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Unsupervised Extractive Summarization with BERT

Unsupervised Extractive Summarization with BERT

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Andreea - Nicoleta Dutulescu Mihai Dascalu Stefan Ruseti Computer Science Department University Politehnica of Bucharest Bucharest Romania

the task of document summarization became more pressing as the information volume increased exponentially from news websites to scientific writings. As such, the necessity for tools that automatically summarize written text, while keeping its meaning and extracting relevant information, increased. Extractive summarization is an NLP task that targets the identification of relevant sentences from a document and the creation of a summary with those phrases. While extensive research and large datasets are available in English, Romanian and other low-resource languages lack such methods and corpora. In this work, we introduce a new approach for summarization using a Masked Language Model for assessing sentence importance, and we research several baselines for the Romanian language including K-Means with BERT embeddings, an MLP considering handcrafted features, and PacSum. In addition, we also present an evaluation corpus to be used for assessing current and future models. the unsupervised methods do not require large datasets for training and make use of low computational power. All of the proposed approaches consider BERT, a state-of-the-art Transformer used for generating contextualized embeddings. the obtained ROUGE score of 56.29 is comparable with state-of-the-art scores and the METEOR average of 51.20 supersedes the most advanced current model.

关键词： Training scientific computing Computational modeling Bit error rate Pressing Writing Transformers

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Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification

Exploring the potential of prototype-based soft-labels data ...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Radu-Andrei Rosu Mihaela-Elena Breaban Henri Luchian Faculty of Computer Science Alexandru Ioan Cuza University of Iasi Sentic Lab SRL Romania Faculty of Computer Science Alexandru Ioan Cuza University of Iasi Romania

Dataset distillation aims at synthesizing a dataset by a small number of artificially generated data items, which, when used as training data, reproduce or approximate a machine learning (ML) model as if it were trained on the entire original dataset. Consequently, data distillation methods are usually tied to a specific ML algorithm. While recent literature deals mainly with distillation of large collections of images in the context of neural network models, tabular data distillation is much less represented and mainly focused on a theoretical perspective. the current paper explores the potential of a simple distillation technique previously proposed in the context of Less-than-one shot learning. the main goal is to push further the performance of prototype-based soft-labels distillation in terms of classification accuracy, by integrating optimization steps in the distillation process. the analysis is performed on real-world data sets with various degrees of imbalance. Experimental studies trace the capability of the method to distill the data, but also the opportunity to act as an augmentation method, i.e. to generate new data that is able to increase model accuracy when used in conjunction with - as opposed to instead of - the original data.

关键词： Measurement Machine learning algorithms scientific computing Neural networks Training data Boosting Data models

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Complexity of Monomial Prediction in Cryptography and Machine Learning

Complexity of Monomial Prediction in Cryptography and Machin...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Pranjal Dutta Mahesh Sreekumar Rajasree Santanu Sarkar School of Computing National University of Singapore (NUS) Singapore Department of Computer Science and Engineering Indian Institute of Technology Delhi Delhi India Department of Mathematics Indian Institute of Technology Madras Tamil Nadu India

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

In this paper, we focus on the monomial prediction problem in two settings: (1) Decide whether a particular mono-mial $m$ is present in a composite function $f:=f_{r}\circ f_{r-1}\circ\ldots f\mathrm{o}$ , where $fi$ are quadratic boolean functions, (2) Decide whether a particular monomial $m$ is present in a composite function $f:= f_{r}\circ f_{r-1}\circ\ldots f0$ , where polynomials $fi$ are efficiently computable by Probabilistic Generating circuits over rationals. Probabilistic generating circuits (PGCs) are economical representations of multivariate probability generating polynomials (PGPs), which capture many tractable probabilistic models in machine learning. the first problem has a strong connection with the security of symmetric-key primitives. Dinur and Shamir proposed the cube attack for distinguishing a cryptographic primitive from a random function, which can be thought of as an efficient monomial prediction. In a general setting, over any large finite field or integers, monomial prediction is known to be NP-hard. Here, we show that in the quadratic setting, the problem is $\oplus \mathbf{P}$ complete. $\oplus \mathbf{P}$ is an interesting complexity class that is not known to contain NP, however, it is believed to contain computationally hard problems. On the other hand, we also present several new zero-sum distinguishers for 5-round Ascon, which is one of the ten finalists for NIST light weight cryptography standardization competition. We show that the second problem is #P-complete. It is known that PGCs have efficient inference, i.e. given a monomial, one can efficiently output (which signifies the probability) its coefficient in the polynomial computed by the circuit. However, a composition of such functions makes the inference hard. Composition of prob-abilistic models and their efficient inference play a crucial role in the semantic contextualization and framework of uncertainty theories in graphical modelling.

关键词： Computational modeling Biological system modeling Circuits Probabilistic logic Polynomials numerical models Complexity theory Cryptography Integrated circuit modeling Context modeling

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On Using Perceptual Loss within the U-Net Architecture for the Semantic Inpainting of Textile Artefacts with Traditional Motifs

On Using Perceptual Loss within the U-Net Architecture for t...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Catalin Stoean Nebojsa Bacanin Ruxandra Stoean Leonard Ionescu Cristian Alecsa Mircea Hotoleanu Miguel Atencia Gonzalo Joya Department of Computer Science Faculty of Sciences University of Craiova Romania Artificial Intelligence and Machine Learning Team Romanian Institute of Science and Technology Cluj-Napoca Romania Faculty of Informatics and Computing Singidunum University Belgrade Serbia Restoration and Conservation Lab Oltenia Museum History and Archaeology Section Craiova Romania Universidad de Málaga Spain

It is impressive when one gets to see a hundreds or thousands years old artefact exhibited in the museum, whose appearance seems to have been untouched by centuries. Its restoration had been in the hands of a multidisciplinary team of experts and it had undergone a series of complex procedures. To this end, computational approaches that can support in deciding the most visually appropriate inpainting for very degraded historical items would be helpful as a second objective opinion for the restorers. the present paper thus attempts to put forward a U-Net approach with a perceptual loss for the semantic inpainting of traditional Romanian vests. Images taken of pieces from the collection of the Oltenia Museum in Craiova, along with such images with garments from the Internet, have been given to the deep learning model. the resulting numerical error for inpainting the corrupted parts is adequately low, however the visual similarity still has to be improved by considering further possibilities for finer tuning.

关键词： Deep learning Visualization scientific computing Semantics Museums Image restoration numerical models

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Motorage - Computer vision-based self-sufficient smart parking system

Motorage - Computer vision-based self-sufficient smart parki...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Bogdan Budihală Todor Ivașcu Sebastian Ștefănigă Department of Computer Science West University of Timisoara Timisoara Romania

As devices get ever so interconnected, the concept of Smart City is becoming less of a distant aspiration and more of a reality. One major discomfort which is persistent in traditional cities, from the perspective of a vehicle motorist, is the lack of guidance or coordination when in need of a parking space, be it in a shopping center, at the airport, at work or any other overcrowded point of interest. Motorists are frequently presented with little to no adequate information regarding the parking space availability within a parking lot, and in turn end up spending excessive time in hopes of finding an open spot. this work tackles the problem of improving operational efficiency regarding the widely known problem of finding a parking space, through automation and surveillance, in hopes of providing higher quality allocation and management services. We propose a smart parking system centered around environment monitoring with the help of motion sensors and live-feed cameras, as well as efficient parking space management through a reservation system. As a result, the entire process is automated and the end user is kept up to date, with regard to parking space availability, no longer needing to wander around in order to find a vacant spot. Given that the system does not require any human intervention, it efficiently manages the incoming traffic by ensuring all reservation expectations are met, and it does so effortlessly.

关键词： Space vehicles Automation Smart cities scientific computing Surveillance Cameras Airports

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On the bases of Zn lattice

On the bases of Zn lattice

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Shashank K Mehta Mahesh Sreekumar Rajasree Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India

the ${\mathbb{Z}^n}$ lattice is the lattice generated by the set of all orthogonal unit integer vectors. Since it has an orthonormal basis, the shortest vector problem and the closest vector problem are easy to solve in this particular lattice. But, these problems are hard to solve when we consider a rotation of ${\mathbb{Z}^n}$ lattice. In-fact, even though it is known that the ${\mathbb{Z}^n}$-isomorphism problem is in NP ∩ Co-NP, we still don’t have an efficient algorithm to solve it. Motivated by the above, in this paper we investigate the properties of the bases of ${\mathbb{Z}^n}$ lattice which are the sets of column/row vectors of unimodular matrices. We show that an integer primitive vector of norm strictly greater than 1 can be extended to a unimodular matrix U such that the remaining vectors have norm strictly smaller than the initial primitive vector. We also show a reduction from SVP in any lattice isomorphic to ${\mathbb{Z}^n}$ to SVP in n − 1 dimensional sublattice of ${\mathbb{Z}^n}$. We define two new classes of lattice bases and show certain results related to ${\mathbb{Z}^n}$ bases. Finally, we study the relation between any solution to Successive Minima Problem and the set of Voronoi relevant vectors and present some bounds related to the compact bases of ${\mathbb{Z}^n}$.

关键词： scientific computing Lattices Zinc

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Algorithm for intersecting symbolic and approximate linear differential varieties

Algorithm for intersecting symbolic and approximate linear d...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Siyuan Deng Zahra Mohammadi Gregory Reid Department of Mathematics University of Western Ontario London Canada Department of Mathematics & Statistics University of Guelph Guelph Canada

this article provides algorithms for systems of approximate linear partial differential equations that exploit exact subsystems. Such exact systems have rational function coefficients over $\mathbb{Q}$ and can be reduced to forms (e.g. differential Gröbner bases) by a finite number of differentiations and eliminations using available computer implementations. We will use the rifsimp algorithm in Maple for this purpose. Such algorithms use solvers based on orderings (rankings) of their derivatives, are coordinate dependent, and are prone to instability when applied to approximate input. In contrast, our Geometric Involutive Form algorithm, uses a sequence of geometric differentiations (prolongations) and projections to complete approximate linear systems to geometric involutive form. In particular, it uses numerical linear algebra (especially the SVD) to monitor dimension criteria for termination. However, this latter method can be expensive as the size of the matrices rapidly increases with the number of variables and order of derivatives *** differential systems in applications often have exact subsystems and this motivated us to develop the hybrid method described in this article. the first step of the method is to partition the input into an exact subsystem and an approximate subsystem. the exact subsystem is reduced by using our rifsimp algorithm. the reduced exact subsystem is used to simplify the approximate subsystem. the previous partition, reduction and simplification steps are repeated until no new exact equations are found. then the reduced exact subsystem is used to simplify prolongations of the approximate subsystem. Checking that the jointly prolonged system is geometrically involutive is done by computing dimension criteria of the simplified prolonged approximate system and using the differential Hilbert function of the reduced exact *** algorithm is illustrated by determination of approximate symmetry properties of a gravitati

关键词： Linear systems scientific computing Partial differential equations Symbols Approximation algorithms Mathematical models numerical models

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the Impact of Convolutional Neural Network Parameters in the Binary Classification of Mammograms

The Impact of Convolutional Neural Network Parameters in the...

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international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Mădălina Dicu Laura Dioşan Anca Andreica Camelia Chira Alin Cordoş Faculty of Mathematics and Computer Science Babeş-Bolyai University Cluj-Napoca Romania PIXELDATA Cluj-Napoca Romania

Breast cancer is the most commonly diagnosed type of cancer. It is essential to classify patients as quickly as possible into groups with a high or low risk of cancer, to provide adequate treatment. this paper aims to address the impact of the parameters of convolutional neural networks in the binary classification of mammograms. In this paper, we treat two types of binary classification, namely: classification between normal and abnormal tissues, respectively classification between benign and malignant tumors. In the analysis, we investigate the correlation and impact of batch size and learning rate in increasing the performance of the proposed model. Following the experiments on the MIAS dataset, we concluded that for the treated problems, it is appropriate to choose a learning rate lower than 0.001. For the classification of tissues (normal/abnormal), we obtained the fact that training the model on a batch size of 32 brings the best results, namely an accuracy of 0.67, and for the classification of tumors (benign/malignant), it is more appropriate to use a batch size of 8, for which we obtained an accuracy of 0.63. For the best results configurations, we continued the experiments by investigating the impact of data augmentation. We have increased the number of training data by applying horizontal flip and rotation operations. Following these attempts, we noticed an improvement only for the tissue classification, for which we obtained an accuracy of 0.70.

关键词： Training Correlation scientific computing Malignant tumors Neural networks Training data Mammography

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Computation Results of the Riemann Zeta Search Project 20

Computation Results of the Riemann Zeta Search Project

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20th international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

作者： Tihanyi, Norbert Kovacs, Attila Eotvos Lorand Univ Fac Informat Dept Comp Algebra Budapest Hungary

ISBN: (纸本)9781728106250

the paper summarizes the computation results of the Riemann Zeta Search Project. the aim of the project was to find extremely large values of the Riemann zeta function on the critical line. the computing method is based on the RS-PEAK algorithm which was presented in the 16th SYNASC conference in 2014. the computation environment was served by the SZTAKI Desktop Grid operated by the Laboratory of Parallel and Distributed Systems at the Hungarian Academy of Sciences. Applying the RS-Peak algorithm 5597001 candidates were found where large values of the Riemann-Siegel Zfunction are expected. the largest known values are presented and published on the project website https://***.

关键词： Riemann zeta function Distributed computing Large Z(t) values

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Proceedings - 18th international symposium on symbolic and numeric algorithms for scientific computing, SYNASC 2016

Proceedings - 18th International Symposium on Symbolic and N...

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18th international symposium on symbolic and numeric algorithms for scientific computing, SYNASC 2016

ISBN: (纸本)9781509057078

the proceedings contain 63 papers. the topics discussed include: using machine learning to decide when to precondition cylindrical algebraic decomposition with Groebner bases;combinatorics of hybrid sets;effective nondeterministic positive definiteness test for unidiagonal integral matrices;various enhancements for extended Hensel construction of sparse multivariate polynomials;a numerical method for analyzing the stability of bi-parametric biological systems;approximate duals of Gabor-like frames based on realizable multi-window spline-type constructions;linking Fourier and PCA methods for image look-up;identifiability for gauge regularizations and algorithms for block-sparse synthesis in compressive sensing;numerical results for the coupling of a simple neutronics diffusion model and a simple hydrodynamics low mach number model without coupling codes;convergence acceleration of iterative methods for inverting real matrices using Frobenius norm minimization;parallel simulations for fractional-order systems;the quest for perfect and compact symmetry breaking for graph problems;resource bounding for non-preemptive task scheduling on a multiprocessor platform;coverability analysis of time basic Petri nets with non-urgent behavior;a hybrid test generation approach based on extended finite state machines;and a duality-aware calculus for quantified Boolean formulas.

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