Hyperspectral image (HSI) data possess complex spatial structures and high-dimensional spectral information. Mamba has been applied to address the limitations of general methods in HSI classification, including restricted receptive fields and high computational complexity. However, the scan mechanism of traditional Mamba unreasonably constructs spatial distance relationships between neighboring row pixels and fails to adaptively construct the optimal scanning path based on the spectral similarity of pixels. Additionally, the characteristic of traditional Mamba scanning each channel independently overlooks the feature extraction from high-dimensional spectral information. This work proposes a Spectral State Fusion Tree Mamba (SSFTM) architecture to resolve these limitations. The Tree Scan (TS) mechanism computes cosine distances among spatial neighboring pixels and spectral channels to construct adaptive minimum spanning trees in both the spatial and spectral domains, thereby establishing reasonable spatial-spectral relationships and enabling efficient joint feature extraction. The Spectral State Fusion (SSF) mechanism applies multi-layer one-dimensional dilated convolutions along the spectral dimension to the state space vectors, enabling inter-channel interaction and promoting multi-scale spectral feature extraction. The proposed SSFTM demonstrates superior classification accuracy across multiple datasets compared to SOTA methods and exhibits acceptable computational complexity. The code is available at https://github.com/copawloroous/SSFTM.

Traditional supervised deep learning (DL) methods for hyperspectral image (HSI) classification are severely limited by the quality and quantity of labels. Furthermore, existing feature extraction methods generally lack the fusion of multiscale feature information, struggling to handle complex scenarios. To counter these problems, this work investigates a feature extraction module based on self-supervised graph masked autoencoders (SGMAEs). It innovatively employs graph masked autoencoders to achieve self-supervised label-free feature extraction for the complete set of samples, utilizing a multiscale graph convolutional network encoder (MGCNE) and cross correlation decoder (CCD) to extract and fuse multiscale spatial-spectral features of HSI data, respectively. Specifically, the HSI data is first converted into an edge-masked perturbed graph to label-freely extract multiscale feature representations of all pixel samples, and then fed into the MGCNE to obtain multilayer feature vectors for the pixel nodes. To reconstruct the masked edges for the fusion of multiscale features, the CCD applies cross correlation calculations to the nodes of the true edges at the masked positions and the fake edges at the random positions. The contrastive learning loss function is proposed for training of the autoencoder, which calculates the loss for the existence estimates of edges generated by cross correlation calculations. The pretrained MGCNE possesses an efficient self-supervised multiscale spatial-spectral feature extraction capability, along with strong generalizability, which significantly improves the accuracy of various mainstream models in downstream classification tasks. Extensive experiments and analyses on multiple HSI datasets demonstrate that our proposed SGMAE significantly enhances the model performance of various supervised classifiers and achieves superior performance in comparison to mainstream models.

The security landscape of software systems has witnessed considerable advancements through dynamic testing methodologies, especially fuzzing. Traditionally, fuzzing involves a sequential, cyclic process where software is tested to identify crashes. These crashes are then triaged and patched, leading to subsequent cycles that uncover further vulnerabilities. While effective, this method is not efficient as each cycle potentially reveals new issues previously obscured by earlier crashes, thus resulting in vulnerabilities being discovered sequentially.

3D building reconstruction from monocular remote sensing images is an important and challenging research problem that has received increasing attention in recent years, owing to its low cost of data acquisition and availability for large-scale applications. However, existing methods rely on expensive 3D-annotated samples for fully-supervised training, restricting their application to large-scale cross-city scenarios. In this work, we propose MLS-BRN, a multi-level supervised building reconstruction network that can flexibly utilize training samples with different annotation levels to achieve better reconstruction results in an end-to-end manner. To alleviate the demand on full 3D supervision, we design two new modules, Pseudo Building Bbox Calculator and Roof-Offset guided Footprint Extractor, as well as new tasks and training strategies for different types of samples. Experimental results on several public and new datasets demonstrate that our proposed MLS-BRN achieves competitive performance using much fewer 3D-annotated samples, and significantly improves the footprint extraction and 3D reconstruction performance compared with current state-of-the-art. The code and datasets of this work will be released at https://github.com/opendatalab/MLS-BRN.git.

3D building reconstruction from monocular remote sensing imagery is an important research problem that has been extensively studied for several decades. Although monocular remote sensing imagery is a more economic data source compared with the LiDAR data and multi-view imagery, its limited information results in great challenges and restricts the performance of existing monocular reconstruction methods. Moreover, the expensive cost and the limited quantity of 3D annotations also restrict the application scenes of existing methods, which are mostly based on fully-supervised learning. In our previous work, we have proposed MTBR-Net, a monocular building reconstruction method that consists of a fully-supervised multi-task network and a post-processing module for optimizing the reconstruction results. In this work, we further propose WS-MTBR-Net, a weakly-supervised building reconstruction network that uses fewer 3D annotations and achieves better performance in an end-to-end manner. Specifically, our WS-MTBR-Net fully leverages the relation between different components of a 3D building instance and the property of off-nadir images to improve the footprint segmentation boundary, based on six modified tasks and a new network structure with an improved feature warping module to support weakly-supervised learning. We also design a new training strategy via a hybrid loss function that enables utilizing the training samples with different annotation levels, i.e., complete 3D annotations, 2D footprint annotations, and image-level angle annotations. Results on BONAI Shanghai and Xi’an test datasets demonstrate that our method achieves competitive performance when using 50% fewer 3D-annotated samples, and improves the footprint segmentation F1-score by around 4% compared with current state-of-the-art.

Kernel debloating is a practical mechanism to mitigate the security problems of the operating system kernel by reducing its attack surface. Existing kernel debloating mechanisms focus on specializing a kernel to run a target application based on its dynamic traces collected in the past - they remove functions from the kernel which are not used by the application according to the traces. However, since the dynamic traces do not ensure full coverage, false removals of required functions are unavoidable. This paper proposes Hacksaw, a novel mechanism to debloat a kernel for a target machine based on its hardware device inventory. Hacksaw accurately debloats a kernel without false removals because figuring out which hardware components are attached to the machine as well as which device drivers manage them is comprehensive and deterministic. Hacksaw removes not only inoperative device drivers that do not control any attached hardware components but also other kernel modules and functions which are associated with the inoperative drivers according to three dependency analysis approaches: call-graph, driver-model, and compilation-unit analyses. Our evaluation shows that Hacksaw effectively removes inoperative kernel modules and functions (i.e., their respective reduction ratios are 45% and 30% on average) while ensuring validity and compatibility.

Load balancing plays a critical role in large-scale heterogeneous edge computing, aiming to enhance the accuracy of computing resource matching and reduce processing delays caused by imbalanced resource allocation. In highly heterogeneous distributed computing environments, scalability and accuracy in matching computing resources are essential. The Consistent Hashing (CH) algorithm is well-known for its simplicity and strong scalability, enabling extensive and flexible scheduling strategies. It achieves load balancing by evenly distributing requests among different nodes. However, the CH algorithm primarily focuses on load balancing in storage resource scenarios and often neglects load balancing for computing resources, potentially leading to reduced accuracy in matching computing resources and prolonged processing delays. This paper introduces an improved CH algorithm called chordDC, specifically designed for distributed computing scenarios. It takes into account the unified representation of user tasks and computing resources, incorporating additional discriminative criteria in the selection of computing nodes to achieve a more precise resource allocation. Through a comparative analysis with two baseline algorithms, the proposed algorithm demonstrates effective performance improvements, enhancing the accuracy of computing resource matching by 40%-50% and significantly reducing computing processing latency.

Sophisticated attackers find bugs in software, evaluate their exploitability, and then create and launch exploits for bugs found to be exploitable. Most efforts to secure software attempt either to eliminate bugs or to add mitigations that make exploitation more difficult. In this paper, we propose a new defensive technique called chaff bugs, which instead targets the bug discovery and exploit creation stages of this process. Rather than eliminating bugs, we instead add large numbers of bugs that are non-exploitable. Attackers who attempt to find and exploit bugs in software will, with high probability, find an intentionally placed non-exploitable bug and waste precious resources in trying to build a working exploit. In a prototype, we demonstrate two strategies for ensuring non-exploitability for memory safety bugs in C/C++ programs and use them to automatically add thousands of non-exploitable bugs to real-world software such as nginx and libFLAC; we show that the functionality of the software is not impaired and demonstrate that our bugs look exploitable to current triage tools. We believe that chaff bugs can serve as an effective deterrent against both human attackers and automated bug-finding tools.

Embedded and IoT devices often come with a wide range of hardware functionality, but any particular end user may only use some small subset of these features. However, even unused hardware features are accompanied by potentially buggy driver code, which increases the attack surface of the device. In this paper, we introduce IRQDebloat, a system for disabling unwanted hardware features through automated firmware rewriting. Building on the insight that external inputs to the system are typically delivered through interrupt requests (IRQs), IRQDebloat systematically explores the interrupt handling code in the target firmware, identifies the handler function for each peripheral, and finally rewrites target firmware to disable the handlers that correspond to undesired hardware features. In our experiments we demonstrate IRQDebloat’s effectiveness and generality by identifying IRQ handlers across four different operating systems (Linux, FreeBSD, VxWorks, and RiscOS) and seven different embedded platforms, and disabling selected peripherals on real-world hardware (a Raspberry Pi and a Valve Steam Link). On the Steam Link, we survey the attack surface and find that disabling selected peripherals could block up to 44 CVEs found in the Linux kernel over the past five years.