Reviewerly/exHarmony huggingface dataset:
Analysis of Politeness Strategies and the Maxim Violation in a Movie Script of Enola Holmes

The purpose of this research were to find out (1) whether the types of politeness strategies in Enola Holmes movie are in accordance with politeness strategies proposed by Brown and Levinson (1987) or not, (2) what type of politeness strategies most frequently appeared in Enola Holmes movie, (3) which character(s) use politeness strategies most frequently than the others, and (4) what type of violating maxims based on Grice’s maxim most frequently appeared in Enola Holmes movie. The design of this research is a descriptive qualitative research. The data of the research were in the form of utterances that contained politeness strategies and cooperation and implicature (Violating of maxim). The result shows that (1) the types of politeness strategies in Enola Holmes movie are in accordance with politeness strategies proposed by Brown and Levinson (1987); (2) Bald on Record is the most frequent politeness strategies appeared in Enola Holmes movie. Bald on Record is mostly used by the characters because there is a power, social status, or close relationship among the characters.; (3) there are three characters who used politeness strategies more frequently, they are Enola Holmes, Sherlock Holmes, and Tewkesbury (4) Violating maxim of relevance is the most frequently appeared in Enola Holmes movie.
Online learning technologies as a modern direction of learning in communities

This article discusses the implementation of technologies of online learning in the mode of massive open online courses, namely, online classes, which are considered as a platform for distance learning and interactive communication between students and teacher. These technologies allow to provide the choice of content, rate, forms of work for students, to increase level of individualization and personification of education, to facilitate learning irrespective of place and time of being, to decrease subjectiveness of evaluation of learning results, to make the learning process more accessible. Theoretical foundations of connectivism are considered as a learning theory implementing the learning process on the basis of online information technologies. This approach changes the role of the teacher, who, acting as a tutor, regulates the pace and volume of students’ work, organizes the exchange of information, thereby creating a community. This article determines the reasons and features of creation of communities of teachers and students during studying in massive open online course. In the frames of this study, network community is considered as a group of students and teachers participating in interactive communication by means of network tools, social networks and messengers. This communication format allows to solve the problems of formation not only of subject but also of meta-subject results of learning, namely, communicative properties of students. The features of construction of communicative activity in these communities are exemplified by the course titled Preparing students for USE in mathematics (profile level), implemented in Volgograd State Socio-Pedagogical University on Miroznai platform. Examples of classes are discussed, each being devoted to solution to one of the task types presented in ESU in mathematics of profile level. Analysis of execution of tests proposed in the course demonstrates that the results of the considered students are somewhat higher than those of students studying in isolation.
Enhancing disk input output performance in consolidated virtualized cloud platforms using a randomized approximation scheme

Abstract In a virtualized computer system with shared resources, consolidated virtual services (VSs) fiercely compete with each other to obtain the required capacity of resources, and this causes significant system's performance degradation. The performance of input output (I/O)‐bound applications running inside their own VS is mainly determined by the total time required to schedule every read/write request, plus the actual time needed by the device driver to complete the request. To achieve a right performance isolation of shared resources (e.g., the last level cache, memory bandwidth, and the disk buffer), it is essential to limit the performance degradation level among collocated applications, as simultaneously several I/O operations are requested by VSs, perhaps with different priorities. This article proposes a resource allocation controller that uses a fully polynomial‐time randomized approximation scheme to enable performance isolation of concurrent I/O requests in a shared system with multiple consolidated VSs. This controller uses a Monte Carlo sampling approach to measure and estimate the unknown attributes of operational requests originating from each VS. This is formalized as an optimization problem with the aim to minimize the degree of total quality of service (QoS) violation incidents in the entire platform. We associated a reward function to every working machine that represents the fulfillment degree of quality of service metric among all running VSs. The conducted comprehensive set of experiments showed that the proposed algorithm can reduce the QoS violation incidents by 32%, compared with the result which is obtained by employing the default resource allocation policy embedded in the existing Linux container layer.
Modelling of multi-path transmission system of various priority multimodal information

Today multimodal approach is relevant in any sphere of our digital life. The technologies aim to suggest the best and the most comfortable way for human-human and human-machine interaction. One of the direction to do that is the usage of multimodal interaction. We consider that modalities may be different by priority. It means that some modalities are more significant than others in a particular task. In this paper, we propose the mathematical model of the multi-path transmission system of various priority multimodal information based on queueing theory. We present a multipath multimodal data transmission system as a multi-phase queuing system (QS), consisting of two typical nodes. The first node (QS1) defines the queue of packets of modalities of different priority, the second node (QS2) distributes the packets over available access networks. We build a simulated model on AnyLogic tool. We show that the rate of packets losses is significant depends on the priority level.
Research and Application of 360<sup>○</sup>Surrounding View Stitching Technology

360° surrounding view stitching technology makes the images obtained from multiple surrounding view cameras applied by SURF algorithm. Through image correction and stitching and optimization, the four adjacent area images are fused using OpenCV's image stitching technology to obtain. This technology can present more clearly in various environments and is a technology with development potential.
(Re)Thinking Design with New Materialism: Towards a Critical Anthropology of Design

The article proposes an empirical and discursive understanding of design as engaging and intensifying uneven power relations. By affiliating with the ontological turn in anthropology, such re-defined reading of design acknowledges design's complicity with extractive capitalism while aiming to open up possibilities to think design otherwise. In recent years, inspired by the resurgence of materialism, abstract notions of design as mediating practice between human and environment have gained popularity. Yet, these more-than-human-centred design theories tend to obscure the material and immaterial infrastructures that still shape human and nonhuman realities. By utilising the example of sand's transformation into land and tracing its journey across sites, actors and continents, the infrastructures of planetary transformation – as well as what eludes them – are investigated. Turning matter into medium emphasises thresholds and ruptures in the human-material relationship and thus transcends both a socially constructed and material reading of reality. Through a historical and empirical relocation of the current more-than-human-centred design discourse, the research presented in this article aims to support the establishment of a critical anthropology of design.
Can Submerged Buoys be Reliably Used for Monitoring Tsunamis in the Indian Ocean?

Abstract The article proposes a novel and robust Continuously Homing Submerged Autonomous Tsunami Underwater System (CHATUR) in which the buoy is submerged at a depth of 300 m, collecting sea-level data during normal conditions and surfacing during a tsunami event to transmit the event data. By on-demand reliability analysis, a system in the Bay of Bengal requires health monitoring at an interval of 13 days. By offshore experiments and numerical simulations, the configuration increases the tsunami warning time by ~2 min.
Spoken Language Understanding with Sememe Knowledge as Domain Knowledge

Spoken language understanding (SLU) is a key procedure in task-oriented dialogue systems, its performance has been improved a lot due to deep neural network with pre-trained textual features. However, data sparsity and ASR error usually influence the model performance. Previous studies showed that pre-defined rules and domain knowledge such as lexicon features seems to be helpful for solving these issues. However, those methods are not flexible. In this study, we propose a new domain knowledge, ontology based sememe knowledge, and apply it in SLU task via a weighted sum network. To do so, we construct a sememe knowledge base by identifying slots' meanings and extracting the corresponding sememes from HowNet. We extract sememe sets for characters in given utterance and use them as domain knowledge in SLU task by means of the weighted sum network. Due to the weighted combinations of the sememe sets can extend words' meanings, the proposed method can help the model to flexibly match a sparse word to a specific slot. Evaluation on a Mandarin corpus showed that the proposed approach achieved better performance comparing to a leading method, and it also showed the robustness to ASR error.
Identification Method of Taxi Drop-off Location Based on Combination Retrieval

The past, the GPS locating closest to drop-off time was selected as the taxi passenger’s alighting position, but due to equipment failure, urban buildings, and complicated network, the accuracy of identifying the alighting position was low. In order to solve this problem, we designed a taxi drop-off location identification system based on combined retrieval. By searching the historical database, the taxi trajectory is reckoned, and the drop-off position is calculated based on the trajectory. Experiments show that this method has higher computational efficiency, and the accuracy is significantly improved compared with the GPS position matching method.
FFDNN: Feature Fusion Depth Neural Network Model of Recommendation System

Faced with the various colorful items on the ecommerce platforms, it becomes a problem that how to effectively match the items to those who need. An indispensable tool for solving the problem above is recommendation system. Focusing on the massive data faced by the current recommendation algorithms, the sparse user-project interaction matrix, the cold start of new items and users, and this paper proposes an effective neural network-based recommendation model. Firstly, the user-item scoring matrix is constructed and filled to alleviate the sparseness problem. Secondly, the user and item rating eigenvector are extracted by matrix decomposition, whiles text eigenvectors are obtained by using text description information. Furthermore, user and item feature vectors are constructed by rating features and text features together. Finally, the constructed eigenvector of users and items are used as input to the long short-term memory (LSTM). The eigenvectors of users and items are trained, and the output of the LSTM network is processed by multi-layer perception (MLP) to predict the correlation between users and projects. The experimental and comparative results show that the proposed model can effectively alleviate the cold start problem of projects and users, and improve the recommendation performance.
Origination and Development of Ideas about “Educational Environment” as Fundamental Unit of Education System: Theoretical Review

The purpose of the study is to consider the notion “educational environment” in the context of origination and development of the presented definition. Using a historical and theoretical analysis, the author has determined that interest in educational environment manifested itself throughout entire societal development from ancient times to the present. At the initial stage of its formation, educational environment was considered one of the conditions of upbringing and only at the turn of the XIX-XX centuries, it emerged as the main condition of education. Scientific novelty of the research lies in description and structural presentation of the main stages of origination and development of ideas about educational environment. As a result, it is determined that the notion “educational environment” originated as early as in ancient times, underwent a fairly large number of interpretations over time and subsequently, through methodological understanding of its nature, it began to be considered as a fundamental unit of the education system.
Predicting Revenues of Seoul Commercial Alley using Neural Tensor Factorization

Those who want to start their own businesses must decide a location and service to start. In order to make the decision, they must know characteristics of the location and service, such as average revenues and floating population. However, it is usually very difficult to collect and analyze these characteristics. Therefore, we propose a novel deep learning model named Neural Tensor Factorization (NeuralTF) that automatically analyzes the characteristics for predicting revenues, and a method for recommending appropriate location or service to start their businesses based on the predicted revenues. NeuralTF is a combination of Tensor Factorization(TF) and Deep Neural Network(DNN). We compare NeuralTF with other machine learning models using Seoul Commercial Alley dataset. In addition, we compare performances of NeuralTF when TF and DNN components share the embedding space and when they do not.
Dynamics of Academic Motivation and Professional Choice Motivation among Students of Linguo-Didactic Training Programmes

The purpose of the research is to determine the current level of academic motivation of students - future foreign language teachers, to examine dynamics of academic motivation throughout years of study. The article presents the research findings and the factors that affect academic motivation of students of linguo-didactic training programmes. Scientific novelty of the work lies in examining the dynamic aspect peculiar to motivation of students of linguo-didactic training programmes. The research findings reveal a drop in academic motivation of students - future foreign language teachers as they approach their final years of study.
An Effective Approach of Evaluating Cognitive Weight and Equivalent Distribution of Programming Problems

Equal distribution of programming problems among the students is so significant during the examination within limited times. But how could we determine that how student feels about whether a given program is either easy or difficult? So Complexity measurement according to their brain thinking capability is necessary. In this article, we have improved the approach of complexity measurement procedure that gives us more accurate value of cognitive weight which contract the constraints of existing measures. The idea of cognitive weight of any basic control structure is ethically based on the logical capacity of the human brain. As per our cognitive data gathering from various level of students, we have built up a new software tool using Java Standard Edition and MySQL for equivalent distribution of programming problems dependent on our developed algorithm. The problems is categorized according to three difficulty level (simple, medium, hard) for each level of students. This will help instructor allocate problems while maintaining an equivalent level of problems for students.
Development of Hindi Mnemonics for generating private keys used in Hierarchical Deterministic wallets

In the cryptography the most important part is the security of the wallet. The wallets are secured till date by some alphanumeric private keys. The generation of the private keys are playing most important role in the security of the whole system which is directly proportional to password security. Therefore the security of the password and also the usability of the password is the main concern for all the administrators. Several strategies are analyzed for password formation. In this research work we proposed a novel technology of using Hindi words for the development of the security keys makes them more strong as binary function works on Hindi dictionary are very few common. In this certain sets of algorithm has been developed for the selection of the Hindi words, their character length and there way of representation. This article introduced a technique of the words to be selected that will be used in future for the development of private keys and makes wallets more secure system. Hindi mnemonics aides joins the request for composing strokes of letters with secret phrase age to assist clients with making protected and essential passwords.
Artificial Intelligence Sustainability Ensured by Convergent Cognitive Semantics

The work addresses ensuring the sustainability of group decision-making processes supported by artificial intelligence (AI) with cognitive and denotative semantics of its models. The latter can be represented by applying the traditional digital and symbolic means like big data and images. Cognitive semantics cannot be represented by such means. This semantics reflects human consciousness, experiences, feelings, thoughts, etc. The interpretation of cognitive semantics is beyond the possibility of digital computers, logical predicates, knowledgebase, neuron networks, big data, and images. Cognitive semantics is a source of divergence and unsustainability in group decision-making, especially during brainstorming. This work shows that the problem of creating cognitive semantics can be resolved indirectly. Some methods from the electrodynamic, quantum field theory, chaos control, category theories are suggested for this aim. Combining such methods is carried out with the author’s convergent approach, which creates a synergy. This approach is based on an inverse problem-solving method, genetic algorithms, cognitive modeling with its big data verification. Some practical results of the implementation are demonstrated.
Application of machine learning in the development of databases of recommendation resources and music services

The problem of using computer-assisted learning technologies for organization musical recommendations according to user's preferences. The main content of work is an analysis of spectrograms of musical compositions of various styles and directions and its further processing using the algorithms of neural network. In this research the problem of making new way of musical classification is solving and proving the idea about the fact that musical preferences can be developed on basics of compiling several packs of digital tags, that were stated to compositions because of the work of neural network.
Random Alpha Hybrid Encryption and Decryption Algorithm

Data breach is a major concern in the current world-wide heterogeneous networks. Data can be forged, stolen or damaged during or after transmission. Hence, data security irrespective of its type is a pressing problem for network designers. Encryption is one of the methodologies by which data could be secured from leakage. Security analysts have proposed multiple encryption algorithms with variation in the number of keys, number of rounds, confusion and diffusion. This paper attempts to produce different cipher text every time it executes. In this proposed key generation algorithm, keys are randomly generated. Random key generation and unique output after each execution strengthen the algorithm and hence the hacker cannot predict the original message. This randomness also protects the message from known plaint-text, chosen cipher text, Replay, Brute -Force and man-in-the middle attacks.
A Formal Model for Behavior Trees Based on Context - Free Grammar

In the last two decades, several studies have been carried out to translate Behavior Trees (BTs) into other formal languages. However, as BTs are usually drawn directly from natural languages, there is no formal grammar to define what is a valid BT. In this research, we first propose a normal form for requirement BT as a building block for a valid BT, and then design a context-free grammar that can generate and verify all valid BTs. This work provides a solid foundation for BT research and will improve the quality of requirements modeling by identifying some common requirement defects.
Multi-Agent Recommendation and Aspect Level Sentiment Analysis in B2B CRM Systems

In today's world of big data, multi-tenant cloud Customer Relationship Management Systems with an ever increasing pressure to customize offerings for each prospect, there is a need for distributed problem solving that can help salespeople win sales using data driven recommendations. With this work we are proposing a Decision Support System constructed as a multi-agent architecture for Business to Business Customer Relationship Management Systems that can guide salespeople during the conversion of a prospect into a client. The implementation and the results using real-world CRM data are presented and discussed in this paper.
Application of VR Technology in Japanese Education

With the continuous development and progress of science and technology, there are many new technologies. As the most advanced technology, VR technology has attracted worldwide attention and attention from its application and development. In order to meet the needs of future development, Japanese education has begun to try to combine education with VR technology to promote the development of Japanese education industry. This paper studies the application of VR technology in the field of Japanese, using the methods of literature review and questionnaire survey, deeply studies the concept, essence and characteristics of VR technology, constructs vr virtual simulation training scene, analyzes the application of VR technology in Japanese education research, and draws a conclusion. At present, VR technology accounts for the highest proportion in the field of education, reaching 50.8%; teachers’ understanding of VR technology reaches 80%, which is very helpful for the application of VR technology in Japanese education. The research has certain research significance and practical value, which can promote the reform and innovation of Japanese education methods, better improve the teaching effect and improve students’ interest in learning Japanese.
Design of Convolution Operation Accelerator based on FPGA

Because of the high complexity and long time of CPU in convolution operation, the power consumption of GPU is high and it can not be deployed to small hardware devices. Field Programmable Logic Gate Array (FPGA) has parallelism and fast speed , Low power consumption and etc. Therefore, this paper proposes an improved RLeNet model, which optimizes model parameters and used a parallel technology to accelerate CNN hardware. The improved model parameter data volume is reduced to 3.2% of the original. Under the MNIST data set, the accuracy of the prediction result is 98.5%. Finally, on the Xilinx Nexys 4 DDR: Artix-7 development board, the prediction process of the convolutional neural network for handwriting recognition is implemented. When the system predicts with a 200MHz clock, the time to predict a picture is 31.8μs.