Zhuosheng Lin

Marawan Elbatel, Mohamed Ghonim, Jiaji Mao et al.

Automated segmentation of liver lesions on non-contrast computed tomography (NCCT) is clinically important but fundamentally challenging, particularly in low-resource settings across Africa and Asia where contrast agents are frequently unavailable. Progress has been limited by the absence of annotated NCCT benchmarks. Here we describe the TriALS challenge for automated liver lesion segmentation under contrast-limited conditions, supported by a multi-centre dataset of 150 cases with four-phase CT acquisitions (600 volumes) from Egyptian and Chinese institutions. Algorithms were evaluated on 70 cases from three institutions, including an independent external cohort. The top-performing method achieved a mean venous-phase Dice of 0.754, consistent with human-level performance, yet dropped to 0.57 on NCCT. On external validation, the leading method outperformed off-the-shelf models by up to 28% in Dice on NCCT. Algorithm performance was most strongly predicted by training data scale and pre-training strategy. A cross-year comparison exposed a persistent perceptual barrier on NCCT that scaling pre-training alone cannot overcome. Data, annotations, and code are available at https://github.com/xmed-lab/TriALS.

Xin Wu, Fei Teng, Yue Feng et al.

Partial multi-label learning aims to extract knowledge from incompletely annotated data, which includes known correct labels, known incorrect labels, and unknown labels. The core challenge lies in accurately identifying the ambiguous relationships between labels and instances. In this paper, we emphasize that matching co-occurrence patterns between labels and instances is key to addressing this challenge. To this end, we propose Semantic Co-occurrence Insight Network (SCINet), a novel and effective framework for partial multi-label learning. Specifically, SCINet introduces a bi-dominant prompter module, which leverages an off-the-shelf multimodal model to capture text-image correlations and enhance semantic alignment. To reinforce instance-label interdependencies, we develop a cross-modality fusion module that jointly models inter-label correlations, inter-instance relationships, and co-occurrence patterns across instance-label assignments. Moreover, we propose an intrinsic semantic augmentation strategy that enhances the model's understanding of intrinsic data semantics by applying diverse image transformations, thereby fostering a synergistic relationship between label confidence and sample difficulty. Extensive experiments on four widely-used benchmark datasets demonstrate that SCINet surpasses state-of-the-art methods.

Zhuosheng Lin, Christophe Guyeux, Qianxue Wang et al.

To guarantee the integrity and security of data transmitted through the Internet, hash functions are fundamental tools. But recent researches have shown that security flaws exist in the most widely used hash functions. So a new way to improve their security performance is urgently demanded. In this article, we propose new hash functions based on chaotic iterations, which have chaotic properties as defined by Devaney. The corresponding diffusion and confusion analyzes are provided and a comparative study between the proposed hash functions is carried out, to make their use more applicable in any security context.

Zhuosheng Lin, Christophe Guyeux, Simin Yu et al.

Investigating how to construct a secure hash algorithm needs in-depth study, as various existing hash functions like the MD5 algorithm have recently exposed their security flaws. At the same time, hash function based on chaotic theory has become an emerging research in the field of nonlinear information security. As an extension of our previous research works, a new chaotic iterations keyed hash function is proposed in this article. Chaotic iterations are used both to construct strategies with pseudorandom number generator and to calculate new hash values using classical hash functions. It is shown that, by doing so, it is possible to apply a kind of post-treatment on existing hash algorithms, which preserves their security properties while adding Devaney's chaos. Security performance analysis of such a post-treatment are finally provided.

Qiuye Gan, Simin Yu, Chengqing Li et al.

This paper proposes a chaotic map-based multicast scheme for multiuser speech wireless communication and implements it in an ARM platform. The scheme compresses the digital audio signal decoded by a sound card and then encrypts it with a three-level chaotic encryption scheme. First, the position of every bit of the compressed data is permuted randomly with a pseudo-random number sequence (PRNS) generated by a 6-D chaotic map. Then, the obtained data are further permuted in the level of byte with a PRNS generated by a 7-D chaotic map. Finally, it is operated with a multiround chaotic stream cipher. The whole system owns the following merits: the redundancy in the original audio file is reduced effectively and the corresponding unicity distance is increased; the balancing point between a high security level of the system and real-time conduction speed is achieved well. In the ARM implementation, the framework of communication of multicast-multiuser in a subnet and the Internet Group Manage Protocol is adopted to obtain the function of communication between one client and other ones. Comprehensive test results were provided to show the feasibility and security performance of the whole system.