Swarm UAV autonomous flight for Embodied Long-Horizon (ELH) tasks is crucial for advancing the low-altitude economy. However, existing methods focus only on specific basic tasks due to dataset limitations, failing in real-world deployment for ELH tasks. ELH tasks are not mere concatenations of basic tasks, requiring handling long-term dependencies, maintaining embodied persistent states, and adapting to dynamic goal shifts. This paper presents U2UData+, the first large-scale swarm UAV autonomous flight dataset for ELH tasks and the first scalable swarm UAV data online collection and algorithm closed-loop verification platform. The dataset is captured by 15 UAVs in autonomous collaborative flights for ELH tasks, comprising 12 scenes, 720 traces, 120 hours, 600 seconds per trajectory, 4.32M LiDAR frames, and 12.96M RGB frames. This dataset also includes brightness, temperature, humidity, smoke, and airflow values covering all flight routes. The platform supports the customization of simulators, UAVs, sensors, flight algorithms, formation modes, and ELH tasks. Through a visual control window, this platform allows users to collect customized datasets through one-click deployment online and to verify algorithms by closed-loop simulation. U2UData+ also introduces an ELH task for wildlife conservation and provides comprehensive benchmarks with 9 SOTA models.

The co-design of neural network architectures, quantization precisions, and hardware accelerators offers a promising approach to achieving an optimal balance between performance and efficiency, particularly for model deployment on resource-constrained edge devices. In this work, we propose the JAQ Framework, which jointly optimizes the three critical dimensions. However, effectively automating the design process across the vast search space of those three dimensions poses significant challenges, especially when pursuing extremely low-bit quantization. Specifical, the primary challenges include: (1) Memory overhead in software-side: Low-precision quantization-aware training can lead to significant memory usage due to storing large intermediate features and latent weights for backpropagation, potentially causing memory exhaustion. (2) Search time-consuming in hardware-side: The discrete nature of hardware parameters and the complex interplay between compiler optimizations and individual operators make the accelerator search time-consuming. To address these issues, JAQ mitigates the memory overhead through a channel-wise sparse quantization (CSQ) scheme, selectively applying quantization to the most sensitive components of the model during optimization. Additionally, JAQ designs BatchTile, which employs a hardware generation network to encode all possible tiling modes, thereby speeding up the search for the optimal compiler mapping strategy. Extensive experiments demonstrate the effectiveness of JAQ, achieving approximately 7% higher Top-1 accuracy on ImageNet compared to previous methods and reducing the hardware search time per iteration to 0.15 seconds.

Accuracy anomaly detection in user-level network traffic is crucial for network security. Compared with existing models that passively detect specific anomaly classes with large labeled training samples, user-level network traffic contains sizeable new anomaly classes with few labeled samples and has an imbalance, self-similar, and data-hungry nature. Motivation on those limitations, in this paper, we propose Meta-UAD, a Meta-learning scheme for User-level network traffic Anomaly Detection. Meta-UAD uses the CICFlowMeter to extract 81 flow-level statistical features and remove some invalid ones using cumulative importance ranking. Meta-UAD adopts a meta-learning training structure and learns from the collection of K-way-M-shot classification tasks, which can use a pre-trained model to adapt any new class with few samples by few iteration steps. We evaluate our scheme on two public datasets. Compared with existing models, the results further demonstrate the superiority of Meta-UAD with 15% - 43% gains in F1-score.

Modern perception systems for autonomous flight are sensitive to occlusion and have limited long-range capability, which is a key bottleneck in improving low-altitude economic task performance. Recent research has shown that the UAV-to-UAV (U2U) cooperative perception system has great potential to revolutionize the autonomous flight industry. However, the lack of a large-scale dataset is hindering progress in this area. This paper presents U2UData, the first large-scale cooperative perception dataset for swarm UAVs autonomous flight. The dataset was collected by three UAVs flying autonomously in the U2USim, covering a 9 km$^2$ flight area. It comprises 315K LiDAR frames, 945K RGB and depth frames, and 2.41M annotated 3D bounding boxes for 3 classes. It also includes brightness, temperature, humidity, smoke, and airflow values covering all flight routes. U2USim is the first real-world mapping swarm UAVs simulation environment. It takes Yunnan Province as the prototype and includes 4 terrains, 7 weather conditions, and 8 sensor types. U2UData introduces two perception tasks: cooperative 3D object detection and cooperative 3D object tracking. This paper provides comprehensive benchmarks of recent cooperative perception algorithms on these tasks.

In today's Internet, bandwidth dynamics are inevitable, and hence, the bitrate for live streaming applications should also be dynamically adjusted. However, in existing HTTP-based adaptive streaming (HAS), bitrate switching can only be performed at segment boundaries, making decisions unresponsive and often inaccurate. In this paper, we start from a close investigation on the impact of the segment length in HAS and accordingly presentVHAS, an extension towards intelligent variable-length segmentation, which makes client-side decisions based on the massive amount of real-time information from the network and viewers. VHAS implements a smart trigger mechanism that balances accuracy and overhead for variable-length segmentation. We further develop an adaptive bitrate switching algorithm with data-driven I-frame prediction, which is tailored to individual viewers to minimize bitrate mismatches. We evaluate VHAS via extensive trace-driven simulations, and our results demonstrate that compared with state-of-the-art solutions, VHAS achieves 15%–49% gains in QoE, with a noticeable bandwidth reduction of 37%–57%.

Anomaly detection in encrypted traffic is a growing problem, and many approaches have been proposed to solve it. However, those approaches need to be trained in the massive of normal traffic and specific-class abnormal traffic, so to achieve good results in that specific-class. For a new anomaly class with few labeled samples, the effectiveness of existing approaches will decline sharply. How to train a model using only a few anomaly samples to detect unseen new anomaly classes in training is a huge challenge. In this paper, we propose a Few-shot Class-adaptive Anomaly Detection framework (FCAD) with model-agnostic meta-learning (MAML) to meet this challenge. Given an input network flow, FCAD first extracts statistical features by feature extractor and feature selector, and time-series features using LSTM-based AutoEncoder. Then, FCAD designs a MAML-based few-shot anomaly detection model, relying on the episodic training paradigm and learning from the collection of K-way-M-shot classification tasks, which can mimic the few-shot regime faced at test time during training. Finally, FCAD uses the pre-trained model to adapt the new class by a few iterations steps. Our goal is to detect anomaly traffic in a before unseen anomaly class with only a few samples. A reliable solution to few-shot anomaly detection will have huge potential for real-world applications since it is expensive and arduous to collect a massive amount of data onto the new anomaly class; extensive experimental results demonstrate the effectiveness of our proposed approach.

With the rise of time-critical and interactive scenarios, ultra-low latency has become the most urgent requirement. Adaptive bitrate (ABR) schemes have been widely used in reducing latency for live streaming services. However, the traditional solutions suffer from a key limitation: they only utilize coarse-grained chunk to solve the I-frame misalignment problem in different bitrate switching process at the cost of increasing latency. As a result, existing schemes are difficult to guarantee the timeliness and granularity of control in essence. In this paper, we use a frame-based approach to solve the I-frame misalignment problem and propose a video adaptation bitrate system (Vabis) in units of the frame for time-critical live streaming to obtain the optimal quality of experience (QoE). On the server-side, a Few-Wait ABR algorithm based on Reinforcement Learning (RL) is designed to adaptively select the bitrate of future frames by state information that can be observed, which can subtly solve the problem of I-frame misalignment. A rule-based ABR algorithm is designed to optimize the Vabis system for the weak network. On the client-side, three delay control mechanisms are designed to achieve frame-based fine-grained control. We construct a trace-driven simulator and the real live platform to evaluate the comprehensive live streaming performance. The results show that Vabis is significantly better than the existing methods with decreases in an average delay of 32%-77% and improvements in average QoE of 28-67%.

Anomaly detection using KPIs (Key Performance Indicators) is key to AIOps (Artificial Intelligence for IT Operations). Recent anomaly detection approaches have adopted machine learning to detect anomalies on the perspective of individual time points more than events. These approaches do not make effective use of the labels of continuous anomaly intervals, nor do they pay attention to the differences among anomaly points. The detection performance is therefore not high enough. In this paper, we propose an anomaly detection approach named ALSR, which uses a label screening model and a relearning model to analyze and utilize the continuous anomaly intervals of KPIs in finer granularity. The label screening algorithm takes advantage of the continuity of anomaly intervals to remove unnecessary data from the training set, so as to better suit to interval-oriented anomaly detection. The relearning algorithm reclassifies the true/false positive points within range of detected anomalies, thus effectively reduces the number of false positive points. ALSR uses statistical characteristics and time series models for feature extraction, and the feature set is proved to better describe the characteristics of KPIs. We conduct comprehensive experiments on 25 KPIs, and the total F-score of ALSR is 0.965, which outperforms state-of-the-art anomaly detection approaches.

With the growing popularity and prosperity of living streaming applications, it is naturally confronting users' quality of experience (QoE) degradation issues especially under dynamic environments arised from nonnegligible factors such as high latency and intermittent bitrate. In this paper, we propose an efficient adaptive bitrate (ABR) algorithm called BitLat to achieve both bitrate-control and latency-control. BitLat is based on reinforcement learning to get strong adaptability for dealing with the complex and changing network conditions. More specifically, in our work, we determine the specific value of latency threshold with the help of current advanced algorithm, and design the structure of the neural network in reinforcement learning, the features used in the training process, and the corresponding reward function. Additional, we use the Dynamic Reward Method to further enhance the performance. Comprehensive experiments are conducted to demonstrate BitLat outperforms the state-of-the-art ABR algorithms, with improvements in average QoE of 20%-62%.