Reviewerly/exHarmony huggingface dataset:
Amalgamation of Blockchain Technology and Cloud Computing for a Secure and More Adaptable Cloud

In the recent past, the Internet has been revolutionized by blockchain. Blockchain is a state-of-the-art, ingenious technology which has arisen as an enticing option for countering security and performance-based issues, especially in distributed systems. As blockchain is a decentralized network based on a consensus mechanism it can eliminate the need for trusted third parties and hence can be used in services based on cloud computing which otherwise requires guaranteed data provenance, data distribution, and auditing. On the other hand, cloud computing presents its users with a large number of benefits like enhanced productivity, cost related benefits, increased performance, and efficiency. Undeniably cloud computing offers major benefits to its users and also provides a platform for new enterprises and start-ups with reduced capital. This entire feature has made people shift towards cloud technology, though a major concern persists that usage of the cloud is a security risk. This chapter focuses on the existing cloud computing model and is an in-depth review of existing cloud computing security measures and related constraints. Through this chapter the authors have also tried to incorporate a systematic understanding about the basics of blockchain technology and how it may be integrated with cloud computing in order to enhance the latter’s existing security mechanisms and provide for a more adaptable cloud.
Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation

Recent works have found evidence of gender bias in models of machine translation and coreference resolution using mostly synthetic diagnostic datasets. While these quantify bias in a controlled experiment, they often do so on a small scale and consist mostly of artificial, out-of-distribution sentences. In this work, we find grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments (e.g., female nurses versus male dancers) in corpora from three domains, resulting in a first large-scale gender bias dataset of 108K diverse real-world English sentences. We manually verify the quality of our corpus and use it to evaluate gender bias in various coreference resolution and machine translation models. We find that all tested models tend to over-rely on gender stereotypes when presented with natural inputs, which may be especially harmful when deployed in commercial systems. Finally, we show that our dataset lends itself to finetuning a coreference resolution model, finding it mitigates bias on a held out set. Our dataset and models are publicly available at github.com/SLAB-NLP/BUG. We hope they will spur future research into gender bias evaluation mitigation techniques in realistic settings.
MANAJEMEN SIRKULASI STOK ATK PADA PT.ANGKASA PURA I BANJARMASIN MEMANFAATKAN SISTEM INFORMASI

PT. Angkasa Pura I (Persero) Syamsuddin Noor Airport, is located in Syamsudin Noor Village, Landasan Ulin District, Banjarbaru City, South Kalimantan. PT. Angkasa Pura I Banjarmasin has very dense office administration activities, circulation or the use of office stationery which is routinely used every day at all levels of management of PT. Angkasa Pura I Banjarmasin. Calculation of stock taking is done once every 1 month. Employees who handle the circulation of the use of office stationery still use the Microsoft Excel application every month. The problem felt by the employees who manage the circulation of office stationery is the difficulty in calculating the stock of office stationery considering the large number and variety of office stationery items ranging from pens, pencils, rulers, paper, to ink equipment and printer cartridges that are used up. Difficulty making reports on the use of stock cards for office stationery items that are used every month and takes a long time to make reports, human calculation errors in recording inventory. The information system created records the income of stationery supplied from the Jakarta head office and records work units requesting the use of stationery so that the remaining available office stationery is monitored easily and quickly. The system makes reports on ATK income and circulation of office stationery usage every month quickly and accompanied by correct and accurate data.
Digital Theses: A Revolution Through the Lens of Boadicea

Theses are an extraordinarily significant part of the scholarly ecosystem. For researchers, they are often the cornerstone of their career by establishing and communicating their professional knowledge. Boadicea’s journey and contribution to the overthrow of the Romans is used as a metaphor for the transition of theses to participate in and conquer the new digital environment through the activities libraries. The contemporary scholarly ecosystem provides for a transformation that takes theses from a place on the “dusty shelves” of libraries to works which have high impact and achieve international visibility. This paper reports on activities across Australia and New Zealand to open access to theses, together with a deep dive to reveal the community demand for theses in the fields of humanities and social science. The relationship of theses to the scholarly knowledge system and perceived barriers are assessed using a survey of academics. Key issues for the future in terms of the open access policy environment in the context of researchers’ careers are identified.
Robust Deep Reinforcement Learning through Adversarial Loss

Deep neural networks, including reinforcement learning agents, have been proven vulnerable to small adversarial changes in the input, thus making deploying such networks in the real world problematic. In this paper, we propose RADIAL-RL, a method to train reinforcement learning agents with improved robustness against any $l_p$-bounded adversarial attack. By simply minimizing an upper bound of the loss functions under worst case adversarial perturbation derived from efficient robustness verification methods, we significantly improve robustness of RL-agents trained on Atari-2600 games and show that RADIAL-RL can beat state-of-the-art robust training algorithms when evaluated against PGD-attacks. We also propose a new evaluation method, Greedy Worst-Case Reward (GWC), for measuring attack agnostic robustness of RL agents. GWC can be evaluated efficiently and it serves as a good estimate of the reward under the worst possible sequence of adversarial attacks; in particular, GWC accounts for the importance of each action and their temporal dependency, improving upon previous approaches that only evaluate whether each single action can change under input perturbations. Our code is available at this https URL.
STAMP: A Real-Time and Low-Power Sampling Error Based Stochastic Number Generator

In this paper, we introduce STAMP — a real-time and low-power sampling error based stochastic number generator — for stochastic computing circuits. STAMP exploits the stochastic nature of sampling error; its name is derived from ‘stochastic’ and ‘sampling’. A unique feature of STAMP which distinguishes it from other random generators is the ability to control the output probability of the generated stochastic bit stream in real-time with no area overhead. STAMP is implemented in 180 nm CMOS. Measurements have shown that STAMP passes all tests in NIST suit and outperforms the benchmark random number generators in terms of randomness quality. STAMP performs 140 Mb/s throughput with energy consumption of 77 pJ/bit.
Vector fields created by Fourier transform of video signals

The power spectrum and autocorrelation are widely used in video signals processing. The phase-frequency spectrum contains more information than the amplitude spectrum. However, the amplitude spectrum is used more often in the video signal process. It is possible to combine the advantages of these two spectra to obtain a vector function such as phase-energy spectrum. This spectrum is sensitive even to a small difference in pixel brightness but at the same time it is as stable as the power spectrum. In this paper, we explored the properties of the phase-power spectrum and related amplitude of partials vector field. We showed that phase-power spectrum has conservative as well as solenoidal components and so it creates curl’s vector field. We also found the components of phase-power spectra that contain pixels with a different degree of contrast on the uniform background. We showed the possibility to use solenoidal component of the phase-power vector field to find the circulation (i.e. field work). We concluded that the circulation along any of brightness contour constants, which is symmetrical to the origin, is equal zero. We explored two-dimensional vector field of the phase-power spectrum. Finally, we determined an effect of pixels with different degree of brightness and their relative position to the image’s edges on vector field’s components formation.
Analysing the Practices of Geochemistry: Philosophical and Historical Insights

From the early metallurgy developped by alchemists to isotope geochronology, from the studies carried out in order to understand the kinetics and thermodynamics of weathering within the critical zone to very up-to-date chemical oceanography, from petrochemistry to organic geochemistry and biogeochemistry, from the scrutiny of the stratosphere to the cosmogeochemistry, geochemists have always been connecting and transforming experimental procedures, methods and principles coming from different scientific domains.Involving philosophy of science, and especially philosophy of chemistry, as well as historial issues, this paper investigates the ways geochemists actually work and take part in the understanding of environmental issues.
Feature Extraction Approach in Hyperspectral Data

In general, feature extraction deals with the problem of finding the most informative, distinctive, and reduced set of features and improve the success of data processing.The features should contain information required to distinguish between classes, be insensitive to irrelevant variability in the input, and also be limited in number, to permit, efficient computation of the applied functions and to limit the amount of data required.In many cases, it is an important step in the solutions of many tasks aiming to extract the relevant information from the available large datasets.The aim of this study is to apply a feature extraction approach to a hyperspectral image and extract different features from the dataset and reduce its dimensionality into meaningful orthogonal features.The final analysis was performed in a test site situated in central Mongolia using 242 band Hyperion data.Overall, the study indicated that the Hyperion hyperspectral data could be effectively reduced into meaningful features through a feature extraction process.
New Aspects and an Artificial Intelligence Approach for the Detection of Cervical Abnormalities

The COVID-19 pandemic has challenged health systems worldwide by decreasing their reserves and effectiveness. In this changing landscape, the urge for reallocation of financial and human resources represents a top priority. In screening, effectiveness and efficiency are most relevant. In the quest against cervical cancer, numerous molecular ancillary techniques detecting HPV DNA or mRNA or other related biomarkers complement morphological assessment by the Papanicolaou test. However, no technique is perfect as sensitivity increases at the cost of specificity. Various approaches try to resolve this issue by incorporating several examination results, such as artificial intelligence are proposed. In this study, 1,258 cases with a complete result dataset for cytology, HPV DNA, HPV mRNA, and p16 were used to evaluate the performance of a self-organizing map (SOM), an unsupervised artificial neural network. The results of the SOM application were encouraging since it is capable of producing maps discriminating the necessary tests and has improved performance.
Investigating emergent coordination in small music groups

It is people that make group music work. For researchers, this provides many interesting and diverse opportunities for study. This chapter focuses on ways in which musicians establish coordination in musical contexts with particular consideration of methods of investigation. It takes a high-level view of coordination relating to the alignment of ideas, intentions, and actions in creative collaboration processes. It outlines observational methods for real-life contexts, coding schemes for group behaviors, and the increased employment of mixed-methods that observe and measure interaction in lab and ecological settings. The chapter closes with a consideration of the relevance of longitudinal studies of ensembles that showcase emergent coordination, and an example is offered of an investigation of the development of behavioral interactions over time in two vocal quintets.
Institutional Factors and Financial Development in Sub-Saharan Africa for the period 2004-2018 : Control of Corruption, Rule of Law, Political Stability and Absence of Violence, and Voice and Accountability

The purpose of this study is to have an in-depth understanding of the importance of the institutional environment for financial development in 43 Sub-Saharan African countries during the years 2004 ...
The Study of 3D Reaching Tasks on Visual Servoing Using Quaternion

The image-based monocular visual servoing is difficult to realize the grasping task on three-dimension. This study presented a method of quaternion on 3D reaching tasks of visual servoing. This method used a camera and a laser scanner to acquire the information of the images and the depth. Then the parameters of Jacobian matrix could be determined by the method of the quaternion. A particular constraint mechanism was also proposed to optimize the reaching trajectory of the robot. The simulation results demonstrated our proposed method could implement the 3D reaching tasks successfully and had a significant performance improvement compared to conventional methods.
Fast Federated Learning in the Presence of Arbitrary Device Unavailability

Federated Learning (FL) coordinates with numerous heterogeneous devices to collaboratively train a shared model while preserving user privacy. Despite its multiple advantages, FL faces new challenges. One challenge arises when devices drop out of the training process beyond the control of the central server. In this case, the convergence of popular FL algorithms such as FedAvg is severely influenced by the straggling devices. To tackle this challenge, we study federated learning algorithms under arbitrary device unavailability and propose an algorithm named Memory-augmented Impatient Federated Averaging (MIFA). Our algorithm efficiently avoids excessive latency induced by inactive devices, and corrects the gradient bias using the memorized latest updates from the devices. We prove that MIFA achieves minimax optimal convergence rates on non-i.i.d. data for both strongly convex and non-convex smooth functions. We also provide an explicit characterization of the improvement over baseline algorithms through a case study, and validate the results by numerical experiments on real-world datasets.
Analyzing English Vocabulary In English Textbooks For EFL Learners By Using The Range Program

English textbooks are of paramount importance for English as a foreign language (EFL) learners. As the books are specially designed for the learners, they are likely to provide comprehensible inputs for the learners. Comprehensible inputs allow the learners to acquire the language. Even so, there is still one thing that English teachers of EFL learners need to do, that is to find out what types of English vocabulary the books cover, and whether the types of English vocabulary existing in the books are useful for the students. Therefore, this article is intended to describe the Range Program, as one tool do analyze English vocabulary in texts, the procedures of analyzing by the program, and the result of the analysis through the Range Program provide.Keywords: Range program, word family, textbooks, EFL learners, high school
Educating Children in Science – Ideas from John Amos Comenius

The article attempts to synthetically present the thought of John Amos Comenius, as a precursor of preschool pedagogy. An attempt has been made to determine what makes his thought still current in theory and pedagogical practice in kindergarten. These findings make it possible to compare what we know and what we are guided by at present. The analysis of Comenius’ whole life is a combination of the value of the work itself, the format of research, organizational and journalistic activities, and the features of the times in which we live today.
Bbrp21 Technique Used To Store And Secure The Data In Cloud Storage Database

The current globe is data globe. This data produced using online media; this data is unhindered data; this data could be store in cloud storage database; this data have not incredible security; hereafter to beat this issue we apply the Salsa technique. This technique successfully hack the data from the software engineers. BBRP21 strategy has 5 phases. 1. To apply the mystery key S. 2. To discover the n esteem with the assistance of k. 3. Apply the n esteem proper condition. 4. To trade a and b esteems from left in matrix. 5. To find the mystery prime key S. 6. To find the X1 and X2 values from prime numbers. 7. To find the X 1 and X 2 values. 8. To find the standard deviation values with the help of equation 3 and 4. 9. To trade a and b esteems from left in matrix. 10. To locate the Ttest values and pair it that numbers from left to right. After applied these steps will be stored in cloud storage. The BBRP21 strategy gives extraordinary security while appearing differently in relation to Salsa technique.
Indonesian Language Learning Model Training

Indonesian language learning in schools must be carried out with various methods so that students are not bored in learning. The cooperative learning model is one of the learning models that can activate students so that it is very suitable for use in learning Indonesian. The results of the author's observations show that teachers in the field often only use conventional learning models because they rarely master various learning models that activate students. This community service activity aims to provide training in the application of various cooperative learning models for teachers in Wanaraja District, Garut Regency. Through this service activity, it is hoped that teachers will be able and skilled in using various cooperative learning models in teaching and learning activities in the classroom. The benefits obtained from this activity are that with the skills possessed by the teacher, students can learn Indonesian subjects in a fun method so that all the basic competencies proclaimed in the learning curriculum can be achieved.
Засвоєння граматичної категорії відмінка школярами із тяжкими порушеннями мовлення

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
Sentence Bottleneck Autoencoders from Transformer Language Models

Representation learning for text via pretraining a language model on a large corpus has become a standard starting point for building NLP systems. This approach stands in contrast to autoencoders, also trained on raw text, but with the objective of learning to encode each input as a vector that allows full reconstruction. Autoencoders are attractive because of their latent space structure and generative properties. We therefore explore the construction of a sentence-level autoencoder from a pretrained, frozen transformer language model. We adapt the masked language modeling objective as a generative, denoising one, while only training a sentence bottleneck and a single-layer modified transformer decoder. We demonstrate that the sentence representations discovered by our model achieve better quality than previous methods that extract representations from pretrained transformers on text similarity tasks, style transfer (an example of controlled generation), and single-sentence classification tasks in the GLUE benchmark, while using fewer parameters than large pretrained models.
WRENCH: A Comprehensive Benchmark for Weak Supervision

Recent \emph{Weak Supervision (WS)} approaches have had widespread success in easing the bottleneck of labeling training data for machine learning by synthesizing labels from multiple potentially noisy supervision sources. However, proper measurement and analysis of these approaches remain a challenge. First, datasets used in existing works are often private and/or custom, limiting standardization. Second, WS datasets with the same name and base data often vary in terms of the labels and weak supervision sources used, a significant hidden source of evaluation variance. Finally, WS studies often diverge in terms of the evaluation protocol and ablations used. To address these problems, we introduce a benchmark platform, \benchmark, for a thorough and standardized evaluation of WS approaches. It consists of 22 varied real-world datasets for classification and sequence tagging; a range of real, synthetic, and procedurally-generated weak supervision sources; and a modular, extensible framework for WS evaluation, including implementations for popular WS methods. We use \benchmark to conduct extensive comparisons over more than 100 method variants to demonstrate its efficacy as a benchmark platform. The code is available at \url{this https URL}.
Teachers are Deprofessionalized when Control of the Curriculum is Taken away from them: Discuss in Relation to High School Teachers in China

In this paper, the writer will use a literature analysis approach to explore the issue of teacher autonomy and professionalism in both centralized and decentralized curriculum models. Through literature analysis, the paper argues that centralized and decentralized curricula each have their own positive and negative effects on teachers' autonomy and professional development. Under the current curriculum model in China, teachers' autonomy and professionalism in curriculum development are both enhanced and limited to some extent. Based on the analysis of the literature, this paper will propose recommendations for enhancing Chinese high school teachers' autonomy and professionalism in curriculum development.
Expected Validation Performance and Estimation of a Random Variable’s Maximum

Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we analyze three statistical estimators for expected validation performance, a tool used for reporting performance (e.g., accuracy) as a function of computational budget (e.g., number of hyperparameter tuning experiments). Where previous work analyzing such estimators focused on the bias, we also examine the variance and mean squared error (MSE). In both synthetic and realistic scenarios, we evaluate three estimators and find the unbiased estimator has the highest variance, and the estimator with the smallest variance has the largest bias; the estimator with the smallest MSE strikes a balance between bias and variance, displaying a classic bias-variance tradeoff. We use expected validation performance to compare between different models, and analyze how frequently each estimator leads to drawing incorrect conclusions about which of two models performs best. We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE.