERT method serves to obtain shallow text tags. Furthermore, text enhancement is used to mitigate the problem of imbalanced samples that emerge when the number of labels is large. Finally, the word2vec model is utilized for deep text analysis. Experiments demonstrate the proposed model's efficiency in automating customer complaint labeling. Consequently, the proposed model supports enterprises in improving their service quality while simultaneously reducing labor costs.
Interval type-2 fuzzy aggregation operator in decision making and its application

Abstract Type-2 fuzzy sets (T2FSs) can deal with higher modeling and uncertainties which exist in the real-world application, specifically in the control systems. Particularly the climate changes are always uncertain and thus, the type-2 fuzzy controller is an effective system to handle those situations. Polyhouse is a methodology used to cultivate the plants. It breaks the seasonal hurdle of the formulation and it is also suitable for the conflictive climate conditions. Controlling and directing internal parameters of the polyhouse play an essential role in the growth of the plant. Among those, humidity is an important element when one deals with the growth of the plant in polyhouse. It affects the weather, as well as the global change of the climate and hence, the inner climate of the polyhouse will be disturbed. In this paper, operational laws for triangular interval type-2 fuzzy numbers and derived triangular interval type-2 weighted geometric (TIT2WG) operator with their desired mathematical properties using Dombi triangular norms. Also, humidity control is analyzed using interval type-2 fuzzy controller (IT2FC) with the use of derived aggregation operator which is the aim of the paper. Further stability of the system has been analyzed by applying four different defuzzification methods and the method is recommended which gives a better response.
Identifying the Topology of Undirected Networks From Diffused Non-Stationary Graph Signals

We address the problem of inferring an undirected graph from nodal observations, which are modeled as non-stationary graph signals generated by local diffusion dynamics that depend on the structure of the unknown network. Using the so-called graph-shift operator (GSO), which is a matrix representation of the graph, we first identify the eigenvectors of the shift matrix from observations of the diffused signals, and then estimate the eigenvalues by imposing desirable properties on the graph to be recovered. Different from the stationary setting where the eigenvectors can be obtained directly from the covariance matrix of the measurements, here we need to estimate first the unknown diffusion (graph) filter - a polynomial in the GSO that preserves the sought eigenbasis. To carry out this initial system identification step, we exploit different sources of information on the arbitrarily-correlated input signal driving the diffusion on the graph. We first explore the setting where the observations, the input information, and the unknown graph filter are linearly related. We then address the case where the relation is given by a system of matrix quadratic equations, which arises in pragmatic scenarios where only the second-order statistics of the inputs are available. While such a quadratic filter identification problem boils down to a non-convex fourth-order polynomial minimization, we discuss identifiability conditions, propose algorithms to approximate the solution, and analyze their performance. Numerical tests illustrate the effectiveness of the proposed topology inference algorithms in recovering brain, social, financial, and urban transportation networks using synthetic and real-world signals.
Mitigating Edge Machine Learning Inference Bottlenecks: An Empirical Study on Accelerating Google Edge Models

As the need for edge computing grows, many modern consumer devices now contain edge machine learning (ML) accelerators that can compute a wide range of neural network (NN) models while still fitting within tight resource constraints. We analyze a commercial Edge TPU using 24 Google edge NN models (including CNNs, LSTMs, transducers, and RCNNs), and find that the accelerator suffers from three shortcomings, in terms of computational throughput, energy efficiency, and memory access handling. We comprehensively study the characteristics of each NN layer in all of the Google edge models, and find that these shortcomings arise from the one-size-fits-all approach of the accelerator, as there is a high amount of heterogeneity in key layer characteristics both across different models and across different layers in the same model. We propose a new acceleration framework called Mensa. Mensa incorporates multiple heterogeneous ML edge accelerators (including both on-chip and near-data accelerators), each of which caters to the characteristics of a particular subset of models. At runtime, Mensa schedules each layer to run on the best-suited accelerator, accounting for both efficiency and inter-layer dependencies. As we analyze the Google edge NN models, we discover that all of the layers naturally group into a small number of clusters, which allows us to design an efficient implementation of Mensa for these models with only three specialized accelerators. Averaged across all 24 Google edge models, Mensa improves energy efficiency and throughput by 3.0x and 3.1x over the Edge TPU, and by 2.4x and 4.3x over Eyeriss v2, a state-of-the-art accelerator.
Opinion Building Based on the Argumentative Dialogue System BEA

In this work, we introduce BEA, an argumentative Dialogue System that assists the user in his or her opinion forming regarding a certain controversial topic. To this end, we establish an opinion model based on weighted bipolar argumentation graphs that allows the system to infer the influence of preferences expressed by the user on all related aspects and is updated by the system in real time during the interaction. The system and the model are tested and discussed by use of an argument structure consisting of 72 components in a proof of principal scenario, showing a high sensitivity of the employed model regarding the expressed preferences.
A Theorem of the Alternative for Personalized Federated Learning

A widely recognized difficulty in federated learning arises from the statistical heterogeneity among clients: local datasets often come from different but not entirely unrelated distributions, and personalization is, therefore, necessary to achieve optimal results from each individual's perspective. In this paper, we show how the excess risks of personalized federated learning with a smooth, strongly convex loss depend on data heterogeneity from a minimax point of view. Our analysis reveals a surprising theorem of the alternative for personalized federated learning: there exists a threshold such that (a) if a certain measure of data heterogeneity is below this threshold, the FedAvg algorithm [McMahan et al., 2017] is minimax optimal; (b) when the measure of heterogeneity is above this threshold, then doing pure local training (i.e., clients solve empirical risk minimization problems on their local datasets without any communication) is minimax optimal. As an implication, our results show that the presumably difficult (infinite-dimensional) problem of adapting to client-wise heterogeneity can be reduced to a simple binary decision problem of choosing between the two baseline algorithms. Our analysis relies on a new notion of algorithmic stability that takes into account the nature of federated learning.
Text-independent speaker recognition based on adaptive course learning loss and deep residual network

Abstract Text-independent speaker recognition is widely used in identity recognition that has a wide spectrum of applications, such as criminal investigation, payment certification, and interest-based customer services. In order to improve the recognition ability of log filter bank feature vectors, a method of text-independent speaker recognition based on deep residual networks model was proposed in this paper. The deep residual network was composed of a residual network (ResNet) and a convolutional attention statistics pooling (CASP) layer. The CASP layer could aggregate frame-level features from the ResNet into an utterance-level features. Extracting speech features for each speaker using deep residual networks was a promising direction to explore, and a straightforward solution was to train the discriminative feature extraction network by using a margin-based loss function. However, a margin-based loss function often has certain limitations, such as the margins between different categories were set to be the same and fixed. Thus, we used an adaptive curriculum learning loss (ACLL) to address the problem and introduce two different margin-based losses for this problem, i.e., AM-Softmax and AAM-Softmax. The proposed method was applied to a large-scale VoxCeleb2 dataset for extensive text-independent speaker recognition experiments, and average equal error rate (EER) could achieve 1.76% on VoxCeleb1 test dataset, 1.91% on VoxCeleb1-E test dataset, and 3.24% on VoxCeleb1-H test dataset. Compared with related speaker recognition methods, EER was improved by 1.11% on VoxCeleb1 test dataset, 1.04% on VoxCeleb1-E test dataset, and 1.69% on VoxCeleb1-H test dataset.
A Decision Guidance for Solving Success Rate Political Campaign Using Distance Weighted kNN in Nassi-Shneiderman Framework

Campaigns on online social media are becoming intriguing because all parties can compete with each other.Various content can be created to support or defeat one another.Interaction is important to control the battlefield in the face of disinformation.The campaign team must be able to increase interaction and reporting it as a form of achievement.The reality is that the campaign team has not been able to provide a measurable report on the success rate of political campaign interaction on social media.This is because there is no standardization in measuring these interactions.This fact inspires to build a model for measuring campaign interaction on social media.This model will be able to help the campaign team.This study uses a dataset from a governor election campaign in Indonesia which is then tested on a presidential election campaign dataset in America.The model was built using machine learning with the k classification method Nearest Neighbor (kNN) and Distance Weight kNN (DWkNN).The research stages were arranged using the Nassi-Shneiderman Framework.This framework describes the stages and comparison of the use of the KNN and the DWkNN from the training to testing stages in an easy to understand manner.The kNN and DWkNN training stages showed excellent accuracy results of nearly 100%.Furthermore, at the testing stage using the crossvalidation method with a variety of fold 5 to 20 variations showed excellent results, namely 99.89% on the governor election dataset and a range of 98% on the American presidential election dataset.This classification model has been tested using several datasets from several candidate social media accounts.We also created a dataset campaign interaction.This study shows that the proposed model can outperform previous studies.So, that it provides novelty in the form of a campaign interaction guide model for the campaign team.
Symmetric multiqudit states: Stars, entanglement, and rotosensors

A constellation of $N=d-1$ Majorana stars represents an arbitrary pure quantum state of dimension $d$ or a permutation-symmetric state of a system consisting of $n$ qubits. We generalize the latter construction to represent in a similar way an arbitrary symmetric pure state of $k$ subsystems with $d$ levels each. For $d\geq 3$, such states are equivalent, as far as rotations are concerned, to a collection of various spin states, with definite relative complex weights. Following Majorana's lead, we introduce a multiconstellation, consisting of the Majorana constellations of the above spin states, augmented by an auxiliary, "spectator" constellation, encoding the complex weights. Examples of stellar representations of symmetric states of four qutrits, and two spin-$3/2$ systems, are presented. We revisit the Hermite and Murnaghan isomorphisms, which relate multipartite states of various spins, number of parties, and even symmetries. We show how the tools introduced can be used to analyze multipartite entanglement and to identify optimal quantum rotosensors, i.e., pure states which are maximally sensitive to rotations around a specified axis, or averaged over all axes.
Environment Inference for Invariant Learning

Learning models that gracefully handle distribution shifts is central to research on domain generalization, robust optimization, and fairness. A promising formulation is domain-invariant learning, which identifies the key issue of learning which features are domain-specific versus domain-invariant. An important assumption in this area is that the training examples are partitioned into "domains" or "environments". Our focus is on the more common setting where such partitions are not provided. We propose EIIL, a general framework for domain-invariant learning that incorporates Environment Inference to directly infer partitions that are maximally informative for downstream Invariant Learning. We show that EIIL outperforms invariant learning methods on the CMNIST benchmark without using environment labels, and significantly outperforms ERM on worst-group performance in the Waterbirds and CivilComments datasets. Finally, we establish connections between EIIL and algorithmic fairness, which enables EIIL to improve accuracy and calibration in a fair prediction problem.
IDCNN-CRF-based domain named entity recognition method

Entity recognition is the foundation of natural language processing. For traditional methods, a large number of manual annotations are required, and the accuracy of the model is low and the speed is slow. The word vector model cannot recognize unregistered words well. This paper proposes an entity recognition method based on character embedding, Iterated Dilated Convolutional Neural Networks (IDCNN) and Conditional Random Fields (CRF). Combining the characteristics of iterative dilation convolutional neural network GPU parallel operation and long-term and short-term memory, the ability of word vectors to express the meaning of unregistered words, and the excellent learning ability of conditional random fields for labeling rules, a character+IDCNN+CRF named entity is constructed Identify the model. Based on the corpus in the field of military equipment, the experiment shows that the method can distinguish the equipment name and organization name excellently in a certain dimension character vector. The F-1 value in the test corpus exceeds 94%. For military equipment domain entity recognition has a better effect, and the prediction speed has been greatly improved compared to before.