Reviewerly/exHarmony huggingface dataset:
Approximating to the Real Translation Quality for Neural Machine Translation via Causal Motivated Methods

It is hard to evaluate translations objectively and accurately, which limits the applications of machine translation. In this article, we assume that the above phenomenon is caused by noise interference during translation evaluation, and we handle the problem through a perspective of causal inference. We assume that the observable translation score is affected by the unobservable true translation quality and some noise simultaneously. If there is a variable that is related to the noise and independent to the true translation quality, the related noise can be eliminated by removing the effect of that variable from the observed score. Based on the above causality hypothesis, this article studies the length bias problem of beam search for neural machine translation (NMT) and the input related noise problem of translation quality estimation (QE). For the NMT length bias problem, we conduct the experiments on four typical NMT tasks (Uyghur–Chinese, Chinese–English, English–German, and English–French) with different scales of datasets. Comparing with previous approaches, the proposed causal motivated method is model-agnostic and does not require supervised training. For QE tasks, we conduct the experiments on the WMT’20 submissions. Experimental results show that the denoised QE results gain better Pearson’s correlation scores with human assessed scores compared to the original submissions. Further analyses on the NMT and QE tasks also demonstrate the rationality of the empirical assumptions made on our methods.
Towards Informative and Diverse Dialogue Systems Over Hierarchical Crowd Intelligence Knowledge Graph

Knowledge-enhanced dialogue systems aim at generating factually correct and coherent responses by reasoning over knowledge sources, which is a promising research trend. The truly harmonious human-agent dialogue systems need to conduct engaging conversations from three aspects as humans, namely (1) stating factual contents (e.g., records in Wikipedia), (2) conveying subjective and informative opinions about objects (e.g., user discussions on Twitter), and (3) impressing interlocutors with diverse expression styles (e.g., personalized expression habits). The existing knowledge base is a standardized and unified coding for factual knowledge, which could not portray the other two kinds of knowledge to make responses more informative and expressive diverse. To address this, we present CrowdDialog , a crowd intelligence knowledge-enhanced dialogue system, which takes advantage of “crowd intelligence knowledge” extracted from social media (with rich subjective descriptions and diversified expression styles) to promote the performance of dialogue systems. Firstly, to thoroughly mine and organize the crowd intelligence knowledge underlying large-scale and unstructured online contents, we elaborately design the C rowd I ntelligence K nowledge G raph ( CIKG ) structure, including the domain commonsense subgraph, descriptive subgraph, and expressive subgraph. Secondly, to reasonably integrate heterogeneous crowd intelligence knowledge into responses while ensuring logicality and fluency, we propose the G ated F usion with D ynamic Knowledge- D ependent ( GFDD ) model, which generates responses from the semantic and syntactic perspective with the context-aware knowledge gate and dynamic knowledge decoding. Finally, extensive experiments over both Chinese and English dialogue datasets demonstrate that our approach GFDD outperforms competitive baselines in terms of both automatic evaluation and human judgments. Besides, ablation studies indicate that the proposed CIKG has the potential to promote dialogue systems to generate fluent, informative, and diverse dialogue responses.
Optimizing direct normal irradiance prediction of BP neural network based on Logistic Sparrow search algorithm

Abstract With the wide application of photothermal power stations in power systems, Direct Normal Irradiance(DNI) prediction is very important to improve the economic benefits of photothermal power stations and the utilization of solar energy. The short-term prediction of wind power by traditional Sparrow search algorithm (SSA) optimized BP(SSA-BP) neural network is prone to fall into local optimum, slow convergence rate, and low prediction accuracy. A short-term DNI prediction method based on the Logistic Sparrow search algorithm (Logistic-SSA) optimized BP(Logistic-SSA-BP) neural network is proposed. This paper takes a photothermal power station in Northwest China as the research object. Firstly, the Pearson correlation coefficient is introduced to analyze the environmental data set with a strong correlation with the direct illumination radiation index as the input of the model, so as to avoid redundant data affecting the prediction of the direct illumination radiation index. Secondly, the SSA algorithm and Logistic-SSA algorithm are used to improve BP neural network. Finally, the measured historical data of the photothermal power station is used to simulate the prediction models. The simulation results show that the prediction model based on Logistic-SSA-BP has better prediction accuracy than SSA-BP, and is more in line with the production and operation requirements of the photothermal power station.
Trajectory planning for unmanned skid-steering vehicle

Abstract To make a trajectory enough to track unmanned skid-steering vehicles, a planning method is proposed in this paper. The planner consists of velocity planning, curvature planning, and path planning. Subject to the constraints of jerk and acceleration, velocity planning has five possibilities. Then taking curvature constraints into account, curvature planning has three possibilities. And according to the velocity sequence and curvature sequence, the path information of the trajectory can be obtained by the kinematic model. To verify the presented method, a simulation scenario is designed. The test result shows that the proposed method could generate a sufficiently smooth trajectory with satisfying all the constraints, including acceleration, jerk, and curvature.
A Substation Live Detection Method on Communication Protocol Data Parsing and Data-level Fusion of Neural Networks

Abstract In this paper, a data parsing method based on database communication protocol technology and a data fusion method based on the neural network are proposed, which solves the shortcomings in the data communication process of the substation, and provides an intelligent method for data analysis and fusion. The data parsing, transforming method, process, and implementation scheme based on the communication protocol is described in this paper. Firstly, the digital expression method of the data message is proposed. Secondly, the message recognition method based on the feature field is introduced. Through the analysis and heuristic of the feature fields, the protocols of the messages are determined. then the data interface relationships in the system are stored in a separate database. When the parsing process is completed, we use data pre-processing, data cleaning, data integration, and other methods to fuse the data of each source. Finally, the unstructured data fusion method based on the VGG-19 model is used to fuse the data. At the same time, this paper also describes the scheme of some principles step by step and introduces the relevant technical details.
Electrical equipment identification method in infrared images based on VGG

Abstract Temperature is widely used to detect the state of electric equipment. Almost all the data is collected and sorted. And image analyzed by manual, which cause some trouble. In order to improve the intelligence level of power equipment detection, this paper proposes an infrared scene identification method for 220 kV current transformers, including target identification, temperature extraction, and image fusion. Firstly, a semantic segmentation model is built on VGG, which can effectively extract the target temperature information in complex environments. Then combined with the method of data set expansion, 3224 images of 220 kV current transformers were used for training. Finally, an image fusion method is used to extract equipment temperature. The experimental show that the method proposed in this paper for semantic segmentation and identification of 220 kV current transformers accuracy reaches 99.68%. In contrast, the traditional CNN identification method is 44%, effectively improving the target identification efficiency of 220 kV current transformers and has a good reference for electric equipment intelligence detection.
MODERN TOOLS FOR INCREASING THE EFFICIENCY OF DISTANCE EDUCATION IN THE CONDITIONS OF DIGITALIZATION

The article considers digitalization tools as a means of improving the quality of education. An analysis of the didactic possibilities of innovative digital technologies and tools is presented. It is shown that this form of education requires the use of fundamentally new tools and mechanisms of the educational process, which, in their application, can significantly change the form, process and end results of education.
Proposal for a Maturity Continuum Model for Open Research Data

As a contribution to the general effort in research to generalize and improve the practices of Open Research Data (ORD), we developed a model conceptualizing the degrees of maturity of a research community in terms of ORD. This model may be used to assess the ORD capacity or maturity level of a specific research community, to strengthen the use of standards with respect to ORD within this community, and to increase its ORD maturity level.&#x0D; We present the background and our motivations for developing such an instrument as well as the reasoning leading to its design. We present its elements in detail and discuss possible applications.
Cohesion-Based Equivalence in Translating English Texts into Arabic: Problems and Strategies

This paper attempts to cast light on an important issue which may pass unnoticed nearly by most student translators in the process of translating English texts into Arabic. Actually, the paper is about how to cohesively reproduce equivalent target texts. The study aims at (1) distinguishing a text from a non-text. (2) highlighting the main cohesive devices in English and how they are realized in Arabic. (3) pointing out some cohesion-related problems a translator may face when rendering English texts into Arabic. The study hypothesizes that unawareness on the part of student translators of the very nature of texts as instances of language in use rather than language as an abstract system nor a sum of its constituent parts may cause them to produce incohesive renderings where they fail to combine sentences into well-organized whole. Bad translation renders a text into a non-text in that it seems foreign even though no faulty vocabulary was found and no apparent grammatical mistakes were detected. The study concludes that nearly all the student translators failed to produce unified and cohesive texts since they transferred the cohesive devices of the source text which do not go along with the norms of writing the target text.
Online Teaching - Learning During the Pandemic Coronavirus Disease (Covid-19)

At the end of 2019, the world community was shocked by the outbreak of a new virus, namely the coronavirus disease.In Indonesia itself, various efforts have been made to prevent the spread of the virus, including by implementing Large-Scale Social Restrictions or PSBB.The implementation has an impact on several sectors, including education.Learning that was initially carried out faceto-face has been replaced with online learning.STMIK Rosma, as one of the tertiary institutions, applies online learning or online teaching-learning.By using various tools and methods, it turns out that running online teaching-learning has various obstacles.This study aims to determine how online teaching-learning was during the COVID-19 pandemic and identify the online teaching-learning platform often used and considered the best during the Covid-19 pandemic.This study uses qualitative and quantitative methods (mixed methods) with qualitative grounded theory and quantitative percentages.The analysis results show that the perception of the resource persons about online learning at STMIK Rosma is quite good, and the online learning platforms used and considered the best are Moodle, Google Meet, and YouTube.The results of this study are expected to help STMIK Rosma improve online learning in the future.
Publications of Toba Bataknese Culture (Multimodal Analysis of ZONABATAK Instagram Account Uploads)

The existence of social media, such as Instagram, Facebook, and Twitter, makes it easier to promote and introduce a product or work, whether for personal, group, ethnic, or national interests.Especially in a pandemic situation, literary works and regional (traditional) arts and culture, usually shown up in open performances and concerts, cannot be expected to recognize and promote the traditional arts and culture.The Toba Batak tribe, one of the minor tribes in Indonesia, is very significant in culture, literature, art, and local wisdom.By the existence of social media, especially in this study, Instagram can maintain its existence, recognition, and interest in the culture and arts.This study was applied using a qualitative analysis method with a study approach of several uploads on the Instagram account of "Zona Batak".The results of this study are seen in terms of effectiveness in terms of the number of likes and impressions assessed in terms of placement, semiotics, and messages of local wisdom.
Research on Classroom Teaching Leadership in Colleges and Universities under the Background of Multimedia Technology

In order to further improve the teaching effect of college classrooms, this paper analyzes the main performance of college teachers' classroom teaching leadership under the background of multimedia technology.Using the quadrant model to analyze the performance of classroom teaching leadership in colleges and universities, the final conclusion is that classroom teaching leadership in colleges and universities under the background of multimedia technology is actually a kind of teacher personality influence.This influence is mainly reflected in the love for students, but also includes normative requirements from the teaching profession.This provides a feasible idea for effective classroom teaching in colleges and universities under the background of multimedia technology.
Design and Application of MOOC Teaching Platform for College English Based on MOODLE System

In order to promote the informatization reform of college English education in China, and solve the problems existing in current English teaching, such as single teaching form, limited communication between teachers and students, and insufficient environmental conditions for practicing listening and speaking skills, this paper takes the online teaching mode of MOOC as a reference, and establishes a college English online teaching platform based on moodle system using PHP language and MySQL database.The platform has developed functional modules of online classroom, listening practice, oral practice, homework questions and communication forum.It effectively enriches the English teaching methods in colleges and universities in China, provides online channels for students and teachers to answer English questions, and makes up for the lack of environment for practicing English listening and speaking ability in colleges and universities in China, thus further helping colleges and universities to improve the quality of college English teaching.
A Meta-learning Approach to Recommend the Meta-heuristic Algorithm Based on Instance Features

This paper studies a framework for implementing meta-heuristic algorithm selection based on meta-learning approach, which is used to recommend the most suitable meta-heuristic algorithm for different problem instances instantly.Therefore, a small sample of instances for capacitated vehicle routing problem (CVRP) is selected as an experimental data set, artificial bee colony, particle swarm optimization, ant colony, artificial fish colony and genetic algorithm which are selected as the recommended algorithms.This study establishes the classification label corresponding to the problem and the algorithm by running the optimization algorithms.The meta-knowledge base corresponding to the feature and the label is generated by extracting a set of instance features.When a new instance is given, its features only need to be extracted to recommend an algorithm.In the process, three meta-learning algorithms of random forest, BP neural network and K-nearest neighbor are used to train meta-model, and comparative analysis is applied.The experimental results show that the average recommendation accuracy is about 80%.The algorithm recommendation framework based on instance features can be extended to similar applications.
Data Mining for State Evaluation and Analysis of Thermal Power Generators

At this stage, with the progress of industrial science, my country's thermal power units are constantly supplementing the shortcomings, the total installed capacity has increased, and some power plants that have been eliminated by the times and are not conducive to environmentally friendly development have been shut down.The thermal power units currently in use are not only safe and reliable, but also attach great importance to intelligent power generation and refined tubes, especially the maintenance and management of thermal power generators.At present, a new data mining technology can be applied to the condition maintenance of thermal power units.This technology obtains potential information from data to help power plant personnel identify the operating status of the unit, so as to achieve better operation and maintenance of thermal power generating units.Based on this It can improve the economy and reliability of thermal power generators and promote the improvement of the intelligent level of power plants.
How Data Security Could Be Achieved in The Process of Cloud Data Governance?

Cloud computing has been widely deployed and consumed across different application domains.It has been defined as a shared pool of computing resources.In cloud computing, data governance plays a critical role in enhancing overall performance and ensuring data security.This paper analyzes how to achieve data security in the scope of data governance regarding the organization structure and existing techniques by reviewing related research findings.The second part discusses security's role in an organization's data governance framework.The third part analyses the specific security risk and the expected risk mitigation methods reviewed.
The Critical-Period Hypothesis and its Implications from Western Scholars

The Critical Period Hypothesis is still controversial recently, so it cannot be said to be absolutely confirmed to be false. It is more important to realize the significance of application. Although the exact properties of this phenomenon remain unclear and the literature is divided, more and more Chinese mothers choose to teach English or other languages as a second language to their children from a very early age (0-3 years old). This paper discusses whether these attempts can help children achieve the future goals set by their parents through literature review. The results attempt to support the existence of the Critical Period Hypothesis, because from the end of adolescence, the ability to learn grammar declines sharply, and the ability to reach native-like proficiency depends largely on the age at which learning begins.
Kernel Choice in One-Class Support Vector Machines for Novelty Detection

This work concentrates on novelty detection, a semi-supervised learning problem concerned with deciding if the new observation is sufficiently different from the ones seen so far.This paper mainly considers a variant of the support vector classification approach, which estimates the contours of the distribution of the initial observations and then can be used to decide if the new observations are abnormal.We try to estimate a negative function on the outlier points in the input space and a positive on the complement.A kernel expansion gives this decision function.The effectiveness of this kernel method is closely related to the choice of kernel functions and hyperparameters.Due to the demand for general and effective hyperparameter selection regulations, we investigate three approaches, including GridSearch in Python, median heuristic and Bayesian kernel learning.Some relevant experiments are performed in this paper.According to the experiments, we have learned that the choice of kernels and parameters can greatly influence the detection result.
Research on Teacher Ability Improvement in Higher Vocational Colleges under the background of new engineering

The new engineering construction is a major strategic choice of higher education reform and innovation. In order to comprehensively promote the construction and reform of new engineering, the improvement of teachers’ core competence is the key. Teachers are the main force in the construction of new engineering. Both discipline construction and talent innovation training need a group of high-level teachers with excellent informatization vocational ability, strong ideological and political ability, high innovation and entrepreneurship ability, and strong teaching and research ability. Based on the construction of new engineering, this paper expounds the ability of teachers in higher vocational colleges and the significance of improving teachers. Through the analysis of the practical problems facing the ability improvement, explore the path of ability improvement, and promote the high-quality development of vocational education.
Research on the integration of production and education of new business talents training under the background of digital economy

With the advent of the digital economy era, people’s production and life style has undergone great changes, and the new business has emerged at the historic moment. The cultivation of business talents has become a common concern of foreign scholars. By exploring the current situation and problems of the development and construction of the integration of middle class and education in the cultivation of new business talents, this paper puts forward feasible suggestions and countermeasures to provide action reference for the cooperation between universities and industrial enterprises.
Research on the implementation of 1+X Certificate System in Vocational Education reform

Since February 2019, “National Vocational Education Reform implementation program” sounded the 1+X certificate system reform horn. The 1+X certificate system is an innovative design to promote the transformation of the talent training mode in vocational education. It impels the reform of the type development of vocational education, and it is also related to the promotion of the overall quality of talents in the future and the establishment of the economic modernization system in our country. In the view of the fuzzy-conflict theory, this paper reflects the causes of the present situation with the established ought-to-be model of policy implementation, and obtains the causes of the present situation by grasping the overall development direction. At present, the problems of resource passivity, planning backwardness and insufficient function lead to the suspension of reform, and the difficulty of path dependence is difficult to surmount. In order to Kalanchoe pinnata the floating 1+X certificate system, this paper combines the practice patterns and their causes to return to the three dimensions of resources, planning and function, based on the relationship between top-level design and bottom-level promotion, concept remolding and action breakthrough, supply optimization and demand matching, to construct the 1+X certificate system, which is coordinated from top to bottom, internal and external, left and right, to optimize the implementation practice strategy system.