Reviewerly/exHarmony huggingface dataset:
Development of Automatic Rule-based Semantic Tagger and Karaka Analyzer for Hindi

Hindi is the third most-spoken language in the world (615 million speakers) and has the fourth highest native speakers (341 million). It is an inflectionally rich and relatively free word-order language with an immense vocabulary set. Despite being such a celebrated language across the globe, very few Natural Language Processing (NLP) applications and tools have been developed to support it computationally. Moreover, most of the existing ones are not efficient enough due to the lack of semantic information (or contextual knowledge). Hindi grammar is based on Paninian grammar and derives most of its rules from it. Paninian grammar very aggressively highlights the role of karaka theory in free-word order languages. In this article, we present an application that extracts all possible karakas from simple Hindi sentences with an accuracy of 84.2% and an F1 score of 88.5%. We consider features such as Parts of Speech tags, post-position markers (vibhaktis), semantic tags for nouns and syntactic structure to grab the context in different-sized word windows within a sentence. With the help of these features, we built a rule-based inference engine to extract karakas from a sentence. The application takes in a text file with clean (without punctuation) simple Hindi sentences and gives back karaka tagged sentences in a separate text file as output.
Sharing of Research Data by Blockchain

This study investigates the new method of sharing research data by a blockchain. We focus on the sharing not only among researchers but also between a group of researchers and test subjects from whom researchers collect data. To explain the use of blockchain technology, we explain the use of blockchain in securely handling research data in comparison with a simple real-world use case.
Design considerations introducing analytics as a “dual use” in complex industrial embedded systems

Embedded systems are today often self-sufficient with limited and predefined communication. However, this traditional view of embedded systems is changing through advancements in technologies such as, communication, cloud technologies, and advanced analytics including machine learning. These advancements have increased the benefits of building Systems of Systems (SoS) that can provide a functionality with unique capabilities that none of the included subsystems can accomplish separately. By this gain of functionality the embedded system is evolving towards a “dual use” purpose <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> In this paper we define dual usage as a control system having two purposes. In other contexts such as politics, diplomacy and export control, the term “dual-use” refers to technology that can be used for both peaceful and military aims, e.g., nuclear power technology., The use is dual in the sense that the system still needs to handle its original task, e.g., control and protect of an asset, and it must provide information for creating the SoS. Larger installations, e.g., industry plants, power systems and generation, have in most cases a long expected life-cycle, some up to 30–40 years without significant updates, compared to analytical functions that evolve and change much faster, i.e., requiring new types of data sets from the subsystems, not know at its first deployment. This difference in development cycles calls for new solutions supporting updates related to new requirements inherent in analytical functions. In this paper, within the context of “dual usage” of systems and subsystems, we analyze the impact on an embedded system, new or legacy, when it is required to provide analytic data with high quality. We compare a reference system, implementing all functions in one CPU core, to three other alternative solutions: a) a multi-core system where we are using a separate core for analytics, b) using a separate analytics CPU and c) analytics functionality located in a separate subsystem. Our conclusion is that the choice of analytics information collection method should to be based on intended usage, along with resulting complexity and cost of updates compared to hardware cost.
Personal results achievement by students of Pedagogical College as a result of cooperation with Hi-Tech Park for Children “Quantorium”

We consider the connection of the personal results of pedagogical college students with the universal competencies provided by the Federal State Educational Standard. The personal results are an integral part of the universal competencies. Educational work should be aimed at the formation and development of the intellectual, cultural, creative, moral personality of a student, a future specialist combining professional knowledge and skills, high moral qualities, possessing a legal and communicative culture, an active civic position. A special approach to the use of new forms of organization of educational work, as well as to the search for new social partners is necessary. The experience of cooperation of the Tambov Pedagogical College with the organization of additional education Hi-Tech Park for Children “Quantorium” is described. We analyze the application of innovative ways of organizing leisure time for pedagogical college students through personal growth trainings, master classes, projects, and meetings with interesting people, competitions that allow students to effectively achieve personal results outside of college, using new platforms and through the involvement of other specialists.
The Agricultural Greenhouse Management Platform based on IoT and SSM Architecture

This paper introduces the agricultural greenhouse management platform, a WEB-based system constructed from the SSM architecture. The platform integrates big data technologies with IoT and can be used to promote the informationization and intelligentization of greenhouses and farms. Our system provides many functions such as real-time monitoring of crop environmental parameters, real-time prompt of greenhouse warning information, data analysis and download, crop information management, traceability information management, employee information management, etc. We employ HTML5, CSS, AJAX, Express, Nunjucks, Highcharts and other front-end development technologies to design the web pages, and adopt the SSM three-tier architecture to develop the back-end business processing modules. Our platform has been tested in some local farms and help them improve the management efficiency of greenhouses and farms.
Accurate Measurement of Strain in Noncontact Surface Deformation Using Subset-Based Digital Image Correlation

The measurement of strain using some contact techniques has some drawbacks like less accuracy and it takes larger computation time for finding each location of subpixels. Thus, a faster noncontact Digital Image Correlation (DIC) mechanism is utilized along with the traditional techniques to measure the strain. The Newton-Raphson (NR) technique is considered to be an accepted mechanism for accurate tracking of different intensity relocation. Generally, the issue regarding the DIC mechanism is its computational cost. In this paper, an interpolation technique is utilized to accomplish a high precision rate and faster image correlation; thereby it reduces the computation time required for finding the matched pixel and viably handles the rehashing relationship process. Hence, the proposed mechanism provides better efficiency along with a reduced number of iterations required for finding the identity. The number of iterations can be reduced using the Sum of Square of Subset Intensity Gradients (SSSIG) method. The evaluation of the projected scheme is tested with different images through various parameters. Finally, the outcome indicates that the projected mechanism takes only a few milliseconds to match the best matching location, whereas the prevailing techniques require 16 seconds for the same operation with the same step size. This demonstrates the effectiveness of the proposed scheme.
PCSLA: Autonomy and Detecting SLA Violations over Applications in the Cloud Computing Environment

Cloud computing is an universal term for something which concerns the provision of hosted services on the Internet. In fact, services are separated into 3 broad main classes such as: IaaS, PaaS and SaaS. Cloud components are provided on the basis of SLAs, which is one of the important features of providing Quality of Service (QOS) guarantees, such as warranty over time of executions. However, due to system malfunctions on software resources, application monitoring proves to be a difficult task due to the metrics being monitored on the platform which can’t be facilely scheduled to the intended application level metrics, SLAs may be infringed. So as to get away with expensive SLA infractions, a reliable and consistent design of SLA achievement is required. This article provides a design called PCSLA. The PCSLA design supervises and discerns SLA damages over the application level and comprises tools for resource provisioning, planning, and deployment. PCSLA focuses on application supervising, which is pertinent whenever several users consume the common resources in a cloud area. We assess the design by using framework that sets out performance to study efficient proportion for control of distinct categories of applications. The reports obtained prove that PCSLA, with a weak level, is capable to control, spot SLA infractions and propose efficient measures for different workloads.
Feature Learning and Data Generative Models for Facial Expression Recognition

During communication process, people’s emotions are usually shown on the face (we call it expression). Capturing expression signals is very important to communication among individuals. In this article, we develop a neuron network architecture for automatic recognition of seven facial expressions: angry, disgust, fear, happy, sad, surprise and neutral. Furthermore, we also developed an CycleGAN architecture used to learn the data allocation of the training data set to solve the problem of data imbalance of the problem need solving. The efficiency of the architectures was evaluated on the FER2013 data set with an accuracy of 72.24%, which is higher than the result of the winner in the FER2013 competition (71.16%).
Case study of convolutional neural network implemented on FPGA

Convolutional neural network (CNN) has become a key research area in artificial intelligent technology. A case study of garbage identification and classification using convolutional neural network is presented in this paper. The CNN model with five convolutional layers is used to extract feature maps from garbage pictures, and classifies six types of domestic garbage. ZYNQ-7020 is employed to deploy the CNN model in order to take advantage of high efficiency, low power consumption and flexibility of FPGA platform. Additionally, the classification results are obtained at the FPGA terminal by running Python code through PYNQ. The study demonstrates an efficient approach to practically deploy CNN model and implement image classification on FPGA platform.
Facilitating the co-evolution of semantic descriptions in standards and models

Standardised specifications for sophisticated technologies are subdivided in multiple documents maintained by different working groups, typically accompanied by models and other formalised artefacts. As the specifications and the models evolve, ensuring their consistency at scale becomes challenging. While previous work developed a methodology for facilitating the co-evolution of models and standards, based on the Network Function Virtualisation (NFV) Information Model (IM) and models extracted from the related standardised specifications, the methodology focused on structural aspects only. This article refines the methodology, enabling the alignment of semantic descriptions of information elements and attributes, both across specifications and across information elements. To enable the alignment of semantic descriptions, we extend the methodology by using statistical and visual analyses of terms used in the specifications. The underlying meta-model for the information extracted from the specifications is extended to accommodate the capturing of additional semantic information. We report on our experiences with the application of a prototypical implementation of the methodology during the continued alignment and maintenance of the IM and the related standardised specifications. More than 400 potential inconsistencies were identified, leading to more than 100 contributions, some of which addressed multiple findings. Feedback from the working group provided insights on how to refine the methodology further. Models shall play a more central role and be better integrated throughout the specification development and implementation processes, helping to ensure and maintain consistency among specifications. Our experiences may provide useful insights into ongoing and future initiatives where similar challenges are faced.
A new application in cancer diagnosis based on convolutional neural network

Cancer detection has become much more significant in recent times than it was before since the number of patients suffering from cancer is rising year by year. But the actual case is that not all cancerous symptoms can be detected even by those experienced physicians by looking at their patients’ histopathologic images through their eyes. To improve this situation, we proposed a model based on convolutional neural networks to help classify the histopathologic images into 2 categories: benign and malignant. Our model, in this way, can extract main features from a certain image and learn from these specific features for classification. We then evaluate its performance on an online public dataset consisting of over two hundred thousand different training images and over fifty thousand validating images. Our model returns a relatively high accuracy on the test data which the dataset provides, and it does outperform some of the existing models in making the binary classification. In addition, we also designed a user interface to put our model into practice. With the help of this interface, users can simply upload an image from their local directory for our pre-trained model to process.
Research on Object Measurement Based on 3D Stereo Vision

In traditional measurement methods, the measurement tools used by people have strong specificity, poor versatility, and time-consuming. They are often only measured for two-dimensional plane geometric quantities. It is impossible to accurately measure some complex three-dimensional surfaces. Therefore, the stereo measurement technology based on 3D reconstruction has increasingly become a hot issue of research. This paper improves the fusion method of the binocular stereo vision measurement system and the TOF measurement system. In order to make the results accurate, before starting the measurement, the binocular and TOF are calibrated, and then the data obtained by the TOF camera is filtered and denoised. On the basis of ensuring accuracy, fast dense reconstruction can be completed. This article will use binocular and TOF camera to detect foam board, bucket and fire extinguisher in order to describe the above process in detail.
Consideration of approaches to forming a team when preparing students to participate in competitive events

The competitiveness of a modern university graduate is determined by his professional knowledge and skills, personal qualities, as well as the ability to make decisions, plan and organize work, and fulfill his role in team activities. Formation of the student’s ability to fulfill his role in a team is a general requirement for the training of a specialist in any field. The process of preparing university students for competitive events has great potential for the development of teamwork competencies. The aim of the study was to consider the specifics of preparing students for competitive events in the aspect of the approach of uniting students into teams. The peculiarities of preparing students for competitive programming in case of role-based and interpersonal methods of team formation were considered. The study used methods of observation, interviewing and questioning. The results of the study showed that the rolebased approach is more conducive to the development of student independence, the ability to make decisions, be responsible for the consequences of decisions, while the interpersonal approach allows to create conditions for the formation of team members’ ability to respond flexibly to changing circumstances. Thus, we can conclude about the various didactic possibilities of using role-based and interpersonal approaches to team building, about the need to use different methods for determining the composition of team members to solve educational problems in the learning process.
Image Segmentation Algorithm Based on Jump Feature Fusion and Rich Features

With the development of deep learning, convolution neural networks have become the mainstream of computer vision algorithms. In recent years, the biggest problem of applying convolution neural network to image segmentation is that it can not achieve accurate segmentation at the last layer, and it will cause resolution loss when extracting features. In order to solve these two problems, we add jump feature fusion methods after Entry, Middle, ExitFlow and ASPP module respectively, so that the feature loss will not be serious when extracting features. In the process of feature restoration, a module combining bilinear upsampling and deconvolution is added to further enrich the feature graph and make the features robust. The experimental results show that the results exceed the performance of other previous algorithms. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012, achieving the test set performance of 85.5%.
Instance Search via Fusing Hierarchical Multi-level Retrieval and Human-object Interaction Detection

Aiming to retrieve specific persons with specific actions, instance-based video search (INS) has attracted rising attention with the development of video understanding. In this paper, a novel hierarchical multi-task INS retrieval frame-work is proposed. Firstly, a multi-level action recognition framework and a face matching scheme are introduced to obtain initial action and person retrieval scores separately. In particular, a novel graph-based human-object interaction (HOI) detection model, named interaction-centric graph parsing network (iCGPN), is proposed to recognize interactions between human and objects. Secondly, an improved query extension strategy is adopted to re-rank the initial person retrieval results. Thirdly, more elaborate action features are extracted to recognize complicated actions. Finally, a specially designed fusion strategy is used to integrate the retrieval results of persons and actions to generate the final INS ranking list. The experimental results show the effectiveness of the proposed framework.
The digital humanities and re-imagined language description: A linguistic model of Malay with potential for other languages

Abstract The declaration of 2019 as the International Year of Indigenous Languages raised the question how it will be possible in practice for hitherto uninvestigated languages to be investigated at all. The suggestion put forward in this article is that traditional language description reimagined in the context of the Digital Humanities has the capacity to create models, which can be re-used mutatis mutandis for other languages. The methodology makes use of MaLex, which is a collection of procedures and datasets designed for Malay in the first instance, but with the potential to be modified for other languages. MaLex is placed in its historical context within the Humanities, and digital procedures are explicitly connected to traditional scholarship. The use of digital methods creates new opportunities to scale up practical linguistic analysis using large datasets. MaLex procedures include a stemmer, a tagger, a parser, and a spelling-to-phoneme algorithm, and make a rough English translation of Malay text. MaLex simulates the approach of the practical linguist analysing a new language, and as new components are added, they are made to integrate with existing components, so that MaLex works like a single machine, drawing on an integrated infrastructure.
Analysis of the Status Quo of College English Learning Based On Big Data Technology

As the "next natural resource of the human world", big data has affected all aspects of society and CE learning. Therefore, using this technology to analyze the status quo of college English learning has become a hot spot in education. The purpose of this article is to analyze the status quo of CE learning based on big data technology. This article first analyzes the impact of Big Data technology on CE learning, discusses the challenges faced by Big Data technology on CE learning, and reflects on the application of Big Data in the process of CE learning. This article takes English majors of school A as the research object, uses questionnaires to explore the English learning of Big Data in English learning, and analyzes the status quo of CE learning, so as to provide a certain level for teachers and students of related majors to improve learning efficiency and learning interest in the teaching and learning process. The survey data shows that when college students are learning English, 24.1% of English learners want to improve their listening ability; 33.3% of learners want to practice oral English on learning devices, indicating that CE learning pays more attention to the cultivation and improvement of listening and speaking ability.
Neural Incremental Speech Recognition Toward Real-Time Machine Speech Translation

Real-time machine speech translation systems mimic human interpreters and translate incoming speech from a source language to the target language in real-time. Such systems can be achieved by performing low-latency processing in ASR (automatic speech recognition) module before passing the output to MT (machine translation) and TTS (text-to-speech synthesis) modules. Although several studies recently proposed sequence mechanisms for neural incremental ASR (ISR), these frameworks have a more complicated training mechanism than the standard attention-based ASR because they have to decide the incremental step and learn the alignment between speech and text. In this paper, we propose attention-transfer ISR (AT-ISR) that learns the knowledge from attention-based non-incremental ASR for a low delay end-to-end speech recognition. ISR comes with a trade-off between delay and performance, so we investigate how to reduce AT-ISR delay without a significant performance drop. Our experiment shows that AT-ISR achieves a comparable performance to the non-incremental ASR when the incremental recognition begins after the speech utterance reaches 25% of the complete utterance length. Additional experiments to investigate the effect of ISR on translation tasks are also performed. The focus is to find the optimum granularity of the output unit. The results reveal that our end-to-end subword-level ISR resulted in the best translation quality with the lowest WER and the lowest uncovered-word rate.
Stream Symmetric Cipher “Strumok”

This chapter presents the results of substantiating the structure of the stream symmetric cipher, the key system and the main cryptographic elements of the new stream cipher “Strumok”. In particular, the mathematical structure of the main cryptographic transformations is verified.