Reviewerly/exHarmony huggingface dataset:
Automatic evaluation of the quality of machine translation of a scientific text: the results of a five-year-long experiment

We report on various approaches to automatic evaluation of machine translation quality and describe three widely used methods. These methods, i.e. methods based on string matching and n -gram models, make it possible to compare the quality of machine translation to reference translation. We employ modern metrics for automatic evaluation of machine translation quality such as BLEU, F-measure, and TER to compare translations made by Google and PROMT neural machine translation systems with translations obtained 5 years ago, when statistical machine translation and rule-based machine translation algorithms were employed by Google and PROMT, respectively, as the main translation algorithms [6]. The evaluation of the translation quality of candidate texts generated by Google and PROMT with reference translation using an automatic translation evaluation program reveal significant qualitative changes as compared with the results obtained 5 years ago, which indicate a dramatic improvement in the work of the above-mentioned online translation systems. Ways to improve the quality of machine translation are discussed. It is shown that modern systems of automatic evaluation of translation quality allow errors made by machine translation systems to be identified and systematized, which will enable the improvement of the quality of translation by these systems in the future.
Ontology-Driven Approach to Research and Educational Organization Information Representation

The paper presents an approach to the representation of various types of information that is accumulated at the level of different scientific and educational organizations with the purpose of further assessment of the quality of their functioning. This approach is based on ontological modelling. Ontology allows structuring and organizing scientific and educational organizations information aiming its representation and analyses. General ontology of scientific and educational organizations information is divided into several ontologies in order to represent all aspects and important indicators of organizations performance. Ontological system elements are described. Practical implementation of proposed ontology and its filling with information was performed using cognitive IT-platform “POLYHEDRON”.
Design and Development of Hospital Indoor Navigation System

The hospital indoor navigation system is a system that can fully display the distribution of internal departments and intelligent diagnosis guidance in the hospital. It mainly realizes the display of the distribution of internal departments in the hospital. The main functions of this system are: search for the target department, map movement, map Zoom in and zoom out, switch between 2D map and 3D map. This system is based on the CAD indoor layout of the Second Hospital of Baoding City. The technical route of making indoor maps through JOSM software, obtaining and analyzing geojson format map data files, canvas rendering 2D interface, and three.js rendering 3D interface has been developed and realized. A set of hospital indoor positioning and navigation system. This system can fully display the distribution of the internal departments of the hospital, so that the doctor can check the layout of the hospital in advance at home, so as to plan the route in advance and avoid the chaos and disorder of the hospital. This system uses apache-tomcat-9.0.12 as the server, Windows8 as the system server, Josm and Sublime as the development tools, html5 and javascript as the main development languages, and cooperates with canvas and three.js to realize 2D and 3D maps.
Inclusive Education for Inclusive Economic Participation: The Financial Access Channel

Purpose – The study assesses how inclusive education affects inclusive economic participation through the financial access channel.Design/methodology/approach – The focus is on 42 sub-Saharan African countries with data for the period 2004-2014. The empirical evidence is based on the Generalised Method of Moments.Findings – The following findings are established. First, inclusive secondary education moderates financial access to exert a positive net effect on female labour force participation. Second, inclusive “primary and secondary school education” and inclusive tertiary education modulate financial access for a negative net effect on female unemployment. Third, inclusive secondary education and inclusive tertiary education both moderate financial access for an overall positive net effect on female employment. In order to provide more gender macroeconomic management policy options, inclusive education thresholds for complementary policies are provided and discussed. Originality/value – Policy implications are discussed in the light of challenges of economic development in the sub-region and Sustainable Development Goals.
A Data-driven Approach for Charging Characteristic Parameter Identification Method of Electric Vehicles

Abstract In order to accurately identify new energy electric vehicles charging behaviour characteristic parameter, the theory of consistency is put forward based on the k-means clustering method. The complex coupling network including consistency control is introduced into the data clustering analysis to accurately describe the consistency characteristics of the electric vehicles charging load data in different periods, and the dissimilarity measure with the state update of the adjacent cluster data is used to quickly calculate the initial cluster center. The k-means method is used to quickly identify the expected value of EV initial charging time in typical scenes, and to accurately extract the probability distribution function of EV charging probability and charging initial time. Combined with a practical case, it is verified that the proposed method has the advantages of simple calculation, fast clustering.
Negative Resistance and Its Impact on a RC-DC Driven Electric Circuit

It is a common misconception that electric "resistance" always is a positive defined electric element.i.e., the plot of the voltage across the resistor, V vs. its current, i is a slanted straight line with a positive slope.Esaki diode also known as tunnel diode is an exception to this character.For a certain voltage range, the current recedes resulting in a line with a negative slope; it is interpreted as negative resistance.In this research flavored report, we investigate the impact of the negative resistance in a typical classic electric circuit.E.g., a tunnel diode, D is inserted in a classic electric circuit that is composed of an ohmic resistor, R and a capacitor, C which are all in series with a DC power supply.The circuit equation for the RCD circuit is a nonlinear ordinary differential equation (NLODE).In line with the ever-growing popular Computer Algebra System (CAS), this is solved numerically utilizing two distinctly different CASs.The consistency of the solutions confidently leads to the understanding of the impact of the negative resistance.The circuit characteristics are compared to the classic analogous RC circuit.The report embodies an atlas of characteristics of the circuits making the analysis visually comprehensible.
Design and Analysis of Finishing and Detection Device for Knitting Needle

In view of the slow efficiency of manual inspection of knitting needles and the uneven detection consistency caused by human factors, and the increase in labor costs year by year, automation technology has become a trend to replace manual detection. Based on the modular idea, the research group designed a knitting needle knitting needle finishing and detection device, so as to realize the needle finishing, conveying, positioning, detection, and collection problems in the needle inspection process. The rationality of the structure is verified by analyzing the statics model of the linkage mechanism and using the Solidworks Simulation module to analyze the stability of the mechanical claw structure. The whole virtual assembly shows no interference, the linkage mechanism can realize the specified trajectory movement, and the mechanical claws of the key components meet the structural design requirements. The designed knitting needle finishing and detection device is stable and reasonable, and meets the design task and action requirements, and provides a feasible solution for the automatic detection of knitting needles.
Research on a Micro-Expression Recognition Algorithm based on 3D-CNN

Micro expression is a kind of natural human expression, which lasts for a short time and is not easy to detect. Due to the subtle spatiotemporal variation of micro-expressions, the recognition of micro-expressions is still a big challenge. Although many scholars have made some attempts in the recognition of micro-expressions, the accuracy of the recognition problem is still not ideal. In order to take advantage of 3D convolution, we propose an improved model of micro expression recognition based on 3D convolution neural network (3D-CNN). In the sequential model based on the deep learning framework of Keras, 3D convolution, pooling, batch normalization and other layers are added to construct the sequence. The recognition rate of this model on SMIC database can reach 76.92%, and it also shows good recognition rate on other databases. This method is superior to or partially superior to the classical methods and the current mainstream methods.
Chatbots: Autoexpansion Approach to Improve Natural Language Automatic Dialogs

Chatbots belong to a large family of software robots that aim to gracefully integrate human spoken interactions as interface. Most of the proposals in the field intend to solve severe limitations of the automatic processing of natural language. This paper is part of a project called PTAH, that implements a prototype able to ask and answer about a topic in Spanish. Although previous work solved much of the training and language-based strategies, there is still poor resources to make the bot understand alternatives to answer specific questions or problems. This paper presents an approach called auto-expansion with an original combination of Morphosyntactic-Linguistic-Wavelets and certain Machine Learning techniques. As part of the scope, the basic preliminary theory is introduced, along with detailed description of the self-expansion proposal and some of the prototype's state of implementation, tests and statistical analysis. Authors intend to show the goodness of the proposal.
An Arithmetically Complete Predicate Modal Logic

This paper investigates a first-order extension of GL called \(\textup{ML}^3\). We outline briefly the history that led to \(\textup{ML}^3\), its key properties and some of its toolbox: the \emph{conservation theorem}, its cut-free Gentzenisation, the ``formulators'' tool. Its semantic completeness (with respect to finite reverse well-founded Kripke models) is fully stated in the current paper and the proof is retold here. Applying the Solovay technique to those models the present paper establishes its main result, namely, that \(\textup{ML}^3\) is arithmetically complete. As expanded below, \(\textup{ML}^3\) is a first-order modal logic that along with its built-in ability to simulate general classical first-order provability―"\(\Box\)" simulating the the informal classical "\(\vdash\)"―is also arithmetically complete in the Solovay sense.
Computer Teacher Teaching Innovation Team Construction in Higher Vocational Education under the Background of 1+X Certificate System

As proposed by "National Vocational Education Reform Implementation Plan" issued in China in January 2019, the pilot work of the 1+X certificate system will be initiated to encourage students from vocational colleges to actively obtain different types of vocational skill level certificates while acquiring academic certificates, expanding employment and entrepreneurial skills. Therefore, it is particularly crucial to establish a teaching system integrating academic certificates and vocational skill level certificates. This move is conductive to unifying the comprehensiveness, systemicity, universality, adaptability, pertinence, and flexibility of talent training, as the fundamental guarantee for continuously enhancing the ability of vocational education to serve economic and social development. This paper will briefly elaborate this issue.
Machine Learning Based Recommendation System For Android Apps

Due to the extensive and ever-growing collection of android apps, it has become quite difficult for users to find apps that they are truly interested in. When looking for android app, the user has a particular goal in mind and wishes to find an app that can truly meets their needs and expectations. App recommendation systems play a vital role in this regard by recommending apps to users based on their preference and requirements. The goal of this project was to develop a machine learning based app recommendation system to recommend Android Apps to the users. We collected the screenshots and metadata of a total of 12000 android apps, divided across ten different categories. CNN model architecture was trained on Vgg16 ImageNet weights. The model’s prediction was followed by Feature Extraction which was further used as a basis for Linear Regression to finally recommend an app of interest to the users.
The use of real technical tasks for the implementation of websites for the formation of professional skills of web developers

Abstract The paper deals with the problem of the formation of professional skills in web development. The task is solved through the use of real technical tasks for the implementation of websites in teaching, the inclusion in the curriculum of the educational program of the disciplines “Methods and tools for designing information systems”, “Programming network databases” and “Web programming”. The developed methodology was tested at Sholom-Aleichem Priamursky State University, Osh State University, Pacific National University and North-Eastern Federal University during the 2019-2020 and 2020-2021 academic years. 42 students of the direction “Information systems and technologies” took part in the training. The content of the disciplines included obligatory project team work on an assignment from an industrial partner. An important element of preparation was the final exam conducted in the format used in WordlSkills championships. The developed websites were assessed by independent qualified third-party experts in the following areas: work organization and management, communication and interpersonal skills, design and layout, server and client-side programming, database and web portal design. As a result, 74% of students implemented the web portal and submitted the project on time. As a result of passing disciplines and implementing web portals, students studied the principles of rapid development of web systems, received professional competencies and interaction with real organizations.
String Theory Algorithm

This chapter, a detailed descriptionString Theory Algorithm of the algorithm proposed methodProposed method is presented.
Fabricate-Vanish: An Effective And Transferable Black-Box Adversarial Attack Incorporating Feature Distortion

Adversarial examples have emerged as increasingly severe threats for deep neural networks. Recent works have revealed that these malicious samples can transfer across different neural networks, and effectively attack other models. The state-of-the-art methodologies leverage Fast Gradient Sign Method to generate obstructing textures, which can cause neural networks to make incorrect inferences. However, the over-reliance on task-specific loss functions makes the adversarial examples less transferable across networks. Moreover, recent de-noising based adaptive defences provide promising performance against aforementioned attacks. Therefore, to achieve better transferability and attack effectiveness, we propose a novel attack, referred to as the Fabricate-Vanish (FV) attack, which is able to erase benign representations and generate obstruction textures simultaneously. The proposed FV attack treats the adversarial example transferability as latent contribution for each layer of deep neural networks, and maximizes the attack performance by balancing transferability and task specific loss function. Our experimental results on ImageNet show that the proposed FV attack achieves the best attack performance and better transferability by degrading the accuracy of classifiers 3.8% more on average compared to the state-of-the-art attacks.
Increasing the Situation Awareness and Response Time of K9 Units Using a Smart Integrated Vest for the Canine Companion

First responders are tasked to intervene in small-scale emergencies and major natural or human-made disasters under unknown environments. They are required to operate around the clock, in situations that are life-threatening and potentially hazardous, with limited awareness of the operational situation, mission progress, and time sensitivity. They more often than not risk their own personal safety and well-being in order to keep civilians safe. During the most demanding and extended incidents, first responders operate under complex response operation plans that involve the collaboration of multiple disciplines and teams including K9 units. Within the context of the INGENIOUS project, a K9 vest for the canine of the unit is developed, aiming to improve their response time, enhance their situational awareness, support the collaboration between agencies, and most importantly, increase their safety during missions.
Complexity of Scorpion Solitaire and applications to Klondike

Scorpion is a puzzle game that falls under the category of Solitaires. The problem of determining whether a starting configuration of a Solitaire game (generalized to contain an arbitrary number of cards) can lead to a winning configuration has been studied from the perspective of Computational Complexity for some of the most popular Solitaires, namely Free Cell [1], Klondike [2] and Spider [3], all resulting in hardness proofs. Scorpion, though, has a unique twist compared to the aforementioned ones: each card can be moved to cover only one other card. This property narrows the set of possible moves and makes a worthwhile issue investigating whether the same problem proved NP-complete for Free Cell, Klondike and Spider falls in P as far as Scorpion is considered. In this paper we prove that the problem of deciding whether an n-card s-suits initial configuration of Scorpion allows for a winning sequence of moves is NP-complete for any s≥1, and it remains so also when the initial configuration contains no face down (locked) cards and under a constant ratio constraint between deck size and tableau size. As a side effect of our results, we have also succeeded in negatively answering a question posed in [2], in that we prove that it is NP-complete to decide whether an n-card one red suit-one black suit configuration of the Klondike Solitaire allows for a winning sequence of moves.
Deep learning and collaborative training for reducing communication barriers with deaf people

Building bridges to facilitate communication between deaf and hearing people is a pending issue that can no longer be postponed in a technologically advanced society. In this work, we propose a multiplatform service that allows gradually building a collaborative platform to bring the interests of deaf and hearing people closer together. The proposal consists of a client-server architecture with two main parts: a multiplatform interface for video capture and playback, and a sign language recognizer system composed of a skeleton data extractor and a Graph Convolutional Neural Network. The result is a proof-of-concept of the first thematic dictionary accessible through sign language.
Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks

In this paper, we tackle the problem of human-robot coordination in sequences of manipulation tasks. Our approach integrates hierarchical human motion prediction with Task and Motion Planning (TAMP). We first devise a hierarchical motion prediction approach by combining Inverse Reinforcement Learning and short-term motion prediction using a Recurrent Neural Network. In a second step, we propose a dynamic version of the TAMP algorithm Logic-Geometric Programming (LGP) [1]. Our version of Dynamic LGP, replans periodically to handle the mismatch between the human motion prediction and the actual human behavior. We assess the efficacy of the approach by training the prediction algorithms and testing the framework on the publicly available MoGaze dataset [2].
Engineering of an artificial intelligence safety data sheet document processing system for environmental, health, and safety compliance

Chemical Safety Data Sheets (SDS) are the primary method by which chemical manufacturers communicate the ingredients and hazards of their products to the public. These SDSs are used for a wide variety of purposes ranging from environmental calculations to occupational health assessments to emergency response measures. Although a few companies have provided direct digital data transfer platforms using xml or equivalent schemata, the vast majority of chemical ingredient and hazard communication to product users still occurs through the use of millions of PDF documents that are largely loaded through manual data entry into downstream user databases. This research focuses on the reverse engineering of SDS document types to adapt to various layouts and the harnessing of meta-algorithmic and neural network approaches to provide a means of moving industrial institutions towards a digital universal SDS processing methodology. The complexities of SDS documents including the lack of format standardization, text and image combinations, and multi-lingual translation needs, combined, limit the accuracy and precision of optical character recognition tools.
Dev-Zero: A Chess Engine

Dev-Zero is a fully functional chess engine that is used to play through various standards such as with human, self-play and also with some Universal Chess Interface (UCI) compatible chess engines. The distinguishable characteristic is that it is designed to be universally accessible, and can be concurrently played by people with different abilities and intelligence. Minimax algorithm along with alpha-beta pruning is used simultaneously with chess books to select the next move. Based on parameters like King safety, mobility of each piece, structure and centre control, the board is evaluated using a heuristic function that checks for checkmate as well as stalemate conditions using values from initialised piece square tables and calculated material score.Developers can create fascinating Graphical User Interface (GUI) and use the proposed algorithm in developing their chess games.