Reviewerly/exHarmony huggingface dataset:
Feasibility Analysis of the Application of BaKuLiKan Blended Learning in Mathematics Lessons

A feasibility study has been conducted with subject of the application of the BaKuLiKan blended learning model by utilizing hypermedia learning. The main purpose of the study is to determine the usefulness of the model to improve students' thinking skills in mathematics. The mathematics subject was chosen because until now the process of learning mathematics in distance or face-to-face learning is limited. There are still many complaints. The feasibility study involved one partner school, 1 model teacher and 12 students. The location for the feasibility study is a junior high school boarding school in the district of Bawen. The observation method is used for the data collection process. The results of the feasibility study show that 21st century students' skills are improving. The 21st century skills are literacy skills, critical thinking skills, creative skills and HOTS. Apart from 21st century skills, character strengthening is also observed in the learning process. The character of students is strengthened because learning involves social relationships, both when studying independently and learning with teacher guidance. Students can work collaboratively and dynamically when the See and Do learning process occurs.
Evaluasi Topik Tersembunyi Berdasarkan Aspect Extraction menggunakan Pengembangan Latent Dirichlet Allocation

Recently, Sentiment Analysis is used for expression detection of products or services. Sentiment Analysis is one category type with a level of aspect focused on extracting product aspects. One of the common methods used for aspect extraction is Latent Dirichlet Allocation (LDA) using random topic identification, but this method has not been able to find an acceptable topic with some aspects having been found. Undeterminable topics are referred to as the hidden topics. This study purpose is to evaluate and compare the suitability of identifying hidden topics between human and computer evaluation. The study is also focused on aspect extraction using a variety of LDA innovations. The data used in this study used case studies on e-Commerce. Data were processed using feature selection and grouped using LDA development. Then the data results are processed using Latent Topic Identification based on subjective and objective evaluations. The identification of hidden topic results was evaluated using several semantic and lexicon tests. The evaluation results indicate the comparison of two hidden topic identification assessment values is quite relevant with the average difference in value reaching 6%. As a result, computer calculations assist humans in determining topics if each topic has a low coherence value.&#x0D;
Image Retrieval using Hybrid Order Dither Block Truncation Code

A new approach is proposed to index images in database using features generated from the HODBTC compressed data stream. This indexing technique can be extended for CBIR. HODBTC compresses an image into a set of color quantizers and a bitmap image. The proposed image retrieval system generates two image features namely CCF and BPF from the minimum quantizer, maximum quantizer and bitmap image respectively by involving the visual codebook.
Applying cluster analysis to seismic tomography models: Uncovering similarities and differences in the spatial and spectral domains

&amp;lt;p&amp;gt;In recent years, several types of Machine Learning (ML) methods have been employed by Earth scientists to extract patterns and structures from multi-dimensional feature spaces. In this regard, images of the mantle obtained by different seismic tomography (ST) models are diverse datasets with varying structures due to their different theoretical approximations and input data. In this work, we apply an unsupervised ML method, K-means clustering, on ST models to explore their similarities and differences to improve our physical understanding of the Earth&amp;amp;#8217;s interior. The K-means clustering method requires ST models to be standardized in a three-dimensional domain. For this purpose, we implement a weighted average technique to resample ST models to radial structural zones with uniform horizontal grid resolutions. However, the homogenized ST models still have 10&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;-10&amp;lt;sup&amp;gt;4&amp;lt;/sup&amp;gt; parameters, which need to be distilled into a small number of summary features. Feature selection is thus a key part of this study: features should be independent from unphysical effects of inversion choices (e.g., the damping factor) and should instead capture the essence of the geological structure. Preliminary results obtained using the center of mass as the attribute to represent the longest wavelength part of the mantle structure show that P-wave and S-wave models do not cluster separately. Therefore, compositional anomalies do not play an essential role at these spatial scales. We plan to expand our analysis by including more summary attributes from both the spatial as well as the frequency domain.&amp;amp;#160;&amp;lt;/p&amp;gt;
Lightweigth Convolutional Neural Networks for Person Re-Identification

Person Re-Identification (Re-ID) is an important task in video surveillance systems. Computer vision algorithms can be used to search, retrieve and localize the person of interest in a camera network. Person Re-ID research is an active research, and most of the researches use deep backbone networks, such as ResNet-50 and GoogleNet, which are complex networks with many parameters to train. However, it is computationally complex and time consuming to train these networks for Person Re-ID especially when it is lacked to have a good computational power. Therefore, effective lightweight networks are needed to perform Person Re-ID with low computational power capacity. In this paper, we evaluate and compare some lightweight networks which proved themselves in object recognition tasks. We compare their accuracies and complexities. Evaluation is conducted on a commonly used Market-1501 dataset.
Changes in Crime Types and Countermeasures in the Fourth Industrial Revolution

제4차 산업혁명의 주요기술들인 로봇, 인공지능, 사물인터넷, 빅데이터 등을 통해 삶의 질이 향상되는 긍정적이 측면이 있다. 그러나 이런 기술들이 악용되어 새로운 유형의 범죄가 발생할 것으로 보이는 부정적인 측면도 나타날 것으로 보인다. 본 연구는 제4차 산업혁명시대의 핵심 기술들이 관련된 대표적인 범죄사례에 대하여 살펴보았고, 이를 바탕으로 향후 사이버범죄 수준을 넘어서는 새로운 범죄유형을 예상해 보면, 데이터 보안성 저하에 따른 사생활 침해와 감시위험의 증가, 민간사물 인터넷을 이용한 지능형 해킹범죄의 등장, 드론과 자율주행차를 이용한 국가중요시설의 침범과 공격, 3D 프린팅 기술을 이용한 생체조직 재생과 불법총포류의 제작, 인공지능 로봇 등 악용한 범죄, 사이버불링과 사이버테러, 비트코인 악용범죄 등이 나타날 것으로 보인다. 급속한 과학기술의 발달에 따른 여러 범죄는 정보화 사회의 본질적 부분에 까지 영향을 미칠 것으로 예상되므로 정보기술의 효과는 최대한 상승시키면서 문제점은 최소한으로 축소될 수 있도록 여러 정책을 수립하여 대응하도록 해야 한다. 시민이 참여하고 의견이 수용되는 범죄예방 체계가 구축될 수 있도록 과학기술의 연구개발과 정책연구를 통해 대응방안의 마련이 요구되는데 사이버위험과 사이버보안의 중요성에 대한 사회적 인식의 확산, 사이버보안관련법률제정 및 조직 강화, 개인정보 보호제도의 강화, 사이버위험 관리체계 구축, 형사절차의 보완, 형사사법기관의 변화, 사이버보안 국제협력체계 구축, 처벌규정의 정비, 신종범죄의 전문수사인력 양성, 비트코인을 악용한 범죄에 대한 기술적 대응방안, 사이버테러 관련 정보강화, 운영체제 다변화 및 방화벽 프로그램 권고와 같은 대응방안의 마련이 요구된다.This study aims to analyze violations of citizens privacy and increased risk of surveillance due to reduced data security, the emergence of intelligent hacking crimes using the Internet of Private Things, the invasion and attack of national critical facilities using drones and self-driving cars, the production of biocas and illegal guns using 3D printing technology, crimes using artificial intelligence robots, cyberbullying and cyber terrorism, and bitcoin abuse crimes that are all expected to emerge in the future. It is necessary to prepare countermeasures through research and development and policy research of science and technology so that a crime prevention system where citizens can participate and accept opinions can be established. It will be recommended to develop social awareness on cyber risks and the importance of cyber security, enact and strengthen cyber-security related laws and organizations, strengthen personal information protection systems, establish a cyber risk management system, supplement criminal procedures, establish a criminal judicial system, establish a cybersecurity international cooperation system, overhaul punishment regulations, train professional investigators for new crimes, strengthen cyber terrorism-related information, diversify operating systems and establish a secure firewall program.
Survey towards Human Activity Recognition using IoT Domain

This task presents a novel framework dependent on the Internet of Things (IOT) to Human Activity Recognition (HAR) by observing fundamental signs distantly.Here it is use raspberry pi, wearable sensors, computational psychiatry.Also have used AI calculations to decide the movement done inside four pre-set up classes (walk, climbing and run).With an increased availability in wearable sensors we explore a better understanding of human needs.Then, it can give input during and after the movement is performed, utilizing a distant checking segment with far off perception and programmable alerts.This framework was effectively executed.
Non-classical properties and generation schemes of superposition of multiple-photon-added two-mode squeezed vacuum state

In this paper, we study some non-classical properties and propose the generation schemes of the superposition of multiple-photon-added two-mode squeezed vacuum state (SMPA-TMSVS). Based on the Wigner function, we clarify that this state is a non-Gaussian state, while the original two-mode squeezed vacuum state (TMSVS) is a Gaussian state. Besides, the SMPA-TMSVS is sum squeezing, as well as difference squeezing. In particular, the manifestation of the sum squeezing and the difference squeezing in the SMPA-TMSVS becomes more pronounced when increasing parameters r and e. In addition, by exploiting the schemes of photon-added superposition in the usual order, we give some schemes that the SMPA-TMSVS can be generated with the higher-order photon-added superposition by using some optical devices.
Analysis on the Methods of Improving Teaching Quality Based on OBE Concept

Based on the outcome oriented education theory (OBE), this paper carries out the reform of the teaching status of the course Fundamentals of Programming, and optimizes and improves the teaching quality of the course through the arrangement of teaching practice. The purpose of this paper is to provide reference for the sustainable application of results oriented education theory in higher education curriculum reform.
A Deterministic Neural Network Approach to Playing Gin Rummy

This paper describes a deterministic approach to building a fixed-strategy gin rummy player. In the paper, we develop and evaluate both heuristic and neural network models for informing draw, discard, and knock decisions in the game. In this empirical study, we test performance of the models through competitive game play, show which best inform strategy, and demonstrate statistical significance of the improvement over a simple strategy. Through this empirical study, we indicate features that we expect to be helpful in future improvements to Gin Rummy play.
Análise dos indicadores temáticos da competência em informação na produção científica brasileira

It presents an analysis of the thematic indicators of information competence identified in the research developed in the Graduate Programs in Information Science in Brazil. In order to identify the thematic perspectives on information competence in Brazilian scientific production, it established as a source of information the Capes Thesis and Dissertations Catalog with the time frame of 2010-2019. The research is configured by using the exploratory method, a quanti-quali approach. To understand the data, he sought to contribute to the content analysis technique, using the indicators proposed by Belluzzo as a category for analyzing the content of each research. With the systematized data, it was possible to verify a national panorama of the productivity of the theme, verifying the years of higher and lower productivity, and the quantity of research by institutions and regions. The results show the southeast region and the central-west region with the highest and lowest number of productions, the year 2014 with the highest rate of investigations on the subject. It can be said that some subjects are recurrent and almost unanimous in academic productions, but there are gaps that require more contributions to the construction of a theoretical basis for information competence in Brazil.
Linguistic Summarization of Data in Decision-Making on Performance Evaluation

The process of evaluating the Human Resources performance of a project generates a large amount of information that conditions timely decision-making at runtime and serves as a basis for study to perfect or correct future management strategies. When the volume of information generated is very high, it is difficult to identify the truly important information. This research proposes a solution to the problems posed from the implementation of a method based on Linguistic Summarization of Data for the construction of Linguistic Summaries from the generation of Association Rules.
A multi-commodity network model for optimal quantum reversible circuit synthesis

Quantum computing is a newly emerging computing environment that has recently attracted intense research interest in improving the output fidelity, fully utilizing its high computing power from both hardware and software perspectives. In particular, several attempts have been made to reduce the errors in quantum computing algorithms through the efficient synthesis of quantum circuits. In this study, we present an application of an optimization model for synthesizing quantum circuits with minimum implementation costs to lower the error rates by forming a simpler circuit. Our model has a unique structure that combines the arc-subset selection problem with a conventional multi-commodity network flow model. The model targets the circuit synthesis with multiple control Toffoli gates to implement Boolean reversible functions that are often used as a key component in many quantum algorithms. Compared to previous studies, the proposed model has a unifying yet straightforward structure for exploiting the operational characteristics of quantum gates. Our computational experiment shows the potential of the proposed model, obtaining quantum circuits with significantly lower quantum costs compared to prior studies. The proposed model is also applicable to various other fields where reversible logic is utilized, such as low-power computing, fault-tolerant designs, and DNA computing. In addition, our model can be applied to network-based problems, such as logistics distribution and time-stage network problems.
Sammon Projected Feature Selection Based Linear Support Vector Regression for Big Data Predictive Analytics

In big data analytics, classification is a key problem to be resolved for providing efficient prediction results. Recently, many research works have been designed for big data classification. However, accuracy was not increased with minimal time complexity when considering large dataset as input. In order to handle such issues, Sammon Projected Feature Selection based Linear Support Vector Regression (SPFS-LSVR) Method is introduced. The SPFS-LSVR method is developed to minimize the time and space complexity involved in the classification with better accuracy. At first, SPFS-LSVR method takes big dataset as input where it includes number of features and data for executing predictive analytics. Then theSammon Projection is employed in SPFS-LSVR methodfor performing the feature selection in high dimensional data. Sammon Projection helps in finding the relevant features by mapping the high-dimensional space to the lower-dimensionality space. This in turns, the time and space complexity occurred during the classification is reduced. After that, SPFS-LSVR method uses a Linear Support Vector Regression (LSVR) model for examining the selected features of input data with higher accuracy. The designed model uses hyperplane for producing the exact prediction result with higher accuracy. The LSVR model determines the relationship between independent data (i.e., features of input data) and the dependent data (i.e., prediction outcomes) with the help of Laplace kernel function. From that, the maximum relationship between the input features of data is classified into different classes. This helps in SPFS-LSVR method to enhance the performance of classification with maximum accuracy and minimal error rate. Through the efficient classification performance of big data, proposed SPFS-LSVR method improves the big data predictive analytics process as compared to state-of-the-art works. Experimental evaluation is carried out using big dataset on factors such as prediction time, prediction accuracy, false positive rate and space complexity with respect to number of data.
Aplikasi Web Service Berbasis Android untuk Pencarian Kost Lingkungan Kampus Universitas Indraprasta PGRI

Human needs are the reason technology is developing rapidly. One of the benefits of technological development is making information very easy to obtain. Everyone can get various kinds of information, such as searching for a list of houses, rented houses to boarding houses. Many people get that information easily, but not a few people have difficulty getting the same information. One example that often occurs is overseas students who find it difficult to get boarding information. Indraprasta University students complain that it is difficult to get information on boarding houses that are close to campus. From this problem, it prompted the author to create an Android-based Boarding House Search Application. In making this application the author uses Android Studio as a code editor, and exchanges data on the web service using JSON. Searching for boarding houses in this application uses two ways, first a boarding search based on a list and second a boarding house search based on the user's closest location. The menu on the main page makes this application easy to use by anyone.
A Proposed Method for Age Detection of Person Based on the size of Face to the size of the Eye of a Face

Age Detection scheme is to estimate and predict the real age or age group of a person.  Facial expressions, emotions, posture, illumination, hair style and aging can be determined from facial appearance. A person at the age of 20 and at the age of 80 can never be identical in respect of facial expression. The structure of face is an important factor in determining age of a person. In this paper age detection technique has been presented with an aim to detect age of a person.
A Lightweight mutual authentication based on Attribute-based Encryption

Recently, With the development of Internet of Things, 5G and AI, the term smart medical is actually the combination of technology and medical. Our study takes smart as an example, which lies in the layout of the wards On the above. After pairing all connected sensor devices with the patient, which includes the measurement of the patient’s blood pressure, pulse, ECG... and other related physiological signals. However, some of the data is highly private. If there is no effective encryption and verification mechanism, these data may be leaked out or used improperly. Our study uses Attribute-based Encryption as the main framework. Users can choose to match the patient’s attribute characteristics to formulate some access policies. Patient data can only be accessed by certain access rules. Access by a specific user. Even if other users steal the data, they cannot obtain the plaintext content through encryption. In complete verification phase, ABE is used as the basis, and in the lightweight verification phase, the patient’s physiological data and timestamp are used as the seed, and the PRNG is used to generate random numbers. In this case, there are only three parties (patients, doctor, hospital server) know to calculate the random number, so that even if the others steal the parameter, they cannot complete the verification because they don't know how to make the random number. In addition, hash function and XOR operations are added to make the overall encryption and verification more complete.
Hybrid Recommendation System Approach for appropriate developer selection in Bug Repositories

The essential destination of this research is to develop a hybrid recommendation system methodology to enhance the overall performance accuracy of such existed systems, this recommendation approach normally utilized to assign or propose a few counted numbers of programmers or developers that capable of resolving system's bug reports generated automatically from an open source bug repository, meaning the system decides which programmers or developers should be taken into account to be in charge of finding a solution the bugs mentioned in the bug's report.The definition of the bug selection problems in bug repositories are the activities that developers achieve within program maintenance to fix some specific bugs.Because of lot of bugs are created daily, many developers required are quite large, therefore it is difficult to specify the accurate programmers or developers to find a solution for the issues for specific bug inside the code.The article also aims to improve the accuracy results obtained than existed traditional approaches for this purpose.Besides, we have considered the case of prioritization of system developers, the case can be utilized to find an appropriate grade of developers' achievements as prior knowledge to assist the system in assigning of bug report issue.The results have found that the importance of developers could support the bug triage worker more and help software tasks to solve the bugs fast and within required time during development and support cycles of the software.
Pengaruh Sistem Informasi Akuntansi Terhadap Kinerja Karyawan Pada PT. BPRS Lantabur Tebuireng Jombang

Every organization strives to always improve performance. This encourages companies to utilize accounting information systems in carrying out daily activities. The application of information systems within the organization can simplify and speed up the completion of tasks by each individual. This study aims to determine how the influence of accounting information systems on employee performance. This type of research is quantitative, the population in this study were all employees of PT. BPRS Lantabur Tebuireng in the district of Jombang totaling 31 respondents. Sampling using a saturated sample technique. While data analysis uses simple regression analysis techniques. The results of this study state that the accounting information system has a positive influence on employee performance, so the better the accounting information system, the more employee performance will increase in the company.
The impact of digitalization on educational achievement: a literature review from a sociological perspective

There is still a great optimism concerning the impact of ICT (Information and Communications Technology), including CAI (Computer Assisted Instruction) on the effectiveness of education. The article is a brief synthesis, mostly with a sociological leaning, of the current empirical literature devoted to the various ways in which digitalization has an influence upon the results of education. We found that the investigations of educational impact of digitalization follows the trend from the general literature concerning the social effects of ICT which reveals an empirical perspective of multi-layered divisions in which the first and the second levels (or orders) are the most thoroughly researched and theorized while current developments tentatively regard issues of third, fourth and international digital divisions. As a general overview of the domain it can be concluded that as it develops the more and more the initial optimism vanes making space for anguish over the possible socially divisive potential of ICT use in education. The digital revolution in education not only does not solves previous issues but apparently creates new ones as differences in access, in skills and in use patterns are able to increase the various offline gaps between people of different socio-economic backgrounds.