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Due to the continuous development of few-shot learning, there have been notable advancements in methods for few-shot object detection in recent years. However, most existing methods in this domain primarily focus on natural images, neglecting the challenges posed by variations in object scales, which are usually encountered in remote sensing images. This article proposes a new few-shot object detection model designed to handle the issue of object scale variation in remote sensing images. Our developed model has two essential parts: a feature aggregation module (FAM) and a scale-aware attention module (SAM). Considering the few-shot features of remote sensing images, we designed the FAM to improve the support and query features through channel multiplication operations utilizing a feature pyramid network and a transformer encoder. The created FAM better extracts the global features of remote sensing images and enhances the significant feature representation of few-shot remote sensing objects. In addition, we design the SAM to address the scale variation problems that frequently occur in remote sensing images. By employing multiscale convolutions, the SAM enables the acquisition of contextual features while adapting to objects of varying scales. Extensive experiments were conducted on benchmark datasets, including NWPU VHR-10 and DIOR datasets, and the results show that our model indeed addresses the challenges posed by object scale variation and improves the applicability of few-shot object detection in the remote sensing domain.

Abstract Malware detection is an important task for the ecosystem of mobile applications (APPs), especially for the Android ecosystem, and is vital to guarantee the user experience of Android APPs. There have been some exiting methods trying to solve the problem of malware detection, but the methods suffer from several defects, such as high time complexity and mediocre accuracy, which seriously decrease the practicability of existing methods. To solve these problems, in this study, we propose a novel Android malware detection framework, where we contribute an efficient Application Programming Interface (API) call sequences extraction algorithm and an investigation of different types of classifiers. In API call sequences extraction, we propose an algorithm for transforming the function call graph from a multigraph into a directed simple graph, which successfully avoids the unnecessary repetitive path searching. We also propose a pruning search, which further reduces the number of paths to be searched. Our algorithm greatly reduces the time complexity. We generate the transition matrix as classification features and investigate three types of machine learning classifiers to complete the malware detection task. The experiments are performed on real‐world Android Packages (APKs), and the results demonstrate that our method significantly reduces the running time and produces high detection accuracy.

A large number of Web APIs have been released as services in mobile communications, but the service provided by a single Web API is usually limited. To enrich the services in mobile communications, developers have combined Web APIs and developed a new service, which is known as a mashup. The emergence of mashups greatly increases the number of services in mobile communications, especially in mobile networks and the Internet-of-Things (IoT), and has encouraged companies and individuals to develop even more mashups, which has led to the dramatic increase in the number of mashups. Such a trend brings with it big data, such as the massive text data from the mashups themselves and continually-generated usage data. Thus, the question of how to determine the most suitable mashups from big data has become a challenging problem. In this paper, we propose a mashup recommendation framework from big data in mobile networks and the IoT. The proposed framework is driven by machine learning techniques, including neural embedding, clustering, and matrix factorization. We employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the mashups. We also develop a novel Joint Matrix Factorization (JMF) model to complete the mashup recommendation task, where we design a new objective function and an optimization algorithm. We then crawl through a real-world large mashup dataset and perform experiments. The experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.

Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities, especially in the new era of 5G and Internet-of-Things (IoT) environments. A mobile bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation systems. In the real world, the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such as the uncertainty of bike usage. In addition to pure manual rebalancing, in several works, attempts were made to predict the demand for bikes. In this paper, we devised a bike-sharing service with highly accurate demand prediction using collaborative computing and information fusion. We combined the information of bike demands at different time periods and the locations between stations and proposed a dynamical clustering algorithm for station clustering. We carefully analyzed and discovered the group of features that impact the demand of bikes, from historical bike-sharing records and 5G IoT environment data. We combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return demand. We performed sufficient experiments on two real-world datasets. The results confirm that compared to some existing methods, our method produces superior prediction results and performance and improves the availability of bike-sharing service in 5G IoT environments.