Reviewerly/exHarmony huggingface dataset:
Basic Statistical Analyses using R

Abstract What are normal data? What are non-normal data? How do you tell them apart and how do you know what sort of data you have? Chapter 5 begins teaching statistical analysis with a focus on understanding the shape of one’s data and how to measure it. The RxP dataset is explored and the principal focus in this chapter is on normal data and on conducting an Analysis of Variance (ANOVA), although classic non-parametric statistics such as a Kruskal-Wallis test and Mann-Whitney U test are also introduced. Readers are taught how to test for data normality, assess model fit, interpret the summary output from the model, calculate summary statistics and p-values, and how to conduct post-hoc tests (Tukey’s HSD) to compare different levels of a single predictor.
Eksistensi Koleksi Jurnal Tercetak Di Era Digital (Studi Koleksi Berseri UPT Perpustakaan Universitas Syiah Kuala)

Abstrak Salah satu jenis koleksi perpustakaan adalah koleksi serial, koleksi serial memiliki beberapa jenis yaitu jurnal, majalah, bulletin, dan surat kabar. Perkembangan teknologi informasi telah mempengaruhi semua lini kehidupan begitu juga dengan kemas informasi, dulunya kemasan informasi hanya terdiri dari bentuk tercetak berjalan dengan perkembangan masa maka koleksi informasi telah berubah menjadi elektronik. UPT perpustakaan USK sebagai salah satu Perpustakaan perguruan berkewajiban menyediakan koleksi yang menjadi rujukan bagi civitas akademika salah satu rujukan yang wajib dimiliki adalah koleksi berseri yang utama adalah untuk jenis jurnal. UPT perpustakaan USK memiliki koleksi jurnal dalam bentuk tercetak dan elektronik, dari hasil penelitian ditemukan bahwa adanya perbedaan sangat signifikan dimana pengaksesan koleksi tercetak lebih sedikit dari koleksi elektronik. Key word: Layanan perpustakaan, koleksi serial tercetak, koleksi serial elektronik Abstract One type of library collection is serial collection, serial collection has several types, namely journals, magazines, bulletins, and newspapers. The development of information technology has affected all lines of life as well as information packaging, in the past, information packaging only consisted of printed forms. With the development of the times, the collection of information has turned into electronic. The USK library's UPT as one of the university libraries is obliged to provide a collection that is a reference for the academic community. One of the mandatory references is the serial collection, which is the main type of journal. The USK library's UPT has a collection of journals in both printed and electronic forms, from the results of the study it was found that there was a very significant difference where access to printed collections was less than electronic collections. Key words : Library service, printed serial collection, electronic serial collection
IMAGE ENCRYPTION AND DECRYPTION USING AES ALGORITHM

Cryptography is art of converting one form of data into another form and in turn make the data unreadable for the end users. This cryptography has two methods like encryption and decryption. The process of converting plain text into cipher text is known as encryption and the process of converting cipher text into plain text is known as decryption. In this application we try to use AES algorithm for encryption or decryption of either text or image data
Microsoft, Oracle, Salesforce join effort to develop COVID-19 vaccination digital passport

The technology would allow individuals to obtain a copy of their immunization records through a digital wallet of their choice, such as Apple Wallet or Google Pay, while those without smartphones would receive paper printed with QR codes containing verifiable credentials.
Adversarial Defense Through High Frequency Loss Variational Autoencoder Decoder and Bayesian Update With Collective Voting

In recent years, Deep Neural Network (DNN) approaches for computer vision tasks have shown tremendous promise and potential. However, they are vulnerable to data that are carefully crafted with adversarial attacks, which can cause mis-prediction and raise security risk to real-world deep learning systems. To make the DNN-based approaches more robust, we propose a defense strategy based on High Frequency Loss Variational Autoencoder Decoder (VAE) and randomization among multiple post-VAE classifiers' predictions. The main contributions of the proposed defense framework are: 1) a new adversarial defense framework that features randomization process to effectively mitigate adversarial attacks; 2) reconstruction of high-quality images from adversarial samples with the VAE enhanced with spatial frequency loss; 3) use of a Bayesian process to jointly combine the collective voting results and the targeted classifier's prediction for final decision. We evaluate our approach and compare it with existing approaches on CIFAR10 and Fashion-MNIST data sets. The experimental study shows that the proposed method outperforms existing methods.
EVALUASI KELENGKAPAN RESEP UNTUK MENCEGAH MEDICATION ERROR

A prescription is a written request from a doctor or dentist to a pharmacist, either in paper or electronic form to provide and deliver medicine for patients in accordance with applicable regulations. Writing a complete recipe must be in the entire recipe format. Incomplete prescribing formats can lead to medication errors. The purpose of this study was to find out the complete description of drug prescription writing at the Puskesmas Kapas, Kota Bojonegoro. The design of this research is descriptive. The population of this study is all prescription at the puskesmas in February 2019 with a total sampling technique. The results of this study showed that 94.67% of prescription writing were incomplete and 5.33% were complete. Incomplete prescription writing causes medication errors, so it is necessary to screen prescriptions at the beginning of receiving prescriptions. Keywords : Prescription, Completeness of Prescription, medication error
IMPLEMENTASI MEDIA KARTU KOSAKATA DALAM MENINGKATKAN KEMAMPUAN BERBICARA SISWA KELAS V SD NEGERI 096113 TANJUNG SARIBU

The purposes of this study are (1) to find out how to improve speaking skills using vocabulary card media in fourth grade students of SD Negeri 096113 in the 2020/2021 academic year; (2) to determine the form of planning and implementation of learning to speak using vocabulary card media: (3) to find out the obstacles and results obtained from learning to speak using vocabulary card media, consisting of 20 students. This study uses a qualitative approach, because the study seeks to examine more deeply about the improvement of learning outcomes in speaking skills using vocabulary cards in Indonesian learning, which aims to help students express ideas and ideas well. This study also uses 2 (two) cycles starting from February to May 2021. From the results of the learning activities, it can be concluded as follows: (1) learning by using vocabulary card media has a positive impact on improving speaking skills marked by an increase in students' speaking in each cycle, namely the first cycle obtained an average of 66.25 and in the second cycle obtained an average of 80; (2) the application of vocabulary card media in speaking skills has a positive influence, namely students are able to express and express ideas, and the results of students' scores meet the minimum completeness criteria.
Century Enterprise: Pee-Cut Machine / Muhammad Aniq Hazman Hairozie ...[et al.]

Century Enterprise offers a product that combines two functions in a machine which are peeling and cutting. By this combination, Pee-Cut Portable Machine was made as an idea to ease people's activities in daily life. Household areas is a place that was surveyed which 63.6% from 70 respondents facing difficulties in cutting and peeling fruit for their daily uses. The major problem is the difficulty in peeling and cutting manually which is tiring and taking your time if you are inexperienced. Next, sharp tools as knives that were used in cutting and peeling can also give harms to people where danger is everywhere. So, by combining peeling and cutting, those difficulties are slowly will be easier and dangers can be avoided.
NeuraCrypt is not private.

NeuraCrypt (Yara et al. arXiv 2021) is an algorithm that converts a sensitive dataset to an encoded dataset so that (1) it is still possible to train machine learning models on the encoded data, but (2) an adversary who has access only to the encoded dataset can not learn much about the original sensitive dataset. We break NeuraCrypt privacy claims, by perfectly solving the authors' public challenge, and by showing that NeuraCrypt does not satisfy the formal privacy definitions posed in the original paper. Our attack consists of a series of boosting steps that, coupled with various design flaws, turns a 1% attack advantage into a 100% complete break of the scheme.
LibGuides: Demonstrate Impact and Manage Your Scholarly Reputation: Researcher IDs - e.g. ORCID

Resources and tools for demonstrating the impact of your scholarly and creative works, and managing your online scholarly presence and reputation.
From Detective to Vigilante: An Examination of the Vimal Series by Surender Mohan Pathak

When it comes to Hindi crime fiction, Surender Mohan Pathak and his cohorts moulded their novels on the same framework as that of crime fiction published in English, only the frame percolated to them through the writings of Ibne Safi. Surender Mohan Pathak's legend also disseminates and, at best, rests mainly on his crime series centring on its curiously named protagonist Sardar Surender Singh Sohal A.K.A. Vimal. Pathak's Vimal series depart from this route as he alters recognised patterns of detective fiction. He uses the genre to reflect the mind of a criminal. &quot;Ishtahari Mujrim (History Sheeter)&quot; Vimal is a detective who works out of the framework of detective fiction. He is a person dislocated from the inner sanctum of society and therefore takes on the garb of a vigilante to not only be acknowledged by the said society but also have a hold, although nefarious, over it.
COMPARISON BETWEEN APRIORI AND FP-GROWTH ALGORITHMS ON INVENTORY MODEL OF ITEM AVAILABILITY

In this study, it will be discussed the comparison between apriori and fp-growth algorithms on aninventory model of item availability. The research about this topic becomes important and interestingto be studied because it illustrates the implementation of applied mathematics by constructing thematematical model, namely an inventory model of item availability. Then, the model will be solved byusing apriori and fp-growth algorthms related to the application of probability theory. In this case,apriori and fp-growth algorithms are used to specifythe pattern of dependency relationships between an item and other items so that the probability ofitem purchase based on other goods can be discovered. Moreover, the number of items which shouldbe provided by a seller in a shop or a supermarket can be calculated. In order to figure out the outputof this research which is the analysis of the comparison between apriori and fp-growth algorithms onthe inventory model of item availbility, then choosing and categorizing kinds of items based on thesales, using the apriori and fp-growth algorithms, constructing the model, solving and interpreting itare established
Decentralized Incognito Limpid e-Voting System

Voting is a constitutional part of governmental systems which gives the people of the nation the liberty to express their opinions. The contemporary system constitutes Electronic Voting Machines (EVM) that is a pile-up of data natively and centralized, making it implausible. Since the data is amassed locally over the EVM(s) prior to the election's cessation, it could be hacked. Furthermore, there have been events of booth hijacking in some regions during the elections. Ethereum blockchain technology addresses concerns regarding integrity, security, and accessibility of current voting systems. Expanding e-voting into Ethereum based blockchain technology is one potential application of the emerging blockchain technology. This paper presents a decentralized, incognito, and limpid e-voting system named DILE. It will escalate accessibility as the users could cast their votes without paying a visit to the polling booths. DILE makes practical and effective use of Ethereum's blockchain technology and smart contracts for its implementation.
APLIKASI PERINGATAN BENCANA BANJIR BERBASIS ANDROID

Many victims are caused by floods every year due to inadequate warning systems that exist today. Making a flood disasterwarning application is used to receive information in the form of the latest water level conditions that are designed usingultra sonic sensors, So as to minimize the number of flood victims. Ultrasonic sensors detect water levels above 11cm, soArduino sends a signal to Android to instruct the Android to send a water warning SMS that has reached the standby pointto the user using the SMS Gateway Method, so users can receive SMS notifications about water levels in real time.
Discovering and Achieving Goals via World Models

How can artificial agents learn to solve many diverse tasks in complex visualenvironments in the absence of any supervision? We decompose this question intotwo problems: discovering new goals and learning to reliably achieve them. Weintroduce Latent Explorer Achiever (LEXA), a unified solution to these thatlearns a world model from image inputs and uses it to train an explorer and anachiever policy from imagined rollouts. Unlike prior methods that explore byreaching previously visited states, the explorer plans to discover unseensurprising states through foresight, which are then used as diverse targets forthe achiever to practice. After the unsupervised phase, LEXA solves tasksspecified as goal images zero-shot without any additional learning. LEXAsubstantially outperforms previous approaches to unsupervised goal-reaching,both on prior benchmarks and on a new challenging benchmark with a total of 40test tasks spanning across four standard robotic manipulation and locomotiondomains. LEXA further achieves goals that require interacting with multipleobjects in sequence. Finally, to demonstrate the scalability and generality ofLEXA, we train a single general agent across four distinct environments. Codeand videos at https://orybkin.github.io/lexa/
Based on the Teaching Quality under the View of the Field of Vision――The Construction and Empirical Research of the Teaching Mode of “Exploration and Cooperation” in Public Sports

By using the method of consulting document, questionnaire, teaching experiment and mathematical statistics, this paper analyzes the connotation and theoretical basis of inquiry and cooperation teaching mode, and constructs the inquiry and cooperation teaching mode.
Hide your home in new Apple Maps viewer and protect your privacy

Don't want to be a part of Apple Maps new Look Around feature in iOS 13? Find out how you can opt out by censoring your home.
Modern language olympiad: unleashing potentials for 21st century skills / Ahmad Mazli Muhammad … [et al.]

The challenge of becoming employable 21st century graduates to meet current industry demands was the basis of this innovation. With the aim to equip a group of students with soft skills on one hand, and another with industry-specific communicative abilities on the other, the Modern Language Olympiad (MLO) was established under Akademi Pengajian Bahasa, UiTM Shah Alam, as the first multi-language competition held internationally that is fully student-led. Five categories of competitions were designed with close engagement with the industry, which were strategic storytelling, idea pitching, versatile presenting, and radio drama across three languages (English, Bahasa Melayu, and Arabic) from 29 October to 2 November 2019. Preparations to conduct this international competition commenced a year before that. In total, 330 participants from 11 public universities, 6 private universities and 4 foreign universities and 10 UiTM branch campuses participated in the event, which was run by 40 student committee and 100 student volunteers, guided by 30 academic advisors. A total of 40 judges from the industry were involved in the competitions. The event, which amassed a total profit of RM58,416.45, managed to develop the student committee’s critical thinking, problem solving, time management, project management, and budgeting skills, while the participants gained immeasurable benefits in enhancing their communication skills through feedback from the industry and by observing others. This innovation is a step forward to improve students’ soft skills and communication skills using the two-pronged approach through running a student-led, student-driven, wide-scale international event.
Bitcoin reaches $16K, highest level since 2018

Bitcoin on Thursday reached a record valuation of $16,118, its highest level since 2018. The cryptocurrency is up 123% year-over-year and 50% this quarter, according to data from the California-based digital currency exchange Coinbase.
Evaluation of Text-Summarization Technique

Text summarization refers to the process of automatically shortening a document to obtain a cohesive summary without losing its inherent intent. It is challenging for humans to prune large documents and gather relevant information manually. Hence, it serves as an essential technique to make the internet and large data sets accessible. Text summarization can be broadly classified as Abstractive and Extractive. Extraction-based summarization involves picking out words, sentences, and paragraphs occurring in the original document, which combine to form a summary. Whereas abstraction-based summarization is the method in which the original text is interpreted and rewritten using language processing methods. It relies on the original text’s information to create entirely new shortened phrases and sentences, eliminating the problems in extraction-based ways. In this chapter, an evaluation of five popular extraction-based summarization algorithms is presented. The data set of BBC news articles from 2004 to 2005 of four genres (Entertainment, Politics, Tech and Business) is used.
DE-striping Augmented Images of Blood Cells using Deep Convolutional Neural Network

The aim of this biomedical image processing-based research paper is to use augmented images of blood sample and Deep Convolutional Neural Network (DCNN) for the purpose of de-striping on Hyperion data to de-stripe on multicore platforms GPU. In order to reach our goal, we have started by analyzing the challenges associated with de-striping, Hyperion data and DCNN. A novel implementation pipeline of training, validating and evaluating stating from input an augmented blood image sample with the help analyzing the Hyperion data leading to the de-striping of augmented image blood sample by removing all the kernel black. What is clear is the high importance of applying the adequate pre-processing on Hyperion data because of low signal-to-noise ratio. By comparing the known layers of DCNN model for de-striping augmented images. The results obtained by applying the mentioned methods, it is revealed that all the higher stripes in an image as well as black color has been reduced and entirely associated with the Hyperion data alteration, and in contrast, the Hyperion imagery successfully corresponds to the de-striping of augmented image with an accuracy of 91.89% using DCNN model. The proposed DCNN is capable of reaching high accuracy within 150s after the launch of the evaluation phase and never reaches low accuracy. The pre-trained DCNN model approach would be an adequate solution considering de-striping as its high inference time is lower compared existing available methods which are not as efficient for de-striping.
As far as the AI can see

Staging of early esophageal adenocarcinoma (EAC) involves a complex process associated with profound clinical implications. Central to this process is the histologic evaluation of depth of invasion. Cancer infiltration limited to the mucosa (T1a) is associated with a lower risk of lymph node metastasis and overall improved survival compared with infiltration that extends into the submucosa (T1b) [1]. Consequently, endoscopic therapy can be considered curative in a subset of patients with T1a EAC but plays a limited role in patients with T1b EAC for which esophagectomy is associated with improved overall survival [2]. Ideally, staging of T1 EAC would be performed without the need for resection, avoiding an unnecessary intervention in patients with T1b EAC and thus preventing associated complications of esophageal perforation, bleeding, and stricture formation [3] [4] [5]. Unfortunately, imaging modalities, including endoscopic ultrasound, have demonstrated suboptimal performance in differentiating T1a from T1b EAC [6]. As a result, the endoscopist performing the staging procedure plays a fundamental role in determining whether an early-stage cancer can be safely resected and the appropriate technique to do so. In this process, the morphology, size, and adequate lift of the lesion away from the muscularis propria are taken into consideration. These factors are of critical importance not only to ensure adequate resection of the cancer but to mitigate the risk of complications. Most often, however, the only way to truly know if a lesion can be resected is to attempt its resection.
Stochastic Scene-Aware Motion Prediction

A long-standing goal in computer vision is to capture, model, and realistically synthesize human behavior. Specifically, by learning from data, our goal is to enable virtual humans to navigate within cluttered indoor scenes and naturally interact with objects. Such embodied behavior has applications in virtual reality, computer games, and robotics, while synthesized behavior can be used as a source of training data. This is challenging because real human motion is diverse and adapts to the scene. For example, a person can sit or lie on a sofa in many places and with varying styles. It is necessary to model this diversity when synthesizing virtual humans that realistically perform human-scene interactions. We present a novel data-driven, stochastic motion synthesis method that models different styles of performing a given action with a target object. Our method, called SAMP, for Scene-Aware Motion Prediction, generalizes to target objects of various geometries while enabling the character to navigate in cluttered scenes. To train our method, we collected MoCap data covering various sitting, lying down, walking, and running styles. We demonstrate our method on complex indoor scenes and achieve superior performance compared to existing solutions. Our code and data are available for research at this https URL.
The Effect of Employee Happiness on Performance of Employees in Public Organization in United Arab Emirates

Employee happiness has received less attention compared with other constructs. This study aims to examine the effect of employee happiness on employee performance in the public sector in the United Arab Emirates (UAE). The study operationalized employee happiness into performance appraisal, flexible working hours, promotion, rewards, and recognition, income, peer and supervisor support, workload, and workplace environment. The data was collected from 319 employees working in Abu Dhabi and Dubai using a questionnaire “The effect of employee spirituality and emotional wellness toward the relationship between employee happiness and employee performance 2019”. The findings were derived using Smart Partial Least Squares (PLS). The findings showed that performance appraisal, income, workplace environment, promotion, reward, and recognition, and peer and supervisor support are significant predictors of employee performance. Flexible Working Hours (FWH) and workload have insignificant effect on employee performance. Findings were discussed and decision makers are advised to increase employee happiness among employees in UAE.