Reviewerly/exHarmony huggingface dataset:
An Automatic Website Menu Comparison among Indonesia’s University Websites for Designing Labeling System of an Indonesia University Website

Label on websites represents the contents of information.To avoid delivery of incorrect information, website developers need to design a good labeling system, thus the labels can provide the information that the website owners want to convey to their users.One of the approaches to design a labeling system on a website is by comparing and studying the labeling system used on competitor websites.In this research, it was compared the labels of website menus, used by 11 university websites in Indonesia, by calculating the similarities of the label using Levenshtein distance.The result of label comparison was analyzed, which then can be used as designing source of labeling system for building and improving labeling systems of university website menus in Indonesia.
Reversible Data Hiding in Permutation-based Encrypted Images with Strong Privacy

Reversible data hiding in encrypted images (RDHEI) provides some real-time cloud applications; i.e. the cloud, acting as a data-hider, automatically embeds timestamp in the encrypted image uploaded by a content owner.Many existing methods of RDHEI only satisfy user privacy in which the data-hider does not know the original image, but leaks owner privacy in which the receiver can obtains the original image by decryption and extraction.In the literature, the method of Zhang et al. is the one providing weak content-owner privacy in which the content-owner and data-hider have to share a data-hiding key.In this paper, we take care of the stronger notion, called strong content-owner privacy, and achieve it by presenting a new reversible data hiding in encrypted images.In the proposed method, image decryption and message extraction are separately controlled by different types of keys, and thus such functionalities are decoupled to solve the privacy problem.At the technique level, the original image is segmented along a Hilbert filling curve.To keep image privacy, segments are transformed into an encrypted image by using random permutation.The encrypted image does not reveal significant information about the original one.Data embedment can be realized by using pixel histogram-style hiding, since this property, can be preserved before or after encryption.The proposed method is a modular method to compile some specific reversible data hiding to those in encrypted image with content owner privacy.Finally, our experimental results show that the image quality is 50.85dB when the averaged payload is 0.12bpp.
A Methodology for Automating the Cloud Data Center Availability Assessment

Organizations worldwide are rapidly migrating their IT services and infrastructure to cloud data centers in order to reduce costs and guarantee high availability, scalability, and security. Considering that service downtime translates into major financial losses, new mechanisms need to be developed to assess the availability of cloud data center dynamically. However, data center availability analysis remains a complex task and one that is prone to human error due the large number of components and their interconnections. In this work we propose a methodology for acquiring information about a data center infrastructure and, automatically, generating computational models to assess its availability. We make use of the Redfish standard to acquire information about the data center infrastructure, the main standard for data center management. To demonstrate the applicability of our proposal, we conduct a study to analyze availability and failure costs of an application hosted in a cloud and we compare different scenarios with redundant servers according to the TIA-942 data center standard. Results show that a lower tier level with redundant servers, in some cases, is more suitable (more available and less costly) than higher tier levels without redundant servers hosting a cloud application.
Is Korean education properly preparing future generations for an era of change?

본 연구는 급격한 사회변동이 진행되고 있는 상황에서 과연 한국 교육이 미래 세대의 삶을 적절히 준비시키고 있는지 고찰하기 위해 수행되었다. 이에 본 연구는 먼저 근래 우리가 경험한 주요 사회변동의 배경, 실상 및 유산을 살펴보았다. 이와 관련해서는 세계화와 4차 산업혁명이 집중적으로 논의되었다. 다음으로 한국 교육의 주요 문제를 검토하였다. 여기에서는 성적지상주의 교육, 사교육의 번창, 계층 간 교육격차 심화, 비정상적 평생학습곡선, 시대 적합성을 결여한 교육패러다임 등이 논의될 것이다. 이 과정에서 현재의 한국 교육이 미래 세대의 삶을 준비하는 데 적합한지도 개괄적으로 평가하고자 한다. 아울러 앞서 논의한 주요 교육문제에 대한 진단을 바탕으로 한국 교육의 핵심 과제를 고찰하도록 하겠다. 이와 관련해서는 교육철학 및 비전의 정립, 교육패러다임의 전환, 사교육비 경감, 계층 간 교육격차 해소, 4차 산업혁명 대비 교육개혁 등을 다루게 될 것이다. 마지막으로 이러한 논의들을 바탕으로 간략하게 정책대안을 제시하도록 하겠다.In this article, I examine whether Korean education is properly preparing future generations for an era of rapid social change. Firstly, I delve into the background, realities, and legacies of the major social changes that we have undergone in recent years. In this regard, globalization and the fourth industrial revolution are the focus of discussion. Secondly, I assess whether the current Korean education is well suited to preparing the students for the future. Here, I discuss in some detail such education problems as academic achievement-oriented education practices, the flourishing of private education, the widening education gap between classes, abnormal lifelong learning curve, and outdated educational paradigms. Thirdly, I present key issues based on the diagnosis of the main problems of Korean education discussed earlier. In this regard, I discuss the establishment of educational philosophy and vision, the transformation of educational paradigm, the reduction of private education expenses, the easing of educational gap between classes, and education reform to prepare for the fourth industrial revolution. Lastly, based on these discussions, I briefly present policy alternatives.
A Block Based Scheme using Tuned Tri-threshold Fuzzy Intensification Operators for Underwater Images

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Rethinking the teaching of grammar from the perspective of corpus linguistics

Despite calls from many composition and rhetoric scholars for instructors of writing to stop teaching prescriptive grammar, a vast number of handbooks intended for college writing classes encourage this tradition. For example, Hacker’s Pocket Style Manual has a section on grammar with instructions for students on how to write appropriately. While Hacker may not intend for her instructions to be taken as dictums, they often are, and much time is spent in many classrooms teaching students these rules of grammar. This article uses the Corpus of Contemporary American English (COCA) to support the calls from composition and rhetoric scholars that prescriptive instruction in grammar is more a hindrance to writing instruction than a benefit. Focusing on a few specifics from frequently used grammar handbooks and illustrating how big data shows the “rules” are incorrect at best, this article argues that, just as scholars of English have begun using big data to better understand literary history, scholars of rhetoric and composition might better understand how to help students to write by understanding patterns within big data. Certainly, this argument recognizes that “common usage” may not necessarily be the most eloquent usage. In making this argument, this article focuses on the [neither or either of X] + Verb construction, where the Verb may have either a plural or a singular form. Our findings illustrate that the “real world” writing is different from what textbooks dictate, and we suggest the data-driven observations need to be appropriately incorporated in writing classes. (University of Minnesota Duluth)
Computational and Technological Models of Cognitive Monitoring Systems

The approach to the construction of promising cognitive monitoring systems is considered.Based on the analysis of known solutions, the problem of automatic synthesis of computational models of cognitive monitoring is posed.In the synthesis of models, it is proposed to reduce them to relatively finite operational automata.An example of a synthesized model of cognitive monitoring is given.
Recent Technology For Translation Study

This article aimed to offer the recent technology which can be used for translation study.This technology which, surely, mostly provided through the internet access has the purpose to assist the translator especially translation students in finishing their translation works.Basically, the most common assistance comes from these two technology innovations: computer-assisted translation tools (CAT tools) and machine translation (MT).There are many options from this two technologies which can be utilized to ease and smoothen the translation works, such as trados, wordfast and Atril DéjàVu as the choise for CAT tools and two ranks of commercial and free machine translation as the choice for MT.This two technologies have been developt significantly by the provider.However, human intervention still helds an important influence on their quality improvement.Therefore, human translation as the user of this two recent technologies should know how to wisely used them.
ViewSeeker: An Interactive View Recommendation Tool.

View recommendation has emerged as a powerful tool to assist data analysts in exploring and understanding big data. Existing view recommendation approaches proposed a variety of utility functions in selecting useful views. Even though each utility function might be suitable for specific scenarios, identifying the most appropriate ones along with their tunable parameters, which represent the user’s intention during an exploration, is a challenge for both expert and non-expert users. This paper presents the first attempt towards interactive view recommendation by automatically discovering the most appropriate view utility functions in an exploration based on the user’s preferences. In particular, our proposed ViewSeeker uses a novel active learning method to discover the view utility function by interactively refining the set of k view recommendations. The effectiveness and efficiency of ViewSeeker was experimentally evaluated using both synthetic and real data sets.
Skeletal muscle modeling by fractional multi-models: analysis of length effect

Many experiments and measures have been carried out on skeletal muscles of different species, such as frog, salamander, rabbit, and rat. Exploiting these measures for system identification through non-integer models has led to many promising results. These models are useful in medicine as they help understanding their organ working or dysfunction. Moreover, they are used for the rehabilitation of deficient muscles via electrical stimulation. Finally, these models are particularly interesting for bio-inspired robotics; indeed, they enable piloting an actuator in order to more precisely reproduce a natural movement of an articulation or a limb. Non-integer modeling has the advantage of ensuring parametric parsimony (or parametric compactness), thus enabling an easier implementation (“embeddable” model). A muscle is composed of muscular fiber groups that are innervated by several motor neurons called α motor neurons. The motor units are classified as three different kinds according to their physiological and contractile properties: motor units of type S (Slow), motor units of type FF (Fast, Fatigable), and motor units of type FR (Fast, Resistant). A non-integer multi-model is able to predict the time response of a motor unit (whatever its type) to an electrical stimulation during an isometric contraction. In order to make the multi-model more representative of the real dynamics of a motor unit, the muscle length influence on the model parameters has been studied to determine the best strategy to take into consideration this phenomenon.
A Comparative Study in Online Movie Ticket Booking System

Due to increase in technology and digitalization online movie system has become much more popular. Instead of wasting time in queues of movie counter, it provides easy and efficient way to book the tickets online. It is quite fascination to sit at home browses the movies which have been released, read the review and then book the tickets according to our feasibility. Movie booking system is actually in action for the last 15 years but it gained its popularity after 2004 and its reach increased several folds in the past 6–10 years because of increasing net accessibility. In the proposed research paper we have sorted out some of the major issues related to online movie management system. Due to increased traffic server problem and transaction failure are the major problems with online movie booking system. To sort out these Node JS and Mongo DB connectivity has been used. Also, there are some more features which have been addend and are new to movie booking system like mailing the review and trailer of the movie to the user while they book the ticket and showing the view of screen from different orientation of screen. For some trustworthy review we have designed our own questionnaire so that it can give exact view of what is the true review of movie. These reviews will cross checked by the editorial team and then published on the web site. So basically it¶s an advanced version of online movie booking system in which few new features has been added as well as the problems with previous system has been rectified.
An Empirical Examination of the Effects of IT Leadership on Information Security Risk Management in USA Organizations

Although organizations face continuously evolving Information Security (IS) risks, the scholarly literature is unclear as to whether transformational, transactional, and passive-avoidant leadership styles influence IS risk management. The study was conducted using a quantitative, non-experimental, and descriptive research design. The sample consisted of senior IT leaders with a range of titles including Chief Information Officer (CIO), Chief Information Security Officer (CISO), Director of IT, and IT Manager. This population is characterized by extensive knowledge of IT and IS issues, and these individuals are generally responsible for directing an organization’s approach to IS risk management. Data from 250 participant surveys were analyzed using the Pearson product-moment coefficient correlation and multiple regression analysis. The results of the analysis demonstrated that both IT leadership is significantly related to IS risk management.
Data cryptography based on musical notes on a fingerboard along with a dice

&lt;p&gt;The security of an online system is the foremost necessity nowadays. With huge growth of the IT power and with the invention of new technologies, the number of threats a user faces is growing exponentially. Cryptography is a combination of security engineering and mathematics. It is the best technology for securing distributed systems. Cryptography consists in processing plain information by applying a cipher and producing encoded output, unknown to a third-party who does has no idea about the key. In cryptography both encryption and decryption phase are processed by one or more keys. Encryption is extremely important for a safe and secure environment for the computers and the Internet.&lt;/p&gt;
Strategies and Tools for Digital Repository Selection and Migration

As digital repositories evolve, so to do the needs of institutions who employ them. Increasingly, institutions are faced with the daunting task of migrating content from one repository to another. But what strategies exist to help institutions identify suitable repositories and effectively and efficiently plan and execute a migration? This workshop aims to explore the issues and strategies of repository (re)selection and migration. Participants will learn about the different phases of a migration process including: system evaluation and selection, migration planning, and implementation strategies and tools. Throughout the workshop, participants will actively explore these phases as they relate to their organizational context and come away with questions and next steps for planning for system selection and/or a migration at their own institution. Target Audience: Digital repository managers, metadata librarians, digital services librarians Format: Half Day Tutorial Expected Number of Participants: 20 Tutorial History: Completed one session at the 2019 Digital Initiatives Symposium
The Self-efficacy of Primary School Teachers in Teaching Mathematics: A Comparative Research on Teacher Training Program Products

This research aims at comparing the self-efficacy of primary school (SD – Sekolah Dasar) teachers who have taken teacher training program (PPG – Program Pelatihan Guru) on mathematics teaching and those who have not, which was seen from the personal belief sub-factors in teaching mathematics and the expected mathematics teaching outcomes. This research employed quantitative method with comparative research type to 198 samples. The data were analysed through T-test using SPSS. The result showed that there was no significant self-efficiency differences between primary school teachers who have taken PPG on mathematics teaching and those who have not. The results of this research indicated that self-efficacy was an important variable in pedagogical competence as it could influence the way teachers manage the classroom to attain maximum students’ achievement.
Recognition of Partial Textual Entailment for Indian Social Media Text

Textual entailment (TE) is a unidirectional relationship between two expressions where the meaning of one expression called Hypothesis (H), infers from the other expression called Text (T). The definition of TE is rigid in a sense that if the H entails from T but lacks minor information or have some additionalinformation, then the pair treated as non-entailed. In such cases we could not measure the relatedness of a T-H pair. Partial textual entailment (PTE) is a possible solution of this problem which defines partial entailment relation between a T-H pair. PTE relationship can plays an important role in different Natural Language Processing (NLP) applications like text summarization and question-answering system by reducing redundant information. In this paper we investigate the idea of PTE for Indian social media text (SMT). We developed a PTE annotated corpus for Bengali tweets and proposed a Sequential Minimal Optimization (SMO) based PTE recognition approach. We also evaluated our proposed approach through experiment results.
Special Section on Big Data and Cloud Technologies

This paper presents a system for Complete Coverage Path Planning to survey an outdoor area with a flock of Unmanned Aerial Vehicles (UAVs). The proposed system is able to manage the presence of no-fly zones, by decomposing the area into smaller subregions via a Morse-based decomposition approach. Furthermore, negotiation for UAV-subregion assignment is managed, taking into account environment geometry and both starting and desired final positions for the UAVs, once the coverage mission is completed. The back-and-forth motion is chosen as coverage pattern inside each subregion. All these features have been implemented with an integrated software framework, capable of working with georeferenced maps and data structures, thus obtaining a software tool compatible with commonly used geographic information systems.
Policy Gradient using Weak Derivatives for Reinforcement Learning

Reinforcement Learning (RL) is a form of implicit stochastic adaptive control where the optimal control policy is estimated without directly estimating the underlying model parameters. This paper considers reinforcement learning for an infinite horizon discounted cost continuous state Markov decision process. In a MDP, actions affect the Markovian state dynamics and result in rewards for the agent. The objective is to find a map from the states to actions, also known as policy, that results in the accumulation of largest expected return over an infinite horizon. There are many ways to estimate such a policy: policy iteration, Q-learning (which operates in the so called “value” space), policy-gradients (which operates in the policy space); see [1, 14] for a detailed discussion of these methods.
Deep Policies for Width-Based Planning in Pixel Domains

Width-based planning has demonstrated great success in recent years due to its ability to scale independently of the size of the state space. For example, Bandres et al. (2018) introduced a rollout version of the Iterated Width algorithm whose performance compares well with humans and learning methods in the pixel setting of the Atari games suite. In this setting, planning is done on-line using the “screen” states and selecting actions by looking ahead into the future. However, this algorithm is purely exploratory and does not leverage past reward information. Furthermore, it requires the state to be factored into features that need to be pre-defined for the particular task, e.g., the B-PROST pixel features. In this work, we extend width-based planning by incorporating an explicit policy in the action selection mechanism. Our method, called π-IW, interleaves width-based planning and policy learning using the state-actions visited by the planner. The policy estimate takes the form of a neural network and is in turn used to guide the planning step, thus reinforcing promising paths. Surprisingly, we observe that the representation learned by the neural network can be used as a feature space for the width-based planner without degrading its performance, thus removing the requirement of pre-defined features for the planner. We compare π-IW with previous widthbased methods and with AlphaZero, a method that also interleaves planning and learning, in simple environments, and show that π-IW has superior performance. We also show that π-IW algorithm outperforms previous width-based methods in the pixel setting of Atari games suite.
Correction to: Breast cancer classification in pathological images based on hybrid features

The article Breast cancer classification in pathological images based on hybrid features, written by Cuiru Yu, Houjin Chen, Yanfeng Li, Yahui Peng, Jupeng Li and Fan Yang, was originally published electronically on the publisher’s internet portal (SpringerLink) on March 16, 2019 with open access.