Recent text-to-image (T2I) diffusion models have made significant strides in generating high-quality images from diverse textual prompts. Despite this progress, these models often face challenges in accurately understanding and synthesizing complex prompts, primarily due to their limited compositional capabilities. In this study, we propose a novel approach for compositional T2I synthesis using layout-guided diffusion models, which do not require additional training. Specifically, we leverage the chain-of-code prompting technique of large language models to interpret textual prompts and generate object layouts with spatial coherence. To enhance the alignment between generated images and textual descriptions, we introduce two innovative layoutguided loss functions: Patch-oriented Cross-Attention (PCA) loss and Region-oriented Cross-Attention (RCA) loss. The PCA loss emphasizes high activation values for image patches that attend to all tokens in the prompt across the layout. The RCA loss enhances the average attention within the layout, thereby increasing the accuracy of generating objects and their associated attributes within specified regions. These proposed loss functions reassign cross-attention in diffusion models during the denoising process. Our comprehensive experiments consistently demonstrate the effectiveness of our approach in improving semantic alignment between generated images and a diverse range of textual prompts, while ensuring high usability as a ready-to-use plugin. Our code is available athttps://github.com/gxl-groups/Compositional-T2I.

With the rise of the internet of things (IoT), ensuring operational efficiency and security for resource-constrained nodes has become crucial. This paper proposes two certificate-less key agreement protocols based on confidential message entanglement, addressing limitations of traditional key management such as certificate reliance and high computational costs. A smart contract-driven, dynamically switchable distributed key management framework is also introduced to enhance adaptability under varying attack frequencies. Simulation results indicate reduced communication and computational overhead. Protocols 1 and 2 show computational losses of 5.53 and 6.47, respectively, while smart contract-based scenarios range from 4.28 to 4.39. In smart agriculture applications, dynamic switching reduced overhead from 6.47 to 4.28, optimising performance by 33.84%. The research supports the development of lightweight, secure, and adaptable communication frameworks for IoT applications in smart cities, industrial monitoring, and telemedicine, offering strong theoretical and practical value.

The ever-increasing web application programming interfaces (APIs) in various service-sharing communities (e.g., ProgrammableWeb.com and Mashape.com) have enabled software developers to quickly create their interested mashups conveniently and economically. However, the big volume of candidate web APIs and their differences often make it hard for software developers to discover a set of appropriate web APIs for mashup creation by considering API functions and API quality performances (e.g., popularity, compatibility, and diversity) simultaneously. These decrease the mashup development success rate and the mashup developers’ satisfaction significantly. In view of these challenges, a novel web APIs’ recommendation method named the popularity-aware and diverse method of web API compositions’ recommendation (PD-WACR) is proposed in this article. In concrete, we model web APIs’ functions, popularity, and compatibility with an API correlation graph. Afterward, correlation graph-based web APIs’ recommendation is performed with popularity and compatibility guarantee. Moreover, a top-$k$strategy is adopted in the recommendation process, so as to diversify the final recommended web APIs’ results. Finally, massive experiments are carried out on a real-world web API dataset crawled from ProgrammeableWeb.com. Experimental comparisons with related methods show the advantages and innovations of the proposed PD-WACR method.

In recent years, the number of web services grows explosively. With a large amount of information resources, it is difficult for users to quickly find the services they need. Thus, the design of an effective web service recommendation method has become the key factor to satisfy the requirements of users. However, traditional recommendation methods often tend to pay more attention to the accuracy of the results but ignore the diversity, which may lead to redundancy and overfitting, thus reducing the satisfaction of users. Considering these drawbacks, a novel method called DivMTID is proposed to improve the effectiveness by achieving accurate and diversified recommendations. First, we utilize users’ historical scores of web services to explore the users’ preferences. And we use the TF-IDF algorithm to calculate the weight vector of each web service. Second, we utilize cosine similarity to calculate the similarity between candidate web services and historical web services and we also forecast the ranking scores of candidate web services. At last, a diversification method is used to generate the top- K recommended list for users. And through a case study, we show that DivMTID is an effective, accurate, and diversified web service recommendation method.