The security analysis of Keccak, the winner of SHA-3, has attracted considerable interest. Recently, some attention has been paid to distinguishing Keccak sponge function from random permutation. In EUROCRYPT'17, Huang et al. proposed conditional cube tester to recover the key of Keccak-MAC and Keyak and to construct practical distinguishing attacks on Keccak sponge function up to 7 rounds. In this paper, we improve the conditional cube tester model by refining the formulation of cube variables. By classifying cube variables into three different types and working the candidates of these types of cube variable carefully, we are able to establish a new theoretical distinguisher on 8-round Keccak sponge function. Our result is more efficient and greatly improves the existing results. Finally we remark that our distinguishing attack on the the reduced-round Keccak will not threat the security margin of the Keccak sponge function.

CAST-128 is a block cipher used in a number of products, notably as the default cipher in some versions of GPG and PGP. It has been approved for Canadian government use by the Communications Security Establishment. Haruki Seki et al. found 2-round differential characteristics and they can attack 5-round CAST-128. In this paper, we studied the properties of round functions F1 and F3 in CAST-128, and identified differential characteristics for F1 round function and F3 round function. So we identified a 6-round differential characteristic with probability 2-53 under 2-23.8 of the total key space. Then based on 6-round differential characteristic, we can attack 8-round CAST-128 with key sizes greater than or equal to 72 bits and 9-round CAST-128 with key sizes greater than or equal to 104 bits. We give the summary of attacks on reduced-round CAST-128 in Table 10.

Recognition of second language (L2) speech is a challenging task even for state-of-the-art automatic speech recognition (ASR) systems, partly because pronunciation by L2 speakers is usually significantly influenced by the mother tongue of the speakers. Considering that the expressions of non-native speakers are usually simpler than those of native ones, and that second language speech usually includes mispronunciation and less fluent pronunciation, we propose a novel method that maximizes unified acoustic and linguistic objective function to derive a phoneme set for second language speech recognition. The authors verify the efficacy of the proposed method using second language speech collected with a translation game type dialogue-based computer assisted language learning (CALL) system. In this paper, the authors examine the performance based on acoustic likelihood, linguistic discrimination ability and integrated objective function for second language speech. Experiments demonstrate the validity of the phoneme set derived by the proposed method.

Recognition of second language (L2) speech is still a challenging task even for state-of-the-art automatic speech recognition (ASR) systems, partly because pronunciation by L2 speakers is usually significantly influenced by the mother tongue of the speakers. The authors previously proposed using a reduced phoneme set (RPS) instead of the canonical one of L2 when the mother tongue of speakers is known, and demonstrated that this reduced phoneme set improved the recognition performance through experiments using English utterances spoken by Japanese. However, the proficiency of L2 speakers varies widely, as does the influence of the mother tongue on their pronunciation. As a result, the effect of the reduced phoneme set is different depending on the speakers' proficiency in L2. In this paper, the authors examine the relation between proficiency of speakers and a reduced phoneme set customized for them. The experimental results are then used as the basis of a novel speech recognition method using a lexicon in which the pronunciation of each lexical item is represented by multiple reduced phoneme sets, and the implementation of a language model most suitable for that lexicon is described. Experimental results demonstrate the high validity of the proposed method.

Infrared and visible image fusion can combine the thermal radiation information and the textures to provide a high-quality fused image. In this letter, we propose a hybrid variational fusion model to achieve this end. Specifically, an ℓ0 term is adopted to preserve the highlighted targets with salient gradient variation in the infrared image, an ℓ1 term is used to suppress the noise in the fused image and an ℓ2 term is employed to keep the textures of the visible image. Experimental results demonstrate the superiority of the proposed variational model and our results have more sharpen textures with less noise.

This paper describes a novel method to improve the performance of second language speech recognition when the mother tongue of users is known. Considering that second language speech usually includes less fluent pronunciation and more frequent pronunciation mistakes, the authors propose using a reduced phoneme set generated by a phonetic decision tree (PDT)-based top-down sequential splitting method instead of the canonical one of the second language. The authors verify the efficacy of the proposed method using second language speech collected with a translation game type dialogue-based English CALL system. Experiments show that a speech recognizer achieved higher recognition accuracy with the reduced phoneme set than with the canonical phoneme set.