Reviewerly/exHarmony huggingface dataset:
Tumor-assisted Diagnosis based on U-Net Network

With the increase of the number of patients in China, the diagnostic efficiency of doctors has decreased, and the rate of misdiagnosis has increased. With the help of medical imaging intelligent assisted diagnosis technology, doctors can obtain a preliminary diagnosis through an assisted diagnosis system. On this basis, doctors further inspect and review the diagnosis results, which greatly improve the efficiency and accuracy of cancer diagnosis, and reduce the rate of misdiagnosis. In this paper, the system focuses on rectal cancer to do research. Whether there is lymph node metastasis in patients with rectal cancer has an important impact on the decision-making of the treatment plan and the prognosis of patients, so the accurate judgment of lymph node metastasis is an important step in the treatment of rectal cancer, but there is no way to accurately judge the lymph node metastasis before the operation. Therefore, in this paper, the proposed U-net-based tumor-assisted diagnosis system can evaluate the status of lymph node metastasis by judging the characteristics of CT images of rectal cancer. The data set used is the seventh "Teddy Cup" data mining challenge-data set B. The system uses the U-Net network structure, using a cross-entropy loss function and Adam optimizer. After adjusting the parameters, the experiment used a piece of NVIDIA TESLA K80 to train for 50 rounds at a learning rate of 0.001 for about two days and finally kept the accuracy rate at about 85%. Obtained results have demonstrated that the proposed system is promising.
“Surface-line-point”—analysis of the development of artificial intelligence in the Yangtze River Delta

Abstract Artificial intelligence technology is booming all over the world, profoundly affecting the social and economic development model and people’s lifestyle. The Yangtze River Delta urban agglomeration, which has a very high strategic position in the country’s modernization and comprehensive opening up, is an important support for China’s participation in international competition and a gathering place for China’s cutting-edge technology. The Yangtze River Delta has become the leader of my country’s artificial intelligence technology and industry by virtue of its advantageous geographical location, active economic development, high-density talent pool and years of development and accumulation in the field of artificial intelligence. From “surface-line-point”, the article analyzes the advantages and disadvantages of the rapid development of artificial intelligence in the Yangtze River Delta; combines the education industry to analyze the integration of artificial intelligence and education and possible bottlenecks; conforms to the trend of the times and shows the new Internet celebrities of programming world—python.
Integrated approach to foreign language teaching: sustainable development of higher education

The article discusses development of an integrated approach of teaching English to students majoring in Management and IT technologies in the Ural State University of Economics and describes the experience of its implementation. Current trends in education require the use of non-traditional means in class to activate students’ potential and help them master the necessary competences. This integrated approach meets the requirements of the educational standard and is based on the survey among students held to reveal their needs and interests in a foreign language. It exceeds the boundaries of the textbook (which is still the basis for learning) and includes participation in multi-university projects and scientific conferences, use of on-line resources and creative tasks.
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains

Pre-trained language models have been applied to various NLP tasks with considerable performance gains. However, the large model sizes, together with the long inference time, limit the deployment of such models in real-time applications. One line of model compression approaches considers knowledge distillation to distill large teacher models into small student models. Most of these studies focus on single-domain only, which ignores the transferable knowledge from other domains. We notice that training a teacher with transferable knowledge digested across domains can achieve better generalization capability to help knowledge distillation. Hence we propose a Meta-Knowledge Distillation (Meta-KD) framework to build a meta-teacher model that captures transferable knowledge across domains and passes such knowledge to students. Specifically, we explicitly force the meta-teacher to capture transferable knowledge at both instance-level and feature-level from multiple domains, and then propose a meta-distillation algorithm to learn single-domain student models with guidance from the meta-teacher. Experiments on public multi-domain NLP tasks show the effectiveness and superiority of the proposed Meta-KD framework. Further, we also demonstrate the capability of Meta-KD in the settings where the training data is scarce.
The Assisted Environment Information for Blind based on Video Captioning Method

Our proposed system is to help the blind to recognize the environment information. In view of the booming development of image recognition technology, many different models of recent video description methods have been developed. However, there is still a problem that the generated subtitles are not accurate enough to describe the image. In this paper, we propose a model RNN-LSTM to identify objects in a movie and generate correct subtitles. Our model uses a layer of RNN to provide the function of identifying images, and a layer of LSTM to analyze and generate subtitles. This paper uses the MSVD data set and thirty customized movie data sets for experiments, and compares the results of the S2VT model and the RNN-LSTM model on the data set.
Key Aspects of Algorithmization of Collaborative Educational and Cognitive Activity of Students at a Technical University in the Conditions of Digitalization of Education

This article presents a quantitative assessment of pedagogical support aimed at improving collaborative education at a modern technical university. The article analyzes the structural composition of the universal communicative competence in a foreign language to identify the advantages of the proposed content detailization. An algorithm for constructing educational and speech actions of students with the use of collaborative digital technology, regarding the monitoring and control of tools, is developed and theoretically justified. Microsoft Teams is offered as a platform for implementing digital collaborative learning technology. The didactic possibilities and methodological functions of Microsoft Teams in foreign language teaching are revealed. The digital technology of the collaborative learning algorithm is relevant for building collaborative actions is relevant, since it contributes to the development of the ability to solve common professional tasks. The algorithm of the dialogic communication, including problem, contradiction, conflict, intellectual difficulty, having general professional context as a stimulus to enhance the interaction in pair work is proposed as pedagogical support. The analysis and quantitative assessment of students' oral responses showed that the dialogue in a foreign language, based on the algorithmic component, is more structured and stimulates students' communication on relevant professional-oriented topics.
Signal noise filtering using wavelet coefficient temporal correlation techniques

The objective of this work is to develop a robust procedure to analyze flight data by wavelet transforms, eliminate or reduce the noise of the data and to use the filtered data to determine the aeroelastic characteristics of the aircraft such that flutter condition, limit cycle oscillations and the aircraft dynamic model from the filtered measured data. Time and frequency analysis, by the use of the Continuous Wavelet transform, is used to filter noise in signals while retaining possible nonlinear terms which might otherwise be lost. Under some basic assumptions as to the nature of noise affecting signals, a technique based on temporal correlation is implemented to reduce the presence of noise by correlating wavelet coefficients. Cleaned reconstructed time domain signals are used to compute dynamic properties of systems.
Strategi Meningkatkan Kemampuan Berbahasa Inggris

Language has been the target of studies by experts, scholars, researchers and writers in the field of language to reveal various things related to it. One issue that has often been under the highlight is language skills. As widely known, language skills are determined by two general skills, namely receptive and productive skills. Receptive skills consist of listening and reading, while productive skills consist of speaking and writing. Unfortunately, not all of the suggested findings can help foreign language learners to master it completely. Therefore, this study published in this report is the result of the implementation of Community Service activities carried out during the Covid-19 pandemic by using Zoom. Based on the results of this community service activity, this report recommends several strategies for improving language skills. This study begins with reviewing existing theories provided by experts and continued by exploring them through exploratory qualitative studies. The data in this study were collected through a literature study of language theories and language skills and strategies to improve them. The data were analyzed by sorting out theories about appropriate strategies to improve language skills separately and to improve all language skills. The result of the study shows that productive and receptive skills are better improved through routine and repetitive exercises, including communicating with ourself and others. Listening to music is an effective way to improve listening skills. Learners must be able to find a learning stimulus through the use of various electronic and online media as well. To strengthen this, teachers must combine various strategies in teaching one language skill so that students are able understand learning easily.
Synthesis and Pruning as a Dynamic Compression Strategy for Efficient Deep Neural Networks

The brain is a highly reconfigurable machine capable of task-specific adaptations. The brain continually rewires itself for a more optimal configuration to solve problems. We propose a novel strategic synthesis algorithm for feedforward networks that draws directly from the brain’s behaviours when learning. The proposed approach analyses the network and ranks weights based on their magnitude. Unlike existing approaches that advocate random selection, we select highly performing nodes as starting points for new edges and exploit the Gaussian distribution over the weights to select corresponding endpoints. The strategy aims only to produce useful connections and result in a smaller residual network structure. The approach is complemented with pruning to further the compression. We demonstrate the techniques to deep feedforward networks. The residual sub-networks that are formed from the synthesis approaches in this work form common sub-networks with similarities up to ~90%. Using pruning as a complement to the strategic synthesis approach, we observe improvements in compression.
Cycle-consistent Generative Adversarial Networks for Neural Style Transfer using data from Chang'E-4

Generative Adversarial Networks (GANs) have had tremendous applications in Computer Vision. Yet, in the context of space science and planetary exploration the door is open for major advances. We introduce tools to handle planetary data from the mission Chang'E-4 and present a framework for Neural Style Transfer using Cycle-consistency from rendered images. The experiments are conducted in the context of the Iris Lunar Rover, a nano-rover that will be deployed in lunar terrain in 2021 as the flagship of Carnegie Mellon, being the first unmanned rover of America to be on the Moon.
An Efficient Method for Training Deep Learning Networks Distributed

Training deep learning (DL) is a computationally intensive process; as a result, training time can become so long that it impedes the development of DL. High performance computing clusters, especially supercomputers, are equipped with a large amount of computing resources, storage resources, and efficient interconnection ability, which can train DL networks better and faster. In this paper, we propose a method to train DL networks distributed with high efficiency. First, we propose a hierarchical synchronous Stochastic Gradient Descent (SGD) strategy, which can make full use of hardware resources and greatly increase computational efficiency. Second, we present a two-level parameter synchronization scheme which can reduce communication overhead by transmitting parameters of the first layer models in shared memory. Third, we optimize the parallel I/O by making each reader read data as continuously as possible to avoid the high overhead of discontinuous data reading. At last, we integrate the LARS algorithm into our system. The experimental results demonstrate that our approach has tremendous performance advantages relative to unoptimized methods. Compared with the native distributed strategy, our hierarchical synchronous SGD strategy (HSGD) can increase computing efficiency by about 20 times.
Disentangling Label Distribution for Long-tailed Visual Recognition

The current evaluation protocol of long-tailed visual recognition trains the classification model on the long-tailed source label distribution and evaluates its performance on the uniform target label distribution. Such protocol has questionable practicality since the target may also be long-tailed. Therefore, we formulate long-tailed visual recognition as a label shift problem where the target and source label distributions are different. One of the significant hurdles in dealing with the label shift problem is the entanglement between the source label distribution and the model prediction. In this paper, we focus on disentangling the source label distribution from the model prediction. We first introduce a simple but overlooked baseline method that matches the target label distribution by post-processing the model prediction trained by the cross-entropy loss and the Softmax function. Although this method surpasses state-of-the-art methods on benchmark datasets, it can be further improved by directly disentangling the source label distribution from the model prediction in the training phase. Thus, we propose a novel method, LAbel distribution DisEntangling (LADE) loss based on the optimal bound of Donsker-Varadhan representation. LADE achieves state-of-the-art performance on benchmark datasets such as CIFAR-100-LT, Places-LT, ImageNet-LT, and iNaturalist 2018. Moreover, LADE outperforms existing methods on various shifted target label distributions, showing the general adaptability of our proposed method.
Formation of an Education Digitalization Strategy in Present-Day Conditions.

The article is devoted to the formation of a strategy for the digitalization of education in present-date conditions. It is established that the very structure of training and the organization of the educational process change fundamentally in the course of digitalization. These changes will require both the selection of material for creating courses and organizing them, as well as for the management of the educational institution. It is proved that the resulting educational environment can be implemented according to several algorithms, of which each has the priority of existence. Cost-free developments of specialized Internet resources, as well as paid platforms are among the most significant options to implement educational content. It is revealed that the use of new information and communication technologies is only an initial condition for the further development of digital learning, whose assessment criterion is its usefulness for students in the current context.
Predicting Violence Rating Based on Pairwise Comparison

With the rapid development of multimedia, violent video can be easily accessed in games, movies, websites, and so on. Identifying violent videos and rating violence extent is of great importance to media filtering and children protection. Many previous studies only address the problems of violence scene detection and violent action recognition, yet violence rating problem is still not solved. In this paper, we present a novel video-level rating prediction method to estimate violence extent automatically. It has two main characteristics: (1) a two-stream network is fine-tuned to construct effective representations of violent videos; (2) a violence rating prediction machine is designed to learn the strength relationship among different videos. Furthermore, we present a novel violent video dataset with a total of 1,930 human-involved violent videos designed for violence rating analysis. Each video is annotated with 6 fine-grained objective attributes, which are considered to be closely related to violence extent. The ground-truth of violence rating is given by pairwise comparison method. The dataset is evaluated in both stability and convergence. Experiment results on this dataset demonstrate the effectiveness of our method compared with the state-of-art classification methods.
SOME IMPACTS OF COVID-19 TO HIGHER EDUCATION IN MEXICO

Appears in: ICERI2020 Proceedings Publication year: 2020Pages: 3691-3700ISBN: 978-84-09-24232-0ISSN: 2340-1095doi: 10.21125/iceri.2020.0831Conference name: 13th annual International Conference of Education, Research and InnovationDates: 9-10 November, 2020Location: Online Conference
Virtual Block Augmented Reality Game Using Freehand Gesture Interaction

Augmented Reality (AR) manages to bring a virtual environment into a real-world environment seamlessly. As AR has been recognised as advancing technology, AR brings future changes to the learning process. The goal of this study is to use freehand gestures to create a virtual block game in AR. First of all, the stages of this study are to explore block games and freehand movements by using Leap Motion. Secondly, the design and development of Leap Motion virtual block games, and thirdly, the implementation of free-hand gesture interaction virtual block games. The paper explains about virtual blocks AR game using freehand gesture. AR tracking system with real hand gesture recognition system is merged to execute the freehand gesture. A prototype virtual block has been described in this paper. The paper ends with the conclusion and future works.
A Survey of Online Card Payment Fraud Detection using Data Mining-based Methods

Card payment fraud is a serious problem, and a roadblock for an optimally functioning digital economy, with cards (Debits and Credit) being the most popular digital payment method across the globe. Despite the occurrence of fraud could be relatively rare, the impact of fraud could be significant, especially on the cardholder. In the research, there have been many attempts to develop methods of detecting potentially fraudulent transactions based on data mining techniques, predominantly exploiting the developments in the space of machine learning over the last decade. This survey proposes a taxonomy based on a review of existing research attempts and experiments, which mainly elaborates the approaches taken by researchers to incorporate the (i) business impact of fraud (and fraud detection) into their work , (ii) the feature engineering techniques that focus on cardholder behavioural profiling to separate fraudulent activities happening with the same card, and (iii) the adaptive efforts taken to address the changing nature of fraud. Further, there will be a comparative performance evaluation of classification algorithms used and efforts of addressing class imbalance problem. Forty-five peer-reviewed papers published in the domain of card fraud detection between 2009 and 2020 were intensively reviewed to develop this paper.
Soft Subspace Clustering With Entropy Constraints

This paper investigates a class of soft subspace clustering algorithms which integrates the negative entropy term in the objective functions. It is necessary and hard to determine the coefficient of negative entropy in practice, which prevents the algorithms from applications. In order to solve the problem of parameter selection, a modified objective function with entropy constraints is proposed by moving the negative entropy from the objective function of ERKM (Entropy Regularization K-Means) algorithm to the constraints. The updating rules are given by theoretical analysis and the performance is evaluated experimentally using ten UCI datasets. The experimental studies demonstrate that the results of the proposed algorithm (ERKM+) outperform the original ERKM and other two k-means-type clustering algorithms in most cases.
A fast automatic identification method for seismic belts based on Delaunay triangulation

Abstract Earthquake prediction practice and a large number of earthquake cases show that there may be abnormal images of small earthquake belts near the epicenter before strong earthquakes occur. For a static small earthquakes spatial distribution, due to the complexity of exhaustive algorithm, the fast automatic identification method of seismic belts has not yet been realized. Visual identification is still the main method of seismic belt discrimination. Based on the Delaunay triangulation, this paper presents a fast automatic identification method of seismic belts. The effectiveness of this method is proved by a 1000 random points test and an actual example of the 4-magnitude belts before the 2005 Jiujiang M 5.7 Earthquake. The results show that: (1) Using Delaunay triangulation method, we can fast get the spatial relationship between two neighboring points; (2) using the two neighboring relationships, it can automatically extend to cluster, which carries the key information of seismic belt; (3) using the technology of minimum enclosing rectangle (MER) for the identified cluster, we can get the shape and structural information of the MER, which can be called the “suspect seismic belt”; (4) after using the other restrictions to sort and filter the suspect seismic belt, we complete the identification of seismic belt; (5) the random and actual earthquakes trial calculation shows that the Delaunay triangulation method can realize a fast automatic identification of seismic belts; and (6) this automatic identification method may provide a research basis for earthquake prediction.
The Center of quantitative data on geology: Current state and prospects for development

Open access" to scientific data led to develop new approaches in data management.The study describes methods and approaches for creation of subject geological system of quantitative information management.The types of existing open sources of quantitative data sets are considered.Examples of sources of data sets of various types with a brief description are given.The international standards used in the construction of the system are described.The proposed technological solution is based on principles of DataCite, FAIR-systems and OAI protocols.The system allows to automatically generate data collections from geographically distributed sources: repositories, databases, world data centers.The system is being developed as part of the work on creating an Information-Analytical Geological Environment, which provides a single access point to geological data on the territory of Russia and their processing systems.The software modules and solutions that were used to create the system based on the proposed approach are described.The subject adaptation of the interface is also described.