Traditional image editing typically relies on manual prompting, making it labor-intensive and inaccessible to individuals with limited motor control or language abilities. Leveraging recent advances in brain-computer interfaces (BCIs) and generative models, we propose LoongX, a hands-free image editing approach driven by multimodal neurophysiological signals. LoongX utilizes state-of-the-art diffusion models trained on a comprehensive dataset of 23,928 image editing pairs, each paired with synchronized electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), photoplethysmography (PPG), and head motion signals that capture user intent. To effectively address the heterogeneity of these signals, LoongX integrates two key modules. The cross-scale state space (CS3) module encodes informative modality-specific features. The dynamic gated fusion (DGF) module further aggregates these features into a unified latent space, which is then aligned with edit semantics via fine-tuning on a diffusion transformer (DiT). Additionally, we pre-train the encoders using contrastive learning to align cognitive states with semantic intentions from embedded natural language. Extensive experiments demonstrate that LoongX achieves performance comparable to text-driven methods (CLIP-I: 0.6605 vs. 0.6558; DINO: 0.4812 vs. 0.4637) and outperforms them when neural signals are combined with speech (CLIP-T: 0.2588 vs. 0.2549). These results highlight the promise of neural-driven generative models in enabling accessible, intuitive image editing and open new directions for cognitive-driven creative technologies. The code and dataset are released on the project website: https://loongx1.github.io.

The primary objective of reverse time migration (RTM) is to image the spatial positions of subsurface reflective interfaces using reflected seismic data. As a linearized inversion scheme, least-squares reverse time migration (LSRTM) is capable of producing higher-quality images compared to RTM. However, current LSRTM utilizes scattered seismic data to image the relative perturbations in medium parameters rather than the reflective interfaces positions. Consequently, existing LSRTM methods, whether in acoustic and elastic case, do not iteratively update the RTM images. To address this limitation, we introduce a linearized scalar reflectivity function of acoustic impedance as the imaging target and propose a reflected wave LSRTM method designed to iteratively update acoustic RTM image. For elastic media, we synthesize prior research and employ the least-squares inversion to derive vector reflection de-migration operators, enabling the iterative update of elastic RTM images. As a result, we clarify the relationship between conventional RTM and proposed LSRTM methods in both acoustic and elastic media. Numerical examples and field data test demonstrate that the proposed LSRTM methods can effectively iteratively update RTM images and provide high-quality imaging results.

Unmanned aerial vehicles (UAVs), such as drones, can carry high-performance computing devices (e.g., servers) to provide flexible and on-demand data processing services for the users in the network edge, leading to the so-called mobile edge computing. In mobile edge computing, researchers have already explored how to optimize the computation offloading and the trajectory planning of UAVs, as well as how to perform the service handover when mobile users move from one location to another. However, there is one critical challenge that has been neglected in past research, which is the limited battery life of UAVs. On average, commercial-level drones only have a battery life of around 30 minutes to 2 hours. As a result, during operation, mobile edge computing carriers have to frequently deal with service handovers that require shifting users and their computing jobs from low-battery UAVs to new fully-charged UAVs. This is the first work that focuses on addressing this challenge with the goal of providing continuous and uninterrupted mobile edge computing service. In particular, we propose a seamless service handover system that achieves minimum service downtime when handling the duty shift between low-battery UAVs and new fully-charged UAVs. In addition, we propose a novel UAV dispatch algorithm that provides guidelines about how to dispatch new fully-charged UAVs and where to retrieve low-battery UAVs, with the objective of maximizing UAVs' service time. The effectiveness of the proposed service handover system and the proposed UAV dispatch algorithm is demonstrated through comprehensive simulations using a time-series event-driven simulator.

An increasing number of cloud services are operated globally, where the service data are frequently replicated across geographically distributed datacenters to improve service quality and reliability. Such replication generates many one-to-many bulk data transfers over inter-datacenter networks from one datacenter to many receiver datacenters. To provide end-users with guaranteed services, these data transfers are usually required to be completed within designated deadlines. Despite the exponential growth in data demand, there has been little work on guaranteeing deadlines for one-to-many transfers, which is the subject of this paper. This paper proposes a centralized admission control coupled with a scheduling algorithm, named deAdline-Guaranteed transfEr (AGE), to guarantee the deadline of admitted data transfers and utilize the network capacity efficiently. The key idea is to flexibly select the source datacenter for receiver datacenters and allow the remaining receivers to obtain a replica from either the original source or the other receivers that have already received a copy. By jointly allocating the source for receivers and the bandwidth and routing paths for every data transfer, AGE maximizes the number of deadline-satisfied transfers. Our simulations show that compared to the state-of-the-art, AGE guarantees the deadline for up to 70 percent more transfers, achieves at least 2× higher network throughput, and reduces the completion time up to 80 percent.

Empowered by the rapid advancement of fiber optic sensing techniques in recent years, network carriers are able to upgrade their network infrastructure beyond the basic communication services with extra sensing applications and services (e.g., monitoring traffic and road condition, leakage detection, etc.), thus evolving to a new era of Infrastructure-as-a-Sensor (IaaSr) or Network-as-a-Sensor (NaaSr). When network carriers upgrade their network infrastructures with distributed fiber optic sensing (DFOS) technique to provide IaaSr services, there will arise a critical challenge: how to provide survivable (or reliable) IaaSr services against network failures (e.g., fiber cut). In this work, for the first time, we investigate the problem of survivable DFOS placement against single link failure. More specifically, we study where to place the primary and backup sensors and how to assign the primary and backup fiber sensing routes, with the objective of minimizing the number of sensors used. We formulate the problem using Integer Linear Programming (ILP) to facilitate the optimal solution. In addition, we propose a set of efficient heuristic algorithms to solve the problem in a fast manner. In particular, the proposed Shared-one algorithm provides a cost-efficient shared protection, through a one-step global optimization of the assignment of primary and backup DFOS placement. We conduct extensive simulations to evaluate the performance of the proposed solutions. We find out that Shared-one can achieve a close-to-optimal performance, compared to the ILP optimal results, while outperforming the other heuristic solutions with an average performance improvement by at least 16%.

As the Internet of Things (IoT) continues to grow, there arises concerns and challenges with regard to the security and privacy of the IoT. Cyber attacks such as man-in-the-middle and distributed denial of service (DDoS) are typical threats to the IoT. In this paper, we propose a FOg CompUting-based Security (FOCUS) system to protect the IoT against malware cyber attacks. The proposed FOCUS system leverages the virtual private network (VPN) to secure the communication channels to the IoT devices. In addition, FOCUS adopts a challenge-response authentication to protect the VPN server against potential DDoS attacks, which can further enhance the security of the IoT system. Such a double protection scheme is effective in mitigating various malicious attacks and can provide a high standard security for the IoT system. Furthermore, FOCUS is implemented in fog computing that is close to the end users, thus achieving a fast response and an efficient network consumption. We demonstrate FOCUS in a proof-of-concept prototype, and conduct experiments to evaluate its performance. The results show that FOCUS can effectively filter out malicious attacks with a low response time and a small amount of network bandwidth consumption.

This paper presents the design and evaluation of a university-sponsored summer STEM recruitment program for young women between their junior and senior year in high school. The College of Engineering, Computer Science, and Technology (ECST) currently has a female enrollment of only 15%, which is below the national average of 20%. The College has set a goal of increasing the percentage of women to 25% by 2025. The recruitment program described in this paper is one of the strategies the ECST will use to reach its goal. The objectives of the recruitment program, LAunchPad, were to increase the students' interest in engineering and computer science, and to achieve a 50% participant matriculation rate into the College of Engineering, Computer Science, and Technology. LAunchPad established a gender-inclusive learning environment, with scaffolded instruction and hands-on activities to reinforce the learning. The students were also introduced to two groups of female role models; the first group was early-career alumni that held technical positions with local companies, and the second group was mid- to senior-level engineers, computer scientists, and executives. A post-event survey revealed that the students' interest in pursuing an engineering or computer science career increased by 29%; seventy percent applied for admission to the University for the following Fall. Results suggest that contributing factors to the students' increased interest are an increased understanding of engineering and computer science, increased confidence, access to diverse role models, and the ability to establish peer networks.

Many modern cloud services are operated across geographically distributed datacenters, and they are usually associated with a demand of transferring bulk data among datacenters for achieving a high performance and reliability. These transfers (e.g., data replications and synchronizations) may require the inter-datacenter networks to deliver data from one Point (or datacenter) to Multiple Points (or datacenters) and impose a deadline on the transfer for providing a guaranteed service to end users. However, very little work has been done to manage an efficient point-to- multipoint bulk transfer while considering the deadline requirements. In this paper, we investigate the deadline- aware point-to-multipoint (P2MP) transfer problem and propose a centralized deAdline- Guaranteed transfEr (AGE) approach that can guarantee the deadline for P2MP transfers while efficiently utilizing the inter-datacenter bandwidth resources. For each arriving request, AGE jointly determines the transfer source selections and bandwidth allocations such that the number of deadline-guaranteed transfers can be maximized. Our simulation experiments show that AGE can accommodate up to 53.3% more transfers whose deadline requirements are met. In addition, AGE can achieve 20% higher network utilization than prior bulk transfer approaches.

Many large-scale compute-intensive and mission-critical online service applications are being deployed on geo-distributed datacenters, which require transfers of bulk business data over Wide Area Networks (WANs). The bulk transfers are often associated with different requirements on deadlines, either a complete transfer before a hard deadline or a best-effort delivery within a soft deadline. In this paper, we study the online bulk transfer problem over inter-datacenter WANs, while taking into consideration the requests with a mixture of hard and soft deadlines. We use Linear Programming (LP) to mathematically formulate the problem with the objective of maximizing a system utility represented by the service provider's revenue, taking into account the revenue earned from deadline-met transfers and the penalty paid for deadline-missed ones. We propose an online framework to efficiently manage mixed bulk transfers and design a competitive algorithm that applies the primal-dual method to make routing and resource allocation based on the LP. We perform theoretical analysis to prove that the proposed approach can achieve a competitive ratio of (e-1)/e with little link capacity augmentation. In addition, we conduct comprehensive simulations to evaluate the performance of our method. Simulation results show that our method irrespective of the revenue model, can accept at least 25% more transfer requests and improve the network utilization by at least 35%, compared to prior solutions.

In this paper, we present ICN-FC, which is an Information-Centric Networking (ICN) based framework for efficient functional chaining (FC). The key enabling techniques for ICN-FC includes naming semantics, Interest & Data processing and an efficient FC forwarding strategy. By using the proposed solutions, a functional chaining request, which consists of the name of raw data and an ordered set of functions, can be executed seamlessly, dynamically and flexibly in the network. In addition, the novel FC forwarding strategy can be used to improve the forwarding efficiency for functional chaining requests. The overall feasibility and efficiency of the proposed solutions are validated by using both experimental prototype and network simulation. The results show that the proposed solutions outperform previous works such as named function networking to support functional chaining applications.

As the Internet of Things (IoT) continues to grow, there arises concerns and challenges with regard to the security and privacy of the IoT system. In this paper, we propose a FOg CompUting-based Security (FOCUS) system to address the security challenges in the IoT. The proposed FOCUS system leverages the virtual private network (VPN) to secure the access channel to the IoT devices. In addition, FOCUS adopts a challenge-response authentication to protect the VPN server against distributed denial of service (DDoS) attacks, which can further enhance the security of the IoT system. FOCUS is implemented in fog computing that is close to the end users, thus achieving a fast and efficient protection. We demonstrate FOCUS in a proof-of-concept prototype, and conduct experiments to evaluate its performance. The results show that FOCUS can effectively filter out malicious attacks with a very low response latency.

Network Function Virtualization (NFV) is promising to lower the network operator's capital expenditure and operational expenditure by replacing proprietary hardware-based network equipment with software-based virtual network functions that can be consolidated into telecom clouds. In particular, NFV provides an efficient way to deploy network services using service function chains that consist of a set of virtual network functions interconnected by virtual links. A practical and yet theoretically challenging issue related to NFV Management and Orchestration is how to jointly optimize the topology design and mapping of multiple service function chains, which is called the JTDM problem. In this paper, we develop an Integer Linear Programming (ILP) model to formulate the JTDM problem with the objective of minimizing the bandwidth consumption in the physical substrate. We propose a novel heuristic algorithm, namely Closed-loop with Critical Mapping Feedback (CCMF), to efficiently address this problem. Through comprehensive simulations, we demonstrate that the CCMF algorithm is efficient in terms of the bandwidth consumption in various scenarios, and can achieve a bandwidth consumption that is close to the minimum obtained by ILP.

Network Function Virtualization (NFV) provides an effective way to reduce the network provider's cost by allowing multiple Virtual Networks (VNs) to share the underlying physical infrastructure. In the NFV environment, especially when supporting multicast service over the VNs, reliability is a critical requirement in the process of VN mapping since the failure of one virtual node can cause the malfunction of all the subsequent nodes that receive multicasting data from it. In this paper, for the first time, we study how to efficiently map VNs for reliable multicast services, while taking into consideration the max-min fairness of the reliability among distinct VNs. We propose a Mixed Integer Linear Programming (MILP) model to determine the upper bound on the max-min fairness reliability. In addition, an efficient heuristic, namely Uniform Reliability Mutation based Genetic (URMG) algorithm, is developed to address reliable multicast VN mapping with a low computational complexity. By encoding multicast tree construction and link mapping into path selection, taking into consideration the max-min reliability fairness goal, and the networking reliability factors during mutation, URMG can globally optimize the reliability and its fairness of all the multicast VN requests. Through extensive simulations, we demonstrate that URMG achieves close to the optimal reliability fairness with a much lower time complexity than the MILP and yields a significant performance improvement in terms of reliability fairness, bandwidth consumption and transmission delay comparing with other heuristic solutions.

Network Function Virtualization (NFV) allows multiple Virtual Networks (VNs) to share the underlying physical infrastructure via VN mapping, thus improving the utilization of physical resources. In this paper, for the first time, we study the multicast service-oriented VN mapping that can support big data applications over Elastic Optical Networks (EONs). Since the problem of minimizing the spectrum consumption in multicast service-oriented VN mapping is NP-hard, we propose an efficient heuristic algorithm, called Integrated Genetic and Simulated Annealing (IGSA) algorithm to address the problem with low computational complexity. By encoding node mapping, multicast tree construction, link mapping and spectrum requirements in the same gene and auto-adjusted evolution, and utilizing simulated annealing to find the fittest multicast requests mapping order, IGSA can perform joint optimization for all the multicast requests in a global way. Through extensive simulations, we demonstrate that IGSA outperforms the other heuristic solutions in terms of spectrum consumption, blocking probability and normalized throughput, while achieving close to minimum spectrum consumption with a much lower time complexity than MILP.

Recent innovations in Network Virtualization and Elastic Optical Networks (EONs) enable flexible deployment of optical networks as a service. However, one open challenge is how to embed Virtual Optical Network (VON) requests onto the physical substrate network to maximize the sharing of physical resources, which is the so called Virtual Network Embedding (VNE) problem. EONs are prone to the fragmentation of spectral resources during the process of routing and spectrum allocation. The fragmentation of spectral resources in the substrate fiber links may lead to the blocking of incoming virtual network requests. This degrades the utilization of the physical resources of the Infrastructure Providers and also, decreases the revenue of the Service Providers. In this paper, we propose a novel virtual network embedding algorithm called Alignment and Consecutiveness-aware Virtual Network Embedding (ACT-VNE), which takes into account the spectrum alignment and relative loss in spectrum consecutiveness when mapping virtual nodes/links onto the physical substrate nodes/links. We also propose a min-max reconfiguration scheme called Relative Consecutiveness Loss-aware and Misalignment-aware Virtual Network Reconfiguration (RCLM-VNR) that minimizes relative consecutiveness loss and maximizes alignment with adjacent links when reconfiguring the virtual network. Simulation results show that ACT-VNE and RCLM-VNR yield a lower blocking probability and a higher link utilization ratio, which leads to better utilization of the physical resources and increased revenue.

Spectrum-sLICed Elastic optical path (SLICE) networks enable elastic and flexible allocation of spectral resources. SLICE networks distribute data on a number of sub-carriers overlapped in frequency domain to provide efficient sub-wavelength and super-wavelength traffic accommodation. In SLICE networks, a routing and spectrum allocation algorithm assigns a spectrum path to any demand with just enough contiguous sub-carriers while following the sub-carrier consecutiveness and spectrum-continuity constraints. In this paper, we propose novel sub-carrier allocation algorithms that employ the proposed Interference Graph technique to assign sub-carriers to a spectrum path based on the historic traffic profile. These algorithms try to achieve minimal disruptions to the live connections while minimizing the blocking probability. Simulation results show that the proposed schemes can effectively accommodate the dynamic traffic while minimizing the network reconfiguration cost in SLICE networks, with or without the traffic prediction.

Traffic grooming can effectively utilize the transmission capacity of WDM networks by properly multiplexing low-speed traffic flows onto high-capacity wavelength channels. In order to maximize the wavelength resource and cut down network costs associated with, e.g. OEO conversion for time-varying yet predictable traffic, we propose a novel predictive and incremental (PI) traffic grooming scheme, named PI-grooming. A conventional traffic grooming approach for fluctuated traffic is to run an algorithm that (re)assigns the traffic flows to as a few wavelengths as possible based only on the current traffic demands of these flows. This however will lead to a lot of OEO traffic. The proposed PI-grooming considers the existing flow assignment, the current traffic demands, and the expected traffic demands in the near future. We show that, compared with the conventional approach, PI-grooming can effectively minimize the amount of OEO traffic while still using a very small number of wavelengths.