Reviewerly/exHarmony huggingface dataset:
ANALISIS FAKTOR-FAKTOR KESULITAN BELAJAR SISWA DALAM PEMBELAJARAN DARING (DALAM JARINGAN) PADA MATA PELAJARAN EKONOMI KELAS XII DI SMA NEGERI 1 KOTA JAMBI TAHUN AJARAN 2020/2021

Abstract This study aims to determine the factors of student learning difficulties in online learning in Economics class XII at SMA Negeri 1 Jambi City. This type of research is a descriptive study and a quantitative approach. The population in this study was 174 people. The sampling technique in this study was random sampling, so that 121 students were selected as the research sample. The data collection technique in this study was to distribute questionnaires to research respondents. The data analysis technique in this study is factor analysis with the SPSS For Windows Version 16 program. The results of this study indicate that (1) the learning difficulty variable is in the sufficient category with an average of 3.40 and the Respondents' Achievement Level (TCR) of 67.92. and (2) there are 11 (eleven) dominant factors that influence student learning difficulties, namely (a) factor 1 which is given the identity of the Learning Environment, (b) factor 2 which is given the identity of Learning Interests, (c) factor 3 which is given the identity of Talent, (d) factor 4 which is given the identity of Learning Motivation, (e) factor 5 which is given the identity of Learning Habits, (f) factor 6 which is given the identity of Intelligence, (g) factor 7 which is given the identity of Student Characteristics, (h) factor 8 which is given School climate identity, (i) factor 9 which is given the identity of the Learning Method, (j) factor 10 which is given the identity of Learning Tools, and (k) factor 11 which is given the identity of Family Roles. Keywords: Learning Difficulties.
APPLICATION OF E-GOVERNMENT IN IMMIGRATION MANAGEMENT INFORMATION SYSTEM (SIMKIM) AT DIRECTORATE GENERAL OF IMMIGRATION

&#x0D; &#x0D; The government is currently implementing an e-government system to provide services to the community using technology. E-government or electronic government is the use of information technology by the government to provide information and services to its citizens. Presidential Instruction No. 3 of 2003 concerning national policies and strategies for e-government development and Presidential Regulation of the Republic of Indonesia Number 95 of 2018 concerning Electronic-Based Government Systems are the legal basis for designing SPBE in Indonesia. The application of e-government at the Directorate General of Immigration can be seen in its centralized services using SIMKIM. This research uses a qualitative approach with descriptive analysis method. In the discussion this research has been based on the selection of research problems related to the suitability of e-government design in SIMKIM at the Directorate General of Immigration based on Presidential Instruction No. 3 of 2003 and Presidential Decree No. 95 of 2018 and the implementation of e-government at the Directorate General of Immigration according to the stages of the Deloitte and Touche framework. The results of the discussion stated that the implementation of e-government at the Directorate General of Immigration was in accordance with the two regulations and the Director General had entered the second stage based on the Deloitte and Touche framework. It is hoped that this research can be used for further research development.&#x0D; &#x0D; &#x0D;
An Overview of the Concepts, Classifications, and Methods of Population Initialization in Metaheuristic Algorithms

Metaheuristic algorithms are typically population-based random search techniques. The general framework of a metaheuristic algorithm consisting of its main parts. The sections of a metaheuristic algorithm include setting algorithm parameters, population initialization, global search section, local search section, and checking the stopping conditions in a metaheuristic algorithm. In the parameters setting section, the user can monitor the performance of the metaheuristic algorithm and improve its performance according to the problem under consideration. In this study, an overview of the concepts, classifications, and different methods of population initialization in metaheuristic algorithms discussed in recent literature will be provided. Population initialization is a basic and common step between all metaheuristic algorithms. Therefore, in this study, an attempt has been made that the performance, methods, mechanisms, and categories of population initialization in metaheuristic algorithms. Also, the relationship between population initialization and other important parameters in performance and efficiency of metaheuristic algorithms such as search space size, population size, the maximum number of iteration, etc., which are mentioned and considered in the literature, are collected and presented in a regular format.
Fighting email phishing attacks utilizing brand icon (Favicon) : An Empirical Research

The release of tablets and smartphones, which offer more advanced communication and computing capabilities, has led to the strong emergence of communication media on the market. Email systems are continuously being improved. In addition to facilitating communication between businesses and customers, users and businesses are proposing new communication methods such as App-based chat and instant messengers. Nevertheless, email is still the most popular means of communication in use. Google’s Gmail is one of the most widely used email service applications in the email market. It is top-notch at filtering out spam and offers useful extras such as quick links to track deliveries, amend reservations, and more without opening the email and looking for a link. Google has interpreted “know your user” and designed a system that can be accessed even by the novice computer user [1]. As of May 2016, Google announced in their I/O Developers Conference that Gmail now has more than 1.5 billion monthly active users, giving Gmail the largest email user base [2]. Usability becomes a key factor in the adoption of these applications, which are often used by people who have problems when using mobile devices, spammers and hackers, and those who have limited experience with technology. The aim of this paper is to conduct an empirical study to evaluate the Gmail Sender’s Icon feature to stop phishing. The result of the study will provide recommendations that may help improve the efficiency and effectiveness of email communication and avoid phishing, scam.
Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization

In this paper, we present a semi-supervised training technique using pseudo-labeling for end-to-end neural diarization (EEND). The EEND system has shown promising performance compared with traditional clustering-based methods, especially in the case of overlapping speech. However, to get a well-tuned model, EEND requires labeled data for all the joint speech activities of every speaker at each time frame in a recording. In this paper, we explore a pseudo-labeling approach that employs unlabeled data. First, we propose an iterative pseudo-label method for EEND, which trains the model using unlabeled data of a target condition. Then, we also propose a committee-based training method to improve the performance of EEND. To evaluate our proposed method, we conduct the experiments of model adaptation using labeled and unlabeled data. Experimental results on the CALLHOME dataset show that our proposed pseudo-label achieved a 37.4% relative diarization error rate reduction compared to a seed model. Moreover, we analyzed the results of semi-supervised adaptation with pseudo-labeling. We also show the effectiveness of our approach on the third DIHARD dataset.
Arduino Based Motion Tracking Keyboard Using Machine Learning

In spite of the emergence of various sensors and input devices, the current methods have not been able to provide an accurate recognition system for gestures. Recognition system such as the one in Kinect using machine learning algorithms shows some of the use cases for these gestures to newer system and domains. The one in our paper provides a far more accurate result along with faster computational speed as it uses the support vector machine (SVM) algorithm. This motion recognition system tracks motion made in mid-air by device in a 3D space, log its speed, angular velocity, distance covered and some other variables at real time, and in turn convert the device captured data of motion into characters of English alphabets. The device presented in this paper presents solution based on support vector algorithms and discusses about some concepts raised from the device.
블렌디드 러닝 일상 한자어휘 교육으로 창의적 사고역량, 지식정보처리역량 기르기

This research makes second-year students of M Girls' Middle School and U Middle School feel that Chinese characters are needed for the right daily language life. And also it aims to develop the creative thinking skills and knowledge information processing skills of students at both schools with the Chinese character vocabulary of everyday language life. First of all, this researcher made them feel the need to learn Chinese characters through finding, reading, and writing themselves Chinese character vocabulary in newspapers, broadcasts, songs, and textbooks, through coining Chinese character vocabulary using Coinage Method. Second, this researcher made them develop creative thinking skills through Brainstorming to make Chinese character vocabulary based on Chinese character vocabulary of everyday language life, Forced Combination of writing using any three Chinese character vocabulary, Lotus Technique to write using 8 Chinese character vocabulary, Scamper Method to change Korean song lyrics to Chinese character song lyrics, and Control Association Method for Chinese character word chain. Third, this study made them develop the knowledge information processing capabilities by utilizing smartphone application Webtoon character, Word cloud, Auto lab, Singplay, and Flipa Clip. In terms of developing creative thinking capabilities, M Girls' Middle School has some meaningful results, but U Middle School did not. However, U Middle School has some meaningful results in terms of knowledge and information processing capabilities, but M Girls' Middle School did not. Also M Girls' Middle School has some meaningful results in terms of class satisfaction and the necessity of learning Chinese characters, but U Middle School did not.
ChaCha for Online AutoML

We propose the ChaCha (Champion-Challengers) algorithm for making an online choice of hyperparameters in online learning settings. ChaCha handles the process of determining a champion and scheduling a set of `live' challengers over time based on sample complexity bounds. It is guaranteed to have sublinear regret after the optimal configuration is added into consideration by an application-dependent oracle based on the champions. Empirically, we show that ChaCha provides good performance across a wide array of datasets when optimizing over featurization and hyperparameter decisions.
Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernels

Graph is an usual representation of relational data, which are ubiquitous in manydomains such as molecules, biological and social networks. A popular approach to learningwith graph structured data is to make use of graph kernels, which measure the similaritybetween graphs and are plugged into a kernel machine such as a support vector machine.Weisfeiler-Lehman (WL) based graph kernels, which employ WL labeling scheme to extract subtree patterns and perform node embedding, are demonstrated to achieve great performance while being efficiently computable. However, one of the main drawbacks of ageneral kernel is the decoupling of kernel construction and learning process. For moleculargraphs, usual kernels such as WL subtree, based on substructures of the molecules, consider all available substructures having the same importance, which might not be suitable inpractice. In this paper, we propose a method to learn the weights of subtree patterns in the framework of WWL kernels, the state of the art method for graph classification task [14]. To overcome the computational issue on large scale data sets, we present an efficient learning algorithm and also derive a generalization gap bound to show its convergence. Finally, through experiments on synthetic and real-world data sets, we demonstrate the effectiveness of our proposed method for learning the weights of subtree patterns.
Shape of Elephant: Study of Macro Properties of Word Embeddings Spaces

Pre-trained word representations became a key component in many NLP tasks. However, the global geometry of the word embeddings remains poorly understood. In this paper, we demonstrate that a typical word embeddings cloud is shaped as a high-dimensional simplex with interpretable vertices and propose a simple yet effective method for enumeration of these vertices. We show that the proposed method can detect and describe vertices of the simplex for GloVe and fasttext spaces.
An objective definition of seasons for the Mediterranean region based on synoptic weather types

&amp;lt;p&amp;gt;An objective definition of seasons for the Mediterranean region is performed based on the intra-annual variation of the frequencies of eight (8) objectively defined synoptic Weather Types (WTs). The data used for the determination of the 8 WTs are daily NCEP/NCAR Reanalysis grid point values of 2m air temperature, total cloud cover, 2m zonal and meridional wind components, 500hPa and 1000hPa geopotential height, 500hPa and 850hPa air temperature, 850hPa specific humidity and precipitable water over the Mediterranean region, for the period 1949-2018. Firstly, Principal Component Analysis (PCA) is applied to the inter-annual variations of the above parameters in order to reduce the dimensionality and then k-means Cluster Analysis (CA) is applied to the resultant Principal Components (PCs) in order to group dates with similar patterns of the above parameters, resulting in 8 synoptic WTs. Then, PCA and CA are applied again, now on the intra-annual variations of the frequencies of the resultant WTs and group dates of the year (seasons) with similar frequency distribution among the 8 WTs. According to the results, four (4) seasons are defined for the 70-year period 1949-2018. The same methodology is applied also for the five overlapping 30-year sub-periods 1949-1978, 1959-1988, 1969-1998, 1979-2008 and 1989-2018, leading to 4 seasons too. Although the characteristics of these seasons generally correspond to the ones of the four conventional seasons, there are differences regarding the onset and cessation dates and the duration. In general, it is found that winter and summer last about 4 months (115 and 114 days respectively), spring has a duration of about 2.5 months (72 days) and autumn lasts about 2 months (64 days). The most remarkable long-term changes of the seasons&amp;amp;#8217; characteristics are: (i) the colder winters and warmer autumns during the last decades, (ii) the shortening of winter and spring due to later onset and earlier cessation dates, respectively, and (iii) the extension of autumn and summer due to later cessation and earlier onset dates, respectively.&amp;lt;/p&amp;gt;
SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain

In the pursuit of natural language understanding, there has been a long standing interest in tracking state changes throughout narratives. Impressive progress has been made in modeling the state of transaction-centric dialogues and procedural texts. However, this problem has been less intensively studied in the realm of general discourse where ground truth descriptions of states may be loosely defined and state changes are less densely distributed over utterances. This paper proposes to turn to simplified, fully observable systems that show some of these properties: Sports events. We curated 2,263 soccer matches including time-stamped natural language commentary accompanied by discrete events such as a team scoring goals, switching players or being penalized with cards. We propose a new task formulation where, given paragraphs of commentary of a game at different timestamps, the system is asked to recognize the occurrence of in-game events. This domain allows for rich descriptions of state while avoiding the complexities of many other real-world settings. As an initial point of performance measurement, we include two baseline methods from the perspectives of sentence classification with temporal dependence and current state-of-the-art generative model, respectively, and demonstrate that even sophisticated existing methods struggle on the state tracking task when the definition of state broadens or non-event chatter becomes prevalent.
Skillful Use of Enterprise Risk Management (ERM) in Leveraging Strategy Success and Holistic BSC-based Hotel Performances

Realizing that enterprise risk management (ERM) is not a straightforward topic or a competence to master, it is important empirical research can find a way to convince the management team to adapt ERM. This research exploits the concept of strategic management, underpinning on a logic that a strategic match of both internal management focus and external environmental condition is crucial for strategy success. Based on the structural equation model (SEM) model tests using Maximum Livelihood (ML) function, it confirms that, as the competitive environment becomes more challenging, the roles of mission clarity and core value communicated to the organizational members, and establishing risk governance system and cultures, become very important and should be aligned. This research extends beyond this fundamental concept, stressing that risk management can function to reduce loss possibility of strategy execution, which leads to SEM confirming ERM as a significant mediator. Besides, this research contributes further to exhibit linkages of both strategy success and ERM to BSC-based performances, which makes the ERM and strategic management more holistic in nature. The accomplishment was based on 215 valid returned participations of the hotels located in Uttaradit and Phitsanulok, Thailand. Besides the theoretical contributions, this research makes use of statistical comparative analyses of many demographic variables (i.e. position, type of job, number of hotel rooms, service types, HR and hotel operations) to offer a rich spectrum of practical implications.
Research on Education Management System Based on Big Data and B/s Three-Tier Structure

Due to the rapid development and widespread use of computer technology and network technology, the management and working methods of universities are undergoing major changes. Educational administrative management in the daily work of each school is an indispensable and important link, which directly affects the quality of education and is part of whether the school can carry out the education work smoothly. The advancement of information technology has brought bright prospects for the innovation of school education management. Big data is a new thing developed with the rapid growth of massive data. It is different from conventional data because it has a large amount of diverse data, and the correlation between the data is very complicated. Various application information management systems using computer technology and network technology play an important role in the management and education of universities. According to the characteristics and requirements of education management, this article discusses the design ideas and implementation schemes of an education management system using B/S (Browser/Server) model and big data technology.
Metacognición y formación del estudiante universitario. Un estudio en la carrera Licenciatura en Educación especialidad Pedagogía-Psicología

La relevancia de la relacion entre la metacognicion y el proceso de formacion de la personalidad posee un amplio marco teorico. Sin embargo, la trasferencia de estos supuestos al proceso de formacion del estudiante universitario exige nuevos analisis. El estudio que se presenta asume los postulados teoricos de base, pero profundiza en las caracteristicas normativas y practicas que pueden obstaculizar o facilitar que la metacognicion se convierta en un eje articulador del proceso formativo. En este proposito, identificar las regularidades que concreta la relacion metacognicion-formacion del estudiante en la carrera Licenciatura en Educacion especialidad Pedagogia Psicologia, se asume como objetivo de esta comunicacion. La metodologia sustenta el uso de metodos y tecnicas cualitativas que permiten aportar a la comprension del problema y la proyeccion necesaria para resolverlo.Palabras clave: Formacion inicial, caracterizacion metacognitiva, experiencia inclusiva, psicopedagogos. ABSTRACTThe relevance of the relationship between metacognition and the personality formation process has a broad theoretical framework. However, the transfer of learning from these assumptions to the university student training process requires new analyzes. The study that is presented assumes the basic theoretical postulates, but delves into the normative and practical characteristics that can hinder or facilitate metacognition from becoming an articulating axis of the training process. For this purpose, identifying the regularities that the metacognition relationship between the student's formation in the Bachelor of Education specializing in Pedagogy Psychology is assumed as the objective of this communication. The methodology supports the use of qualitative methods and techniques that allow to contribute to the understanding of the problem and the necessary projection to solve it.Keywords: Initial training, metacognitive characterization, inclusive experience, psychopedagogues.
i-AEGIS-128: An Improved Authenticated Encryption Based on AEGIS-128

Authenticated Encryption (AE) is the latest cryptographic algorithm standard introduced through CAESAR (Com-petition for Authenticated Encryption: Security, Applicability, and Robustness). AE schemes provide a process for simultaneously achieving the security goals of confidentiality and integrity assurance. The constructions of authentication encryption is secure and efficient to protect the modern electronic information systems. In this research paper, we proposed an Improved AEGIS-128 Algorithm (i-AEGIS-128) for improving the standard AEGIS-128 algorithm to enhance the performance in which quarter round-based AES (Advanced Encryption Standard) is used for the encryption and decryption process. The proposed i-AEGIS-128 is based on an AEGIS-128 AES-round permutation modification for hardware implementation. The Results of i-AEGIS-128 on Intel(R)Core(TM) i7-3700 Processor running at 3.40 GHz speed with 16GB of RAM using C/C++ and HDL (Hardware Description Language) languages and platforms to determine the encryption and decryption time, throughput processing unit memory during encryption and decryption. Finally we compared the efficiency and power analysis of the hardware. The i-AEGIS-128 algorithm relies on the use of ¼ AES-round in authentication encryption to reduce the area consumed during processing, perform fast encryption and decryption, use less power and provide the best efficiency.