School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China Emails: {23b903088, zhangzhaoxin, 22s030153, li.ning, 22b303010}@stu.hit.edu.cn School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China Email: xinran_Liu@bupt.edu.cn Abstract --Fine-grained IP geolocation plays a critical role in applications such as location-based services and cybersecurity. Most existing fine-grained IP geolocation methods are regression-based; however, due to noise in the input data, these methods typically encounter kilometer-level prediction errors and provide incorrect region information for users. T o address this issue, this paper proposes a novel hierarchical multi-label classification framework for IP region prediction, named HMCGeo. This framework treats IP geolocation as a hierarchical multi-label classification problem and employs residual connection-based feature extraction and attention prediction units to predict the target host region across multiple geographical granularities. Furthermore, we introduce probabilistic classification loss during training, combining it with hierarchical cross-entropy loss to form a composite loss function. IP region prediction experiments on the New Y ork, Los Angeles, and Shanghai datasets demonstrate that HMCGeo achieves superior performance across all geographical granularities, significantly outperforming existing IP geolocation methods. P geolocation is a technique used to predict the geographical location of a host based on its IP address [1], playing a crucial role in location-based services, network topology optimization, and cybersecurity [2], [3], [4], [5], [6], [7], [8]. Using IP geolocation technology, online services and applications infer the geographical location of users to deliver localized weather updates, news, and event notifications [3]. Internet service providers (ISPs) estimate the approximate location of target hosts to optimize traffic transmission paths, reduce network latency, and improve transmission efficiency [4]. Network analysts examine the geographical origins of incoming traffic to assess security threats from suspicious addresses. This research was supported by the National Key R&D Program of China (2024QY1103, 2018YFB18002). Based on the accuracy of prediction results, IP geolocation is categorized into coarse-grained and fine-grained geolocation. Coarse-grained IP geolocation predicts the location of a target host by utilizing allocation information such as Autonomous System Numbers (ASN), ISP, and BGP, or by analyzing the relationship between latency and distance. These methods construct geolocation databases that provide location information at the country or city level. Building on this foundation, fine-grained IP geolocation reduces prediction errors to a few kilometers in certain regions by leveraging richer landmarks or employing more effective prediction methods.

Even the AI has been widely used and significantly changed our life, deploying the large AI models on resource limited edge devices directly is not appropriate. Thus, the model split inference is proposed to improve the performance of edge intelligence, in which the AI model is divided into different sub models and the resource-intensive sub model is offloaded to edge server wirelessly for reducing resource requirements and inference latency. However, the previous works mainly concentrate on improving and optimizing the system QoS, ignore the effect of QoE which is another critical item for the users except for QoS. Even the QoE has been widely learned in EC, considering the differences between task offloading in EC and split inference in EI, and the specific issues in QoE which are still not addressed in EC and EI, these algorithms cannot work effectively in edge split inference scenarios. Thus, an effective resource allocation algorithm is proposed in this paper, for accelerating split inference in EI and achieving the tradeoff between inference delay, QoE, and resource consumption, abbreviated as ERA. Specifically, the ERA takes the resource consumption, QoE, and inference latency into account to find the optimal model split strategy and resource allocation strategy. Since the minimum inference delay and resource consumption, and maximum QoE cannot be satisfied simultaneously, the gradient descent based algorithm is adopted to find the optimal tradeoff between them. Moreover, the loop iteration GD approach is developed to reduce the complexity of the GD algorithm caused by parameter discretization. Additionally, the properties of the proposed algorithms are investigated, including convergence, complexity, and approximation error. The experimental results demonstrate that the performance of ERA is much better than that of the previous studies.

In this paper, we present an adaptive graph-based personalized recommendation method based on co-ranking and query-based collaborative diffusion. By utilizing the unique network structure of n-partite heterogeneous graph, we attempt to address the problem of personalized recommendation in a two-layer ranking process with the help of reasonable measure of high and low order relationships and analyzing the representation of user’s preference in the graph. The experiments show that this algorithm can outperform the traditional CF methods and achieve competitive performance compared with many model-based and graph-based recommendation methods, and have better scalability and flexibility.