The rapid advancement of wireless networks towards Artificial Intelligence (AI)-driven solutions attracts many vendors to build resilient and intelligent capabilities for Open Radio Access Networks (O-RAN). However, besides the benefits of achieving flexibility and intelligence, openness in native AI-driven O-RAN functions is also the target of severe AI-related security threats, e.g., adversarial attacks. This work addresses the security matter for the AI-powered solutions in the physical layer of O-RAN, specifically within the context of deep reinforcement learning (DRL)-based resource allocation. We introduce a new adversarial attack variant that manipulates the environment parameters and misleads the agent's observation during the inference phase. The attack can cause incorrect allocation decisions and significant degradation in the transmission data rate. Our evaluation results show that the attack degrades user data and packet delivery rates by up to 40% and 77.74%, respectively, particularly in ultra-low-latency services. We also found that the major weakness of DRL-driven radio resource allocation is the environment observation stage, where a group of compromised users or jammers can spoof noises and signal power to mislead environment interaction. In our context, the proposed policy infiltration attack is the most efficient approach to cause sustained network inefficiencies or reduced throughput for benign users.

Identifying significant shots in a rally is important for evaluating players’ performance in badminton matches. While there are several studies that have quantified player performance in other sports, analyzing badminton data has remained untouched. In this article, we introduce a badminton language to fully describe the process of the shot, and we propose a deep-learning model composed of a novel short-term extractor and a long-term encoder for capturing a shot-by-shot sequence in a badminton rally by framing the problem as predicting a rally result. Our model incorporates an attention mechanism to enable the transparency between the action sequence and the rally result, which is essential for badminton experts to gain interpretable predictions. Experimental evaluation based on a real-world dataset demonstrates that our proposed model outperforms the strong baselines. We also conducted case studies to show the ability to enhance players’ decision-making confidence and to provide advanced insights for coaching, which benefits the badminton analysis community and bridges the gap between the field of badminton and computer science.

The customer base is the most valuable resource to E-commerce companies. A comprehensive understanding of customers’ preferences and behavior is crucial to developing good marketing strategies, in order to achieve optimal customer lifetime values (CLVs). For example, by exploring customer behavior patterns, given a marketing plan with a limited budget, a set of potential customers is able to be identified to maximize profit. In other words, personalized campaigns at the right time and in the right place can be treated as the last stage of consumption. Moreover, effective future purchase estimation and recommendation help guide the customer to the up-selling stage. The proposed willing-to-pay prediction model (W2P) exploits the transaction data to predict customer payment behavior based on a probabilistic graphical model, which provides semantic explanation of the estimated results and deals with the sparsity of payment data from each customer. Existing work in this domain ranks the customers by their probabilities of purchase in different conditions. However, the customer with the highest purchase probability does not necessarily spend the most. Therefore, we propose a CLV maximization algorithm based on the prediction results. In addition, we improve the model by behavioral segmentation wherein we group the customers by payment behaviors to reduce the size of the offline models and enhance the accuracy for low-frequency customers. The experiment results show that our model outperforms the state-of-the-art methods in purchase behavior prediction.

Identifying significant shots in a rally is important for evaluating players’ performance in badminton matches. While there are several studies that have quantified player performance in other sports, analyzing badminton data is remained untouched. In this paper, we introduce a badminton language to fully describe the process of the shot and propose a deep learning model composed of a novel short-term extractor and a long-term encoder for capturing a shot-by-shot sequence in a badminton rally by framing the problem as predicting a rally result. Our model incorporates an attention mechanism to enable the transparency of the action sequence to the rally result, which is essential for badminton experts to gain interpretable predictions. Experimental evaluation based on a real-world dataset demonstrates that our proposed model outperforms the strong baselines. The source code is publicly available at https://github.com/wywyWang/Shot-Influence.