We present Dist-Meter, a distributed semi-supervised learning system designed for scalable and adaptive water meter reading in smart utility networks. The system employs a distributed edge-cloud architecture, enabling automatic scalability and rapid model updates as the number of deployed meters increases. Dist-Meter incorporates Semi-Meter, a semi-supervised object detection algorithm that improves recognition accuracy through a dual student-teacher framework to mitigate overfitting, a distribution matching regularization to maintain balanced predictions under long-tailed data, and a structural prior mechanism that refines detections by leveraging the geometric alignment of dial readings. Experimental results demonstrate that Dist-Meter achieves robust, low-latency, and scalable performance, making it an effective solution for intelligent and distributed visual metering applications.

Oceanariums serve as a vital bridge between humans and marine ecosystems, fostering public engagement and education. However, most contemporary oceanariums still rely on information panels to introduce marine life, lacking interactivity and immersive experiences, which limits tourist engagement and educational effectiveness. Marine organism detection offers a promising solution to enhance interactivity, yet achieving high detection performance with lightweight models remains a critical challenge, particularly in computational resource-constrained environments. To address this issue, we propose AquaLite, a mobile-oriented lightweight detector optimized for efficiency while maintaining high detection accuracy. We integrate the Faster Extract (FE) and VoVEnhanced (VoVE) modules into AquaLite's backbone and neck to reduce computational redundancy and refine feature representation, leveraging their synergy for improved efficiency. Additionally, we construct the AquaLife dataset, a high-quality benchmark for marine organism detection, to support further research and algorithm development. The experimental results on the AquaLife and Aquarium Combined Dataset demonstrate that AquaLite outperforms the baseline with mAP improvements of 3.5% and 3.6%, and reductions in parameters and FLOPs of 11.5% and 9.2%, respectively, and achieving real-time inference at 24 FPS on mobile device. The artifacts are available upon request.

Vision-Language Models (VLMs) have advanced cross-modal understanding and generation, yet their domain adaptability remains limited. To address the lack of high-quality captions for fish recognition and description in oceanarium scenes, we introduce AquaVLM, a domain-specific VLM incorporating a Categorical-Spatial Descriptor (CSD) and multi-type prompt templates for joint modeling of category and spatial information. A Low-Rank Adaptation (LoRA) scheme enables parameter-efficient fine-tuning to enhance domain transfer. To support this task, we build AquaCap, an image-text dataset of 2,722 oceanarium images with aligned descriptions. Experiments show that AquaVLM substantially outperforms representative VLMs on AquaCap, achieving a BERTScore of 0.9217, indicating superior semantic and descriptive quality. Ablation studies confirm the effectiveness of the CSD and multi-template design, while successful 4-bit quantized deployment on a mobile-cloud architecture highlights its potential for resource-limited applications. The artifacts are available upon request.

Accurate reading of mechanical water meters is crucial for automated water billing and resource management. When a digit wheel advances between two positions, it often displays overlapping parts of two digits, forming a halfcharacter transition. Conventional vision models struggle to interpret these cases, leading to sequence-level misreads. To address this challenge, we present TransMeter, a robust reading framework that combines object detection with multimodal semantic reasoning. Specifically, YOLOv11n is employed for precise digit-wheel detection, while the vision-language large model CogVLM2 performs fine-grained reasoning to identify half-character states. A position-aware confidence fusion module then integrates visual and semantic cues to produce coherent readings. Experiments on a self-built dataset demonstrate that TransMeter corrects 40 misread cases (26 detection errors and 14 reasoning self-corrections) and improves overall accuracy from$\mathbf{9 3. 5 \%}$to$\mathbf{9 7. 1 \%}$, validating the effectiveness of vision-language fusion for transition-digit recognition.

Abstract The encryption of user data is crucial when employing electronic health record services to guarantee the security of the data stored on cloud servers. Attribute-based encryption (ABE) scheme is considered a powerful encryption technique that offers flexible and fine-grained access control capabilities. Further, the multi-user collaborative access ABE scheme additionally supports users to acquire access authorization through collaborative works. However, the existing multi-user collaborative access ABE schemes do not consider the different weights of collaboration users. Therefore, using these schemes for weighted multi-user collaborative access results in redundant attributes, which inevitably reduces the efficiency of the ABE scheme. This paper proposes a revocable and verifiable weighted attribute-based encryption with collaborative access scheme (RVWABE-CA), which can provide efficient weighted multi-user collaborative access, user revocation, and data integrity verification, as the fundamental cornerstone for establishing a robust framework to facilitate secure sharing of electronic health records in a public cloud environment. In detail, this scheme employs a novel weighted access tree to eliminate redundant attributes, utilizes encryption version information to control user revocation, and establishes Merkle Hash Tree for data integrity verification. We prove that our scheme is resistant against chosen plaintext attack. The experimental results demonstrate that our scheme has significant computational efficiency advantages compared to related works, without increasing storage or communication overhead. Therefore, the RVWABE-CA scheme can provide an efficient and flexible weighted collaborative access control and user revocation mechanism as well as data integrity verification for electronic health record systems.

Abstract ID-based encryption with equality test (IBEET) allows a tester to compare ciphertexts encrypted under different public keys for checking whether they contain the same message. In this paper, we first introduce group mechanism into IBEET and propose a new primitive, namely group ID-based encryption with equality test (G-IBEET). With the group mechanism: (1) group administrator can authorize a tester to make comparison between ciphertexts of group users, but it cannot compare their ciphertexts with any ciphertext of any user who is not in the group. Such group granularity authorization can make IBEET that adapts to group scenario; (2) for the group granularity authorization, only one trapdoor, named group trapdoor, should be issued to the tester, which can greatly reduce the cost of computation, transmission and storage of trapdoors in traditional IBEET schemes; (3) G-IBEET can resist the insider attack launched by the authorized tester, which is an open problem in IBEET. We give definitions for G-IBEET and propose a concrete construction with an efficient test algorithm. We then give its security analysis in the random oracle model.

In this paper the concept of fuzzy IBE schemes with some fixed attributes (SAA-FIBE) is proposed and one construction of it is presented. SAA-FIBE scheme can be viewed as a variant of SW scheme described in which demanding no fixed positive or negative attributes. In our scheme, a user with identity omega can decrypt the message that is encrypted with a set of attributes, omega', if and only if |omega' capomega| ges d and omega must have or must have not some attributes described in encryption policy. The scheme are both error-tolerant and secure against collusion attacks in the SPID-FIBE attack model.