Millimeter-wave (mmWave) communication technology with high throughput and high reliability attracts much attention in both academic and industrial fields. This technology plays a pivotal role in next-generation communication networks, offering promising solutions for high-speed data transfer. Localization of mobile mmWave communication devices is essential in this context, as it can effectively guide mmWave beam steering, thereby eliminating the need for cumbersome beam alignment processes. However, existing approaches for commercial mmWave communication devices suffer channel state fluctuations and can only work on static devices. To provide accurate localization for mobile mmWave devices, we propose MobiLoc , a neural network-based approach to enhance localization accuracy in mobile scenarios. Our method leverages Channel Frequency Response (CFR) and the angular spectrum for assistance to determine the positions. We first analyze the feasibility of classifying sensing data into different qualities. Then we implement a neural network architecture specifically designed to identify the sensing data with high quality. The effectiveness of our approach is demonstrated through comprehensive experiments conducted on commercial off-the-shelf (COTS) mmWave communication devices. Results show that MobiLoc can increase the localization accuracy significantly and reduce the median angle estimation error of mobile devices to 1.33ˆ with only single items of CFR measurements.

Urban land use, intrinsically linked to people’s daily activities, undergoes continuous evolution, presenting a complex interplay that remains partially understood. To bridge this gap, our study leverages fine-grained human mobility data to predict these changes, adopting a novel approach that conceptualizes “community-level” land use shifts as a regression problem and represents citywide changes through dynamic graphs. We harness recent advancements in graph neural networks (GNNs), which, despite their success in various applications, face challenges in directly predicting land use changes due to the temporal mismatch between the slow evolution of urban land and the immediacy of human mobility data. Our research stands out by introducing a temporal skeleton for dynamic GNNs to synchronize human activity graphs with urban land use changes, a dynamic heterogeneous GNN approach for integrating diverse human activity data to capture essential temporal dependencies, and a novel algorithm powered by causal inference to elucidate the primary factors influencing land use predictions at the community level, all of which contribute to a training process informed by the generated causal graph. Empirically validated on three real-world datasets, our model demonstrates a performance leap over state-of-the-art baselines, marking a pivotal step toward understanding and predicting the dynamics of urban land use.

The current deployment of extremely shallow-shared-buffer switches in data center networks has posed challenges to widely adopted ECN-based congestion control algorithms, leading to the issue of ECN failure. Switches may not allocate sufficient buffer space for each port, leading to the possibility that the ECN marking threshold exceeds the buffer limit per port. This results in excessive packet loss during bursts, even before the ECN markings take effect. To address this problem, we propose BBQ, an automatic ECN tuning system based on reinforcement learning. BBQ ensures that the ECN threshold does not exceed the buffer capacity allocated to the port, thus avoiding the ECN failure issue. Besides, BBQ is designed to adapt to switches with varying buffer sizes ensuring generalization. We validate the effectiveness of BBQ through experiments conducted with shallow buffering and high bursts. The results show that BBQ efficiently controls the packet loss rate of incast flows to within 3%, 1.2 times lower than State-of-the-Arts in shallow-buffered environments.

Network feedback, representing the impact of congestion control actions on the network, is crucial for evaluating the advantages of the actions taken. Previous reinforcement learning (RL)-based congestion control algorithms use average performances over fixed periods to measure network feedback, which fails to accurately capture the impact of an action and leads to performance degradation. In this paper, we propose Zygos, which accurately estimates network feedback. This accurate feedback can benefit other RL-based congestion control algorithms. Zygos contains a distribution-based reward correction mechanism that leverages a RL model to generate relevance distributions for the sequence rewards of each state-action pair, and then aggregates the rewards by weighted average. Zygos also adopts metagradient RL to capture network feedback offset patterns, thereby updating the relevance generation model during the training of the congestion control algorithm. Experiments show that the RL congestion control method using Zygos achieves an average 20–30% improvement in throughput and 20% decrease in delay compared to the original method, highlighting substantial enhancements in RL-based congestion control algorithms.

Time series forecasting in the sensing system aims to predict future values based on historical records that sensors have collected. Previous works, however, usually focus on improving model structure or algorithm for better performance but the perspective of learning proper numeric representations is overlooked. The inappropriate and coarse numeric representations are not expressive enough to capture the intrinsic characteristics of numbers, which will obviously degrade the prediction performance. In this article, we propose Num2vec, an algorithmic framework to learn numeric representations. Specifically, Num2vec lists three main logic characteristics of numbers: arithmetic, direction, and periodicity. By representing numbers into a transition space, Num2vec can translates numbers agilely to different Internet of Things tasks through selecting the corresponding characteristics. According to such a design, Num2vec enjoys flexible numeric representations to fit different Internet of Things time series tasks. Extensive experiments on four real-world datasets show that the approach achieves the best performance when compared with state-of-the-art baselines.

For sequential recommendation, it is essential to capture and predict future or long-term user preference for generating accurate recommendation over time. To improve the predictive capacity, we adopt reinforcement learning (RL) for developing effective sequential recommenders. However, user-item interaction data is likely to be sparse, complicated and time-varying. It is not easy to directly apply RL techniques to improve the performance of sequential recommendation.

Automatic crime classification is a fundamental task in the legal field. Given the fact descriptions, judges first determine the relevant violated laws, and then the articles. As laws and articles are grouped into a tree-shaped hierarchy (i.e., laws as parent labels, articles as children labels), this task can be naturally formalized as a two layers' hierarchical multi-label classification problem. Generally, the label semantics (i.e., definition of articles) and the hierarchical structure are two informative properties for judges to make a correct decision. However, most previous methods usually ignore the label structure and feed all labels into a flat classification framework, or neglect the label semantics and only utilize fact descriptions for crime classification, thus the performance may be limited. In this paper, we formalize crime classification problem into a matching task to address these issues. We name our model as Hierarchical Matching Network (HMN for short). Based on the tree hierarchy, HMN explicitly decomposes the semantics of children labels into the residual and alignment components. The residual components keep the unique characteristics of each individual children label, while the alignment components capture the common semantics among sibling children labels, which are further aggregated as the representation of their parent label. Finally, given a fact description, a co-attention metric is applied to effectively match the relevant laws and articles. Experiments on two real-world judicial datasets demonstrate that our model can significantly outperform the state-of-the-art methods.