The explosive growth of multi-modal data in the era of big data has significantly heightened the urgent need for efficient cross-modal retrieval methods. While traditional real-valued retrieval approaches struggle with high storage costs and slow query speeds, hashing techniques have emerged as a promising solution by mapping high-dimensional data into compact binary codes. Among various hashing paradigms, short-length hashing offers superior advantages in terms of retrieval speed and storage efficiency, making it particularly suitable for resource-constrained edge devices and large-scale real-time applications. However, existing short-length hashing methods typically suffer from weak classification boundaries and significant information loss due to the extremely limited capacity of the hash bits. Most state-of-the-art methods treat the hash code as a holistic vector, failing to maximize the distinctiveness of individual bits. To tackle these challenges effectively, this paper proposes a novel method termed Bit-Level Semantic Representation Hashing (BLSRH). First, by establishing a Bit Semantic Learning Network (BSLN), we enhance the representational and discriminative capabilities of each bit in short-length hash codes independently. Additionally, a contrastive learning mechanism is introduced between bits to improve semantic consistency across modalities and reduce semantic redundancy among bits. Furthermore, to preserve the manifold structure of the original data, an adapted global similarity-preserving method is designed. Finally, a modal alignment loss based on soft-constraint is proposed to bridge the heterogeneity gap and reduce quantization errors, which replaces strict discrete constraints with flexible symbolic constraints. Comprehensive experimental results on three benchmark datasets demonstrate that BLSRH significantly outperforms state-of-the-art baselines, including recent transformer-based approaches, particularly in low-bit scenarios.

Behavior trees(BTs) provide a systematic and structured control architecture extensively employed in game AI and robotic behavior control, owing to their modularity, reactivity, and reusability. Nonetheless, manual BTs design requires significant expertise and becomes inefficient as task complexity increases. Recent automation technologies have avoided manual work, but often have high application barriers and face challenges in adapting to new tasks, making it difficult to easily configure them to specific requirements. Code-BT introduces a novel approach that utilizes large language models(LLMs) to automatically generate BTs, representing the task planning process as the process of coding and organizing sequences. By retrieving control flow information from the generated code, BTs can be efficiently constructed to address the complexity and diversity of task planning challenges. Rather than relying on manual design, Code-BT uses task instructions to guide the selection of relevant APIs, and then systematically assembles these APIs into modular code to align with the BTs structure. Finally, action sequences and control logic are extracted from the generated code to construct the BTs. Our approach not only ensures the automation of BTs generation but also guarantees the scalability and adaptability for long-term tasks. Experimental results demonstrate that Code-BT substantially improves LLM performance in BTs generation, achieving improvements ranging from16.67% to 29.17%.

Recently, there exists an increased research interest in embodied artificial intelligence (EAI), which involves an agent learning to perform a specific task when dynamically interacting with the surrounding 3D environment. There into, a new challenge is that many unseen objects may appear due to the increased number of object categories in 3D scenes. It makes developing models with strong zero-shot generalization ability to new objects necessary. Existing work tries to achieve this goal by providing embodied agents with massive high-quality human annotations closely related to the task to be learned, while it is too costly in practice. Inspired by recent advances in pre-trained models in 2D visual tasks, we attempt to boost zero-shot generalization for embodied reasoning with vision-language pre-training that can encode common sense as general prior knowledge. To further improve its performance on a specific task, we rectify the pre-trained representation through masked scene graph modeling (MSGM) in a self-supervised manner, where the task-specific knowledge is learned from iterative message passing. Our method can improve a variety of representative embodied reasoning tasks by a large margin (e.g., over 5.0% w.r.t. answer accuracy on MP3D-EQA dataset that consists of many real-world scenes with a large number of new objects during testing), and achieve the new state-of-the-art performance.

Non-Chinese speakers hold increasing opportunities and need to process Chinese information and communicate in Chinese. This paper, with the purpose of facilitating the handwriting input of Chinese characters for non-Chinese speakers, is directed towards the development of the handwriting rules and vocabulary for Latin-style anti-cursive characters and the ways of their selection and classification. This aims to build a practical platform by utilizing three characteristics of wavelet neural network — automatically ascertaining the number of hidden layer unit, converging rapidly and never running into the partial minimum of networks — for a simple Latin-style online handwriting input and processing, meanwhile, taking the customary handwriting habits of non-Chinese speakers. The paper, based on profound information of cursive characters, deciphered the genetic code of ancient cursive symbols and made clear the rules for characters changing into its cursive style. As a result, it breaks the bottleneck, which enables non-Chinese speakers to easily input information through handwriting Chinese characters.

A novel self-adaptive chaotic artificial bee colony algorithm based on Tent map (STOC-ABC) is proposed to enhance the global convergence and the population diversity. In the STOC-ABC, Tent chaotic opposition-based learning initialization method is presented to diversify the initial individuals and obtain good initial solutions. Furthermore, the self-adaptive Tent chaotic searching is implemented at the zones nearby individual optimum solution to help the artificial bee colony (ABC) algorithm to escape from the local optimum effectively. Moreover, the tournament selection strategy in onlooker bee phase is employed to increase the ability of the algorithm and avoid premature convergence. Experiments on six complex benchmark functions with high-dimension, the results further demonstrate that, the STOC-ABC not only accelerates the convergence rate and improves solution precision, but also provides excellent performance in dealing with complex high-dimensional functions.