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The current research in Point-of-Interest (POI) recommendation primarily aims to decipher users’ transitional patterns to predict their future location visits. Traditional approaches often intertwine various features to model these check-in ...

The deep semantic information mining extracts deep semantic features from textual data and effectively utilizes the world knowledge embedded in these features, so it is widely researched in recommendation tasks. In spite of the extensive ...

• Proposes a novel global and local hypergraph learning framework for POI recommendation Effectively captures long-term and short-term higher-order collaborative preferences.
• Develops a deep semantic enhancement method exploiting pre-...

In the next Point-of-Interest recommendation, sparse and uneven location data generate biases, resulting in homogeneous recommendation outcomes that fail to reflect user preferences. Although there are many related unbiased studies, they still ...

• Analyze impact of attraction on variables by structural causal graph.
• Build unified debiasing paradigm for different biases by intervening on attraction.
• Utilize causal inference to balance the positive and negative impacts of ...

Protecting system observability records (logs) from compromised OSs has gained significant traction in recent times, with several note-worthy approaches proposed. Unfortunately, none of the proposed approaches achieve high performance with tiny log ...

The challenge of semantic segmentation with scarce pixel-level annotations has induced many self-supervised works, however most of which essentially train an image encoder or a segmentation head that produces finer dense representations, and when ...

Dynamic recommendation systems, where users interact with items continuously over time, have been widely deployed in real-world online streaming applications. The burst of interaction stream causes a rapid evolution of both users and items. To update ...

Existing personalized federated learning frameworks fail to significantly improve the personalization of user preference learning in next Point-Of-Interest (POI) recommendations, causing notable performance deficits. These frameworks do not fully ...

Android malware attacks have posed a severe threat to mobile users, necessitating a significant demand for the automated detection system. Among the various tools employed in malware detection, graph representations (e.g., function call graphs) have ...

Optimizing heterogeneous elastic material distribution on a 3D part to achieve desired deformation behavior is an important task in computer-aided design and additive manufacturing. This paper presents a solution to this problem, which involves ...

• A geometric deformation-based design is incorporated into the FEM-based optimization.
• An L0-formulation is proposed for determining the material distribution from a given set of base materials.
• The adjoint method is used to derive ...

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Aiming at the problem of poor real-time performance and low precision of traditional pavement crack detection, an improved YOLOv8 algorithm is proposed to realize the automatic pavement crack detection and identification. Firstly, the crack image is ...

Traditional POI recommendation systems use a centralized data storage approach to train models, posing significant risks of privacy breaches. Federated learning offers an effective solution to address user privacy concerns. However, in existing ...

Semantics play a crucial role in many AI tasks, yet the lack of high-quality textual data in LBSNs hampers deep semantic feature learning. Sparse user check-in records, characterized by significant spatial or temporal intervals (cut points), ...

User interfaces (UIs) is the main channel for users to interact with mobile apps. As such, attackers often create similar-looking UIs to deceive users, causing various security problems, such as spoofing and phishing. Prior studies identify these similar ...

Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, ...

Edge clouds are expected to be a key revenue growth driver for cloud vendors in the next decade; however, simply replicating the network infrastructure for the public cloud to the edge experiences deployment issues. At the edge, the challenge for cloud ...

Colonoscopy has attached great importance to early screening and clinical diagnosis of colon cancer. It remains a challenging task to achieve fine segmentation of polyps. However, existing State-of-the-art models still have limited segmentation ...

• A region self-attention enhancement network (RSAFormer) is proposed.
• RSAFormer uniquely uses a dual decoder structure to generate various feature maps.
• A novel region self-attention enhancement (RSA) module is proposed.

The advent of smart terminals and the IOT era has prompted the emergence of the multiplex network system. With the rapid information transmission in multiplex networks, security incidents caused by malicious attackers occur frequently. In light of the ...

Under the current paradigm, POI (Point-of-interest) recommendation tasks are mainly focused on representation learning. Therefore, the quality of the trajectory embeddings plays a key role in prediction. Existing methods mainly focus on learning ...

• Establish causal transfer between check-ins through the medium of semantics.
• Learning the deep semantic feature of trajectory.
• Propose temporal interval encoding to avoid losing temporal information.
• Fusing deep semantic ...

Given the rapid growth of the mobile internet market, high-priced telecom packages are limiting the growth of mobile internet use. Telecom operators and internet companies are working together to offer free-flow packages. This paper proposes a ...

• A complete content selection and package pricing scheme (CSPPS) is proposed.
• A content selection scheme is developed via modeling users’ internet behaviors.
• A tensor decomposition factor model is built to detect users’ online ...

Predicting the next Point-of-Interest (POI) is a persistent issue in the realm of Location-Based Social Networks (LBSN). To discover the user’s dynamic interests and the dependence between different POIs in user trajectory sequences, ...

• Learn user interest from both local and global views.
• Personalized ...