Reviewerly/exHarmony huggingface dataset:
Developing Teacher Professional Competency Under the Integration of Russian Universities into the World Academic Area

The article notes the importance of studying the interconnection of professional competency and integration processes in higher education for their subsequent use in the development of competitive higher education. The research objective is to identify the key competences of University scientific and pedagogical staff as an integral part of professional competency influenced by internationalization and integration of Russian universities into the global educational area. To rich this objective the authors put forward the principle of convergence. In the process of convergence, all ideas and concepts are aligned, penetrate each other, diffuse thus enriching each other. The authors also highlight the difference between “competence” and “competency” and define teacher professional competency is a result of acquisition or developing a set of competences getting through experience in various fields including professional area. Teacher professional competency is a part of teacher professional excellence. The foundation for teacher professional excellence is provided by the hierarchical structure of the individual which has three levels. Teacher professional competency correlates with the second level of the hierarchical structure of a person which in its turn consists of: 1. individual knowledge, 2. skills (competences), 3. acquired skills, 4. habits. The authors define four levels of individual knowledge development and classify skills by orientation, by levels of organization of activities and by levels of acquisition. Using theoretical studies the authors have already developed the IDUTE programme (International Dimension of University Technical Teacher Education) designed to enhance the international dimensions of various components of university teacher education programmes, to transform courses in order to facilitate change and improvement in reaching internationalization goals of their work and to help provide university teachers with greater global knowledge and competences.
DeepCatch: Predicting Return Defaulters in Taxation System using Example-Dependent Cost-Sensitive Deep Neural Networks

Tax evasion is most common in several nations. Taxpayers evade tax by using thoughtful and well-considered techniques, which hinders the economic progress of the nation. Delaying the filing of returns by taxpayers is the most primitive form of tax evasion. Taxpayers who delay the filing of returns are called return defaulters. It is the most brazen form of tax evasion. To tackle this problem, we introduce an example-dependent cost-sensitive deep learning model to identify potential return defaulters. This model takes example-dependent costs into account and makes predictions that aim to minimize the overall cost instead of minimizing the total number of misclassifications. Applying our method, we show cost savings of about 55%. This work is designed and implemented for the Commercial Taxes Department Government of Telangana, India.
An Evolutionary-Based Additive Tree for Enhanced Disease Prediction

The increased usage of machine learning in healthcare applications requires the highly accurate decision support models which are good in predictive performance and intuitive explanation. Although the models like additive tree could balance both the factors, it can be further enhanced by applying the evolutionary methods. This paper thus provides a decision support system by introducing an enhanced additive tree which could provide an accurate disease prediction in the medical domain. Along with good increase in accuracy, it also scales down the nodes in the tree by performing dimensionality reduction. The proposed model is also as accurate as the linear classifier algorithms and ensemble models.
Integration of intelligent diagnosis system and augmented reality for electric motors

Abstract With the rapid development of industrial automation technology, electric motor control has also become an important technology. However, motor failure has many possible causes, and it is not easy to detect in advance. Therefore the fault diagnosis is an important issue for motors. The augmented reality (AR) technology is also developing towards various industrial and educational application in recent years. This research is focused on developing the intelligent diagnosis system and AR application for electric motors. The intelligent diagnosis system applies cloud data management and machine learning methods to predict the health status of electric motors. And the time-recurrent Long Short-Term Memory (LSTM) neural network algorithm is applied to establish a motor health diagnostic model. The experimental results show that the motor diagnosis method can predict the health status of motor effectively. The proposed system will also provide more industrial application services in the future.
Accuracy Analysis of K-Nearest Neighbor and Naïve Bayes Algorithm in the Diagnosis of Breast Cancer

In the medical field, there are many records of disease sufferers, one of which is data on breast cancer. An extraction process to fine information in previously unknown data is known as data mining. Data mining uses pattern recognition techniques such as statistics and mathematics to find patterns from old data or cases. One of the main roles of data mining is classification. In the classification dataset, there is one objective attribute or it can be called the label attribute. This attribute will be searched from new data on the basis of other attributes in the past. The number of attributes can affect the performance of an algorithm. This results in if the classification process is inaccurate, the researcher needs to double-check at each previous stage to look for errors. The best algorithm for one data type is not necessarily good for another data type. For this reason, the K-Nearest Neighbor and Naïve Bayes algorithms will be used as a solution to this problem. The research method used was to prepare data from the breast cancer dataset, conduct training and test the data, then perform a comparative analysis. The research target is to produce the best algorithm in classifying breast cancer, so that patients with existing parameters can be predicted which ones are malignant and benign breast cancer. This pattern can be used as a diagnostic measure so that it can be detected earlier and is expected to reduce the mortality rate from breast cancer. By making comparisons, this method produces 95.79% for K-Nearest Neighbor and 93.39% for Naïve Bayes
Development and Application of an Intensive Care Medical Data Set for Deep Learning

A large number of patient healthcare data have been collected in the process of diagnosis and treatment of intensive care medicine, which provides major benefits for patient safety and quality. Unfortunately, the application of medical data is greatly limited. Key barriers to the use of the data include difficulties in data extraction and cleaning, and the construction of high-quality data sets promotes the research of medical big data analysis. In China, there is few intensive care data set built by clinicians has been used for clinical outcome prediction. This study developed and evaluated an Intensive Care Medical (ICM) data set for critically care patients that can be used for deep learning. The ICM data set contained four types of data collected routinely in Chinese hospitals, including all-cause characteristics of administrative information, vital signs, laboratory tests, and intravenous medication records. A total of 17,291 ICU admissions involving 12,815 patients aged 14 years and older were extracted from the data set. Deep learning model achieved high accuracy for tasks in hospital mortality predicting (AUROC[area under the receiver operator curve] reach 0.8941). We believe that the ICM data set can be used to create accurate predictions for a variety of clinical scenarios.
Blockchain based mechanism to eliminate frauds and tampering of land records

Data tampering and fraud in land records have increased drastically in the modern world. A data storage model using Blockchain and Interplanetary File System (IPFS) is proposed in this work. Land records and the farmer’s information are stored inside the Interplanetary file system. To avoid data faking, the hash address of the respective data generated by IPFS is stored in the blockchain. This proposed system when deployed on a large scale can outperform the existing methods of securing user data. One of the latest technological advancements in the software industry is the innovation of Blockchain Technology. This new technology has opened up a new business relationship platform that delivers feasibility, protection, and cheap rates. It provides a new foundation of trust for transactions that can facilitate a very streamlined workflow and a faster economy.
Design of a Quadrotor Control Software Experimental Validation Platform Based on Real-Time Hardware-in-the-Loop Simulation

High quality simulation environment can accelerate the development of the control software. Also, it is a good tool for the beginner to study how the control software works. The goal of this paper is aiming at designing a real time HIL experimental platform for quadrotor autopilot software design, validation and optimization. The platform is mainly composed of four blocks: A quadrotor dynamics model, a flight control system, a communication agent and hardware devices for control input and output. The flight dynamics model of a quadrotor build in this paper is to simulate the physics involved with its movement. It receives input signals from the autopilot controller, and returns the quadrotor's real time pose, position, velocities, and etc. resulting from the input signals to the autopilot controller. A communication agent implements bidirectional communication between the flight dynamics model and the autopilot controller. Hardware devices for input and output is used to change the flight mode, indicate the flight state and test the control law. This paper focuses primarily on developing an experiment platform that works for control software validation. It allows for the safe testing of experimental code and settings, especially for aggressive and agile flight test.
Fault‐tolerant quantum implementation of conventional decoder logic with enable input

Decoherence is the greatest obstacle to the physical realization of scalable quantum computer, jeopardises coherent superposition of the qubit, and makes qubit extremely fragile. Quantum Error Correction Code (QECC), and Fault-tolerant quantum computation collectively could protect qubit and improve scalability. On the other hand, the conventional logic circuit is no more useful in quantum computing due to much difference from quantum logic. However, quantum computer has to perform classical tasks which can be addressed by translating to its equivalent quantum algorithm. Herein, zero-garbage-based reversible and fault-tolerant quantum circuit for 1 : 2, and 2 : 4 Decoder with enable signal using Clifford + T-group are proposed. Further, the design approach to implement n : 2n decoder on fault-tolerant quantum logic in linear T − depth is extended. Besides, performance parameters likely T − count, T − depth, and garbage output have been evaluated for n : 2n decoder.
A Comparative Study of Information Technology Curriculum Standards in Primary and Secondary Schools in China and the United States, Japan and Britain

With the continuous changes in information and communication technology, countries in the world are paying more and more attention to information technology and information technology education, and the information technology curriculum standards of various countries are constantly updated and improved. This article compares and analyzes the curriculum standards of China, Britain, the United States, and Japan from three aspects: policy environment, curriculum objectives, and curriculum content, hoping to provide ideas for subsequent research on information technology education courses.
Union-Retire: A New Paradigm for Single-Pass Connected Component Analysis

Most connected component labelling and analysis algorithms are based on some variant of Union-Find. In this paper, it is shown the Find operation is unnecessary for single-pass algorithms, leading to the Union-Retire approach where the focus is on connectivity rather than labelling. The computational complexity of the resulting algorithm is linear with the number of pixels within an image. It is shown that the resulting algorithm requires significantly less memory than existing algorithms, making it ideal for embedded image processing.
Automatic splicing of Chinese single-sided shreds based on character feature and typesetting characteristics

Abstract In this paper we proposed an automatic splicing algorithm for regular cross-cut single-sided shreds of pure Chinese document, which was based on the characteristics of character and typesetting. The algorithm began with extracting the characteristics including character width, character height, line height and line spacing, after which the shreds meeting the typesetting requirement were spliced according to the edge similarity. Afterwards, the horizontal projection vectors of the shreds were corrected and the shreds were clustered by row according to horizontal projection similarity. A greedy algorithm with rejection strategy was then used to complete the intra-cluster horizontal splicing of shreds based on edge similarity. The experiment results revealed that the algorithm proposed in this paper had the characteristics of both high splicing speed and high accuracy.
Shape design of magnetic circuit of the magnetic lens based on medial axis transform

This paper proposes a method of designing a magnetic lens circuit based on axial transform theory. A unified mathematical description of the magnetic circuit profile is given within a medial axial transform in differential geometry. The objective constraint and geometric constraint equations of the magnetic circuit form a closed set of differential equations. Lastly, the optimized magnetic circuit is obtained using the solutions of the equations. The optical performance of the magnetic lens is significantly improved.
Action Unit Driven Facial Expression Synthesis from a Single Image with Patch Attentive GAN

Abstract Recent advances in generative adversarial networks (GANs) have shown tremendous success for facial expression generation tasks. However, generating vivid and expressive facial expressions at Action Units (AUs) level is still challenging, due to the fact that automatic facial expression analysis for AU intensity itself is an unsolved difficult task. In this paper, we propose a novel synthesis‐by‐analysis approach by leveraging the power of GAN framework and state‐of‐the‐art AU detection model to achieve better results for AU‐driven facial expression generation. Specifically, we design a novel discriminator architecture by modifying the patch‐attentive AU detection network for AU intensity estimation and combine it with a global image encoder for adversarial learning to force the generator to produce more expressive and realistic facial images. We also introduce a balanced sampling approach to alleviate the imbalanced learning problem for AU synthesis. Extensive experimental results on DISFA and DISFA+ show that our approach outperforms the state‐of‐the‐art in terms of photo‐realism and expressiveness of the facial expression quantitatively and qualitatively.
Delivering Viewport-Adaptive 360-Degree Videos in Cache-Aided MEC Networks

The high bit-rate and low transmission latency requirements of 360-degree videos streaming impose many technical challenges. By introducing non-homogeneous spatial content quality, viewport adaptive streaming provides a bandwidth efficient solution for the delivery of 360-degree videos. Typically, two different implementations of viewport-adaptive streaming are considered: the projection-based and tile-based. Besides, cache-aided Mobile Edge Computing (MEC) network has been identified as a promising solution for supporting the streaming of 360-degree videos. In this paper, we design delivery mechanisms for the two types of viewport-adaptive streaming in the context of MEC. By considering the specific characteristics of projection-based and tile-based videos, we develop queue and multi-queues based streaming mechanisms for guaranteeing both transmission and queuing delays. Finally, our propositions are evaluated via realistic simulations that leverage a real data-set of user consumption of 360-degree videos.
Empirical Evaluation of Federated Learning with Local Privacy for Real-World Application

As Machine Learning-based applications become increasingly pervasive, a growing concern is how to balance the need for large, representative data sets with the need to respect user data privacy. The increased compute and connectivity capabilities of edge devices (e.g. phones, PCs) presents us with new avenues for achieving this balance, including a promising approach known as federated learning with local privacy. However, today we have gaps in practical knowledge about applicability, trade-offs, and benefits for large-scale realworld implementation. In this paper, using large-scale data from a real-world Windows Update ML-driven application (as well as the publicly available CIFAR-10 data set to enhance reproducibility), we report empirical evaluations of four practical considerations: heterogeneity in device availability that may cause bias, resiliency of federated learning with local differential privacy, benefits of time-varying adaptive configurations, and data transmission/storage savings based on the Pareto principle. We discuss the implications of these findings for practitioners and researchers.
A Semantic Segmentation Algorithm Based on Improved Attention Mechanism

In the field of intelligent driving, while ensuring real-time capability, how the semantic segmentation task can more accurately grasp the boundary information to obtain accurate segmentation is an urgent problem which wants to be solved. Based on this, the article designs a semantic segmentation network GSANet (Global and Selective Attention Network) based on the visual attention mechanism. The article mainly designs an ASPP structure GASPP (Global Atrous Spatial Pyramid Pooling) with global attention information to provide long-distance detailed information for the semantic segmentation model better. Then, a new SAM (Selective Attention Module) is introduced in the decoder stage to provide different attention weight information for different spatial positions. The results show that both the ASPP with global attention and the decoder with selective attention mechanism can significantly improve the accuracy.
The Analysis of Web Search Snippets Displaying User's Knowledge

In recent years, due to the widespread use of the Internet, the number of opportunities to search the Web using search engines has been increasing. In conventional search engines, information retrieval is achieved by repeatedly entering a query and selecting and browsing each page in the search engine result pages (SERPs). The search engines present titles, snippets, and other information to help users select suitable Web pages. However, there are cases in which people view Web pages one by one due to lack of prior knowledge or failure of search strategies. To solve this problem, we present keywords from unvisited results in the SERPs, so that users can predict the content of the Web pages. We propose two kinds of feature words as extended snippets to be presented in each search result: a content word to indicate the central content of a Web page and known-topic and unknown-topic words to indicate the degree of knowledge that one would gain by browsing the Web page. The extraction of those is based on the clustering of words in snippet sentences using the distributed representation of the words and the clustering of words in the visited pages, respectively. We investigated the impact of the proposed extended snippet on user search behavior. The experimental findings indicate that our method was useful in certain types of search, as it decreased the time necessary to complete the search. Furthermore, the participants' ratings of the extended snippets were favorable, especially those of the unknown-topic words.
Prototyping a collaborative data curation service for coastal science

The growing push for open data resulted in an abundance of data for coastal researchers, which can lead to problems for individual researchers related to data discoverability. One solution is to explicitly develop services for coastal researchers to help curate data for discovery, hosting discussions around reuse, community building, and finding collaborators. To develop the idea of a coastal data curation service, we investigate aspects of the UNESCO International Coastal Atlas Network member sites that could be used to build a curation service. We develop a minimal example of a coastal data curation service, deploy this as a website, and describe the next steps to move beyond the prototype phase. We envision a coastal data curation service as a way to cultivate a community focused on coastal data discovery and reuse.
Massively Parallel Random Number Generation

Random numbers are of high importance for many applications, e.g. simulation, optimization, and data mining. Unlike in information security, in these applications the demands on the quality of the random numbers are only moderate while the most important issue is the runtime efficiency. We propose in this paper new SIMD (Single Instruction, Multiple Data) and MIMD (Multiple Instructions, Multiple Data) parallel methods for Linear Congruential Generators (LCG), the most widespread class of fast pseudo-random number generators. In particular, we propose algorithms for the well-known 48-bit LCG used in the Java-class Random and in the method drand48() of C++ for processors using AVX (Advanced Vector eXtensions) and OpenMP. Our focus is on consistency with the original methods which facilitates debugging and enables the user to exactly reproduce previous non-parallel experiments in a SIMD and MIMD environment. Our experimental evaluation demonstrates the superiority of our algorithms.
A massive images classification method based on MapReduce parallel fuzzy C-means clustering

Aiming at the low performance of classifying images under the computing model of single node. With GLCM (Gray Level Co-occurrence Matrix) which fuses gray level with texture of image, a parallel fuzzy C-means clustering method based on MapReduce is designed to classify massive images and improve the real-time performance of classification. The experimental results show that the speedup ratio of this method is more than 10% higher than that of the other two methods, moreover, the accuracy of image classification has not decreased. It shows that this method has high real-time processing efficiency in massive images classification.
Deep Neural Networks for Epistatic Sequence Analysis

We report a step-by-step protocol to use pysster, a TensorFlow-based package for building deep neural networks on a broad range of epistatic sequences such as DNA, RNA, or annotated secondary structure sequences. Pysster provides users comprehensive supports for developing, training, and evaluating the self-defined deep neural networks on sequence data. Moreover, pysster allows users to easily visualize the resulting perditions, which is helpful to uncover the “black box” of deep neural networks. Here, we describe a step-by-step application of pysster to classify the RNA A-to-I editing regions and interpret the model predictions. To further demonstrate the generalizability of pysster, we utilized it to build and evaluated a new deep neural network on an artificial epistatic sequence dataset.