Semi-Supervised semantic segmentation aims to improve segmentation performance by leveraging a limited amount of labeled data along with a large set of unlabeled data. Existing methods mainly focus on enforcing image-level consistency between weakly and strongly augmented images in high-confidence regions. However, such approaches often neglect class similarity in feature space and discard low-confidence regions, leading to suboptimal representation learning. To address these issues, we propose a Dual-Level Consistency Framework with Prototype Contrastive Learning (DLC-PC). First, the prototype contrastive learning (PCL) is designed to enhance intra-class consistency and inter-class discrepancy of feature distribution. Specifically, representations are aligned with identical-class global prototypes, while being pushed away from different-class local prototypes. Second, the dynamic contrast threshold (DCT) is proposed to facilitate feature learning by progressively incorporating more low-confidence pixels into the contrastive learning process. Extensive experiments on two benchmarks demonstrate the state-of-the-art performance of the proposed framework.

The Matter protocol has rapidly become the new standard for secure and interoperable IoT connectivity, adopted by major industry players and integrated into millions of devices. A core feature of Matter is its ability to support device sharing across users and controllers. However, as vendors independently implement Matter and blend it with their proprietary ecosystems, significant inconsistencies emerge. These inconsistencies result in heterogeneous user capabilities depending on which Matter Controller (MC) or OEM app is used, introducing a new and largely unexplored class of security risks. In this work, we present the first systematic study on security risks stemming from heterogeneous Matter controller implementations in shared device environments. We analyze 18 major IoT vendors and uncover a novel category of vulnerabilities, which we term MCG (Matter Controller Gaps), where differences in controller capabilities can enable unauthorized access or stealthy device manipulation. To uncover these flaws at scale, we develop MCG-Checker, a semi-automated analysis tool that combines large language models and UI automation to detect control disparities across Matter controllers and OEM apps. Using MCG-Checker, we evaluate 14 Matter controllers and 8 OEM apps, discovering 5 previously unknown attack vectors affecting top vendors such as Google, Apple, and Amazon Alexa. Our work reveals critical design and implementation issues in current Matter deployments. We offer concrete recommendations for protocol designers, vendors, and end users to address these gaps, contributing to more secure and predictable IoT ecosystems.

Hyperspectral pansharpening is an efficient approach to obtaining high-resolution hyperspectral images (HR-HSIs) by fusing low-resolution hyperspectral images (LR-HSIs) with high-resolution panchromatic images (HR-PANs). However, the spatial and spectral distortions in reconstructed HR-HSIs are almost inevitable due to the modal gap between LR-HSIs and HR-PANs. Therefore, the performance of multi-source features fusion largely hinges on the ability to extract and align heterogeneous features across modalities. Most of the existing methods focus on integrating decoupled spatial and spectral information from different sources directly, which poses a dual challenge in aligning both spatial and spectral features effectively. To address the issues above-mentioned, a novel method named Interactive-Guided Fusion Network (IGFNet) is proposed, which is built upon a multi-stage progressive fusion framework. A high-resolution branch (HR) is introduced to interactively guide the alignment between cross-modal features, by fusing up-sampled HSI and PAN as a joint spatial-spectral guidance signal. Furthermore, the alignment is progressively conducted across stages, narrowing the modality gap and enhancing the representation of HR spatial-spectral feature. Additionally, we designed parameter-free spatial, spectral, and spatial-spectral attention mechanisms to extract global and local features effectively. Extensive experiments on reduced-resolution and full-resolution datasets demonstrate that IGFNet outperforms state-of-the-art across various metrics. Specifically, with a scaling factor of 4 on the Pavia University dataset, our method achieves a 2.09% relative improvement in PSNR, a 0.99% relative increase in SSIM, a 2.6% relative reduction in SAM, while reducing the parameter count by compared to the baseline MDA-Net.

Semantic segmentation of remote sensing images (RSIs) is vital for geospatial intelligence. However, traditional methods face challenges with mixed pixels and complex land cover types. Convolutional neural networks and transformers have led the field of RSI semantic segmentation by learning visual features in the spatial domain, but they often overlook the rich spectral features which can be well-described in the frequency domain, resulting in inadequate context modeling. In this paper, we present FreqFormer, a frequency transformer that enhances semantic segmentation by incorporating both spectral and spatial information through a devised frequency attention (FA) module. FA refines representations in the frequency domain through two parallel branches. Specifically, the high-frequency branch (HFB) utilizes a convolution layer with a Canny kernel to preserve high-frequency details, followed by multi-head self-attention to model high-frequency context. Followed by an element summation, high-frequency and low-frequency contexts are aggregated. Then, the formed FreqFormer block is sequentially deployed in the encoder stage with patch merging for spatial contraction. As for the decoder, the mask transformer decoder applies a scalar product to predict patch-wise semantics before upsampling. In experiments, FreqFormer outperforms state-of-the-art models on the ISPRS Potsdam and LoveDA datasets, demonstrating significant improvements in numerical evaluations. The integration of HFB significantly boosts the model’s ability to capture fine details, highlighting its potential for geospatial analysis.

Modern IoT device manufacturers are taking advantage of the managed Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) IoT clouds (e.g., AWS IoT, Azure IoT) for secure and convenient IoT development/deployment. The IoT access control is achieved by manufacturer-specified, cloud-enforced IoT access policies (cloud-standard JSON documents, called IoT Policies) stating which users can access which IoT devices/resources under what constraints. In this paper, we performed a systematic study on the security of cloud-based IoT access policies on modern PaaS/IaaS IoT clouds. Our research shows that the complexity in the IoT semantics and enforcement logic of the policies leaves tremendous space for device manufacturers to program a flawed IoT access policy, introducing convoluted logic flaws which are non-trivial to reason about. In addition to challenges/mistakes in the design space, it is astonishing to find that mainstream device manufacturers also generally make critical mistakes in deploying IoT Policies thanks to the flexibility offered by PaaS/IaaS clouds and the lack of standard practices for doing so. Our assessment of 36 device manufacturers and 310 open-source IoT projects highlights the pervasiveness and seriousness of the problems, which once exploited, can have serious impacts on IoT users' security, safety, and privacy. To help manufacturers identify and easily fix IoT Policy flaws, we introduce P-Verifier, a formal verification tool that can automatically verify cloud-based IoT Policies. With evaluated high effectiveness and low performance overhead, P-Verifier will contribute to elevating security assurance in modern IoT deployments and access control. We responsibly reported all findings to affected vendors and fixes were deployed or on the way.

In some special equipment, the engine assembles nozzles to drive the equipment and adjust movement posture. The nozzles action test is an essential step during the whole test session. The current engine action test relies on the judgment observed by testers. It is difficult to identify some actions when the nozzles swing too fast or slightly. This paper focuses on the research in automatic engine motion tests by using computer vision technology instead of manual observation. We are also dedicated to developing an integrated system used to solve the problems during the test, which can effectively solve the difficulties of inefficiency and inability to record. Algorithms based on the image detection techniques are designed to accomplish this work. At the same time, the algorithms integrate into the hardware and software platform. The proposed system is equipped with some hardware control and monitoring functions. The algorithm designed in this paper achieves 100% accuracy in nozzle target detection and motion detection in real scenarios. The automated monitoring platform supports more than eight cameras for motion monitoring, the algorithm computation speed is higher than 20FPS, and the delay time is not longer than 3s.

Past studies on Cooperative Multi-Point(CoMP) Processing/Base Station Cooperation (BSC) technique have shown its potential for system capacity improvement. However, many of the previous studies ignore a fact that in a network where multiple CoMP groups co-exist, the interference between CoMP groups would significantly decrease each of the CoMP groups' performance. To combat this Interference between CoMP groups, we propose a novel precoding technique - Maximum Eigenmode Transmission(MET) CoMP precoding, which is able to effectively combat such interference and achieves 90% better performance than the conventional method in a multi-group CoMP network.

Previous studies on Base Station Cooperation (BSC) have shown its significant capacity gains. These studies usually ignore the fact that a practical cellular network has a large number of Base Stations (BSs) and to coordinate these BSs will incur heavy signalling overhead, high transmission latency. In order to utilize BSC, a network has to be divided into separate groups, each of which cooperate independently, limiting the number of BSs in one group. This arrangement will make a new kind of interference, which we name it Out of cooperative Group Interference (OGI). We show that OGI can cause severe performance degradation through our simulation study. To solve the problem, we propose a novel Noise Balancing Block Diagonal-ization (NB-BD) precoding algorithm to both cancel self-group interference and accommodate the OGI as extra noise. This NB-BD is simpler, more robust than traditional BD joint precoding and is shown to provide significant performance gains.