Reviewerly/exHarmony huggingface dataset:
AISPEECH-SJTU accent identification system for the Accented English Speech Recognition Challenge

This paper describes the AISpeech-SJTU system for the accent identification track of the Interspeech-2020 Accented English Speech Recognition Challenge. In this challenge track, only 160-hour accented English data collected from 8 countries and the auxiliary Librispeech dataset are provided for training. To build an accurate and robust accent identification system, we explore the whole system pipeline in detail. First, we introduce the ASR based phone posteriorgram (PPG) feature to accent identification and verify its efficacy. Then, a novel TTS based approach is carefully designed to augment the very limited accent training data for the first time. Finally, we propose the test time augmentation and embedding fusion schemes to further improve the system performance. Our final system is ranked first in the challenge and outperforms all the other participants by a large margin. The submitted system achieves 83.63\% average accuracy on the challenge evaluation data, ahead of the others by more than 10\% in absolute terms.
Face recognition: fighting the fakes

Facial biometrics have become increasingly popular authentication tools for three main reasons. First, the user experience is familiar. Onboarding is easy – basically like taking a selfie – and since cameras are already on mobile devices, they're ubiquitously accessible. Second, they remove much, if not all of the friction that users encounter trying to remember passwords. Third, the technology works – and it's getting better every year.
Spectral Domain Spline Graph Filter Bank

In this paper, we present a structure for two-channel spline graph filter bank with spectral sampling (SGFBSS) on arbitrary undirected graphs. Our proposed structure has many desirable properties; namely, perfect reconstruction, critical sampling in spectral domain, flexibility in choice of shape and cut-off frequency of the filters, and low complexity implementation of the synthesis section, thanks to our closed-form derivation of the synthesis filter and its sparse structure. These properties play a pivotal role in multi-scale transforms of graph signals. Additionally, this framework can use both normalized and non-normalized Laplacian of any undirected graph. We evaluate the performance of our proposed SGFBSS structure in nonlinear approximation and denoising applications through simulations. We also compare our method with the existing graph filter bank structures and show its superior performance.
Research on Sports Competition Information Management Based on Computer Database Technology

Abstract Sports competition has the characteristics of long cycle, high investment, and many risks. Among them, sports competition has a far-reaching influence. Therefore, it is very necessary to build the sports competition data security monitoring management system. At present, China’s information management is still in the initial stage of development, and many aspects need to be improved, such as the function of the information system is simple, low integration. To this end, this paper first on the sports competition data security monitoring management computer information system design and application to elaborate, and then in-depth computer information system structure, function, application and other aspects of research and analysis, hoping to have a positive impact.
Research on the Application of Deep Learning in Campus Security Monitoring System

Abstract This paper summarizes the background of the construction of campus video security monitoring system, points out that deep learning has an important impact on the development of campus security monitoring system, and puts forward the design principles and ideas of campus video security system. On the whole, the architecture of campus security monitoring system is constructed. The application of deep learning algorithm to campus security monitoring system will realize the precision of campus security management, improve the ability of campus security emergency command and decision, realize the intelligence of campus security operation, improve the efficiency of campus security work, improve the level of campus security management, and ensure the campus more safe and stable.
VR Serious Games for Neuro-cognitive Rehabilitation of Patients with Severe Memory Loss

When developing new products, brand designers must analyse related products, which is a complicated and time-consuming process.Modern product design often requires complex engineering processes; product development requires extensive knowledge but there is also a demand for shorter product design cycles.Therefore, we propose a method based on extension theory and the analytic hierarchy process for identifying product-related knowledge, to aid the development of new products.First, based on our understanding of extenics, matter-element and relational meta-models of product form, function, and structure are established.Then, we define different primitives of brand identity.Finally, using an "extensional analytic hierarchy process" (EAHP), a hierarchy is established and the weights of different primitives are calculated.Various combinations of primitives are used to facilitate knowledge transfer for computer-aided intelligent design.Design data for multiple cases are analysed to verify the feasibility and effectiveness of the method.The method was verified in a physiological signal experiment, and the results show that the method can effectively accumulate product knowledge.Rapid data mining is important for market competitiveness.
Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis

Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images. However, a continuing challenge has been its sensitivity to the variability of acquisition protocols. Attempting to segment images that have different contrast properties from those within the training data generally leads to significantly reduced performance. Furthermore, heterogeneous data sets cannot be easily evaluated because the quantitative variation due to acquisition differences often dwarfs the variation due to the biological differences that one seeks to measure. In this work, we describe an approach using alternating segmentation and synthesis steps that adapts the contrast properties of the training data to the input image. This allows input images that do not resemble the training data to be more consistently segmented. A notable advantage of this approach is that only a single example of the acquisition protocol is required to adapt to its contrast properties. We demonstrate the efficacy of our approaching using brain images from a set of human subjects scanned with two different T1-weighted volumetric protocols.
A Survey of Open Source Statistical Software (OSSS) and Their Data Processing Functionalities

This paper discusses the definitions of open source software, free software and freeware, and the concept of big data. The authors then introduce R and Python as the two most popular open source statistical software (OSSS). Additional OSSS, such as JASP, PSPP, GRETL, SOFA Statistics, Octave, KNIME, and Scilab, are also introduced in this paper with function descriptions and modeling examples. They further discuss OSSS's capability in artificial intelligence application and modeling and Popular OSSS-based machine learning libraries and systems. The paper intends to provide a reference for readers to make proper selections of open source software when statistical analysis tasks are needed. In addition, working platform and selective numerical, descriptive and analysis examples are provided for each software. Readers could have a direct and in-depth understanding of each software and its functional highlights.
VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

We present the first edition of "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges. We offer four data-impaired challenges, where models are trained from scratch, and we reduce the number of training samples to a fraction of the full set. Furthermore, to encourage data efficient solutions, we prohibited the use of pre-trained models and other transfer learning techniques. The majority of top ranking solutions make heavy use of data augmentation, model ensembling, and novel and efficient network architectures to achieve significant performance increases compared to the provided baselines.
Analisis Pengaruh Motivasi Belajar Ekstrinsik Dalam Pembelajaran Daring Terhadap Mutu Hasil Belajar Di AMIK Bina Sriwijaya Palembang

&lt;p&gt;&lt;em&gt;The goal of this research was to determine the effect of extrinsic learning motivation (learning motivation that comes from lecturers in online lectures) on Discrete Mathematics quality of learning outcomes. Data was collected from 30 lecture students in Informatics Management Faculty of AMIK Bina Sriwijaya Palembang, who take Discrete Mathematics as their subject, by using a random sampling method. The measured variables are extrinsic learning motivation and discrete mathematics quality of learning outcomes. Simple regression analysis is used in testing the effect of extrinsic learning motivation on Discrete Mathematics quality of learning outcomes. The results of the research using 30 respondents showed that the correlation coefficient of 0.449, which means the corelation between the two variables in the medium category and the coefficient of determination 0.201 which means that lecture students quality of learning outcomes are influenced by extrinsic learning motivation (learning motivation originating from lecturers in online lectures) by 20.1%. Based on data analysis, the regression coefficient is positive (+) so it can be concluded that the increasing extrinsic learning motivation (learning motivation originating from lecturers in online lectures) will affect the increase in lecture students quality of learning outcomes.&lt;/em&gt;&lt;/p&gt;
Motion planning for a pair of tethered robots

Considering an environment containing polygonal obstacles, we address the problem of planning motions for a pair of planar robots connected to one another via a cable of limited length. Much like prior problems with a single robot connected via a cable to a fixed base, straight line-of-sight visibility plays an important role. The present paper shows how the reduced visibility graph provides a natural discretization and captures the essential topological considerations very effectively for the two robot case as well. Unlike the single robot case, however, the bounded cable length introduces considerations around coordination (or equivalently, when viewed from the point of view of a centralized planner, relative timing) that complicates the matter. Indeed, the paper has to introduce a rather more involved formalization than prior single-robot work in order to establish the core theoretical result—a theorem permitting the problem to be cast as one of finding paths rather than trajectories. Once affirmed, the planning problem reduces to a straightforward graph search with an elegant representation of the connecting cable, demanding only a few extra ancillary checks that ensure sufficiency of cable to guarantee feasibility of the solution. We describe our implementation of A $${}^\star $$ search, and report experimental results. Lastly, we prescribe an optimal execution for the solutions provided by the algorithm.
Developing Online Learning Applications for People with Hearing Disabilities

To make a communication with a hearing-impaired person, who is someone who has a problem with hearing ability, a special form of communication using sign language is required in order to make the purpose of the conversation convey properly. It is clearly that providing a proper and appropriate education for hearing impaired person is very important. Android technology is the best and useful solution for hearing-impaired person in learning as today's technological developments. The purpose of this research was to make an Android-based application for hearing impaired person. Scrum method was used to find and utilize existing libraries as well as the needs for application development. This application provides various kinds of subject from videos and documents uploaded by the teachers. It can be downloaded of it, if it is needed. It means that the users can study anytime and anywhere without concerning of limited time and internet access. Other features of the application are quiz, make schedule, event, chat, memory game, and other features to maximize the online learning process for hearing-impaired person.
A multi-cue guidance network for depth completion

Depth completion is to predict a dense depth image from a raw sparse depth image with missing values, which is an important yet challenging problem in a myriad of vision, robotics, and multimedia applications. While previous studies have made substantial progress in this issue, most of them directly fuse RGB and depth features without considering the multiple latent cues in data. In this paper, we propose a multi-cue guidance network model for depth completion, which introduces multi-cue features to guide the regression of residual values. This network includes two major parts: multi-cue guidance structure and residual regression structure. The multi-cue guidance structure composed of two parallel convolutional sampling streams extracts features from the raw depth image and the corresponding RGB image, respectively. The extracted features are used as multi-cue guidance to combine with the depth image to feed the residual regression structure, where the estimated residual values are combined with the raw depth values to output the final dense depth values. By virtue of the multi-cue guidance and residual regression, the proposed model can leverage the multiple latent cues in the data to predict more accurate depth values. The proposed method was tested on two challenging datasets of outdoor and indoor scenes: KITTI benchmark and NYUv2 dataset. The experimental results show that the proposed method outperforms the comparison methods. Extensive ablation studies demonstrate the effectiveness of different modules in the proposed method.
Face Super-Resolution via Hierarchical Multi-Scale Residual Fusion Network

Exploring the structural information as prior to facial images is a key issue of face super-resolution (SR). Although deep convolutional neural networks (CNNs) own powerful representation ability, how to accurately use facial structural information remains challenges. In this paper, we proposed a new residual fusion network to utilize the multi-scale structural information for face SR. Different from the existing methods of increasing network depth, the bottleneck attention module is introduced to extract fine facial structural features by exploring correlation from feature maps. Finally, hierarchical scales of structural information is fused for generating a high-resolution (HR) facial image. Experimental results show the proposed network outperforms some existing state-of-the-art CNNs based face SR algorithms.
Overview of the 2020 ALTA Shared Task: Assess Human Behaviour

The 2020 ALTA shared task is the 11th in stance of a series of shared tasks organised by ALTA since 2010. The task is to classify texts posted in social media according to human judgements expressed in them. The data used for this task is a subset of SemEval 2018 AIT DISC, which has been annotated by domain experts for this task. In this paper we introduce the task, describe the data and present the results of participating systems.
Peg Solitaire on Cartesian Products of Graphs

Abstract In 2011, Beeler and Hoilman generalized the game of peg solitaire to arbitrary connected graphs. In the same article, the authors proved some results on the solvability of Cartesian products, given solvable or distance 2-solvable graphs. We extend these results to Cartesian products of certain unsolvable graphs. In particular, we prove that ladders and grid graphs are solvable and, further, even the Cartesian product of two stars, which in a sense are the “most” unsolvable graphs.
Design of Trusted Aviation Data Exchange Platform Based on Blockchain

Based on the characteristics of blockchain technology, an aviation data exchange platform is designed to realize the rapid exchange of aviation data between business departments. The platform design adopts alliance chain and private chain. Each data production unit, such as relevant departments of air traffic control, airlines, airports, etc., has its own private chain. Multiple private chains form an alliance chain. In each private chain, there are servers, which store encrypted aviation data. The alliance chain builds alliance block based on the hash value of private chain block, and uses re-encryption to exchange data by changing encryption, the platform achieves data integrity, access control, exchange security and other security goals.
Ecosystem Functions of Individual Style in a Digital Educational Environment

The relevance of the study is due to the development of digital technology and modeling of the ecosystem of digital education. The purpose of the article is to reveal the ecosystem functions of an individual style, as an integral phenomenon of personalization and the pedagogical conditions of its formation in a digital educational environment. Based on a questionnaire survey of 310 teachers of secondary vocational education institutions, the authors come to the conclusion that the personalization of the learning process is inextricably linked with the formation of an individual style (of activity, learning, communication, etc.). It was found that 45.1% of all the surveyed create digital materials by themselves, and do not stimulate students’ self-learning activities. For 33.2% of teachers, students’ activity in the online learning environment was only the subject of observation and analysis. Only a minimal group of respondents (6.5%) actively took part in students’ online activities.
Goodpoint: unsupervised learning of key point detection and description

Subject of Research.The paper presents the study of algorithms for key point detection and description, widely used in computer vision.Typically, the corner detector acts as a key point detector, including neural key point detectors.For some types of images obtained in medicine, the application of such detectors is problematic due to the small number of detected key points.The paper considers a problem of a neural network key point detector training on unlabeled images.Method.We proposed the definition of key points not depending on specific visual features.A method was considered for training of a neural network model meant for detecting and describing key points on unlabeled data.The application of homographic image transformation was basic to the method.The neural network model was trained to detect the same key points on pairs of noisy images related to a homographic transformation.Only positive examples were used for detector training, just points correctly matched with features produced by the neural network model for key point description.Main Results.The unsupervised learning algorithm is used to train the neural network model.For the ease of comparison, the proposed model has a similar architecture and the same number of parameters as the supervised model.Model evaluation is performed on the three different datasets: natural images, synthetic images, and retinal photographs.The proposed model shows similar results to the supervised model on the natural images and better results on retinal photographs.Improvement of results is demonstrated after additional training of the proposed model on images from the target domain.This is an advantage over a model trained on a labeled dataset.For comparison, the harmonic average of such metrics is used as: the accuracy and the depth of matching by descriptors, reproducibility of key points and image coverage.Practical Relevance.The proposed algorithm makes it possible to train the neural network key point detector together with the feature extraction model on images from the target domain without costly dataset labeling and reduce labor costs for the development of the system that uses the detector.
Development of Android-based application: An effort to enhance students’ conceptual understanding in fundamental physics

In the Physics Education study program, Fundamental Physics 1 is a compulsory course and one of the most fundamental substantial courses for the graduates. Since high school, students are mostly used to learn physics by applying physics in calculation with lacking conceptual understanding. Hence, this study was aimed to develop an application using the Nature of Science perspective to enhance students’ conceptual understanding. Thus, an Android-based mobile application was developed and supporting the learning management system (LMS) of Universitas Negeri Malang called SIPEJAR. The application itself is “Nucleon-Fisika Dasar 1” which is connected to SIPEJAR using a specific button in the application. Both the media are supporting and complementing each other. In the process, the learning was emphasized on the conceptual understanding aspect. Pretest and posttest used concept inventory assessment to find out the effectiveness of the learning process and the effect on students’ conceptual understanding. This application had been validated by experts in learning media and limited trials involving 25 students. The average score of media validation is 87.89% meanwhile the result of the limited trial shows a percentage of 91.05%. Thus, the Nucleon-Fisika Dasar 1 application is categorized as valid and feasible to be used for Physics Education students in Universitas Negeri Malang.
Educational ecosystem development based on quality management standards

The educational system is sensitive to all changes in the world space. The transition to a digital society is determined by significant changes in the philosophical foundations of human activity and creating products, especially intellectual ones. The information and digital revolutions have significantly changed the spheres of cognition, perception and human activity, putting them on the rails of continuous education in an actively developing dynamic world. In order to evolve, it is necessary to transfer the existing system of knowledge and sciences to the new, digital generation of people, to form mechanisms of continuous learning, to generate innovations - the solution to this problem lies with the education system, referring to all its levels. The change in the education model is not only about the digitalization of educational processes. The “human-centered” approach is global. The authors consider the methodology for the development of educational ecosystems as a new norm of educational activity, revealing the concepts of educational ecology and analyzing the principles of implementing the ecosystem approach from the standpoint of achieving the quality of education.