Reviewerly/exHarmony huggingface dataset:
The Realistic Dilemma and Breakthrough Ways of Improving the Quality of Online Teaching Resources of Physical Education in Colleges and Universities

With the rapid development of information technology, the Ministry of education has put forward new requirements for the quality of online teaching resources.In addition, the improvement of the quality of online teaching resources will directly affect the improvement of course teaching quality, which directly leads to the continuous improvement of the requirements of online teaching resources quality in Colleges and universities.This paper mainly analyses several modes of online teaching of physical education course in Colleges and universities, expounds the realistic dilemma of improving the quality of online teaching resources of physical education courses in Colleges and universities, and puts forward substantive suggestions for the high-quality development of online teaching resources of physical education courses in Colleges and universities, so as to find a breakthrough path and promote the quality of online teaching resources of physical education courses in Colleges and universities, so as to promote our country The development of online teaching resources of physical education.
Grad-LAM: Visualization of Deep Neural Networks for Unsupervised Learning

Nowadays, the explainability of deep neural networks is an essential part of machine learning. In the last years, many methods were developed to visualize important regions of an input image for the decision of the deep neural network. Since almost all methods are designed for supervised trained models, we propose in this work a visualization technique for unsupervised trained autoencoders called Gradient-weighted Latent Activation Mapping (Grad-LAM). We adapt the idea of Grad-CAM and propose a novel weighting based on the knowledge of the autoencoder's decoder. Our method will help to get insights into the highly nonlinear mapping of an input image to a latent space. We show that the visualization maps of Grad-LAM are meaningful on simple datasets like MNIST and the method is even applicable to real-world datasets like ImageNet.
General Formulas for the Central and Non-Central Moments of the Multinomial Distribution

We present the first general formulas for the central and non-central moments of the multinomial distribution, using a combinatorial argument and the factorial moments previously obtained in Mosimann (1962). We use the formulas to give explicit expressions for all the non-central moments up to order 8 and all the central moments up to order 4. These results expand significantly on those in Newcomer (2008) and Newcomer et al. (2008), where the non-central moments were calculated up to order 4.
Handwritten Digit Recognition Using Machine Learning

Technology is getting more and more involved in our lives, and so are algorithms. These algorithms speed up work and reduce workload. Especially machine learning algorithms are improving day by day by imitating human behaviours. Handwriting recognition systems are also stand out on this field. In this study, handwriting digit recognition process has been done with algorithms having different working methods. These algorithms are Support Vector Machine (SVM), Decision Tree, Random Forest, Artificial Neural Networks (ANN), K-Nearest Neighbor (KNN) and K- Means Algorithm. The working logic of the handwriting digit recognition process was examined, and the efficiency of different algorithms on the same database was measured. A report was presented by making comparisons on the accuracy.
An Image Segmentation Algorithm Based on a Local Region Conditional Random Field Model

To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image.The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field.The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps.Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction.The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.
XLP at SemEval-2020 Task 9: Cross-lingual Models with Focal Loss for Sentiment Analysis of Code-Mixing Language

In this paper, we present an approach for sentiment analysis in code-mixed language on twitter defined in SemEval-2020 Task 9. Our team (referred as LiangZhao) employ different multilingual models with weighted loss focused on complexity of code-mixing in sentence, in which the best model achieved f1-score of 0.806 and ranked 1st of subtask-Sentimix Spanglish.The performance of method is analyzed and each component of our architecture is demonstrated.
A Key-Based Mutual Authentication Framework for Mobile Contactless Payment System Using Authentication Server

This paper presents a framework for mutual authentication between a user device and a point of sale (POS) machine using magnetic secure transmission (MST) to prevent the wormhole attack in Samsung pay. The primary attribute of this method is authenticating the POS terminals by an authentication server to bind the generated token to a single POS machine. To secure the system from eavesdropping attack, the data transmitted between the user device and the machine is encrypted by using the Elgamal encryption method. The keys used in the method are dynamic in nature. Furthermore, comparison and security analysis are presented with previously proposed systems.
Construction of Curriculum Knowledge Map based on Ontology

With the rapid development of science and technology, knowledge update is accelerating, which puts forward higher requirements for teachers and students in Colleges and universities, and needs to grasp the latest development trends of curriculum knowledge related fields faster, more comprehensive and more accurate. Many schools have digitized their educational resources. However, the traditional sharing of educational resources lacks a unified knowledge representation structure, which makes the sharing and reuse of learning resources unsatisfactory. Curriculum is the core of school knowledge teaching. It evaluates the curriculum system and discipline system in order to achieve certain teaching objectives. Curriculum knowledge includes explicit knowledge and tacit knowledge.
Can an Artificial Intelligence Decision Aid Decrease False-Positive Breast Biopsies?

This study aimed to evaluate the effect of an artificial intelligence (AI) support system on breast ultrasound diagnostic accuracy.In this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved retrospective study, 200 lesions (155 benign, 45 malignant) were randomly selected from consecutive ultrasound-guided biopsies (June 2017-January 2019). Two readers, blinded to clinical history and pathology, evaluated lesions with and without an Food and Drug Administration-approved AI software. Lesion features, Breast Imaging Reporting and Data System (BI-RADS) rating (1-5), reader confidence level (1-5), and AI BI-RADS equivalent (1-5) were recorded. Statistical analysis was performed for diagnostic accuracy, negative predictive value, positive predictive value (PPV), sensitivity, and specificity of reader versus AI BI-RADS. Generalized estimating equation analysis was used for reader versus AI accuracy regarding lesion features and AI impact on low-confidence score lesions. Artificial intelligence effect on false-positive biopsy rate was determined. Statistical tests were conducted at a 2-sided 5% significance level.There was no significant difference in accuracy (73 vs 69.8%), negative predictive value (100% vs 98.5%), PPV (45.5 vs 42.4%), sensitivity (100% vs 96.7%), and specificity (65.2 vs 61.9; P = 0.118-0.409) for AI versus pooled reader assessment. Artificial intelligence was more accurate than readers for irregular shape (74.1% vs 57.4%, P = 0.002) and less accurate for round shape (26.5% vs 50.0%, P = 0.049). Artificial intelligence improved diagnostic accuracy for reader-rated low-confidence lesions with increased PPV (24.7% AI vs 19.3%, P = 0.004) and specificity (57.8% vs 44.6%, P = 0.008).Artificial intelligence decision support aid may help improve sonographic diagnostic accuracy, particularly in cases with low reader confidence, thereby decreasing false-positives.
A Public Key Cryptosystem Using a Group of Permutation Polynomials

Abstract In this paper we propose an efficient multivariate encryption scheme based on permutation polynomials over finite fields. We single out a commutative group ℒ( q, m ) of permutation polynomials over the finite field F q m . We construct a trapdoor function for the cryptosystem using polynomials in ℒ(2, m ), where m =2 k for some k ≥ 0. The complexity of encryption in our public key cryptosystem is O ( m 3 ) multiplications which is equivalent to other multivariate public key cryptosystems. For decryption only left cyclic shifts, permutation of bits and xor operations are used. It uses at most 5 m 2 +3 m – 4 left cyclic shifts, 5 m 2 +3 m + 4 xor operations and 7 permutations on bits for decryption.
Image Caption Generation for News Articles

In this paper, we address the task of news-image captioning, which generates a description of an image given the image and its article body as input.This task is more challenging than the conventional image captioning, because it requires a joint understanding of image and text.We present a Transformer model that integrates text and image modalities and attends to textual features from visual features in generating a caption.Experiments based on automatic evaluation metrics and human evaluation show that an article text provides primary information to reproduce news-image captions written by journalists.The results also demonstrate that the proposed model outperforms the state-of-the-art model.In addition, we also confirm that visual features contribute to improving the quality of news-image captions.
Phase Transitions in Transfer Learning for High-Dimensional Perceptrons

Transfer learning seeks to improve the generalization performance of a target task by exploiting the knowledge learned from a related source task. Central questions include deciding what information one should transfer and when transfer can be beneficial. The latter question is related to the so-called negative transfer phenomenon, where the transferred source information actually reduces the generalization performance of the target task. This happens when the two tasks are sufficiently dissimilar. In this paper, we present a theoretical analysis of transfer learning by studying a pair of related perceptron learning tasks. Despite the simplicity of our model, it reproduces several key phenomena observed in practice. Specifically, our asymptotic analysis reveals a phase transition from negative transfer to positive transfer as the similarity of the two tasks moves past a well-defined threshold.
Automatic Sentiment Analysis of Texts: The Case of Russian

Abstract The chapter considers the problems of automatic sentiment analysis of texts including processing multiple opinions, implicit and explicit sentiment, ambiguity of sentiment words, sentiment modifiers, irreal context, comparisons, et cetera. Main approaches to sentiment analysis are briefly presented, including the types of sentiment vocabularies. Most attention is given to Russian-specific components of automatic sentiment analysis: publicly available vocabularies and sentiment-related shared tasks.
G-Tric: generating three-way synthetic datasets with triclustering solutions

Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations [Formula: see text] features [Formula: see text] contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output.G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters.Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric's potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.
Single-Object Tracking Algorithm Based on Two-Step Spatiotemporal Deep Feature Fusion in a Complex Surveillance Scenario

Object tracking has been one of the most active research directions in the field of computer vision. In this paper, an effective single-object tracking algorithm based on two-step spatiotemporal feature fusion is proposed, which combines deep learning detection with the kernelized correlation filtering (KCF) tracking algorithm. Deep learning detection is adopted to obtain more accurate spatial position and scale information and reduce the cumulative error. In addition, the improved KCF algorithm is adopted to track and calculate the temporal information correlation of gradient features between video frames, so as to reduce the probability of missing detection and ensure the running speed. In the process of tracking, the spatiotemporal information is fused through feature analysis. A large number of experiment results show that our proposed algorithm has more tracking performance than the traditional KCF algorithm and can efficiently continuously detect and track objects in different complex scenes, which is suitable for engineering application.
Application of Machine Learning Methods for Determining the Stage of Cancer

The paper covers the analysis of standards for the registration of patients with cancerous. The eighth revision of the international TNM system for determining the stage of the breast and skin cancer is presented. The paper presents the results of conducted researches, on the base created regression models with machine learning methods for determine the stage of cancerous the breast, skin, bronchus and lung. In the present studies are used three methods of machine learning - Decision Tree(DT), Support Vector Machine(SVM) and Ensemble Algorithms(EA). The models were compared and evaluated using the Root Mean Squared Error(RMSE), Mean Absolute Error(MAE), Mean Squared Error(MSE), and coefficient of determination.
SA2-MCD: Secured Architecture for Allocation of Virtual Machine in Multitenant Cloud Databases

Cloud computing is one of the most demanding technology in today's era. At one side, where cloud computing is offering unlimited amount of IT resources to provide exceptional performance in computing but on the other side, it is facing the security issues majorly with public cloud for multitenant cloud environment. Due to the security issues with public cloud, most of the government and private organizations are not moving their private and sensitive data over the public cloud and compromising with the limited IT resources available to them and limited performance from available resources. A solution to secure private space over the public cloud will resolve the aforesaid issues. This paper presents SA2-MCD: Secured Architecture for Allocation of Virtual Machine in Multitenant Cloud Databases. This proposed architecture is used for public cloud which provides the private space over multitenant public cloud environment. Moreover, SA2-MCD is used for efficient distribution of transactions in distributed databases over the public cloud to offer the secured and remarkable performance. It is useful for both software and hardware level security. Random encryption selection method (RESM) for software level security and N_port zoning, F_port zoning, logical unit number (LUN) masking, node_port_ID virtualization (NPIV), raw device mapping (RDM) have been used for hardware level security in SA2-MCD architecture. The proposed architecture has been evaluated mathematically and simulated through CloudSim 3.0.3 tool. The experimental results reveal that the proposed architecture SA2-MCD gives the better performance as compared to the existing architectures such as T-MMORRS, MGA, DPRA and PRP.
From Simulation to Real World Maneuver Execution using Deep Reinforcement Learning

Deep Reinforcement Learning has proved to be able to solve many control tasks in different fields, but the behavior of these systems is not always as expected when deployed in real-world scenarios. This is mainly due to the lack of domain adaptation between simulated and real-world data together with the absence of distinction between train and test datasets. In this work, we investigate these problems in the autonomous driving field, especially for a maneuver planning module for roundabout insertions. In particular, we present a system based on multiple environments in which agents are trained simultaneously, evaluating the behavior of the model in different scenarios. Finally, we analyze techniques aimed at reducing the gap between simulated and real-world data showing that this increased the generalization capabilities of the system both on unseen and real-world scenarios.
Multiple Linear Regression of Multi-class Images in Devices of Internet of Things

The correct classification of images is an important application in the monitoring of Internet of things (IoT). In the research of IoT images, a key issue is to recognize multi-class images at a high accuracy. As a result, this paper puts forward a classification method for multi-class images based on multiple linear regression (MLR). Firstly, the convolutional neural network (CNN) was improved to automatically generate a network from the IoT terminals, and used to classify images into disjoint class sets (clusters), which were processed by the subsequently constructed expert network. After that, the MLR was introduced to evaluate the accuracy and robustness of the classification of multi-class images. Finally, the proposed method has been verified on CIFAR-10, CIfar-100 and MNIST, etc. benchmark data sets. Our method was found to outperform other methods in classification, and improve the accuracy of the classic AlexNet by 2%. The research results provide theoretical evidence and lay practical basis for the classification of multi-class IoT images.
Privacy-sensitive Objects Pixelation for Live Video Streaming

With the prevailing of live video streaming, establishing an online pixelation method for privacy-sensitive objects is an urgency. Caused by the inaccurate detection of privacy-sensitive objects, simply migrating the tracking-by-detection structure applied in offline pixelation into the online form will incur problems in target initialization, drifting, and over-pixelation. To cope with the inevitable but impacting detection issue, we propose a novel Privacy-sensitive Objects Pixelation (PsOP) framework for automatic personal privacy filtering during live video streaming. Leveraging pre-trained detection networks, our PsOP is extendable to any potential privacy-sensitive objects pixelation. Employing the embedding networks and the proposed Positioned Incremental Affinity Propagation (PIAP) clustering algorithm as the backbone, our PsOP unifies the pixelation of discriminating and indiscriminating pixelation objects through trajectories generation. In addition to the pixelation accuracy boosting, experiment results on the streaming video data we built show that the proposed PsOP can significantly reduce the over-pixelation ratio in privacy-sensitive object pixelation.
Predicting Undergraduate Admission: A Case Study in Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh

The university admission tests find the applicant's ability to admit to the desired university.Nowadays, there is a huge competition in the university admission tests.The failure in the admission tests makes an examinee depressed.This paper proposes a method that predicts undergraduate admission in universities.It can help students to improve their preparation to get a chance at their desired university.Many factors are responsible for the failure or success in an admission test.Educational data mining helps us to analyze and extract information from these factors.Here, the authors apply three machine learning algorithms XGBoost, LightGBM, and GBM on a collected dataset to estimate the probability of getting admission to the university after attending or before attending the admission test.They also evaluate and compare the performance levels of these three algorithms based on two different evaluation metricsaccuracy and F1 score.Furthermore, the authors explore the important factors which influence predicting undergraduate admission.
Accounting for Unobserved Confounding in Domain Generalization

The ability to extrapolate, or generalize, from observed to new related environments is central to any form of reliable machine learning, yet most methods fail when moving beyond i.i.d data. In some cases, the reason lies in a misappreciation of the causal structure that governs the data, and in particular as a consequence of the influence of unobserved confounders that drive changes in observed distributions and distort correlations. In this paper, we argue for defining generalization with respect to a broader class of distribution shifts (defined as arising from interventions in the underlying causal model), including changes in observed, unobserved and target variable distributions. We propose a new robust learning principle that may be paired with any gradient-based learning algorithm. This learning principle has explicit generalization guarantees, and relates robustness with certain invariances in the causal model, clarifying why, in some cases, test performance lags training performance. We demonstrate the empirical performance of our approach on healthcare data from different modalities, including image and speech data.