Inconsistencies in manufacturing features, sampling settings, and cryptographic implementations amongst the profiling and target devices can lead to the failure of profiling side-channel analysis (SCA). Various techniques, such as preprocessing, multi-device training, and transfer learning, have been proposed to mitigate this portability problem in profiling SCA. However, many techniques of block ciphers, such as tweaks, key-dependent components, and customized elements, might have uncertain effects from the perspective of cryptographic implementations, requiring further insightful analysis on their impact on portability. This paper investigates the portability of profiling SCA from a case study using adjustable implementations of block ciphers. First, we theoretically analyze the variation in leakage distribution under adjustable implementations. To support our theoretical results, a dataset of deep-learning SCA is built from AES, Pilsung, and Skinny. Specifically, we reveal how to reverse the parameterized components and recover the key from these adjustable implementations. According to our experiment on an 8-bit AVR microcontroller, the computational complexities of the attacks based on our model are less than 9 × 216within 4500 traces. Moreover, the effectiveness of our proposed method is demonstrated under the combinatorial effect with adjustable implementations and device characteristics. Our case study provides insights into the results of adjustable implementations of block ciphers, which strengthens both the theoretical and practical understanding of the portability of profiling SCA.

Large language models have been extended to the speech domain, leading to the development of speech large language models (SLLMs). While existing SLLMs demonstrate strong performance in speech instruction-following for core languages (e.g., English), they often struggle with non-core languages due to the scarcity of paired speech-text data and limited multilingual semantic reasoning capabilities. To address this, we propose the semi-implicit Cross-lingual Speech Chain-of-Thought (XS-CoT) framework, which integrates speech-to-text translation into the reasoning process of SLLMs. The XS-CoT generates four types of tokens: instruction and response tokens in both core and non-core languages, enabling cross-lingual transfer of reasoning capabilities. To mitigate inference latency in generating target non-core response tokens, we incorporate a semi-implicit CoT scheme into XS-CoT, which progressively compresses the first three types of intermediate reasoning tokens while retaining global reasoning logic during training. By leveraging the robust reasoning capabilities of the core language, XS-CoT improves responses for non-core languages by up to 45% in GPT-4 score when compared to direct supervised fine-tuning on two representative SLLMs, Qwen2-Audio and SALMONN. Moreover, the semi-implicit XS-CoT reduces token delay by more than 50% with a slight drop in GPT-4 scores. Importantly, XS-CoT requires only a small amount of high-quality training data for non-core languages by leveraging the reasoning capabilities of core languages. To support training, we also develop a data pipeline and open-source speech instruction-following datasets in Japanese, German, and French.

White-box block cipher (WBC) has been proposed by Chow \textit{et al.} to prevent the secret key to be extracted from its implementation in an untrusted context. A pivotal technique behind WBC is to convert the iterated round functions into a series of look-up tables (LUTs) with encodings. The construction of encoded LUTs consists of matrix operations, such as multiplication and inversion. The widely-used matrix libraries in applications, such as open-source NTL and M4RI, are primarily designed for large dimensional matrix operations. Therefore, they might not be suitable for WBC implementations which are mainly based on small-scale matrices and vectors. In this paper, we propose a new matrix library named WBMatrix for the optimization of WBC implementations. WBMatrix reduces the operating steps of multiplication and simultaneously generates pairwise invertible matrices as encodings. The performance comparison supports that WBMatrix improves the table construction and encryption phases on Intel x86 and ARMv8 platforms. Moreover, WBMatrix also boosts the initialization and encryption phases of LowMC/LowMC-M block ciphers and enhances the performance for the generation of key-dependent Sbox.

This work describes an encoder pre-training procedure using frame-wise label to improve the training of streaming recurrent neural network transducer (RNN-T) model.Streaming RNN-T trained from scratch usually performs worse than nonstreaming RNN-T.Although it is common to address this issue through pre-training components of RNN-T with other criteria or frame-wise alignment guidance, the alignment is not easily available in end-to-end manner.In this work, frame-wise alignment, used to pre-train streaming RNN-T's encoder, is generated without using a HMM-based system.Therefore an allneural framework equipping HMM-free encoder pre-training is constructed.This is achieved by expanding the spikes of CTC model to their left/right blank frames, and two expanding strategies are proposed.To our best knowledge, this is the first work to simulate HMM-based frame-wise label using CTC model for pre-training.Experiments conducted on LibriSpeech and MLS English tasks show the proposed pre-training procedure, compared with random initialization, reduces the WER by relatively 5%∼11% and the emission latency by 60 ms.Besides, the method is lexicon-free, so it is friendly to new languages without manually designed lexicon.