Reviewerly/exHarmony huggingface dataset:
MEMe: A Mutually Enhanced Modeling Method for Efficient and Effective Human Pose Estimation

In this paper, a mutually enhanced modeling method (MEMe) is presented for human pose estimation, which focuses on enhancing lightweight model performance, but with low complexity. To obtain higher accuracy, a traditional model scale is largely expanded with heavy deployment difficulties. However, for a more lightweight model, there is a large performance gap compared to the former; thus, an urgent need for a way to fill it. Therefore, we propose a MEMe to reconstruct a lightweight baseline model, EffBase transferred intuitively from EfficientDet, into the efficient and effective pose (EEffPose) net, which contains three mutually enhanced modules: the Enhanced EffNet (EEffNet) backbone, the total fusion neck (TFNeck), and the final attention head (FAHead). Extensive experiments on COCO and MPII benchmarks show that our MEMe-based models reach state-of-the-art performances, with limited parameters. Specifically, in the same conditions, our EEffPose-P0 with 256 × 192 can use only 8.98 M parameters to achieve 75.4 AP on the COCO val set, which outperforms HRNet-W48, but with only 14% of its parameters.
MegaStitch: Robust Large-Scale Image Stitching

We address fast image stitching for large image collections while being robust to drift due to chaining transformations and minimal overlap between images. We focus on scientific applications where ground-truth accuracy is far more important than visual appearance or projection error, which can be misleading. For common large-scale image stitching use cases, transformations between images are often restricted to similarity or translation. When homography is used in these cases, the odds of being trapped in a poor local minimum and producing unnatural results increases. Thus, for transformations up to affine, we cast stitching as minimizing reprojection error globally using linear least-squares with a few, simple constraints. For homography, we observe that the global affine solution provides better initialization for bundle adjustment compared to an alternative that initializes with a homography-based scaffolding and at lower computational cost. We evaluate our methods on a very large translation dataset with limited overlap as well as four drone datasets. We show that our approach is better compared to alternative methods such as MGRAPH in terms of computational cost, scaling to large numbers of images, and robustness to drift. We also contribute ground-truth datasets for this endeavor.
Procrustes Analysis with Deformations: A Closed-Form Solution by Eigenvalue Decomposition

Generalized Procrustes Analysis (GPA) is the problem of bringing multiple shapes into a common reference by estimating transformations. GPA has been extensively studied for the Euclidean and affine transformations. We introduce GPA with deformable transformations, which forms a much wider and difficult problem. We specifically study a class of transformations called the Linear Basis Warps, which contains the affine transformation and most of the usual deformation models, such as the Thin-Plate Spline (TPS). GPA with deformations is a nonconvex underconstrained problem. We resolve the fundamental ambiguities of deformable GPA using two shape constraints requiring the eigenvalues of the shape covariance. These eigenvalues can be computed independently as a prior or posterior. We give a closed-form and optimal solution to deformable GPA based on an eigenvalue decomposition. This solution handles regularization, favoring smooth deformation fields. It requires the transformation model to satisfy a fundamental property of free-translations, which asserts that the model can implement any translation. We show that this property fortunately holds true for most common transformation models, including the affine and TPS models. For the other models, we give another closed-form solution to GPA, which agrees exactly with the first solution for models with free-translation. We give pseudo-code for computing our solution, leading to the proposed DefGPA method, which is fast, globally optimal and widely applicable. We validate our method and compare it to previous work on six diverse 2D and 3D datasets, with special care taken to choose the hyperparameters from cross-validation.
Deep Generation of Heterogeneous Networks

Heterogeneous graphs are ubiquitous data structures that can inherently capture multi-type and multi-modal interactions between objects. In recent years, research on encoding heterogeneous graph into latent representations have enjoyed a rapid increase. However, its reverse process, namely how to construct heterogeneous graphs from underlying representations and distributions have not been well explored due to several challenges in 1) modeling the local heterogeneous semantic distribution; 2) preserving the graph-structured distributions over the local semantics; and 3) characterizing the global heterogeneous graph distributions. To address these challenges, we propose a novel framework for heterogeneous graph generation (HGEN) that jointly captures the semantic, structural, and global distributions of heterogeneous graphs. Specifically, we propose a heterogeneous walk generator that hierarchically generates meta-paths and their path instances. In addition, a novel heterogeneous graph assembler is developed that can sample and combine the generated meta-path instances (e.g., walks) into heterogeneous graphs in a stratified manner. Theoretical analysis on the preservation of heterogeneous graph patterns by the proposed generation process has been performed. Extensive experiments <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> on multiple real-world and synthetic heterogeneous graph datasets demonstrate the effectiveness of the proposed HGEN in generating realistic heterogeneous graphs. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/lingchen0331/HGEN
Sentiment Analysis on Different Domains Using Machine Learning Algorithms

In sentiment analysis, we try to find out the writer's view about any product, events, government policy, services, topics, individual, etc., through the text written by them on social media platforms like Twitter, Facebook, etc. This study has considered two datasets (STS-Gold and IMDb) on a different domain and with varying lengths of text. The objective of this study is to know which classification algorithm performs better on two domains of text with different length. We have applied six machine learning algorithms (support vector machine, logistic regression, K-Nearest Neighbors, random forest, Naïve Bayes, and decision tree) and compared them on the basis f-score, precision, recall, and accuracy. In the IMDb dataset, logistic regression performs better among all and gives the highest accuracy of 96.3% and f-score of 80.6%. The second highest is achieved with Naïve Bayes with 95.89 and 80.05% f-score. Naïve Bayes gives the highest accuracy of 81.08% and an f-score of 42.45% in the STS-Gold dataset. The second highest is achieved with logistic regression giving an accuracy of 80.09 and 41.52% f-score. We found that logistic regression and Naïve Bayes are performing better among all the algorithms on both datasets.
Experimenting quantum phenomena on NISQ computers using high level quantum programming

We execute the quantum eraser, the Elitzur-Vaidman bomb, and the Hardy's paradox experiment using high-level programming language on a generic, gate-based superconducting quantum processor made publicly available by IBM. The quantum circuits for these experiments use a mixture of one-qubit and multi-qubit gates and require high entanglement gate accuracy. The results aligned with theoretical predictions of quantum mechanics to high confidence on circuits using up to 3 qubits. The power of quantum computers and high-level language as a platform for experimenting and studying quantum phenomena is henceforth demonstrated.
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision

Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection. Today's best neural stance detectors need large volumes of training data, which is difficult to curate given the fast-changing landscape of social media text and issues on which users opine. Homophily properties over the social network provide strong signal of coarse-grained user-level stance. But semi-supervised approaches for tweet-level stance detection fail to properly leverage homophily. In light of this, We present SANDS, a new semi-supervised stance detector. SANDS starts from very few labeled tweets. It builds multiple deep feature views of tweets. It also uses a distant supervision signal from the social network to provide a surrogate loss signal to the component learners. We prepare two new tweet datasets comprising over 236,000 politically tinted tweets from two demographics (US and India) posted by over 87,000 users, their follower-followee graph, and over 8,000 tweets annotated by linguists. SANDS achieves a macro-F1 score of 0.55 (0.49) on US (India)-based datasets, outperforming 17 baselines (including variants of SANDS) substantially, particularly for minority stance labels and noisy text. Numerous ablation experiments on SANDS disentangle the dynamics of textual and network-propagated stance signals.
Convolutional Neural Network Based Career Recommender System for Pakistani Engineering Students

In recent years, Recommender systems are utilized in a variety of areas. One reason behind why we want a recommender system in current society is that an individual has many alternatives to use because of the pervasiveness of the Internet. A recommender system seeks to estimate and predict user content preference. Old recommender systems used State-of-the-art recommender algorithms like content based filtering to predict ratings. Career Recommender system provides Engineering candidates the best possible available jobs relevant to their skills, qualification, etc. Four to six major engineering disciplines are covered in this recommender system. The proposed approach is tested using a career recommendation dataset which is collected from many students of different disciplines of various universities. A deep NLP based CNN model is used to predict the best jobs with maximum precision. 512 hidden layers are used to increase the performance of this system. Career recommendation takes care of the users and saves their cost and time spending on traditional job searching methods. Comparative study demonstrates that the proposed methodology of prediction of the best jobs achieves better results with an accuracy of 84% when matched with content based filtering technique where 81% accuracy is gained for content based career recommender system.
A Reasoning Approach to Financial Data Exchange with Statistical Confidentiality

Motivated by our experience with the Bank of Italy, in this work we present Vada-SA, a reasoning framework for financial data exchange with statistical confidentiality. By reasoning on the interplay of the features that may lead to identity disclosure, the framework is able to guarantee explainable, declarative, and context-aware confidentiality.
Using a Data Mining Approach to Detect Automobile Insurance Fraud

The number of policyholders involved in fraudulent activities has increased dramatically in recent years. Intentionally misleading insurers by missing facts when claiming insurance has resulted in massive losses for insurers. There is indeed a lack of a robust system for rigorously addressing insurance fraud. Throughout the paper, we presented a data mining approach to detect fraudulent claims. Following two sampling methods (SMOTE, ROSE) to remove the class imbalance and experimenting with two different features subsets (the first composed of 23 predictors, the second of 5 predictors), we employed Random Forests and Logistic Regression. For validation, we experimented with a (75:25) split ratio for a real dataset of automobile insurance claims to test the performance of our proposed models. The results revealed that the models built using the second feature selection perform slightly better with a higher rate of correctly classified fraudulent claims (Random Forest recall = 95.24%); further, statistically, there is an insignificant difference between SMOTE and ROSE. Finally, the study has demonstrated that random forest outperforms logistic regression.
An Attention-based Hybrid 2D/3D CNN-LSTM for Human Action Recognition

Human Action Recognition (HAR) is a challenging problem in computer vision that has received a great deal of attention in the last decade. With the advent of new deep learning techniques such as convolutional neural networks (CNNs), the recognition performance of HAR systems has improved significantly over traditional methods, mainly due to the powerful representation capabilities of CNNs. In most of the literature, 2D CNNs or their 3D counterparts have been used to learn spatial and temporal image-level features of videos. In this paper, we developed an end-to-end HAR framework based on a hybrid 2D/3D CNN. The hybrid CNN feature extractor aims to exploit the potential collaboration between 2D and 3D CNNs. The CNN features extracted from the video sequences are then fed into a Long Short-Term Memory (LSTM) network to capture the short- and long-term dependencies in the data structure. Inspired by human visual attention mechanisms, a visual attention module was used in this study to focus semantically on relevant salient features in visual representations. The developed model was trained and evaluated using the KTH dataset and achieved promising recognition performance compared to state-of-the-art methods.
Diversified Subgraph Query Generation with Group Fairness

This paper investigates the problem of subgraph query generation with output that satisfies both diversity and fairness constraints. Given a set of groups with associated cardinality requirements, it is to compute subgraph queries with diversified output that meanwhile covers the groups with the desired cardinality. Such need is evident in web and social search with fairness constraints. We formalize subgraph query generation as a bi-criteria optimization problem on the diversity and fairness properties of queries, and verify its hardness and approximability. We show that the problem is in Σp2 , and remains NP-complete even for single-node queries. Despite the hardness, (1) we show that approximations exist whenever a corresponding subset selection process provides good solutions, and provide feasible algorithms with performance guarantees for two practical query generation scenarios. We also present a fast heuristic algorithm for the general problem, which early terminates without enumerating queries. We experimentally verify that our algorithms can efficiently generate queries with desired diversity and coverage properties for targeted groups.
MANAGING PROJECT ACTIVITIES OF SCHOOL STUDENTS DURING DISTANCE LEARNING

The issue of organizing and managing project activities of school students during distance learning is highlighted. The article describes the possibilities of using online services to manage the project activities of secondary school students. The article considers the organization of computer science project with the use of Trello online tool as an example. Trello allows users to visualize the project tasks and control the deadlines effectively. The article presents the definition of the learning project, the stages of work on the project and the features of managing project activities of students during distance learning.
PREUNN: Protocol Reverse Engineering using Neural Networks

The ability of neural networks to universally approximate any function enables them to learn relationships between arbitrary kinds of data. This offers great potential in information security topics such as protocol reverse engineering (PRE), which has seen little usage of neural networks (NNs) so far. In this paper, we provide a novel approach for implementing PRE with solely NNs, demonstrating a simple yet effective reverse engineering of text-based protocols. This approach is modular by design and allows for the exchange of neural network models at any step with better performing models. The architectures used include a convolutional neural network (CNN), an autoencoder (AE), a generative adversarial net (GAN), a long short-term memory (LSTM), and a self-organizing map (SOM). All of these models combine for a new protocol reverse engineering approach. The results show that the widespread application layer protocols HTTP and FTP can successfully be mimicked by artificial intelligen
Ontosemiotic Analysis of the Use of Multibase Material in Mathematics Textbooks for Primary Education in Chile

Background : The multibase material is a widely used and recommended resource for mathematics teaching in the first school grades, given its potential in learning numerical concepts. For this reason, we consider analysing the proposal suggested in the textbooks on its use in primary education. Objective : To analyse the use of multibase material in the teaching of mathematics in primary education in Chile. Design : A qualitative methodology of exploratory-descriptive level is followed. Setting and Participants : Sample made up of 12 math textbooks. Data collection and analysis : The levels of analysis of mathematical activity and the construct epistemic configuration of primary objects, theoretical and methodological elements of the Onto-Semiotic-Approach (OSA) were used. Results : Multibase blocks are used to 1) indicate the cardinality of a set; 2) explain a procedure; 3) explain a concept; 4) represent a number, and 5) compare numbers. Conclusions : The analysis of the textbooks allowed us to identify how the multibase material is used in primary education. This information can serve the teachers in service to enhance their use in the instructional process.
An Examination of Verbal Aggression in Politically-Motivated Digital Discourse

Verbal aggression in digital discourse has emerged as a substantial focus of interest for scholars wishing to examine how conflicts begin, unfold and end on Twitter, YouTube, blogs, and WhatsApp. However, despite all the scholarly attention that Facebook has received since its origin, verbal aggression on Facebook is still relatively under-researched. This paper examines verbal aggression online in the political discourse of Congo-Brazzaville Facebook users. More specifically, quantitative along with qualitative analyses of a dataset of 9,330 Facebook comments were jointly carried out. One critical finding is that explicit aggressive comments are overwhelmingly pervasive and on the increase, pointing to the conclusion that historical, social, sociolinguistic, and political factors are to a larger extent instigators of verbal aggression on Facebook. Furthermore, users employ different strategies to express or intensify verbal aggression, favoring explicit expressions of aggression such as insult and abuse over others like teasing.
Data Mining Model of English Translation Teaching Based on Virtual Reality Technology

In modern society, the development of information technology is becoming more and more rapid, especially virtual reality technology and data mining, which have become one of the hot spots of the current era. From the current point of view, computer technology has become an important application tool in the field of English translation. However, in the translation process, the traditional English teaching model can no longer meet the needs of translation work. In traditional English translation teaching, teachers often explain and analyze the knowledge points learned in the classroom. However, some translation knowledge points are abstract and complicated and difficult for students to understand, which leads to lower learning efficiency. Therefore, in order to better improve the teaching effect and cultivate high-quality English translators, it is necessary to use virtual technology and data mining methods to assist the establishment of corresponding links between translation methods and methods, and to achieve effective integration, thereby helping English translation Professionals perform language output work more quickly in the classroom. This article uses questionnaire survey and data analysis methods to explore the current status of existing English translation teaching models, and then uses virtual technology and data mining methods to deal with existing problems, thereby further enhancing the effect of English translation teaching and improving teaching quality. According to the survey results, most of the interviewed teachers and students believe that virtual technology has played a certain auxiliary role in the development of teachers’ teaching work and students’ English translation training, and has certain application value to the teaching mode of English translation.
Fraud and Anomaly Detection

In this chapter, an intelligent fraud and anomaly detection system using statistical, supervised, and unsupervised machine learning techniques is presented. The following sections will show how a non-traditional approach can be leveraged by internal auditors to gain additional insight into the data. The chapter is organized like a recipe – goal of the recipe, ingredients, instructions, and variation and serving. The accompanying code is available at the GitHub repository specified in Chapter 20.
Event Extraction of Chinese Electronic Medical Records Based on BiGRU-CRF

Electronic medical record (EMR) is the product of medical information system, which contains rich medical knowledge, and plays an important role in medical question answering and assistant decision-making [1]. However, as an unstructured text, EMRs cannot be directly utilized by machine. Therefore, how to use information extraction technology to obtain a large amount of accurate knowledge, which is closely related to patients, from EMRs has become a research hotspot. In order to solve the problem of free-text extraction in EMRs, this paper proposes an event extraction model, which finds the corresponding short sentences from the long texts by trigger words, and then recognizes the entities of the short sentences. The method is as follows: Firstly, the text is preprocessed by word segmentation and vectorization, and then the word frequency co-occurrence method is used to identify the trigger words related to events. Secondly, using ID3 algorithm to evaluate the validity of the trigger words. Finally, locate short sentences by triggering words, and using the BiGRU (Bidirectional Gate Recurrent Unit)-CRF (Conditional Random Field) algorithm for sequence labeling to complete the task of event extraction. The result shows that the single-task independent extraction model based on BiGRU-CRF is superior to other deep learning models in the recognition effect of the three tasks: the F1 value of "primary site of tumor" is 96.43%, the F1 value of "primary lesion size" is 98.04%, and the F1 value of "metastatic site" is 88%. Moreover, through experimental comparison, it is found that the single-task independent extraction effect proposed in this paper is better than the multi-task joint extraction effect.