Reviewerly/exHarmony huggingface dataset:
Evaluation Analysis of Using Tracer on Medical Record Storage

Tracer is one of the facilities in the medical record filing room that can be used as medical record storage to facilitate the detection of its existence. Several studies show that there are still missfiles in medical record files. This can make services to patients often delayed and not timely because medical records are very important for patients. The purpose of this study will be to analyze the importance of evaluating the use of tracers in order to achieve the best quality of health institutions. The research method used is qualitative research with a literature review. The secondary data used for the literature review are15 peer-reviewed journal articles published between 2019-2021 on evaluation of the use of tracers of health agencies spread throughout Indonesia. The findings indicate that there is a need for a good medical record document storage system in the form of a tracer to maximize the service quality of health agencies, both puskesmas and hospitals throughout Indonesia.
Learning Scene Dynamics from Point Cloud Sequences

Understanding 3D scenes is a critical prerequisite for autonomous agents. Recently, LiDAR and other sensors have made large amounts of data available in the form of temporal sequences of point cloud frames. In this work, we propose a novel problem -- sequential scene flow estimation (SSFE) -- that aims to predict 3D scene flow for all pairs of point clouds in a given sequence. This is unlike the previously studied problem of scene flow estimation which focuses on two frames. We introduce the SPCM-Net architecture, which solves this problem by computing multi-scale spatiotemporal correlations between neighboring point clouds and then aggregating the correlation across time with an order-invariant recurrent unit. Our experimental evaluation confirms that recurrent processing of point cloud sequences results in significantly better SSFE compared to using only two frames. Additionally, we demonstrate that this approach can be effectively modified for sequential point cloud forecasting (SPF), a related problem that demands forecasting future point cloud frames. Our experimental results are evaluated using a new benchmark for both SSFE and SPF consisting of synthetic and real datasets. Previously, datasets for scene flow estimation have been limited to two frames. We provide non-trivial extensions to these datasets for multi-frame estimation and prediction. Due to the difficulty of obtaining ground truth motion for real-world datasets, we use self-supervised training and evaluation metrics. We believe that this benchmark will be pivotal to future research in this area. All code for benchmark and models will be made accessible.
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training

Innovations in neural architectures have fostered significant breakthroughs in language modeling and computer vision. Unfortunately, novel architectures often result in challenging hyper-parameter choices and training instability if the network parameters are not properly initialized. A number of architecture-specific initialization schemes have been proposed, but these schemes are not always portable to new architectures. This paper presents GradInit, an automated and architecture agnostic method for initializing neural networks. GradInit is based on a simple heuristic; the norm of each network layer is adjusted so that a single step of SGD or Adam with prescribed hyperparameters results in the smallest possible loss value. This adjustment is done by introducing a scalar multiplier variable in front of each parameter block, and then optimizing these variables using a simple numerical scheme. GradInit accelerates the convergence and test performance of many convolutional architectures, both with or without skip connections, and even without normalization layers. It also improves the stability of the original Transformer architecture for machine translation, enabling training it without learning rate warmup using either Adam or SGD under a wide range of learning rates and momentum coefficients. Code is available at https://github.com/zhuchen03/gradinit.
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization

Stochastic model-based methods have received increasing attention lately due to their appealing robustness to the stepsize selection and provable efficiency guarantee. We make two important extensions for improving model-based methods on stochastic weakly convex optimization. First, we propose new minibatch model-based methods by involving a set of samples to approximate the model function in each iteration. For the first time, we show that stochastic algorithms achieve linear speedup over the batch size even for non-smooth and non-convex (particularly, weakly convex) problems. To this end, we develop a novel sensitivity analysis of the proximal mapping involved in each algorithm iteration. Our analysis appears to be of independent interests in more general settings. Second, motivated by the success of momentum stochastic gradient descent, we propose a new stochastic extrapolated model-based method, greatly extending the classic Polyak momentum technique to a wider class of stochastic algorithms for weakly convex optimization. The rate of convergence to some natural stationarity condition is established over a fairly flexible range of extrapolation terms. While mainly focusing on weakly convex optimization, we also extend our work to convex optimization. We apply the minibatch and extrapolated model-based methods to stochastic convex optimization, for which we provide a new complexity bound and promising linear speedup in batch size. Moreover, an accelerated model-based method based on Nesterov's momentum is presented, for which we establish an optimal complexity bound for reaching optimality.
Sustainable Modular Debiasing of Language Models

Unfair stereotypical biases (e.g., gender, racial, or religious biases) encoded in modern pretrained language models (PLMs) have negative ethical implications for widespread adoption of state-of-the-art language technology. To remedy for this, a wide range of debiasing techniques have recently been introduced to remove such stereotypical biases from PLMs. Existing debiasing methods, however, directly modify all of the PLMs parameters, which – besides being computationally expensive – comes with the inherent risk of (catastrophic) forgetting of useful language knowledge acquired in pretraining. In this work, we propose a more sustainable modular debiasing approach based on dedicated debiasing adapters, dubbed ADELE. Concretely, we (1) inject adapter modules into the original PLM layers and (2) update only the adapters (i.e., we keep the original PLM parameters frozen) via language modeling training on a counterfactually augmented corpus. We showcase ADELE, in gender debiasing of BERT: our extensive evaluation, encompassing three intrinsic and two extrinsic bias measures, renders ADELE, very effective in bias mitigation. We further show that – due to its modular nature – ADELE, coupled with task adapters, retains fairness even after large-scale downstream training. Finally, by means of multilingual BERT, we successfully transfer ADELE, to six target languages.
Comparative Performance of Machine Leaning Algorithms in Prediction of Cervical Cancer

Cervical cancer is among the most common types of cancer affecting women around the world despite the advances in prevention, screening, diagnosis, and treatment during the past decade. Cervical cancer can be treated if diagnosed in its early stages. Machine learning algorithms like multi-layer perceptron, decision trees, random forest, K-Nearest Neighbor, and Naive-Bayes have been used for the prediction of cervical cancer to aid in its early diagnoses. In this study, we compare the performance of ensemble methods (AdaBoost, Stochastic Gradient Boosting, Random Forests, and Extra Trees), and classification algorithms (K-Nearest Neighbor and Support Vector Machine) in the prediction of cervical cancer basin g risk factors. Ensemble methods and classification algorithms were used during this study. Ensemble methods were selected because they combine several machine learning techniques into one model to decrease variance, bias, or improve performance while the classification methods were selected because our dataset was generally categorical and therefore could work well with our problem domain. Experimental results revealed that all the algorithms did not perform well on the “imbalanced” dataset. Experiments on balanced revealed an improved performance. The performance metrics used include Fl-score, Area Under Curve (AUC), and Recall. Extra Trees performed better than the rest when using the Fl-score metric, Stochastic Gradient Boosting and Random Forest performed better than the rest when using the AUC metric, K-Nearest Neighbors outperformed the rest using the recall metric, and Extra Trees had the best accuracy 0.96. The application of machine learning methods in the prediction of cervical cancer using risk factors may lead to early detection of the disease which can be treated if diagnosed early. Six algorithms have been considered in this study. The general performance reveals that ensemble methods performed better than classification methods using both imbalanced and balanced datasets.
SoK: Automated Market Maker (AMM) based Decentralized Exchanges (DEXs)

As an integral part of the Decentralized Finance (DeFi) ecosystem, AutomatedMarket Maker (AMM) based Decentralized Exchanges (DEXs) have gained massivetraction with the revived interest in blockchain and distributed ledgertechnology in general. Most prominently, the top six AMMs -- Uniswap, Balancer,Curve, Dodo, Bancor and Sushiswap -- hold in aggregate 15 billion USD worth ofcrypto-assets as of March 2021. Instead of matching the buy and sell sides,AMMs employ a peer-to-pool method and determine asset price algorithmicallythrough a so-called conservation function. Compared to centralized exchanges,AMMs exhibit the apparent advantage of decentralization, automation andcontinuous liquidity. Nonetheless, AMMs typically feature drawbacks such ashigh slippage for traders and divergence loss for liquidity providers. In thiswork, we establish a general AMM framework describing the economics andformalizing the system's state-space representation. We employ our framework tosystematically compare the mechanics of the top AMM protocols, deriving theirslippage and divergence loss functions.
Explicit Explore, Exploit, or Escape ($$E^4$$): near-optimal safety-constrained reinforcement learning in polynomial time

Abstract In reinforcement learning (RL), an agent must explore an initially unknown environment in order to learn a desired behaviour. When RL agents are deployed in real world environments, safety is of primary concern. Constrained Markov decision processes (CMDPs) can provide long-term safety constraints; however, the agent may violate the constraints in an effort to explore its environment. This paper proposes a model-based RL algorithm called Explicit Explore, Exploit, or Escape ( $$E^{4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>E</mml:mi> <mml:mn>4</mml:mn> </mml:msup> </mml:math> ), which extends the Explicit Explore or Exploit ( $$E^{3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>E</mml:mi> <mml:mn>3</mml:mn> </mml:msup> </mml:math> ) algorithm to a robust CMDP setting. $$E^4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>E</mml:mi> <mml:mn>4</mml:mn> </mml:msup> </mml:math> explicitly separates exploitation, exploration, and escape CMDPs, allowing targeted policies for policy improvement across known states, discovery of unknown states, as well as safe return to known states. $$E^4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>E</mml:mi> <mml:mn>4</mml:mn> </mml:msup> </mml:math> robustly optimises these policies on the worst-case CMDP from a set of CMDP models consistent with the empirical observations of the deployment environment. Theoretical results show that $$E^4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>E</mml:mi> <mml:mn>4</mml:mn> </mml:msup> </mml:math> finds a near-optimal constraint-satisfying policy in polynomial time whilst satisfying safety constraints throughout the learning process. We then discuss $$E^4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>E</mml:mi> <mml:mn>4</mml:mn> </mml:msup> </mml:math> as a practical algorithmic framework, including robust-constrained offline optimisation algorithms, the design of uncertainty sets for the transition dynamics of unknown states, and how to further leverage empirical observations and prior knowledge to relax some of the worst-case assumptions underlying the theory.
Dynamic Grained Encoder for Vision Transformers

Transformers, the de-facto standard for language modeling, have been recently applied for vision tasks. This paper introduces sparse queries for vision transformers to exploit the intrinsic spatial redundancy of natural images and save computational costs. Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region. Thus it achieves a fine-grained representation in discriminative regions while keeping high efficiency. Besides, the dynamic grained encoder is compatible with most vision transformer frameworks. Without bells and whistles, our encoder allows the state-of-the-art vision transformers to reduce computational complexity by 40%-60% while maintaining comparable performance on image classification. Extensive experiments on object detection and segmentation further demonstrate the generalizability of our approach. Code is available at https://github.com/StevenGrove/vtpack.
Perancangan Sistem Informasi Prestasi Mahasiswa Berbasis Website pada Institut Teknologi Bandung

Campus life is inseparable from the various types of activities or activities which are at the same time a means of student self-development towards the expansion of insight, increased reasoning, professionalism, interests, talents and unity. The students are expected not only to master the occupied field of science (hard-skills), but also to master other fields that can support their success in the future (soft-skills). Against this background, it is needed an achievement information system to record and manage achievement data which aims to motivate students and improve the rank of campus student affairs at Ristekdikti. This system is made by the waterfall method, using the php programming language, mysql database, codeigniter framework. The implementation of the system is in the form of a website portal that can manage achievement data, collect achievement data to submit in the Simkatmawa Ristekdikti portal and provide outstanding student recommendations for rewarding.
Learning Generalized Gumbel-max Causal Mechanisms.

To perform counterfactual reasoning in Structural Causal Models (SCMs), one needs to know the causal mechanisms, which provide factorizations of conditional distributions into noise sources and deterministic functions mapping realizations of noise to samples. Unfortunately, the causal mechanism is not uniquely identified by data that can be gathered by observing and interacting with the world, so there remains the question of how to choose causal mechanisms. In recent work, Oberst & Sontag (2019) propose Gumbel-max SCMs, which use Gumbel-max reparameterizations as the causal mechanism due to an intuitively appealing counterfactual stability property. In this work, we instead argue for choosing a causal mechanism that is best under a quantitative criteria such as minimizing variance when estimating counterfactual treatment effects. We propose a parameterized family of causal mechanisms that generalize Gumbel-max. We show that they can be trained to minimize counterfactual effect variance and other losses on a distribution of queries of interest, yielding lower variance estimates of counterfactual treatment effect than fixed alternatives, also generalizing to queries not seen at training time.
Redes, tecnologías de registro distribuido, blockchain y defensa de la competencia

espanolEl articulo configura la primera entrega, de cuatro, de la evaluacion que los autores realizan acerca de la relacion entre la Tecnologia de Registro distribuido, blockchain, y la Defensa de la competencia. Asi, el presente documento se centra en una cuestion tecnica, mas de naturaleza economica que de naturaleza juridica, que resulta esencial para comprender las potenciales implicaciones de la blockchain en el ecosistema competitivo y en el derecho de la libre competencia —o antitrust— que intenta salvaguardarle: la relativa a la taxonomia de las redes, los registros y las cadenas de bloques Englishhe paper configures the first outcome of four, about the analysis of the authors regarding the relationship between Distributed Ledger Technology, blockchain, and antitrust. Thus, this document points out a technical issue that is more economical than juridical and it is also essential to understand the possible implications of the blockchain on markets and on the part of the law that protects them: the one related to the taxonomy of the networking, the ledgers, and the chains of blocks.
Evaluation of Unsupervised Automatic Readability Assessors Using Rank Correlations

Automatic readability assessment (ARA) is the task of automatically assessing readability with little or no human supervision.ARA is essential for many second language acquisition applications to reduce the workload of annotators, who are usually language teachers.Previous unsupervised approaches manually searched textual features that correlated well with readability labels, such as perplexity scores of large language models.This paper argues that, to evaluate an assessors' performance, rank-correlation coefficients should be used instead of Pearson's correlation coefficient (ρ).In the experiments, we show that its performance can be easily underestimated using Pearson's ρ, which is significantly affected by the linearity of the output readability scores.We also propose a lightweight unsupervised readability assessor that achieved the best performance in both the rank correlations and Pearson's ρ among all unsupervised assessors compared.
A Max-Min Entropy Framework for Reinforcement Learning

In this paper, we propose a max-min entropy framework for reinforcement learning (RL) to overcome the limitation of the soft actor-critic (SAC) algorithm implementing the maximum entropy RL in model-free sample-based learning. Whereas the maximum entropy RL guides learning for policies to reach states with high entropy in the future, the proposed max-min entropy framework aims to learn to visit states with low entropy and maximize the entropy of these low-entropy states to promote better exploration. For general Markov decision processes (MDPs), an efficient algorithm is constructed under the proposed max-min entropy framework based on disentanglement of exploration and exploitation. Numerical results show that the proposed algorithm yields drastic performance improvement over the current state-of-the-art RL algorithms.
The Effectiveness of Using Demonstration Method in Teaching Writing Procedure Text (An Experimental Research at The Seventh Grade of MTs Al-Rahmah Islamic Boarding School Walantaka-Serang)

Januar Eka Nugraha, 122301406. 2019. The Effectiveness of Using Demonstration Method in Teaching Writing Procedure Text (An Experimental Research at The Seventh Grade of MTs Al-Rahmah Islamic Boarding School Walantaka-Serang). Under graduate research paper, department of English education, Faculty of Edycation Teacher Training, The state University for Islamic Studies „Sultan Maulana Hasanudin Banten” Adviser As‟ari, S.S., M.Si and Drs. H Busthomi Ibrahim, M.Ag.This research investigates “The Effectiveness of Using Demonstration Method in Teaching Writing Procedure Text”. Demonstration Method is one of interesting method for students in learning English. The students‟ can easier understand and can write so well by this method. Beside, Demonstration Method is one of ways that can be used in teaching writing procedure text. The writer conducts the research based on the main problem: 1) How does demonstration method in teaching improve students achievement in writing procedure text at the seventh grade student of Mts Al-RAhmah Islamic Boarding School Walantaka-Serang.? 2) How does demonstration method influence students achievement in writing procedure text at the seventh grade student of Al-RAhmah Islamic Boarding School Walantaka-Serang? it is oriented towards the following objective: 1) To see the process of demonstration method in teaching to improve students achievement in writing procedure text at the seventh grade student of Al-Rahmah Islamic Boarding School Walantaka-Serang. 2) To know the influence of demonstration method in teaching to improve students achievement in writing procedure text at the seventh grade student of Al-RAhmah Islamic Boarding School Walantaka-Serang.To answer the questions, the researcher collect data from 52 students as a samples in two classes, one class experimental class and another one as the control class, by applying quasi-experimental research method. The population is seventh grade of MTs. Al-Rahmah Islamic Boarding school divide in two classes, as a sample class A as the experimental class and class B as the control class. The data is gathered through pre-test and post-test.The result of data analysis of the research is shown that the value of .to is higher than tt. to = 43,75 > tt = 2,06 (5 %), on . to = 43,75 > tt = 2,80 (1 %). It means the alternative hypothesis (Ha) is accepted and the null hypothesis (Ho) is rejected. It means that demonstration method gives significant influences toward students‟ writing skill on procedure text. The writer recommends that the research using demonstration method can motivate the students to be more spirited and get better result. This method also makes the students more active and creative in learning English. It means that there is significant effect of procedure text writing ability in experimental class. The effect is students‟ procedure text writing ability using demonstration method is better than those who do not use demonstration.
Iterative Teacher-Aware Learning

In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. The teacher adjusts her teaching method for different students, and the student, after getting familiar with the teacher's instruction mechanism, can infer the teacher's intention to learn faster. Recently, the benefits of integrating this cooperative pedagogy into machine concept learning in discrete spaces have been proved by multiple works. However, how cooperative pedagogy can facilitate machine parameter learning hasn't been thoroughly studied. In this paper, we propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function and learn provably faster compared with the naive learning algorithms used in previous machine teaching works. We give theoretical proof that the iterative teacher-aware learning (ITAL) process leads to local and global improvements. We then validate our algorithms with extensive experiments on various tasks including regression, classification, and inverse reinforcement learning using synthetic and real data. We also show the advantage of modeling teacher-awareness when agents are learning from human teachers.