Reviewerly/exHarmony huggingface dataset:
Smart contracts based on hyperledger fabric blockchain for the purpose of health and life insurance services

Nowadays the standard health and life insurance service has devolved into an inefficient industry. In many cases the time for reimbursement is crucial for customers, because when they most need help, they can end up fighting in vain for reimbursement from insurance companies, whose profits too often depend on avoiding paying out. The main goal of using Blockchain technology in health and life insurance is to make the claim process more efficient, to enable lower operational costs and to reduce the direct payments. This paper presents a solution, based on smart contracts on a Blockchain, whereby the insurer pays directly to the hospital for the services provided in favor of the insured and only if the sum insured is insufficient, the patient pays the hospital. This solution is much faster for the insured, reduces operating costs for experts, does not require unnecessary money transfers (between patient and hospital and between insurer and insured patient) and at a time when the patient's health is at risk, reduces emotional and his/her physical discomfort, as it does not require activities for filing a claim with the insurer.
Agents Incorporating Identity and Dynamic Teams in Social Dilemmas

We present our preliminary work on a multi-agent system involving the complex human phenomena of identity and dynamic teams. We outline our ongoing experimentation into understanding how these factors can eliminate some of the naive assumptions of current multi-agent approaches. These include a lack of complex heterogeneity between agents and unchanging team structures. We outline the human social psychological basis for identity, one's sense of self, and dynamic teams, the changing nature of human teams. We describe our application of these factors to a multi-agent system and our expectations for how they might improve the system's applicability to more complex problems, with specific relevance to ad hoc teamwork. We expect that the inclusion of more complex human processes, like identity and dynamic teams, will help with the eventual goal of having effective human-agent teams.
THE IMPACT OF COVID-19 ON THE DESIGN OF ONLINE LANGUAGE TEACHING THROUGH ZOOM

Since 2020, because of the Covid 19 pandemic, university education has been converted to a flipped classroom format, integrating asynchronous and synchronous forms. The change in the concept of learning required teachers to construct new lessons’ designs. Rethinking the didactic approach led us to choose social constructivism as a pedagogical method. The main points of social constructivism used in this article are: learning is learner-centered process; design of learning scenario is built to develop problem-solving, decision-making and innovation capabilities of students; interaction and collaboration play very important role in the learning process; knowledge is co-constructed during interaction with other people. We concluded that communicating about health care during a time of pandemic is a necessary sociocultural competence. Using communicative approaches (task-based and content and language integrated), a structured task was designed. The students worked synchronously on the Zoom platform on the topic “How to survive during pandemic Covid19?” They were offered to complete tasks that involved in-depth vocabulary study and developed skills of reception, production, interaction and mediation. Then, using Zoom’ breakout rooms function, the students were able to practice interviewing about the symptoms of diseases, simulating a dialogue with a doctor. Having learnt the vocabulary necessary for effective communication, at the next stage the students independently wrote a text on a given topic, as suggested by the homework assignment. This task fully realized the possibilities of the creative task to construct meaning. The results showed that using Zoom to develop sociocultural competencies for an online class offers students the needed opportunities for successful vocabulary acquisition.
Generalized mixtures of finite mixtures and telescoping sampling

Within a Bayesian framework, a comprehensive investigation of mixtures of finite mixtures (MFMs), i.e., finite mixtures with a prior on the number of components, is performed. This model class has applications in model-based clustering as well as for semi-parametric density estimation and requires suitable prior specifications and inference methods to exploit its full potential. We contribute by considering a generalized class of MFMs where the hyperparameter $\gamma_K$ of a symmetric Dirichlet prior on the weight distribution depends on the number of components. We show that this model class may be regarded as a Bayesian non-parametric mixture outside the class of Gibbs-type priors. We emphasize the distinction between the number of components $K$ of a mixture and the number of clusters $K_+$, i.e., the number of filled components given the data. In the MFM model, $K_+$ is a random variable and its prior depends on the prior on $K$ and on the hyperparameter $\gamma_K$. We employ a flexible prior distribution for the number of components $K$ and derive the corresponding prior on the number of clusters $K_+$ for generalized MFMs. For posterior inference, we propose the novel telescoping sampler which allows Bayesian inference for mixtures with arbitrary component distributions without resorting to reversible jump Markov chain Monte Carlo (MCMC) methods. The telescoping sampler explicitly samples the number of components, but otherwise requires only the usual MCMC steps of a finite mixture model. The ease of its application using different component distributions is demonstrated on several data sets.
Junior High School Students’ Difficulties in Writing Descriptive Text

Writing is one of the skills that must be possessed by students when learning English. It is one way of expressing ideas, feelings, thoughts, even arguments and knowledge into a piece of paper. Descriptive text is a text which purpose is to describe someone, a place, or something. Students considered writing descriptive text is difficult to do because it is done in English which is a foreign language for them. This research aimed to understand students’ difficulty in writing descriptive text and the causes. This research was using the narrative inquiry method in one of Junior High School in Karawang with a total of five eight-grade students as the participants. The data was collected through a structured personal interview with an interview guideline as the instruments and analyzed based on Miles &amp; Huberman (1994): data reduction, data display, and drawing conclusion/verification. The findings showed that one of the biggest factors causes students to have difficulty in writing descriptive text is their dislike of the English language itself and even though the students know about writing descriptive in general, it doesn't make them want to learn about it. Also, there are several difficulties that the students face when they are asked to write descriptive texts such as they do not understand grammar, they have very little vocabulary, they do not know the meaning of each vocabulary, and they have difficulty constructing sentences when write descriptive text which cause by their dislike on learning English, teaching techniques that make them bored, feeling lazy in learning grammar and vocabulary, and difficulty in reading and translating vocabulary due to lack of vocabulary they have.
Consistent sharing of personal health data in cloud using attribute based encryption (ABE)

Personal Health Record (PHR) is a booming model of patient’s health data updating and periodic maintenance. The patients' power over admittance to track their own PHRs is a challenging and recent technology. Attribute based Encryption (ABE) is advanced technologies were rules are framed based on their characteristics rather than identities. It is a secure key of the patient and is dependent on their characteristics .This paper focus on numerous information of the patients and the clients, to fit them in the PHR framework. The decryption of the secure key is done only when the characteristics match with the data base in the cloud. The security access can be a key or a text data. If numerous keys are created the cloud will grant access only when at least one key attribute gets match with the cloud data. This technology provides an additional dynamic tuning of access or record credits, to uphold the skill-ful on-request client/property denial and access under emergency situations. Other than this a Thresh-old Identity Based Encryption (TIBE) conspire is accommodated upgrading solid unscrambling consistency.
Semantic-based Data Augmentation for Math Word Problems

It's hard for neural MWP solvers to deal with tiny local variances. In MWP task, some local changes conserve the original semantic while the others may totally change the underlying logic. Currently, existing datasets for MWP task contain limited samples which are key for neural models to learn to disambiguate different kinds of local variances in questions and solve the questions correctly. In this paper, we propose a set of novel data augmentation approaches to supplement existing datasets with such data that are augmented with different kinds of local variances, and help to improve the generalization ability of current neural models. New samples are generated by knowledge guided entity replacement, and logic guided problem reorganization. The augmentation approaches are ensured to keep the consistency between the new data and their labels. Experimental results have shown the necessity and the effectiveness of our methods.
BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling

This paper describes the BLCU-ICALL system used in the SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings, the Definition Modeling subtrack, achieving 1st on Italian, 2nd on Spanish and Russian, and 3rd on English and French. We propose a transformer-based multitasking framework to explore the task. The framework integrates multiple embedding architectures through the cross-attention mechanism, and captures the structure of glosses through a masking language model objective. Additionally, we also investigate a simple but effective model ensembling strategy to further improve the robustness. The evaluation results show the effectiveness of our solution. We release our code at: https://github.com/blcuicall/SemEval2022-Task1-DM.
Provable Convergence of Nesterov's Accelerated Gradient Method for Over-Parameterized Neural Networks

Momentum methods, such as heavy ball method~(HB) and Nesterov's accelerated gradient method~(NAG), have been widely used in training neural networks by incorporating the history of gradients into the current updating process. In practice, they often provide improved performance over (stochastic) gradient descent~(GD) with faster convergence. Despite these empirical successes, theoretical understandings of their accelerated convergence rates are still lacking. Recently, some attempts have been made by analyzing the trajectories of gradient-based methods in an over-parameterized regime, where the number of the parameters is significantly larger than the number of the training instances. However, the majority of existing theoretical work is mainly concerned with GD and the established convergence result of NAG is inferior to HB and GD, which fails to explain the practical success of NAG. In this paper, we take a step towards closing this gap by analyzing NAG in training a randomly initialized over-parameterized two-layer fully connected neural network with ReLU activation. Despite the fact that the objective function is non-convex and non-smooth, we show that NAG converges to a global minimum at a non-asymptotic linear rate $(1-\Theta(1/\sqrt{\kappa}))^t$, where $\kappa > 1$ is the condition number of a gram matrix and $t$ is the number of the iterations. Compared to the convergence rate $(1-\Theta(1/{\kappa}))^t$ of GD, our result provides theoretical guarantees for the acceleration of NAG in neural network training. Furthermore, our findings suggest that NAG and HB have similar convergence rate. Finally, we conduct extensive experiments on six benchmark datasets to validate the correctness of our theoretical results.
A Convergence Analysis of Nesterov's Accelerated Gradient Method in Training Deep Linear Neural Networks

Momentum methods, including heavy-ball~(HB) and Nesterov's accelerated gradient~(NAG), are widely used in training neural networks for their fast convergence. However, there is a lack of theoretical guarantees for their convergence and acceleration since the optimization landscape of the neural network is non-convex. Nowadays, some works make progress towards understanding the convergence of momentum methods in an over-parameterized regime, where the number of the parameters exceeds that of the training instances. Nonetheless, current results mainly focus on the two-layer neural network, which are far from explaining the remarkable success of the momentum methods in training deep neural networks. Motivated by this, we investigate the convergence of NAG with constant learning rate and momentum parameter in training two architectures of deep linear networks: deep fully-connected linear neural networks and deep linear ResNets. Based on the over-parameterization regime, we first analyze the residual dynamics induced by the training trajectory of NAG for a deep fully-connected linear neural network under the random Gaussian initialization. Our results show that NAG can converge to the global minimum at a $(1 - \mathcal{O}(1/\sqrt{\kappa}))^t$ rate, where $t$ is the iteration number and $\kappa > 1$ is a constant depending on the condition number of the feature matrix. Compared to the $(1 - \mathcal{O}(1/{\kappa}))^t$ rate of GD, NAG achieves an acceleration over GD. To the best of our knowledge, this is the first theoretical guarantee for the convergence of NAG to the global minimum in training deep neural networks. Furthermore, we extend our analysis to deep linear ResNets and derive a similar convergence result.
Heavy Rain Face Image Restoration: Integrating Physical Degradation Model and Facial Component Guided Adversarial Learning

With the recent increase in intelligent CCTVs for visual surveillance, a new image degradation that integrates resolution conversion and synthetic rain models is required. For example, in heavy rain, face images captured by CCTV from a distance have significant deterioration in both visibility and resolution. Unlike traditional image degradation models (IDM), such as rain removal and superresolution, this study addresses a new IDM referred to as a scale-aware heavy rain model and proposes a method for restoring high-resolution face images (HR-FIs) from low-resolution heavy rain face images (LRHR-FI). To this end, a 2-stage network is presented. The first stage generates low-resolution face images (LR-FIs), from which heavy rain has been removed from the LRHR-FIs to improve visibility. To realize this, an interpretable IDM-based network is constructed to predict physical parameters, such as rain streaks, transmission maps, and atmospheric light. In addition, the image reconstruction loss is evaluated to enhance the estimates of the physical parameters. For the second stage, which aims to reconstruct the HR-FIs from the LR-FIs outputted in the first stage, facial component guided adversarial learning (FCGAL) is applied to boost facial structure expressions. To focus on informative facial features and reinforce the authenticity of facial components, such as the eyes and nose, a face-parsing-guided generator and facial local discriminators are designed for FCGAL. The experimental results verify that the proposed approach based on physical-based network design and FCGAL can remove heavy rain and increase the resolution and visibility simultaneously. Moreover, the proposed heavy-rain face image restoration outperforms state-of-the-art models of heavy rain removal, image-to-image translation, and superresolution.
Simple Video Generation using Neural ODEs

Despite having been studied to a great extent, the task of conditional generation of sequences of frames, or videos, remains extremely challenging. It is a common belief that a key step towards solving this task resides in modelling accurately both spatial and temporal information in video signals. A promising direction to do so has been to learn latent variable models that predict the future in latent space and project back to pixels, as suggested in recent literature. Following this line of work and building on top of a family of models introduced in prior work, Neural ODE, we investigate an approach that models time-continuous dynamics over a continuous latent space with a differential equation with respect to time. The intuition behind this approach is that these trajectories in latent space could then be extrapolated to generate video frames beyond the time steps for which the model is trained. We show that our approach yields promising results in the task of future frame prediction on the Moving MNIST dataset with 1 and 2 digits.
TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement

Low-light image enhancement is a challenging low-level computer vision task because after we enhance the brightness of the image, we have to deal with amplified noise, color distortion, detail loss, blurred edges, shadow blocks and halo artifacts. In this paper, we propose a Two-Stage Network with Channel Attention (denoted as TSN-CA) to enhance the brightness of the low-light image and restore the enhanced images from various kinds of degradation. In the first stage, we enhance the brightness of the low-light image in HSV space and use the information of H and S channels to help the recovery of details in V channel. In the second stage, we integrate Channel Attention (CA) mechanism into the skip connection of U-Net in order to restore the brightness-enhanced image from severe kinds of degradation in RGB space. We train and evaluate the performance of our proposed model on the LOL real-world and synthetic datasets. In addition, we test our model on several other commonly used datasets without Ground-Truth. We conduct extensive experiments to demonstrate that our method achieves excellent effect on brightness enhancement as well as denoising, details preservation and halo artifacts elimination. Our method outperforms many other state-of-the-art methods qualitatively and quantitatively.
Adaptivity Gaps for the Stochastic Boolean Function Evaluation Problem

We consider the Stochastic Boolean Function Evaluation (SBFE) problem where the task is to efficiently evaluate a known Boolean function $f$ on an unknown bit string $x$ of length $n$. We determine $f(x)$ by sequentially testing the variables of $x$, each of which is associated with a cost of testing and an independent probability of being true. If a strategy for solving the problem is adaptive in the sense that its next test can depend on the outcomes of previous tests, it has lower expected cost but may take up to exponential space to store. In contrast, a non-adaptive strategy may have higher expected cost but can be stored in linear space and benefit from parallel resources. The adaptivity gap, the ratio between the expected cost of the optimal non-adaptive and adaptive strategies, is a measure of the benefit of adaptivity. We present lower bounds on the adaptivity gap for the SBFE problem for popular classes of Boolean functions, including read-once DNF formulas, read-once formulas, and general DNFs. Our bounds range from $\Omega(\log n)$ to $\Omega(n/\log n)$, contrasting with recent $O(1)$ gaps shown for symmetric functions and linear threshold functions.
PENGGUNAAN PEMBATAS ALIRAN (KATUP) JENIS SWING CHECK PADA KERAN AIR UNTUK EFISIENSI AIR WUDU

In Islam, the most frequent need for water is when performing ablution activities. Prophet Muhammad SAW used only 1 mudd of water (about two-thirds of a liter) or about 0.67 liters for ablution. This study aims to determine the efficiency of using a Swing Check type of flow limiter on a water faucet related to ablution activities. The results showed that the flow limiter (valve) of the Swing Check type was able to efficiently use water in an average of 43%. With the Swing Check valve wudu volume per individual is 17.68 l/person before the flow limiter is used this valve and 10.09 liters/person. With this research, it is hoped that in the future we can update technology that can save water, not only in saving water but also in maintaining the existence of water, especially the involvement of water conservation also plays an important role in water conservation.
Roboviz: A Game-Centered Project for Information Visualization Education

Due to their pedagogical advantages, large final projects in information visualization courses have become standard practice. Students take on a client--real or simulated--a dataset, and a vague set of goals to create a complete visualization or visual analytics product. Unfortunately, many projects suffer from ambiguous goals, over or under-constrained client expectations, and data constraints that have students spending their time on non-visualization problems (e.g., data cleaning). These are important skills, but are often secondary course objectives, and unforeseen problems can majorly hinder students. We created an alternative for our information visualization course: Roboviz, a real-time game for students to play by building a visualization-focused interface. By designing the game mechanics around four different data types, the project allows students to create a wide array of interactive visualizations. Student teams play against their classmates with the objective to collect the most (good) robots. The flexibility of the strategies encourages variability, a range of approaches, and solving wicked design constraints. We describe the construction of this game and report on student projects over two years. We further show how the game mechanics can be extended or adapted to other game-based projects.
ACTION SONG: PROMOTING A JOYFUL AND ENTERTAINING VOCABULARY LEARNING FOR KIDS

This research aims to describe the best implementation of Action Songs in improving young learners’ body parts vocabularies mastery at ACCEL Kids Center Ubud. This is a CAR which has done in ACCEL Kids Center Ubud. There are 6 children who participate in this study because they have low vocabulary mastery especially in term of Body Parts. The findings of this research indicate the use of action songs as the best implementation of action songs as a media to improve young learners' body parts vocabulary mastery was the action songs that (1) could encourage young learners to focus and concentrate during the activity, (2) less of the materials that could distracted young learners coordination and concentration,(3) has the list of enough body parts vocabularies based, and (4) not too much words of intro lyric that not highlight to the core of vocabulary being taught. The teachers should use Action Song because it makes the situation of learning is enjoyment and entertaining. And enjoyment is the best primary modal that must be noticed as a way to get young learners' interest in learning. If the young learners have a high interest in the use of action songs, it might no longer be too challenging for teachers to carry the object lesson. Additionally, young learners may be able to open their thoughts on accepting the object lesson without problems.