To approximate sums of values in key-value data streams, sketches are widely used in databases and networking systems.They offer high-confidence approximations for any given key while ensuring low time and space overhead.While existing sketches are proficient in estimating individual keys, they struggle to maintain this high confidence across all keys collectively, an objective that is critically important in both algorithm theory and its practical applications.We propose ReliableSketch, the first to control the error of all keys to less than Λ with a small failure probability Δ, requiring only 𝑂 (1 + Δ ln ln( 𝑁 Λ )) amortized time and 𝑂 ( 𝑁 Λ + ln( 1 Δ )) space.Furthermore, its simplicity makes it hardware-friendly, and we implement it on CPU servers, FPGAs, and programmable switches.Our experiments show that under the same small space, ReliableSketch not only keeps all keys' errors below Λ but also delivers competitive throughput among accuracy-oriented baselines, outperforming * Both authors contributed equally to this research.

In data stream mining, monitoring high-speed users and segregating their excessive use, known as “Overspeed items,” is crucial for preventing system overload and maintaining fairness in messaging and network systems. Current approaches, however, face scalability challenges with large user bases, primarily due to increasing memory requirements proportional to user numbers. We have pinpointed the inefficiency in allocating memory for all users, recognizing that only a small fraction exhibit overspeed behavior at any given time. Addressing this, we employed the sketching technique, a type of approximate algorithm, and designed the first sketch algorithm for finding Overspeed items, named SpeedSketch: (1) Scalability. SpeedSketch can scale user numbers (saving memory space) to a factor of 6430 while maintaining a low average error rate of 0.1% in real-world datasets. (2) Accuracy. In theory, SpeedSketch stands out as the only sketch algorithm offering a per-user relative error bound. (3) Speed. SpeedSketch is implemented on a high-speed programmable switch with a throughput capacity of 4.8 billion items per second. All codes are available on GitHub for reference.

Extracting effective spatial-temporal information is significantly important for video-based action recognition. Recently 3D convolutional neural networks (3D CNNs) that could simultaneously encode spatial and temporal dynamics in videos have made considerable progress in action recognition. However, almost all existing 3D CNN-based methods recognize human actions only using RGB videos. The single modality may limit the performance capacity of 3D networks. In this paper, we extend 3D CNN to depth and pose data besides RGB data to evaluate its capacity for spatiotemporal multimodal learning for video action recognition. We propose a novel multimodal two-stream 3D network framework, which can exploit complementary multimodal information to improve the recognition performance. Specifically, we first construct two discriminative video representations under depth and pose data modalities respectively, referred as depth residual dynamic image sequence (DRDIS) and pose estimation map sequence (PEMS). DRDIS captures spatial-temporal evolution of actions in depth videos by progressively aggregating the local motion information. PEMS eliminates the interference of cluttered backgrounds and describes the spatial configuration of body parts intuitively. The multimodal two-stream 3D CNN deals with two separate data streams to learn spatiotemporal features from DRDIS and PEMS representations. Finally, the classification scores from two streams are fused for action recognition. We conduct extensive experiments on four challenging action recognition datasets. The experimental results verify the effectiveness and superiority of our proposed method.

With the help of convolutional neural networks (CNNs), video-based human action recognition has made significant progress. CNN features that are spatial and channel-wise can provide rich information for powerful image description. However, CNNs lack the ability to process the long-term temporal dependency of an entire video and further cannot well focus on the informative motion regions of actions. Aiming at the two problems, we propose a novel video-based action recognition framework in this paper. We first represent videos with dynamic image sequences (DISs), which effectively describe videos by modeling the local spatial-temporal dynamics and dependencies. Then a channel and spatial-temporal interest points (STIPs) attention model (CSAM) based on CNNs is proposed to focus on the discriminative channels in networks and the informative spatial motion regions of human actions. Specifically, channel attention (CA) is implemented by automatically learning channel-wise convolutional features and assigning different weights for different channels. STIPs attention (SA) is encoded by projecting the detected STIPs on frames of dynamic image sequences into the corresponding convolutional feature map space. The proposed CSAM is embedded after CNN convolutional layers to refine the feature maps, followed by global average pooling to produce effective feature representations for videos. Finally frame-level video representations are fed into an LSTM to capture the temporal dependencies and make classification. Experiments on three challenging RGB-D datasets show that our method has better performance and outperforms the state-of-the-art approaches with only depth data.