Abstract High-concurrency systems often suffer from performance bottlenecks [1]. This is often caused by waiting and context switching caused by fierce competition between threads for locks. As a cloud computing company, we place great emphasis on maximizing performance. In this regard, we transform the lightweight spin-lock and propose a concise parameter fine-tuning strategy, which can break through the system performance bottleneck with minimal risk conditions. This strategy has been validated in Apache RocketMQ [2], a high-throughput message queue system. Through this strategy, we achieved performance improvements of up to 37.58% on X86 CPU and up to 32.82% on ARM CPU. Furthermore, we confirmed the method’s consistent effectiveness across different code versions and IO flush strategies, demonstrating its broad applicability in real-world settings. This work offers not only a practical tool for addressing performance issues in high-concurrency systems but also highlights the practical value of formal techniques in solving engineering problems.

We present an automatic generation pipeline of interactive nonlinear video for online apparel shopping navigation. Our approach was inspired by Google's “Messy Middle” theory, which suggests that people mentally are faced with two tasks—exploration and evaluation—before purchasing online. Given a set of apparel product presentation videos, our navigation UI organizes them to optimize users' product exploration and automatically generates interactive videos for users' product evaluation. To support automatic methods, we proposed a video clustering similarity ($\operatorname{CSIM}$) and a camera movement similarity ($\operatorname{MSIM}$), as well as a comparative video generation algorithm for product recommendation, presentation, and comparison. To evaluate our pipeline's effectiveness, we conducted several user studies. The results showed that our pipeline can help users complete the consumption process more efficiently, making it easier for them to understand and choose a product.

As a team from Alibaba Cloud, we have developed and open-sourced RocketMQ, a cloud-native “messaging, eventing, streaming” real-time data processing platform that covers cloud-edge-device collaboration scenarios. During the development of RocketMQ, we also formulated RocketHA, a log-based storage high availability design theory that provides a robust solution for distributed log storage software used in industrial applications. RocketHA comprises six fundamental components that enable automatic cluster recovery from failures such as crashes and partitions. This design paradigm has been successfully implemented in the open-source RocketMQ. Our evaluation demonstrates that RocketHA ensures high availability, fast recovery, high throughput, and data loss prevention. We hope that RocketHA will inspire and guide the development of high-availability solutions for all log-based storage systems.

Deep learning has been broadly leveraged by major cloud providers, such as Google, AWS and Baidu, to offer various computer vision related services including image classification, object identification, illegal image detection, etc. While recent works extensively demonstrated that deep learning classification models are vulnerable to adversarial examples, cloud-based image detection models, which are more complicated than classifiers, may also have similar security concern but not get enough attention yet. In this paper, we mainly focus on the security issues of real-world cloud-based image detectors. Specifically, (1) based on effective semantic segmentation, we propose four attacks to generate semantics-aware adversarial examples via only interacting with black-box APIs; and (2) we make the first attempt to conduct an extensive empirical study of black-box attacks against real-world cloud-based image detectors. Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100 percent, and the semantic segmentation based attacks have a success rate over 90 percent among different detection services, such as violence, politician, and pornography detection. We also proposed several possible defense strategies for these security challenges in the real-life situation.

Adversarial examples (AEs) against deep neural networks (DNNs) raise wide concerns about the robustness of DNNs. Existing detection mechanisms are often limited to a given attack algorithm. Therefore, it is highly desirable to develop a robust detection approach that remains effective for a large group of attack algorithms. In addition, most of the existing defences only perform well for small images (e.g. MNIST and Canadian institute for advanced research (CIFAR)) rather than large images (e.g. ImageNet). In this paper, the authors propose a robust and effective defence method for analysing the sensitivity of various AEs, especially in a much harder case (large images). Their method first creates a feature map from the input space to the new feature space, by utilising 19 different feature mapping methods. Then, a detector is learned with the machine‐learning algorithm to recognise the unique distribution of AEs. Their extensive evaluations on their proposed detector show that their detector can achieve: (i) low false‐positive rate (<1%), (ii) high true‐positive rate (higher than 98%), (iii) low overhead (<0.1 s per input), and (iv) good robustness (work well across different learning models, attack algorithms, and parameters), which demonstrate the efficacy of the proposed detector in practise.

Various media information in life can have an important impact on our understanding of music. In this paper, we present a demo, Mind Band, which is a Cross-Media artificial intelligent composing platform using our life elements such as emoji, image and humming. In practice, we base our system on the valence-arousal model. We use emotion analysis of life elements to map them to music pieces, which are generated by a Variational Autoencoder - Generative Adversarial Networks model. We provide users with immersive experience by uploading emoji/image/humming and retrieving emotionally related music pieces back. With this platform, everyone can be a composer.