Reviewerly/exHarmony huggingface dataset:
Intersection Point Determination of Parabolic Antenna Reflectors Using Close-range Photogrammetry

In the construction of complex antenna structures, a number of feature points are inevitably formed on the main reflector surface of the antenna structure by bolting, riveting, cabling, and welding.In this study, the intersection point determination method using close-range photogrammetry was investigated.3D position coordinates of many feature points distributed on the inner surface of a parabolic reflector were extracted using close-range photogrammetry.The fitting lines were estimated for the feature points arranged in the radial direction of the parabolic reflector, which tend to intersect at the center point of the reflector.The intersection point between the estimated fitting lines was calculated.The numerical variation of the intersection points on the three axes, which are accumulated through regular time-series calculations, can be used to intuitively predict and analyze not only the deformation rates but also the deformation directions for the shape of the parabolic reflector.
Empirical Study of DiachronicWord Embeddings for Scarce Data

Word meaning change can be inferred from drifts of time-varying word embeddings.However, temporal data may be too sparse to build robust word embeddings and to discriminate significant drifts from noise.In this paper, we compare three models to learn diachronic word embeddings on scarce data: incremental updating of a Skip-Gram from Kim et al. ( 2014), dynamic filtering from Bamler and Mandt (2017), and dynamic Bernoulli embeddings from Rudolph and Blei (2018).In particular, we study the performance of different initialisation schemes and emphasise what characteristics of each model are more suitable to data scarcity, relying on the distribution of detected drifts.Finally, we regularise the loss of these models to better adapt to scarce data.
IMPLEMENTASI ALGORITMA HORSPOOL PADA APLIKASI ISTILAH FASHION

The word fashion is synonymous with fashion, in today's digital era fashion has become a lifestyle that can introduce personality through fashion on social media. In the term fashion, there are many terms that form the basis of knowledge to match one's fashion. The term fashion is generally studied using foreign languages so that many ordinary people find it difficult to understand in more detail related to fashion terms so that there is a need for applications that adopt science related to the term fashion. An application that was built with the main objective to search for information such as fashion terms is inseparable from the existence of string matching. String matching can be interpreted as an activity that works to match something that is sought in a container with the contents stored in the container. Algortima string matching which is implemented to search for the meaning of the term fashion in this study is the Horspool algorithm. The horspool algorithm is a boyer-moore derivative algorithm, to go through the process when there is a mismatch of character patterns with text characters, the horspool algorithm uses bad-matches repeatedly until all the character patterns are found to have a match against the text. The results of the application built in this study can facilitate ordinary people who have difficulty finding information related to fashion terms more easily.Keywords: Implementation, Fashion Terms, Algorithms, Horspool
A Study of College English Teaching Reform in Application-Oriented Undergraduate Colleges and Universities under the Background of New Engineering

This article, through the methods of logical analysis, expert interview and questionnaire survey, combs the deficiencies in the process of College English teaching reform in the Application-oriented Undergraduate Colleges and Universities under the background of new engineering, and strives to put forward the improvement strategies in the reform of College English Teaching in the Application-oriented Universities Based on the theories of pedagogy, management and other disciplines, so as to make it continuously innovate for the education of our country in the future Progress helps.
Probability Distribution Based Resource Management for Transient Resource

Co-locating latency-critical jobs and batch jobs is an effective way to increase utilization and reduce costs in modern datacenters. The idle resources of latency-critical jobs can be used to run batch jobs, but these transient resources must be revoked whenever latency-critical jobs require them again for guaranteeing their strict quality of service (QoS). The main challenge of this approach is interference between colocated jobs on transient resources. We propose a transient resource management scheme based on probability distribution. Our resource management scheme consist of two components: Tail Latency Aware Allocation (TLAA) and Cost-Effective Resource Revocation (CERR). We character the resource consumption and the completion time of the task of batch jobs and get the probability distribution function of them. Based on the probability distribution of the task resource consumption, we design TLAA to use transient resources efficiently while ensuring tail delay requirements. Based on the probability distribution of the task completion time, we design CERR to revoke transient resources with minimal eviction costs, thereby reducing the impact of resource revocation on batch tasks. Experimental results demonstrate the efficiency of our resource management. TLAA can improve the utilization of transient resources and reduce the completion time of batch jobs to varying degrees under different tail delay requirements. CERR can reduce the impact of resource revocation on batch jobs, and perform better than existing resource revocation strategies.
Factors Influencing Research Productivity of Private Higher Education Institutions Lecturers in Kopertis Region XII

A researcher is said to be productive after publishing their findings.The analysis of the Science and Technology Index portal, called SINTA, shows the productivity of researches of private higher education institutions lecturers in Kopertis Region XII Maluku and North Maluku is still very low.The ranking of private higher education institutions in Kopertis Region XII compared with other universities in Indonesia further strengthens the opinion.This research aimed to determine the factors influencing the research productivity of private higher education institutions lecturers in Kopertis Region XII.The method used was an online survey on 155 samples of lecturers in Maluku and North Maluku.The data obtained were analyzed statistically descriptive.The results showed that research productivity is influenced by individual and institutional factors.Individually, the biggest driving factor is the perception of the function and benefits of research for human life, while the smallest one is the desires to be famous and financial benefits.The biggest inhibiting factor is the limited cost of research and publication, while the smallest is the motivation to research and publish.Institutionally, library facilities are deemed inadequate, thus contributing less to the increase of research productivity.
Automatic Spelling Detection and Correction in the Medical Domain: A Systematic Literature Review

Automatic spelling correction is one of the most important problems in natural language processing. Its difficulty increases in medical corpora, due to the intrinsic particularities that have these texts. These features include the use of specific terminology, abbreviations, acronyms and the presence of writing errors. In this article we present a systematic review of the literature on automatic spelling detection and correction for the medical domain. There are many works on detection and automatic correction, but there is no review delving into the process of automatic correction in the medical domain. Therefore, we intend to synthesize all the existing information on this research topic and the types of studies that have been carried out to date. We present the main techniques and resources, and finally also the limitations and specific challenges. The results reflect the importance of compiling an exhaustive dictionary. In addition, the results show the ordinary use of distance algorithms of spelling and phonetic similarity, as well as with statistical techniques. The improvement of performance in recent years is especially relevant because of the use of context-based methods, such as linguistic models or neural embeddings.
Surface Temperature and NDVI Behavior Analysis in September in Manaus/AM City

The city of Manaus has been going through a process of accelerated and disorganized urban growth, which causes situations that characterize many challenges and environmental problems.Among these, we highlight the change in the local climate, causing the so-called Urban Heat Island (UHI) that arises in the area of the metropolitan region and is characterized by the rise in surface temperature when compared to the predominant temperature in the regions of urban sites.This phenomenon relates to anthropic activities on the surface and its consequence in the lower troposphere, which results in the increase in average temperatures and the number of hot days, intensifying thermal discomfort in the population.Thus, the objective of this work is to analyze the evolution of the heat island in the geographic space of the city of Manaus in the last 10 years, using remote sensing data, to estimate the values of surface terrestrial temperature, using Thermal maps generated from composites of AQUA satellite images, MODIS sensor: Surface Temperature and NDVI from THE LPDAAC (Land Processes Distributed Active Archive Center, NASA.Based on the data and analyses performed, it was possible to see an increase in temperature with a temporal variation in the years 2010 to 2019 as well as the expansion of warmer spaces, which invigorates the influence of UHI in the amazonense metropolis.
Voice Identification Using Neural Network And Mel Frequency Cepstrum Coefficients

Voice as a communication media for human and computer communication is developed by using voice recognition/identification technology.One of its applications is differentiate between male and female voices.Some speech recognition research have been proposed in disseminating male and female voices, this paper performs male and female voice extraction by using Mel Frequency Cestrum Coefficients (MFCC) as the characteristic vector in back propagation artificial neural network (ANN).The weight change cycle or EPOCH (Exponential Decay) is used to initialize male and female voice identification by using multiple recorded voices.As results, the female voice identification is better than of male with error less than 1.
Graph-Based Clustering via Group Sparsity and Manifold Regularization

Clustering refers to the problem of partitioning data into several groups according to the predefined criterion. Graph-based method is one of main clustering approaches and has been shown impressive performance in many literatures. The core issue of graph-based clustering is how to construct a good adjacency graph. A large number of works employ the sparse representation of data as the similarity measure by ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization. However, due to the flat nature of the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm, such methods solve the sparse representation of each data point individually, which do not take into account the global structure of data. To exploit the global and essential structure in data, in contrast to existing methods, we propose to learn a graph with group sparsity. To incorporate more information into the graph, we also use the manifold regularization with adaptive similarity during the process of group sparse self-representation. The resulting model is handled by Alternating Direction Method of Multipliers (ADMM). Further, we employ Iterative Re-weighted Least Squares (IRLS) algorithm and threshold operator to solve the ADMM subproblems. Experimental results on real-world datasets demonstrate the superiority of our method compared to the competing clustering methods.
Attention-Aware Invertible Hashing Network

In large-scale image retrieval tasks, hashing methods based on deep convolutional neural networks (CNNs) play an important role due to elaborate semantic feature representation. However, they usually progressively discard information during feature transformation, thus leading to incomplete and unsatisfactory hashing codes for image retrieval. This study tries to design an invertible architecture to maintain image information, meanwhile focus on necessary parts of image features. Consequently, in this paper, we propose a novel attention-aware invertible hashing network (AIHN) for image retrieval. By invertible feature representations, the final hash codes can be completely obtained from input images without any information loss. For highlighting informative regions, we present a novel attention-aware invertible block as the basic module of AIHN, which can promote generalization ability by spatial attention mechanism. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of our invertible feature representation on hash code generation, and show the promising performance on image retrieval of our methods against the state-of-the-arts.
ENHANCING UNIVERSITY STUDENTS� ENGLISH WRITING SKILLS ON CONTENT AREA

Writing, as an instrument of communication, is nowadays accomplished mostly in English to ensure information understood globally via digital platforms. This creates a transformation in job fields into utilizing technologies to textually deliver messages. Therefore, it is vital to generate high qualified future employees competing in the work places. Accordingly, university students must be equipped with English writing competencies as well as strategies focusing on content area, in addition to forms, so as to promote meaning-making concerning critical and logical thinking skills, besides to comprise comprehensive realization. Thisqualitative research utilized a critical literature review by conducting in-depth data collection, organization, integration, and classification of writing strategies. It offers suggested maneuvers to overcome higher education learners writing problems: lack of content maturation practices, through implementing collaborative writing discussions with either or both verbal or / and online discussions. It can as well integrate (intensive / extensive) reading and writing instructions in contextual cognitive processes concentrating on intellectual meaning development.DOI: 10.24071/llt.2019.220202
A Shift-Reduce Dependency Parser Based on Reinforcement Learning

Dependency parsing is a task of extracting the relationships between words in a sentence. Researchers have achieved great success in transition-based and graph-based methods recently. However, there are problems of error propagation and high time complexity. This paper proposes dependency parser based on reinforcement learning to improve transition-based parser. We regard the actions in transition-based method as RL agentâ€™s actions. Meanwhile, we introduce a BACK action, so that the agent can get back to the previous state after entering the error state. We use the Universal Dependencies dataset to make experiments on different language. Experiments show that this method effectively solves the problem of error propagation of transition-based method and improve the accuracy significantly.
Concept Factorization with Optimal Graph Learning for Data Representation

In recent years, concept factorization methods become a popular data representation technique in many real applications. However, conventional concept factorization methods cannot capture the intrinsic geometric structure embedded in data using the fixed nearest neighbor graph. To overcome this problem, we propose a novel method, called Concept Factorization with Optimal Graph Learning (CF_OGL), for data representation. In CF_OGL, a novel rank constraint is imposed on the Laplacian matrix of the initial graph model, which encourages the learned graph with exactly c connected components for the data with c clusters. Then the learned optimal graph regularizer is integrated into the model of concept factorization. Therefore, this learned structure is benefit to the clustering analysis. In addition, we develop an efficient and effective iterative optimization algorithm to solve our proposed model. Extensive experimental results on three benchmark datasets have demonstrated that our proposed method can effectively improve the performance of clustering.
Development of an intelligent UAV path planning approach to minimize the costs in flight distance, time, altitude, and obstacle collision

This paper presents a UAV path planning approach to consider flight cost functions. The UAV flight paths are generated using quintic Hermite interpolation. These paths are constant in speed, and the algorithm generates the paths by iteration to ensure the path segments are smooth. We designed a Waypoint-Matrix to store the points of the path. The paths are aimed to reach the defined destination by passing the waypoints and avoiding obstacles. In this approach, we developed flight cost functions to evaluate the paths, and path length, flight time, altitude, and collision. This approach is validated by simulation results.
Indication of Moral Erosion in Children’s Fiction

Children literature has an important role in children development. Based on the research, there is a different between intellectual and character from child who is a bookworm and who is not. Reading fiction is a favorite of children. Children tend to identify themselves into a character in a fiction, therefore proper rules needs to be put into consideration. This paper aims at underlining problems in children fiction which are potentially eroding children’s morality. Analysis is done by studying the value and structure in children’s literature. It needs to be reconstructed, so reading habit of children will not drop them down to moral erosion.
Trends and transformation of secondary education in the USA, Great Britain and Ukraine through reforms, innovations and alternative education

In this article, the author discovers some aspects of the transformational processes of secondary education in the United States and Great Britain through the prism of innovation and alternative education. The paper highlights the link between innovation and alternative education and changes in secondary education in the US and British schools. An analysis of the works of some American and British scholars dealing with the problems of secondary education development and the results of their researches are highlighted. The article reveals and identifies the core competencies in learning essential for the development of the personality of the student recommended by the Council of Europe.The author also highlights the transformational processes that take place in Ukrainian school education, in particular the new school reform called “New Ukrainian School” and the identification of the new core competencies for the development of a successful personality.
Comprehensive Technology Function Product Matrix for Intelligent Chatbot Patent Mining

Conversational intelligence and the rise of its agent through “chatbots” are disrupting businesses worldwide. Chatbots enable natural interactions via text or voice leveraging technologies. Chatbots are used for various practical purposes such as information acquisition, customer service, and virtual assistance. Google Assistant, Apple Siri, and Amazon Alexa are some of the most widely used virtual assistants. Further, several use cases demonstrate a customer preference for chatbot interface against conventional graphical user interfaces. This has led to rapid growth in chatbot research and consecutively has led to a growth in the filing of patents. The disclosed patents represent major advances in the technology and offer hints about the directions conversational chatbots will grow in the future. Businesses wanting to integrate chatbot technology must, therefore, conduct an exhaustive patent analysis to identify required technologies, functions, and their associated product applications. This research presents a text mining based Technology Function Product Matrix (TFPM) generation approach to analyze a corpus of more than 400 chatbot technology patent documents. The analysis systematically identifies hotspot distributions across the patent landscape with respect to the key technologies, functional advances, and novel applications as current developments in chatbot technologies.
Towards Functionally Similar Corpus Resources for Translation

The paper describes a computational approach to produce functionally comparable monolingual corpus resources for translation studies and contrastive analysis.We exploit a text-external approach, based on a set of Functional Text Dimensions to model text functions, so that each text can be represented as a vector in a multidimensional space of text functions.These vectors can be used to find reasonably homogeneous subsets of functionally similar texts across different corpora.Our models for predicting text functions are based on recurrent neural networks and traditional feature-based machine learning approaches.In addition to using the categories of the British National Corpus as our test case, we investigated the functional comparability of the English parts from the two parallel corpora: CroCo (English-German) and RusLTC (English-Russian) and applied our models to define functionally similar clusters in them.Our results show that the Functional Text Dimensions provide a useful description for text categories, while allowing a more flexible representation for texts with hybrid functions.
Impact of fertilizer urea on liver histopathology of fresh water snake head, Channa punctata

The widespread use of agrochemicals such as fertilizers in agricultural practices have been predicted to show adverse effects on fish, since bulk of the fertilizers sprayed on agricultural crops find way into water bodies.The effect of commonly used fertilizer, urea on histopathology of fresh water teleost fish Channa punctata was studied.The median lethal concentration after exposing fish to urea for 96 hours (96-h LC 50 ) was found to be 19.75 gL -1 and for the chronic toxicity tests spanning for 28 days, sublethal concentrations of urea, viz., 1.97 g L -1 (1/10 th 96-h LC 50 ) and 9.87 g L -1 (1/2 of 96-h LC 50 ) were considered.Accordingly, two test groups having 3 replicates with 5 fish in each one were exposed to 1.97 g L -1 and 9.87 g L -1 urea.Control in tap water without urea was simultaneously run.After 28 days of chronic exposure fish liver samples were collected, processed, stained in Hematoxylin-Eosin and observed under light microscope for histopathological alterations.The liver of control group were normal while the fish exposed to urea showed signs of hepatic alterations such as dilation of sinusoids, vacuolation and necrosis, with severity increasing with dose.This study revealed that urea, which seems innocuous, could actually induce various histopathological anomalies in fish, posing a threat to ecosystem.
Dense Hierarchy Decomposition for Bipartite Graphs

Dense subgraphs detection is a well known problem in Computer Science. Hierarchical organization of graphs as dense subgraphs, however, goes beyond simple clustering as it allows the analysis of the network at different scales. Despite the fact there are several works on hierarchical decomposition for unipartite graphs, only a few works for the bipartite case have been proposed. In this work we explore the problem of hierarchical decomposition of bipartite graphs. We propose an algorithm which we call weighted linking that produces denser and more compact hierarchies. The proposed algorithm is evaluated experimentally using several datasets and provided gains on most of them.