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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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Covariance-Aware Feature Alignment with Pre-Computed Source Statistics for Test-Time Adaptation to Multiple Image Corruptions

Covariance-Aware Feature Alignment with Pre-Computed Source ...

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IEEE International Conference on Image Processing

作者： Kazuki Adachi Shin’Ya Yamaguchi Atsutoshi Kumagai NTT Computer and Data Science Laboratories Kyoto University

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.

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Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation 12

Recurrent Neural Networks for Learning Long-term Temporal De...

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12th IEEE International Conference on Big Knowledge, ICBK 2021

作者： Ohno, Kentaro Kumagai, Atsutoshi NTT Computer Data Science Laboratories Japan

ISBN: (纸本)9781665438582

Recurrent neural networks with a gating mechanism such as an LSTM or GRU are powerful tools to model sequential data. In the mechanism, a forget gate, which was introduced to control information flow in a hidden state in the RNN, has recently been re-interpreted as a representative of the time scale of the state, i.e., a measure how long the RNN retains information on inputs. On the basis of this interpretation, several parameter initialization methods to exploit prior knowledge on temporal dependencies in data have been proposed to improve learn-ability. However, the interpretation relies on various unrealistic assumptions, such as that there are no inputs after a certain time point. In this work, we reconsider this interpretation of the forget gate in a more realistic setting. We first generalize the existing theory on gated RNNs so that we can consider the case where inputs are successively given. We then argue that the interpretation of a forget gate as a temporal representation is valid when the gradient of loss with respect to the state decreases exponentially as time goes back. We empirically demonstrate that existing RNNs satisfy this gradient condition at the initial training phase on several tasks, which is in good agreement with previous initialization methods. On the basis of this finding, we propose an approach to construct new RNNs that can represent a longer time scale than conventional models, which will improve the learnability for long-term sequential data. We verify the effectiveness of our method by experiments with real-world datasets. © 2021 IEEE.

关键词： Time series analysis

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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

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. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Edit and Alphabet-Ordering Sensitivity of Lex-Parse 49

Edit and Alphabet-Ordering Sensitivity of Lex-Parse

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49th International Symposium on Mathematical Foundations of computer science, MFCS 2024

作者： Nakashima, Yuto Köppl, Dominik Funakoshi, Mitsuru Inenaga, Shunsuke Bannai, Hideo Department of Informatics Kyushu University Fukuoka Japan Department of Computer Science and Engineering University of Yamanashi Kofu Japan M&D Data Science Center Tokyo Medical and Dental University Japan NTT Communication Science Laboratories Kyoto Japan

ISBN: (纸本)9783959773355

We investigate the compression sensitivity [Akagi et al., 2023] of lex-parse [Navarro et al., 2021] for two operations: (1) single character edit and (2) modification of the alphabet ordering, and give tight upper and lower bounds for both operations (i.e., we show Θ(log n) bounds for strings of length n). For both lower bounds, we use the family of Fibonacci words. For the bounds on edit operations, our analysis makes heavy use of properties of the Lyndon factorization of Fibonacci words to characterize the structure of lex-parse. © Yuto Nakashima, Dominik Köppl, Mitsuru Funakoshi, Shunsuke Inenaga, and Hideo Bannai.

关键词： Sensitivity analysis

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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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Meta-ticket: finding optimal subnetworks for few-shot learning within randomly initialized neural networks 22

Meta-ticket: finding optimal subnetworks for few-shot learni...

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

作者： Daiki Chijiwa Shin'ya Yamaguchi Atsutoshi Kumagai Yasutoshi Ida NTT Computer and Data Science Laboratories NTT Corporation NTT Computer and Data Science Laboratories NTT Corporation and Kyoto University

ISBN: (纸本)9781713871088

Few-shot learning for neural networks (NNs) is an important problem that aims to train NNs with a few data. The main challenge is how to avoid overfitting since over-parameterized NNs can easily overfit to such small dataset. Previous work (e.g. MAML by Finn et al. 2017) tackles this challenge by meta-learning, which learns how to learn from a few data by using various tasks. On the other hand, one conventional approach to avoid overfitting is restricting hypothesis spaces by endowing sparse NN structures like convolution layers in computer vision. However, although such manually-designed sparse structures are sample-efficient for sufficiently large datasets, they are still insufficient for few-shot learning. Then the following questions naturally arise: (1) Can we find sparse structures effective for few-shot learning by meta-learning? (2) What benefits will it bring in terms of meta-generalization? In this work, we propose a novel meta-learning approach, called Meta-ticket, to find optimal sparse subnetworks for few-shot learning within randomly initialized NNs. We empirically validated that Meta-ticket successfully discover sparse subnetworks that can learn specialized features for each given task. Due to this task-wise adaptation ability, Meta-ticket achieves superior meta-generalization compared to MAML-based methods especially with large NNs.

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Few-shot learning for feature selection with hilbert-schmidt independence criterion 22

Few-shot learning for feature selection with hilbert-schmidt...

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

作者： Atsutoshi Kumagai Tomoharu Iwata Yasutoshi Ida Yasuhiro Fujiwara NTT Computer and Data Science Laboratories NTT Communication Science Laboratories

ISBN: (纸本)9781713871088

We propose a few-shot learning method for feature selection that can select relevant features given a small number of labeled instances. Existing methods require many labeled instances for accurate feature selection. However, sufficient instances are often unavailable. We use labeled instances in multiple related tasks to alleviate the lack of labeled instances in a target task. To measure the dependency between each feature and label, we use the Hilbert-Schmidt Independence Criterion, which is a kernel-based independence measure. By modeling the kernel functions with neural networks that take a few labeled instances in a task as input, we can encode the task- specific information to the kernels such that the kernels are appropriate for the task. Feature selection with such kernels is performed by using iterative optimization methods, in which each update step is obtained as a closed-form. This formulation enables us to directly and efficiently minimize the expected test error on features selected by a small number of labeled instances. We experimentally demonstrate that the proposed method outperforms existing feature selection methods.

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Fast Binary Network Hashing via Graph Clustering

Fast Binary Network Hashing via Graph Clustering

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IEEE International Conference on Big data

作者： Yasuhiro Fujiwaray Masahiro Nakanoy Atsutoshi Kumagai Yasutoshi Idaz Akisato Kimura Naonori Ueda NTT Communication Science Laboratories NTT Computer and Data Science Laboratories

ISBN: (纸本)9781665480468

Network hashing converts each node of a graph into a compact binary code, and it is a useful graph analytics tool since it can reduce memory cost. INH-MF is a network hashing approach to factorize the high-order proximity matrix representing similarities between nodes. However, since it cuts small nonzero elements from the proximity matrix, it fails to effectively extract insights from the graph. Moreover, it incurs high memory and computational costs since the proximity matrix is large and dense. We propose Graph Clustering-based Network Hashing, a novel network hashing approach. To compute the proximities effectively, it uses the structural relationships between nodes and clusters obtained from a graph clustering approach. Moreover, it can efficiently compute hash codes from eigenvectors of the matrix corresponding to the graph Laplacian by using its low-rank property. Experiments show that it can more efficiently and effectively compute hash codes than previous approaches.

关键词： Laplace equations Costs Binary codes Big data Computational efficiency Matrix converters

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META-LEARNING FOR OUT-OF-DISTRIBUTION DETECTION VIA DENSITY ESTIMATION IN LATENT SPACE

arXiv

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arXiv 2022年

作者： Iwata, Tomoharu Kumagai, Atsutoshi NTT Communication Science Laboratories NTT Computer and Data Science Laboratories

Many neural network-based out-of-distribution (OoD) detection methods have been proposed. However, they require many training data for each target task. We propose a simple yet effective meta-learning method to detect OoD with small in-distribution data in a target task. With the proposed method, the OoD detection is performed by density estimation in a latent space. A neural network shared among all tasks is used to flexibly map instances in the original space to the latent space. The neural network is meta-learned such that the expected OoD detection performance is improved by using various tasks that are different from the target tasks. This meta-learning procedure enables us to obtain appropriate representations in the latent space for OoD detection. For density estimation, we use a Gaussian mixture model (GMM) with full covariance for each class. We can adapt the GMM parameters to in-distribution data in each task in a closed form by maximizing the likelihood. Since the closed form solution is differentiable, we can meta-learn the neural network efficiently with a stochastic gradient descent method by incorporating the solution into the meta-learning objective function. In experiments using six datasets, we demonstrate that the proposed method achieves better performance than existing meta-learning and OoD detection methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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