Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Salt and Pepper Removal Techniques for Binary Images

Binarization is the most important step in the OCR system that converts the gray level or colored images into bi-level form. In the case of degraded images, results after binarization mostly contain noises. Salt and pepper noise of different sizes is the most prevalent noise in binary images. For the better results of OCR process, it is necessary to denoise image before proceeding to the next stage. This paper conducts experiments with different existing salt and pepper noise removal methods such as median filter-based techniques and kFill algorithm-based techniques for binary document images. The statistical measures, namely, PSNR, SSIM, and EPI are used to evaluate the performance.
Privacy Protection in Location Based Service by Secure Computation

Mobile devices with Global Positioning System enable various kinds of location information searches using location based services (LBS). Location information and query contents of searchers are privacy information. Privacy protection requires to hide location information and query contents of searchers from not only third parties such as eavesdroppers but also location database servers which answer queries. This paper focuses on privacy protection in nearest neighbor search. There are several researches of secure computation in nearest neighbor search for other than LBS. This paper proposes a new method of privacy protection in LBS. The method is based on Asymmetric Scalar-product Preserving Encryption. This paper also shows that the proposed method is faster and more secure than conventional privacy protection methods in LBS.
Marine Education Web-Site Usability Analysis Case Study Busan Marine Education Guide

The purpose of this study is to contribute to enhancement of user accessibility of web-site by searching for problems and improvement plan through user analysis of web site for Busan Marine Education Guide. Prior to the opening of the Busan Marine Education Guide web-site, which is currently operated by Young-Nam SeaGrant, we are going to look at Jacob Nielsen"s user analysis for key points to consider for user-centered web-site operation. According to Jacob Nielsen"s usability test, we looked at the information and helping and information items on the web page. First, according to gender, differences in usability of Busan Marine Education Guide web-sites are found to be different in Visibility and Error prevention factors. Second, the usability difference according to the information retrieval ability showed that the difference between the flexibility and efficiency of the person with high information retrieval ability and the ordinary person.
Adversarial Network Traffic: Towards Evaluating the Robustness of Deep Learning-Based Network Traffic Classification

Network traffic classification is used in various applications such as network traffic management, policy enforcement, and intrusion detection systems. Although most applications encrypt their network traffic and some of them dynamically change their port numbers, Machine Learning (ML) and especially Deep Learning (DL)-based classifiers have shown impressive performance in network traffic classification. In this paper, we evaluate the robustness of DL-based network traffic classifiers against Adversarial Network Traffic (ANT). ANT causes DL-based network traffic classifiers to predict incorrectly using Universal Adversarial Perturbation (UAP) generating methods. Since there is no need to buffer network traffic before sending ANT, it is generated live. We partition the input space of the DL-based network traffic classification into three categories: packet classification, flow content classification, and flow time series classification. To generate ANT, we propose three new attacks injecting UAP into network traffic. AdvPad attack injects a UAP into the content of packets to evaluate the robustness of packet classifiers. AdvPay attack injects a UAP into the payload of a dummy packet to evaluate the robustness of flow content classifiers. AdvBurst attack injects a specific number of dummy packets with crafted statistical features based on a UAP into a selected burst of a flow to evaluate the robustness of flow time series classifiers. The results indicate injecting a little UAP into network traffic, highly decreases the performance of DL-based network traffic classifiers in all categories.
Automatic thresholding by sampling documents and estimating recall : ILPs@UVA at Tar task 2.2

In this paper, we describe the participation of the Information and Language Processing System (ILPS) group at CLEF eHealth 2019 Task 2.2: Technologically Assisted Reviews in Empirical Medicine. This task is targeted to produce an efficient ordering of the documents and to identify a subset of the documents which contains as many of the relevant abstracts for the least effort. Participants are provided with systematic review topics with each including a review title, a boolean query constructed by Cochrane experts, and a set of PubMed Document Identifiers (PID's) returned by running the boolean query in MEDLINE. We handle the problem under the Continuous Active Learning framework by jointly training a ranking model to rank documents, and conducting a “greedy” sampling to estimate the real number of relevant documents in the collection. We finally submitted four runs.
A Research on Dynamic Target Tracking with Camera Based on Kalman Filter

Abstract With the popularization of the camera, all kinds of video network communication technologies have developed towards civil use and become increasingly popular. For household application, a common PC is used to obtain the frame data from the home network, while the constant frame is set as the background. The update function is used to update background in real time, and thereby forming an inter-frame relation matrix. Based on the matrix, Kalman filter can match and track targets. This method can effectively complete the calculation on the PC, perform precise detection and deal with the complexity of the household application. Finally, with the test results, this paper further proves that the method is feasible and can be used in actual situations.
Improved Privacy Preserving Score-Based Location K-Anonymity in LBS

The extensive use of the location-based services in today’s communication world has created tremendous interest. Considering the importance of these services, the demand for applications using location-based services is also growing rapidly. While working with the applications of these services, there are many threats related to the issue of security. Security to the user’s data is to be provided from the unauthorized parties in the network. The main idea lies in preserving the privacy of the user using anonymization techniques. In this paper, a method for improving the location privacy of the user is proposed by the popular K-anonymity technique, and the implementation algorithm is also discussed.
Dynamic Load Balancing Based Cloud Computing in Network Traffic

Over the years the term cloud computing has become quite popular, mainly because of its ability to provide software, infrastructure and storage services to millions of users over the internet with the help of acquisition and release of resources from data centers. However, as more and more users have started using cloud computing services, the efficient usage of cloud computing resources has become a major challenge. Often cloud computing services suffer due to an overload in the number of requests from various users. One such algorithm has been discussed in this work where load of different virtual machines are balanced by considering minimum traffic in network and existing load in virtual machines. Further, a comparison has been drawn up between the proposed model and some of the existing algorithms like Round Robin and algorithms [1, 2].
Decentralized Maintenance Event Documentation with Hyperledger Fabric

Due to the transparency and redundancy of blockchain, previously required intermediaries become obsolete, which enables the technology to align and digitize business processes in sectors with strong competition and low trust. On the example of the aviation industry, this contribution considers the application of blockchain in supply chains. Complete life cycle documentation is mandatory for safety-related aircraft parts. This work presents and evaluates an IT artefact storing the information of maintenance event certificates in a Hyperledger Fabric blockchain. Our research responds to the calls for practical applications in the blockchain research field. An existing proof of concept is advanced towards a more naturalistic environment by further decentralizing the system. The results suggest a growth in performance in regard to transaction throughput, latency and memory usage by distributing the system on different physical machines. Projectable patterns are identified that can be applied to a broad solution space in similar industry sectors.
Modelling Human Active Search in Optimizing Black-box Functions

Modelling human function learning has been the subject of in-tense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available and must be learned through function evaluations. In this paper we focus on the relation between the behaviour of humans searching for the maximum and the probabilistic model used in Bayesian Optimization. As surrogate models of the unknown function both Gaussian Processes and Random Forest have been considered: the Bayesian learning paradigm is central in the development of active learning approaches balancing exploration/exploitation in uncertain conditions towards effective generalization in large decision spaces. In this paper we analyse experimentally how Bayesian Optimization compares to humans searching for the maximum of an unknown 2D function. A set of controlled experiments with 60 subjects, using both surrogate models, confirm that Bayesian Optimization provides a general model to represent individual patterns of active learning in humans
How Can Industrial Internet of Things (IIoT) Improve Enterprise Productivity?

This chapter gives brief information about internet of things (IoT) and then detailed knowledge of industrial internet of things (IIoT). Internet of things applications can be seen in different areas, such as smart cars, smart homes, smart cities, agriculture, healthcare, industry, etc. This study focuses on the industrial part. Industrial internet of things (IIoT) means internet of things (IoT) applications for industrial usage. IIoT give a chance to enterprise for tracking supply chains, monitoring production line operations, and real-time consumption of energy, managing stock, and transportation decisions. This study used case study method for developing theory about IIoT's contribution to enterprise productivity. IIoT applications can be adapted to which operations of the enterprise, and how it will contribute to enterprise productivity is explained in this chapter. The chapter discusses the projects that are within the vision of IIoT but not yet implemented and concludes with suggestions for future studies.
Mensajes, publicaciones, comentarios y otros textos breves de la comunicación digital

espanolEl objetivo de este trabajo es caracterizar el texto breve como genero emergente del discurso digital. Partimos de la hipotesis de que los generos discursivos son respuestas linguisticas tipificadas a ciertas necesidades comunicativas, que en el caso del discurso digital, estan influidos de forma directa por las caracteristicas del medio. Para abordar nuestros objetivos de investigacion optamos por una propuesta metodologica que combina el analisis de textos reales, con los resultados de unas entrevistas abiertas y una encuesta de habitos sociales sobre los textos breves en internet. A partir de estos instrumentos, se identificaron dos subtipos pertenecientes al genero discursivo del texto breve, el mensaje y la publicacion/comentario, que se distinguen por el caracter privado o publico del intercambio. EnglishThe aim of this work is short text characterization as an emerging discursive genre from digital discourse. Our hypothesis is that discursive genres are typified linguistic responses to communicative purposes that, in the case of digital discourse, a re directly influenced by the characteristics of the medium. To aproach our research objectives, we opted for a mixed methodological proposal that combines the analysis of real texts, with the results of open conversation and a test over short texts on the Internet. We conclude to separated two short text subtypes, the message and the publication/commentary, which are distinguished by the private or public nature of the exchange.
Mosaic Method for Insulator Images Based on Region Characteristics

Abstract Based on the analysis of image registration methods based on region and feature, an image mosaic method based on edge statistical features is proposed. By using the mean and mean square deviation of RGB parameters of edge region pixels as matching criteria, this method improves the shortcomings of general region registration algorithm, such as large computational complexity, sensitivity to brightness and scale changes, poor real-time performance and adaptability, and simplifies the calculation of global region features of images to that of local region features. This method makes full use of the relevant information of the RGB parameters of the pixels. Compared with the registration calculation based on gray level, it has the characteristics of abundant optional features and high practicability.
Keypoints-Based 2D Virtual Try-on Network System

Image-based Virtual Try-On Systems are among the most potential solution for virtual fitting which tries on a target clothes into a model person image and thus have attracted considerable research efforts. In many cases, current solutions for those fails in achieving naturally looking virtual fitted image where a target clothes is transferred into the body area of a model person of any shape and pose while keeping clothes context like texture, text, logo without distortion and artifacts. In this paper, we propose a new improved image-based virtual try-on network system based on keypoints, which we name as KP-VTON. The proposed KP-VTON first detects keypoints in the target clothes and reliably predicts keypoints in the clothes of a model person image by utilizing a dense human pose estimation. Then, through TPS transformation calculated by utilizing the keypoints as control points, the warped target clothes image, which is matched into the body area for wearing the target clothes, is obtained. Finally, a new try-on module adopting Attention U-Net is applied to handle more detailed synthesis of virtual fitted image. Extensive experiments on a well-known dataset show that the proposed KP-VTON performs better the state-of-the-art virtual try-on systems.
STRUKTUR DAN UNSUR KEBAHASAAN TEKS EKSPLANASI SISWA KELAS XI SMA NEGERI 2 BATANG ANAI

ABSTRACT The Purpose of this study were. (1)to describe the use of explanatory the text structure of the student of class XI SMA Negeri 2 Batang Anai. (2) to describe the use of linguistic element in explanatory the text of the strudent of class XI SMA Negeri 2 Batang Anai. (3) to describe accuracy of element and structure of the explanatory text of the student of class XI SMA Negeri 2 Batang Anai. This type of research is qualitative using descriptive methods. Data from this study are the structure and linguistic element of the explanatory text. The data source used in this study a document. The document used is a collection of explanatory text of the student of class XI SMA Negeri 2 Batang Anai. The instrument in this study was the researcher himself. Kata kunci: Struktur, Unsur Kebahasaan, Teks Eksplanasi
Efficient Approaches for Updating Sequential Patterns

Mining sequential patterns is to find the sequential purchasing behaviors for most of the customers in a transaction database. By using sequential patterns, it is possible to predict which products will be purchased in the future after the customer purchases certain commodities. Nowadays, transaction data is continuously added to the database. It is an important issue to update the sequence pattern efficiently in this environment. The previous efficient approach is to store the transactions in a tree structure. When the transactions were added, the tree structure could be updated according to the newly added items. It still needs to re-find the sequential patterns from the updated tree structure and re-scan the original transactions, without using the previous patterns. Therefore, we propose two algorithms for mining and maintaining the discovered sequential patterns when the transactions are added into the database. Our algorithms do not need to re-scan the original transactions and re-generate the existing sequential patterns, which just need to process the added transactions to update the existing sequential patterns. The experimental results also show that our algorithms outperforms the previous approaches.
Trusted Path Routing using Remote Attestation

There are end-users who believe encryption technologies like IPSecalone are insufficient to protect the confidentiality of their highlysensitive traffic flows. This specification describes two alternativesfor protecting these sensitive flows as they transit a network. Inboth alternatives, protection is accomplished by forwarding sensitiveflows across network devices currently appraised as trustworthy.
KSL 배경 학령기 학습자에 대한 연구 동향 분석

The purpose of this study is to analyze the research trends of elementary, middle and high school students among KSL learners from the perspective of procedural literature analysis, and further present the direction of follow-up research based on the results of the analysis. It has been more than a decade since the government-led policy on KSL learners for school-age began, and it is necessary to look at the trends in research on KSL learners during school years and analyze the tendency of the research subject. As the focus of preceding studies related to this was on trend analysis, the focus was placed on describing the percentage of research topics in each year through a quantitative approach, and the subjects of the studies included adult learners, or often limited to specific academic journals or dissertations. Therefore, this study included both dissertation and academic journal papers for trend analysis of KSL learners for school-age. After the criteria were established by applying a systematic literature review, the analysis of the topics by year was conducted considering the task by period, and the learners’ types and the school level were considered. The analysis suggests that despite the short period of research, various subjects were studied in a compressed manner, and Korean language education is now being studied as an independent field as it comes to recent years. However, there were an imbalance in the theoretical research areas, and the “academic Korean language competence” part, which should be discussed fully, has yet to achieve. (Daegu Catholic University)
Social Marketing On The Russian Market: Problems And Prospects

The social responsibility of business in constantly changing conditions of enterprises functioning is an urgent and the most significant task for the success of companies in a competitive environment. The changing needs and preferences of customers are increasingly inclined towards environmental care, saving of resources and support of unprotected layers of the society by more successful and financially stable market entities. Creation of positive image, respectable reputation and a memorable brand is the key task of marketing activities of companies focusing on the market, on the needs and preferences of customers, both real and potential. The concept of social and ethical marketing takes into account not only consumer preferences management but also the responsibility of companies for the social aspects of society. Social marketing determines the directions for the further development of companies not only in making profit or increasing market share but also in creating socially significant projects relevant both for individual segments of society and for the state. The use of marketing tools and approaches to the effective management of market opportunities and prospects led to the strengthening of positions and the development of social marketing at Russian enterprises aiming not only at satisfying the needs of the market and the external environment. Social marketing is of strategic importance for enterprises, as it develops marketing programs, focusing on long-term interaction with consumers, suppliers, intermediaries and other business partners; on building mutually beneficial and promising channels of interaction, taking into account the existing requirements of the social environment.
Effective Data Redistribution Based on User Queries in a Distributed Graph Database

The problem of data distribution in NoSQL databases is particularly difficult in the case of graph databases since the data often represent a large, highly connected graph. We face this task with monitoring of user queries, for which we created a logging module providing information serving as an input to a redistribution algorithm which bases on a lightweight method of Adaptive Partitioning but incorporates our enhancements overcoming its present drawbacks (local optima, balancing, edge weights). The results of our experiments show 70% – 80% reduction of communication between cluster nodes which is a comparable result to other methods, which, however, are more computationally demanding or suffer from other shortcomings.
Quality Evaluation and Key Defects Correction for Handwritten Chinese Characters

A technique of quality evaluation and key defects correction for handwritten Chinese characters is proposed. Firstly, by analyzing the relative centers of Chinese characters' strokes, this technique can give a proper score for the standardization degree of a handwritten Chinese character. Secondly, by clustering handwritten strokes with K-means algorithm and calculating their penalty values, this technique can effectively recognize and correct the key defective strokes in handwritten Chinese characters. The experimental results show that this technology has good application effect and can achieve unattended Chinese character writing practice.
A stochastic subspace approach to gradient-free optimization in high dimensions

We present a stochastic descent algorithm for unconstrained optimization that is particularly efficient when the objective function is slow to evaluate and gradients are not easily obtained, as in some PDE-constrained optimization and machine learning problems. The algorithm maps the gradient onto a low-dimensional random subspace of dimension $\ell$ at each iteration, similar to coordinate descent but without restricting directional derivatives to be along the axes. Without requiring a full gradient, this mapping can be performed by computing $\ell$ directional derivatives (e.g., via forward-mode automatic differentiation). We give proofs for convergence in expectation under various convexity assumptions as well as probabilistic convergence results under strong-convexity. Our method extends the well-known Gaussian smoothing technique to descent in subspaces of dimension greater than one, opening the doors to new analysis of Gaussian smoothing when more than one directional derivative is used at each iteration. We also provide a finite-dimensional variant of a special case of the Johnson-Lindenstrauss lemma. Experimentally, we show that our method compares favorably to coordinate descent, Gaussian smoothing, gradient descent and BFGS (when gradients are calculated via forward-mode automatic differentiation) on problems from the machine learning and shape optimization literature.