Reviewerly/exHarmony huggingface dataset:
An enhanced krill herd optimization technique used for classification problem

In this paper, this method is intended to improve the optimization of the classification problem in machine learning. The EKH as a global search optimization method, it allocates the best representation of the solution (krill individual) whereas it uses the simulated annealing (SA) to modify the generated krill individuals (each individual represents a set of bits). The test results showed that the KH outperformed other methods using the external and internal evaluation measures.
Analysis on the Construction of Cultivation Mechanism of the College Counselors’ Scientific Research Ability with the Computer Technique Support in the Internet Age

With the development of the Internet age, the improvement of scientific research ability within college counselors becomes the basic content of the construction of college counselors and It is also an inevitable choice to promote the professionalization and specialization of counselors’ development. Based on the scientific definition of the counselor’s scientific research ability, this paper analyses the constituent elements of counselors’ ability. As a result, a mechanism for cultivating the scientific research ability of college counselors is constructed, which is based on the guarantee mechanism, focused on the education mechanism, promoted by the computer technology support mechanism, supported by the discipline platform construction mechanism, and tested by the interaction of theory and practice. And this mechanism aims at providing theoretical reference for strengthening the cultivation of scientific research ability of college counselors in the Internet age.
Scalable Requirements: One Size Can Fit All

Software and systems engineering practitioners have the option to work with a very wide range of requirements specification methods. I view these as requirements languages. A subset of practitioners additionally has a compelling interest in using requirements methods that are suitable for very large-scale enterprise projects. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-4</sup>
Weighted Cross-Product Constraint Transformation to Optimize Spatial Structure of Data

The research of data spatial structure optimization is significant in data mining. Many methods optimize the space based on a specific criterion, which in a certain extent destroys the original spatial distribution of data with uncertain optimization effects. Based on the heterogeneous distribution spatial structure of the data and the importance of the data attributes to distinguish categories, we proposed the weighted cross-product constraint transformation (WCCT) method to improve the accuracy of data analysis. This method retains the spatial distribution of the original data and maps the data to a limited high-dimensional space with constraints, which increases the proximity between the same data category and the discrimination between different data categories. Moreover, we established a statistical evaluation system of spatial structure to prove the effectiveness of this method in optimizing the spatial structure of data attributes. Experimental analysis on real data demonstrates that the proposed method obtains excellent classification performance with different machine learning classifiers.
Data Analysis on the Communication Traits of NBA Predictive News

The research proposes the concept of NBA predictive news based on the observation of NBA scoops as well as the academic discussions under the instant news and sports rumors. Through the content analysis, the research codes the NBA predictive news in the designated pool, making the data mated with cases that reveal the characteristics of NBA predictive news-ambiguous truth, instant release, good predictability and the significant differentiation. With the support of specific cases, the analysis further explores the formation mechanism of NBA predictive news to understand the stakeholders behind it as well as its potential detriments. The research also tries to give suggestions for the governance of NBA predictive news. In the near future, it is possible to establish the connections between the textual characteristics of news and its authenticity or predictability, so as to provide references for the design of scientific algorithm to identify and manage the false, the low-predictive pieces.
Bind Video Watermarking Based On Wavelet Transform

A blind design that doesn't demand the first image or it's characteristics for digital video watermarking is proposed which reinforces the safety. Watermark is embedded within the digital video and maybe extracted for the longer term or perceive. The security of the design is stabilized by employing the watermark image in the LL and HH sub-bands obtained and also during the extraction of the watermark. Individually frame is collected from the digital video which contains RGB frames converted into YUV frames. Discrete Wavelet Transform (DWT) is implemented to all luminance segments of an individual frame disintegrating it into different sub-bands. The respective subband is divided into severa lsub-bands. The highest entropy bands are chosen of individual sub-band and each frame is converted using Principal Component Analysis (PCA) of all chosen bands. Then, the Watermark turned into vector representation of zeros and one is inserted into the preferred proper values of each subbands. The suggested design is usually examined using various video strings. Inspection outcomes display great imperceptibility. The intended design exhibits tremendous robustness on different attacks like Gaussian noise addition, automatic equalization, gamma correction, and adjustment of contrast in the cases of variedvideos.
Corner Detection Based on Scalable Masks

Scalable masks for the selection of angular structures in two-dimensional (2D) digital images are considered. The mask is a 2D window sliding over the image and convolved with image fragments. We propose the model of a scalable 2D mask based on expanding smaller mask along its sides and edges. In this case, the submatrices remain unchanged, and the generation of new elements consists in repeating the elements of the submatrix, preserving the structure of the corner. Numerical experiments with test images are performed.
Research on Online Teaching Practice of Specialized Course

Based on the online teaching practice of diesel engine course, a new hybrid online teaching mode is proposed in this paper. The online teaching of diesel engine course is comprehensively designed from three aspects: preparation before class, teaching design and course education. According to the online teaching data and teaching results of diesel engine course in our university, the online teaching mode and integrated design method adopted can effectively improve cadet's interest, initiative and learning quality in online learning, and achieve the teaching objectives of the course.
Role of Cryptocurrency in Digital Marketing

Today, the world has become a digital platform where technology has become an important part of day-to-day life. The world is growing at a rapid pace where there is a new innovation every other day. As days are passing, every aspect of life has become online. Companies have also identified the scope and opportunity of digital marketing. This chapter discusses how cryptocurrency is a challenge for digital marketing. This study is an exploratory study that involves secondary data collection. The objective of the research is to identify the influence of cryptocurrency on digital marketing. The findings of the study suggest that digital marketing is a data-driven marketing technique, and cryptocurrency is a payment mode that hides data; thus, the popularity of cryptocurrency as a mode of payment is a challenge for digital marketing.
세종학당 교원 연수 프로그램 설계 및 지원 체계 개발 연구

The purpose of this study is to design a teacher training program that enhances the professionalism of teachers at King Sejong Institute, and to establish a support system that can lead to active participation in the training program. To this end, the status of the teacher training program provided by the King Sejong Institute Foundation was analyzed and a survey for the teacher training program was conducted targeting King Sejong Institute teachers and related persons. Based on the analysis and the results of the survey, the design principles of the King Sejong Institute teacher training program were presented. The program was designed to comprehensively consider the training operating conditions, to prepare a program that can be jointly managed by the King Sejong Institute Foundation and the local institute, and to provide training in consideration of the life cycles and career characteristics of teachers. This study intended to present systematic training programs by designing the programs as job training, qualification training, special training, and self-training. In addition, it was proposed to operate a mileage system that utilizes the results of training participation for personnel management so that teachers can actively participate in teacher training programs. (Hansung University·Korea University·Korea University·Korea University·Korea University)
Performance Analysis Similarity Matrix, Responsibility Matrix, Availability Matrix, Criterion Matrix of Affinity Propagation

Abstract Each process in classifying data into several clusters or grouping so that the data in one cluster has a maximum similarity level and between clusters has a minimum similarity is called clustering. Clustering is divided into 2 approaches in its development, namely the partitioning and hierarchical approach to clustering[1]. The Water Quality Status dataset has 8 attributes, 4 classes and 120 instances, Class distribution is good condition (30 instances), lightly polluted (30 instances), medium polluted (30 instances) and heavily polluted (30 instances). 70% of the data will be used as training data and 30% of the data will be used as randomized test data. The simplify the process of completing the performance calculation of the clustering model, the research implementation was carried out using the MATLAB function. That iteration is carried out with the number of clusters generated from 100 to 2,500 iterations with the results of the number of clusters as many as 10 clusters. In the experiment, iteration amounted to 5000 and there was a change in the results of the number of clusters by 9 clusters. After re-testing using the number of iterations of 10,000-50,000 iterations, but the number of clusters produced did not change anything at all. So that the conclusion in testing the AP method produces the most optimal number of clusters of 10 clusters.
TheCoin: Privacy and security considerations within blockchain transactions

TheChain is a solution to many problems such as monopoly, heavy state transition, and security vulnerabilities. TheChain solves these problems by introducing the intersection of regions as an incentive before allowing validators to nest a client directory. Intersecting their operating territories forces them to keep a watch over each other. The definition of privacy can take many forms, starting from the right to be forgotten beside being away from public attention. Although the pseudonymity of the user within the network can enhance the user's privacy, several pieces of research have studied the techniques to take advantage of the network structure to identify the users of pseudonyms. Moreover, two models have been used to record the updated exchange of values within the blockchain system, which are the unspent transaction output (UTXO) and the balance model. The UTXO suffers from duplication of information and the balance model suffers from having a single point of entry. This paper introduces TheCoin model that defines the protocol of the exchange of valuable datum within TheChain system. The solution has introduced a novel approach of initiating the transaction from the receiver side by taking advantage of mobile agents empowering a topology hiding to the network. Billing within the platform has been introduced to allow advanced contractual logic to be adopted into the system on the information level. Moreover, traceable fuzziness has been used to eliminate duplication. The paper presents an evaluation of the TheCoin model in terms of system security, block size, and search performance.
Method for Processing Document with Tables in Russian-Language Automated Information Systems

This paper presents a method for processing images of documents with tables and extracting information from them for automated information systems. The key feature of this method is the decomposition of the process of extracting information from documents into 2 parts: processing tables and non-tabular text. This combined approach allows for processing different types of documents. We specify the scope of the method, describe its stages, compare and select solutions for each stage. The proposed method consists of 5 stages of processing scanned images of documents. The main of them are composition of analytical and machine learning approaches. An experiment was conducted separately for the tasks of processing tables and non-tabular text. We obtain results that prove the effectiveness of the selected solutions. For training and validation, we used both the ICDAR-2013 table competition dataset and our own dataset of 317 scans of layout heavy business documents, particularly financial documents. In conclusion, we provide recommendations for organizing a process of extracting information from images of financial documents that contain information inside and outside of tables. Based on this method, a software intelligent module is developed that is used in the ERP system.
Determining the Number of Groups in Cluster Analysis Using Classical Indexes and Stability Measures—Comparison of Results

In the context of taxonomic methods, in recent years, much attention has been paid to the issue of the stability of these methods, i.e., the answer to the question: to what extent the structure discovered by a given method is actually present in the data. This criterion examines whether the groups that were created as a result of using clustering method to a set of objects are real (the structure is stable), or whether they appeared accidentally. Most often this criterion is used when selecting the number of groups (k), for which should be clustered a set of data. The aim of the article is to compare the results in terms of the indicated correct number of groups by classical indexes and stability measures.
Akademik Amaçlı İngilizce Öğreniminde Her An Her Yerde Bilgisayar Teknolojilerinin Kullanımı / Use of Ubiquitous Computing Technology in Learning English for Academic Purposes

This case study aims at revealing EAP (English for academic purposes) learners’ experiences in and perceptions about using ubiquitous computing technology. Data were collected through interviews carried out with eleven people being prepared for the foreign language (English) exams for academic purposes in a private language course in Turkey. Fifteen open-ended questions were involved in face-to-face interviews. Data were collected through the audio recordings of interviews and then examined by content analysis method, and afterwards, codes, categories and themes were obtained. The findings of this study revealed that all of the participants were continuously and actively using ubiquitous computing technologies, they thought ubiquitous computing technologies had several advantages in learning English for academic purposes such as continuous and permanent learning, quick access to information, preventing waste of time, and interacting with other learners. However, some disadvantages and limitations of ubiquitous computing technologies such as technical and infrastructural problems, information pollution, and inadequacies in translation of terms were also tackled.
An Indonesian Sentiment Classification Model Based on Multi-task Learning

With the rapid development of social network, people are keen on giving voice to their feelings on social media containing their attitude towards products or services, which is significant for company to have a better understanding of the customers’ opinions. Nowadays, many researchers are concentrating on sentiment classification research in universal language such as English and Chinese. However, sentiment classification research has not been widely applied for low-resource language. In this paper, we constructed an Indonesian sentiment classification dataset consisting of 108,000 hotel comments, dividing into positive, negative and neutral polarities. Moreover, we transformed three-label sentiment classification into three binary classification models and proposed a multi-task learning (MTL) model for Indonesian sentiment classification. We utilized Bi-LSTM as the general representation layer shared across three binary sentiment classification models, aiming to better capture the bidirectional semantic dependency information. In the sentiment-specific representation layer, the sentiment-specific features will be extracted through the max-pooling layer, and the three polarities probability distributions were predicted respectively through the sigmoid non-liner layer. The experimental results demonstrated that, both on the existing open source dataset and the self-constructed dataset, our model outperforms other models that we used as baseline.
Web Crawler for an Anonymously Processed Information Database

The Japanese Act on the Protection of Personal Information (Act No. 57 of 2003) allows companies to deidentify personal data and to provide them to any third party without obtaining the data subject’s consent. Officially, the “Anonymously Processed Information” will eventually improve public convenience and aid big-data business. In this paper, we aim to clarify the landscape of anonymously processed information databases in Japan. Focusing on disclosed statements that specify the production and provisioning processes for anonymously processed information, we have developed an automated Web crawler system that detects such statements using heuristics associated with legal statements. We demonstrate that our crawler system performs very well in terms of processing time and detection accuracy. In addition, the resulting statistics may prove useful to others exploring the landscape of Japanese personal-data business structures.
COMPUTER DIAGNOSTICS OF THE LEVEL OF SELF-ESTEEM IN THE PROCESS OF SOLVING MATHEMATICAL PROBLEMS

The diagnostics of the dynamics of self-assessment of the level of independence of educational activity of students in teaching the solution of mathematical problems is considered. The groups of students with overestimated, average and low self-esteem were identified. It is shown that the self-assessment of algorithmic activity among male students is on average higher than among female students.
Semantic feature refine module for nucleus segmentation using Mask R-CNN

Nucleus segmentation is a fundamental prerequisite in the digital pathology analysis. However, automated nucleus segmentation is challenging due to clustered arrangement and possible occlusion. Additionally, some nuclei exhibit large variability between images and have fuzzy boundaries. Most of the previous works solve the task through FCN, which requires well-designed post-processing methods to separate instances. In contrast, Mask R-CNN segments objects based on region proposal with no post-processing methods to separate instances, but usually confuses the foreground and background. In this paper, we propose a Semantic Feature Refine Module (SFRM) to enhance its ability to distinguish foreground and background. We first add a semantic segmentation branch to enhance the semantic feature of FPN. Besides, we utilize the feature of semantic segmentation branch to yield an attention pyramid for FPN to enhance its semantic feature further at the same time. Experiments on CoNSeP and PanNuke datasets verify the effectiveness of our method.
Design and build a queuing system for the office of population and civil registration of Kuningan Regency based on Android

Abstract Queue system based on Population and Civil Registry of Kuningan Regency based on android is used in the process of managing the queue so that the queue stacking can be overcome. This queuing system provides a queue number for the next few days besides that the call of the queue number screen is provided in this system so that the community can more easily install the queue so that it is not at the location. With this system, the people of Kuningan Regency are freed by the large number of queues or scattered queues, then people who are far from the office location are made easy to get a queue number without having to come to the office.
A New Authentic Cloud Dataset from a Production Facility for Anomaly Detection

As technology advances and modern Industry 4.0 solutions are becoming more widespread, the need for better-suited datasets is rising. The commonly used datasets for training machine learning focus on simple data of often publicly available information. Within the industry, there is only a handful of datasets publicly available to use. In this paper, we present a new authentic industrial cloud data (AICD) dataset collected from an actual operating pick-and-place machine handling items with variations in shape, size, and weight. The AICD dataset contains various analogue sensor values and states of the machine, collected from an existing cloud solution. Within the data, an error is present when the machine fails. Therefore, this dataset is suited for testing and developing predictive maintenance and anomaly detection algorithms to be used in the industry. Moreover, the paper also presents a baseline implementation as a performance indicator for future models.
A Study on the Characteristics of College Students’ Consumption Behavior Based on Clustering and Association Rules

In 21st century, the development of information technology has led to an overall improvement in people’s material level. Especially the big consumer group of college students. Campus information technology has now become a trend, which means that there is a large amount of consumer data accumulation. Behind the huge amount of behavioral data, there is potentially valuable information. By analyzing the data, we can understand the characteristics and needs of the consumption behavior of college students, so that we can better promote the process of campus informationization and help campus management.
Functional encryption for cubic polynomials and implementation

Functional encryption (FE), which provides fine-grained access control on encrypted data, is becoming a new hot spot in the field of cryptography. Recent applications, such as outsourcing computation, searchable encryption and so on, suggest that FE has unlimited possibilities. It especially shows great feasibility to construct indistinguishability obfuscation and reuseable garbled circuits. Furthermore, bounded collusion functional encryption is an extension of FE which is against more than one key query and protects the security of messages under more than one function keys. In this paper, we proposed a bounded collusion FE for cubic polynomials, which follows from Agrawal and Rosen's work on TCC 2017. Our construction only invokes the Regev public key encryption and a linear FE scheme which avoids complex encodings defined recursively. What's more, we proposes an FE scheme for all circuit with FULL-SIM security. Finally, we also implement these schemes and do some analyses on parameters' size, time and space performance.