by jsendak | Feb 5, 2024 | Computer Science
With the exponential growth of data centers, there is a need for re-thinking and re-designing networks to meet the demands of these large-scale operations. The concept of central control, once deemed ineffective in the Internet era, is gaining popularity in data centers due to their structured topologies and the ability to have a single entity in control of the entire network resources.
In this article, we focus on two specific problems related to central controller-assisted prioritization of interactive flow in data center networks. The first problem addressed is the scalability issue of Fastpass, a centralized “zero-queue” data center network. While Fastpass has proven to be an efficient solution, its central arbiter does not scale well beyond 256 nodes or 8 cores. To tackle this issue, the authors have re-designed the timeslot allocator of the central arbiter, resulting in linear scalability up to 12 cores and support for approximately 1024 nodes and 7.1 Terabits of network traffic. This enhancement enables Fastpass to handle larger data center networks without sacrificing performance.
The second problem investigated in this thesis is congestion control in a software-defined network (SDN). The authors propose a framework where the controller, equipped with a global view of the network, actively participates in congestion control decisions for end TCP hosts by appropriately setting the ECN bits of IPv4 packets. What makes this framework particularly appealing is its ease of deployment, requiring no modifications to the end node TCPs or SDN switches. The authors demonstrate significant performance improvements, reporting a 30x enhancement over TCP cubic and a 1.7x improvement over Random Early Detection (RED) in flow completion times for one implementation of this framework.
This research brings innovative solutions to the challenges faced by data center networks. By addressing issues such as scalability and congestion control, the authors have paved the way for more efficient and responsive data center networks. As data centers continue to grow in size and complexity, the insights and techniques presented in this thesis will undoubtedly play a crucial role in shaping the future of data center networking.
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by jsendak | Feb 2, 2024 | Computer Science
The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life. Although beneficial in numerous fields, this content presents significant risks, including potential misuse, societal disruptions, and ethical concerns. Consequently, detecting multimedia generated by LAIMs has become crucial, with a marked rise in related research. Despite this, there remains a notable gap in systematic surveys that focus specifically on detecting LAIM-generated multimedia. Addressing this, we provide the first survey to comprehensively cover existing research on detecting multimedia (such as text, images, videos, audio, and multimodal content) created by LAIMs. Specifically, we introduce a novel taxonomy for detection methods, categorized by media modality, and aligned with two perspectives: pure detection (aiming to enhance detection performance) and beyond detection (adding attributes like generalizability, robustness, and interpretability to detectors). Additionally, we have presented a brief overview of generation mechanisms, public datasets, and online detection tools to provide a valuable resource for researchers and practitioners in this field. Furthermore, we identify current challenges in detection and propose directions for future research that address unexplored, ongoing, and emerging issues in detecting multimedia generated by LAIMs. Our aim for this survey is to fill an academic gap and contribute to global AI security efforts, helping to ensure the integrity of information in the digital realm. The project link is https://github.com/Purdue-M2/Detect-LAIM-generated-Multimedia-Survey.
Expert Commentary: The Rise of AI-Generated Multimedia and the Need for Detection
The rapid advancement of Large AI Models (LAIMs) has ushered in a new era where AI-generated multimedia is becoming increasingly integrated into our daily lives. From text and images to videos and audio, these AI models have the ability to create highly realistic and convincing content. While this has numerous benefits in various fields, it also presents significant risks.
One of the key concerns surrounding AI-generated multimedia is the potential for misuse. In a world where anyone can create highly realistic fake videos, images, or text, the implications for misinformation and propaganda are immense. Detecting multimedia generated by LAIMs has therefore become crucial in ensuring the integrity of information in the digital realm.
In response to this need, researchers have been actively working on developing detection methods for LAIM-generated multimedia. However, despite the growing interest in this area, there has been a lack of systematic surveys that comprehensively cover the existing research. Addressing this gap, the authors of this article have provided the first survey that focuses specifically on detecting multimedia created by LAIMs.
The survey introduces a novel taxonomy for detection methods, categorized by media modality, such as text, images, videos, audio, and multimodal content. This taxonomy helps researchers and practitioners better understand the different approaches to detecting LAIM-generated multimedia. Additionally, the authors also highlight two perspectives: pure detection and beyond detection. Pure detection aims to enhance detection performance, while beyond detection adds attributes like generalizability, robustness, and interpretability to detectors.
Furthermore, the authors provide an overview of generation mechanisms, public datasets, and online detection tools, making this survey a valuable resource for those working in this field. By identifying current challenges in detection and proposing directions for future research, this survey aims to contribute not only to academic knowledge but also to global AI security efforts.
From a multidisciplinary perspective, this content touches upon various disciplines within the field of multimedia information systems. The integration of AI-generated multimedia into daily life requires a deep understanding of how different media modalities can be effectively detected. This involves knowledge from computer vision, natural language processing, signal processing, and human-computer interaction.
Moreover, the concepts presented in this survey are closely related to the wider fields of animations, artificial reality, augmented reality, and virtual reality. The ability to detect LAIM-generated multimedia becomes crucial in maintaining the trust and user experience in these immersive environments. Without proper detection mechanisms, these technologies run the risk of being misused and causing societal disruptions.
In conclusion, this comprehensive survey fills an academic gap and provides insights into detecting multimedia generated by LAIMs. With the rise of AI-generated content, it is essential to develop robust detection methods to ensure the reliability and integrity of information. By highlighting current research, challenges, and future directions, this survey contributes to the broader field of multimedia information systems and the development of secure AI technologies.
Reference:
Detect-LAIM-generated-Multimedia-Survey. Retrieved from https://github.com/Purdue-M2/Detect-LAIM-generated-Multimedia-Survey
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by jsendak | Feb 2, 2024 | Computer Science
Analysis of “Queue-Aware Coding Scheduling Transmission for B5G Edge Networks”
Introduction
The article “Queue-Aware Coding Scheduling Transmission for B5G Edge Networks” explores the challenges faced in achieving low-latency and high-reliability transmissions in edge networks. It specifically focuses on the issue of potential eavesdroppers and proposes a solution called SCLER, which is a Protocol Data Units (PDU) Raptor-encoded multi-path transmission method.
The Challenges in B5G Edge Networks
B5G (Beyond 5G) edge networks require efficient and secure data transmissions between edge computing nodes and terminal devices. However, there are several challenges that need to be addressed. First, the larger attack surface in Concurrent Multipath Transfer (CMT) makes it susceptible to potential eavesdroppers. Second, the presence of asymmetric delay and bandwidth can introduce excessive delays in data transmission. Lastly, the lack of interaction among PDU session bearers poses a hindrance to achieving reliable and secure communication.
SCLER: Secure and Reliable Transmission Scheme
To overcome these challenges, the article proposes SCLER as a solution. SCLER utilizes Raptor encoding and distribution to ensure secure and reliable transmission. Additionally, it incorporates a queue length-aware encoding strategy, which takes into account the number of data packets in the queue. This strategy is modeled using a Constrained Markov Decision Process (CMDP), enabling optimal decision-making based on a threshold strategy.
Benefits and Practical Applicability
According to the numerical results presented in the article, SCLER effectively reduces data leakage risks while achieving an optimal balance between delay and reliability. This ensures data security in B5G edge networks. Moreover, the proposed system is compatible with current mobile networks, making it practically applicable in real-world scenarios.
Expert Insights
As an expert commentator, I find the proposed SCLER system to be a promising solution for secure and reliable transmission in B5G edge networks. The inclusion of Raptor encoding and distribution addresses the issue of potential eavesdroppers, providing an added layer of security. The queue length-aware encoding strategy, modeled using CMDP, allows for intelligent decision-making, optimizing the trade-off between delay and reliability.
It is worth noting that the practical applicability of SCLER to current mobile networks is a significant advantage. This ensures that the proposed system can be integrated into existing infrastructure without requiring major changes or disruptions.
Moving forward, it would be interesting to see further research and development in the implementation of SCLER in real-world scenarios. Field trials and performance evaluations could provide valuable insights into the system’s effectiveness and scalability. Additionally, exploring the potential impact of SCLER on other aspects of B5G edge networks, such as energy efficiency and network resource utilization, would contribute to a more comprehensive understanding of its capabilities.
In conclusion, “Queue-Aware Coding Scheduling Transmission for B5G Edge Networks” presents a novel approach to address the challenges of secure and reliable data transmissions in B5G edge networks. The proposed SCLER system shows promising results and practical applicability, making it an important contribution to the field of edge network communications.
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by jsendak | Feb 1, 2024 | Computer Science
We present a framework for learning cross-modal video representations by directly pre-training on raw data to facilitate various downstream video-text tasks. Our main contributions lie in the pre-training framework and proxy tasks. First, based on the shortcomings of two mainstream pixel-level pre-training architectures (limited applications or less efficient), we propose Shared Network Pre-training (SNP). By employing one shared BERT-type network to refine textual and cross-modal features simultaneously, SNP is lightweight and could support various downstream applications. Second, based on the intuition that people always pay attention to several “significant words” when understanding a sentence, we propose the Significant Semantic Strengthening (S3) strategy, which includes a novel masking and matching proxy task to promote the pre-training performance. Experiments conducted on three downstream video-text tasks and six datasets demonstrate that, we establish a new state-of-the-art in pixel-level video-text pre-training; we also achieve a satisfactory balance between the pre-training efficiency and the fine-tuning performance. The codebase are available at https://github.com/alipay/Ant-Multi-Modal-Framework/tree/main/prj/snps3_vtp.
Analysis and Expert Insights on Cross-Modal Video Representations
This article presents a framework for learning cross-modal video representations by pre-training on raw data. This approach aims to facilitate various downstream video-text tasks and addresses the limitations of existing pixel-level pre-training architectures.
Multi-disciplinary Nature of the Concepts
The concepts discussed in this content are highly multi-disciplinary, drawing from fields such as machine learning, natural language processing, computer vision, and multimedia information systems. The integration of these domains is crucial for developing robust and efficient methods in the area of cross-modal video representations.
By leveraging the power of pre-training on raw data, the framework promotes the joint learning of textual and cross-modal features. This allows for a more comprehensive understanding of video content and enhances the performance of downstream tasks that involve video and text interactions.
Relation to Multimedia Information Systems
The field of multimedia information systems focuses on the efficient organization, retrieval, and analysis of multimedia data. The framework presented in this article aligns with this field by proposing a pre-training approach that can learn meaningful representations from raw video data.
By improving the pre-training efficiency and fine-tuning performance, the framework facilitates the development of multimedia information systems that can handle complex video-text interactions. This has practical applications in areas such as video captioning, video summarization, and video search.
Connection to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The concepts discussed in this article have connections to animations, artificial reality, augmented reality, and virtual realities. These fields often involve the integration of visual and textual elements to create immersive and interactive experiences.
The framework’s ability to learn cross-modal representations from raw data can be valuable in the development of animations, where textual descriptions are often used to guide the creation of visuals. Similarly, in augmented and virtual reality applications, the framework can enhance the understanding of video content and enable more seamless interactions between the virtual and real worlds.
Conclusion
The presented framework for learning cross-modal video representations is a significant contribution to the field of multimedia information systems. By directly pre-training on raw data and incorporating novel proxy tasks, the framework achieves state-of-the-art performance in pixel-level video-text pre-training.
The multi-disciplinary nature of the concepts discussed in this article highlights the importance of integrating machine learning, natural language processing, computer vision, and multimedia information systems in the advancement of cross-modal video representations. This framework has implications for various fields, including animations, artificial reality, augmented reality, and virtual realities. By improving our ability to understand and analyze video content, this research contributes to the development of more immersive and interactive multimedia experiences.
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by jsendak | Feb 1, 2024 | Computer Science
As an expert commentator on this project on the synchronization of mixed machine-converter power grids, I find it fascinating how the framework evaluated in this study utilizes a model-matching approach. By actuating synchronous machines with mechanical torque injections and converters with DC-side current injections, the researchers have managed to retain physical interpretation while providing extensions to the swing-equations model.
The use of the DC-side voltage measurement to drive the converter’s modulation angle and assigning its modulation amplitude analogously to the electrical machine’s excitation current is a clever way to achieve frequency synchronization while stabilizing the angle configuration and bus voltage magnitude. This method allows for the design of controllers that can achieve various objectives, such as maintaining a prescribed optimal power flow (OPF) set-point.
One of the key challenges addressed in this project is decentralization issues. Clock drifts, loopy graphs, model reduction, energy function selection, and characterizations of operating points are all important factors to consider when dealing with decentralized systems. It is crucial to design controllers that can handle these issues effectively and ensure stable and synchronized operation of the power grid.
In terms of numerical evaluation, the researchers have performed experiments on three- and two-bus systems. This approach allows for a comprehensive assessment of the proposed framework and provides valuable insights into its performance under different scenarios. It would be interesting to see how the framework performs in larger and more complex power grids, as well as in real-world implementations.
In conclusion, this project contributes significantly to the field of synchronization of mixed machine-converter power grids. The utilization of a model-matching approach and the consideration of decentralization issues provide valuable insights into designing controllers for stable and synchronized operation. Further research and experimentation could help refine and enhance this framework for practical applications in larger power systems.
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by jsendak | Jan 31, 2024 | Computer Science
In current virtual try-on tasks, only the effect of clothing worn on a person is depicted. In practical applications, users still need to select suitable clothing from a vast array of individual clothing items, but existing clothes may not be able to meet the needs of users. Additionally, some user groups may be uncertain about what clothing combinations suit them and require clothing selection recommendations. However, the retrieval-based recommendation methods cannot meet users’ personalized needs, so we propose the Generative Fashion Matching-aware Virtual Try-on Framework(GMVT). We generate coordinated and stylistically diverse clothing for users using the Generative Matching Module. In order to effectively learn matching information, we leverage large-scale matching dataset, and transfer this acquired knowledge to the current virtual try-on domain. Furthermore, we utilize the Virtual Try-on Module to visualize the generated clothing on the user’s body. To validate the effectiveness of our approach, we enlisted the expertise of fashion designers for a professional evaluation, assessing the rationality and diversity of the clothing combinations and conducting an evaluation matrix analysis. Our method significantly enhances the practicality of virtual try-on, offering users a wider range of clothing choices and an improved user experience.
Introducing the Generative Fashion Matching-aware Virtual Try-on Framework
In the field of multimedia information systems, virtual try-on technology has gained significant attention. It allows users to visualize how clothing items would look on them without physically trying them on. However, existing virtual try-on systems have focused only on showing the effect of clothing worn on a person, without considering the needs of users and providing personalized recommendations.
This is where the Generative Fashion Matching-aware Virtual Try-on Framework (GMVT) comes in. This framework aims to address this limitation by generating coordinated and stylistically diverse clothing for users. The Generative Matching Module plays a key role in this process, leveraging a large-scale matching dataset to effectively learn matching information. This knowledge is then transferred to the virtual try-on domain to offer personalized recommendations.
Furthermore, the GMVT framework utilizes the Virtual Try-on Module to visualize the generated clothing on the user’s body. This allows users to see how the recommended clothing combinations would look and make informed choices. By enlisting the expertise of fashion designers, the framework has undergone a professional evaluation to assess the rationality and diversity of the generated clothing combinations.
In the wider field of multimedia information systems, this framework demonstrates the multi-disciplinary nature of virtual try-on technology. It incorporates concepts from computer vision, machine learning, and fashion design to provide an enhanced user experience. The use of generative algorithms and matching datasets showcases the potential of artificial intelligence in fashion-related applications.
This framework also intersects with other areas such as animations, artificial reality, augmented reality, and virtual realities. By visualizing the generated clothing on the user’s body, it creates a virtual reality experience where users can experiment with different outfits. Augmented reality could be integrated into the framework to allow users to virtually try on clothing items in real environments.
Future Possibilities
The GMVT framework serves as a stepping stone for future advancements in virtual try-on technology. By incorporating user feedback and preferences, the framework could further refine its recommendation system. Machine learning algorithms could continuously learn from user interactions to offer more personalized and accurate clothing suggestions.
Expanding the dataset used by the GMVT framework could also lead to improved results. Incorporating a wider variety of fashion styles, cultural influences, and body types would enhance the diversity of the clothing combinations generated. This could cater to a broader range of users and provide more inclusive recommendations.
Incorporating real-time feedback from fashion designers during the virtual try-on process could elevate the framework’s capabilities. Designers could provide instant feedback on the feasibility and aesthetic appeal of the clothing combinations generated, helping users make better choices.
The GMVT framework opens the door to exciting developments in the field of virtual try-on technology and its integration with multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. With ongoing advancements in artificial intelligence and computer vision, the possibilities for enhancing the user experience and providing personalized recommendations are endless.
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