Reviewerly/exHarmony huggingface dataset:
Research on Recognition of Faces with Masks Based on Improved Neural Network

At present, the new crown virus is spreading around the world, causing all people in the world to wear masks to prevent the spread of the virus. Problem. People with masks have found a lot of trouble for face recognition. Finding a feasible method to recognize faces wearing masks is a problem that needs to be solved urgently.This paper proposes a mask recognition algorithm based on improved YOLO-V4 neural network and the integrated SE-Net and DenseNet network and introduces deformable convolution.Compared with other target detection networks, the improved YOLO-V4 neural network used in this paper improves the accuracy of face recognition and detection with masks to a certain extent.
Particle Cloud Generation with Message Passing Generative Adversarial Networks

In high energy physics (HEP), jets are collections of correlated particles produced ubiquitously in particle collisions such as those at the CERN Large Hadron Collider (LHC). Machine-learning-based generative models, such as generative adversarial networks (GANs), have the potential to significantly accelerate LHC jet simulations. However, despite jets having a natural representation as a set of particles in momentum-space, a.k.a. a particle cloud, to our knowledge there exist no generative models applied to such a dataset. We introduce a new particle cloud dataset (JetNet), and, due to similarities between particle and point clouds, apply to it existing point cloud GANs. Results are evaluated using (1) the 1-Wasserstein distance between high- and low-level feature distributions, (2) a newly developed Frechet ParticleNet Distance, and (3) the coverage and (4) minimum matching distance metrics. Existing GANs are found to be inadequate for physics applications, hence we develop a new message passing GAN (MPGAN), which outperforms existing point cloud GANs on virtually every metric and shows promise for use in HEP. We propose JetNet as a novel point-cloud-style dataset for the machine learning community to experiment with, and set MPGAN as a benchmark to improve upon for future generative models.
A Novel Approach With Bayesian Networks to Multi-Robot Task Allocation in Dynamic Environments

Abstract In the modern world, building an autonomous multi-robot system is essential to coordinate and control robots to help humans because using several low-cost robots becomes more robust and efficient than using one expensive, powerful robot to execute tasks to achieve the overall goal of a mission. One research area, multi-robot task allocation (MRTA), becomes substantial in a multi-robot system. Assigning suitable tasks to suitable robots is crucial in coordination, which may directly influence the result of a mission. In the past few decades, although numerous researchers have addressed various algorithms or approaches to solve MRTA problems in different multi-robot systems, it is still difficult to overcome certain challenges, such as dynamic environments, changeable task information, miscellaneous robot abilities, the dynamic condition of a robot, or uncertainties from sensors or actuators. In this paper, we propose a novel approach to handle MRTA problems with Bayesian Networks (BNs) under these challenging circumstances. Our experiments exhibit that the proposed approach may effectively solve real problems in a search-and-rescue mission in centralized, decentralized, and distributed multi-robot systems with real, low-cost robots in dynamic environments. In the future, we will demonstrate that our approach is trainable and can be utilized in a large-scale, complicated environment. Researchers might be able to apply our approach to other applications to explore its extensibility.
METHODOLOGIES AT COVID-19 ERA: A BIBLIOMETRIC ANALYSIS

Appears in: ICERI2021 Proceedings Publication year: 2021Pages: 5154-5158ISBN: 978-84-09-34549-6ISSN: 2340-1095doi: 10.21125/iceri.2021.1175Conference name: 14th annual International Conference of Education, Research and InnovationDates: 8-9 November, 2021Location: Online Conference
The Effect of Physical Activity Levels of Middle School Students on Health Care Capabilities and Academic Stress

The purpose of this study is to provide basic data for physical and mental health care of students at school field by analyzing the effects of physical activity level of middle school students on healthcare capabilities and academic stress. Among 492 middle school students that expressed their voluntary participation, attending five different middle schools in Jeollanam-do, the study selected 408 students (188 males, 215 females) except 89 students who were unfaithfully responded to the survey. the result of this study showed that in physical activity level according to gender of middle school students, both male and female students do medium-intensity activity the most. In high-intensity activity, about 30% of male students responded doing high-intensity activity, while only 6% of female students responded, which is statistically considerable differences. In healthcare capabilities, high-intensity activity group showed higher average of healthcare capabilities compared to medium-intensity and low-intensity activity groups in all factors except for health responsibility factor. This result showed statistically considerable differences. In academic stress, high-intensity activity group was under low stress in all factors. This result has statistically considerable meaning. summing up the above results, participation in high-intensity physical activity by middle school students has a positive impact on their healthcare capabilities and academic stress. Therefore, it is considered that middle school students’ participation in high-intensity physical activity to foster students’ healthcare capabilities and reduce academic stress is important.
Framework Prototype to Test Decision Support System for Avionics Analytics

The use of ontologies is an attractive approach to develop systems to assist Air Traffic Controllers (ATCs) and pilots while making decisions, including complex airspace situations involving Unmanned Aerial Systems (UASs). Within the last decade, an Avionics Analytics Ontology (AAO) has been developed as a cognitive engine of a Decision Support System (DSS) for avionics analytics for application such as Air Traffic Management (ATM). The Federal Aviation Administration (FAA) Next Generation Air Transportation System (NextGen) and the Single European Sky ATM Research (SESAR) promote the use of ontologies to support avionics. However, just few research groups that support the FAA NextGen and the SESAR systems have reported on UAS cyber protection, including defense from drone swarms. This paper presents a framework prototype to test an AAO-based DSS for ATM (AODSATM). The AODSATM prototype is a proof-of-concept framework built of a software application and a hardware platform. The software part consists of the AAO, algorithms for data analytics, and routines for enterprise access. The hardware part consists of small aircraft required to emulate different roles such as a commercial airplane which is about to takeoff and drones like intruders in the airspace nearby the airplane. The challenge for the AAO-based DSS is to assist ATCs and pilots on what is the best course of action to do to avoid air collision when drones are flying around the aircraft. This paper also discusses design details of the DSS software application and shows test cases and a list of planned test procedures for the prototype trials. Concluding remarks and further steps for this research are also discussed.
The $$\alpha \mu $$ Search Algorithm for the Game of Bridge

\(\alpha \mu \) is an anytime heuristic search algorithm for incomplete information games that assumes perfect information for the opponents. \(\alpha \mu \) addresses and if given enough time solves the strategy fusion and the non-locality problems encountered by Perfect Information Monte Carlo search (PIMC). Strategy fusion is due to PIMC playing different strategies in different worlds when it has to find a unique strategy for all the worlds. Non-locality is due to choosing locally optimal moves that are globally inferior. In this paper \(\alpha \mu \) is applied to the game of Bridge and outperforms PIMC.
On Classifying whether Two Texts are on the Same Side of an Argument

To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed.In contrast to actual stance classification, which requires a substantial amount of domain knowledge to identify whether an argument is in favor or against a certain issue, it is argued that, for S3C, only argument similarity within stances needs to be learned to successfully solve the task.We evaluate several transformer-based approaches on the dataset of the recent S3C shared task, followed by an in-depth evaluation and error analysis of our model and the task's hypothesis.We show that, although we achieve state-of-the-art results, our model fails to generalize both within as well as across topics and domains when adjusting the sampling strategy of the training and test set to a more adversarial scenario.Our evaluation shows that current state-of-the-art approaches cannot determine same side stance by considering only domain-independent linguistic similarity features, but appear to require domain knowledge and semantic inference, too.
Modern Information Technologies and Services in Public Institutions

The article considers the issues of modern information technologies and services in public institutions (on the example of educational institutions). The aim of the article is to study the application of modern information technologies and services, in particular Google services, in such public institutions as educational institutions during the pandemic and to identify opportunities for their wider use. Based on a study to assess the economic benefits of using Google services, it has been found that their wider use in educational institutions will also have a positive effect. It is determined that the use of the service "Google" allows you to solve problems of information support of management and educational processes in the educational institution, work together on different types of documents, avoid distortion of information while working on them by different users by setting the appropriate level of document access. Based on the study, it is proposed to classify Google services for the purposes of scientific and pedagogical work of teachers of educational institutions on the following grounds: general services; services for self-organization and productivity; services for educational purposes; services for scientific purposes; services for commercial and other purposes. It is established that in order to increase the efficiency of different goals, it is advisable to combine Google services for different purposes, such as general, educational and to increase productivity. It has also been established that the collaboration with Google services is organized at the excellent level necessary to work with documents and quickly access the information, taking into account the current conditions of the pandemic. The analysis of the directions of education reform and the possibilities of application of information technologies during their implementation gives grounds to assert that the decisive factor in achieving the main goals of education reform is its informatization. And the use of modern information technologies and services helps to increase the self-organization and productivity of the administration of the educational institution, teachers and students, to optimize educational and management processes. The results of the study are the basis for further research in terms of more effective use of modern information technology and services in public institutions on the example of educational institutions.
Motion-constrained Road User Tracking for Real-time Traffic Analysis

Reliability of numerous smart traffic applications are highly dependent on the accuracy of underlying road user tracker. Demand on scalability and privacy preservation pushes vision-based smart traffic applications to sense and processimages on edge devices and transmit only concise information to decision/fusion nodes. One of the requirements for deploying a vision algorithm on edge devices is its ability to process captured images in real time. To meet these needs, we propose a real-time road user tracker which outperforms state-of-the-art trackers. Our approach utilizes double thresholding on detector responses to suppress initialization of false positive trajectories while assuring corresponding detector responses required for updating trajectories are not wrongly discarded. Furthermore, our proposed Bayes filter reduces fragmentation and merging of trajectories which highly effect the performance of subsequent smart traffic applications. The performance of our tracker is evaluated on the real life traffic data in turning movement counting (TMC) application and it achieves a high precision of 96% and recall of 95% while state-of-the-art tracker in comparison achieves 92% on precision and 87% on recall. Indirect performance comparison to other vision-based traffic analysis platform (Telraam) indicates that our method is more reliable.
A TDOA-FDOA Passive Positioning Algorithm Based on the Semi-Definite Relaxation Technique

In the passive location of moving target, the closed-form solution can reach Cramer-Rao Lower Bound (CRLB) under the low noise level, but these algorithms often can not adapt to the large measurement noise condition. For this problem, this paper proposes a passive positioning algorithm based on the Semi-Definite Relaxation (SDR) using Time Difference Of Arrival (TDOA) and Frequency Difference Of Arrival (FDOA). Firstly, this method constructs the pseudo-linear equation of the typical closed-form solution. Secondly, the idea of Stochastic Robust Least Squares (SRLS) and the nonlinear relationship between the target parameters and the additional variables are used to transform the localization problem into the least squares problem with quadratic equality. Using Semi-Definite Programming (SDP) technique, constrained least squares problem is then converted into the SDP problem, which is finally solved by the optimization toolbox. The proposed method does not require an initial priori information and simulations show the effectiveness of the proposed method.
Self-Supervised Learning with Kernel Dependence Maximization

We approach self-supervised learning of image representations from a statistical dependence perspective, proposing Self-Supervised Learning with the Hilbert-Schmidt Independence Criterion (SSL-HSIC). SSL-HSIC maximizes dependence between representations of transformed versions of an image and the image identity, while minimizing the kernelized variance of those features. This self-supervised learning framework yields a new understanding of InfoNCE, a variational lower bound on the mutual information (MI) between different transformations. While the MI itself is known to have pathologies which can result in meaningless representations being learned, its bound is much better behaved: we show that it implicitly approximates SSL-HSIC (with a slightly different regularizer). Our approach also gives us insight into BYOL, since SSL-HSIC similarly learns local neighborhoods of samples. SSL-HSIC allows us to directly optimize statistical dependence in time linear in the batch size, without restrictive data assumptions or indirect mutual information estimators. Trained with or without a target network, SSL-HSIC matches the current state-of-the-art for standard linear evaluation on ImageNet, semi-supervised learning and transfer to other classification and vision tasks such as semantic segmentation, depth estimation and object recognition.
Automatic short answer grading with SBERT on out-of-sample questions

We explore how different components of an Automatic Short Answer Grading (ASAG) model affect the model’s ability to generalize to questions outside of those used for training. For supervised automatic grading models, human ratings are primarily used as ground truth labels. Producing such ratings can be resource heavy, as subject matter experts spend vast amounts of time carefully rating a sample of responses. Further, it is often the case that multiple raters must come to a census before a final ground-truth rating is established. If ASAG models were developed that could generalize to out-of-sample questions, educators may be able to quickly add new questions to an auto-graded assessment without a continued manual rating process. For this project we explore various methods for producing vector representations of student responses including state-of-the-art representation methods such as Sentence-BERT as well as more traditional approaches including Word2Vec and Bag-of-words. We experiment with including previously untapped question-related information within the model input, such as the question text, question context text, scoring rubric information and a question-bundle identifier. The out-of-sample generalizability of the model is examined with both a leave-one-question-out and leave-one-bundle-out evaluation method and compared against a typical student-level cross validation.
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement

Advances in object-centric generative models (OCGMs) have culminated in the development of a broad range of methods for unsupervised object segmentation and interpretable object-centric scene generation. These methods, however, are limited to simulated and real-world datasets with limited visual complexity. Moreover, object representations are often inferred using RNNs which do not scale well to large images or iterative refinement which avoids imposing an unnatural ordering on objects in an image but requires the a priori initialisation of a fixed number of object representations. In contrast to established paradigms, this work proposes an embedding-based approach in which embeddings of pixels are clustered in a differentiable fashion using a stochastic, non-parametric stick-breaking process. Similar to iterative refinement, this clustering procedure also leads to randomly ordered object representations, but without the need of initialising a fixed number of clusters a priori. This is used to develop a new model, GENESIS-V2, which can infer a variable number of object representations without using RNNs or iterative refinement. We show that GENESIS-V2 outperforms previous methods for unsupervised image segmentation and object-centric scene generation on established synthetic datasets as well as more complex real-world datasets.
Attentive 3D-Ghost Module for Dynamic Hand Gesture Recognition with Positive Knowledge Transfer

Hand gesture recognition is a challenging topic in the field of computer vision. Multimodal hand gesture recognition based on RGB-D is with higher accuracy than that of only RGB or depth. It is not difficult to conclude that the gain originates from the complementary information existing in the two modalities. However, in reality, multimodal data are not always easy to acquire simultaneously, while unimodal RGB or depth hand gesture data are more general. Therefore, one hand gesture system is expected, in which only unimordal RGB or Depth data is supported for testing, while multimodal RGB-D data is available for training so as to attain the complementary information. Fortunately, a kind of method via multimodal training and unimodal testing has been proposed. However, unimodal feature representation and cross-modality transfer still need to be further improved. To this end, this paper proposes a new 3D-Ghost and Spatial Attention Inflated 3D ConvNet (3DGSAI) to extract high-quality features for each modality. The baseline of 3DGSAI network is Inflated 3D ConvNet (I3D), and two main improvements are proposed. One is 3D-Ghost module, and the other is the spatial attention mechanism. The 3D-Ghost module can extract richer features for hand gesture representation, and the spatial attention mechanism makes the network pay more attention to hand region. This paper also proposes an adaptive parameter for positive knowledge transfer, which ensures that the transfer always occurs from the strong modality network to the weak one. Extensive experiments on SKIG, VIVA, and NVGesture datasets demonstrate that our method is competitive with the state of the art. Especially, the performance of our method reaches 97.87% on the SKIG dataset using only RGB, which is the current best result.
Ethics and privacy in computational social science

No other topic has stirred up as much controversy and division as the ethics of computational social science research. For one, massive amounts of data are readily available for download in the form of digital trace data on individuals and their interactions, providing opportunities for unprecedented insight into social science research questions. Complicating matters is that developments in computational approaches and methods are rapid, while policy, legal frameworks, ethics guidelines, and pedagogy lag behind. In this chapter, we discuss four major challenges that computational social science scholars confront with regard to ethical questions and dilemmas, often linked to data interpretation and user privacy: 1) data representativity, 2) aggregation and disaggregation, 3) research data archiving, and 4) data linkage. In addition, we also provide a roadmap for computational social science scholars to address these issues using best practices informed by exemplary research. We conclude by stressing the relevance of building on existing ethics guidelines and integrating new legal frameworks into research practice. We argue that central to computational social science is a constant engagement with ethics and new regulations as well as an active commitment to pedagogy, to teaching the principles of ethics in computational social science projects.
Relation-aware Bidirectional Path Reasoning for Commonsense Question Answering

Commonsense Question Answering is an important natural language processing (NLP) task that aims to predict the correct answer to a question through commonsense reasoning.Previous studies utilize pre-trained models on large-scale corpora such as BERT, or perform reasoning on knowledge graphs.However, these methods do not explicitly model the relations that connect entities, which are informational and can be used to enhance reasoning.To address this issue, we propose a relationaware reasoning method.Our method uses a relation-aware graph neural network to capture the rich contextual information from both entities and relations.Compared with methods that use fixed relation embeddings from pre-trained models, our model dynamically updates relations with contextual information from a multi-source subgraph, built from multiple external knowledge sources.The enhanced representations of relations are then fed to a bidirectional reasoning module.A bidirectional attention mechanism is applied between the question sequence and the paths that connect entities, which provides us with transparent interpretability.Experimental results on the CommonsenseQA dataset illustrate that our method results in significant improvements over the baselines while also providing clear reasoning paths.