Reviewerly/exHarmony huggingface dataset:
Visualizing risk factors of dementia from scholarly literature using knowledge maps and next-generation data models

Abstract Scholarly communication of knowledge is predominantly document-based in digital repositories, and researchers find it tedious to automatically capture and process the semantics among related articles. Despite the present digital era of big data, there is a lack of visual representations of the knowledge present in scholarly articles, and a time-saving approach for a literature search and visual navigation is warranted. The majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation, storage, and dynamic visualization. To address this limitation, the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital repositories. Contemporary topics of research and practice, including modifiable risk factors leading to a dramatic increase in Alzheimer’s disease and other forms of dementia, warrant deeper insight into the evidence-based knowledge available in the literature. The goal is to provide researchers with a visual-based easy traversal through a digital repository of research articles. This paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge, such as dementia risk factors. The model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research articles. It also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly articles. Further, it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations. In this study, the implementation of the proposed model in the Neo4j graph data repository, along with the results achieved, is presented as a proof of concept. Using scholarly research articles on dementia risk factors as a case study, automatic knowledge extraction, storage, intelligent search, and visual navigation are illustrated. The implementation of contextual knowledge and its relationship for a visual exploration by researchers show promising results in the knowledge discovery of dementia risk factors. Overall, this study demonstrates the significance of a semantic visualization with the effective use of knowledge maps and paves the way for extending visual modeling capabilities in the future.
University professor’s assessment in the conditions of digital transformation

Professional and personal competences of a university professor are considered not only as one of the theoretical directions of pedagogical research, but also as a factor in ensuring the quality of modern higher education. Despite the constant efforts of individual researchers and international organizations dealing with educational problems on a global scale, the composition of the unified system of professor’s competences has not been drawn up yet due to the differences in national, methodological and practical approaches. The task of assessing a university professor, determining the degree of effectiveness of his educational and research activities is no less urgent. In connection with the existing tendency in modern pedagogy to assess a professor by means of the identification of his competences, the article analyses the changes in the basic competences that occurred during the period of forced remote learning. As a result of using a number of theoretical and empirical research methods, the most popular tools and techniques for assessing the professor’s performance have been identified, the attitude of the professors to the use of these methods and the objectivity of experts has been demonstrated. The directions of modernization of methods, which can be used to assess the work of university professors on the basis of modern electronic technologies in the context of digitalization of education are outlined.
Optimizing Synchronization of Tennis Professional League Live Broadcast Based on Wireless Network Planning

The live broadcast of the common league mainly adopts the design of streaming media data block by block. The secondary processing of streaming data is carried out on the server-side. It results in a higher delay during a live broadcast. The higher delay has numerous disadvantages during broadcasting for delay-sensitive applications. We have to solve the delay problems during a live broadcast. Therefore, in this paper, we optimize the synchronization of the tennis professional league live broadcast based on wireless network planning. In the proposed scheme, the 3D detection target model is first constructed, and the background is extracted from the moving video image. The extraction is performed using the interframe difference elimination algorithm, and the target motion trajectory is predicted. Then the phase difference characteristics of the image foreground trajectory are analyzed by the Hilbert transform to detect target missing points. By observing the vertex structure of the target missing point, the phase Fourier transform frame is constructed. Finally, the synchronization of the league live broadcast is encoded and decoded. Based on the timestamp, the synchronization optimization of the tennis professional league live broadcast is completed. The experimental results show that the synchronization optimization method has good synchronization, high resolution, short reaction time, and good detection effect.
Adversarial Pose Regression Network for Pose-Invariant Face Recognitions

Face recognition has achieved significant progress in recent years. However, the large pose variation between face images remains a challenge in face recognition. We observe that the pose variation in the hidden feature maps is one of the most critical factors to hinder the representations from being pose-invariant. Based on the observation, we propose an Adversarial Pose Regression Network (APRN) to extract pose-invariant identity representations by disentangling their pose variation in hidden feature maps. To model the pose discriminator in APRN as a regression task in its 3D space, we also propose an Adversarial Regression Loss Function and extend the adversarial learning from classification problems to regression problems in this paper. Our APRN is a plug-and-play structure that can be embedded in other state-of-the-art face recognition algorithms to improve their performance additionally. The experiments show that the proposed APRN consistently and significantly boosts the performance of baseline networks without extra computational costs in the inference phase. APRN achieves comparable or even superior to the state-of-the-art on CFP, Multi-PIE, IJB-A and MegaFace datasets. The code will be released, hoping to nourish our proposals to other computer vision fields
It’s All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning

Commonsense reasoning is one of the key problems in natural language processing, but the relative scarcity of labeled data holds back the progress for languages other than English.Pretrained cross-lingual models are a source of powerful language-agnostic representations, yet their inherent reasoning capabilities are still actively studied.In this work, we design a simple approach to commonsense reasoning which trains a linear classifier with weights of multi-head attention as features.To evaluate this approach, we create a multilingual Winograd Schema corpus by processing several datasets from prior work within a standardized pipeline and measure cross-lingual generalization ability in terms of out-of-sample performance.The method performs competitively with recent supervised and unsupervised approaches for commonsense reasoning, even when applied to other languages in a zero-shot manner.Also, we demonstrate that most of the performance is given by the same small subset of attention heads for all studied languages, which provides evidence of universal reasoning capabilities in multilingual encoders.* Equal contribution.The town councilors refused to give the demonstrators a permit because they feared violence.
Automation of Data Prep, ML, and Data Science

As machine learning (ML), artificial intelligence (AI), and Data Science grow in practical importance, a large part of the ML/AI software industry claims to have built tools and platforms to automate the entire workflow of ML. That includes vexing problems of data preparation (prep), studied intensively by the database (DB) community for decades, with basically no resolution so far. Such claims by the ML/AI industry face a stunning lack of scientific scrutiny from the DB and ML research worlds, largely due to the lack of meaningful, large, and objective benchmarks. As such tools rapidly gain adoption among enterprises and other customers, this panel will debate whether the new ML/AI industry is basically selling "snake oil" to such users, how to evolve away from the status quo by instituting meaningful new benchmarks, creating new partnerships between industry and academia for this, and other pressing questions in this important arena. We aim to spur vigorous conversations that will hopefully lead to genuine new cures for an age-old affliction in Data Science.
Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans

Health insurance companies cover half of the United States population through commercial employer-sponsored health plans and pay 1.2 trillion US dollars every year to cover medical expenses for their members. The actuary and underwriter roles at a health insurance company serve to assess which risks to take on and how to price those risks to ensure profitability of the organization. While Bayesian hierarchical models are the current standard in the industry to estimate risk, interest in machine learning as a way to improve upon these existing methods is increasing. Lumiata, a healthcare analytics company, ran a study with a large health insurance company in the United States. We evaluated the ability of machine learning models to predict the per member per month cost of employer groups in their next renewal period, especially those groups who will cost less than 95\% of what an actuarial model predicts (groups with "concession opportunities"). We developed a sequence of two models, an individual patient-level and an employer-group-level model, to predict the annual per member per month allowed amount for employer groups, based on a population of 14 million patients. Our models performed 20\% better than the insurance carrier's existing pricing model, and identified 84\% of the concession opportunities. This study demonstrates the application of a machine learning system to compute an accurate and fair price for health insurance products and analyzes how explainable machine learning models can exceed actuarial models' predictive accuracy while maintaining interpretability.
Facilitating team-based data science: Lessons learned from the DSC-WAV project

While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have limited exposure to team-based data science and the principles and tools of collaboration that are encountered outside of school. In this paper, we describe the DSC-WAV program, an NSF-funded data science workforce development project in which teams of undergraduate sophomores and juniors work with a local non-profit organization on a data-focused problem. To help students develop a sense of agency and improve confidence in their technical and non-technical data science skills, the project promoted a team-based approach to data science, adopting several processes and tools intended to facilitate this collaboration. Evidence from the project evaluation, including participant survey and interview data, is presented to document the degree to which the project was successful in engaging students in team-based data science, and how the project changed the students' perceptions of their technical and non-technical skills. We also examine opportunities for improvement and offer insight to other data science educators who may want to implement a similar team-based approach to data science projects at their own institutions.
Textual Analysis and Timely Detection of Suspended Social Media Accounts

Suspended accounts are high-risk accounts that violate the rules of a social network. These accounts contain spam, offensive and explicit language, among others, and are incredibly variable in terms of textual content. In this work, we perform a detailed linguistic and statistical analysis into the textual information of suspended accounts and show how insights from our study significantly improve a deep-learning-based detection framework. Moreover, we investigate the utility of advanced topic modeling for the automatic creation of word lists that can discriminate suspended from regular accounts. Since early detection of these high-risk accounts is crucial, we evaluate multiple state-of-the-art classification models along the temporal dimension by measuring the minimum amount of textual signal needed to perform reliable predictions. Further, we show that the best performing models are able to detect suspended accounts earlier than the social media platform.
Foreign Object Detection Algorithm Based on Multi-scale Convolutional Network

Abstract In order to detect foreign matter invading the track and prevent the intrusion of foreign matter from causing railway safety accidents, the detection algorithm of foreign matter intrusion on the track is studied. Aiming at the problem that the rail transit scene is complex and the obstacle scale changes in the acquired image information, this research proposes a multi-scale target detection algorithm based on the YOLO (you only look once) algorithm. First, an adaptive feature fusion module is designed to make the feature maps used for detection have strong semantic information at various scales; then, a new loss function is designed to alleviate the problem of uneven sample distribution and optimize the training process. Experiments show that the algorithm has obvious advantages in multi-scale detection, which not only improves the accuracy of target detection, especially the accuracy of small targets, but also does not significantly increase the inference time and the amount of parameters, and has high real-time performance.
Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text Generation

Table -to-text generation aims at automatically generating natural text to help people conveniently obtain salient information in tables.Although neural models for table-to-text have achieved remarkable progress, some problems are still overlooked.Previous methods cannot deduce the factual results from the entity's (player or team) performance and the relations between entities.To solve this issue, we first build an entity graph from the input tables and introduce a reasoning module to perform reasoning on the graph.Moreover, there are different relations (e.g., the numeric size relation and the importance relation) between records in different dimensions.And these relations may contribute to the data-to-text generation.However, it is hard for a vanilla encoder to capture these.Consequently, we propose to utilize two auxiliary tasks, Number Ranking (NR) and Importance Ranking (IR), to supervise the encoder to capture the different relations.Experimental results on ROTOWIRE and RW-FG show that our method not only has a good generalization but also outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering.
Multimodal MR image registration using weakly supervised constrained affine network

Multimodal image registration is an important technique for many clinical applications. However, it is particularly challenging to obtain good spatial alignment. This paper introduces a novel architecture named the constrained affine network, which combines deformable image registration with affine transformation for multimodal MR image registration. A weakly supervised manner is adapted to train the network and anatomical labels are used in training. The network directly learns to predict a displacement vector field (DVF) between pairs of input images. Different from the existing deformable image registration methods based on the convolutional neural network (CNN), the method proposes a global constrained affine module, which can predict an affine transformation by pre-computing the range of affine parameters, and the model can be combined with a deformable registration network. We evaluated the proposed method on 3D multimodal medical images. Experimental results indicate that the proposed method has better performance.
Virtual reality as a main basis for forming modern educational technologies

In the work from the position of modern pedagogical science, a number of difficulties (risks) associated with the use of virtual reality for educational purposes are considered. The practical significance of the results lies in solving the problem under study, which formulates general provisions on the specifics of VR as a method and means of education, notes the growth of digital educational technologies and the wide potential for their application in the future. Virtual reality in education is used as the information space, in which the student can not only obtain the necessary information, but also enter into the contact with the fictitious objects. Sinking (immersion) into the educational medium helps students to acquire the habits of interaction with the virtual objects, to develop methods of collective collaboration and to find confidence in the course presentation of its own projects. The process of creating the resources with the elements of interactivity, and also the selection of the necessary instrument means is a technological and a systematic problem for the teachers, who are mastering new information technologies. The purpose of the article is construction and substantiation of the effectiveness of the model of the use of technologies of virtual reality in the course of the instruction of schoolchildren in the system of additional formation.
Composition of Web Services of Multi-Population Adaptive Genetic Algorithm Based on Cosine Improvement

Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition.How to select the web service composition with the highest comprehensive QoS is a NP hard problem.In this paper, an improved multi population genetic algorithm is proposed.Cosine adaptive operator is added to the algorithm to avoid premature algorithm caused by improper genetic operator and the disadvantage of destroying excellent individuals in later period.Experimental results show that compared with the common genetic algorithm and multi population genetic algorithm, this algorithm has the advantages of shorter time consumption and higher accuracy, and effectively avoids the loss of effective genes in the population.
GCN2-NAA: Two-stage Graph Convolutional Networks with Node-Aware Attention for Joint Entity and Relation Extraction

Joint extraction of entities and relations is critical for many tasks of Natural Language Processing (NLP), which aims to extract all triplets in the text. However, the huge challenge is that a sentence usually contains overlapping triplets. In this paper, we propose a joint extraction framework named GCN2-NAA based on a two-stage Graph Convolutional Neural networks (GCN) and Node-Aware Attention mechanism. We obtain multi-granularity representations and regional features of words by stacking multiple feature encoders and 1st-phase GCN. Besides, the node-aware attention mechanism and 2nd-phase GCN to capture the soft attention correlation matrix between all words in each relation type. Based on the constructed soft attention correlation matrix, we utilize GCN to further obtain the interaction between entities, relations, and triplets. Experiment results show that GCN2-NAA outperforms baseline models by 6.5% and 11.4% in terms of F1 score on NYT and WebNLG datasets, respectively.
Exposure Fusion Using a Relative Generative Adversarial Network

At present, the generative adversarial network (GAN) plays an important role in learning tasks. The basic idea of a GAN is to train the discriminator and generator simultaneously. A GAN-based inverse tone mapping method can generate high dynamic range (HDR) images corresponding to a scene according to multiple image sequences of a scene with different exposures. However, subsequent tone mapping algorithm processing is needed to display it on a general device. This paper proposes an end-to-end multi-exposure image fusion algorithm based on a relative GAN (called RaGAN-EF), which can fuse multiple image sequences with different exposures directly to generate a high-quality image that can be displayed on a general device without further processing. The RaGAN is used to design the loss function, which can retain more details in the source images. In addition, the number of input image sequences of multi-exposure image fusion algorithms is often uncertain, which limits the application of many existing GANs. This paper proposes a convolutional layer with weights shared between channels, which can solve the problem of variable input length. Experimental results demonstrate that the proposed method performs better in terms of both objective evaluation and visual quality.
Models of implementation of the virtual educational environment in the process of teaching

This article deals with the problem of choosing the most optimal teaching model for teaching Russian as a foreign language. The purpose of the research is to identify and analyze possible models for the introduction of a virtual educational environment in the process of teaching Russian as a foreign language, and to determine the criteria for the effectiveness of these models. The article is based on the following practical methods: observation of classroom and extracurricular communication between teachers and foreign students, questioning of teachers and students, the method of experiment. Five models of the introduction of virtual educational environment into the practice of teaching Russian as a foreign language are identified and analyzed, and the functional capabilities of platforms and applications used in online learning are described in detail. The main problems that arise for foreign students while fully converting Russian language teaching into a distance format are identified. Russian language teaching quality is determined by three main criteria that affect the quality of teaching Russian to foreign students in a distance format using a virtual educational environment: the level of Russian language proficiency, the motivation of the student, and the language/discipline section being studied. The mixed learning model, which combines traditional classroom learning with parallel online broadcasting of classes in a virtual space, is currently the most optimal model for implementing a virtual educational environment in the teaching process. When using the mixed learning model, the necessary competencies are acquired by both students studying in the classroom and those students who, due to various circumstances, study in a distance format. The mixed education model allows the student to make a deliberate choice between traditional and innovative models, making it possible to switch from one model to another directly in the learning process.