Reviewerly/exHarmony huggingface dataset:
Analysis of Research Results of SSCI Environmental Bear Capacity in Recent Twenty Years Based on CiteSpace-II

Abstract According to the data on Social Science Citation Index (SSCI), an international literature retrieval tool for humanities and social sciences in the past 20 years, and based on Citespace II data visualization software developed by Dr. Chen Qimei, Changjiang Scholars of Dalian University of Technology based on Java, an all-round subject knowledge graph is constructed on the research hotspots and main research fields of environmental bear capacity, academic schools and subject structures, and the relationship with neighboring disciplines. Through “a picture is better than a thousand characters”, the research on scattered environmental bear capacity will develop towards digitalization and spatial visualization on the basis of quantification, and will be presented in an image-based and interactive way. It shows the development trend and trend of a discipline or knowledge domain in a certain period of time and forms the evolution process of several research frontier fields.
Working Memory in Chinese Text Comprehension

本研究旨在探討中文文字理解中工作記憶與文章一致性的關係。八十六名以粵語為母語的參與者完成了操作字元工作記憶跨度任務(分為低工作記憶組和高工作記憶組), 以及一項閱讀理解任務 (閱讀不一致文章的時間以及記憶不一致文章的準確性)。結果顯示高工作記憶組用更少時間檢測文章的不一致性, 和更準確地記憶文章的內容。低工作記憶組需要更長時間檢測文章的不一致性, 和更不準確地記憶文章的內容。此外, 參與者花更多的時間閱讀不一致的文章, 扭曲較多的內容, 並替換更多資訊。然而, 不一致的文章並不有助於記憶。本研究擴展了關於工作記憶和中文文字理解的文獻, 除了測量反應時間, 本研究新增測量回憶文章內容, 並介紹替換為另一個閱讀不一致文章的錯誤方法。
An Ensemble of Feature Extraction with Whale Optimization Algorithm for Content Based Image Retrieval System

In recent days, content based image retrieval (CBIR) becomes a hot research area, which aims to determine the relevant images to the query image (QI) from the available large sized database. This paper presents an optimal hybrid feature extraction with similarity measure (OHFE-SM) for CBIR. Initially, histogram equalization of images takes place as a preprocessing step. Then, texture, shape and color features are extracted. The texture features include Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) is extracted, where the optimal number of features will be chosen by whale optimization algorithm (WOA). Afterwards, the shape feature extraction takes place by Crest lines and color feature extraction process will be carried out using Quaternion moments. Finally, Euclidean distance will be applied as a similarity measure to determine the distance among the feature vectors exist in the database and QI. The images with higher similarity index will be considered as relevant images and is retrieved from the database. A detailed experimental validation takes place against Corel10K dataset. The simulation results showed that the proposed OHFE-SM model has outperformed the existing methods with the higher average precision of 0.915 and recall of 0.780.
THE ROLE OF INFORMATION CULTURE IN THE UNIVERSITY KNOWLEDGE MANAGEMENT SYSTEM

The article is devoted to the role of information culture in ensuring the effectiveness of knowledge management at the university. Based on the ideas of P. Drucker and I. Nonaka on the need for continuous self-education of the individual using professional collective experience in the process of practical training, means of improving the efficiency of knowledge management are considered, in particular, introduction of innovative leadership models, research activity expansion, creation of conditions for improving the system of advanced training, the formation of an electronic knowledge base as a reflection of the collective intellectual potential and labor market analytics with the subsequent involvement of advanced ideas to improve the professional competencies of graduates. In the knowledge management at the university, information culture performs a number of important functions: educational, ideological, communicative, technological, the function of preserving social memory. Proposed approaches to the analysis of information culture are: information (as a body of knowledge, skills and abilities to effectively meet information needs), communicative (the required level of communication competence), culturological (providing a holistic vision of the world based on the values of its spiritual development), technological (skills and abilities to work in the IT-sphere), methodological (methods of operating information arrays), management (effective management of information resources). The conclusions emphasize that the information culture at the university is formed by cognitive and epistemic expectations anticipated in the implementation of tasks and decision-making. The information culture of the university is based on organizational values, leadership, norms and conduct regarding the exchange and use of information, and the possibility of digital rethinking of knowledge management is largely determined by a clear organizational strategy aimed at innovation. Measures aimed at improving the level of students’ information culture mobilize analytical approaches in the evaluation of information, improve the quality of work with information resources and network systems, contribute to the generation of knowledge and their practical use in future professional experience.
WITHDRAWN: Psycholinguistic evaluation of ASC based semantic similarity measures

To determine how good any two concepts match with each other, it is important to compute the similarity of the concepts. The measures for Semantic similarity measures are classified into four categories. First are the Ontology Based measures that depend on the closeness of the concepts in the taxonomy. These measures use the path which links the concepts and its position in the hierarchy while computing the similarity. In Second method Information Content of concepts is considered to find the semantic similarity. Other two methods are Feature based similarity and Hybrid similarity. Computing Semantic similarity is very much useful in the areas of Data Processing, Information Retrieval, and Computational Linguistics. Several measures to semantic similarity have been proposed in all the four approaches and applied in wide areas of applications. For any semantic similarity measure proposed under any category the quality of the measure has to assessed or evaluated to prove its accuracy and performance. There exist many evaluation approaches. Many researchers consider the Psycholinguistic evaluation to justify the similarity measure quality. The comparison of the proposed semantic similarity measure values are done against the human judgment values. These computed values are correlated with the human assessments. Pearson Correlation Coefficient is used to test the quality of the similarity measure. The proposed measures can be superior when its correlation is closely related to human assessment value. This paper explains in brief the proposed ASC semantic similarity measure and its Psycholinguistic evaluation over human assessments.
Predicting Programming Behavior in OSS Communities: A Case Study of NLP-based Approach

Prediction of developers' programming behaviors is an effective way to improve their development efficiency and optimize the organization of project modules and files. However, little research exists investigating on this direction. In order to address this knowledge gap, we proposed a NLP-based approach to predict the programming behaviors in OSS (Open Source Software) communities. The proposed approach i) embeds the historical programming behavior data of a project into a multi-dimensional vector space to capture the potential laws in the data, ii) forms an eigenvector matrix reflecting the semantic relationship of the development behavior data, and iii) predicts the next programming behavior of a specific developer based on the eigenvector matrix. Our experiments on five OSS projects show that the prediction accuracy rate of the proposed prediction approach can reach up to about 50%, indicating that it can summarize the development behavior data law and effectively predict the programming behavior of developers. Our work can provide valuable assistance for developers' programming and projects' maintenance in practice.
Review of Face Recognition Techniques for Secured Cloud Data Surveillance using Machine Learning

Face recognition techniques are used in the cloud environment for securing the cloud data from intrusion activities. Face recognition techniques help detect any kind of intrusion activities and in protecting cloud data from intrusion activities. Face recognition techniques help extract and secure the information embedded into cloud data by using different machine learning-based methods. In a cloud environment, recognition techniques can be used in identifying accurate information from the image as well as speech signals. Machine learning and deep learning-based techniques help increase the accuracy of recognition in the cloud environment. The main aim of this research is to identify efficient face recognition techniques that can be implemented in the cloud environment for securing data stored on the cloud network. Cloud data, behaviour detection, and recognition are the major components that help develop an efficient system to be implemented in a cloud environment for achieving secure data surveillance and to secure data stored on the cloud environment from any network intrusion activities. Analysis and evaluation of these components help in developing an efficient system based on machine learning techniques that help in recognizing different activities and in detecting intruder activities in the cloud environment. Classification of all the system components helps in identifying efficient machine learning-based face recognition system for obtaining secure cloud data surveillance.
Investigation of mechanical properties for Non-Homogeneous by image tracking method

We have investigated new approach to predict mechanical properties for non-homogeneous materials. The investigation used copy paper 100 gsm as sampling object using image tracking method and based on bending of cantilever slender beam. The copy paper 100 gsm was shaped on non-homogeneous geometris than placing it with curvature angle variations (0°, 45° and 90°). We snapshot the copy paper sample using digital camera later tracked the coordinates at several points after divided the beam image into 16 segments. Futhermore, we calculated Modulus Young using elasticity equation for non-homogeneous beam. The results presented show that it is possible to obtain a good Modulus Young value due to approach Modulus Young value obtained by direct measurement using tensile strength device. This method is expected to become a reference for developing Modulus Young estimation for non-homogeneous geometry materials without direct contact.
A Framework Enhanced by Mutual Information Cross Entropy for Time Series Anomaly Detection Under Noise

In this paper, an anomaly detection framework for time series is proposed, which is enhanced by mutual information cross entropy. Based on the prediction method, the mutual information cross entropy is used to select blocks that are most related to the prediction results. Considering the effects caused by various noise rates, a prediction module including a set of recurrent neural networks is trained and reserved under different noise environments, then a classification module consists of convolutional neural networks is used to choose suitable prediction model. Based on the errors between the predicted series and the test series, anomaly detection is implemented by Neyman Pearson criterion.
An Novel Text Extraction Technique Based on Pattern Matching and Automatic Backtracking

Most Web documents are described in a DOM tree structure, and therefore, the extraction of Web key information usually requires a process of traversing the DOM tree. The key entities to be extracted are not only determined by the extracted content itself, but also depend on the surrounding environment of the extracted entities. In this paper, we introduce a method for Web key information extraction through non-deterministic algorithm, which uses automatic backtracking algorithms and pattern matching to concisely describe the key text content that needs to be extracted, while significantly simplifying the extraction process.
Fuzzy Logic and Simple Additive Weighting Implementation on River Flow Controlling System

Abstract Floods are natural disasters that are very difficult to predict and come from a variety of factors. One of the causes of flooding is an increase in the amount of water discharge. The water discharge comes from various sources, among which the most is high water in rivers and high rainfall. While the river is a system of water that flows from upstream to highlands to downstream in the lowlands with a very long and has branched routes. Fuzzy logic is logic that can overcome ambiguous matters, in this case, rainfall and water discharge. In river branching, river opening and closing systems are needed to prevent rain, so the Simple Additive Weighting (SAW) algorithm is used. In this research prototyping was carried out to implement Fuzzy Logic and SAW for sluice control, using the Water Level Sensor and the Rain Sensor Module to control the Servo Motor. Servo Motor is used to control the opening and height of sluice openings. This study as a whole system has an error of 0.099% so that the prototype system can be categorized quite well and can be applied for further research.
Digital Fiat Currency (DFC): A Taxonomy for Automatic Sleep Stage Classification

Deep learning is the latest phenomena, which is being used to get the results for automatic classification, segmentation, image processing in various medical fields. This technology basically helps in reducing processing time and to avoid manual classification and identification. In recent years, convolution neural network in deep learning has been used for getting automatic results from the raw data. [1], [2] This technology is quite popular in automatic sleep stage classification, these days. It is basically used for automatic sleep stage classification, as manual classification is very time consuming and complex. [3] In previous times, the classification of sleep stages, was done with the help of manual human vision inspection, which were very time consuming and complex. To fasten this process and to reduce complexity, deep learning neural network models are used for classification. These neural network models help to improve this process and give better results than manual scoring of sleep stages. [4], [5] In this proposed DFC taxonomy, these components are implemented to validate the sleep stage classification in deep convolution neural network. [6]. After validation, evaluation and verification of this Digital Fiat Currency (DFC) taxonomy, it can improve the results of classification to large extent, which involves the major components of deep learning to improve the accuracy. In addition, this proposed method, is simple and easy to adapt for other methods.
Automatic Essay Scoring Model Based on Multi-channel CNN and LSTM

In essay marking, manual grading will waste a lot of manpower and material resources, and the subjective judgment of marking teachers is easy to cause unfair phenomenon. Therefore, this paper proposes an automatic essay grading model combining multi-channel convolution and LSTM. The model adds a dense layer after the embedding layer, obtains the weight assignment of text through softmax function, then uses the multi-channel convolutional neural network to extract the text feature information of different granularities, and the extracted feature information is fused into the LSTM to model the text. The model is experimented on the ASAP composition data set. The experimental results show that the model proposed in this paper is 6% higher than the strong baseline model, and the automatic scoring effect is improved to a certain extent.
Generalised Zero-shot Learning with Multi-modal Embedding Spaces

Generalised zero-shot learning (GZSL) methods aim to classify previously seen and unseen visual classes by leveraging the semantic information of those classes. In the context of GZSL, semantic information is non-visual data such as a text description of the seen and unseen classes. Previous GZSL methods have explored transformations between visual and semantic spaces, as well as the learning of a latent joint visual and semantic space. In these methods, even though learning has explored a combination of spaces (i.e., visual, semantic or joint latent space), inference tended to focus on using just one of the spaces. By hypothesising that inference must explore all three spaces, we propose a new GZSL method based on a multimodal classification over visual, semantic and joint latent spaces. Another issue affecting current GZSL methods is the intrinsic bias toward the classification of seen classes - a problem that is usually mitigated by a domain classifier which modulates seen and unseen classification. Our proposed approach replaces the modulated classification by a computationally simpler multidomain classification based on averaging the multi-modal calibrated classifiers from the seen and unseen domains. Experiments on GZSL benchmarks show that our proposed GZSL approach achieves competitive results compared with the state-of-the-art.
The Power of Modern Digital Technologies for the Specialist Training in Artificial Intelligence

Purpose: The purpose of the article is to present a set of methodological recommendations to introduce Modern Digital Technologies in the educational process that can be used for training in Artificial Intelligence including preparation for Bachelors’ and Masters’ degrees. Design/Methodology/Approach: The authors note that in the modern education system the navigation through educational online resources and competent use of such sources of information to obtain relevant knowledge and skills are becoming important. We must accept that contemporary development of education changes the role of a lecturer. A professor is no longer a central figure in order to transfer the knowledge to students. Currently a professor should be a moderator as well so that students can obtain the necessary competence. The authors describe approaches to organizing full-time student learning using a set of Modern Digital Technology tools such as the Kaggle platform, the Google Service—Colaboratory (Google Colab) and the Stepik educational platform. Website Kaggle demonstrates a World-Class level of development in Artificial Intelligence. The Colaboratory resource reduces entry barriers associated with limited technical and software resources for studying Deep Learning and online experiments with neural networks. Stepik is a service to organize online learning. Findings: It is shown that the use of digital tools provides an increase in the effectiveness of the educational process, mobility and a differentiated approach to study training materials. Originality/Value: A set of methodological recommendations on the use of digital education tools was developed. The proposed methodological recommendations give an opportunity to introduce the powers of digital resources for educational purposes. The advantages of using Modern Digital Technologies providing opportunities to students for professional growth and personal development are shown.
Research on Information Services Integrated Method Based on SOA

Abstract The paper firstly analyze the current problems in the information services integration, then the architecture of information services integration based on SOA is designed. The paper also gives a integration method based on ESB and Web Service. Compare the differences between SOA and traditional integration methods. We know that the integration method based on SOA makes full advantage of the information services system. Results show that the method is scientific and easy to use.
Understanding user feedback on learning management system of SIPEJAR by using text mining techniques

Abstract SIPEJAR ( Sistem Pengelolaan Pembelajaran ) as a learning management system can organize various activities in e-learning such as assignments, exams, learning materials, etc. This study aimed to use text mining techniques to extract information from the answers given by respondents toward questions about SIPEJAR. Text mining is one of the techniques used to extract and analyze useful information from the unstructured data. The data were collected using an online survey, and the total data collected is 10,456 but only 9,315 were used. The results show that course material, assignment, and search for information are the three most important factors that motivate students to access SIPEJAR. In the contrary, the factors that lead students to reluctant using SIPEJAR is bad quality network, difficulty to use, and frequently server down. In addition, the factors that need to be improved from SIPEJAR is a user interface, features, service, notification, and simplicity.
The Research on News Production and Distribution Under Artificial Intelligence Technology Environment

Every technological revolution will completely transform the media format, and the increasingly mature artificial intelligence technology will have a profound impact on the news industry. News production is a crucial link in the entire news dissemination process, and artificial intelligence is changing news production. Starting from the production and distribution of news, this article analyzes the impact of artificial intelligence on all aspects of news production, and analyzes the problems that artificial intelligence may bring in the field of news dissemination, and how to better embrace artificial intelligence.
Nearest Centroid: A Bridge between Statistics and Machine Learning

In order to guide our students of machine learning in their statistical thinking, we need conceptually simple and mathematically defensible algorithms. In this paper, we present the Nearest Centroid algorithm (NC) algorithm as a pedagogical tool, combining the key concepts behind two foundational algorithms: K-Means clustering and K Nearest Neighbors (kNN). In NC, we use the centroid (as defined in the K-Means algorithm) of the observations belonging to each class in our training data set and its distance from a new observation (similar to k-NN) for class prediction. Using this obvious extension, we will illustrate how the concepts of probability and statistics are applied in machine learning algorithms. Furthermore, we will describe how the practical aspects of validation and performance measurements are carried out. The algorithm and the work presented here can be easily converted to labs and reading assignments to cement the students' understanding of applied statistics and its connection to machine learning algorithms, as described toward the end of this paper.
An Empirical Evaluation of the Credibility, Trust Perceptions, and Compatibility with Cloud-Based Services: The Case of Higher Education in Malaysia

This paper investigates the influence of credibility on trust as it received scant attention in academia, especially in cloud computing, and to test compatibility against the intention towards usage or adoption of SaaS services. A quantitative survey instrument used, and the authors selected respondents from Malaysian public universities from different schools, races, ages, and genders. Valid responses were 233. Partial least squares structural equation modeling (PLS-SEM) used to analyze the model. The main results show a statistically positive and significant effect of credibility and trust on attitude, compatibility on behavior intention, and behavior intention on the outcome. Notably, credibility exerted a potent significant positive effect on trust. The framework has proven its appropriateness, predictive accuracy, and relevance.