With the advancement of service computing technology, software developers tend to consume one or more web APIs, a practice that helps them avoid the behavior of reinventing the wheel. These web APIs are capable of providing services or data on the Internet; developers can use them to create feature-rich mashups. Against this backdrop, the number of web APIs across various platforms is growing rapidly, making it increasingly challenging to identify suitable ones for upcoming mashup creation. Consequently, web API recommendation has emerged as an effective means to facilitate web API discovery. We have proposed a novel web API recommendation method called R2API, which constructs the interactions between mashups and web APIs, as well as their tag usage records, into multiple homogeneous hypergraphs and then adopts HGNNs with multi-task learning to learn entity vectors for the recommendation task. While the result is encouraging, R2API’s limitation is its use of simple homogeneous hypergraphs to describe entities, failing to characterize them comprehensively and accurately. To further enhance recommendation performance, this work proposes a novel cross-view HGNNs-based web API recommendation method, namely CVH-REC. First, CVH-REC models the interactions between mashups and web APIs, as well as their tag usage records, into a multi-view knowledge graph to characterize entities more comprehensively and accurately. This knowledge graph comprises a global main hypergraph and four sub-hypergraphs from local views. Second, CVH-REC adopts a contrastive learning and multi-task learning framework to drive multiple HGNNs in jointly learning entity vectors. Third, CVH-REC leverages the SBERT model to derive the semantic vector from the mashup requirement and transfers it into the vector space of the knowledge graph with an MLP. This process enables the generation of a higher-quality requirement vector. By comparing the vector of the mashup requirement with those of web APIs, CVH-REC generates a recommendation list for mashup creation. Extensive experiments on a real-world dataset demonstrate that the proposed method outperforms baseline methods.

The Android platform provides a series of animation APIs, with which app developers can improve the implementation efficiency of UI animations—specifically, reducing the effort and time required to implement them. To assist app developers in quickly finding the suitable animation APIs, we have proposed two recommendation models called Animation2API and U-A2A. Animation2API has the capability to generate a list of available animation APIs for the UI animation task using the collaborative filtering algorithm. In contrast, U-A2A can encode both the animation API context and the UI animation task, and then predict the next animation API for the current animation implementation based on the joint encoding of the two modalities. Since U-A2A can provide real-time recommendations throughout the process of animation implementation, it is effective in assisting developers in using animation API resources. Nevertheless, U-A2A has three key limitations. First, its GRU encoder for the animation API context has difficulty in adequately capturing the long-distance dependencies and the global information. Second, its 3D CNN encoder for the UI animation task fails to effectively extract the long-distance dependencies between video frames and the spatiotemporal features at different scales. Third, U-A2A consistently treats the two modalities equally when fusing their encodings, despite the need to adaptively adjust their contribution levels according to the actual situation. To address these limitations, the paper introduces a novel animation API recommendation model named AC2Next. AC2Next adopts an encoder component based on the self-attention mechanism to encode the animation API context and the UI animation task. Specifically, it uses GRU with the self-attention mechanism as the encoder of the animation API context and applies ViViT, a Transformer architecture with self-attention mechanisms, to encode the UI animation task. Meanwhile, AC2Next utilizes its adaptive weight layer to assign appropriate weights to the animation API context and the UI animation task during the information fusion process. The experimental results show that AC2Next can outperform U-A2A in any stage of the animation implementation. When considering 1, 3, 5, and 10 animation APIs, AC2Next achieves an improvement of 31.56%, 10.01%, 5.57%, and 3.34% respectively in recommendation accuracy compared to U-A2A.

In device-to-device (D2D) networks, co-channel interference is one of the main reasons that causes high power consumption, which further reduces the life time of mobile devices. In this paper, we adopt the superposition coding between macro and D2D users, which is expected to effectively eliminate the co-channel interference. In particular, we develop two power allocation methods to minimize the power consumption in cooperative and non-cooperative modes, respectively. Then, we use mode selection to obtain the minimum overall power consumption of the whole system. In power allocation, we model the average power consumption as a function of channel gain, power allocation factor, transmission rate, and noise power. Then, we obtain the close-form solution. Our results indicate that the proposed method outperforms the conventional non-cooperative methods in terms of power consumption, outage probability, and energy efficiency.