Reviewerly/exHarmony huggingface dataset:
Authentication Verification System

The field of Handwritten Authentication Verification has been extensively inquired about in themost recent decades, yet stays an open research problem. People are acquainted with pen and papers forconfirmation and approval in legal exchange. Because of expanding the use of handwritten authentications, it isimportant that an individual manually writes authentication to be recognized uniquely. Authentication is a socialbiometric described by a social attribute that a person learns and obtains over a timeframe and turns into his uniqueidentification. This paper clarifies the importance of the offline system and presents an overview of differentmethodologies being pursued in various territories. This being a beginning territory under look into, the overviewcovers a portion of the instances of the ways. The goal of an independent confirmation system is to separate if agiven authentication is certifiable (created by the guaranteed individual), or duplicity (delivered by an impostor).This has exhibited to be a difficult job, specifically in the offline (static) situation, that utilizes pictures ofexamined authentications, where the dynamic data about the objectivism procedure is not accessible.
Multiparty probabilistic controlled joint remote preparation of an arbitrary &amp;lt;italic&amp;gt;m&amp;lt;/italic&amp;gt;-qudit equatorial state with a pure entangled quantum channel

<p indent="0mm">The quantum equatorial state, a special kind of quantum state, has attracted much attention because of its potential for applications in quantum information processing. The remote preparations of arbitrary two-qudit, three-qudit equatorial and <italic>m</italic>-qudit equatorial states have been studied via different entangle quantum channels. In this paper, we propose two protocols for the controlled joint remote preparation of an arbitrary <italic>m</italic>-qudit equatorial state with a pure entangled quantum channel. The first is for the controlled joint remote preparation of the <italic>m</italic>-qudit equatorial state by using <italic>m</italic> multiparticle GHZ-class states as the quantum channel, and the second is for the controlled joint remote preparation of the <italic>m</italic>-qudit equatorial state via a multiparticle entangled state. All senders share information about the prepared state and perform <italic>m</italic>-particle projective measurements on their entangled particles according to their information about the prepared state. All controllers perform generalized X-basis measurements on their entangled particles. If a receiver cooperates with all the senders and controllers, he can prepare the original state probabilistically by performing unitary operations on his entangled particles and an auxiliary qubit. This protocol is more convenient for applications because it only requires nonmaximal entanglement for the controlled joint remote preparation of an arbitrary <italic>m</italic>-qudit equatorial state.
Implementing a digital media lab: Defining needs and planning for space and service

The creation of digital content is increasingly expected in higher education for both students and faculty, but campus facilities supporting this work are often inaccessible to the community. By implementing digital media labs—spaces that house the technology to create digital content—academic libraries can position themselves to meet this need for their patrons. This article presents a case study of implementing a digital media lab at a mid-sized regional comprehensive university, the planning that went into it, the lessons learned, and how the experiences can inform other academic libraries interested in implementing a similar service.
Replica Sets and Atlas

So far, we have considered performance tuning singleton MongoDB servers – servers that are not part of a cluster. However, most production MongoDB instances are configured as replica sets, since only this configuration provides sufficient high availability guarantees for modern "always-on" applications.
The Data Communication Security Design on IoT Based Systems with the Port Knocking Method

Technology is the thing that is familiar among the public and the Internet is something that is always used in everyday to help the work . With the technology and the internet, IOT (Internet of Things) can be realized in any device that is there to build the Smart City. However, there are still people who use the internet to commit crimes. To prevent it is not enough with a firewall alone but need the system of security that is over. In the article it discusses how port knocking would be able to help secure a system that is based IOT it.
The making of Android-based search engine applications with Elastic-search algorithm to improve programming competence

Abstract This research aims to create an Android-based search engine application using the Elastic-search algorithm which can later be used for students learning object-oriented programming. This media contains a search for material that makes it easy to learn and do assignments. The method used in developing this application is 4D from Thiagarajan and using Java language programming. The results of this study indicate that this application is valid for use in terms of Quality of visual and auditory perception, Ease of interaction and collaboration, Ease of learning to operate, Ease of use. It can be concluded that the Search engine application with Elastic Search algorithm is declared valid to be used as learning for students who want to learn about programming.
Foreign Direct Investment, Information Technology and Economic Growth Dynamics in Sub-Saharan Africa

The research assesses how information and communication technology (ICT) modulates the effect of foreign direct investment (FDI) on economic growth dynamics in 25 countries in Sub-Saharan Africa for the period 1980-2014. The employed economic growth dynamics areGross Domestic Product (GDP) growth, real GDP and GDP per capita while ICT is measured by mobile phone penetration and internet penetration. The empirical evidence is based on the Generalised Method of Moments. The study finds that both internet penetration and mobile phone penetration overwhelmingly modulate FDI to induce overall positive net effects on all three economic growth dynamics. Moreover, the positive net effects are consistently more apparent in internet-centric regressions compared to “mobile phone”-oriented specifications. In the light of negative interactive effects, net effects are decomposed to provide thresholds at which ICT policy variables should be complemented with other policy initiatives in order to engender favorable outcomes on economic growth dynamics. Practical and theoretical implications are discussed.
Skimming Techniques to Improve Student Understanding in Reading Learning in Class IV Elementary Schools

This research is motivated by reading activities that occur in learning Indonesian which has problems, namely low reading skills. The purpose of this study was to answer the formulation of the problem of how the interest in learning to read using skimming techniques, how to learn reading activities using skimming techniques, and how the learning outcomes of reading with skimming techniques in improving students' reading comprehension. The research method used is classroom action research. This has been done by researchers accompanied by data collection by tests and observations through observation, field notes, and interviews. The data were obtained from research subjects, namely students of grade IV SDN Simpang I, totaling 25 people consisting of 12 boys and 13 girls. The results showed that there was an increase in reading skills accompanied by high understanding in the first cycle it was obtained an average final score of 75.4, in the second cycle it obtained an average final value of 82.4, and in the third cycle it obtained the final average value. learning 87.2, this shows that this skimming technique can improve students' reading learning well, so as to improve and optimize the quality of learning in the classroom. Based on the data above, it is clear that the right learning material is the first step that must be determined by a teacher before carrying out learning so that success in learning activities can be achieved.
RISK OF ANEMIA AMONG ELEMENTARY SCHOOL STUDENTS IN EAST KALIMANTAN PROVINCE, INDONESIA: A CASE CONTROL PROTOCOL STUDY

The issue of anemia in Indonesia still remains a homework for the Ministry of Health, as well as other related sectors.  According to data from the Basic Health Research (Riset Kesehatan Dasar) in 2013, there are 31% female adolescents in Indonesia who suffers from anemia.  However, this number increased to 48.9% according to data from the Basic Health Research in 2018, with the most proportion of anemia found in the 15-24 and 24-34 age groups.  These cases clearly confirms that the health state of adolescents highly determines the success of health development, especially in the effort to establish the quality of the next generation in the future.  This research uses case control design, where the case group population are students at Muhammadiyah Elementary Schools located in the city of Samarinda, East Kalimantan Province, whereas the control group population are students from Islamic Elementary Schools in the city of Samarinda, East Kalimantan Province. The statistic analysis being used is the multiple regression analysis to look for risk factor with the highest effect. This is a retrospective research, that is extracting past variable information from respondents so recall bias, which can cause wrong information, is at risk of occurance.  Furthermore, the bias that might occur is that  the respondent is bias by giving false answers.  Another mistake is in the form of other cofounding variables not included in the research, which results in risk estimation value error.
Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews

In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brandassociated polarity-bearing topics from product reviews.Different from existing models for sentiment-topic extraction which assume topics are grouped under discrete sentiment categories such as 'positive', 'negative' and 'neural', BTM is able to automatically infer real-valued brand-associated sentiment scores and generate fine-grained sentiment-topics in which we can observe continuous changes of words under a certain topic (e.g., 'shaver' or 'cream') while its associated sentiment gradually varies from negative to positive.BTM is built on the Poisson factorisation model with the incorporation of adversarial learning.It has been evaluated on a dataset constructed from Amazon reviews.Experimental results show that BTM outperforms a number of competitive baselines in brand ranking, achieving a better balance of topic coherence and uniqueness, and extracting better-separated polaritybearing topics.
Improving Deep Learning based Multi-document Summarization through Linguistic Knowledge

Multi-document summarization is one of the most important tasks in the field of Natural Language Processing (NLP) and it gains increasing attention in recent years. It aims to generate one summary across several topic-related documents. Compared with extractive summarization, abstractive summarization is more similar to human-written ones. Proposing effective and efficient abstractive multi-document summarization models is significant to the NLP community. Existing deep learning based multi-document summarization models rely on the exceptional ability of neural networks to extract distinct features. However, they have missed out important linguistic knowledge such as dependencies between words since linguistics information in texts is full of meaningful knowledge with respect to the input documents. Besides, how models automatically evaluate the quality of the summary is crucial to design a high-performance summarization model since the evaluation indicator objectively measures the effectiveness of a method. In this proposal, we bring forward two research questions and corresponding solutions for the abstractive multi-document summarization task.
MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?

The paper describes the MilaNLP team’s submission (Bocconi University, Milan) in the WASSA 2021 Shared Task on Empathy Detection and Emotion Classification. We focus on Track 2 - Emotion Classification - which consists of predicting the emotion of reactions to English news stories at the essay-level. We test different models based on multi-task and multi-input frameworks. The goal was to better exploit all the correlated information given in the data set. We find, though, that empathy as an auxiliary task in multi-task learning and demographic attributes as additional input provide worse performance with respect to single-task learning. While the result is competitive in terms of the competition, our results suggest that emotion and empathy are not related tasks - at least for the purpose of prediction.
China as a Neural-Information Megamatrix: Digital Technologies for Structuring Cognitive Ensembles of Order

In China, the strategy for the national information infrastructure development is implementing a large-scale and unparalleled project for developing artificial intelligence and its application to solving critical problems. Threats and risks of the current stage left no choice to China. The best Soviet and Russian developments of centralized planning and modeling are actively used, advanced American experience in applying neuroinformation technologies to influence society is being adopted. In fact, a neuroinformational mega-matrix is being formed, which will allow the Chinese authorities to keep the seething mass of people and organizations within a controlled circuit. Configuring cognitive communications in the socio-technical system “person — infocommunication environment — the state” allows to develop a social credit model for forming behavior vectors in large groups of the population with culling disloyal in behaviour (implemented now) and disloyal in thinking (soon to be implemented) human units of the China-community.
Prototype Development of Android-Based Thesis Information System at Institute Informatics and Business (IIB) Darmajaya Bandar Lampung

The rapid progress of Android-based mobile device technology has propelled the development of various applications that add diversified functions to mobile devices. Advanced systems in Android-based applications have also been utilized in many different sectors of life, including in education, from K-12 to university. Such advanced systems will be able to effectively solve distance and time limitations. This study tries to develop an Android-based system of thesis/final project consultation service that will help us solve some issues in availability schedule between students and lecturers. It is not designed to replace conventional consultation service, but rather to serve as an additional channel that connects students and lecturers in planning the thesis/final project consultation process. The ultimate aim, therefore, is to smooth the way for consultation process and to help more students complete their theses/final projects without considerable delays. In addition, the online consultation system will minimize paper consumption since it offers a paperless procedure. Keywords: Android, consultation, thesis
Applying the Affective Aware Pseudo Association Method to Enhance the Top-N Recommendations Distribution to Users in Group Emotion Recommender Systems

Recommender Systems are a subclass of information retrieval systems, or more succinctly, a class of information filtering systems that seeks to predict how close is the match of the user's preference to a recommended item. A common approach for making recommendations for a user group is to extend Personalized Recommender Systems' capability. This approach gives the impression that group recommendations are retrofits of the Personalized Recommender Systems. Moreover, such an approach not taken the dynamics of group emotion and individual emotion into the consideration in making top_N recommendations. Recommending items to a group of two or more users has certainly raised unique challenges in group behaviors that influence group decision-making that researchers only partially understand. This study applies the Affective Aware Pseudo Association Method in studying group formation and dynamics in group decision-making. The method shows its adaptability to group's moods change when making recommendations.
Optimization of Coagulation and Flocculation in Concrete Wastewater of Precast Industry

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; The population increase until the end of 2019 reached 267 million. This is in line with developments that support activities. This is inseparable from the actions of one of the precast concrete companies PT.WB Precast Plant Karawang in supporting development that produces waste in the form of wastewater. The parameter measured as a reference in carrying out processing is the Minister of Health Regulation Number 32 of 2017. This study's method refers to turbidity as a physical parameter, namely 25NTU and hardness total as a chemical parameter, which is 500 mg / l. CaCO3. &lt;strong&gt;Objectives:&lt;/strong&gt;&lt;strong&gt; &lt;/strong&gt;The purpose of this study was to determine the optimization of the use of PAC as a coagulant and flocculant to reduce the turbidity and total hardness and determine the optimum detention time of flocculation.&lt;strong&gt; &lt;/strong&gt;&lt;strong&gt;Method and results:&lt;/strong&gt;&lt;strong&gt; &lt;/strong&gt;The sampling method in this study is observation. The population in this study is concrete wastewater with parameter values that are not in accordance with the standard.&lt;strong&gt; Conclusion:&lt;/strong&gt; By using PAC 10% the optimum doses is 140 ppm, and the optimum detention time of flocculation is 5 minute that can reduce the turbidity from 275 NTU to be 11.615 NTU or 95.7% and can reduce total hardness from 948.75 mg/l CaCO3 to be 491.25 mg/l CaCO3 or 48.2%.&lt;/p&gt;
Computer Aided Melanoma Detection Using GLCM Matrix Analysis

Melanoma skin cancer is very dangerous type of skin cancer among the other types of skin cancer. It starts in the melanocytes due to the unwanted growth of the cells in human body and usually these are in the black or brown in color. It develops anywhere on the skin. It is very serious type of skin cancer among the other types of skin cancer because it spreads very vastly to other parts of the body if not diagnosed and treated early it spreads in distinct areas such as liver, lymph, etc. The aim of this study is to detect melanoma at early stage for the effective treatment and prevention. In this paper computer aided diagnosis system is presented. This system consist pre-processing, segmentation, feature extraction and classification of the image. For which the filtering process is used for pre-processing purpose , the segmentation is done by using K-means clustering algorithm and feature extraction is done by GLCM matrix analysis. From the parameters getting from GLCM matrix through that ANN will be trained to classify the image.
Virtual Pre-Service Teacher Assessment and Feedback via Conversational Agents

Conversational agents and assistants have been used for decades to facilitate learning. There are many examples of conversational agents used for educational and training purposes in K-12, higher education, healthcare, the military, and private industry settings. The most common forms of conversational agents in education are teaching agents that directly teach and support learning, peer agents that serve as knowledgeable learning companions to guide learners in the learning process, and teachable agents that function as a novice or less-knowledgeable student trained and taught by a learner who learns by teaching. The Instructional Quality Assessment (IQA) provides a robust framework to evaluate reading comprehension and mathematics instruction. We developed a system for pre-service teachers, individuals in a teacher preparation program, to evaluate teaching instruction quality based on a modified interpretation of IQA metrics. Our demonstration and approach take advantage of recent advances in Natural Language Processing (NLP) and deep learning for each dialogue system component. We built an open-source conversational agent system to engage pre-service teachers in a specific mathematical scenario focused on scale factor with the aim to provide feedback on pre-service teachers’ questioning strategies. We believe our system is not only practical for teacher education programs but can also enable other researchers to build new educational scenarios with minimal effort.
US and Asian consumer device manufacturers are targeting the Visegrad4 countries to avoid private copying compensation

The European Federation of Journalists (EFJ) has co-signed a letter with representatives of authors, performers, publishers and creative workers at EU ...
2019 JPART Community Survey

﻿﻿This report presents results from the 2019 community survey for JPART. First, we describe the sample, survey administration, and respondent characteristics. We then present the survey data in four sections: perceptions of JPART, mechanisms for improvement, subfields and sections, and reviewer experiences. Each section presents the descriptive data from the survey items and a discussion of how the editorial team is responding to the information.
A group decision-making model for important power user risks based on cloud model and set pair analysis: a case study in China

The safe and stable operation of important power users plays an important role in the harmonious development of society and economy. Different from previous studies which mostly based on reliability model, a group decision-making model based on cloud model and connection number is proposed in this paper. First, an assessment index system is established including operation safety risk, cooperation credit risk, power regulation risk and operating capacity risk. Then, with the advantage in dealing with qualitative language, cloud model is employed to convert the qualitative concepts into quantitative values, which reduce the fuzziness and randomness. Next, in order to integrate the information among different index, the geometric operator of binary connection number is defined. Finally, the comparison method of connection numbers is put forward to rank the results. The example and sensibility analysis verify the effectiveness and applicability of the model in this paper.
Dynamic Shadow Detection and Removal in Evening Time for Vehicle Counting System

Shadow detection and removal are an important process for the further progression in computer vision method, i.e., tracking, recognition, classification, etc. Vehicle counting system in traffic management is not able to count exact number of vehicles without shadow detection and removal especially during evening time. Due to strength of shadow in evening time, the shadow detection is quite difficult in traffic especially in evening time. Many researchers have published paper on shadow detection, but for daylight/noon time dataset. Hence, need arises for determination of shadow detection in evening time. In this paper, HSV color space-based non-deterministic model with background updating concept is proposed for shadow detection during evening traffic dataset. Investigational outcomes suggest that the proposed method has been able to detect dynamic shadows accurately.