Reviewerly/exHarmony huggingface dataset:
Educación superior e investigación en Latinoamérica: Transición al uso de tecnologías digitales por Covid-19

El desarrollo de iniciativas para superar barreras que afectan el ecosistema funcional de la educacion superior y la sostenibilidad de la educacion en America Latina, conlleva al analisis de la educacion superior e investigacion en Latinoamerica, considerando la transicion al uso de tecnologias digitales por COVID-19 Mediante un estudio informetrico sobre contenidos web de 25 Universidades, seleccionadas considerando el indice QS Latin America Ranking y cinco paises Latinoamericanos, en los cuales prevalece mayor crisis por contagios Asimismo, se relacionan los aportes impulsados por organismos gubernamentales y universidades Los resultados determinan, que la informacion sobre los programas academicos, se accede mediante entornos digitales, en materia de investigacion, prevalece una escasa proyeccion sobre los avances en tiempos de pandemia, generando incertidumbre ante la dinamizacion de la trilogia ciencia-tecnologia-humanidades en Latinoamerica Se concluye, que el alcance de las tecnologias, para superar la desercion estudiantil se difunde debilmente En investigacion, se omite la visibilidad de estrategias que garanticen su continuidad, generando paralizacion o retraso, incidiendo en el avance cientifico-tecnico Es necesario trascender del uso exclusivo de tecnologias para docencia, e impulsar la difusion sobre accesibilidad, visibilidad y capacidad de interoperabilidad de las plataformas tecnologicas que responda a requerimientos de un entorno educativo vulnerado por COVID-19 Alternate abstract:The development of initiatives to overcome barriers that affect the functional ecosystem of higher education and the sustainability of education in Latin America, leads to the analysis of higher education and research in Latin America considering the transition to the use of digital technologies by COVID-19 Through a computerized study on web content of 25 universities, selected considering the QS Latin America Ranking index and five Latin American countries in which mayor the contagion crisis prevails Likewise, the contributions promoted by government agencies and universities are listed The results determine that the information on the academic programs is accessed through digital environments, in terms of research, little projection prevails over the advances in times of pandemic, generating uncertainty in the face of the revitalization of the science-technology-humanities trilogy, in Latin America It is concluded that the scope of technologies to overcome student desertion is weakly spread In research, the visibility of strategies that guarantee their continuity is omitted, generating paralysis or delay, influencing scientific-technical progress It is necessary to go beyond the exclusive use of technologies for teaching, and promote the dissemination of accessibility, visibility and interoperability of technological platforms that respond to the requirements of an educational environment violated by COVID-19
COMPETENCY-BASED EVALUATION FOR CREATIVE AND EXPERIMENTAL IMMERSION CHALLENGE

Suffocating" is the name given to the transmedia interactive installation, product of the challenge that 28 students achieved in five weeks as part of the Immersion and Creative Experimentation Block (This document refers to Block as a multidisciplinary project with a design challenge and a scheme consisting of modules, which are evaluated by competencies).Based on the text "Redondillas" by Sor Juana Inés de la Cruz (1689), students seek to make the harassment that women experience on the street daily visible.This Block is part of the new Tec 21 educational model, as an educational innovation strategy of the Entrance to Creative Studies at Tecnológico de Monterrey (first year of undergraduate studies in creative and related disciplines).The Block is composed of four modules that cover Fundamentals of Design, Creative Writing, Audio visual Production and Volumetric Construction, with students profiles of architecture, design, digital art, communication, journalism, music production, Hispanic letters and educational innovation, they work in a collaborative and multidisciplinary way with their different profiles for five weeks, in which they develop transversal and disciplinary skills, which are co-evaluated among peers and evaluated in a collegial way by the professors of each module that makes up the Block, using Canvas and E-Lumen, as key tools in the process of qualifying and feedback to students.
AUV Path Planning with Kinematic Constraints in Unknown Environment Using Reinforcement Learning

Path planning has been considered as a challenging and critical issue in the management of autonomous underwater vehicle (AUV) systems. Although in the past years, the study of AUV path planning has obtained numerous achievements, many researches only considered the optimal path in a known grid-based environment, and ignored nonlinear kinematic characteristics of AUVs, which is unrealistic. In this paper, a reinforcement learning based path planning algorithm is proposed, with the nonlinear kinematic constraints of the AUV in unknown continuous environments. The proposed algorithm utilizes the sonar array to detect the randomly placed obstacles and plans a collision-free path that connects the start and target points even the map data is not known in advance. Extensive analysis and simulations are conducted in unknown continuous environments, and verify the validity and efficiency of the proposed algorithm.
Saturated biomarkers in the estimation of organic geochemical homogeneity of crude oils from four oil fields in Libya

Seven crude oils from four oil fields in the Sirte and Murzuq Basins in Libya were investigated in order to estimate their organic geochemical homogeneity. Saturated biomarkers (n-alkanes and isoprenoids) were analyzed using gas chromatography?mass spectrometry (GC?MS). The parameters calculated from the distributions of n-alkanes and isoprenoid aliphatic alkanes, pristane and phytane were used to interpret the organic geochemical characteristics of the oils. Based on the high relative concentration of lower n-alkane homologues in the C11?C16 range and high API values, the oils were classified as light oils. These results also implied that the oils were not biodegraded. The parameters calculated from the distributions of saturated biomarkers indicated that all investigated oils were generated from source rocks containing organic matter of a similar marine origin but with a contribution of terrestrial organic matter. The results also demonstrated that these source rocks were deposited in an oxic environment. The same source rock or the same type of source rocks for all seven samples were postulated. It was concluded that, if the crude oils from these distant basins originated from the same source rocks, they must have had different lengths of their migration pathways.
How to Make COVID-19 Contact Tracing Apps Work: Insights From Behavioral Economics

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Differentially Private Generation of Social Networks via Exponential Random Graph Models

Many social networks contain sensitive relational information. One approach to protect the sensitive relational information while offering flexibility for social network research and analysis is to release synthetic social networks at a pre-specified privacy risk level, given the original observed network. We propose the DP-ERGM procedure that synthesizes networks that satisfy the differential privacy (DP) via the exponential random graph model (EGRM). We apply DP-ERGM to a college student friendship network and compare its original network information preservation in the generated private networks with two other approaches: differentially private DyadWise Randomized Response (DWRR) and Sanitization of the Conditional probability of Edge given Attribute classes (SCEA). The results suggest that DP-EGRM preserves the original information significantly better than DWRR and SCEA in both network statistics and inferences from ERGMs and latent space models. In addition, DP-ERGM satisfies the node DP, a stronger notion of privacy than the edge DP that DWRR and SCEA satisfy.
Graph Adversarial Networks: Protecting Information against Adversarial Attacks

We study the problem of protecting information when learning with graph-structured data. While the advent of Graph Neural Networks (GNNs) has greatly improved node and graph representational learning in many applications, the neighborhood aggregation paradigm exposes additional vulnerabilities to attackers seeking to extract node-level information about sensitive attributes. To counter this, we propose a minimax game between the desired GNN encoder and the worst-case attacker. The resulting adversarial training creates a strong defense against inference attacks, while only suffering small loss in task performance. We analyze the effectiveness of our framework against a worst-case adversary, and characterize the trade-off between predictive accuracy and adversarial defense. Experiments across multiple datasets from recommender systems, knowledge graphs and quantum chemistry demonstrate that the proposed approach provides a robust defense across various graph structures and tasks, while producing competitive GNN encoders.
Language vitality and transculturalization of european immigrant minorities: pomeranian in Brazil

This work presents part of the studies carried out on linguistic and cultural ethnicity of immigration languages in Brazil in the framework of a bilateral cooperation project between XXXXXX e a XXXXXXXX. The discussion presented here is delimited to the context of Pomeranian immigration in the state of Espírito Santo, precisely in Santa Maria de Jetibá municipality. It aims to show the importance of linguistic and cultural vitality of the immigration variety to the pluri- and multilingual education proposals. This study sees Pomeranian as an autochthonous Brazilian language and it discusses linguistic policy actions that have been implemented to demonstrate the vitality of the immigration variety and the use of bi- plurilingualism with some other varieties in different fields and usage practices. From the results of an ethnographic research, we propose a Portuguese-Pomeranian bilingual literacy in the first grades, subsequently extended to other Germanic language(s) from the concept of intercomprehension.
Ray-Space Epipolar Geometry for Light Field Cameras

Light field essentially represents rays in space. The epipolar geometry between two light fields is an important relationship that captures ray-ray correspondences and relative configuration of two views. Unfortunately, so far little work has been done in deriving a formal epipolar geometry model that is specifically tailored for light field cameras. This is primarily due to the high-dimensional nature of the ray sampling process with a light field camera. This paper fills in this gap by developing a novel ray-space epipolar geometry which intrinsically encapsulates the complete projective relationship between two light fields, while the generalized epipolar geometry which describes relationship of normalized light fields is the specialization of the proposed model to calibrated cameras. With Plücker parameterization, we propose the ray-space projection model involving a 6×6 ray-space intrinsic matrix for ray sampling of light field camera. Ray-space fundamental matrix and its properties are then derived to constrain ray-ray correspondences for general and special motions. Finally, based on ray-space epipolar geometry, we present two novel algorithms, one for fundamental matrix estimation, and the other for calibration. Experiments on synthetic and real data have validated the effectiveness of ray-space epipolar geometry in solving 3D computer vision tasks with light field cameras.
From Pixel to Patch: Synthesize Context-Aware Features for Zero-Shot Semantic Segmentation

Zero-shot learning (ZSL) has been actively studied for image classification tasks to relieve the burden of annotating image labels. Interestingly, the semantic segmentation task requires more labor-intensive pixel-wise annotation, but zero-shot semantic segmentation has not attracted extensive research interest. Thus, we focus on zero-shot semantic segmentation that aims to segment unseen objects with only category-level semantic representations provided for unseen categories. In this article, we propose a novel context-aware feature generation network (CaGNet) that can synthesize context-aware pixel-wise visual features for unseen categories based on category-level semantic representations and pixel-wise contextual information. The synthesized features are used to fine-tune the classifier to enable segmenting of unseen objects. Furthermore, we extend pixel-wise feature generation and fine-tuning to patch-wise feature generation and fine-tuning, which additionally considers the interpixel relationship. Experimental results on Pascal-VOC, Pascal-context, and COCO-stuff show that our method significantly outperforms the existing zero-shot semantic segmentation methods.
OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation

The preparation of parallel corpora is a challenging task, particularly for languages that suffer from under-representation in the digital world. In a multi-lingual country like India, the need for such parallel corpora is stringent for several low-resource languages. In this work, we provide an extended English-Odia parallel corpus, OdiEnCorp 2.0, aiming particularly at Neural Machine Translation (NMT) systems which will help translate English↔Odia. OdiEnCorp 2.0 includes existing English-Odia corpora and we extended the collection by several other methods of data acquisition: parallel data scraping from many websites, including Odia Wikipedia, but also optical character recognition (OCR) to extract parallel data from scanned images. Our OCR-based data extraction approach for building a parallel corpus is suitable for other low resource languages that lack in online content. The resulting OdiEnCorp 2.0 contains 98,302 sentences and 1.69 million English and 1.47 million Odia tokens. To the best of our knowledge, OdiEnCorp 2.0 is the largest Odia-English parallel corpus covering different domains and available freely for non-commercial and research purposes.
Document Analysis and Recognition

The cornerstone of this project is to create a DAR system that will convert its functionality into applications in the areas of research and commercial systems.The system will allow the user to convert printed text into an editable digital format, making recognition easier and more efficient.The system will rely on well-known methods, such as image processing, pattern recognition and artificial intelligence, to which will be added a unique technique, namely natural image processing.Effective use of the CNN algorithm would help reduce image blur while effectively recognizing text.The entire system will be a threestep process of creating a digital document from a hard copy, scanning the digital document, and recognizing templates in the document for correct, error-free results.
A Twig-Based Algorithm for Top-k Subgraph Matching in Large-Scale Graph Data

Subgraph matching is considered as a basis query for graph data management, and is used in many domains, such as semantic web and social network analysis. Subgraph isomorphism is an initial solution for the task, which is an NP-complete problem. To speed up the procedure, graph simulation has been presented to match subgraphs with polynomial complexity. Unfortunately, simulation usually loses topology of matched subgraphs. In this paper, we propose an approximation approach for subgraph matching based on twig patterns. First, we transform query graphs into twig patterns and match candidate substructures in graph data. Second, we present an optimized join strategy along with top-k mechanism, including join order selection based on cost evaluation and optimized pruning based on maximum possible score and minimum possible score. Finally, we design experiments on real-life and synthetic graph data to evaluate the performance of our work. The results show that our approach obviously reduces the time complexity and guarantee the correctness for answering the queries of subgraph matching.
Pretraining Financial Text Encoder Enhanced by Lifelong Learning

As the number of financial literature grows rapidly, Financial text mining is becoming important increasingly. In recent years, extracting valuable information from financial documents, namely financial text mining, gained significant popularity within research communities. Although Deep Learning-based financial text mining has achieved remarkable progress recently, in financial fields it still suffers from issues of lack of task-specific labeled training data. To alleviate these issues, we present a pretraining financial text encoder, named F-BERT, a domain-specific language model pretrained on large-scale financial corpora. Different from original BERT, proposed F-BERT is trained continually on both general corpus and financial domain corpus, and four pretraining tasks can be pretrained through lifelong learning, which can enable our F-BERT to continually capture language knowledge and semantic information. The experimental results demonstrate that proposed F-BERT achieves strong results on several financial text mining tasks. Extensive experimental results show the effectiveness and robustness of F-BERT. The source code and pretrained models of F-BERT are available online.
Feller coupling of cycles of permutations and Poisson spacings in inhomogeneous Bernoulli trials

Feller (1945) provided a coupling between the counts of cycles of various sizes in a uniform random permutation of [n] and the spacings between successes in a sequence of n independent Bernoulli trials with success probability 1/n at the nth trial.Arratia, Barbour and Tavaré (1992) extended Feller's coupling, to associate cycles of random permutations governed by the Ewens (θ) distribution with spacings derived from independent Bernoulli trials with success probability θ/(n-1+θ) at the nth trial, and to conclude that in an infinite sequence of such trials, the numbers of spacings of length are independent Poisson variables with means θ/ .Ignatov (1978) first discovered this remarkable result in the uniform case θ = 1, by constructing Bernoulli (1/n) trials as the indicators of record values in a sequence of i.i.d.uniform [0, 1] variables.In the present article, the Poisson property of inhomogeneous Bernoulli spacings is explained by a variation of Ignatov's approach for a general θ > 0.Moreover, our approach naturally provides random permutations of infinite sets whose cycle counts are exactly given by independent Poisson random variables.
An ASP Semantics for Constraints Involving Conditional Aggregates.

We elaborate upon the formal foundations of hybrid Answer Set Programming (ASP) and extend its underlying logical framework with aggregate functions over constraint values and variables. This is achieved by introducing the construct of conditional expressions, which allow for considering two alternatives while evaluating constraints. Which alternative is considered is interpretation-dependent and chosen according to an associated condition. We put some emphasis on logic programs with linear constraints and show how common ASP aggregates can be regarded as particular cases of so-called conditional linear constraints. Finally, we introduce a polynomial-size, modular and faithful translation from our framework into regular (condition-free) Constraint ASP, outlining an implementation of conditional aggregates on top of existing hybrid ASP solvers.
Monitoring the Health of Emerging Neural Network Accelerators with Cost-effective Concurrent Test

ReRAM-based neural network accelerator is a promising solution to handle the memory-and computation-intensive deep learning workloads. However, it suffers from unique device errors. These errors can accumulate to massive levels during the run time and cause significant accuracy drop. It is crucial to obtain its fault status in real-time before any proper repair mechanism can be applied. However, calibrating such statistical information is non-trivial because of the need of a large number of test patterns, long test time, and high test coverage considering that complex errors may appear in million-to-billion weight parameters. In this paper, we leverage the concept of comer data that can significantly confuse the decision making of neural network model, as well as the training algorithm, to generate only a small set of test patterns that is tuned to be sensitive to different levels of error accumulation and accuracy loss. Experimental results show that our method can quickly and correctly report the fault status of a running accelerator, outperforming existing solutions in both detection efficiency and cost
Protean: VM allocation service at scale

We describe the design and implementation of Protean - the Microsoft Azure service responsible for allocating Virtual Machines (VMs) to millions of servers around the globe A single instance of Protean serves an entire availability zone (10-100k machines), facilitating seamless failover and scale-out to customers The design has proven robust, enabling a substantial expansion of VM offerings and features with minimal changes to the core infrastructure In particular, Protean preserves a clear separation between policy and mechanisms From a policy perspective, a flexible rule-based Allocation Agent (AA) allows Protean to efficiently address multiple constraints and performance criteria, and adapt to different conditions On the system side, a multi-layer caching mechanism expedites the allocation process, achieving turnaround times of few milliseconds A slight compromise on allocation quality enables multiple AAs to run concurrently on the same inventory, resulting in increased throughput with negligible conflict rate Our results from both simulations and production demonstrate that Protean achieves high throughput and utilization (85-90% on a key utilization metric), while satisfying user-specific requirements We also demonstrate how Protean is adapted to handle capacity crunch conditions, by zooming in on spikes caused by COVID-19 © 2020 Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2020 All rights reserved
Machine Learning Shrewd Approach For An Imbalanced Dataset Conversion Samples

The imbalance data applies to at least one of the classes, which are typically exceeded by the other ones. The Machine Learning Algorithm (Classifier) trained with an imbalance dataset predicts the majority class (frequently occurring) ‎ more than the other minority classes (rarely occurring). Training with an imbalance dataset poses challenges for classifiers; ‎ however, applying suitable techniques for reducing class imbalance issues can enhance the classifier’s performance. We take an ‎ imbalanced dataset from an educational context. Initially, all shortcomings regarding classification of imbalanced dataset have ‎ been examined. After that, we apply data-level algorithms for class balancing and compare the performance of classifiers. The ‎ performance of the classifier is measured using the underlying information in their confusion matrices such as accuracy, ‎ precision, recall, and f-measure. It shows that classification with an imbalance dataset may produce higher accuracy but low ‎ precision and recall for the minority class. The analysis confirms that both undersampling and oversampling are effective for ‎ balancing datasets, however, oversampling dominates. ‎
A Scalable Bayesian Inference Accelerator for Unsupervised Learning

This article consists only of a collection of slides from the author's conference presentation.