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检索条件"任意字段=7th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing"

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Semi-Supervised Pipeline for Human Sprites Neural Rendering

Semi-Supervised Pipeline for Human Sprites Neural Rendering

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

作者： Alexandru Ionascu Sebastian Stefaniga Department of Computer Science West University of Timisoara Timisoara Romania

In this paper we are discussing the possibility of a hybrid approach in renderable scenes. the main idea of the presented experiment is to render the human actors by using existing videos of the characters. the input video is first converted to a sprite dataset. the dataset is generated with supervised techniques but human intervention is also required. After that we extract body and pose parameters. Lastly, we render novel poses using a GAN-based approach similar to pix2pix.

关键词： Image segmentation Image resolution scientific computing Pipelines Pose estimation Rendering (computer graphics) Hardware

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Preemptive Phased Execution Models with Scratchpad Memory Requirements

Preemptive Phased Execution Models with Scratchpad Memory Re...

引用

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

作者： Vlad Rădulescu Albert M.K. Cheng Ştefan Andrei Department of Computer Science A. I. Cuza University of Iaşi Iaşi Romania Department of Computer Science University of Houston Houston TX USA

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

there is currently a widespread interest in using phased execution models in the design of real-time systems, stimulated by their ability to reduce contention in accessing shared resources. the main beneficiary of such approaches is the wide category of Commercial Off-the-Shelf (COTS) multi-core systems, where complexity is an underlying issue. However, the advantage of predictability comes at a cost, which often resides in the additional decisions that are usually made when using phased execution models. One such decision is employing non-preemptive scheduling; as is well known, this leads to NP-hardness for most instances of the scheduling problem, while preemption can avoid this level of complexity in many cases. It is no surprise, then, that recent efforts have attempted to explore the possibilities offered by preemptive scheduling in combination with phased execution models. Unlike existing work, the current paper focuses on the scratchpad memory consumption in preemptive phased execution models. the starting point, the model introduced by the authors of a previous article, was meant for the general analysis of the efficiency of preemptive scheduling in phased execution models. Notably, two methods for handling intermediate data are introduced, namely, the Waiting Time Minimizing Preemption (WMPM) and the Overhead Minimizing Preemption (OMPM). the current paper analyses the requirements of each approach in terms of scratchpad memory. It also introduces a hybrid method, in order to achieve a finer balance between memory consumption and schedulability constraints.

关键词： Analytical models scientific computing Processor scheduling Computational modeling Memory management Predictive models Scheduling Real-time systems numerical models Complexity theory

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Empirical evaluation of LZW-Compressed Multiple Pattern Matching algorithms

Empirical evaluation of LZW-Compressed Multiple Pattern Matc...

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

作者： Mario Reja Faculty of Mathematics and Informatics West University of Timisoara Timisoara Romania

Data compression is used to reduce the cost of storing and transmitting increasingly large datasets in various domains ranging from bioinformatics to particle physics and general purpose computing. Most of these areas require the ability to scan the datasets for patterns like DNA sequences, malicious executable code or various other string queries, an operation that is hindered by the altered form of the data. In order to speed up the matching process, several compressed pattern matching algorithms have been previously proposed. this paper presents an overview of the state of the art in multiple pattern matching of Lempel-Ziv-Welch encoded archives, together with experimental results.

关键词： Runtime Costs scientific computing Memory management DNA Automata Static analysis

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Advantages of a neuro-symbolic solution for monitoring IT infrastructures alerts

Advantages of a neuro-symbolic solution for monitoring IT in...

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

作者： Darian M. Onchis Codruta Istin Hogea Eduard-Florin Department of Computer Science West University of Timisoara Timisoara Romania Department of Computers and IT Politehnica University of Timisoara Timisoara Romania

the classification and at the same time the inter-active characterization of both bad connections, called alerts or attacks, as well as normal connections, is a must for monitoring network traffic. For this specific task, we developed in this study a neuro-symbolic predictive model based on Logic Tensor Networks. Moreover, we present in detail the advantages and disadvantages of using our hybrid system versus the usage of a standard feed-forward deep neural network classifier. For a relevant comparison, the same dataset was used during training and the metrics resulted have been compared. An overview shows that while both algorithms have similar precision, the hybrid approach gives also the possibility to have interactive explanations and deductive reasoning over data.

关键词： Training Tensors scientific computing Telecommunication traffic Predictive models Cognition Classification algorithms

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Artificial Conflict Sampling for Real Satisfiability Problems

Artificial Conflict Sampling for Real Satisfiability Problem...

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

作者： James H. Davenport Ali K. Uncu Department of Computer Science University of Bath Bath UK RICAM Austrian Academy of Sciences & University of Bath Linz AT & Bath UK

We outline some preliminary ideas on a guided theory assignment of variables in a real (QF_NRA) satisfiability problem. One objective of this approach is to mix the topdown approach of cylindric algebraic decomposition and the bottom-up approach of partial theory assignments of modern SAT/SMT solvers. We use equational constraints and a single strict inequality at a time to artificially create conflicting variable assignment traces, which can later be used in conflict resolution to enrich the constraints of the original satisfiability problem.

关键词： scientific computing

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Improving the Diagnostic of Contrast Enhanced Ultrasound Imaging using Optical Flow for Focal Liver Lesion Detection

Improving the Diagnostic of Contrast Enhanced Ultrasound Ima...

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

作者： Cristina Laura Sîrbu Georgiana Simion Cătălin Daniel Căleanu Faculty of Electronics Telecommunications and Information Technologies Politehnica University of Timișoara Timișoara Romania

Our proposal aims an automatic method used for obtaining the ultrasound image of a region of interest based on the optical flow computation. Combined with a kernel correlation filter tracking algorithm and a Xception deep convolutional neural network architecture, our solution provides state-of-the-art results (over 90% accuracy) in the automatic diagnosis of liver lesion using contrast enhanced ultrasound.

关键词： Frequency locked loops Optical filters Ultrasonic imaging scientific computing Neural networks Liver Lesions

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On Measuring the Dissimilarity between Biological Sequences

On Measuring the Dissimilarity between Biological Sequences

引用

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

作者： Alina Cărunta Mihai Pleşu Department of Computer Science West University of Timisoara Timisoara Romania

the antimicrobial resistance (AMR) of bacteria to antibiotics is tightly connected with their genetic structure changes. Many methods belonging to various disciplines are applied to analyze these genetic changes. the genes involved in AMR undergo different changes. these changes can be analyzed using distances between different variants (alleles) of a gene. this paper aims to study the dissimilarity between different variants of the sul2 gene using the Levenshtein distance between the corresponding DNA sequences, a distance based on binary representation and the distance induces by the global alignment scores computed using with the Needleman-Wunsch algorithm.

关键词： Microorganisms scientific computing Antibiotics DNA Electrical resistance measurement Immune system

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Homotopy Techniques for Analytic Combinatorics in Several Variables

Homotopy Techniques for Analytic Combinatorics in Several Va...

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

作者： Kisun Lee Stephen Melczer Josip Smolčić Department of Mathematics University of California San Diego La Jolla CA USA Dept. of Combinatorics and Optimization University of Waterloo Waterloo Canada

We combine tools from homotopy continuation solvers with the methods of analytic combinatorics in several variables to give the first practical algorithm and implementation for the asymptotics of multivariate rational generating functions not relying on a non-algorithmically checkable ‘combinatorial’ non-negativity assumption. Our homotopy implementation terminates on examples from the literature in three variables, and we additionally describe heuristic methods that terminate and correctly predict asymptotic behaviour in reasonable time on examples in even higher dimension. Our results are implemented in Julia, through the use of the *** package, and we provide a selection of examples and benchmarks.

关键词： scientific computing Algebra Benchmark testing Prediction algorithms Computational efficiency

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Reducing Adversarial Vulnerability Using GANs

Reducing Adversarial Vulnerability Using GANs

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

作者： Ciprian-Alin Simion Bitdefender Laboratory "Al.I. Cuza" University - Faculty of Computer Science Iaşi România

the Cyber-threat industry is ever-growing and it is very likely that malware creators are using generative methods to create new malware as these algorithms prove to be very potent. As the majority of researchers in this field are focused on new methods to generate better adversarial examples (w.r.t. fidelity, variety or number) just a small portion of them are concerned with defense methods. this paper explores three methods of feature selection in the context of adversarial attacks. these methods aim to reduce the vulnerability of a Multi-Layer Perceptron to GAN-inflicted attacks by removing features based on rankings computed by type or by using LIME or F-Score. Even if no strong conclusion can be drawn, this paper stands as a Proof-of-Concept that because of good results in some cases, adversarial feature selection is a worthy exploration path.

关键词： Industries Machine learning algorithms scientific computing Feature extraction Malware

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Using N-Gram Variations in Static Analysis for Malware Detection

Using N-Gram Variations in Static Analysis for Malware Detec...

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

作者： Marco Radovancovici Darius Galiş Ciprian Pungilă Faculty of Mathematics and Informatics West University of Timişoara Timişoara Romania

Most of intrusion detection systems nowadays employ signature based analysis that often fails when newer or modified malware versions are brought into play. Intrusion detection systems working with cryptanalysis would offer some advantages against obfuscated code or newly derived viruses based on classic exploits. In this paper, we are applying an index of coincidence approach from cryptanalysis with a N-gram pattern-matching technique on recent binaries, to attempt classification of malicious code. those characteristics are studied with the use of modern data mining methods, namely K-means, to discover interesting clusters for classification and properties of malicious behavior. the challenges of gathering, working with, learning from and classifying large amounts of virus datasets with different techniques are explored.

关键词： Codes Computer viruses scientific computing Intrusion detection Clustering algorithms Static analysis Classification algorithms

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