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Optimisation of artificial neural network topology applied in the prosody control in text-to-speech synthesis 27th

27th conference on current trends in theory and practice of Informatics, SOFSEM 2000

作者： Šebesta, Václav Tučková, Jana Institute of Computer Science Academy of Sciences of the Czech Republic Czech Technical University Czech Republic Czech Technical University Institute of Radioengineering and Electronics Academy of Sciences Czech Republic

ISBN: (纸本)3540413480

Multilayer artificial neural networks (ANN) are often used for the solution of classification problems or for the time series forecasting. An appropriate number of learning and testing patterns must be available for the ANN training. Each training pattern is composed of n input parameters and m output parameters. Number m is usually given by the problem formulation, but the number n may be often selected from a greater set of input parameters. An optimal selection of input parameters is a very important task especially in a situation when the number of usable input parameters is great and the analytical relations between the input and output parameters are not known. the number of neurons in all ANN layers must be generally kept as small as possible because of the optimal generalisation ability. In this paper we present a possible way for the selection of significant input parameters (the so called "markers"), which are the most important ones from the point of view of influence on the output parameters. these parameters are later used for the training of ANN. A statistical approach is usually used for this reason [5]. After some experience in the ANN application we recognised that the approach based on mathematical logic, i. e. the GUHA method (General Unary Hypotheses Automaton) is also suitable for the determination of markers. Besides the minimisation of the number of elements in the input layer of ANN, also the number of neurons in hidden layers must be optimised. For this reason standard methods of pruning can be used, described e. g. in [1]. We have used this method in the following applications: – Optimisation of the intervals between the major overhaul of plane engines by the analysis of tribodiagnostic data. Only selected types of chemical pollution in oil can be taken into account.– Prediction of bleeding of patients with chronic lymphoblastic leukemia. Only a part of parameters about the patient is important from this point of view (see [2]).– Optimisati

关键词： Forecasting

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QuPAA: Exploiting Parallel and Adaptive Architecture to Scale up Quantum Computing

QuPAA: Exploiting Parallel and Adaptive Architecture to Scal...

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IEEE International conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Yingxun Fu Yao Sun Tao Li University of Florida Gainesville USA College of Information Science North China University of Technology Beijing China

ISBN: (数字)9781728197104

ISBN: (纸本)9781728197111

Quantum computing has been gradually developed from a theory to practice in recent years. the desire for more powerful computational capability keeps promoting the scaling up of quantum computers. For quantum machines with large scales, limited coherence time and bloated instruction bandwidth may obstruct the continuous scaling up. In this paper, we propose a new quantum computing architecture named QuPAA, which leverages multiple layers of arbitrary waveform generator (AWG) units to exploit the parallelism among quantum operations. QuPAA provides a series of mechanisms, including parallelism exploitation, microcode streaming, and a quantum flexible instruction set (QuFIS). these mechanisms assist QuPAA to generate instructions adaptively and reduce the requirement for coherence time and instruction bandwidth. the evaluation results show that, compared to the baseline quantum computer with a single AWG unit, QuPAA reduces 25.9% to 97.4% coherence time requirement, and saves 26.9% to 79.1 % instruction bandwidth. the improvement effectively breaks the limitations and provides strong support for the extension of the scale for quantum computers.

关键词： computers Qubit Bandwidth computer architecture Parallel processing Coherence time System analysis and design

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4th International workshop on Emerging trends in Software Metrics (WETSoM 2013)

4th International workshop on Emerging Trends in Software Me...

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International conference on Software Engineering (ICSE)

作者： Steven Counsell Ewan Tempero Michele L. Marchesi Aaron Visaggio Department of Information Systems and Computing Brunel University London UK Department of Electrical and Electronic Engineering University of Cagliari Cagliari Italy Department of Computer Science University of Auckland Auckland New Zealand Department of Engineering University of Sannio Benevento Italy

ISBN: (纸本)9781467330756

the International Workshop on Emerging trends in Software Metrics aims at gathering together researchers and practitioners to discuss the progress of software metrics. the motivation for this workshop is the low impact that software metrics has on current software development. the goals of this workshop includes critically examining the evidence for the effectiveness of existing metrics and identifying new directions for metrics. Evidence for existing metrics includes how the metrics have been used in practice and studies showing their effectiveness. Identifying new directions includes use of new theories, such as complex network theory, on which to base metrics.

关键词： Educational institutions Software metrics conferences Software Market research Complex networks

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When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection

When Less is Enough: Positive and Unlabeled Learning Model f...

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IEEE International conference on Automated Software Engineering (ASE)

作者： Xin-Cheng Wen Xinchen Wang Cuiyun Gao Shaohua Wang Yang Liu Zhaoquan Gu School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Central University of Finance and Economics China School of Computer Science and Engineering Nanyang Technological University China

Automated code vulnerability detection has gained increasing attention in recent years. the deep learning (DL)-based methods, which implicitly learn vulnerable code patterns, have proven effective in vulnerability detection. the performance of DL-based methods usually relies on the quantity and quality of labeled data. However, the current labeled data are generally automatically collected, such as crawled from human-generated commits, making it hard to ensure the quality of the labels. Prior studies have demonstrated that the non-vulnerable code (i.e., negative labels) tends to be unreliable in commonly-used datasets, while vulnerable code (i.e., positive labels) is more determined. Considering the large numbers of unlabeled data in practice, it is necessary and worth exploring to leverage the positive data and large numbers of unlabeled data for more accurate vulnerability detection. In this paper, we focus on the Positive and Unlabeled (PU) learning problem for vulnerability detection and propose a novel model named PILOT, i.e., Positive and unlabeled Learning mOdel for vulnerability deTection. PILOT only learns from positive and unlabeled data for vulnerability detection. It mainly contains two modules: (1) A distance-aware label selection module, aiming at generating pseudo-labels for selected unlabeled data, which involves the inter-class distance prototype and progressive fine-tuning; (2) A mixed-supervision representation learning module to further alleviate the influence of noise and enhance the discrimination of representations. Extensive experiments in vulnerability detection are conducted to evaluate the effectiveness of PILOT based on real-world vulnerability datasets. the experimental results show that PILOT outperforms the popular weakly supervised methods by 2.78%-18.93% in the PU learning setting. Compared with the state-of-the-art methods, PILOT also improves the performance of 1.34%-12.46 % in F1 score metrics in the supervised setting. In additio

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Harnessing small projectors and multiple views for efficient vision pretraining 38

Harnessing small projectors and multiple views for efficient...

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ghosh, Arna Agrawal, Kumar Krishna Sodhani, Shagun Oberman, Adam M. Richards, Blake A. Mila - Quebec AI Institute & Computer Science McGill University MontréalQC Canada UC Berkeley CA United States Meta FAIR TorontoON Canada Mila - Quebec AI Institute & Mathematics and Statistics McGill University MontréalQC Canada Neurology & Neurosurgery and Montreal Neurological Institute McGill University MontréalQC Canada CIFAR Learning in Machines & Brains Program TorontoON Canada

Recent progress in self-supervised (SSL) visual representation learning has led to the development of several different proposed frameworks that rely on augmentations of images but use different loss functions. However, there are few theoretically grounded principles to guide practice, so practical implementation of each SSL framework requires several heuristics to achieve competitive performance. In this work, we build on recent analytical results to design practical recommendations for competitive and efficient SSL that are grounded in theory. Specifically, recent theory tells us that existing SSL frameworks are actually minimizing the same idealized loss, which is to learn features that best match the data similarity kernel defined by the augmentations used. We show how this idealized loss can be reformulated to a functionally equivalent loss that is more efficient to compute. We study the implicit bias of using gradient descent to minimize our reformulated loss function, and find that using a stronger orthogonalization constraint with a reduced projector dimensionality should yield good representations. Furthermore, the theory tells us that approximating the reformulated loss should be improved by increasing the number of augmentations, and as such using multiple augmentations should lead to improved convergence. We empirically verify our findings on CIFAR, STL and Imagenet datasets, wherein we demonstrate an improved linear readout performance when training a ResNet-backbone using our theoretically grounded recommendations. Remarkably, we also demonstrate that by leveraging these insights, we can reduce the pretraining dataset size by up to 2× while maintaining downstream accuracy simply by using more data augmentations. Taken together, our work provides theoretically grounded recommendations that can be used to improve SSL convergence and efficiency. © 2024 Neural information processing systems foundation. All rights reserved.

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Rough Sets and Intelligent Systems Paradigms: Second International conference, RSEISP 2014, Held as Part of JRS 2014, Granada and Madrid, Spain, July 9-13, 2014. Proceedings 1

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丛书名： Lecture Notes in computer science 8537 Lecture Notes in Artificial Intelligence

2014年

作者： Marzena Kryszkiewicz, Chris Cornelis, Davide Ciucci, Jesús Medina-Moreno, Hiroshi Motoda, Zbigniew W. Raś (eds.)

ISBN: (数字)9783319087290

ISBN: (纸本)9783319087283;9783319087290

this book constitutes the refereed proceedings of the 23rd Australasian Joint conference on Rough Sets and Intelligent Systems Paradigms, RSEISP 2014, held in Granada and Madrid, Spain, in July 2014. RSEISP 2014 was held along with the 9th International conference on Rough Sets and current trends in Computing, RSCTC 2014, as a major part of the 2014 Joint Rough Set Symposium, JRS 2014. JRS 2014 received 40 revised full papers and 37 revised short papers which were carefully reviewed and selected from 120 submissions and presented in two volumes. this volume contains the papers accepted for the conference RSEISP 2014, as well as the three invited papers presented at the conference. the papers are organized in topical sections on plenary lecture and tutorial papers; foundations of rough set theory; granular computing and covering-based rough sets; applications of rough sets; induction of decision rules - theory and practice; knowledge discovery; spatial data analysis and spatial databases; information extraction from images.

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DF40: Toward Next-Generation Deepfake Detection 38

DF40: Toward Next-Generation Deepfake Detection

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yan, Zhiyuan Yao, Taiping Chen, Shen Zhao, Yandan Fu, Xinghe Zhu, Junwei Luo, Donghao Wang, Chengjie Ding, Shouhong Wu, Yunsheng Yuan, Li School of Electronic and Computer Engineering Peking University China Tencent Youtu Lab China

We propose a new comprehensive benchmark to revolutionize the current deepfake detection field to the next generation. Predominantly, existing works identify top-notch detection algorithms and models by adhering to the common practice: training detectors on one specific dataset (e.g., FF++ [62]) and testing them on other prevalent deepfake datasets. this protocol is often regarded as a "golden compass" for navigating SoTA detectors. But can these stand-out "winners" be truly applied to tackle the myriad of realistic and diverse deepfakes lurking in the real world? If not, what underlying factors contribute to this gap? In this work, we found the dataset (both train and test) can be the "primary culprit" due to the following: (1) forgery diversity: Deepfake techniques are commonly referred to as both face forgery (face-swapping and face-reenactment) and entire face synthesis (especially face). Most existing datasets only contain partial types of them, with limited forgery methods implemented (e.g., 2 swapping and 2 reenactment methods in FF++);(2) forgery realism: the dominated training dataset, FF++, contains out-of-date forgery techniques from the past four years. "Honing skills" on these forgeries makes it difficult to guarantee effective detection generalization toward nowadays' SoTA deepfakes;(3) evaluation protocol: Most detection works perform evaluations on one type, e.g., training and testing on face-swapping types only, which hinders the development of universal deepfake detectors. To address this dilemma, we construct a highly diverse and large-scale deepfake detection dataset called DF40, which comprises 40 distinct deepfake techniques (10 times larger than FF++). We then conduct comprehensive evaluations using 4 standard evaluation protocols and 8 representative detection methods, resulting in over 2,000 evaluations. through these evaluations, we provide an extensive analysis from various perspectives, leading to 7 new insightful findings contributing to

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When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection 23

When Less is Enough: Positive and Unlabeled Learning Model f...

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Proceedings of the 38th IEEE/ACM International conference on Automated Software Engineering

ISBN: (纸本)9798350329964

Automated code vulnerability detection has gained increasing attention in recent years. the deep learning (DL)-based methods, which implicitly learn vulnerable code patterns, have proven effective in vulnerability detection. the performance of DL-based methods usually relies on the quantity and quality of labeled data. However, the current labeled data are generally automatically collected, such as crawled from human-generated commits, making it hard to ensure the quality of the labels. Prior studies have demonstrated that the non-vulnerable code (i.e., negative labels) tends to be unreliable in commonly-used datasets, while vulnerable code (i.e., positive labels) is more determined. Considering the large numbers of unlabeled data in practice, it is necessary and worth exploring to leverage the positive data and large numbers of unlabeled data for more accurate vulnerability *** this paper, we focus on the Positive and Unlabeled (PU) learning problem for vulnerability detection and propose a novel model named PILOT, i.e., PositIve and unlabeled Learning mOdel for vulnerability deTection. PILOT only learns from positive and unlabeled data for vulnerability detection. It mainly contains two modules: (1) A distance-aware label selection module, aiming at generating pseudo-labels for selected unlabeled data, which involves the inter-class distance prototype and progressive fine-tuning; (2) A mixed-supervision representation learning module to further alleviate the influence of noise and enhance the discrimination of representations. Extensive experiments in vulnerability detection are conducted to evaluate the effectiveness of PILOT based on real-world vulnerability datasets. the experimental results show that PILOT outperforms the popular weakly supervised methods by 2.78%-18.93% in the PU learning setting. Compared with the state-of-the-art methods, PILOT also improves the performance of 1.34%-12.46% in F1 score metrics in the supervised setting. In addition,

关键词： software vulnerability detection

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Preface

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 2932卷 V-VI页

作者： Van Emde Boas, Peter Pokorný, Jaroslav Bieliková, Mária University of Amsterdam ILLC - Department of Mathematics and Computer Science Plantage Muidergracht 24 Amsterdam Netherlands Charles University Malostranské nám. 25 Prague 1 Czech Republic Slovak University of Technology Ilkoviova 3 Bratislava Slovakia Academy of Sciences of the Czech Republic Institute of Computer Science Pod Vodárenskou ví 2 Prague 8 Czech Republic

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Preface

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2000年 1963卷 V-VI页

作者： Hlavá, Václav Jeffery, Keith G. Wiedermann, Jií Czech Technical University Department of Cybernetics Karlovo nám. 13 Prague Czech Republic CLRC RAL Information Technology Department Chilton DidcotOxfordshire United Kingdom Academy of Sciences of the Czech Republic Insitute of Computer Science Pod vodárenskou ví 2 Prague Czech Republic

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