Steganography without embedding (SWE), which does not modify the cover content, can effectively resist steganalysis and is a highly secure covert communication technology suitable for the Internet of Things (IoT). However, despite this fundamental security advantage, its practical implementation over lossy network channels still faces challenges, including hiding capacity limitations imposed by dataset scales and the non-trivial trade-off between robustness and behavioral security. To tackle these challenges, we propose DIGM-SWE, a robust SWE method based on dynamic image generation and matching. First, a text-to-image generation model (T2I model) is employed to dynamically generate a theme image dataset, ensuring controllable hiding capacity and secure transmission behavior. Second, an image matrix is constructed by distance-pixel cascading sorting, and a bijective mapping is established between secret message sequences and cover image groups via row-index encoding to achieve high hiding success and non-repetitive use of cover images. Finally, resolution enhancement technology is applied to maintain the consistency of stego images under lossy network channels. Meanwhile, a VP-tree structure of the image dataset is built to improve both the stego image matching accuracy and the secret message extraction accuracy. Experimental results show that, compared to other SWE methods, our method achieves up to 3 times greater actual hiding capacity while guaranteeing a 100% hiding success rate across four experimental datasets. Against common image processing operations, it improves average extraction accuracy by up to 59.44%. In two real-world transmission scenarios, our proposed DIGM-SWE achieves perfect recovery of the secret message.

Low-latency cloud rendering enables mobile users to experience high-quality, real-time 3D graphics but achieving low Motion-to-Photon (MTP) latency while maintaining smooth playback is a significant challenge. Our real-world measurement study identifies Receive-to-Composition (R2C) latency, caused by ineffective jitter buffer management, as the primary factor contributing to increased MTP latency. To address this, we introduce JitBright, a client-side optimization strategy that dynamically reduces MTP latency through adaptive jitter buffer management. By adjusting buffer levels based on smoothing playback probability and implementing proactive keyframe requests to mitigate frame dependency, JitBright minimizes both active and passive waiting times. Our large-scale evaluation, conducted over 591,000 sessions across diverse network conditions (WiFi, 4G, 5G) and device types, demonstrates significant improvements in user experience. JitBright reduces median R2C latency by up to 87.5%, increases the proportion of sessions meeting strict MTP latency requirements by 6%–27%, and decreases the video freeze rate from 2.4%–2.8% to 0.4%–1.0%.

Data intelligence is a pivotal driver of the digital economy, fostering social innovation and high-quality economic growth. Mastering data intelligence equips enterprises with a competitive edge, enabling them to harness technologies, products, and theories to unlock the potential of internal data. The Enterprise Data Intelligence Platform, as a foundational infrastructure, integrates data storage, computation, analysis, and governance, offering end-to-end data life-cycle services. As digital transformation accelerates, building specialized and adaptive data intelligence platforms has become a critical industry focus. This paper explores the evolution, architecture, and implementation of enterprise data intelligence platform, providing insights for enterprise upgrades and envisioning future advancements in the field.

In recent years, with the breakthroughs in generative artificial intelligence technology, AI has become a key approach and effective means for unlocking the value of data elements. The relationship between the big data industry and the AI industry has evolved from a one-way empowerment to a deep integration, giving birth to the data intelligence industry. The collaboration between data and intelligence has together become the core force in transforming the physical world and reshaping the digital world order. The booming development of the data intelligence industry has become the core engine driving innovation and growth in the digital economy. This paper focuses on the topic of data intelligence. It systematically clarifies the technical system of data intelligence, conducts an in-depth analysis of the current status and issues in specific areas of data intelligence, such as data, algorithms, and applications, and also provides an outlook on the future development trends in various fields of the data intelligence industry.

Increasing bandwidth demands of mobile video streaming pose a challenge in optimizing the Quality of Experience (QoE) for better user engagement. Multipath transmission promises to extend network capacity by utilizing multiple wireless links simultaneously. Previous studies mainly tune the packet scheduler in multipath transmission, expecting higher QoE by accelerating transmission. However, since Adaptive BitRate (ABR) algorithms overlook the impact of multipath scheduling on throughput prediction, multipath adaptive streaming can even experience lower QoE than single-path. This paper proposes Chorus, a cross-layer framework that coordinates multipath scheduling with adaptive streaming to optimize QoE jointly. Chorus establishes two-way feedback control loops between the server and the client. Furthermore, Chorus introduces Coarse-grained Decisions, which assist appropriate bitrate selection by considering the scheduling decision in throughput prediction, and Finegrained Corrections, which meet the predicted throughput by QoE-oriented multipath scheduling. Extensive emulation and real-world mobile Internet evaluations show that Chorus outperforms the state-of-the-art MPQUIC scheduler, improving average QoE by 23.5% and 65.7%, respectively.

Low-latency cloud rendering services use high-performance servers to provide mobile device users with exquisite graphics and convenient access experiences. Due to the complexity of the system and the diversity of impacting factors, identifying system bottlenecks has become a significant challenge. To demystify system performance, we build an online cloud rendering system to measure the latency distribution of its key components.

Due to the network fluctuations, congestion control is indispensable to guarantee the quality of experience (QoE) for Real-Time Communication (RTC) users. This component adjusts the sending rate of media data, which determines the video encoding bitrate. However, existing control schemes either only focus on network numerical indicators or fail to adapt to various network environments. Logically, we propose PACC (Perception Aware Congestion Control) for RTC in this paper. Leveraging the convolutional neural network (CNN), we develop a quality sensor to infer the video quality increasing rate. Assisted with the variation trend analysis for user perception, PACC tunes the bitrate towards the direction of better QoE. Extensive tracedriven experiments demonstrate the effectiveness of PACC, which outperforms the existing landmark schemes by 8.2% to 32.4% and 6.8% to 18.0% in terms of transport and application layer QoE metrics, respectively.

The concept of "Lakehouse" was proposed by Databricks in 2020. Since "Lakehouse" was first written into Gartner’s Hype Cycle for Data Management in 2021, as a new technology, "Lakehouse" has received unprecedented attention from the enterprises who need digital transformation. More enterprises believe lakehouse is an important infrastructure for digital transformation. Currently, lakehouse is still in its early stage of development. It is not merely a technical research endeavor, but rather a gradual integration of technologies, representing a transitional phase in the evolution of heterogeneous data platform towards integration. This paper focuses on the lakehouse technology, sorts out the development history of the data platform and the practice path of lakehouse technology. It also lists main manufactures and products of lakehouse and provides the judgments for the future development of lakehouse.

The integrated big data platform aims to provide one-stop development and operation capabilities for massive heterogeneous data. Various components are organically combined and managed in a unified way. At the same time, the underlying technical details are shielded from users, so as to finally achieve the purpose of convenient data development, reducing operation and maintenance costs, and standardizing management processes. This article introduces the functional modules that the platform should have one by one, covering data storage, data integration, data management and governance, data development, data processing and analysis, high availability, operation and maintenance management, and data security. Finally, the typical characteristics and development direction of the integrated big data platform are pointed out.

We report XLINK, a multi-path QUIC video transport solution with experiments in Taobao short videos. XLINK is designed to meet two operational challenges at the same time: (1) Optimized user-perceived quality of experience (QoE) in terms of robustness, smoothness, responsiveness, and mobility and (2) Minimized cost overhead for service providers (typically CDNs). The core of XLINK is to take the opportunity of QUIC as a user-space protocol and directly capture user-perceived video QoE intent to control multi-path scheduling and management. We overcome major hurdles such as multi-path head-of-line blocking, network heterogeneity, and rapid link variations and balance cost and performance.

At present, data security has become a key area of attention from all aspects. The proliferation of data leakage incidents, the acceleration of the introduction of data security laws and regulations, and the rising cost of enterprise compliance punishment jointly urge all enterprises to step up to improve their own data security system. In the construction of data security, data desensitization is always a core technical ability. In view of the current situation and future development trend of data desensitization technology and products, this paper introduces the development of data desensitization technology in detail from desensitization algorithm, anonymization, static data desensitization and dynamic data desensitization. At the same time, from the core function, product form, supply side status and other aspects of the data desensitization technology are described. On this basis, the standardization direction of data desensitization products in terms of core functions, concept definition, performance and stability is proposed. Finally, the development trend of data desensitization technology and products in four aspects of regularization, anonymization, intellectualization and contextualization are put forward.

This paper introduces the E-Collaboration in E-Business. It stresses that HDBIS (Heterogeneous Database Integration System) plays an important role for supporting E-Collaboration in E-Business. HDBIS provides a crucial technique for integration of heterogeneous information source in E-Business. The paper further displays the E-Collaboration model supported by HDBIS through application of clothing companies, which include three layers: manufacturers, sellers and customers. The system architecture, functions and characteristics of HDBIS are presented. The implementation and model of HDBIS using COM/DCOM and ASP is also introduced.