The robustness of intelligent IoT device networking is vital for maintaining communication connectivity within intelligent manufacturing systems, impacting the reliability of the customized Industrial Internet of Things (CuIoT). Current studies enhance network connectivity and resilience against cyber attacks through combinatorial optimization theory by redeploying topologies. However, these approaches often overlook the transformative potential of network motifs in the optimization process. To address this, we introduce CuIoT-MET, an innovative approach that enhances CuIoT robustness by leveraging motif evolutionary transfer knowledge from historical evolution processes. By analyzing changes in connection relationships and emphasizing network motifs' unique contributions, we design a novel robustness metric to optimize the evolutionary trajectory, resulting in more robust CuIoT connection patterns. Extensive experiments show that CuIoT-MET outperforms state-of-the-art methods in improving network robustness.

Sensor data analysis is a crucial task for environmental cognition in smart traffic systems. Recently, vehicle-to-everything (V2X) collaborative analysis has leveraged intermediate feature communication between vehicles and infrastructure to achieve superior analysis performance compared to single-vehicle approaches. However, due to the limited bandwidth of V2X communication links, directly transmitting features can be inefficient, resulting in significant delays that are unacceptable for real-time decision-making. To address this challenge, we propose a compact feature representation method in the bird's eye view (BEV) space for communication-efficient collaborative analysis. As shown in Fig. 1, the proposed method can be viewed as a task-aware distributed coding approach with decoder side information. First, the ego vehicle and the networked infrastructure convert raw LiDAR data into BEV features using a shared PointPillars feature extractor. The infrastructure then applies the proposed BEV codec to transform these BEV features into a compact representation, encoding them into a binary bitstream through entropy coding based on the estimated distribution. The received features are subsequently warped and fused with the ego vehicle's features using a bidirectional attention fusion module, and processed by a single-shot detector to perform 3D object detection. Experimental results on the DAIR-V2X-C dataset demonstrate that the proposed framework achieves more than 1000 times compression compared to directly transmitting floating-point features, while maintaining high analysis performance in real-world V2X scenarios.

Personalized location prediction is key to many mobile applications and services. In this paper, motivated by both statistical and visualized preliminary analysis on three real datasets, we observe a strong spatiotemporal correlation for user trajectories among the visited area-of-interests (AoIs) and different time periods on both weekly and daily basis, which directly motivates our time-aware location prediction model design called "t-LocPred". It models the spatial correlations among AoIs by coarse-grained convolutional processing of the user trajectories in AoIs of different time periods ("ConvAoI"); and predicts his/her fine-grained next visited PoI using a novel memory-augmented attentive LSTM model ("mem-attLSTM") to capture long-term behavior patterns. Experimental results show that t-LocPred outperforms 8 baselines. We also show the impact of hyperparameters and the benefits ConvAoI can bring to these baselines.

Unsupervised domain adaptation (UDA) aims to utilize knowledge from a label-rich source domain to understand a similar yet distinct unlabeled target domain. Notably, global distribution statistics across domains and local semantic characteristics across samples, are two essential factors of data analysis that should be fully explored. Most existing UDA approaches either harness only one of them or fail to closely associate them for efficient adaptation. In this work, we propose a unified framework, called Collaborative Alignment Framework (CAF), which simultaneously reduces the global domain discrepancy and preserves the local semantic consistency for cross-domain knowledge transfer in a collaborative manner. Specifically, for domain-oriented alignment, we utilize adversarial training or minimize the Wasserstein distance between the two distributions to learn domain-level invariant representations. For semantic-oriented matching, we capture the semantic discrepancy between the predictions of two diverse task-specific classifiers and enhance the features of target data to be near the support of the source data class-wisely, which promotes semantic consistency across domains effectively. These two adaptation processes can be deeply intertwined in CAF via collaborative training, thus CAF can learn domain-invariant and semantic-consistent feature representations. Extensive experiments on four popular benchmarks, including DomainNet, VisDA-2017, Office-31, and ImageCLEF, demonstrate the proposed methods significantly outperform the existing methods, especially on the large-scale dataset. The code is available athttps://github.com/BIT-DA/CAF.

Personalized location prediction is key to many mobile applications and services. In this paper, motivated by both statistical and visualized preliminary analysis on three real datasets, we observe a strong spatiotemporal correlation for user trajectories among the visited area-of-interests (AoIs) and different time periods on both weekly and daily basis, which directly motivates our time-aware location prediction model design called “$t$t-LocPred”. It models the spatial correlations among AoIs by coarse-grained convolutional processing of the user trajectories in AoIs of different time periods (“ConvAoI”); and predicts his/her fine-grained next visited PoI using a novel memory-augmented attentive LSTM model (“mem-attLSTM”) to capture long-term behavior patterns. Experimental results show that$t$t-LocPred outperforms 8 baselines. We also show the impact of hyperparameters and the benefits ConvAoI can bring to these baselines.

In this paper, we propose a single-agent Monte Carlo based reinforced feature selection (MCRFS) method, as well as two efficiency improvement strategies, i.e., early stopping (ES) strategy and reward-level interactive (RI) strategy. Feature selection is one of the most important technologies in data prepossessing, aiming to find the optimal feature subset for a given downstream machine learning task. Enormous research has been done to improve its effectiveness and efficiency. Recently, the multi-agent reinforced feature selection (MARFS) has achieved great success in improving the performance of feature selection. However, MARFS suffers from the heavy burden of computational cost, which greatly limits its application in real-world scenarios. In this paper, we propose an efficient reinforcement feature selection method, which uses one agent to traverse the whole feature set, and decides to select or not select each feature one by one. Specifically, we first develop one behavior policy and use it to traverse the feature set and generate training data. And then, we evaluate the target policy based on the training data and improve the target policy by Bellman equation. Besides, we conduct the importance sampling in an incremental way, and propose an early stopping strategy to improve the training efficiency by the removal of skew data. In the early stopping strategy, the behavior policy stops traversing with a probability inversely proportional to the importance sampling weight. In addition, we propose a reward-level interactive strategy to improve the training efficiency via reward-level external advice. Finally, we design extensive experiments on real-world data to demonstrate the superiority of the proposed method.

Spatial crowdsourcing (SC) utilizes the potential of a crowd to accomplish certain location based tasks. Although worker scheduling has been well studied recently, most existing works only focus on the static deployment of workers but ignore their temporal movement continuity. In this paper, we explicitly consider the use of unmanned vehicular workers, e.g., drones and driverless cars, which are more controllable and can be deployed in remote or dangerous areas to carry on long-term and hash tasks as a vehicular crowdsourcing (VC) campaign. We propose a novel deep reinforcement learning (DRL) approach for curiosity-driven energy-efficient worker scheduling, called "DRL-CEWS", to achieve an optimal trade-off between maximizing the collected amount of data and coverage fairness, and minimizing the overall energy consumption of workers. Specifically, we first utilize a chief-employee distributed computational architecture to stabilize and facilitate the training process. Then, we propose a spatial curiosity model with a sparse reward mechanism to help derive the optimal policy in large crowdsensing space with unevenly distributed data. Extensive simulation results show that DRL-CEWS outperforms the state-of-the-art methods and baselines, and we also visualize the benefits curiosity model brings and show the impact of two hyperparameters.

In a hybrid video encoder, the DCT transform or integer transform are adopted to de correlate the residual correlation among neighboring pixels. However, different transform coefficients have very different variances. For example, in a 4x4 integer transform, the 16 coefficients show a decreasing variance in the well-known zigzag scan order as used in H.264. As a result, the coefficients of higher frequency have higher probability of being zeroes after quantization. On the other hand, the coefficients of lower frequency have larger variances after quantization. Such characteristics are exploited by the run-level mapping after zigzag scan to further increase the coding efficiency of entropy coding.

RELIEF is considered one of the most successful algorithms for assessing the quality of features due to its simplicity and effectiveness. It has been recently proved that RELIEF is an online algorithm that solves a convex optimization problem with a margin-based objective function. Starting from this mathematical interpretation, we propose a novel feature extraction algorithm, referred to as LFE, as a natural generalization of RELIEF. LFE collects discriminant information through local learning, and is solved as an eigenvalue decomposition problem with a closed-form solution. A fast implementation is also derived. Experiments on synthetic and real-world data are presented. The results demonstrate that LFE performs significantly better than other feature extraction algorithms in terms of both computational efficiency and accuracy.