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4th International Conference on E-Voting and Identity, Vote-ID 2013

作者： Kaczmarek, Tyler Wittrock, John Carback, Richard Florescu, Alex Rubio, Jan Runyan, Noel Vora, Poorvi L. Zagórski, Filip Department of Computer Science George Washington University United States Network and Information Concepts Group Charles Stark Draper Laboratories United States Personal Data Systems United States Institute of Mathematics and Computer Science Wroclaw University of Technology Poland

ISBN: (纸本)9783642391842

We describe in detail dispute resolution problems with cryptographic voting systems that do not produce a paper record of the unencrypted vote. With these in mind, we describe the design and use of Audiotegrity - a cryptographic voting protocol and corresponding voting system with some of the accessibility benefits of fully-electronic voting systems and some of the dispute resolution properties of paper-ballot-based systems. We also describe subtle issues with coercion-resistance if accessible systems are not well-designed. Audiotegrity was designed in response to a request by Takoma Park election officials, tested in a public test organized by the city in June 2011, and used in its municipal election in November 2011. We are not aware of any other precinct-based end-to-end independently-verifiable election for public office where the protocol enabled participation by voters with visual disabilities. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Cryptography

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Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff

Adversarial Finetuning with Latent Representation Constraint...

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International Conference on computer Vision (ICCV)

作者： Satoshi Suzuki Shin’ya Yamaguchi Shoichiro Takeda Sekitoshi Kanai Naoki Makishima Atsushi Ando Ryo Masumura NTT Computer and Data Science Laboratories Kyoto University NTT Human Informatics Laboratories

This paper addresses the tradeoff between standard accuracy on clean examples and robustness against adversarial examples in deep neural networks (DNNs). Although adversarial training (AT) improves robustness, it degrades the standard accuracy, thus yielding the tradeoff. To mitigate this tradeoff, we propose a novel AT method called ARREST, which comprises three components: (i) adversarial finetuning (AFT), (ii) representation-guided knowledge distillation (RGKD), and (iii) noisy replay (NR). AFT trains a DNN on adversarial examples by initializing its parameters with a DNN that is standardly pretrained on clean examples. RGKD and NR respectively entail a regularization term and an algorithm to preserve latent representations of clean examples during AFT. RGKD penalizes the distance between the representations of the standardly pretrained and AFT DNNs. NR switches input adversarial examples to nonadversarial ones when the representation changes significantly during AFT. By combining these components, ARREST achieves both high standard accuracy and robustness. Experimental results demonstrate that ARREST mitigates the tradeoff more effectively than previous AT-based methods do.

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Sharing knowledge for meta-learning with feature descriptions 22

Sharing knowledge for meta-learning with feature description...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Tomoharu Iwata Atsutoshi Kumagai NTT Communication Science Laboratories Kyoto Japan NTT Computer and Data Science Laboratories Tokyo Japan

ISBN: (纸本)9781713871088

Language is an important tool for humans to share knowledge. We propose a meta-learning method that shares knowledge across supervised learning tasks using feature descriptions written in natural language, which have not been used in the existing meta-learning methods. The proposed method improves the predictive performance on unseen tasks with a limited number of labeled data by meta-learning from various tasks. With the feature descriptions, we can find relationships across tasks even when their feature spaces are different. The feature descriptions are encoded using a language model pretrained with a large corpus, which enables us to incorporate human knowledge stored in the corpus into meta-learning. In our experiments, we demonstrate that the proposed method achieves better predictive performance than the existing meta-learning methods using a wide variety of real-world datasets provided by the statistical office of the EU and Japan.

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A Security Verification Framework of Cryptographic Protocols Using Machine Learning

arXiv

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arXiv 2023年

作者： Ohno, Kentaro Nakabayashi, Misato NTT Computer & Data Science Laboratories Japan NTT Social Informatics Laboratories Japan

We propose a security verification framework for cryptographic protocols using machine learning. In recent years, as cryptographic protocols have become more complex, research on automatic verification techniques has been focused on. The main technique is formal verification. However, the formal verification has two problems: it requires a large amount of computational time and does not guarantee decidability. We propose a method that allows security verification with computational time on the order of linear with respect to the size of the protocol using machine learning. In training machine learning models for security verification of cryptographic protocols, a sufficient amount of data, i.e., a set of protocol data with security labels, is difficult to collect from academic papers and other sources. To overcome this issue, we propose a way to create arbitrarily large datasets by automatically generating random protocols and assigning security labels to them using formal verification tools. Furthermore, to exploit structural features of protocols, we construct a neural network that processes a protocol along its series and tree structures. We evaluate the proposed method by applying it to verification of practical cryptographic protocols. © 2023, CC BY.

关键词： Machine learning

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Full spectrum of novelties in time-dependent urn models

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Physical Review Research 2025年第2期7卷 023127-023127页

作者： Alessandro Bellina Giordano De Marzo Vittorio Loreto Sapienza University of Rome Physics Department I-00185 Rome Italy Centro Ricerche Enrico Fermi I-00184 Rome Italy Sony Computer Science Laboratories–Rome Joint Initiative CREF-SONY Centro Ricerche Enrico Fermi 00184 Rome Italy Complexity Science Hub Vienna 1080 Vienna Austria Center for Data and Methods University of Konstanz Universitaetstrasse 10 78457 Konstanz Germany

Systems driven by innovation, a pivotal force in human society, present various intriguing statistical regularities, from the Heaps' law to logarithmic scaling or somewhat different patterns for the innovation rates. The urn model with triggering (UMT) has been instrumental in modeling these innovation dynamics. Yet a generalization is needed to capture the richer empirical phenomenology. Here we introduce a time-dependent urn model with triggering (TUMT), a generalization of the UMT that crucially integrates time-dependent parameters for reinforcement and triggering to offer a broader framework for modeling innovation in nonstationary systems. Through analytical computation and numerical simulations, we show that the TUMT reconciles various behaviors observed in a broad spectrum of systems from patenting activity to the analysis of gene mutations. We highlight how the TUMT features a “critical” region where both Heaps' and Zipf's laws coexist, for which we compute the exponents.

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Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff

arXiv

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arXiv 2023年

作者： Suzuki, Satoshi Yamaguchi, Shinya Takeda, Shoichiro Kanai, Sekitoshi Makishima, Naoki Ando, Atsushi Masumura, Ryo NTT Computer and Data Science Laboratories Kyoto University Japan NTT Human Informatics Laboratories

关键词： Deep neural networks

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Robust Sparse Approximations for Stochastic Dynamical Systems

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IFAC-PapersOnLine 2017年第1期50卷 14010-14015页

作者： Quinn, Christopher J. Pinar, Ali Gao, Jing Su, Lu School of Industrial Engineering Purdue University West LafayetteIN United States Data Sciences & Cyber Anal. Dept. Sandia National Laboratories LivermoreCA United States Dept. of Computer Science and Engineering University at Buffalo BuffaloNY United States

Inferring the exact topology of the interactions in a large, stochastic dynamical system from time-series data can often be prohibitive computationally and statistically without strong side information. One alternative is to seek approximations of the system topology that nonetheless describe the data well. In recent works, algorithms were proposed to identify sparse approximations which are optimal in terms of Kullback-Leibler divergence. Those algorithms relied on point estimates of statistics from the data. In this work, we investigate the more practical setting where point estimates are not reliable. We propose an algorithm to identify sparse, connected approximations that are robust to estimation error. © 2017

关键词： Approximation algorithms Dynamical systems Identification (control systems) Nonlinear systems Stochastic systems Time series Topology Errors in variables Model reduction Nonparametric methods Robust estimation Time series modeling

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COVARIANCE-AWARE FEATURE ALIGNMENT WITH PRE-COMPUTED SOURCE STATISTICS FOR TEST-TIME ADAPTATION TO MULTIPLE IMAGE CORRUPTIONS

arXiv

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arXiv 2022年

作者： Adachi, Kazuki Yamaguchi, Shin'ya Kumagai, Atsutoshi NTT Computer and Data Science Laboratories Japan Kyoto University Japan

Real-world image recognition systems often face corrupted input images, which cause distribution shifts and degrade the performance of models. These systems often use a single prediction model in a central server and process images sent from various environments, such as cameras distributed in cities or cars. Such single models face images corrupted in heterogeneous ways in test time. Thus, they require to instantly adapt to the multiple corruptions during testing rather than being re-trained at a high cost. Test-time adaptation (TTA), which aims to adapt models without accessing the training dataset, is one of the settings that can address this problem. Existing TTA methods indeed work well on a single corruption. However, the adaptation ability is limited when multiple types of corruption occur, which is more realistic. We hypothesize this is because the distribution shift is more complicated, and the adaptation becomes more difficult in case of multiple corruptions. In fact, we experimentally found that a larger distribution gap remains after TTA. To address the distribution gap during testing, we propose a novel TTA method named Covariance-Aware Feature alignment (CAFe). We empirically show that CAFe outperforms prior TTA methods on image corruptions, including multiple types of corruptions. Copyright © 2022, The Authors. All rights reserved.

关键词： Crime

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Loss factorization, weakly supervised learning and label noise robustness 33

Loss factorization, weakly supervised learning and label noi...

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33rd International Conference on Machine Learning, ICML 2016

作者： Patrini, Giorgio Nielsen, Frank Nock, Richard Carioni, Marcello Australian National University Australia Data61 Australia École Polytechnique France Sony Computer Science Laboratories Inc France Max Planck Institute for Mathematics in the Sciences Germany

ISBN: (纸本)9781510829008

We prove that the empirical risk of most wellknown loss functions factors into a linear term aggregating all labels with a term that is label free, and can further be expressed by sums of the same loss. This holds true even for non-smooth, non-convex losses and in any rkhs. The frrst term is a (kernel) mean operator - the focal quantity of this work - which we characterize as the sufficient statistic for the labels. The result tightens known generalization bounds and sheds new light on their interpretation. Factorization has a direct application on weakly supervised learning. In particular, we demonstrate that algorithms like sgd and proximal methods can be adapted with minimal effort to handle weak supervision, once the mean operator has been estimated. We apply this idea to learning with asymmetric noisy labels, connecting and extending prior work. Furthermore, we show that most losses enjoy a data-dependent (by the mean operator) form of noise robustness, in contrast with known negative results.

关键词： Factorization

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Improving Raft Performance with Bulk Transfers

Improving Raft Performance with Bulk Transfers

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Aoi Yamashita Masahiro Tanaka Yutaro Bessho Yasuhiro Fujiwara Hideyuki Kawashima Graduate School of Keio University NTT Computer and Data Science Laboratories NTT Communication Science Laboratories Keio University

Raft is known as a replicating state machine that tolerates crash faults, and its use enables various distributed systems, including distributed transaction processing systems. The original Raft protocol creates an intra-cluster consensus by having the leader generate a log of each incoming client request and replicate it to its followers. When requests arrive frequently, the number of replications increases, increasing the number of communications and possibly leading to performance degradation. To solve this problem, we propose a bulk-transfer protocol that replicates multiple requests at once. We implemented the Raft system to verify the effect of the proposed method. We measured the performance of the implemented system when multiple requests were transferred in bulk and observed an increase in throughput with an increase in the amount of data replicated at a time. The highest throughput of 214 MB/sec was achieved when replicating 12.5 MB at a time. This is 12,066 times higher than the performance when replicating one request (18 B) at a time. Besides, the size of the log is reduced to 5/9 of the conventional method.

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