Reviewerly/exHarmony huggingface dataset:
Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification

Class-imbalance naturally exists when train and test models in different domains.Unsupervised domain adaptation (UDA) augment model performance with only accessible annotations from the source domain and unlabeled data from the target domain.However, existing state-of-the-art UDA models learn domaininvariant representations and evaluate primarily on class-balanced data across domains.In this work, we propose an unsupervised domain adaptation approach via reinforcement learning that jointly leverages feature variants and imbalanced labels across domains.We experiment with the text classification task for its easily accessible datasets and compare the proposed method with five baselines.Experiments on three datasets prove that our proposed method can effectively learn robust domain-invariant representations and successfully adapt text classifiers on imbalanced classes over domains.
HUBUNGAN SARAPAN PAGI DENGAN TINGKAT KONSENTRASI BELAJAR PADA ANAK KELAS 4-6 SEKOLAH DASAR NEGERI 1 TIYINGGADING

Background: Children who regularly eat breakfast have enough energy to receive lessons at school than children who skip breakfast. Children who skip breakfast often show weakness, dizziness or fainting. These things really do not support the learning process because learning concentration is disturbed. In learning activities, concentration plays an important role in achieving a conducive learning atmosphere because it reflects children's cognitive abilities.&#x0D; Purpose: This study aims to determine the relationship between breakfast and the level of learning concentration in grade 4-6 at Tiyinggading 1 Public Elementary School.&#x0D; Method: This research is a descriptive analytic study with a cross sectional approach. The sampling technique is Total Sampling. The research subjects were 41 students of grade 4-6 at Tiyinggading 1 Public Elementary School. The independent variable includes breakfast habits and the dependent variable is the level of concentration. Data were analyzed using the Chi-Square test. &#x0D; Result: The average percentage of breakfast for children in grades 4-6 at Tiyinggading 1 State Elementary School is 34 people, 83%. The category of learning concentration for children in grades 4-6 at SD Negeri 1 Tiyinggading has a good average, namely 31 people 76% and there is a significant relationship between breakfast and the level of learning concentration with a value of p = 0.001.&#x0D; Conclusion: Based on the results of the study, it can be concluded that breakfast is related to the level of learning concentration, therefore parents are expected to provide their children with breakfast in order to increase brain performance where breakfast has benefits in providing energy to the brain so that it can help improve memory, reduce the risk of anemia. and concentration while studying.&#x0D; &#x0D; Key words: Breakfast, study concentration, pre-school.
Transformer based Urdu Handwritten Text Optical Character Reader

Extracting Handwritten text is one of the most important components of digitizing information and making it available for large scale setting. Handwriting Optical Character Reader (OCR) is a research problem in computer vision and natural language processing computing, and a lot of work has been done for English, but unfortunately, very little work has been done for low resourced languages such as Urdu. Urdu language script is very difficult because of its cursive nature and change of shape of characters based on it's relative position, therefore, a need arises to propose a model which can understand complex features and generalize it for every kind of handwriting style. In this work, we propose a transformer based Urdu Handwritten text extraction model. As transformers have been very successful in Natural Language Understanding task, we explore them further to understand complex Urdu Handwriting.
MaskEval: Weighted MLM-Based Evaluation for Text Summarization and Simplification

In text summarization and simplification, system outputs must be evaluated along multiple dimensions such as relevance, factual consistency, fluency, and grammaticality, and a wide range of possible outputs could be of high quality. These properties make the development of an adaptable, reference-less evaluation metric both necessary and challenging. We introduce MaskEval, a reference-less metric for text summarization and simplification that operates by performing masked language modeling (MLM) on the concatenation of the candidate and the source texts. It features an attention-like weighting mechanism to modulate the relative importance of each MLM step, which crucially allows it to be adapted to evaluate different quality dimensions. We demonstrate its effectiveness on English summarization and simplification in terms of correlations with human judgments, and explore transfer scenarios between the two tasks.
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models

Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive transformers have achieved state-of-the-art performance for anomaly detection in medical imaging. Nonetheless, these models still have some intrinsic weaknesses, such as requiring images to be modelled as 1D sequences, the accumulation of errors during the sampling process, and the significant inference times associated with transformers. Denoising diffusion probabilistic models are a class of non-autoregressive generative models recently shown to produce excellent samples in computer vision (surpassing Generative Adversarial Networks), and to achieve log-likelihoods that are competitive with transformers while having fast inference times. Diffusion models can be applied to the latent representations learnt by autoencoders, making them easily scalable and great candidates for application to high dimensional data, such as medical images. Here, we propose a method based on diffusion models to detect and segment anomalies in brain imaging. By training the models on healthy data and then exploring its diffusion and reverse steps across its Markov chain, we can identify anomalous areas in the latent space and hence identify anomalies in the pixel space. Our diffusion models achieve competitive performance compared with autoregressive approaches across a series of experiments with 2D CT and MRI data involving synthetic and real pathological lesions with much reduced inference times, making their usage clinically viable.
NEEDS ANALYSIS ON ENGLISH LANGUAGE LEARNING AMONG PHARMACY DIPLOMA STUDENTS

English use in tourism sector is different from business or pharmacy field, so English language learning based on needs has to be carried out. To determine the needs for English language learning in certain fields requires a needs analysis whose results can later be applied in the curriculum. This study aims to investigate the needs of Pharmacy Diploma students for English language learning. The subjects of this study were 57 Pharmacy Diploma students who had also worked as pharmaceutical technicians. The data was obtained by using a questionnaire which was then analysed for its frequency, percentage, mean, and standard deviation. Based on the research, the most important skills for Pharmacy Diploma students were Speaking and Translation. Meanwhile, the most significant functions of English were to read scientific articles, operate tools or computers, and communicate with patients and colleagues. Furthermore, the problems experienced by Pharmacy Diploma students in the use of English were difficulties in understanding someone speaking English quickly and also vocabulary related to the health or pharmacy. It then can be concluded that English language learning needed by Pharmacy Diploma students is related to speaking, translation, and also vocabulary enrichment related to health and pharmacy. Compiling curriculum adjusting the needs and the use of English by students is recommended.
Improving Concurrent GC for Latency Critical Services in Multi-tenant Systems

For resource utilization efficiency, latency critical (LC) services are commonly co-located with best-effort batch jobs in datacenter servers. Many LC services, such as Cassandra and HBase, run in Java Virtual Machine (JVM). We find that LC services often experience heavy-tailed latency due to performance interference of the concurrent garbage collection (GC) as well as multi-tenancy. The root cause is a semantic gap of resource allocation between JVM and the underlying Linux OS in multi-tenant systems. That is, the OS is unaware of the characteristics of different kinds of threads in JVM (i.e., GC threads and LC worker threads), which may lead to GC threads competing for CPUs; JVM is unaware of the resource utilization in the OS, which may trigger CPU-intensive GC operations when CPUs are busy. Furthermore, we find that co-located batch jobs can interfere with LC services due to Simultaneous Multi-Threading (SMT).
Implementation of Accounting Information System and Just In Time in Increasing Employee Work Productivity

This study aims to prove the effect of accounting information systems and Just In Time on employee productivity and the effect accounting information system on the implementation of Just In Time. The research method is descriptive quantitative with the technique of taking the sample is simple random and determining the number of samples using the Slovin formula with the number of respondents 77 people. Data collection uses a questionnaire which is distributed directly to employees at PT Distributions Bekasi who work in finance, marketing, delivery of goods, warehouse and administration. The results of the study conclude that the accounting information system and Just In Time partially have a positive and significant effect on employee productivity. likewise, the accounting information system has a positive effect on the implementation of just in time This means that the better the accounting information system and just in time will increase profits and improve the company’s position through good cost control, good delivery of goods and improving the quality of products, services and services to consumers. The results of the study have positive implications for managerial performance in controlling, planning and decision making in company operations.
Efficiency of financial indicators of the Romanian state budget, an objective of economic security during the epidemiological crisis

The financial structure of the state budget is a particularly important objective at the national level, aimed at ensuring economic security by providing resources to meet general objectives of national interest. During the - epidemiological crisis, due to changes in expenditure and revenue structures, the state budget was subject to financial pressures, accumulating deficits five times higher than in the period that followed the previous crisis of 2009-2012 and three times higher than at the beginning of the epidemiological crisis. The present scientific approach observes the main causes of vulnerability and identifies the economic and financial directions predicted based on the results of the last 10 years, taking into account the fact that economic security is a major objective that must be permanently monitored and adjusted through sustainable financial policies by institutional decision makers. This study uses prescriptive analytics methods, modelling data or financial results expressed in the national budgets from 2013 to 2021 in order to quantify the vulnerabilities that led to a large budget deficit (sau “an increased budget deficit”). The results may prove to be useful to stakeholders, institutional decision makers or for the medium-term adjustment of financial policies.
PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs

Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs. However, existing methods are often tailored to specific GAN architectures and are limited to either discovering global semantic directions that do not facilitate localized control, or require some form of supervision through manually provided regions or segmentation masks. In this light, we present an architecture-agnostic approach that jointly discovers factors representing spatial parts and their appearances in an entirely unsupervised fashion. These factors are obtained by applying a semi-nonnegative tensor factorization on the feature maps, which in turn enables context-aware local image editing with pixel-level control. In addition, we show that the discovered appearance factors correspond to saliency maps that localize concepts of interest, without using any labels. Experiments on a wide range of GAN architectures and datasets show that, in comparison to the state of the art, our method is far more efficient in terms of training time and, most importantly, provides much more accurate localized control. Our code is available at: https://github.com/james-oldfield/PandA.
Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers

From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP). It is a challenging and unique task that demands blending surface level text pattern recognition with mathematical reasoning. In spite of extensive research, we are still miles away from building robust representations of elementary math word problems and effective solutions for the general task. In this paper, we critically examine the various models that have been developed for solving word problems, their pros and cons and the challenges ahead. In the last two years, a lot of deep learning models have recorded competing results on benchmark datasets, making a critical and conceptual analysis of literature highly useful at this juncture. We take a step back and analyse why, in spite of this abundance in scholarly interest, the predominantly used experiment and dataset designs continue to be a stumbling block. From the vantage point of having analyzed the literature closely, we also endeavour to provide a road-map for future math word problem research.
Optimizing Prestate Copies in Runtime Verification of Function Postconditions

In behavioural specifications of imperative languages, postconditions may refer to the prestate of the function, usually with an old operator. Therefore, code performing runtime verification has to record prestate values required to evaluate the postconditions, typically by copying part of the memory state, which causes severe verification overhead, both in memory and CPU time. In this paper, we consider the problem of efficiently capturing prestates in the context of Ortac, a runtime assertion checking tool for OCaml. Our contribution is a postcondition transformation that reduces the subset of the prestate to copy. We formalize this transformation, and we provide proof that it is sound and improves the performance of the instrumented programs. We illustrate the benefits of this approach with a maze generator. Our benchmarks show that unoptimized instrumentation is not practicable, while our transformation restores performances similar to the program without any runtime check.
Sentiment Analysis of Indonesian Digital Payment Customer Satisfaction Towards GOPAY, DANA, and ShopeePay Using Naïve Bayes and K-Nearest Neighbour Methods

The utilization of different results from the pace of technological development provides a lot of convenience, benefits, and time efficiency. Now, many related companies or organizations are engaged in technology, offering various services that they have developed to meet the needs of a highly consumptive society, with different terms and conditions. There are many descriptions related to the experience of using the platform that it developed on social media, where everyone can express their feelings and opinions about something since people commonly use Twitter. This study used sentiment analysis and opinion mining to see public satisfaction with digital payment services available in Indonesia by focusing on several available services such as GOPAY, DANA, and ShopeePay. The dataset that became the source of this research was taken from Twitter data through several preparation stages, including data crawling, data cleaning, feature selection, and classification with two machine learning approaches (K-Nearest Neighbour and Naïve Bayes). The raw data held is pre-processed until making the clean data. According to the classification algorithm, a feature search is implemented in this study so that the classification process and data modeling validation provide significant results.
Mastering Quantization is Key for Video Compression

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">This article discusses some key elements and solutions for efficient video compression. More precisely, we introduce key differentiating techniques for video signal quantization within modern hybrid video codecs. First, we recall the fundamentals of scalar quantization and rate-distortion (R-D) theory and then describe the different control points and levels of granularity for optimizing signal quantization in modern video compression standards. From this common knowledge, we discuss approaches in how to jointly optimize those various levels of quantization refinements for improved video encoding quality. Most notably, we explain how to take advantage of a look-ahead module (available in most industrial encoder implementations) to model spatio-temporal coding dependencies, and how to further compute optimal quantization information from an R-D standpoint for a group of pictures (GOPs). Complementary to this first approach, we share some insights about a local quantization refinement (LQR) algorithm. Such an algorithm is often ignored in practical encoder implementation due to its apparent complexity; we develop how and why it can efficiently work in realtime software encoding</i> .
Simple Physics With Python: A Workbook on Introductory Physics With Open-Source Software

Skills in computer science can have great value in studying, doing, and communicating physics. As educators, we asked ourselves how to make students aware of that, and how to offer them a new and appealing approach to physics. We also wondered how to increase students’ engagement, participation, and understanding, particularly when lessons are delivered online. Thus, we began a project to develop study materials for an introductory course in physics for computer science and we chose to use open source software. The materials are organized as a set of Jupyter notebooks hosted on an open GitHub repository. The notebooks deal with the fundamental concepts of physics related to everyday life, offering examples of what can be done with a few lines of Python code. In the notebooks we propose activities to observe phenomena, describe problems, experiment, acquire and analyze data, and model the behavior of systems. The contents are suitable for undergraduates, high-school students, and evergreen students. We have used the materials for lectures, guided laboratory activities, and presentations to freshmen and younger students, and we plan to continue with this project.
Alphabet Recognition with Augmented Reality Technology Based on Android Using Extreme Programming Model

Lack of literacy or interest in reading is the cause of children having difficulty recognizing the letters of the alphabet and assembling them into words or sentences because the letters are similar. Early childhood students have difficulty learning letters because there are too many letters of the alphabet that must be memorized, the number of letters of the alphabet there are 26 letters that must be memorized. In addition, early childhood students complain that reading is very difficult to pronounce because the way the teacher conveys reading techniques for students is difficult to understand so that it is boring for early childhood students. This makes it difficult for young children to pronounce letters. In software development using Extreme Programming (XP) where writing programs in pairs, two programming people work together to write programs. Currently, computer vision technology has been used in various industries, including trade, medicine, education, and so on. Augmented reality is one of the computer vision technologies. The technology of computer vision is to combine synthetic images into the real world or vice versa. By making an android application with an extreme programming method that utilizes Augmented Reality technology that can display 3D, animation, and sound so that it looks real, it makes early childhood interested and makes it easier for them to learn the letters of the alphabet.
ShuffleMixer: An Efficient ConvNet for Image Super-Resolution

Lightweight and efficiency are critical drivers for the practical application of image super-resolution (SR) algorithms. We propose a simple and effective approach, ShuffleMixer, for lightweight image super-resolution that explores large convolution and channel split-shuffle operation. In contrast to previous SR models that simply stack multiple small kernel convolutions or complex operators to learn representations, we explore a large kernel ConvNet for mobile-friendly SR design. Specifically, we develop a large depth-wise convolution and two projection layers based on channel splitting and shuffling as the basic component to mix features efficiently. Since the contexts of natural images are strongly locally correlated, using large depth-wise convolutions only is insufficient to reconstruct fine details. To overcome this problem while maintaining the efficiency of the proposed module, we introduce Fused-MBConvs into the proposed network to model the local connectivity of different features. Experimental results demonstrate that the proposed ShuffleMixer is about 6x smaller than the state-of-the-art methods in terms of model parameters and FLOPs while achieving competitive performance. In NTIRE 2022, our primary method won the model complexity track of the Efficient Super-Resolution Challenge [23]. The code is available at https://github.com/sunny2109/MobileSR-NTIRE2022.
Specific Emitter Identification Based on Paired Sample and Complex Fourier Neural Operator

&lt;p&gt;Specific emitter identification (SEI) techniques utilize the physical-layer fingerprints embedded in their received signals to identify the unique emitter. Fingerprints originate from hardware impairments during transmitter manufacturing. Considering the different manifestations of fingerprints in different domain perspectives, a novel neural operator with the time and frequency domain attention mechanism is proposed, which is named the complex Fourier neural operator (CFNO). And to further enhance the individual discriminability, the demodulation reconstruction waveform and the synchronized waveform are converted into a new sample format, named the paired sample. With the paired sample, the proposed network acquires prior communication knowledge and a baseline of the ideal transmitter. In this work, different network structures are investigated. And performances of the paired sample are fully evaluated. Compared with existing SEI methods based on deep learning models, results show the effectiveness of the CFNO structure, as well as the proposed paired sample scheme. The significance of this paper is that the proposed framework fully utilizes the domain knowledge in the communication field, resulting in excellent performance improvement. &lt;/p&gt;