Reviewerly/exHarmony huggingface dataset:
Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images

Recently, there has been extensive research interest in training deep networks to denoise images without clean reference. However, the representative approaches such as Noise2Noise, Noise2Void, Stein's unbiased risk estimator (SURE), etc. seem to differ from one another and it is difficult to find the coherent mathematical structure. To address this, here we present a novel approach, called Noise2Score, which reveals a missing link in order to unite these seemingly different approaches. Specifically, we show that image denoising problems without clean images can be addressed by finding the mode of the posterior distribution and that the Tweedie's formula offers an explicit solution through the score function (i.e. the gradient of log likelihood). Our method then uses the recent finding that the score function can be stably estimated from the noisy images using the amortized residual denoising autoencoder, the method of which is closely related to Noise2Noise or Nose2Void. Our Noise2Score approach is so universal that the same network training can be used to remove noises from images that are corrupted by any exponential family distributions and noise parameters. Using extensive experiments with Gaussian, Poisson, and Gamma noises, we show that Noise2Score significantly outperforms the state-of-the-art self-supervised denoising methods in the benchmark data set such as (C)BSD68, Set12, and Kodak, etc.
Natural Language Processing: An Approach to Aid Emergency Services in COVID-19 Pandemic

In the recent years, Natural Language Processing (NLP) has been widely adopted in numerous applications to organize and structure knowledge to accomplish tasks like translation, summarization, named entity recognition, relationship extraction and speech recognition. With the advent of deep learning techniques, there has been significant increase in the processing efficiency of NLP based systems. The COVID-19 pandemic situation has challenged the medical research, IT operations, business processes and world economy at large. Numerous solutions are being developed to make lockdown and social distancing successful without causing much inconvenience to public. The current trends and applications of NLP can act as a critical support system for fighting COVID-19 pandemic situation. This paper presents a comparative assessment of various deep learning models for NLP techniques and proposes an NLP framework to develop essential and emergency services support system to minimize human interactions.
(An Empirical study of Egyptian Employees) The Role of Authentic leadership During COVID-19

This paper aims is to examine COVID-19 and its influence on Authentic leadership. The research aims to identify the main items of CoronaVirus affecting Authentic leadership. A questionnaire was used to collect data. The survey included 305 employees from different occupations and different age group. A convenient sample was used. The hypotheses were tested using Correlation and Regression analysis. A reliability analysis was firstly investigated to ensure that the data was reliable once this was ensured a Correlation and Regression analysis was investigated. This research shows how Authentic leaders helps employees in dealing with COVID-19 during pandemic. Findings of this research are interesting and beneficial. The results showed that there is a Correlation between Covid-19 and Authentic leadership. And Covid-19 affected Authentic leadership. Greeting had a major impact then Opinion and Isolation and finally comes wearing masks. The study recommended the importance of increasing the awareness of the Egyptian employees and workers towards Covid-19. As well as giving Authentic leaders workshops to give effective methods to deal during Covid-19 pandemic Keywords: Isolation, Symptoms of COVID-19, Authentic leadership, Ethics, Preferences of others, Listening to opposers, Admitting Mistakes, Considering the opinion of others هدفت هذه الدراسة إل معرفة تاثير کوفيد- 19علي القيادة الحقيقة لدى الموظفين و العمال المصريين ، واستخدمت الدراسة المنهج الوصفي الارتباطي و معامل الانحدار ، حيث تکون مجتمع الدراسة من وظائف مختلفةو اعمار مختلفة. وبلغ حجم عينة الدراسة (305) تم اختبارهم باستخدام العينة المتاحة. ولتحقيق أهداف الدراسة قد تم عمل استبيان لجمع المعلومات و ذلک لقياس عناصر من کوفيد-19. قد تم عمل تحليل الموثوقيية لمعرفة جودة البيانات و فد تم التاکد من ذلک. تم تحليل الاستبيانة باستخدام معامل الانحدار و الارتباط بيرسون لمعرفة طبيعة العلاقة و التاثير بين متغيرات الدراسة وتوصلت الدراسة إلي عدد من النتائج و هي: هناک ارتباط بين کل من کوفيد-19 و القيادةالحقيقة و ان هناک تاثير بعض عوامل لکوفيد-19 علي القيادة الحقيقة. الاکثر تاثيرا التحية وقت کوفيد-19 ثم الاراء ثم الاعراض و اخيرا ارتداء الماسکات. و اوصت الدراسة بضرورة توعية الموظفين و العمال المصريين بمعلومات عن کوفيد-19 و کيفية التعامل معة اتناء الجائحةو التعامل مع القيادة الحقيقة. ممکن عقد دورات تدريبية وورش عمل من اجل التوعية القيادة الحقيقية عن کيفية التعامل في وقت جائحة کورونا الکلمات الافتتاحية: الانعزال , الاعراض , القيادة الحقيقية , الاخلاق , اختارات الاخرين , الاستماع الي المعارضين , الاعتراف بالاخطاء , الاهتمام باراء الاخرين
Representation Learning Beyond Linear Prediction Functions

Recent papers on the theory of representation learning has shown the importance of a quantity called diversity when generalizing from a set of source tasks to a target task. Most of these papers assume that the function mapping shared representations to predictions is linear, for both source and target tasks. In practice, researchers in deep learning use different numbers of extra layers following the pretrained model based on the difficulty of the new task. This motivates us to ask whether diversity can be achieved when source tasks and the target task use different prediction function spaces beyond linear functions. We show that diversity holds even if the target task uses a neural network with multiple layers, as long as source tasks use linear functions. If source tasks use nonlinear prediction functions, we provide a negative result by showing that depth-1 neural networks with ReLu activation function need exponentially many source tasks to achieve diversity. For a general function class, we find that eluder dimension gives a lower bound on the number of tasks required for diversity. Our theoretical results imply that simpler tasks generalize better. Though our theoretical results are shown for the global minimizer of empirical risks, their qualitative predictions still hold true for gradient-based optimization algorithms as verified by our simulations on deep neural networks.
Flexible Ostinati, Groove, and Formal Process in Craig Taborn’s<i>Avenging Angel</i>

Repetition in the music of pianist/composer Craig Taborn engenders diverse temporal experiences at both local and global scales. At the local level, distinctive repetitions that I term flexible ostinati share significant features with the cyclic materials often observed in groove-based musics: they comprise a repetitive, omnipresent stream within the overall texture, provide a rapid isochronous pulse, and imply higher-level metric levels. However, they differ from strict ostinati or the repetitions that Anne Danielsen (2006) terms “basic groove units” in their flexibility at the rhythmic/metric level of temporal experience. This flexibility manifests in two ways: in the material domain, flexible ostinati are often varied significantly across repetitions, while in the interpretive domain, they simultaneously suggest multiple possibilities for pulse, meter, or cyclic beginning/ending. Following Danielsen, who describes groove as emerging from interactions between sounding rhythm and a reference structure, I examine the potential reference structures (meter and cycle) suggested by Taborn’s flexible ostinati. These structures are never fixed, but instead must be constantly negotiated, resulting in engaging grooves that draw the listener into the music’s temporal matrix. I analyze three pieces from Taborn’s 2011 solo album, Avenging Angel, to demonstrate how, across longer stretches of time, diverse forms grow out of these ostinati: “The Broad Day King” layers a surface-level flexible ostinato against slower background cycles that gradually reveal themselves; “Avenging Angel” is structured by the juxtaposition of two contrasting ostinati; and a brief passage in “Neverland” features an elusive groove that dissolves almost as soon as it materializes.
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning

Offline reinforcement learning (RL) algorithms have shown promising results in domains where abundant pre-collected data is available. However, prior methods focus on solving individual problems from scratch with an offline dataset without considering how an offline RL agent can acquire multiple skills. We argue that a natural use case of offline RL is in settings where we can pool large amounts of data collected in various scenarios for solving different tasks, and utilize all of this data to learn behaviors for all the tasks more effectively rather than training each one in isolation. However, sharing data across all tasks in multi-task offline RL performs surprisingly poorly in practice. Thorough empirical analysis, we find that sharing data can actually exacerbate the distributional shift between the learned policy and the dataset, which in turn can lead to divergence of the learned policy and poor performance. To address this challenge, we develop a simple technique for data-sharing in multi-task offline RL that routes data based on the improvement over the task-specific data. We call this approach conservative data sharing (CDS), and it can be applied with multiple single-task offline RL methods. On a range of challenging multi-task locomotion, navigation, and vision-based robotic manipulation problems, CDS achieves the best or comparable performance compared to prior offline multi-task RL methods and previous data sharing approaches.
Security In IoT : Based On Elliptic Curve

The emerging Internet of Things (IoT) technology interconnects billions of embedded devices with each other. These embedded devices are internet-enabled, which collect, share, and analyze data without any human interventions. The integration of IoT technology into the human environment, such as industries, agriculture, and health sectors, is expected to improve the way of life and businesses. The emerging technology possesses challenges and numeroussecurity threats. On these grounds, it is a must to strengthen the security of IoT technology to avoid any compromise, which affects human life. In contrast to implementing traditional cryptosystems on IoT devices, an elliptic curve cryptosystem (ECC) is used to meet the limited resources of the devices. ECC is an elliptic curve-based public-key cryptography which provides equivalent security with shorter key size compared to other cryptosystems such as Rivest–Shamir–Adleman (RSA). The security of an ECC hinges on the hardness to solve the elliptic curve discrete logarithm problem (ECDLP). ECC is faster and easier to implement and also consumes less power and bandwidth. ECC is incorporated in internationally recognized standards for lightweight applications due to thebenefits ECC provides.
Quality of Life Integrated Framework: Perspective of Cloud Computing Usage

This research aims to measure the impact of cloud computing on people's quality of life in the Kingdom of Bahrain and recognize factors that could impact people's intention to use cloud computing services. An online survey has been used to collect primary data for the research. It was distributed to a random sample of 443 respondents in the Kingdom of Bahrain. The achievable sample comprised 394 represent people of different ages and educational levels. The researchers adapted selected factors from the diffusion of innovation (DOI) theory, including relative advantage, complexity, and compatibility. In addition to the quality of life factors consisting of education, healthcare, wellbeing, and entertainment. These factors are used to establishing the framework of this research. The research limitation was in examining only the variables proposed in the framework. Also, as a consequence of the coronavirus's current situation (COVID-19), collecting data was restricted to the quantitative approach using an online survey. Findings show that administrability of cloud computing usage is the most impacting factor on people's quality of life and, more specifically, on people's education.
A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction using Relational Graph Convolutional Networks

Relational Graph Convolutional Networks (RGCNs) are commonly used on Knowledge Graphs (KGs) to perform black box link prediction. Several algorithms have been proposed to explain their predictions. Evaluating performance of explanation methods for link prediction is difficult without ground truth explanations. Furthermore, there can be multiple explanations for a given prediction in a KG. No dataset exists where observations have multiple ground truth explanations to compare against. Additionally, no standard scoring metrics exist to compare predicted explanations against multiple ground truth explanations. We propose and evaluate a method, including a dataset, to benchmark explanation methods on the task of explainable link prediction using RGCNs.
Anatomy of OntoGUM--Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms

SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it difficult to evaluate generalizability to open domain data. Zhu et al. (2021) introduced the creation of the OntoGUM corpus for evaluating geralizability of the latest neural LM-based end-to-end systems. This paper covers details of the mapping process which is a set of deterministic rules applied to the rich syntactic and discourse annotations manually annotated in the GUM corpus. Out-of-domain evaluation across 12 genres shows nearly 15-20% degradation for both deterministic and deep learning systems, indicating a lack of generalizability or covert overfitting in existing coreference resolution models.
Data processing using deep learning of the generative-adversarial neural network (GAN)

The article is devoted to the consideration of one of the areas of application of modern and promising computer technology – machine learning. This direction is based on the creation of models consisting of neural networks and their deep learning. At present, there is a need to generate new, not yet existing, images of objects of different types. Most often, text files or images act as such objects. To achieve a high quality of results, a generation method based on the adversarial work of two neural networks (generator and discriminator) was once worked out. This class of neural network models is distinguished by the complexity of topography, since it is necessary to correctly organize the structure of neural layers in order to achieve maximum accuracy and minimal error. The described program is created using the Python language and special libraries that extend the set of commands for performing additional functions: working with neural networks Keras (main library), integrating with the operating system Os, outputting graphs Matplotlib, working with data arrays Numpy and others. A description is given of the type and features of each neural layer, as well as the use of library connection functions, input of initial data, compilation and training of the obtained model. Next, the implementation of the procedure for outputting the results of evaluating the errors of the generator and discriminator and the accuracy achieved by the model depending on the number of cycles (eras) of its training is considered. Based on the results of the work, conclusions were drawn and recommendations were made for the use and development of the considered methodology for creating and training generative and adversarial neural networks. Studies have demonstrated the procedure for operating with comparatively simple and accessible, but effective means of a universal Python language with the Keras library to create and teach a complex neural network model. In fact, it has been proved that the use of this method allows to achieve high-quality results of machine learning, previously achievable only when using special software systems for working with neural networks.
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms

Missing data is an important problem in machine learning practice. Starting from the premise that imputation methods should preserve the causal structure of the data, we develop a regularization scheme that encourages any baseline imputation method to be causally consistent with the underlying data generating mechanism. Our proposal is a causally-aware imputation algorithm (MIRACLE). MIRACLE iteratively refines the imputation of a baseline by simultaneously modeling the missingness generating mechanism, encouraging imputation to be consistent with the causal structure of the data. We conduct extensive experiments on synthetic and a variety of publicly available datasets to show that MIRACLE is able to consistently improve imputation over a variety of benchmark methods across all three missingness scenarios: at random, completely at random, and not at random.
A Correlative Scrutiny on two Programming Dialects: RUBY Vs PYTHON

The two most dominant rising programming dialects in reality, Ruby and Python have many features in common. High-level object-oriented coding, interactive shell, standard libraries, persistence support are some of the common features of Python and Ruby. However, both vary in their approach to solve problems because of their syntax and uses. Power of a language is based on the libraries and popularity defines the frameworks that have been used. This research paper primarily focuses on the frameworks and libraries of Ruby and Python. Ruby on Rails is one of the most powerful framework of Ruby and Django for python. Python is used for many purposes beyond web development such as data science and machine learning computations. A spike in popularity is seen for both Ruby and Python but still the question of “which to be opted” for developing an application becomes a query most of the times. Hence, this research scrutinizes both Ruby and Python in terms of framework and libraries.
Past 20-years environmental record in the coastal area of Rote Island, East Nusa Tenggara; Evidence from the variation of trace metals anthropogenic in annual banding of coral skeleton of Porites lutea

Changing in the concentrations of trace metals in coastal seawater is in parallel with variations of human activities and geochemical processes. Coral skeleton is an excellent archive of metal proxies to monitor water quality as the trace metals incorporate to the skeleton. To study the response of trace metals in coastal seawater to human activities, the coral skeleton of Porites lutea (KR1 and KR2) from Rote Island, Kupang, East Nusa Tenggara, one of the passageways of Indonesian Throughflow (ITF) were used. Corals were drilled vertically using pneumatic tool with diameter 5 cm. The core was cut into 4 mm thick slabs, rinsed with aqua-bidest in an ultrasonic bath, and oven-dried. Sample were then x-rayed using digital radiography to determine the age and annual banding. Sub-samples were collected from each of annual banding using a hand-held drill. The powder of sub-samples were digested with acid and Milli-Q water and analyzed with Inductively Coupled Plasma-Optical Emission Spectrometer (ICP-OES) for determining of anthropogenic trace metals Cu, Pb, Zn, As, Cd and Cr. Contamination factor was used to assess the pollution degree of the coral. Based on the x-ray radiography, the linear extension rates of KR1 and KR2 are decrease from past 20 years with the average are 1.7 cm/year and 1.4 cm/year, respectively. The average of concentrations of trace metals in KR1 and KR2 followed the order of Cu>Cd>Zn>Cr>Pb>As, and their vertical profiles are slightly increase in the past-20 years. Moreover, the average of concentration all trace metals in KR2 are a bit higher than KR1 which maybe the KR2 is closed to the mainland compare to the KR1. The contamination factors of trace metals are in the level of non-pollution to moderate pollution.
A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling

Table filling based relational triple extraction methods are attracting growing research interests due to their promising performance and their abilities on extracting triples from complex sentences. However, this kind of methods are far from their full potential because most of them only focus on using local features but ignore the global associations of relations and of token pairs, which increases the possibility of overlooking some important information during triple extraction. To overcome this deficiency, we propose a global feature-oriented triple extraction model that makes full use of the mentioned two kinds of global associations. Specifically, we first generate a table feature for each relation. Then two kinds of global associations are mined from the generated table features. Next, the mined global associations are integrated into the table feature of each relation. This "generate-mine-integrate" process is performed multiple times so that the table feature of each relation is refined step by step. Finally, each relation's table is filled based on its refined table feature, and all triples linked to this relation are extracted based on its filled table. We evaluate the proposed model on three benchmark datasets. Experimental results show our model is effective and it achieves state-of-the-art results on all of these datasets. The source code of our work is available at: https://github.com/neukg/GRTE.
SimpleTensor -- a user-friendly Mathematica package for elementary tensor and differential-geometric calculations

In this paper we present a short overview of the new Wolfram Mathematica package intended for elementary "in-basis" tensor and differential-geometric calculations. In contrast to alternatives our package is designed to be easy-to-use, short, all-purpose, and hackable. It supports tensor contractions using Einstein notation, transformations between different bases, tensor derivative operator, expansion in basis vectors and forms, exterior derivative, and interior product.
Interactive Inverse Reinforcement Learning for Cooperative Games.

We study the problem of designing AI agents that can learn to cooperate effectively with a potentially suboptimal partner while having no access to the joint reward function. This problem is modeled as a cooperative episodic two-agent Markov decision process. We assume control over only the first of the two agents in a Stackelberg formulation of the game, where the second agent is acting so as to maximise expected utility given the first agent's policy. How should the first agent act in order to learn the joint reward function as quickly as possible, and so that the joint policy is as close to optimal as possible? In this paper, we analyse how knowledge about the reward function can be gained in this interactive two-agent scenario. We show that when the learning agent's policies have a significant effect on the transition function, the reward function can be learned efficiently.