Reviewerly/exHarmony huggingface dataset:
High-Quality Soft Image Delivery with Deep Image Denoising

Soft image delivery uses pseudo-analog modulation for wireless image transmissions to prevent cliff and leveling effects subject to channel quality fluctuation and to realize graceful quality improvement according to wireless channel quality. Despite its attractive feature of graceful performance, the conventional soft image delivery suffers from low image quality when the analog-modulated symbols are severely impaired by fading and strong channel noise. In this paper, we propose a novel scheme of soft image delivery to reconstruct high-quality images from its low-quality observations. Specifically, the proposed scheme integrates deep convolutional neural network (DCNN)-based image restoration, i.e., deep image prior, into soft image delivery. The deep image prior learns a mapping function from the noisy image to the clean image based on user's perception-aware loss function using multiple training images in prior to soft delivery. The mapping function can restore a clean image even when the received image is distorted by strong fading and additive noise. From the evaluation results, the proposed scheme can remove fading and noise effects from the received images by using DCNN-based image restoration. For example, the proposed scheme achieves up to 0.44 improvement compared with the conventional soft image delivery in terms of structural similarity (SSIM) index at a deep fading channel.
Protecting Consumption Habits with Differential Privacy

Online bank system has been widely deployed to provide financial service worldwide. Big data mining brings serious privacy issues to consumers because consumption records may disclose their activities. Existing authentication and encryption algorithms in privacy-preserving deposit transaction schemes are mostly limited to the case that each consumption has to be reported to online banks directly and an insider adversary can monitor a consumer's bank account during the financial transaction period. Motivated by the above problems, we propose a deposit transaction scheme based on the differential privacy. Considering a mobile payment system as a noise generator, this scheme aims to reduce the relevancy between real consumption and deposit transaction. In addition, we provide in-depth theoretical analysis that our scheme can satisfy the definition of the differential privacy. Experimental results illustrate the consumption pattern can be protected with the differential privacy.
Variasi Faktor Exsposi Pada Pemeriksaan Schedel Proyeksi Anterior Posterior untuk Hasil Radiografi yang Maksimal di Rumah Sakit Al Islam Bandung

The head is an examination that is often done at each hospital, especially Posterior Anterior projection. The quality of the results of a good picture of contrast, density and detail are needed so that the exposure factor will be very influential on the head examination. The study was conducted at Al Islam Hospital in Bandung using the head phantom. Variations in kV and mAs in this study were AP projections with 64 kV and 11.2 mAs, AP schemes with 72 kV and 14.2 mAs, AP heads with 80 kV and 12.6 mAs, AP heads with 88 kV and 14, 2 mAs and AP head radiographs with 96 kV and 16.0 mAs. The maximum exposure factor value on the head phantom in anteroposterior projections based on the data is that the respondent's value has changed and only one is the same or similar from the respondent's perspective, the use of kV 64 and mAs 11.2 has a density value of the ten respondents by 94%, the value of in contrast to the ten respondents by 68%, the sharpness value of the ten respondents by 74%, and the detailed value of the ten respondents by 74%. In the use of kV 72 and mAs 14.2, the density value of the ten respondents was 42%, the contrast value of the ten respondents was 44%, the sharpness value of the ten respondents was 38%, and the retail value of the ten respondents was 38%. The use of kV 80 and mAs 12.6 has a density value of the ten respondents of 56%, the contrast value of the ten respondents by 44%, the sharpness value of the ten respondents by 40%, and the retail value of the ten respondents by 40%. In the use of kV 88 and mAs 14.2, the density value of the ten respondents was 78%, the contrast value of the ten respondents was 64%, the sharpness value of the ten respondents was 64%, and the retail value of the ten respondents was 64%. In the use of kV 96 and mAs 16.0, the density value of the ten respondents was 78%, the contrast value of the ten respondents was 76%, the sharpness value of the ten respondents was 64%, and the retail value of the ten respondents was 80%. According to the evaluation of the ten respondents expose at Al Islam Hospital Bandung, the results of the first radiograph with the exposure factor kV 64 and mAs 11.2 had the highest value. And the lowest index value of each respondent is the exposure factor kV 72 and mAs 14.2.
Are Personality Types Related to Cyberbullying Behavior Trends in Adolescents?

Adolescent growth and development is influenced by 2 factors, namely internal and external factors. Internal factors are factors that influence the fulfillment of developmental tasks originating from within the individual, both physical and psychological, while external factors are factors that influence the fulfillment of developmental tasks originating from outside the individual self. There are several examples of external factors, namely biological and physical environment, psychosocial and depression, family and cultural factors, and economic factors. Psychosocial factors involve psychological and social aspects. The social aspect can be done without having to face to face directly or can be done online using social media. the freedom of a person to use social media causes various abuses of social media, for example cyberbullying. Cyberbullying in adolescents is influenced by several factors, namely personality type, perception of the victim, the role of parent and child interaction. The research aims to determine the relationship of personality types with the tendency of cyberbullying behavior in adolescents. The study was conducted on 10th grade high school teenagers in Malang. The sample was 126 students with a purposive sampling technique. Data was collected using Eysenck Personality Questionare and cyberbullying instruments. Data were analyzed using Chi Square test. There is no significant relationship between personality types and the tendency of cyberbullying behavior in 10th grade teenagers in Malang with Asymp. Sig 0.128 or p-value&gt; 𝞪 (0.05).
Imperative Requirements for Data Security in Cloud Computing

Cloud Computing is a popular buzzword currently in the market, the essential concerns for both Cloud providers and consumers are Confidentiality, Integrity, Availability, Authenticity, and Privacy .Security in Cloud computing is a crucial important and critical aspect, and involves numerous issues and problem associated to it.Cloud service provider and the cloud service consumer must and should make sure that the cloud is stay protected from all the external threats and risks so that the customer does not get any problem such as loss of data or data theft.There is also an instance where a malicious user can entered in to the cloud by acting like a legitimate user, in consequence infecting the entire cloud and that effects many customers who are sharing the affected cloud.In this we will discuss how to administer security for the data from the unauthorized users and accommodating integrity to the users.
Demandas de décadas da Enfermagem se sobressaem no enfrentamento à Pandemia COVID-19

COVID 10 coping efforts are shining a harsh light on the historical demands of nursing These include the improvement of poor working conditions, the qualiquantitative lack of personal protection equipment (and nurse understaffing These and other matters are advocated for by nursing organizations, which have been waiting for over two decades for the approval of bills in the National Congress What to make of authorities claiming that our demands cannot be met because we are too many when compared to other professions? Feelings such as disappointment, lack of recognition, senselessness, incoherence, discrimination, and ignorance about the nature of the profession all contribute to our disbelief It is time society supported this category in such challenges, recognizing the profession's specific characteristics and the leading role played by nurses, providing the category with the conditions it needs to consolidate its social practice with quality and safety.
PERANCANGAN SISTEM INFORMASI DATABASE PEMBELIAN DAN PENJUALAN PRODUK PADA SHAVIRA PANCAKE BANDA ACEH

Purchase and sales are things which often heard in a company. The use of manual recording results in a number of problems in the company. Seeing the results of purchases, sales and transaction data manually would take a long time. This study aims to design a database of purchases and sales information systems to help employees manage purchase and sale transactions easier, therefore reducing errors and speed up the proccess of transaction calculations. The system designed in this study uses Microsoft Access. This study uses descriptive qualitative method, data collection techniques was done by conducting interviews, observations, and literature studies carried out directly with the owner of the company. This study produces an application system that would ease future corporate activities.
Expand and Filter: CUNI and LMU Systems for the WNGT 2020 Duolingo Shared Task

We present our submission to the Simultaneous Translation And Paraphrase for Language Education (STAPLE) challenge.We used a standard Transformer model for translation, with a crosslingual classifier predicting correct translations on the output n-best list.To increase the diversity of the outputs, we used additional data to train the translation model, and we trained a paraphrasing model based on the Levenshtein Transformer architecture to generate further synonymous translations.The paraphrasing results were again filtered using our classifier.While the use of additional data and our classifier filter were able to improve results, the paraphrasing model produced too many invalid outputs to further improve the output quality.Our model without the paraphrasing component finished in the middle of the field for the shared task, improving over the best baseline by a margin of 10-22% weighted F1 absolute.
ANALYSIS OF THE IMPLEMENTATION OF PRICE STRATEGIES AND PRODUCTS DIFERENTIATION IN COMPETITIVE ADVANTAGES (Case Study in Sekolah Alam Purwakarta)

The purpose of this research are to know the price strategies of competitive advantaes that is adopted at the Sekolah Alam Purwakarta and how Sekolah Alam Purwakarta presents products differentiation in provding education that is conceptualized according to student needs. The approach of this research uses a qualitative approach. Which is a descriptive study and tends to use analysis with an inductive approach. The competitive advantage strategies applied for educational institutions through the Sekolah Alam Purwakarta pricing strategy involves various parties from Yasasan, Schools, and Parents, and has a pricing policy that is in accordance with government policy, a pricing channel that involves committees as parents' representatives , a clear factor so that price changes can be accounted for, as well as obstacles that have a solution. The second adventage are presenting product differentiation in the form of typical programs. That are morning activity, outbond, home visit, bussines day, fun advanture (camping/ tracking), farming, cooking spesial events, experiments, aquaplays, educational visits, lunches, midday prayers, suttle using companion teacher, trash bank, interships, and backpackers.
Cross-term suppression of multi-component signals based on improved STFT-Wigner

Cross-term interference exists in the WVD of multi-component signals in time-frequency analysis, and the STFT is limited by Heisenberg uncertainty criterion. For multicomponent signals under noisy background, this paper proposes an improved STFT-Wigner algorithm, which establishes a threshold based on the exponential multiplication result compared to the original algorithm, so as to weaken the cross term and reduce the impact of noise on the signal, and improve the time-frequency aggregation of the signal. Simulation results show that the improved algorithm has higher time-frequency aggregation than other methods. Similarly, for cross-term suppression, our method is superior to many other TF analysis methods in low signal-to-noise ratio (SNR) environment.
Learning and Analyzing the Application of Digital Learning in Technology Enterprises

With the advent of the artificial intelligence era, it is an irresistible trend for enterprises to adopt digital learning instead of traditional corporate training methods.Digital learning plays an active role in the enterprise talent training strategy and brings good economic benefits to the enterprise.Digital teaching will produce complicated data.Learning and analysis technology is to use big data to quantitatively analyze learners' learning behaviors, provide evidence for learners' self-evaluation and corporate learning assessment, and also make decision-making and pre-judgment actions for decision makers.Provide data support.This paper introduces the study analysis, to explore its potential applications in enterprise digital learning, and learning challenges facing the analysis.
SMS Sentiment Classification based on Lexical Features, Emoticons and Informal Abbreviations

In this paper we investigate the influence of emoticons, informal speech, lexical and other linguistic features on the sentiment contained in SMS messages. Using the dataset of ~6,000 samples, we trained a linear SVM classifier able to determine positive, negative and neutral sentiments. The dataset mostly contains messages in Serbian, but also in English and German. The classifier had an average accuracy score of 92.3% in a 5-fold Cross Validation setting, and F1-score of 92.1%, 74.0% and 93.3% in favor of positive, negative and neutral class, respectively.
Image Analytics: A Consolidation of Visual Feature Extraction Methods

Revolutionary advances in computer vision and deep learning have dramatically expanded our opportunities to decipher the merits of images in the business world. Though prior research on computer vision has yielded a myriad of approaches to extract core attributes from images, the esotericism of the advocated techniques hinders scholars from delving into the role of visuals in driving business performance. Consequently, this tutorial aims to consolidate resources of visual features’ extraction tactics that employ both conventional machine learning and deep learning models. We describe resources and techniques based on three visual feature extraction methods, namely calculation-, recognition-, and simulation-based method. Additionally, practical examples are provided to illustrate how image features can be accessed via python and open sourced packages such as OpenCV and TensorFlow.
Exploring Model Consensus to Generate Translation Paraphrases

This paper describes our submission to the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE).This task focuses on improving the ability of neural MT systems to generate diverse translations.Our submission explores various methods, including Nbest translation, Monte Carlo dropout, Diverse Beam Search, Mixture of Experts, Ensembling, and Lexical Substitution.Our main submission is based on the integration of multiple translations from multiple methods using Consensus Voting.Experiments show that the proposed approach achieves a considerable degree of diversity without introducing noisy translations.Our final submission 1 achieves 0.5510 weighted F1 score on the blind test set for the English-Portuguese track.
FLOUTING AND IMPLEMENTING MAXIMS IN SARAH SECHAN TALK SHOW

In performing utterances, some people do not always want to cooperate because they have certain reasons such as to avoid unpleasant situations, to be polite, and to make jokes. This research was conducted to find out the most dominant flouting and fulfillment of maxim in some speech acts. The theory of cooperative principles and the speech act were used to find out problems. The data were taken from Sarah Sechan talk show. The most flouting maxim was the quantity and manner and the fulfillment of the quality maxim in the assertive-to-tell speech acts.
Aplikasi Penyewaan Alat Berat Pada PT. UNITED TRACTORS Pontianak Berbasis Desktop

PT United Tractors Pontianak, is still done manually, starting from recording to storing other data relating to the rental process, so that it is possible that during the process of error occurs in recording, inaccurate reports made and delays in searching the necessary data. The research method is research and data collection methods are observation, interview, and literature. Computerized system is the best solution to solve the problems that exist in PT United Tractors Pontianak, as well as with a computerized system an effective and efficient activity can be achieved to support the activities of PT United Tractors Pontianak. Therefore a computerized system is better than a manual system, so that it runs more effectively and efficiently and the rental system is now more conducive compared to the previous system.
Meeting the 2020 Duolingo Challenge on a Shoestring

What is given below is a brief description of the two systems, called gFCONV and c-VAE, which we built in a response to the 2020 Duolingo Challenge.Both are neural models that aim at disrupting a sentence representation the encoder generates with an eye on increasing the diversity of sentences that emerge out of the process.Importantly, we decided not to turn to external sources for extra ammunition, curious to know how far we can go while confining ourselves to the data released by Duolingo (Mayhew et al., 2020).gFCONV works by taking over a pre-trained sequence model, intercepting the output its encoder produces on its way to the decoder.c-VAE is a conditional variational auto-encoder, seeking the diversity by blurring the representation that the encoder derives.Experiments on a corpus constructed out of the public dataset from Duolingo, containing some 4 million pairs of sentences, found that gFCONV is a consistent winner over c-VAE though both suffered heavily from a low recall.
Research on the Factors Influencing the Efficiency of College Students' Class-room Learning and the Countermeasures

To improve the quality of students' talent training is one of the core tasks of colleges and universities, and classroom teaching is the most common teaching mode used in the teaching process of full-time higher education. Therefore, it is of great significance to study how to improve the learning efficiency of college students in the classroom for improving the quality of talent training. Based on the actual investigation and analysis of College Students' classroom, this paper studies the main factors that affect the efficiency of College Students' classroom learning, and on this basis, gives suggestions and measures to improve the efficiency of College Students classroom learning.
A randomized adaptive trust region line search method

Hybridizing the trust region, line search and simulated annealing methods, we develop a heuristic algorithm for solving unconstrained optimization problems. We make some numerical experiments on a set of CUTEr test problems to investigate efficiency of the suggested algorithm. The results show that the algorithm is practically promising.
Building Blocks for Powerful Ideas: Designing a Programming Language to Teach the Beauty and Joy of Computing

Snap! is a cloud-native graphical programming environment and an online community. It is the programming language made for UC Berkeley’s popular introductory CS course named “The Beauty and Joy of Computing”. Snap! is taught in colleges and high schools across the U.S. from Palo Alto to Philadelphia. It has been translated to more than 40 languages and is used around the world—from Göttingen to Beijing—for teaching and research. Snap! has been designed for inclusion. Its low floor welcomes beginners and its multi-media capabilities invite creative thinkers of all ages. At the same time, Snap! offers sophisticated abstractions that make it suitable for an intellectually rigorous introduction to computer science.
Humanoid robot localization in a soccer field using Deep Learning

With the evolution of humanoid robotics and its increasing use in diverse environments and tasks, it is imperative that the robot can interact with the environment and, therefore, understand it accurately to execute decision making. In this work, we presented the process of collecting a new dataset for simulated soccer scenes. We simulated the RoboCup Humanoid challenge and collected over 200k images that contain up to 4 classes of objects, depth estimation, and bounding boxes. We then trained a modified multiclass version of J-MOD2 to validate the dataset and provide the landmarks distances to a Monte Carlo localization algorithm in order to estimate the robot position on the field.
Generating Ethnographic Models from Communities’ Online Data

In this paper we describe computational ethnography study to demonstrate how machine learning techniques can be utilized to exploit bias resident in language data produced by communities with online presence. Specifically, we leverage the use of figurative language (i.e., the choice of metaphors) in online text (e.g., news media, blogs) produced by distinct communities to obtain models of community worldviews that can be shown to be distinctly biased and thus different from other communities’ models. We automatically construct metaphor-based community models for two distinct scenarios: gun rights and marriage equality. We then conduct a series of experiments to validate the hypothesis that the metaphors found in each community’s online language convey the bias in the community’s worldview.
The Role Of Cross-Cultural Understanding in Interpreting English Recount Texts

This study aimed at analyzing the role of cross-cultural understanding in interpreting English recount texts. The data of this study were obtained by reviewing documents of a book entitled “Histories of Nations: How their identities were forged” (Furtado, 2017). As a qualitative content analysis, all the data were analyzed qualitatively by classifying, coding and interpreting the data gathered that led us to some conclusions and suggestions. The study found that there were some words and phrases in the recount text that requires cross cultural understanding in order to be able to interpret those texts accurately. Therefore, in interpreting the recount text, it is necessary to understand not only the text in literal meaning, but also the cultural context in a comprehensive way. This focuses on cultural context deals with the history of social organization and religion system. Cross cultural understanding eases the readers to interpret the recount text appropriately. The study suggests that cross cultural understanding should be an important topic to be learnt intensively in learning English language at least at university education level as it will enable students to be capable of understanding English discourses comprehensively.