Sequence-based Point-Of-Interest (POI) recommendations are increasingly crucial for location-based services and social platforms, offering nuanced insights into predicting user preferences from historical interaction patterns. However, a significant challenge arises from the non-uniform distribution of user-POI interaction sequences, where user preferences are often obscured by irregular and sporadic activities. This paper proposes an innovative Uniform Sequence Balancing (USB) strategy, addressing the critical issue of non-uniform sequences by utilizing the standard deviation of time intervals to achieve uniformity. Our approach transforms non-uniform sequences into uniform ones, thereby facilitating more accurate preference capture. We leverage the Transformer eXtra Long (Transformer-XL) model, known for its ability to discern long-term dependencies, and integrate it with our USB strategy to propose the Sequential Transformer-XL Recommender (STR). Our comprehensive experiments on two widely used public datasets demonstrate the effectiveness of STR, which significantly outperforms state-of-the-art models. The proposed STR not only optimizes recommendation performance but also paves the way for future research on sequence-based recommendation systems.

The rapid advancement of technologies such as artificial intelligence has led to increasing complexity in human computer fusion systems, which poses significant challenges to the security of these systems. Recent developments in hardware, software, and algorithms have exacerbated the security landscape of human computer fusion complex systems across multiple dimensions, underscoring the need for a comprehensive review of attack and defense technologies in this domain. In this paper, we systematically review security issues in human computer fusion complex systems from various perspectives, with the aim of summarizing the current state of the field and encouraging further exploration by researchers. Specifically, our review is organized into the following key areas based on the security protection targets: (1) Human-machine interaction safety. We explore methods for measuring human-machine trust and discuss strategies for its repair and recalibration in dynamic contexts. (2) Human-computer collaborative safety. We discuss potential issues arising during human-device interactions and communication, alongside security challenges of intelligent algorithms and data privacy. Following a review of representative works, we discuss the experimental findings. Finally, we summarize the challenges in each area and point out some promising directions.

Lithological facies classification is a pivotal task in petroleum geology, underpinning reservoir characterization and influencing decision-making in exploration and production operations. Traditional classification methods, such as support vector machines and Gaussian process classifiers, often struggle with the complexity and nonlinearity of geological data, leading to suboptimal performance. Moreover, numerous prevalent approaches fail to adequately consider the inherent dependencies in the sequence of measurements from adjacent depths in a well. A novel approach leveraging an attention-based gated recurrent unit (AGRU) model is introduced in this paper to address these challenges. The AGRU model excels by exploiting the sequential nature of well-log data and capturing long-range dependencies through an attention mechanism. This model enables a flexible and context-dependent weighting of different parts of the sequence, enhancing the discernment of key features for classification. The proposed method was validated on two publicly available datasets. Results demonstrate a considerably improvement over traditional methods. Specifically, the AGRU model achieved superior performance metrics considering precision, recall, and F1-score.