System software is often complex and hides exploitable security vulnerabilities. Formal verification promises bug-free software but comes with a prohibitive proof cost. We present Spoq2, the first verification framework to highly automate security verification of unmodified system software. Spoq2 is based on the observation that many security properties, such as noninterference, can be reduced to establishing inductive invariants on individual transitions of a transition system that models system software. However, directly verifying such invariants for real system code overwhelms existing SMT solvers. Spoq2 makes this possible by automatically reducing verification complexity. It decomposes transitions into individual execution paths, extends cone-of-influence analysis to the individual transition level, and eliminates irrelevant machine states, clauses, and control-flow paths before invoking the SMT solver. Spoq2 further optimizes how pointer operations are modeled and verified through pointer abstractions that eliminate expensive bit-wise operations from SMT queries. We demonstrate the effectiveness of Spoq2 by verifying security properties of four unmodified, real-world system codebases with minimal manual effort.

Network slicing promotes the development of different industries by dividing multiple logical networks on the same physical network to provide the customized services. However, the complex environment of network slicing deployment makes it difficult to obtain the accurate mathematical models, so that the traditional rule-based heuristic algorithms are difficult to process them efficiently. Therefore, we combine the Graph Convolutional Networks (GCN) and the Deep Deterministic Policy Gradient (DDPG) algorithms and propose the GCN-DDPG (G-DDPG) algorithm in this paper to solve the end-to-end network slicing deployment problem, while taking into account the constraints of Virtual Network Function (VNF) placement, VNF sharing, tolerable latency, and node and link resources limitations. First, the end-to-end network slicing deployment optimization is formulated as a problem of maximizing the weighted sum of system resource utilization and acceptance rate. Second, the physical network features extracted by GCN are combined with the state information of end-to-end network slicing requests as the state space of the optimization problem, and a G-DDPG algorithm is proposed to solve it. Finally, the simulation results demonstrate that our proposed method superior to benchmark solutions in terms of the resource utilization of the system, acceptance rate of end-to-end network slicing.

Cloud-edge-terminal collaborative network (CETCN) has become a key enabler of the next generation wireless network. However, due to the privacy concern, terminals and the edge server may not will to leakage individual data to the cloud server. At the same time, limited computing capability of the edge server and terminals leads to long latency. Therefore, in this paper, we utilize a federated deep reinforce learning (DRL) algorithm named federated learning-based Double Deep Q Network (FL-DDQN) algorithm to solve the task offloading problem in CETCN. To be specific, firstly, we model the task offloading issue as the minimization problem of weighted energy consumption and latency of the CETCN with the constraints of subcarriers and maximum task processing latency. Secondly, we propose a DRL algorithm to obtain the suboptimal solution of the optimization problem. Thirdly, aiming to improve data security and reduce the working pressure of the terminals and the edge server, the FL-DDQN algorithm is further utilized, where the DDQN model is trained cooperatively in the terminals and the edge server. Finally the simulation result demonstrate that our proposed method superior to benchmark solutions in terms of total energy consumption and latency.

Combining the signed distance function (SDF) and differentiable volume rendering has emerged as a powerful paradigm for surface reconstruction from multi-view images without 3D supervision. However, current methods are impeded by requiring long-time per-scene optimizations and cannot generalize to new scenes. In this paper, we present GenS, an end-to-end generalizable neural surface reconstruction model. Unlike coordinate-based methods that train a separate network for each scene, we construct a generalized multi-scale volume to directly encode all scenes. Compared with existing solutions, our representation is more powerful, which can recover high-frequency details while maintaining global smoothness. Meanwhile, we introduce a multi-scale feature-metric consistency to impose the multi-view consistency in a more discriminative multi-scale feature space, which is robust to the failures of the photometric consistency. And the learnable feature can be self-enhanced to continuously improve the matching accuracy and mitigate aggregation ambiguity. Furthermore, we design a view contrast loss to force the model to be robust to those regions covered by few viewpoints through distilling the geometric prior from dense input to sparse input. Extensive experiments on popular benchmarks show that our model can generalize well to new scenes and outperform existing state-of-the-art methods even those employing ground-truth depth supervision. Code will be available at https://github.com/prstrive/GenS.