In the pursuit of scalable and fault-tolerant quantum computing architectures, photonic-based quantum computers have emerged as a leading frontier. This article provides a comprehensive overview of advancements in photonic quantum computing, developed by leading industry players, examining current performance, architectural designs, and strategies for developing large-scale, fault-tolerant photonic quantum computers. It also highlights recent groundbreaking experiments that leverage the unique advantages of photonic technologies, underscoring their transformative potential. This review captures a pivotal moment of photonic quantum computing in the noisy intermediate-scale quantum (NISQ) era, offering insights into how photonic quantum computers might reshape the future of quantum computing.

The cross-domain multicast routing problem in a software-defined wireless network with multiple controllers is a classic NP-hard optimization problem. As the network size increases, designing and implementing cross-domain multicast routing paths in the network requires not only designing efficient solution algorithms to obtain the optimal cross-domain multicast tree but also ensuring the timely and flexible acquisition and maintenance of global network state information. However, existing solutions have a limited ability to sense the network traffic state, affecting the quality of service of multicast services. In addition, these methods have difficulty adapting to the highly dynamically changing network states and have slow convergence speeds. To this end, this paper aims to design and implement a multiagent deep reinforcement learning based cross-domain multicast routing method for SDWN with multicontroller domains. First, a multicontroller communication mechanism and a multicast group management module are designed to transfer and synchronize network information between different control domains of the SDWN, thus effectively managing the joining and classification of members in the cross-domain multicast group. Second, a theoretical analysis and proof show that the optimal cross-domain multicast tree includes an interdomain multicast tree and an intradomain multicast tree. An agent is established for each controller, and a cooperation mechanism between multiple agents is designed to effectively optimize cross-domain multicast routing and ensure consistency and validity in the representation of network state information for cross-domain multicast routing decisions. Third, a multiagent reinforcement learning-based method that combines online and offline training is designed to reduce the dependence on the real-time environment and increase the convergence speed of multiple agents.

The growing interest in cybersecurity has significantly increased articles designing and implementing various Cyber Deception (CYDEC) mechanisms. This trend reflects the urgent need for new strategies to address cyber threats effectively. Since its emergence, CYDEC has established itself as an innovative defense against attackers, thanks to its proactive and reactive capabilities, finding applications in numerous real-life scenarios. Despite the considerable work devoted to CYDEC, the literature still presents significant gaps. In particular, there has not been (i) a comprehensive analysis of the main components characterizing CYDEC, (ii) a generic classification covering all types of solutions, nor (iii) a survey of the current state of the literature in various contexts. This article aims to fill these gaps through a detailed review of the main features that comprise CYDEC, developing a comprehensive classification taxonomy. In addition, the different frameworks used to generate CYDEC are reviewed, presenting a more comprehensive one. Existing solutions in the literature using CYDEC, both without Artificial Intelligence (AI) and with AI, are studied and compared. Finally, the most salient trends of the current state of the art are discussed, offering a list of pending challenges for future research.

Apple has unveiled its artificial intelligence-boosted iPhone 16, showing off the long-awaited phone hours after Chinese rival Huawei's tri-fold phone began racking up orders. "The next generation of iPhone has been designed for Apple Intelligence from the ground up. It marks the beginning of an exciting new era," Chief Executive Tim Cook said at a product launch on Monday. The iPhone 16 will use the new A18 chip and have an aluminium back, as well as a new customizable button that can be used for camera controls. Huawei's website showed on Monday that it had garnered more than three million preorders for its Z-shaped tri-fold phone.

Artificial Intelligence Generated Content (AIGC) services can efficiently satisfy user-specified content creation demands, but the high computational requirements pose various challenges to supporting mobile users at scale. In this paper, we present our design of an edge-enabled AIGC service provisioning system to properly assign computing tasks of generative models to edge servers, thereby improving overall user experience and reducing content generation latency. Specifically, once the edge server receives user requested task prompts, it dynamically assigns appropriate models and allocates computing resources based on features of each category of prompts. The generated contents are then delivered to users. The key to this system is a proposed probabilistic model assignment approach, which estimates the quality score of generated contents for each prompt based on category labels. Next, we introduce a heuristic algorithm that enables adaptive configuration of both generation steps and resource allocation, according to the various task requests received by each generative model on the edge.Simulation results demonstrate that the designed system can effectively enhance the quality of generated content by up to 4.7% while reducing response delay by up to 39.1% compared to benchmarks.

The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application scenarios. This process, known as the customization of domain-specific foundation models, addresses the limitations of general-purpose models, which may not fully capture the unique patterns and requirements of domain-specific data. Despite its importance, there is a notable lack of comprehensive overview papers on building domain-specific foundation models, while numerous resources exist for general-purpose models. To bridge this gap, this article provides a timely and thorough overview of the methodology for customizing domain-specific foundation models. It introduces basic concepts, outlines the general architecture, and surveys key methods for constructing domain-specific models. Furthermore, the article discusses various domains that can benefit from these specialized models and highlights the challenges ahead. Through this overview, we aim to offer valuable guidance and reference for researchers and practitioners from diverse fields to develop their own customized foundation models.

Website Fingerprinting (WF) attacks can effectively identify the websites visited by Tor clients via analyzing encrypted traffic patterns. Existing attacks focus on identifying different websites, but their accuracy dramatically decreases when applied to identify fine-grained webpages, especially when distinguishing among different subpages of the same website. WebPage Fingerprinting (WPF) attacks face the challenges of highly similar traffic patterns and a much larger scale of webpages. Furthermore, clients often visit multiple webpages concurrently, increasing the difficulty of extracting the traffic patterns of each webpage from the obfuscated traffic. In this paper, we propose Oscar, a WPF attack based on multi-label metric learning that identifies different webpages from obfuscated traffic by transforming the feature space. Oscar can extract the subtle differences among various webpages, even those with similar traffic patterns. In particular, Oscar combines proxy-based and sample-based metric learning losses to extract webpage features from obfuscated traffic and identify multiple webpages. We prototype Oscar and evaluate its performance using traffic collected from 1,000 monitored webpages and over 9,000 unmonitored webpages in the real world. Oscar demonstrates an 88.6% improvement in the multi-label metric Recall@5 compared to the state-of-the-art attacks.

We demonstrate mechanical threats classification including jackhammers and excavators, leveraging wavelet transform of MIMO-DFS output data across a 57-km operational network link. Our machine learning framework incorporates transfer learning and shows 93% classification accuracy from field data, with benefits for optical network supervision.

Dell revealed details for new models in its Inspiron and Latitude laptop lines at IFA 2024. The company announced in May that it would be powering several of its new devices with Qualcomm's Snapdragon X Plus, and now we have more information about how those processors will work in Dell's collection of Copilot PCs. The Inspiron 14 and the Latitude 5455 can have either the 8-core or 10-core Snapdragon X Plus processors. The 10-core option has clock speeds up to 3.4GHz while the newly announced 8-core goes up to 3.24 GHz. Both versions have the same NPU for AI tasks, which offers up to 45 TOPS (trillions of operations per second) in machine learning performance in support of Microsoft's Copilot AI platform.

As MTC networks continue to grow rapidly, managing and optimizing resources has become a crucial challenge for ensuring scalability. Additionally, the low-power/complexity of the MTC devices (MTD) present another challenge in terms of data management and network availability. These factors depend on the limited battery lifetime of the devices and their ability to implement algorithms, making energy efficiency and optimization critical enablers for future MTC networks (Shafiq et al., 2020). Characterizing and modeling MTC traffic is crucial for optimizing wireless IoT networks by tailoring management strategies to specific application needs (Sharma & Wang, 2019, 2018). With this, significant energy savings may be achieved, which is crucial due to the limited battery lifespan inherent in IoT networks, thereby improving network efficiency and scalability Shafiq et al. (2020). This may be enabled by the exploitation of accurate machine learning (ML)-based traffic predictors (Kato et al., 2016). For example, idle channel monitoring is responsible for wasting over half of the energy consumed in these networks (Mughees et al., 2020) while the results in (Ruiz-Guirola et al., 2022) indicated that up to 38% of the consumed energy could be saved by exploiting a prediction method when using sleep modes like wake-up radio or discontinuous reception. Unfortunately, the use of ML requires numerous labeled data obtained from extensive, large-scale dataset (Aldahiri et al., 2021). A significant stage of the ML process is the data analysis, which can be a difficult task.