Reviewerly/exHarmony huggingface dataset:
Random Walks with Variable Restarts for Negative-Example-Informed Label Propagation.

Label propagation is frequently encountered in machine learning and data mining applications on graphs, either as a standalone problem or as part of node classification. Many label propagation algorithms utilize random walks (or network propagation), which provide limited ability to take into account negatively-labeled nodes (i.e., nodes that are known to be not associated with the label of interest). Specialized algorithms to incorporate negatively labeled samples generally focus on learning or readjusting the edge weights to drive walks away from negatively-labeled nodes and toward positively-labeled nodes. This approach has several disadvantages, as it increases the number of parameters to be learned, and does not necessarily drive the walk away from regions of the network that are rich in negatively-labeled nodes. We reformulate random walk with restarts and network propagation to enable that is the increased likelihood of restarting at a positively-labeled node when a negatively-labeled node is encountered. Based on this reformulation, we develop CusTaRd, an algorithm that effectively combines variable restart probabilities and edge re-weighting to avoid negatively-labeled nodes. In addition to allowing variable restarts, CusTaRd samples negatively-labeled nodes from neighbors of positively-labeled nodes to better characterize the difference between positively and negatively labeled nodes. To assess the performance of CusTaRd, we perform comprehensive experiments on four network datasets commonly used in benchmarking label propagation and node classification algorithms. Our results show that CusTaRd consistently outperforms competing algorithms that learn/readjust edge weights, and sampling of negatives from the close neighborhood of positives further improves predictive accuracy.
Actor-critic is implicitly biased towards high entropy optimal policies

We show that the simplest actor-critic method -- a linear softmax policy updated with TD through interaction with a linear MDP, but featuring no explicit regularization or exploration -- does not merely find an optimal policy, but moreover prefers high entropy optimal policies. To demonstrate the strength of this bias, the algorithm not only has no regularization, no projections, and no exploration like $\epsilon$-greedy, but is moreover trained on a single trajectory with no resets. The key consequence of the high entropy bias is that uniform mixing assumptions on the MDP, which exist in some form in all prior work, can be dropped: the implicit regularization of the high entropy bias is enough to ensure that all chains mix and an optimal policy is reached with high probability. As auxiliary contributions, this work decouples concerns between the actor and critic by writing the actor update as an explicit mirror descent, provides tools to uniformly bound mixing times within KL balls of policy space, and provides a projection-free TD analysis with its own implicit bias which can be run from an unmixed starting distribution.
Promoviendo la educación artística en secundaria con las píldoras audiovisuales del proyecto de innovación docente Second Round

espanolEl Proyecto “Second Round: Arte y lucha en Secundaria” nacio en 2015 con la intencion de mejorar la situacion de las artes en secundaria. Se trata de una colaboracion entre la Universitat de Valencia junto a 15 institutos valencianos, que cuenta ademas con la participacion de siete universidades. En cada edicion se plantea una tematica concreta. En el curso 2020-2021 el tema ha sido “Comic, ilustracion y videojuegos”, valorando el atractivo que tienen estas manifestaciones artisticas y culturales para el alumnado adolescente. Debido a la situacion que hemos vivido a causa de la pandemia de COVID-19, hemos impulsado la creacion de las “Pildoras Second Round”, un conjunto de videos de corta duracion, con formato atractivo, de modo que tanto el profesorado como el alumnado puedan disponer de este recurso audiovisual para facilitar los aprendizajes online. El esfuerzo realizado ha valido la pena, ya que ahora disponemos de esta coleccion, explicada por docentes, donde se tratan aspectos actuales, y que pueden resultar muy utiles para el profesorado y alumnado de Secundaria. Este proyecto cuenta con una ayuda del SFPIE de la Universitat de Valencia El trabajo esta vinculado a los procesos y resultados del proyecto de innovacion “Second Round: Arte y lucha en Secundaria con comic, EnglishThe Project “Second Round: Art and Struggle in Secondary” was born in 2015 with the intention of improving the situation of the arts in Secondary School. It is a collaboration of the University of Valencia together with 15 Valencian institutes, and also the participation of seven universities. In each edition there is a specific theme. In the 2020-2021 academic year, the theme was “Comic, illustration and video games”, assessing the attractiveness of these artistic and cultural manifestations for adolescent students. Due to the situation of the COVID-19 pandemic we have experienced, we have promoted the creation of the “Second Round Pills”, a set of short videos with an attractive format, so that both teachers and students may have this audiovisual resource to facilitate online learning. The effort made has been worth it, since we now have this collection, explained by teachers, where current aspects are dealt with, and which can be very useful for secondary school teachers and students. This project is supported by the SFPIE of the University of Valencia. The work is linked to the processes and results of the innovation project “Second Round: Art and struggle in Secondary with comics, illustration and video games” participating in the Aid to Development of educational innovation projects for the 2020-2021 academic year , Office for Employment and Training Programs of the University of Valencia. Project reference: UV-SFPIE_PID20-1350597
The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results

In this paper, we introduce the Eval4NLP-2021shared task on explainable quality estimation. Given a source-translation pair, this shared task requires not only to provide a sentence-level score indicating the overall quality of the translation, but also to explain this score by identifying the words that negatively impact translation quality. We present the data, annotation guidelines and evaluation setup of the shared task, describe the six participating systems, and analyze the results. To the best of our knowledge, this is the first shared task on explainable NLP evaluation metrics. Datasets and results are available at https://github.com/eval4nlp/SharedTask2021.
VIRUP : The Virtual Reality Universe Project

VIRUP is a new C++ open source software that provides an interactive virtual reality environment to navigate through large scientific astrophysical datasets obtained from both observations and simulations. It is tailored to visualize terabytes of data, rendering at 90 frames per second in order to ensure an optimal immersion experience. While VIRUP has initially been designed to work with gaming virtual reality headsets, it supports different modern immersive systems like 3D screens, 180 deg. domes or 360 deg. panorama. VIRUP is scriptable thanks to the Python language, a feature that allows to immerse visitors through pre-selected scenes or to pre-render sequences to create movies. A companion video (https://www.youtube.com/watch?v=KJJXbcf8kxA) to the last SDSS 2020 release as well as a 21 minute long documentary, The Archaeology of Light, https://go.epfl.ch/ArchaeologyofLight have been both 100% produced using VIRUP.
Graph Condensation for Graph Neural Networks

Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. To alleviate the concerns, we propose and study the problem of graph condensation for graph neural networks (GNNs). Specifically, we aim to condense the large, original graph into a small, synthetic and highly-informative graph, such that GNNs trained on the small graph and large graph have comparable performance. We approach the condensation problem by imitating the GNN training trajectory on the original graph through the optimization of a gradient matching loss and design a strategy to condense node futures and structural information simultaneously. Extensive experiments have demonstrated the effectiveness of the proposed framework in condensing different graph datasets into informative smaller graphs. In particular, we are able to approximate the original test accuracy by 95.3% on Reddit, 99.8% on Flickr and 99.0% on Citeseer, while reducing their graph size by more than 99.9%, and the condensed graphs can be used to train various GNN architectures.Code is released at https://github.com/ChandlerBang/GCond.
Causal Representation Learning for Context-Aware Face Transfer

Human face synthesis involves transferring knowledge about the identity and identity-dependent face shape (IDFS) of a human face to target face images where the context (e.g., facial expressions, head poses, and other background factors) may change dramatically. Human faces are non-rigid, so facial expression leads to deformation of face shape, and head pose also affects the face observed in 2D images. A key challenge in face transfer is to match the face with unobserved new contexts, adapting the face appearance to different poses and expressions accordingly. In this work, we find a way to provide prior knowledge for generative models to reason about the appropriate appearance of a human face in response to various expressions and poses. We propose a novel context-aware face transfer method, called CarTrans, that incorporates causal effects of contextual factors into face representation, and thus is able to be aware of the uncertainty of new contexts. We estimate the effect of facial expression and head pose in terms of counterfactual inference by designing a controlled intervention trial, thus avoiding the requirement of a large number of observations to cover the pose-expression space well. Moreover, we propose a kernel regression-based encoder that eliminates the identity specificity of target faces when encoding contextual information from target images. The resulting method shows impressive performance, allowing fine-grained control over face shape and appearance under various contextual conditions.
Image Depixelizer Using Enhanced Deep Residual Network

Image reconstruction is challenging now-a-days due to the ill-poseness of the inverse problem and very few number of detected photons. As of late profound deep neural networks have been widely and effectively utilized in PC vision errands and attracted in developing areas like medical imaging, astronomy and many more. In this work, we trained a deep residual convolutional neural network to improve image quality by using the existing training datasets information. We form the target work as an constrained enhancement problem and solve it using the convolutional neural network (CNN) algorithm. The trained datasets are used to evaluate the proposed method. The primary point of Image depixelizer is to change over the given low-resolution image into respective high and super-resolution image. Experimental results shows that the embedded Image depixelizer is quite robust in face of various low-resolution images and provides good results in terms of resolution
Quantifying the Task-Specific Information in Text-Based Classifications

Recently, neural natural language models have attained state-of-the-art performance on a wide variety of tasks, but the high performance can result from superficial, surface-level cues (Bender and Koller, 2020; Niven and Kao, 2020). These surface cues, as the ``shortcuts'' inherent in the datasets, do not contribute to the *task-specific information* (TSI) of the classification tasks. While it is essential to look at the model performance, it is also important to understand the datasets. In this paper, we consider this question: Apart from the information introduced by the shortcut features, how much task-specific information is required to classify a dataset? We formulate this quantity in an information-theoretic framework. While this quantity is hard to compute, we approximate it with a fast and stable method. TSI quantifies the amount of linguistic knowledge modulo a set of predefined shortcuts -- that contributes to classifying a sample from each dataset. This framework allows us to compare across datasets, saying that, apart from a set of ``shortcut features'', classifying each sample in the Multi-NLI task involves around 0.4 nats more TSI than in the Quora Question Pair.
Towards Creating a Standardized Collection of Simple and Targeted Experiments to Analyze Core Aspects of the Recommender Systems Problem

Imagine you are a teacher attempting to assess a student's level in a particular subject. If you design a test with only hard questions, and the student fails, this mostly proves that the student does not understand the more advanced material. A more insightful exam would include different types of questions varying in difficulty to truly understand the student's weaknesses and strengths from different perspectives. In the field of Recommender Systems (RS), more often than not, we design evaluations to measure an algorithm's ability to optimize goals in complex scenarios, representative of the real-world challenges the system would most probably face. Nevertheless, this paper posits that testing an algorithm's ability to address both simple and complex tasks/problems would offer a more detailed view of performance to help identify, at a more granular level, the weaknesses and strengths of solutions when facing different scenarios/domains. We believe the RS community would greatly benefit from creating a collection of standardized, simple, and targeted experiments, which, much like a suite of "unit tests", would individually assess an algorithm's ability to tackle core challenges that make up complex RS tasks. What's more, these experiments go beyond traditional pass/fail "unit tests". Running an algorithm against the collection of experiments allows a researcher to empirically analyze in which type of settings an algorithm performs best and to what degree under different metrics. Not only do we defend this position, in this paper, we also offer a proposal of how these simple and targeted experiments could be defined and shared and suggest potential next steps to make this project a reality.
The Differences between Cognitive Ability Based on Learning Method and Personality Types for Early Childhood in Serang, Banten, Indonesia

Cognitive abilities are needed by children in order to develop knowledge. For this development, efforts need to be made to pay attention to learning methods and personality types. Therefore, research was conducted with the aim of knowing the interaction between learning methods and personality types on students' cognitive abilities. To achieve this goal, a study was conducted on early childhood in Serang City with samples of Al-Kautsar Serang Kindergarten and Amalia Walantaka Kindergarten. The study and interrelationships between variables were carried out with a 2x2 factorial design. The results showed that there was a significant interaction between learning methods and personality types on children's cognitive abilities. The existence of these interactions shows that both methods and personality types need to be considered in the learning process, especially for early childhood education.
Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training Examples

Convolutional Neural Networks (CNNs) for visual tasks are believed to learn both the low-level textures and high-level object attributes, throughout the network depth. This paper further investigates the `texture bias' in CNNs. To this end, we regenerate multiple instances of training examples from each original image, through a process we call `repainting'. The repainted examples preserve the shape and structure of the regions and objects within the scenes, but diversify their texture and color. Our method can regenerate a same image at different daylight, season, or weather conditions, can have colorization or de-colorization effects, or even bring back some texture information from blacked-out areas. The in-place repaint allows us to further use these repainted examples for improving the generalization of CNNs. Through an extensive set of experiments, we demonstrate the usefulness of the repainted examples in training, for the tasks of image classification (ImageNet) and object detection (COCO), over several state-of-the-art network architectures at different capacities, and across different data availability regimes.
Continual Unsupervised Domain Adaptation for Semantic Segmentation

Unsupervised Domain Adaptation (UDA) for semantic segmentation has been favorably applied to real-world scenarios in which pixel-level labels are hard to be obtained. In most of the existing UDA methods, all target data are assumed to be introduced simultaneously. Yet, the data are usually presented sequentially in the real world. Moreover, Continual UDA, which deals with more practical scenarios with multiple target domains in the continual learning setting, has not been actively explored. In this light, we propose Continual UDA for semantic segmentation based on a newly designed Expanding Target-specific Memory (ETM) framework. Our novel ETM framework contains Target-specific Memory (TM) for each target domain to alleviate catastrophic forgetting. Furthermore, a proposed Double Hinge Adversarial (DHA) loss leads the network to produce better UDA performance overall. Our design of the TM and training objectives let the semantic segmentation network adapt to the current target domain while preserving the knowledge learned on previous target domains. The model with the proposed framework outperforms other state-of-the-art models in continual learning settings on standard benchmarks such as GTA5, SYNTHIA, CityScapes, IDD, and Cross-City datasets. The source code is available at https://github.com/joonh-kim/ETM.
Sistem Pendukung Keputusan Penerimaan Peserta Didik Baru Menggunakan Metode Multi Object Optimization on the Basis of Ratio Analysis (moora)berbasis Web (studi Kasus : Man 1 Kuantan Singingi)

Admission of new students is usually done in several SMA/MA, as is done by MAN 1 Kuantan Singingi every year. Prospective students must register for the data collection process and take several tests to determine the selection of new students. The selection process is a problem experienced by the madrasa, because it is only processed with Microsoft Excel and the determination of new student admissions is only estimated and calculated manually from the test results. The method used in the decision support system is the MOORA method, with criteria including the average value of report cards, academic tests and psychological tests. The method is used to optimize two or more conflicting attributes. With the MOORA method, it is hoped that the process of selecting new students will be more accurate. The results of the tests that have been carried out by applying the MOORA method to this decision support system can provide a ranking process for new student admissions. The User Acceptance Testing conducted by the PPDB head has been accepted and the functions have been running well. Then for the usability testing given to the PPDB committee, it can be concluded that the committee strongly agrees that the system for determining new student admission decisions has been able to assist the committee in making decisions with a percentage of 90.53%. In addition, based on testing the accuracy of system calculations and manual calculations, it can be concluded that the system calculations built are the same as manual calculations with an accuracy rate of 100%. Keywords: Multi Object Optimization on the basis of Ratio Analysis (MOORA), User Acceptance Testing, Usability Testing.
SCIENTIFIC-PEDAGOGICAL HERITAGE OF PROFESSOR V.K. SYDORENKO OF THE POLTAVA PERIOD

The article reveals V. Sydorenko's vision of training a teacher of labor education, technology training and drawing; the understanding of this problem is shown as a means of improving labor education and graphic preparation of schoolchildren. Briefly reviewed the biography of the scientist, pedagogical path and his scientific formation are briefly reviewed; the stages of scientific activity are analyzed, the didactic basis of the course of the drawing is substantiated; regularities of formation of graphic knowledge and skills are established; the emphasis is placed on the necessity of forming a graphic culture of pupils; the essence of cooperation between academician and teachers of the Faculty of Technology and Design of Poltava National Pedagogical University named after V.G. Korolenko, his contribution to the development of technological education. The results of complex research in the field of development of scientific and technical creativity, content of scientific and pedagogical heritage; the scientific searches of the professor are described; the main stages of life of V. Sydorenko, pedagogical views, teaching activity are analyzed. Much attention is paid to the Poltava period of the formation of V. Sydorenko, the training of scientific and pedagogical personnel. The basic aspect of activity in the field of technological education is revealed, the most important scientific achievement; V. Sydorenko's theoretical researches on problems of graphic preparation of students, scientific ideas of the professor are analyzed.
Medical Supply Chain Management using Blockchain: An Overview

Blockchain technology has been developed over the past decade in various industries, including banking, government, energy, health, etc., and has gained significant traction. This paper offers a detailed description of medical blockchain technology. In reality, ongoing research in this field is progressing rapidly. We have thus created many modern cases of use of blockchain technology, including electronic medical records sharing, remote patient access, the medical supply chain, etc. Stakeholders in the fields of medical services need to be interoperable, secure, authentic, accountable and seam-free. The Internet-based blockchain platform aims to allow peer-to-Peer and interoperable use of current health data using a patient centered approach that excludes third parties. This technology allows applications to be built to manage and share systemic fraud audit trails that are secure, transparent and immutable. This research analyzes current literature to identify major challenges facing different health workers and to assess the features of blockchain technology that could solve problems identified. We also concentrated on finding the limitations of the approaches examined and eventually addressed open-ended study and further areas of research. However, future research needs to focus on the challenges and disadvantages of this technology.