Miracle-dz/UniFAR-FTUs huggingface dataset:
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Recently proposed image-based virtual try-on (VTON) aims at transferring a target clothing image onto a reference person image, which is a challenging task because the human body and clothes hava high flexibility in shapes and details. A common processing pipeline for this is composed of two stages: first, warping the target try-on clothes to align with the corresponding clothes region on the reference person image, and then the warped clothing is blended with the target person image. However, existing methods suffer from artifacts and distortions, also fails to preserve the corresponding region features due to the the complex semantics. In this paper, we propose a framework based on semantically conditional generative adversarial network for virtual try-on (SCVTON). Firstly, we introduce a semantic labels synthesis module to predict the desired semantic segmentation after try-on. Then a generative adversarial network fuses all information (e.g. reference person image, semantic labels, target clothes) to generate the final try-on result. Moreover ,we propose using the synthetic semantic labels for modulating the activations in normalization layers through a semantic spatially-adaptive normalizations, which can effectively propagate the semantic information through the networks. Qualitative and quantitative results show significant improvements on VTON tasks. Keywords—virtual try-on, generative adversarial networks, semantic synthesis, semantic spatially normalization
Distributionally robust optimization for networked microgrids considering contingencies and renewable uncertainty

In light of a reliable and resilient power system under extreme weather conditions and natural disasters, microgrids integrating local renewable resource in a network have been adopted extensively to supply demands when the main utility experiences blackouts. However, the stochastic nature of renewables and unpredictable contingencies are difficult to be addressed by conventional, deterministic microgrid energy management framework. The paper proposes a distributionally robust chance-constrained (DR-CC) framework for the power dispatch of networked microgrids. The model considers the power flow, dual-mode operation (i.e. on-/off-grid operation) and voltage droop control simultaneously. By analyzing the empirical solar generation forecast and uncertain forecast error distribution, we construct two kinds of ambiguity sets for the DR-CC model, so that the solution is robust for any unimodal forecast error distribution with specific shape and shared moments. We find a special ambiguity set, unimodal & symmetric set, and prove its tractable second-order conic formulation. It meets the reliability requirements verified by the robustness tests and shows less conservativeness in solution, compared with the unimodal ambiguity set in literature.
A multidimensional colored packing approach for network slicing with dedicated protection

Network Function Virtualization (NFV) enables the virtualization of core-business network functions on top of a NFV infrastructure. NFV has gained an increasing attention in the telecommunication field these last few years. Virtual network functions (VNFs) can be represented by a set of virtual network function components (VNFCs). These VNFCs are typically designed with a redundancy scheme and need to be deployed against failures of, e.g., compute servers. However, such deployment must respect a particular resiliency mechanism for protection purposes. Therefore, choosing an efficient mapping of VNFCs to the compute servers is a challenging problem in the optimization of the software-defined, virtualization-based next generation of networks. In this paper, we model the problem of reliable VNFCs placement under anti-affinity constraints using several optimization techniques. A novel approach based on an extension of bin packing is proposed. We perform a comprehensive evaluation in terms of performance under real-world ISP networks along with synthetic traces. We show that our methods can calculate rapidly efficient solutions for large instances.
Benchmarking Kubernetes Container-Networking for Telco Usecases

Kubernetes container orchestration technology is beginning to address telco Network Function Virtualization (NFV) deployment needs of: i) multiple data-plane interfaces and one management-plane interface, and ii) support for various protocols/software stacks targeting I/O performance, including software acceleration technologies and software switch architectures. The purpose of this work is to accurately benchmark different network-virtualization architectures (OvS-DPDK, SR-IOV and VPP) in a Kubernetes deployment for telco data plane configurations. By leveraging industry-standard testing methodologies and an open source testing framework (VSPERF) that can control different hardware-based commercial and open source traffic generators, we benchmark the data plane of the telco NFV deployments. We also study the effects of different resource allocation strategies (CPU-Pinning, compute and storage resource variations, NUMA) on the data plane performance.
Fault Diagnosis based on Domain Adaptive Multi-task learning Convolutional Neural Network

In modern industry driven by machines, effective fault diagnosis of mechanical equipment is the basis for its healthy and safe operation. Current fault diagnosis methods are mostly based on the assumption of independent and identical distribution (IID). However, the characteristics of the same fault are different under different working conditions and not all fault data under all working conditions are available, so using the fault data obtained under certain working conditions to solve the fault diagnosis under all working conditions is of great significance to ensure the reliability and safety of the system. In order to solve these problems, a domain adaptive fault diagnosis method based on convolutional neural network (DAMCNN) is proposed in this paper which consists of three parts: encoder, decoder and tag generator. These three parts are combined to form the supervised and unsupervised learning parts of the model. Then, the multitask learning method is used to minimize the cost function of the source domain and the target domain. Finally, a model for data fault diagnosis in the target domain is obtained.
A Convex Optimization Approach to Chance-Constrained Linear Stochastic Drift Counteraction Optimal Control

In this paper, we propose a convex optimization approach to chance-constrained drift counteraction optimal control (DCOC) problems for linear systems with additive stochastic disturbances. Chance-constrained DCOC aims to compute an optimal control law to maximize the time duration before the probability of violating a prescribed set of constraints can no longer be maintained to be below a specified risk level. While conventional approaches to this problem involve solving a mixed-integer programming problem, we show that an optimal solution to the problem can also be found by solving a convex second-order cone programming problem without integer variables. We illustrate the application of chance-constrained DCOC to an automotive adaptive cruise control example.
Secrecy Outage Probability Analysis for Downlink Untrusted NOMA Under Practical SIC Error

Non-orthogonal multiple access (NOMA) serves multiple users simultaneously via the same resource block by exploiting superposition coding at the transmitter and successive interference cancellation (SIC) at the receivers. Under practical considerations, perfect SIC may not be achieved. Thus, residual interference (RI) occurs inevitably due to imperfect SIC. In this work, we first propose a novel model for characterizing RI to provide a more realistic secrecy performance analysis of a downlink NOMA system under imperfect SIC at receivers. In the presence of untrusted users, NOMA has an inherent security flaw. Therefore, for this untrusted users' scenario, we derive new analytical expressions of secrecy outage probability (SOP) for each user in a two-user untrusted NOMA system by using the proposed RI model. To further shed light on the obtained results and obtain a deeper understanding, a high signal-to-noise ratio approximation of the SOPs are also obtained. Lastly, numerical investigations are provided to validate the accuracy of the desired analytical results and present valuable insights into the impact of various system parameters on the secrecy rate performance of the secure NOMA communication system.
International Journal of Emerging Electric Power Systems Control Strategy for VSC-HVDC Power Quality Improvement under Unbalanced Grid Conditions

This paper presents a novel unified dynamic model and control strategy to improve the power quality for VSC based high voltage direct current transmission system (VSC-HVDC) under unbalanced grid conditions. The unified models present the dynamic behavior of VSC-HVDC in the unique positive synchronously rotating reference (dq-p) frame with respect to both the positiveand negative-sequence components. Based on the unified model, a strategy to either eliminate the dc-link ripple or achieve the balanced currents, along with a rather low level harmonics in each grid is introduced by resorting to the resonant integrator and filter based scheme in the two cascaded control loops. The resonant integrator and filter scheme enables effective control of the positiveand negative-sequence currents, and avoid the sequence component decomposition. The simulation studies in PSCAD/EMTDC verify the improved performance of VSC-HVDC system regarding the power quality and the ride-through capability.
Towards Fine-grained Resource Allocation in NFV Infrastructures

Resource optimization arguably comprises a crucial aspect for Network Function Virtualization (NFV) infrastructures. In this respect, the problem of virtualized network function (VNF) placement commonly entails the selection of the most appropriate server within a single or among multiple Points-of-Presence (PoPs). Nevertheless, CPU cache hierarchy and memory locality in NUMA multi-core servers along with the diversity in NFV resource profiles introduce significant challenges in terms of intra-server resource allocation; a problem that is often overlooked. As such, we stress on the need for fine-grained resource allocation in NFV infrastructures, and, to this end, we study various aspects of CPU allocation for VNF chains. We deem this intra-server resource allocation problem as complementary to the large body of literature that seeks to optimize VNF placement onto virtualized infrastructures. More particularly, we shed light on the intra-server VNF placement problem, treating CPU cores as the main resource allocation unit. To this end, we assess the performance of multiple CPU allocation combinations under varying server utilization levels and processing workloads with diverse requirements in terms of CPU and memory. Our experimentation approach lets us progressively gain useful insights, which ultimately form ground rules that can be leveraged for optimized server resource allocation.
Capacity Optimization for Downlink Multiuser MISO UAV Mobile Communication

The airborne mobile wireless networks (AMWNs) composed of unmanned aerial vehicles (UAVs) communication networks is one of the emerging application scenarios of the Space-Ground-Sea Integrated Networks in 6G. Due to its high mobility, UAV exhibits great advantages in assisted cellular network communication and emergency communication. The multi-antenna technology and quality of service (QoS) can help cellular networks reduce latency and improve network capacity. In this paper, we jointly optimize the transmit power and flight trajectory of the multiuser MISO UAV communication system to improve the total effective capacity of the system. Specifi-cally, zero-forcing (ZF) beamforming and block diagonalization are adopted to eliminate multiuser interference. In addition, the optimization problem with regard to maximizing the total effective capacity of the system under the condition of delay constraint is proposed. Furthermore, an iterative optimization algorithm jointly considering UAV trajectory and subchannel transmit power is considered, in which we use successive convex optimization techniques and the block coordinate descent method to solve the thorny non-convex problem. Simulation results verify the effectiveness and convergence of the proposed algorithm.
Observability and Determinability Characterizations for Linear Switched Systems in Discrete Time

In this article, we study the observability and determinability for discrete-time linear switched systems. Studies for the observability for this system class are already available in literature, however, we use assume here that the switching signal is known. This leads to less conservative observability conditions (e.g. observability of each individual mode is not necessary for the overall switched system to be observable); in particular, the dependencies of observability on the switching times and the mode sequences are derived; these results are currently not available in the literature on discrete-time switched systems. In addition to observability (which is concerned with recovering the state from the initial time onwards), we also investigate the determinability which is concerned with the ability to reconstruct the state value at the end of the observation interval. We provide several simple examples to illustrate novel features not seen in the continuous time case or for unswitched systems.
Estimate-to-State Stability for Hybrid Human-Prosthesis Systems

Control methods for lower-limb powered prostheses remain mostly model-independent and cannot always guarantee stability. Model-dependent prosthesis control methods yield a wider range of stability properties, but require knowledge of the interaction force between the human and prosthesis. Any error in force estimation compromise the formal guarantees. This paper addresses this uncertainty by formalizing the stability of the human-prosthesis system subject to force estimation error. A novel notion of estimate-to-state stability is introduced and provides a means to guarantee exponential convergence of the prosthesis to a set when the controller’s model contains estimation error. Conditions are established to ensure input-to-state stability for the human’s hybrid periodic orbits when subject to disturbances from the prosthesis control action deviating from its nominal control law. A class of estimate-to-state stable prosthesis controllers is proposed and implemented in simulation, demonstrating how the human-prosthesis system converges to a tube around the desired trajectory resulting in stable walking.
Toward Proactive Service Relocation for UAVs in MEC

Multi-Access Edge Computing (MEC) is considered as one of the key enablers of Unmanned Aerial Vehicles (UAVs) use cases. However, the envisioned MEC deployments introduce new challenges related to the management of the mobility of services across the distributed MEC hosts, following the UAVs movements and possible handovers to ensure sustainable Quality-of-Service (QoS). A major challenge for MEC service mobility is the decision-making on where and when to relocate services. In this paper, we motivate the use of the predefined flight plans of UAVs for devising proactive relocation strategies that can deal efficiently with realistic asynchronous relocation processes. Moreover, we formulate the Proactive Service Relocation for UAV (PSRU) problem using linear programming, and we validate the gains introduced by the proactive relocation strategy and the use of the predefined flight plans of UAVs.
Optimizing Information Transfer Through Chemical Channels in Molecular Communication

The optimization of information transfer through molecule diffusion and chemical reactions is one of the leading research directions in Molecular Communication (MC) theory. The highly nonlinear nature of the processes underlying these channels poses challenges in adopting analytical approaches for their information-theoretic modeling and analysis. In this paper, a novel iterative methodology is proposed to numerically estimate achievable information rates. Based on the Nelder-Mead optimization, this methodology does not necessitate analytical for-mulations of MC components and their stochastic behavior, and, when applied to well-known scenarios, it demonstrates consistent results with theoretical bounds and superior performance to prior literature. A numerical example that abstracts communications between genetically engineered cells via simulation is presented and discussed in light of possible future applications to support the design and engineering of realistic MC systems.
FL-PATE: Differentially Private Federated Learning with Knowledge Transfer

Federated learning provides a solution for data privacy protection, while enabling training over the local data samples, without exchanging them. However, it is far from practical and secure because data privacy is still vulnerable due to the well-studied attacks, e.g., membership inference attacks and model inversion attacks. In this paper, to further prevent data leakage against these attacks, we propose FL-PATE, a differentially private federated learning framework with knowledge transfer. Specifically, participants with sensitive data are grouped to train teacher models under federated learning settings, and the knowledge of teacher models is transferred to a publicly accessible student model for prediction via aggregating teacher models' outputs of public datasets. A modified client-level differential privacy mechanism is used to guarantee each participant's data privacy during the corresponding teacher model's training process. The proposed framework preserves participant's privacy against membership inference attacks and the differential privacy cost is fixed. The privacy analysis and experiments demonstrate that trained teacher and student models have an excellent performance in accuracy and robustness theoretically and empirically.
Joint Distributed Beamforming and Backscatter Cooperation for UAV-Assisted WPSNs

Unmanned aerial vehicle (UAV)-assisted wireless powered sensor networks (WPSNs) have emerged as a promising paradigm for charging sensor nodes' batteries in remote areas. However, the sum-throughput of overall sensor nodes can dramatically decrease due to their long-distance transmission to the UAV. In this paper, we propose a joint distributed beamforming and backscatter cooperation (BC) scheme to enhance the sum-throughput of UAV-assisted WPSNs with various types of sensor nodes. In particular, we consider the BC mechanism which leverages other types sensor nodes with constructive multi-path signals to enhance the long-distance transmission of same-type sensor nodes. We maximize the sum-throughput by jointly optimizing the distributed backscattering, distributed beamforming and time allocation. The sum-throughput maximization problem is difficult to be solved directly due to the coupling among optimizing variables. We decompose the problem into a BC subproblem and a time allocation subproblem, and propose a two-step scheme to solve them. First, for the BC subproblem, we derive closed-form low-complexity distributed beamforming solutions and distributed backscattering solutions to maximize the signal-to-noise ratios of the same-type sensor nodes. Second, for the time allocation subproblem, we derive the closed-form solutions according to KKT conditions. Simulation results are provided to demonstrate that the proposed joint distributed beamforming and BC scheme can increase the sum-throughput as compared to conventional distributed beamforming schemes.
Multi-hop Graph Embedding for Botnet Detection

We have developed a novel multi-hop graph embedding technique for botnet detection. It can detect the entire layered architecture of a botnet in the internet backbone traffic by starting from a small set of the components of the botnet. A botnet is a group of hosts collaborating each other to launch a variety of attacks, such as distributed denial-of-service attacks and phishing campaigns. Over 20 years of their existence, botnets have been evolved to employ layered architectures for robust operation and efficient management. Several existing methods leverage graph analysis to detect malicious communications. However, they cannot detect such botnet components that communicating each other through multiple layers, which are represented at more than one hop distance in graphs. To solve this problem, our technique trains separate graph embedding models with samples at different distances and select appropriate features from multiple models to represent multi-hop adjacency for each node. By applying our proposal to real-world Internet traffic, we have confirmed that it can outperform other methods in terms of detecting collaborating botnet components with higher accuracy even if their command and control communications are cascading through multiple layers.
Application of Robust Nonlinear Model Predictive Control to Unstable Tank Reactor

Ensuring nominal stability of nonlinear model predictive control is not trivial. Current work presents application of two approaches for the terminal region characterization of discrete time quasi infinite horizon nonlinear model predictive control around an open loop unstable operating point. Case study presented is on a benchmark continuous stirrer tank reactor system. Results are presented for the nominal case (i.e. without noise case) and for the robust case (i.e. with noise case). Results validate the nominal asymptotic stability result of the discrete time quasi infinite horizon nonlinear model predictive control formulation. State trajectories for the robust case are close to the nominal case as expected due to the bounded disturbance.
Distributed Consensus-Based Multi-Agent Off-Policy Temporal-Difference Learning

In this paper we propose two novel distributed consensus-based temporal-difference algorithms for multi-agent off-policy learning of linear approximation of the value function in Markov decision processes. The algorithms are composed of: 1) local parameter updates based on single-agent off-policy algorithms TD(λ) and ETD(λ), and 2) a linear dynamic consensus scheme. The algorithms are completely decentralized, allowing: 1) efficient parallelization and 2) applications in which all the agents may have completely different behavior policies and different initial state distributions while evaluating a single target policy. Starting from the properties of the underlying Feller-Markov processes, we show that, under nonrestrictive assumptions, the algorithms weakly converge to a unique consensus point. A discussion is given on the asymptotic parameter values at consensus, including estimation bias and variance. The algorithms’ properties are illustrated by characteristic simulation results.
Multi-Agent Trajectory Optimization Against Plan-Deviation Attacks using Co-Observations and Reachability Constraints

In this paper, we focus on using path planning and inter-agent measurements to improve the security of multi-robot systems against possible takeovers from cyber-attackers. We build upon recent trajectory optimization approaches where introspective measurement capabilities of the robots are used in an co-observation schedule to detect deviations from the preordained routes. This paper proposes additional constraints that can be incorporated in the previous trajectory optimization algorithm based on Alternating Direction Method of Multipliers (ADMM). The new constraints provide guarantees that a compromised robot cannot reach a designed safety zone between observations despite adversarial movement by the attacker. We provide a simulation showcasing the new components of the formulation in a multi-agent map exploration task with several safety zones.