Reviewerly/exHarmony huggingface dataset:
Pengenalan Makanan 4 Sehat 5 Sempurna dengan Media Playdough pada Anak Usia 5-6 Tahun di TK Islam Plus Qamarul-Lail Aikmel

Based on the research result and discussion about introduction 4 healthy 5 perfect food using playdough to children aged 5-6 at Islam Plus Qamarul-Lail kindergarten, it can be concluded that the food was introduced using pictures to show the children the image of 4 healthy 5 perfect food, the name of each food was written in the children’s workbook to strengthen their memory and knowledge about the food. The researcher also introduced the materials to make the playdough and explained the steps of making it which would be used to make the replica of food. The result showed that the children were familiar with the name of 4 healthy 5 perfect foods thus showing the score of each descriptor as shown in the table i.e. indicator 1 is 75%, indicator 2 is 70%, and indicator 3 is 71%. Each indicator sets the descriptors that each child should achieve. One of the descriptors depicted that some children encountered a little problem to replicate a glass that contained milk because the process was too complicated for the children. However, each child achieved the most descriptors. The result showed that most children got 3 scores which means the children were familiar with most items in 4 healthy 5 perfect food after they were introduced to the food using pictures at the first stage.
Design of English situational simulation teaching system based on personalized recommendation

The traditional English situation simulation teaching system can only provide students with a unified English situation, which can not stimulate students' interest in learning, resulting in the poor effect of improving students' English ability. Therefore, an English situational simulation teaching system based on personalized recommendation is designed. Design the system hardware, configure the system server parameters, design the software part of the system, analyze the relevant functions of the system, and establish the teaching system database. Using collaborative filtering algorithm to design personalized recommendation module of teaching resources. According to the teaching plan, the simulation teaching situation is introduced to complete the design of the teaching system. Compared with the streaming media based situational simulation teaching system, it is proved that the application of personalized recommendation based teaching system can stimulate students' interest in English learning and improve their English ability.
Computer Visioned Free Interaction Using Leap Motion Controller

Hand signal affirmation for the PC vision organize is so far a subject of glorious concern. In view of their criticalness in human-human correspondence and human-PC association, correspondence by means of motions and semaphores hand movements in express are two key fields of concern. Sets of limit vectors that move after some time can address any hand signal. Because of their capacity to show the long stretch coherent data of transient progressions, Convolution neural frameworks (CNNs) are sensible to survey this sort of set. In this chronicle, a CNN is told using the portrayal of hand following made by the human hands finger bones as traits. A very inconvenient dataset including a significant proportion of movements described by the American Sign Language was at first attempted. A precision of in excess of 96 percent was polished on the last referenced. By then, using the dataset of the Shape Retrieval Contest (SHREC), a wide grouping of manual sign.
Library Guides: Creating a LibGuide: Change Publication Status

This guide will help you design an accessible, user-friendly LibGuide. Instruction on how to change the publication status of a guide.
SURF Algorithm-Based Suspect Retrieval

In this work, we have discussed the method to retrieve the suspect from the gallery of images with the help of a forensic sketch. A forensic sketch is defined as a container of a number of different features: image vector. Face outlines have the fundamental data about the spatial geography and mathematical subtleties of appearances while missing some significant facial credits, for example, identity, hair, eye, and skin color. On the basis of this number of features, the sketch is then iterated through a gallery of images to find the closest match. The proposed idea utilized the sketch and free facial characteristic data to prepare a machine learning model contrasted with the regular sketch-photograph acknowledgment techniques.
FSSRE: Fusing Semantic Feature and Syntactic Dependencies Feature for threat intelligence Relation Extraction

Threat intelligence relation extraction plays an important role in threat intelligence text analysis and processing.To extract the relation between two threat entities in a sentence, we develop a novel framework called FSSRE which fuses sematic feature and syntactic dependencies feature for threat intelligence relation extraction.We utilize graph convolutional networks (GCN) to extract syntactic dependencies features, and utilize Sentence-BERT to extract contextual semantic features.To keep vital information with irrelevant content removed to the most extent, we further apply a novel pruning strategy, SDP-VP, to the input trees.With retaining the shortest path and nodes that are hops away from nodes on the shortest path, we give the edge connected to the verb nodes a weight of times.We create an advanced persistent threat (APT) intelligence entities and intra-sentence relations dataset, APTER-SENT, for that there is no public dataset can be used for relation extraction research in the threat intelligence field.Experimental results on APTER-SENT demonstrate improved performance over competitive baselines.At the same time, we also conducted experiments on the SemEval-2010 dataset.The results of the experiment indicate that our method is still effective on this dataset.
Multi-step Domain Adaption Image Classification Network via Attention Mechanism and Multi-level Feature Alignment

Domain adaption image classification uses source domain task knowledge to enhance the classification effect of target domain tasks. It can reduce the work of data labeling in the target domain and significantly improve the network’s adaptive ability. However, the existing methods can not deal well with the performance degradation in large differences in data distribution between the source domain and the target domain. Therefore, based on the idea of reverse gradient layer, a multi-step domain adaptive image classification network with an attention mechanism aligned with multi-level features is proposed in this paper. Specifically, firstly, the attention mechanism is used to combine the source domain and target domain data to generate an intermediate domain. Secondly, several feature alignment strategies are proposed to align the source domain and the target domain from the pixel and global levels.
Program mengajar bahasa inggris dari rumah selama pandemi Covid 19

The covid 19 virus pandemic has stopped teaching and learning activities in schools and changed teaching methods to online teaching. This makes students feel bored because they cannot feel the learning atmosphere as usual. This activity provides an opportunity for students to be able to feel the learning atmosphere like in school by providing English teaching materials to students. In this activity, using a teaching method and through a socialization and training approach to students by providing understanding and explanation of English learning material in a fun way. This activity was carried out for one full month to provide a forum for teaching English to students affected by the COVID-19 pandemic. It is hoped that in this activity students are able to understand English learning material well and can experience learning again together with their classmates even though they have to follow protocols. health.
Implementation of Learning Model Start with a Question (LSQ) to Improve Activeness and Students’ Learning Result

The purpose of this study was to increase student activity and learning outcomes by applying the Learning Start with A Question (LSQ) learning strategy. This research is a classroom action research conducted in 2 cycles. The subjects of this study were the fifth grade students of SDN Pinggir Nganjuk Regency, totaling 40 odd semester students for the 2020/2021 academic year. The instruments used in this study were the teacher's activity or performance observation sheet, student activity observation sheet and test sheet. Data collection techniques usedwere observation, documentation and tests. The results of the study indicate that the Learning Start with A Question (LSQ) learning model could increase the activity of fifth grade students at SDN Pinggir Nganjuk Regency. This increase could be seen from the increase in the percentage of student activity that could be seen from the average percentage in the first cycle of students reaching 66.95%, while in the second cycle there was an increase of 93.78%. Meanwhile, in the first cycle, the increase in student learning outcomes with a percentage of 51.22%, in the second cycle there was a very good increase again with a percentage of 92.68%. Based on the results of tests that have been carried out on social studies subject matter, it is known that there has been an increase in student learning outcomes.
Hybrid Approach for Image Defogging Process based on Atmospheric Light Estimation Process

Due to unfavorable weather circumstances, images captured from multiple sensors have limited the contrast and visibility. Many applications, such as web camera surveillance in public locations are used to identify object categorization and capture a vehicle's licence plate in order to detect reckless driving. The traditional methods can improve the image quality by incorporating luminance, minimizing distortion, and removing unwanted visual effects from the given images. Dehazing is a vital step in the image defogging process of many real-time applications. This research article focuses on the prediction of transmission maps in the process of image defogging through the combination of dark channel prior (DCP), transmission map with refinement, and atmospheric light estimation process. This framework has succeeded in the prior segmentation process for obtaining a better visualization. This prediction of transmission maps can be improved through the statistical process of obtaining higher accuracy for the proposed model. This improvement can be achieved by incorporating the proposed framework with an atmospheric light estimation algorithm. Finally, the experimental results show that the proposed deep learning model is achieving a superior performance when compared to other traditional algorithms.
Automated data exfiltration detection using netflow metadata

The volume and sophistication of data exfiltration attacks over networks have significantly increased in the last decade. This has resulted in the need for defense mechanisms, to effectively detect both known and unknown data exfiltration scenarios over the network. While methods such as DPI (Deep Packet Inspection) are commonly used to detect data exfiltrations, this mechanism requires a thorough inspection of every payload or packet going out of the network, making it unsuitable for use in some environments, as it is quite resource intensive and can lead to severe data privacy implications. In our work, we use lightweight netflows which are non-privacy invasive to detect data exfiltrations at connection-level granularity. The key intuition behind our proposed solution is that connections involved in data exfiltration tend to differentiate themselves from normal network connections based on certain feature values. The result of this research shows that features extracted from netflows such as the duration of a netflow, the source bytes, the source bytes sent per second, the source bytes sent per packet and the producer-consumer ratio can be used to effectively detect data exfiltration. Subsequently, connections are grouped using k-means, and the robust Z-score of their distances from their respective cluster centroid is used as a statistical and distance-based technique to detect connections involved in a data exfiltration. While this method detects some data exfiltration scenarios, it results in a significant number of false positives. Combining this with the results from the LOF (local outlier factor) and the LoOP (local outlier probability), which are density-based techniques, leads to a more robust model, as it significantly reduces the number of false positives and false negatives. Also, we show that using the smallest clusters formed from k-means for analysis leads to similar detection results as the entire datasets, with a significant reduction in computation time.
Dampak Covid-19 terhadap Tren Belajar dan Bermain Anak Tingkat Sekolah Dasar di Gampong Beurawe

The purpose of this study is to know the effect of Covid-19 on the trend of elementary school children learning and playing that recognized by their parents. This research uses case study methods and qualitative research approaches to gain information on the effect of Covid-19 on the trend of playing and learning. For confidentiality identity, respondents were given the initials R1, R2, R3, R4, R5, and up to R20. The interview was conducted by asking the respondents one by one. the results showed that the level of achievement had decreased and there was a new learning trend. Online learning causes students and parents to adapt to this learning model. In addition, playing games on cellphones has also become the most favorite games during this pandemic.
Assessing the relationship between information transparency through social media disclosure and firm value / Abdalmuttaleb M.A Musleh Al-Sartawi.

The aim of this paper is to show the relationship between social media financial disclosure and firm value of the GCC listed firms. To answer the research questions, the researchers collected cross-sectional data from a sample of 241 firms listed in the financial stock markets of the GCC for the year 2017. An Index was used to calculate the total level of social media financial disclosure where the SMFD for each firm was calculated by dividing the total earned scores of the firm by the total maximum possible scores appropriate for the firm. The findings show that the 84% of firms in the GCC countries use social media, while 70% of these firms use the SM for financial disclosure. The results also confirm the hypothesis that enhanced disclosure levels through various social media channels of GCC listed firms significantly influence the firm value of these firms. These results have implications for both the social media disclosure literature and value relevance literature in the GCC countries.
LibGuides: COVID-19 and Online Learning Resources: Using the CCP Library from off-campus

This guide was created to help CCP students, faculty, and staff navigate information related to the coronavirus and COVID-19, and to compile information that will assist our community with their online courses.
서-한 순차통역 학습자의 증거성 오류 분석 - 전달 증거성(hearsay evidential)을 중심으로

This study analyzes the function and meaning of hearsay evidential markers appearing in students’ interpretations in Spanish- Korean consecutive interpretation class. Evidentiality deals with the source the speaker has for his statement, whether he heard it, saw it or, inferred it from indirect evidence, that is, expresses the means by which the speaker acquired the information he is conveying; it reflects an evaluation of the source of evidence. In Korean, evidentiality can be expressed by various grammatical markers. This paper focuses on the latter, in particular, on the sequence ‘-ㄴ다는 것입니다’, which appears unnecessarily in students’ interpretations. To this end, this study examines the general concept and types of evidentiality, and, in particular, examines the characteristics of the hearsay (or reported) evidential markers in Korean. And then analyzes in which situations the students use these sequences and what this use means in their interpretations.
Asymptotic Separation Between Adaptive and Non-adaptive Strategies in Quantum Channel Discrimination

We present a broad investigation of asymptotic binary hypothesis testing, when each hypothesis represents asymptotically many independent instances of a quantum channel, and the tests are based on using the unknown channel multiple times and observing its output at the end. Unlike the familiar setting of quantum states as hypotheses, there is a fundamental distinction between adaptive and non-adaptive strategies with respect to the channel uses, and we introduce a number of further variants of the discrimination tasks by imposing different restrictions on the test strategies. Our main result is the first separation between adaptive and non-adaptive symmetric hypothesis testing exponents for quantum channels, which we derive from a general lower bound on the error probability for non-adaptive strategies; the concrete example we analyze is a pair of entanglement-breaking channels. Full details in [1].
Sentiments Analysis on Public Land Transport Infrastructure in Davao Region using Machine Learning Algorithms: A Recent Study

Land transport infrastructure has always been an important aspect of a city. People increasingly use social media to express their feelings about city projects such as land transportation. Government organisations are having trouble detecting concerns that develop as a result of people utilising social media to criticise land transportation infrastructure. These text-based social media posts can be examined using sentiment analysis, which is a key task in Natural Language Processing (NLP). Sentiment analysis is a process of the deriving sentiment of a particular statement or sentence. This study used a social networking website to create a model of sentiments on land transportation infrastructure in Region XI (Davao Region), the Philippines, and tested the model's correctness using a data set. There are 1,200 text data sets in total, divided into two categories: test dataset (25 percent) and training dataset (75 percent). To perform sentiments analysis of text data sets, machine learning text classifiers such as Support Vector Machines (SVM), Random Forest (RF), and Multinomial Nave Bayesian (MNB) were utilised.&nbsp; The performance of each classification model is estimated by generating a confusion metric with precision and recall calculations, known as the f1-score. The accuracy rating was calculated as well. A comparison was also made based on the results of the three machine learning classifiers' tests. &nbsp;SVM has the highest accuracy, with 76.12 percent and a f1-score of 71.98 percent, based on the results of the experiment. This research will be used to inform and support policy-making and development of land transportation infrastructure in the Davao Region.
License Trading System for Video Contents Using Smart Contract on Blockchain

Recently, with the spread of SNS (Social Networking Services) such as Twitter and Instagram, many images and videos have been posted and shared. These postings are generally evaluated and spread among SNS users, but basically, there is no direct profit for the posters. However, when the posting of videos and images in a disaster area spreads, the videos and the images are requested and bought by broadcasters such as public media companies. This means that there is often sufficient monetary value in such a video and an image. It is desirable that the negotiation on contents license between a poster and a broadcaster proceeds promptly and easily. The application of the block chain technology outside of the finance region attracts the attention recently. In particular, Ethereum, which is a platform that enables application construction using smart contracts, is currently being studied for many applications. In this paper, we propose a transaction system for buying and selling licenses of video contents using smart contract on a blockchain and a distributed file storage. We implement the proposed system on Ethereum blockchain and a distributed file storage Swarm and evaluate the performance.
Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization

Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given system, up to knowing almost nothing but the results. This paper describes our approach to solving the black-box optimization challenge at NeurIPS 2020 through learning search space partition for local Bayesian optimization. We describe the task of the challenge as well as our algorithm for low budget optimization that we named \texttt{SPBOpt}. We optimize the hyper-parameters of our algorithm for the competition finals using multi-task Bayesian optimization on results from the first two evaluation settings. Our approach has ranked third in the competition finals.
Analisis Retensi Rekam Medis Rawat Jalan Aktif ke Inaktif di UPT Puskesmas Sukarasa

Medical record retention is an activity of transferring medical record documents from an active storage room to an inactive storage room. Based on a preliminary survey at UPT Puskesmas Sukarasa, it is known that the puskesmas has retained outpatient medical record files 2 times in 2018 and 2019. But the retention is not done according to the SOP. The purpose of the study was to determine the implementation of outpatient medical record file retention at UPT Puskesmas Sukarasa. This type of research is descriptive with a retrospective approach. The research instrument was carried out by observation, interviews and checklist forms. The object of the research is the implementation of active to inactive outpatient medical record retention at UPT Puskesmas Sukarasa. The research subjects are the officer in charge of medical records and the person in charge of registration who performs retention of outpatient medical records.It is also known that the UPT Puskesmas Sukarasa already has an SOP on retention of medical records, but the SOP has not been implemented due to the lack of special staff for retention and the accumulation of medical record files on active storage shelves. Files that are retained are files for 2016-2018 without looking at the last date of treatment but based on the year number in the medical record file. Then the file is immediately moved to the inactive storage rack. It can be concluded that the implementation of retention at UPT Puskesmas Sukarasa is not in accordance with the SOP.  It would be better if the retention is carried out according to the SOP with special and scheduled officers so that there is no accumulation of medical record files.
A Study of Performance and Security Across the Virtualization Spectrum

Virtualization is the fundamental technology that enabled the widespread adoption of cloud computing. With the ever-increasing pervasiveness of the cloud computing paradigm, strong isolation guarantees and low performance overhead from isolation platforms are paramount. An ideal isolation platform offers both: an infallible isolation boundary while it retains a negligible performance overhead. In this thesis, we examine various isolation platforms (containers, secure containers, hypervisors, unikernels), and conduct a wide-array of experiments to measure the performance overhead and degree of isolation offered by the platforms. We find that container platforms have the best, near-native, performance. The highest degree of isolation is achieved by unikernels, closely followed by containers.
LibGuides: Developmental Education: How to Search in Databases

This guide provides resources that can be used to supplement the various courses and disciplines covered by developmental education courses at HCC.