Reviewerly/exHarmony huggingface dataset:
Research on Lightning Point Location of Transmission Line Based on Improved Two-Terminal Traveling Wave Method

In the southern region, lightning strikes occur frequently, causing serious harm to the stable and safe operation of the power grid. In order to strengthen the lightning protection level of the line and save investment, it is necessary to strengthen the insulation of poles, towers, lines and adjacent parts those are frequently struck by lightning, especially in hilly and mountainous areas. In Sichuan, Yunnan and Shenzhen and others regions to install a lot of lightning overvoltage online monitoring device, on this basis, put forward based on the improved double terminal method of travelling wave transmission line lightning positioning technology, used for statistics on a line by lightning tower or adjacent lines of by lightning overvoltage waveform on both ends of the acquisition, to extract the first time, calculate the time difference, and have been identified by lightning tower measured time take difference divided by the average of double tower wave propagation time of adjacent tower by direct lightning and reference tower distance of implementation of lightning orientation. Through the detection of measured data, it has good precision and good application prospect. paragraph.
Research on Statistical Model of Japanese-Chinese Name Translation for Japanese Kana

Abstract In view of the lack of bilingual resources in Japanese-Chinese translation, and the naming entity of Chinese character naming entities, especially Japanese pseudonyms, it is difficult to use, so the Japanese-Chinese pure pseudonym translation is a statistical translation model. The model has been applied so far and there have been many problems. The first is limited by the size and quality of the parallel corpus, and the second is the lower accuracy of the translation. Based on this, the paper proposes a statistical model of Japanese pseudonymous Japanese name translation based on inductive learning method. This method can extract Japanese and Chinese named entities from Japanese and Chinese corpora, and convert them into Roman alphabet and Pinyin sequence. Example screening after similarity calculation. Then, the instance-based induction learning method is used to automatically obtain the Japanese-Chinese transliteration rule base of the named entity, and iteratively reconstructs the transliteration rule base through feedback learning. Through experimental research, it is found that the Japanese-Han name translation statistical model method for Japanese kana is simple and efficient, and the translation accuracy is overcome while overcoming the dependence on bilingual data.
Research on the Application and Development Trend of Automation in Mechanical Manufacturing

Abstract With the development of science and technology, automation is increasingly used in industrial production, which has greatly improved production efficiency. Based on this, this article studies the application and development trend of automation in mechanical manufacturing. First of all, automation was summarized, its concept, basic characteristics and development status were introduced, and then the application of automation technology in machinery manufacturing was analyzed, including application principles and practical applications. Next, we explained the development trend of automation in the field of machinery manufacturing, and then several problems in the current development were explained, and finally we put forward corresponding suggestions based on the above analysis. The application of automation technology has great advantages in cost control and improving the core competitiveness of the market. To ensure its long-term development, we must attach importance to the introduction of new technologies and the optimization of production quality.
A comparative study on heterogeneous information network embeddings

Heterogeneous information networks are widely used to represent real world applications in forms of social networks, word co-occurrence networks, and communication networks, etc. However, It is difficult for traditional machine learning methods to analyze these networks effectively. Heterogeneous information network embedding aims to convert the network into low dimensional vectors, which facilitates the following tasks. Thus it is receiving tremendous attention from the research community due to its effectiveness and efficiency. Although numerous methods have been present and applied successfully, there are few works to make a comparative study on heterogeneous information network embedding, which is very important for developers and researchers to select an appropriate method. To address the above problem, we make a comparative study on the heterogeneous information network embeddings. Specifically, we first give the problem definition of heterogeneous information network embedding. Then the heterogeneous information networks are classified into four categories from the perspective of network type. The state-of-the-art methods for each category are also compared and reviewed. Finally, we make a conclusion and suggest some potential future research directions.
Methodology to define an integration process between frameworks SCRUM, Django REST framework y Vue.js, implemented for software development, from quality management approach and agility

Abstract At present, the industry has great challenges for the construction and maintenance of software, in terms of quality, costs and time, for this it is significant to have a methodological framework, which allows the possibility of having effective and practical methods, applying programming techniques from an agile approach, which guarantees the construction of quality code from the beginning of software development. This study analyzes the integration between frameworks SCRUM [1], Django REST framework [2] and Vue.js [3], which are commonly implemented in isolation for the execution of software development projects, and components are also incorporated of the IEEE 830 standard defined for the specification of software requirements [4], and of the ISO 25000 standard for software quality assessment [5]. Likewise, the characteristics of each study framework within the software architecture are analyzed, establishing of traceability between them and obtaining as a result a methodology for the definition of a process map; where each framework is integrated and it is described the procedure for structuring the planning, development and delivery phases. Thus providing a guide for the application of programming techniques from the concept of agility and quality management, from the process of integration of the frameworks analyzed in this study.
Towards an ontology for designing cycling routes in the city of Puerto Madryn

Increasing urban cycling rates has become a main objective of mobility policies due to its contribution to social equity and sustainable urban travel. This objective can only be achieved if the activity is perceived as safe and convenient. The design of cycling routes projecting this perception depends on knowing how people move in the city and what the city looks like. However, acquiring such knowledge is challenging since it is distributed in an open set of heterogeneous data sources belonging to a variety of organizations. This paper documents the first step in an ontology-based data access (OBDA)approach to this problem, i.e., it describes the development of an ontology to support the design of cycling routes in the city of Puerto Madryn. We assume a set of competence questions that urban designers are likely to formulate in this context to understand the mobility needs of Puerto Madryn's inhabitants and the different alternatives they have to design cycling routes covering such needs. We derive concepts, relations, attributes and instances from these questions and characterize them using natural language. We specify the resulting ontology using Graphol, a graphic language for OWL 2 developed as part of an OBDA tool stack.
Application of image content feature retrieval based on deep learning in sports public industry

The image content retrieval can effectively promote the development of the entire industry. At present, sports competition is becoming more and more fierce, and the requirements for image content retrieval are getting higher and higher. In this paper, research has been carried out on image descriptor generation, image feature quantization and coding, accurate nearest neighbor cluster center fast search, multi-dimensional inverted index construction and fast retrieval. Moreover, based on deep learning, this paper constructed an effective detection algorithm for the characteristics of sports images, and compared the image shape and color as examples. It can be seen from the comparative study that the research method of this paper can effectively reduce the size of the candidate set of query results without affecting the accuracy of the query, which is of great significance for improving the speed of image query and has certain significance for promoting the development of sports public industry.
The effect of Indonesian folktales on fourth-grade students’ reading comprehension and motivation

The low level of fourth-grade students’ reading comprehension affects their learning motivation in elementary school. To overcome these problems, this study is aimed: first, to develop indigenous folktales picture books; second, to investigate the effect of Indonesian folktales on students’ reading comprehension and learning motivation. This study used a quasi-experimental design. The participants were 49 fourth-grade students divided into experimental and control groups. The study was conducted at two public Indonesian elementary schools. The Reading Comprehension Test (RCT) and the Motivation to Learn Scale (MLS) were administered as pre-and post-tests. To analyze data, independent and paired t-tests were used at .05 significance level. The results showed that experimental group students had higher scores on both dependent variables compared to their counterparts. It indicated that Indonesian folktales picture books significantly promote reading comprehension and motivation of experimental group students compared to control group students.
Designing Ambient Wanderer

Recommender systems are widely integrated into our everyday activities. These intelligent systems succeed in learning the user's profile to recommend movies, music, news and more. However, for designing context-aware recommendations, new challenges emerge in predicting the situational needs of the user. We prototyped Ambient Wanderer, our personalised and contextualised Point-of-Interest (POI) recommender system and experimented it with new locals, people who have recently relocated to a city. Our key findings include: sudden breakdowns during urban exploration, trust issues with the recommendations from people unlike them, feeling bored as the trigger to POI search, intent to find free activities, information needs on areas-of-interest beyond points-of-interest and the demand to build a new social life. For each of these needs, we present the implications to design mobile recommendations for urban exploration.
Underwater image restoration based on perceptually optimized generative adversarial network

Underwater images are degraded due to the complexity of the underwater environment and medium scattering and absorption. The image degeneration includes low contrast, color distortion, and blur, which makes the task of underwater vision difficult. A perceptually optimized cycle consistency generative adversarial network (CycleGAN-VGG) is proposed to restore distorted underwater images. We adopt the framework of CycleGAN so that our method does not require pairs of distorted underwater images and corresponding ground truth images for training. Inspired by perceptual loss, we use a combination of perceptual loss, cycle-consistent loss, and adversarial loss. The multiterm loss functions guarantee that the output has the same content and structure as the input while color looks like ground truth images. In an objective assessment, our method is significantly higher than other methods in terms of colorfulness metrics and contrast metrics. It confirms that our method effectively restores the color of underwater scenes and enhances some image details and shows a superior performance when compared to other state-of-the-art methods.
Using Semantic Networks for Question Answering - Case of Low-Resource Languages Such as Swahili

Question Answering is an important aspect of language processing. While much research has been done for other languages, low-resourced languages such as Swahili have not been studied much, despite it being an important language spoken in East Africa. Due to lack of language resources, we rely on syntactic analysis of raw text to create rules that extract Subject-Predict-Object to then create a semantic network of Swahili, since Swahili is Subject-Verb-Object (SVO) language. We then use SPARQL to query the network. We got our test corpus from public websites with Swahili Frequently Asked Questions (FAQ) and some Swahili reading comprehension story books. Results for story books show that the semantic network can correctly answer on average three of the five set questions for each sample text. Shortcomings of our method is mainly related to inadequacies of preprocessing tools, where further research is needed. Our method should be applicable in creating networks for any low-resourced language that follows the SVO language pattern, which can then be queried.
Research on the inheritance and protection of data mining technology in national sports

Summary In order to improve the ability of inheritance and protection of national sports, the association data mining model of inheritance and protection of national sports is proposed based on feature extraction of association rules. In this article, a new neural fuzzy normal form system is designed, which can perform both feature analysis and SI(sine) integration. The time series analysis model of the inheritance and protection correlation data in the national sports are constructed, the overall frame model of the inheritance and protection correlation data mining in the national sports is designed. The ability of adaptive data fusion and feature registration is improved. The simulation results show that the proposed method has good accuracy and strong ability of feature matching in the correlation data mining of inheritance and protection in national sports, and it can improve the function of inheritance and protection of national sports.
The Exploration of Government as a Service Through Community Cloud Computing

The current article discusses the concept of government as a service through the use of cloud computing which is managed from a public community, such as the citizens of a municipality. It suggests a potential architecture of a gamified community cloud in order to transform cloud computing management to an enjoyable and valuable experience. The architecture includes the development of an on-site community and an outsourced community cloud computing infrastructure. Moreover, additional discussion in this article includes the potentiality of cloud computing as a tool towards the succesful operation of a self governed community.
Injecting Challenge or Competition in a Learning Activity for Kindergarten/Primary School Students

English and Mathematics have always been considered important subjects in schools all over the world but they can also be subjects that people in certain parts of the world fear. A recent poll done by OECD (Organization of Education and Community Development) shows that in certain countries like Tunisia, Argentina, Brazil and Thailand, teenagers have been found to have the “highest levels of Mathematics anxiety”. Similarly, English is a difficult subject for East Asians (Japanese, Chinese) as there is too much emphasis on grammar in local classrooms with very little time devoted to actual conversational practice by learners. With today’s technology, we can look towards mobile technology in the form of mobile educational game or application. The mobile educational game is one approach that can be beneficial for enhancing pre-school and elementary school education in the world as virtually everyone now owns a mobile smartphone. One will find that current educational games are rather boring and that is because the games were created with a random selection of theme, content and mechanics. This paper attempts to prove that by injecting competition or challenge in a learning activity elevating it to the level of mobile educational game and coupled with the appropriate theme, content and mechanics will lead to better learning experience for kindergarten/primary school students. An experimental study has been carried out and the results are evaluated and discussed at the end of this paper.
VRMouse: Mouse Emulation with the VR Controller for 2D Selection in VR

Selection is one of the fundamental tasks of user interaction in VR. Currently, the most popular selection technique is ray-casting, but whether ray-casting is the most appropriate selection method for 2D selection in VR is questionable due to its inherent limitations. To address the issues associated with ray-casting, we proposed the VRMouse, a new technique that emulates the mouse with the VR controller. VRMouse transforms the VR controller into a mouse by restricting its six degrees of freedom to two and simulating 2D desktop-like cursor control. We evaluated the technique against ray-casting in an experiment based on ISO 9241-9. Results indicated that VRMouse excelled over the ray-casting, in terms of accuracy, comfort, and satisfaction. The findings of this preliminary study imply that the proposed VRMouse technique could enhance 2D selection performance in VR.
Cross-Domain Deep Face Matching for Real Banking Security Systems

Ensuring the security of transactions is currently one of the major challenges that banking systems deal with. The usage of face for biometric authentication of users is attracting large investments from banks worldwide due to its convenience and acceptability by people, especially in cross-domain scenarios, in which facial images from ID documents are compared with digital self-portraits (selfies) for the automated opening of new checking accounts, e.g, or financial transactions authorization. Actually, the comparison of selfies and IDs has also been applied in another wide variety of tasks nowadays, such as automated immigration control. The major difficulty in such process consists in attenuating the differences between the facial images compared given their different domains. In this work, in addition to collecting a large cross-domain face dataset, with 27,002 real facial images of selfies and ID documents (13,501 subjects) captured from the databases of the major public Brazilian bank, we propose a novel architecture for such cross-domain matching problem based on deep features extracted by two well-referenced Convolutional Neural Networks (CNN). Results obtained on the dataset collected, called FaceBank, with accuracy rates higher than 93%, demonstrate the robustness of the proposed approach to the cross-domain face matching problem and its feasible application in real banking security systems.
Application To Measuring Food Business Opportunities For The Development Of Smart Cities

Abstract In the development of technology, more people are using products from technology development to facilitate their daily activities especially in food industry. However, to measuring and determine opportunities when opening a food business covering food material and type of culinary. People usually choose what food ingredients are of public interest and estimates materials and cost to determine a business opportunity. In this situation a Decision Support System (DSS) can be developed, namely apus-kul is a decision makers by using certain data and models to solve some unstructured problems using Simple Additive Weighting (SAW) algorithm. This system is expected to be a means to assist the community when making decisions to choose materials and food businesses for the development of smart city governance systems.
Deep Web Selection Based on Entity Association

Each subject has a vast amount of deep web resources nowadays. It becomes tremendously hard to retrieve the required integrated information from all the related deep web resources for a subject, which drives out the technology on how to select the suitable web resources. In the medical field, the associations among entities are very extensive, as it can improve our overall level of health when all the related association information can be integrated and used. To improve the entities and their associations integration efficiency, we provide a data resource selection method based on the entities and their associations. The entity and association abstract matrix used in the method is built on the weighted entity scores and their association information in the entity and association diagram. Also, based on the entity and association query intention, it provides a new way to calculate the data resource association. After a large amount of tests and experiments on the data sets in the medical field, the new method is shown to provide a better precision and recall, and it can greatly support the study of information integration in the medical field.
A note on linearly constrained Bayes estimator in elliptical models

A constrained Bayes estimator is established for the location parameter of an elliptically contoured distribution, when it is suspected that some linear constraints may hold on the parameter space. Under Jeffrey’s prior, the posterior distribution is proposed and the exact form of the constrained empirical Bayes estimator is derived for a general form of balanced loss function. Performance of the estimators is discussed using bootstrap simulation and a real data is analyzed to show the applicability of the proposal.
Joint Multi-Dimensional Model for Global and Time-Series Annotations

Crowdsourcing is a popular approach to collect annotations for unlabeled data instances. It involves collecting a large number of annotations from several, often naive untrained annotators for each data instance which are then combined to estimate the ground truth. Further, annotations for constructs such as affect are often multi-dimensional with annotators rating multiple dimensions, such as valence and arousal, for each instance. Most annotation fusion schemes however ignore this aspect and model each dimension separately. In this article we address this by proposing a generative model for multi-dimensional annotation fusion, which models the dimensions jointly leading to more accurate ground truth estimates. The model we propose is applicable to both global and time series annotation fusion problems and treats the ground truth as a latent variable distorted by the annotators. The model parameters are estimated using the Expectation-Maximization algorithm and we evaluate its performance using synthetic data and real emotion corpora as well as on an artificial task with human annotations.
Collaborative Filtering Recommendation Algorithm Based on User Preferences

Abstract The personalized recommendation technology provide people an effective method to solve the problem of information overload. Collaborative filtering is one of the key algorithm of recommendation technology, In this paper, the User-Movie (UM) model based on multilayer perceptron algorithm is a kind of item-based collaborative filtering recommendation algorithm. Therefore, the paper puts forward a new method to integrate item similarity in the UM model. Using Jaccard to find the similar items of current item, respectively. Experiments on the well-known datasets Epinions and Movielens show that the algorithm weighted by Jaccard in the case of sparse datasets and less neighbor achieves great improvement of prediction accuracy.