This paper focuses on music-driven dance motion generation, in which current methods often face challenges such as irregular dance movements and the inability to capture the relationship between music and dance emotions. To address these issues, we propose MeloDance, a new dance generation framework, which includes two main modules. The Musical Structure Feature Analysis (MSFA) module is introduced to enhance the consistency of dance movements by considering the global structural features of music. The Emotion Alignment Module for Audio-Dance (EAM-AD) is designed to ensure that the emotional expressions of the movements match those of the music. Through experimental comparisons, our approach outperforms previous methods, generating more harmonious dance movements. Specific examples are available at: https://pkjq11.github.io/melodance-demo/

With the rapid growth of Internet of Things (IoT) applications, ensuring the data privacy and security has become a critical concern. The security of integrated circuits (ICs) in IoT systems is of paramount importance. As IC integration increases, circuit testing becomes increasingly challenging. Design For Testability (DFT) techniques improve testability, but scan-based methods can be easily exploited by unauthorized users to steal confidential information. For this reason, researchers have proposed various defense techniques, but each technique has its limitations. In order to effectively protect the scan chains of ICs from illegal access, this paper presents a novel DFT framework based on hidden, randomly inserted key seeds and Physical Unclonable Function (PUF). A multiple-input shift register is used as a test key generator, which receives the hidden test key seed from randomly selected scan chains, and then generates a unique key for each test pattern. An authentication module is also introduced to verify the validity of the test patterns. Through key verification, if a test vector contains valid key seed, the design will perform normal scan operations. Conversely, if the seed hidden in a test vector cannot generate the expected test key, the scan output data will be scrambled by the random PUF responses, thereby effectively preventing attackers from inferring sensitive information. Theoretical analysis and experimental results show that the proposed secure DFT protects the cryptographic chip from all known scan-based side-channel attacks, while requiring no additional test preparation time and incurring only negligible low area overhead.

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.

Citrus detection is an essential component of the citrus industry. In order to realize the identification, positioning, segmentation, maturity estimation, and quality evaluation of citrus in complex environments, this paper proposes a multi-task deep learning network that can be applied to multiple scenes for citrus detection. The system is based on the Mask R-CNN network framework. By adding multi-task branches, modifying model parameters, and designing multi-task loss function, it can realize multi-task detection of citrus in a complex environment. The mAP on the validation set of the model obtained after training is 91.56%, and it takes an average of 0.35s to detect a citrus image using GeForce GTX 1080 Ti. Through the comparative analysis of the detection effect and performance evaluation index F value of multi-task citrus under different maturity, quality, citrus quantity, and light angle, the experimental results show that the model can effectively and accurately detect the citrus with different maturity and quality in the environment of citrus fruit overlap, tree branch and leaf occlusion, light change and surface shadow. The code is available at https://github.com/wxx-gan/Multitask.