Reviewerly/exHarmony huggingface dataset:
Leveraging Linguistic Knowledge for Accent Robustness of End-to-End Models

Acoustic models are susceptible to the difference in acoustic characteristics between the training distribution and test distributions. Accent variability is a challenging source of variability, and the variations within one accent often do not generalize to others. Consequently, end-to-end models that have only transcriptions as linguistic information need high amounts of data to learn how different accents realize their sounds. To aid with recognition of accented speech, we make use of an accent independent abstraction of phonemes, often called metaphonemes. We force our models to learn hidden representations that are correlated to metaphonemes using multi-task training. Our aim is to obtain a model that is more robust to accented speech and, can, at the same time, adapt faster to different accents through the learned structure. Our experiments on the Common Voice corpus show better generalization when making use of this additional linguistic information, with a word error rate reduction of up to 12.6% when compared to the baseline. Furthermore, the relative improvement when adapting an existing model by making use of the metaphonemes is higher than using Byte Pair Encodings alone.
Research on Advancing Professional Ability of Petty Officers by Micro Course

According to the professional characteristics of petty officers, this paper introduce the definition and characteristics of micro course, analyzes the feasibility of building micro courses by using high-quality teaching resources in military colleges, gives suggestions on how to design micro courses in combination with the process of micro course construction, introduce the production of the micro course, and expounds how to reasonably select teaching mode according to the teaching content, thus can cultivate the ability of petty officers students to study independently, and effectively improve the classroom teaching effect.
Toward Active Sequential Hypothesis Testing with Uncertain Models

This work introduces an active learning approach for hypothesis testing with uncertain likelihood models. Uncertain models appear when hypotheses’ parameters are built from finite and limited training data. As a result, hypothesis testing performance is limited by the dearth of training data even as the number of observations increases asymptotically. Even with large amounts of observational data, decision-making at desired error rates can be impossible. This work proposes various active learning methods to collect as little additional training data as possible and still guarantee desired error bounds. These methods attempt to reduce the amount of observational and training data required sequentially and adaptively for each hypothesis until only one hypothesis is accepted. Finally, various active learning methods are compared in terms of their data collection costs to achieve the desired false rejection rate through simulations.
Level Sensitive Context Aware Translation Suggestions

In this manuscript, we demonstrate a Context Aware Translation Suggestion system that assists humans in translating English stories to other languages by suggesting translations for words and phrases present in a sentence, in real-time. This system can also recommend two separate complexity levels of translation for the same meaning of a word or phrase in the target language, which lets the translator maintain the consistency of the storybooks with their level when translating from English into other languages. In addition to this, parallel corpora have been created for two Indian languages that map English words or phrases with translated words in Odia and Assamese. We have achieved a state-of-the-art accuracy for the suggestions. This system can be used as an assisted language translation for the translation of English stories to other underserved Indian languages.
A Heterogeneous Approach for the Traffic Sign Segmentation and Classification

Deep learning-based image segmentation provides state-of-the-art accuracy and performance for real-time scenarios, but the segmentation of a street is a challenging and complex task. This complexity derives from the huge number of classes that are required to be taken into consideration. In some cases, a vast number of classes can also decrease the classification accuracy of a network. Moreover, the process of image segmentation cannot distinguish between two objects of the same class. In this work, a heterogeneous approach for the traffic sign segmentation and classification is presented. The proposed heterogeneous approach is comprised of an image segmentation block that identifies the different objects on a street especially traffic signs using a deep architecture named ENet (efficient neural network) and the second block distinguishes between segmented traffic signs using Support Vector Machine (SVM) model. The results obtained by the proposed approach are promising.
The Mediating Effect of Learning Emotions on the Relationship between Personal Environmental Factors and Continuous Participation Intention in the Comparative Program of University Students Majoring in Cosmetology

The purpose of this study is to analyze the mediating effects of learning emotions in the relationship between personal environmental factors and continuous participation intention in extracurricular programs for university students majoring in cosmetology. The data collection was conducted on college students majoring in cosmetology who have experienced extracurricular programs while attending a four-year university from September 24 to December 12, 2020. A total of 135 copies of a questionnaires were used for the final analysis. Questionnaires containing unfaithful responses were excluded. The data collected for the final analysis were analyzed using the SPSS 26.0 program. As analysis techniques, exploratory factor analysis, reliability analysis, frequency and percentage calculation, descriptive statistical analysis, correlation analysis, linear regression analysis, and process macro were performed. The results of this analysis showed that personal environmental factors influence learning emotions, continuous participation intention. Learning emotions also affected continuous participation intention. In addition, the mediating effects of learning emotions were identified in the relationship between personal environmental factors and continuous participation intention. This study can be used as a basic data for the development and activation of extracurricular programs for cosmetology students at a time when the importance of extracurricular programs is emphasized.
A Framework and Its User Interface to Learn Machine Learning Models

In order to develop a system related to machine learning (ML), it is necessary to understand various contents such as prerequisite knowledge, implementation procedures, verification methods, and improvement methods. However, although general learning sites on the Web provide extensive learning contents such as videos and textbooks, they are insufficient for acquiring practical skills. In this paper, we propose a framework for learning ML and its user interface. The framework manages the ML learning phases, which includes learning the theory and practical knowledge, implementation, validation, improvement, and completion. In the model validation phase, checks are automatically applied according to the target ML model. Similarly, in the model improvement phase, improvement methods are automatically applied according to the target ML model. As a case study, we have developed contents on linear regression, classification, clustering, and dimensionality reduction.
Health Capital and its Relationship to Economic Growth

The purpose of the article/hypothesis: The purpose of this paper is to attempt to justify the thesis that the health potential of human and society has an impact on economic well-being, contributing to the economic growth of countries, and thus their enrichment.&#x0D; Methodology: In order to examine the significance and strength of the impact of health capital on productivity on a macroeconomic scale, an econometric model of economic growth was used and six estimations were made in which the impact of subsequent components of this capital was examined.&#x0D; Results of the research: It turned out that 4 out of 6 introduced variables of health capital significantly influenced economic growth, and their direction is consistent with the economic theory. This implies necessity for further studies.
A Study on the Development Process and Legal Issues of Internet Healthcare in China

This paper aims to provide useful implications for Korea’s introduction of telemedicine services by studying the development process and legal issues of internet healthcare in China, which is currently rapidly growing. China is promoting the development of internet healthcare based on the ‘Internet+healthcare’ policy in 2015. Since 2018, a number of administrative rules have been enacted to manage internet healthcare services. However, there exist conflicts with the current laws in the restriction of doctors’ business locations, and recognizing internet hospitals and healthcare platforms as medical institutions. For the further development of internet healthcare, the Chinese government needs to solve these legal issues.
A GuessWhat?! Game for Goal-Oriented Visual Dialog: A Survey

Visual Dialog requires an AI-agent to hold a meaningful natural conversation about visual content. For a given image, dialog history, and a current question about the image, the AI-agent is required to understand the question, infer the context from the history and ground this information from the given image to be able to answer the current question correctly. One of the Visual Dialog tasks is a guessing game between two AI-agents for visual object discovery. This work represents a survey over different approaches done for the guessing game task. In this survey, we are showing the methodologies and architectures used by the relevant work as well as the results achieved by them. We present the most recent five approaches for this game from 2017 to 2020. GuessWhat?! is the dataset, introduced mainly for addressing the guessing game for visual dialog task, used in all works we are mentioning here.
Long-Term Person Re-identification Model with a Strong Feature Extractor

Person Re-Identification is a challenging task in the field of computer vision, it has gained a huge space due to its importance in our life. Person Re-ID is mainly utilized in security applications, it is used everywhere like airports, metro stations, banks, etc. Due to its crucial importance many approaches were proposed to perform such task, the explosion of deep learning and its ability to outperform most of the state of-the-art approaches motivated researchers to use it in person re-identification, moreover researchers introduced benchmark person re-id datasets to train these models, which were able to provide remarkable results and provide a solid benchmark for comparison. The only limitation to all these models and datasets is that they were built assuming no pedestrian clothes variations. In our work we aim to address the problem of Long Term Person Re-Identification where the clothes variation exists. We start by experimenting and comparing the most recent state-of-the-art deep learning models that were developed for that task such as OSNet and PCB. After that we introduce our proposal to replace the backbone network of PCB with OSNet to act as a feature extractor. All of these models were trained and tested using NKUP dataset and Market-1501. The benchmark dataset Market-1501 was used to compare our model’s performance with several standard models. Our model was able to outperform the" PCB" model on Market-1501 and improve the accuracy from 87.71% to 91.3%. On the NKUP dataset our model improved the accuracy of" PCB" model from 16.1% to 17.6%.
Costs Technique for Analysis Attacks Graphs in Computer Systems

In this paper, we define a new technique for analysing attack graphs in order to protect networks and computer systems from the risks and threats that can affect them. Our approach is based on logic expression of attacks scenarios, generated by any tool, in order to obtain a minimal set of vulnerabilities to remove. After identifying the dependencies between these vulnerabilities we propose the best cost paths to remove. As a result, we obtain the deleted paths with a minimal cost.
Research on the Function of Automatic Evaluation of English Grades Based on Data Mining

In the development of education innovation, the application of modern technology to evaluate and analyze students' English performance can not only save teachers' time, but also improve the accuracy of student achievement evaluation. Therefore, in the context of scientific and technological exploration and innovation, the automatic English score grading function based on data mining has become the main direction of current scientific research exploration. On the basis of understanding the relevant concepts of data mining in English scores, this paper analyzes how to apply it effectively in practice by studying the automatic evaluation model of English scores based on data mining.
Study of the influence of a suspension of polymeric particles in a physiological solution on hemolysis of human erythrocytes. The effect of polymer films on human blood cells

A series of styrene and α-methyl styrene copolymers were synthesized on the basis of their various derivatives. The hemolytic effect of polymer films was determined in vitro. The influence of polymer particle suspension on human erythrocyte hemolysis, as well as the effect of polymer films on human blood cells, has been established. The data obtained indicate that they are completely hemocompatible.
Residuality and Learning for Nondeterministic Nominal Automata

We are motivated by the following question: which data languages admit an active learning algorithm? This question was left open in previous work by the authors, and is particularly challenging for languages recognised by nondeterministic automata. To answer it, we develop the theory of residual nominal automata, a subclass of nondeterministic nominal automata. We prove that this class has canonical representatives, which can always be constructed via a finite number of observations. This property enables active learning algorithms, and makes up for the fact that residuality -- a semantic property -- is undecidable for nominal automata. Our construction for canonical residual automata is based on a machine-independent characterisation of residual languages, for which we develop new results in nominal lattice theory. Studying residuality in the context of nominal languages is a step towards a better understanding of learnability of automata with some sort of nondeterminism.
Ensuring the Integrity of Cloud Computing Against Account Hijacking Using Blockchain Technology

Cloud computing is a model of the internet that allows us to store our data on a remote server rather than on a local server, It helps us to access data from anywhere with an internet connection. This cloud has several features such as: easy to access, on demand services, high reliability, and elasticity. However, it has some disadvantages, among them are: Not all the data that we access to is secure, also it is easy for the hackers to attack this data. Account hijacking is one of the major attacks in the cloud, and it is considered as one of the most famous technologies that keeps and maintains the integrity of data. Blockchain technology is a digital distributed ledger that collects data in the form of blocks that are linked together with a hash. In this paper, we propose a decentralized cloud that combines two technologies: Blockchain and Private Cloud Computing in order to ensure the data integrity of the user by encrypting the authentication, and the document before send it to the cloud.
Analysis of the Utilization of the Embung Klampeyan, Tlogoadi Village, Mlati District, Sleman Regency, Indonesia

Embung Klampeyan which was established in 2009, has an area of approxi-mately 54 × 329 m2 with a depth of 4.15 m with a water capacity of 15,000 m3 and can drain water up to 25 ha. Currently the Embung Klampeyan is still functioning properly. The dam performance will be reviewed using a system-based approach: physical aspects, utilization aspects, as well as operational and maintenance aspects. Descriptive procedure with quantitative approach is used in this research. Descriptive analysis is intended to describe the results of the research to each of the assessed aspect variables. The Likert scale is used to assess/measure the attitudes, opinions and perceptions of a person or group of people towards research variables in the form of events or social symptoms that specifically determined. The results of the analysis show that the Physical Aspects have a value of 4.02 in very good conditions, the Utilization Aspects have a value of 4.13 in very good conditions, and the Operations and Maintenance Aspects have a value of 4.06 in very good conditions as well. Thus it can be concluded that the Klampeyan Embung is in very good condition or can pro-vide very good benefits for the surrounding community.
Sketch-Based Image Retrieval Using Novel Edge Detector and Feature Descriptor

With the explosive increase of digital images, intelligent information retrieval systems have become an indispensable tool to facilitate users’ information seeking process. Although various kinds of techniques like keyword-/content-based methods have been extensively investigated, how to effectively retrieve relevant images from a large-scale database remains a very challenging task. Recently, with the wide availability of touch screen devices and their associated human-computer interaction technology, sketch-based image retrieval (SBIR) methods have attracted more and more attention. In contrast to keyword-based methods, SBIR allows users to flexibly manifest their information needs into sketches by drawing abstract outlines of an object/scene. Despite its ease and intuitiveness, it is still a nontrivial task to accurately extract and interpret the semantic information from sketches, largely because of the diverse drawing styles of different users. As a consequence, the performance of existing SBIR systems is still far from being satisfactory. In this paper, we introduce a novel sketch image edge feature extraction algorithm to tackle the challenges. Firstly, we propose a Gaussian blur-based multiscale edge extraction (GBME) algorithm to capture more comprehensive and detailed features by continuously superimposing the edge filtering results after Gaussian blur processing. Secondly, we devise a hybrid barycentric feature descriptor (RSB-HOG) that extracts HOG features by randomly sampling points on the edges of a sketch. In addition, we integrate the directional distribution of the barycenters of all sampling points into the feature descriptor and thus improve its representational capability in capturing the semantic information of contours. To examine the efficiency of our method, we carry out extensive experiments on the public Flickr15K dataset. The experimental results indicate that the proposed method is superior to existing peer SBIR systems in terms of retrieval accuracy.
On the Formation of the Aesthetics of Old Russian Cursive Writing (Manuscript f. 115 no. 160 from St. Petersburg Institute of History)

The author addresses anew the manuscript f. 115 no. 160 from the collection of St. Petersburg Institute of History (the so-called ‘Azbuka Fryaskaya’) of 1604. This manuscript, written for the most part by the Muscovite scribe Fyodor Basov, is one of the two oldest Russian calligraphy sample books. The author discusses the relation of the ‘Azbuka Fryaskaya’ to other Old Russian ABC-books and their functions. As the codicological analysis has proved, the quires with cursive writing initially were placed in another part of the codex, next to the examples of ornate lettering. Such placement of the quires with cursive writing, as well as their decoration shows that Fyodor Basov viewed the cursive writing as decorative, ornate lettering par excellence. The paper also discusses the technical and aesthetical principles of the cursive writing, which were basic for Muscovite calligraphers of the late 16th — early 17th century when cursive writing took its shape as a style of writing. Special attention has been paid to the question of the influence of Greek (Byzantine) tradition on the aesthetics of Old Russian calligraphic cursive writing. Both the Russian calligraphic sample-books and the cursive writing are also viewed in the context of Western European calligraphy and its influence. The author concludes that the cursive writing style elaborated by the Muscovite calligraphers was the result of the development of Russian Early Modern writing culture that adopted some tendencies both from the Byzantine and from the Western traditions.
Visual Authentication Codes Generated Using Predictive Intelligence

In this paper we’ll describe the possibilities of using Predictive Intelligence approaches in creation of thematic-based visual security protocols, oriented for user authentication. Such protocols will use human perception abilities and cognition thresholds features evaluated for particular authorized persons.
Design and Implementation of Multimodal Video Retrieval System

With the application of deep learning in various fields, it replaces the extraction of video by manual design. Through the analysis and research of modal video retrieval methods, this paper aims to use text, image or video to carry out different ways of video retrieval through multi-modal video retrieval, and strive to meet the needs of different scenes, different users of video retrieval, and maximize the accuracy and effectiveness of video retrieval. This paper designs and implements a deep learning-based multimodal video retrieval system based on Windows. The system is based on the design concept of program modularization, using MySQL as the database and PyQT4 as the development tool of system interface. It mainly realizes three functional modules of video retrieval based on text, video retrieval based on image and video retrieval based on video segment.
Explaining GBDT by Probabilistic Finite-State Automata

Explainable artificial intelligence becomes vital for human users to understand and trust the decision-making process and results of machine learning methods. Unfortunately, most machine learning models are black-box and the algorithms running behind are opaque. In this work, we propose an approach to interpreting GBDT (Gradient Boosting Decision Tree Explanation) by extracting probabilistic finite-state automata from the trained model. Our method is inspired by and built upon a previous work that extracts probabilistic automata from RNN (Recurrent Neural Networks). To adapt the approach to our situation, we propose a series of techniques to ensure that the extracted probabilistic automaton approximates the GBDT model as accurately as possible. We conduct experiments on real-world datasets and our experimental results show that our method maintains a high level of fidelity of the extracted model as the size of the given GBDT model grows.