Reviewerly/exHarmony huggingface dataset:
The Relationship Between Requirements Subjectivity and Semantics for Healthcare Design Support Systems

Subjectivity exists in requirements described in the healthcare regulatory framework. This is mainly due to the nature of regulatory requirements and the uniqueness of the design process. Past research identified that subjectivity in regulations is a key issue for automated code and rule checking. The aim of this paper is to discuss how requirements subjectivity could be addressed within building models through semantic enrichment, within the context of automated rule and code compliance checking. The paper presents preliminary findings of a research that follows the Design Science Research approach, framed within the UK healthcare design context. Findings suggest that part of the requirements subjectivity exists due to the implicit relationships between the elements of the healthcare built environment, which also include healthcare services. In order to enable automation, implicit relationships from the regulatory framework should be represented in building models – which could potentially be done through semantic enrichment. The paper discusses some complementarity between relationships identified in regulatory requirements and semantic enrichment operators. Moreover, findings indicate that incorporating semantic relationships in building models can be a promising way to deal with requirements subjectivity, rather than eliminating subjective expressions from regulations.
ELABORATION OF THEORETICAL FOUNDATIONS FOR THE CREATION OF THE EDUCATIONAL ENVIRONMENT OF THE EDUCATIONAL INSTITUTION

Informatization of society, the growth of the social role of the individual in the conditions of constant transformation of equipment and technologies require constant development and modernization of the educational environment in order to form and maintain the current level of professional education throughout a person’s life. Knowledge and information in modern information society are becoming the main intellectual resource, while the volume and pace of knowledge accumulation are constantly increasing. The paper considers the problem of creating the educational environment of an educational institution and its impact on the quality and effectiveness of the educational process. The educational environment is defined as a set of conditions affecting the development and formation of abilities, needs, interests, and consciousness of a person in common object space. Modern computerization of education is based primarily on the information approach, remaining, in fact, on the “manual” management of learning, which does not allow to fully individualizing this process. The traditional learning objectives in the form of knowledge, skills, and abilities are being replaced by a competency-based approach. The paper provides a classification of didactic training systems in the coordinates of the parameters: the orientation of the learning process, the type of process control, the nature of control. Such a classification is the basis for the focused design of training information systems with specified characteristics in terms of the acquisition of competencies. Based on the training scheme of L. Rastrygin developed a model of communication of knowledge carrier and students in learning system. The model contains parameters of the level of assimilation of knowledge and characteristics of the learning object with reference to the duration of the study of the discipline and allows to develop and evaluate an individual learning path.
Vector Space Modeling based Evaluation of Automatically Generated Text Summaries

Evaluating automatically generated summaries is not an effortless task. Despite the fact that significant advances have been made in this context during the last two decades, it still remains a challenging resaerch problem. In this paper, we present VSMbM; a new metric for automatically generated text summaries evaluation. VSMbM is based on vector space modelling. It gives insights on to which extent retention and fidelity are met in the generated summaries. Three variants of the proposed metric, namely PCA-VSMbM, ISOMAP-VSMbM and tSNE-VSMbM are tested and compared to Recall-Oriented Understudy for Gisting Evaluation (ROUGE): a standard metric used to evaluate automatically generated summaries. Conducted experiments on the Timeline17 dataset show that VSMbM scores are highly correlated to the state-of-the-art Rouge ones.
RETRACTED: Remote sensing image landmark segmentation algorithm based on improved GSA and PCNN combination

Application of Remote Sensing Technology in the Development of Urban Functions is presented. Remote sensing image processing for ecological protection and monitoring, this paper proposes a remote sensing image landmark segmentation algorithm based on the IGSA and PCNN. Because the GSA algorithm has the disadvantages of premature convergence and is easy to fall into the local optimal solution, the improved IGSA algorithm is used to extract the ratio of the image entropy and energy to IGSA, and the entropy change value is used as the IGSA algorithm. Based on the global search ability of IGSA, the optimal value of the key parameters affecting the segmentation effect in PCNN model is found. Finally, through experimental comparison, the proposed method has strong advantages in segmentation effect, real-time and robustness
Research Trends and the Implications of Generation Related Studies in the Field of Human Resource Development

본 연구의 목적은 국내 HRD 분야의 세대별 연구를 분석하여 세대별 연구의 특징과 연구동향을 탐색하기 위한 것이다. 이를 위하여 2000년부터 2019년까지 기간을 설정하고 전체 세대 간, 개별 세대들을 대상으로 연구된 총 89편의 학술논문을 연구연도, 연구방법, 연구분야, 연구주제를 중심으로 동향을 분석하였다. 먼저 연도별 분석을 살펴보면 세대 간 연구의 경우 2016∼2019년 동안 가장 활발한 것으로 나타났고, 개별 세대 내 연구 역시 모든 세대가 2016∼2019년 동안 연구가 가장 많이 이루어졌다. 연구방법별 분석에서는 세대 간, 세대 내 연구 모두 대체로 양적연구가 가장 많이 활용되었으며 연구분야별 분석에서는 총 20개의 분야에서 세대 별 연구가 다양하게 나타났다. 마지막으로 연구주제별 분석에서는 일과 삶의 균형, 노동시장의 현황, 조직몰입, 직업기준과 직업이동, 세대 간 갈등, 퇴직준비와 퇴직경험, 경력개발, 교육과 학습, 리더십의 공통주제로 범주화하여 전체 세대 간, 각 세대별로 다양하게 나타난 관심 동향을 파악할 수 있었다. 본 논문은 이러한 연구결과를 바탕으로 HRD 분야의 세대별 연구의 특징과 관련된 국내 연구의 동향 및 시사점과 한계점에 관해 논의하였다.The purpose of this paper is to explore the research trends and implications of generation related studies published in domestic journals in the field of Human Resource Development, hereafter referred to by its acronym “HRD”. For this aim, researchers selected 88 papers that included research themes about relations between generations or within a generation in the period between 2000 to 2019. The paper adopted four analytic frameworks, which were research year, method, field, and theme. Firstly, the distinct feature as the result of analysis by research year was that generation related studies were published more from 2016 till 2019 than in any other periods. As the analysis by research methods, the result showed that quantitative methods using surveys were most utilized in both studies about relations between generations and studies within a generation. The analysis by research field indicated that a variety of generation related issues were addressed in a total of 20 fields of study. Lastly, through the analysis by research themes, researchers could understand the research trends by categorizing common themes such as work-life balance, the current status of the labor market, organizational commitment, occupational standards and mobility, conflicts between generations, retirement preparation and experience, career development, education and learning, and leadership. Based on these analysis results, the paper discussed issues and implications that appeared to be in synthesis and provided a review about related studies, while including the limits that this research had in the field of HRD.
Tourism Destination Grouping Systems With Partitioning Clustering Method from Geotagged Photo using Android

Abstract This application is focused on grouping photos that have information about the location of the photo. To get the location of the nearest tourism destination is by taking the location coordinate value from the uploaded photo then grouping it with a maximum distance of 1 kilometer. The main location for data collection is in Gunung Kidul Regency. This application uses Android for its development. For data processing and transforming scripts into the JSON file uses PHP and for storing the data uses MySQL.The metadata of the photo contains detail information about the photo. In the application shown the few information of its metadata such as the date and time took, image size and resolution, camera maker and model, also coordinate latitude and longitude. The location and metadata values will be shown in the application and stored in the database. Partitioning Clustering method used to find the nearest place within the 1 km radius. First of all by taking the coordinates value from an uploaded photo and stored to a database. The coordinate must be converted from Degree Minute Second Format into Latitude and Longitude Coordinate. After that, the calculation process starting with uses the Euclidean Distance formula to get the nearest distance of the attractions. To display the nearest location, sorting the result values from a minimum value to the maximum value as the final result. The application could be a reference for tourists who will visit Gunung Kidul Regency.
Estimating the measurement error of the coordinates of markers on images recorded with a stereoscopic system

This paper proposes a method of estimating the error of measuring the coordinates of markers (corners of cells) on images recorded by a stereoscopic system. This problem needs to be solved in order to determine the error of the three-dimensional geometrical measurements made with such systems. At the design stage, the method is applied to segments of an image synthesized on the basis of the aberrational characteristics of the optical system. At the operating stage, the method is supplemented by an estimate of the parameters describing the noise on the recorded images. The efficiency of the proposed approach is confirmed by computer simulation and experiments. The results of this paper make it possible to connect the design of the optical system and the development of data-processing algorithms into a single procedure when stereoscopic measurement devices are being created, as well as to estimate the errors of three-dimensional geometrical measurements when these devices are being operated.
Performance of authorship attribution classifiers with short texts: application of religious Arabic fatwas

Although authorship attribution is a well-known problem in authorship analysis domain, researches on Arabic contexts are still limited. In addition, examining the performance of the attribution methods on training set with short textual documents is also not considered well in other languages, such as English, Chinese, Spanish and Dutch. Therefore, this current work aims at examining the performance of attribution classifiers in the context of short Arabic textual documents. The experimental part of this work is conducted with well-known classifiers namely: decision tree C4.5 method, naive Bayes model, K-NN method, Markov model, SMO and Burrows Delta method. We experiment with various features combination. The results show that combining the word-based lexical features with the structural features yields the best accuracy. At this end, we use this combination as a baseline for further investigation. We also examine the effect of combining the n-gram features. The results indicate that some classifiers show an improvement while the others do not. In addition, the results show that the naive Bayes method gives the highest accuracy among all the attribution classifiers.
Mobile Expert System for Dengue Fever Based on Certainty Factor Method

Abstract The development of information technology more rapidly, it can be used to study the dangers of dengue fever and know the type of symptoms that resemble dengue fever. In this research, the design and implementation of multimedia-based android application in which there are components of text, images, and animation. The purpose of designed and built application “Dengue Alert” to help users learn and do the prevention of dengue fever. Besides this application also provides information how to deal with dengue fever. This application was developed by utilising Certainty Factors method as a method of decision support. The result of this research is a mobile application that 97.6% can predict Dengue Hemorrhagic Fever (DHF) so that DHF can be detected early.
Large-scale Multi-modal Search and QA at Alibaba

In this talk, we will present ongoing efforts of Alibaba on large-scale multi-modal search ad question-answering, including image search, video search, cross lingual retrieval, and QA based on machine reading and comprehension (MRC). We will also share in this talk the core technologies that are crucial to the development of the related technologies, including representation learning, data augmentation, and robust learning.
An Analysis of the Listening Comprehension in CET-6——Illustrated by the CET-6 Test on June, 2018

The College English Test Band Six (CET-6) is one of the most widely applied exam across China. As a national teaching test, CET-6 is sponsored by the Department of higher education of the Ministry of education of The People’s Republic of China and held twice a year. In this study, 77 sophomores of different majors were tested using the listening test in June 2018. It is ensured that the test method is exactly same as that of CET-6. The purpose of this paper is to analyze the content and specification of CET-6 listening comprehension test, and to evaluate this part in detail according to the real data, including score report and item analysis, trying to provide some enlightenment for the revision of the examination.
Learning-based Online Query Evaluation for Big Data Analytics in Mobile Edge Clouds

The rise of big data brings extraordinary benefits and opportunities to businesses and governments. Enterprise users can analyze their consumers' data and infer the business value obtained, such as purchasing goods correlations, customer preferences, and hidden patterns. Meanwhile, with the emerge of big data processing frameworks, such as Hadoop and Tensor-flow, more and more mobile users are embracing big data analytics by issuing queries to analyze their data. In this paper, we investigate the problem of Quality-of-Service (QoS) aware query evaluation for big data analytics in a mobile edge cloud to maximize the system throughput while minimizing the query evaluation time of each admitted query, by exploring the materialization of intermediate query results. We consider dynamic big-data query evaluations where user queries arrive one by one without the knowledge of future arrivals, and the system needs to respond to each query by accepting or rejecting the query immediately. We propose an online algorithm for query admissions within a finite time horizon, the proposed algorithm can intelligently determine whether some immediate results during a query evaluation need to be materialized for later use of other queries, by making use of the Reinforcement Learning (RL) method with predictions. We finally investigate the performance of the proposed algorithm by simulations, and results show that the performance of the proposed algorithm is promising, by achieving a higher system throughput while reducing the average evaluation cost per query by from 20% to 52% compared to the comparison benchmarks.
A Critical Discourse Analysis of song “Look What You Made Me Do” by Taylor Swift

The aim of this study was to reveal on how the intended meaning of any figurative expressions delivered through the lyrics and also symbolic signs and gestures which delivered on a popular video clip of Taylor Swift “Look What You Made Me Do” in order to figure out its implication toward language learning as one of the most-watched music videos on YouTube. The data of this study were the lyrics of Taylor Swift’s song “Look What You Made Me Do” and the official video clip on YouTube uploaded by VIVO on August 27th, 2017. Both the lyrics and the symbolic cues shown on the video clip were analyzed through Fairclough’s critical discourse analysis by categorizing any kinds of figurative expressions delivered to reveal the intended meaning of the song and interpret its symbolic expressions through semiotic study as research method. Thus the context is intervening the writing of the song. The results showed that there are many repetitions found to emphasize the message delivered which can be considered as something that viewed as highly really matter for the author or the singer of the song then based on the symbolic or semiotic analysis, most of the clues delivered are trying to express the singers’ transformation regarding to her new reputation as a more powerful and tough person from her past image as an innocent sweet girl. The clues are delivered through the symbols in the forms of animals such as snakes and raven which belief as the symbols of transformation.
Resource Regulation Strategy Based on Resource Allocation Benefitstate Transition in 5G Fronthaul

We propose an online resource regulation strategy based on resource allocation benefit-state transition in 5G Fronthaul Network (5G-RAB ST). Results show that the presented system can significantly reduce the waste of resources in 5G fronthaul and highly improve the quality of users' service.
Sentiment analysis from travellers’ reviews using enhanced conjunction rule based approach for feature-specific evaluation of hotels

The evolution of the internet has steered an enormous amount of travel reviews of hotels on the web. People referring to these reviews are often overloaded and confused by the sheer amount of information available. Sentiment analysis techniques have been successful in aggregating the reviews, extracting their sentiments and thereby minimizing the information overload. But lacking in specific feature-based sentiment analysis has restricted customers in getting the actual scenario of hotels entirely. This paper presents a prospective design on lexicon-based approach for feature-based sentiment analysis of travel reviews on hotels or resorts. In particular, an enhanced form of conjuncture-based approach is proposed to segregate sentences into relevant clauses, identifying the feature and the sentiment value associated with it. Overall sentiment score for features like food, service, and location of a hotel is being calculated. The experiment results show significantly better accuracy and precision than the conventional text segregation and sentiment analysis methods, namely trigram and conjunction rule based approach.
Corrigendum to “Local cyclicity of isogeny classes of abelian varieties defined over finite fields” [Finite Fields Appl. 62 (2020) 101628]

We give the correct statement of main Theorem 2.1.
Feature learning for representing sparse networks based on random walks

Identifying features to represent graphs such as social networks, protein graphs is increasingly common in both research and business communities, thanks to the fact that data has increased not only in quantity but also in complexity. This results in the graphs to be sparser because not all nodes a re fully connected. In addition, if this whole graph is used as input data for learning algorithms e.g. neural network, a lot of training time will be required. Substantial efforts have been made to convert the graphs to better yet compact representations, among of which is graph embedding. The traditional methods used to map the original graph to its embedding representation had not yielded significant results until deep learning was invented. Many good approaches in this direction, as examples, are DeepWalk, node2vec. However, their general weakness is many important connections in the original graph could be lost. In this paper, we propose another approach to retain more edge information while ensuring the embedding graph is still sufficiently small, compared to the original one. Our experiment results show that the method also increases the accuracy of latter learning models.
Semi-Supervised Iterative Approach for Domain-Specific Complaint Detection in Social Media

In this paper, we present a semi-supervised bootstrapping approach to detect product or service related complaints in social media. Our approach begins with a small collection of annotated samples which are used to identify a preliminary set of linguistic indicators pertinent to complaints. These indicators are then used to expand the dataset. The expanded dataset is again used to extract more indicators. This process is applied for several iterations until we can no longer find any new indicators. We evaluated this approach on a Twitter corpus specifically to detect complaints about transportation services. We started with an annotated set of 326 samples of transportation complaints, and after four iterations of the approach, we collected 2,840 indicators and over 3,700 tweets. We annotated a random sample of 700 tweets from the final dataset and observed that nearly half the samples were actual transportation complaints. Lastly, we also studied how different features based on semantics, orthographic properties, and sentiment contribute towards the prediction of complaints.
Information theory inspired optimization algorithm for efficient service orchestration in distributed systems

Distributed Systems architectures are becoming the standard computational model for processing and transportation of information, especially for Cloud Computing environments. The increase in demand for application processing and data management from enterprise and end-user workloads continues to move from a single-node client-server architecture to a distributed multitier design where data processing and transmission are segregated. Software development must considerer the orchestration required to provision its core components in order to deploy the services efficiently in many independent, loosely coupled—physically and virtually interconnected—data centers spread geographically, across the globe. This network routing challenge can be modeled as a variation of the Travelling Salesman Problem (TSP). This paper proposes a new optimization algorithm for optimum route selection using Algorithmic Information Theory. The Kelly criterion for a Shannon-Bernoulli process is used to generate a reliable quantitative algorithm to find a near optimal solution tour. The algorithm is then verified by comparing the results with benchmark heuristic solutions in 3 test cases. A statistical analysis is designed to measure the significance of the results between the algorithms and the entropy function can be derived from the distribution. The tested results shown an improvement in the solution quality by producing routes with smaller length and time requirements. The quality of the results proves the flexibility of the proposed algorithm for problems with different complexities without relying in nature-inspired models such as Genetic Algorithms, Ant Colony, Cross Entropy, Neural Networks, 2opt and Simulated Annealing. The proposed algorithm can be used by applications to deploy services across large cluster of nodes by making better decision in the route design. The findings in this paper unifies critical areas in Computer Science, Mathematics and Statistics that many researchers have not explored and provided a new interpretation that advances the understanding of the role of entropy in decision problems encoded in Turing Machines.