We believe this is the first forensic method to be explicitly designed for the specific purpose of identifying Photoshop inpainting. Delicate and professionally inpainted images are handled by the PS-Net's specific design. folding intermediate The system's design incorporates two sub-networks, the principal network (P-Net) and the auxiliary network (S-Net). The P-Net leverages a convolutional network to mine subtle inpainting feature frequency clues, thereby enabling the precise identification of the altered region. The model benefits from the S-Net's capability to mitigate, to a degree, compression and noise attacks by amplifying the importance of features that frequently appear together and by supplying features absent in the P-Net's representation. Moreover, PS-Net incorporates dense connections, Ghost modules, and channel attention blocks (C-A blocks) to enhance its localization capabilities. Empirical evidence demonstrates PS-Net's proficiency in identifying forged areas within intricately inpainted images, surpassing the performance of several cutting-edge solutions. The suggested PS-Net is exceptionally resilient against post-processing actions that are common within the Photoshop environment.
This article proposes a novel scheme for model predictive control (RLMPC) of discrete-time systems, employing reinforcement learning techniques. The policy iteration (PI) method seamlessly integrates model predictive control (MPC) and reinforcement learning (RL), using MPC to formulate policies and RL to assess their performance. Thereafter, the obtained value function is incorporated as the terminal cost within the MPC framework, leading to an improvement in the generated policy. Crucially, this strategy removes the dependence on the offline design paradigm, including the terminal cost, auxiliary controller, and terminal constraint, which are present in standard MPC implementations. Moreover, this article's RLMPC methodology provides a greater range of prediction horizon options, because the terminal constraint is removed, offering a significant potential for minimizing the computational workload. RLMPC's convergence, feasibility, and stability properties are subjected to a rigorous analytical assessment. RLMPC, according to simulation results, achieves a performance essentially similar to that of traditional MPC for linear systems, and surpasses it for nonlinear system control.
Deep neural networks (DNNs) are susceptible to manipulation by adversarial examples, while advanced adversarial attack models, like DeepFool, are emerging rapidly and outperforming detection techniques for adversarial examples. This article introduces a superior adversarial example detector, exceeding the performance of current state-of-the-art detectors in pinpointing the most recent adversarial attacks on image datasets. Adversarial example detection is proposed using sentiment analysis, specifically by analyzing the progressively changing hidden-layer feature maps of the attacked deep neural network in response to an adversarial perturbation. Subsequently, a modular embedding layer with the fewest trainable parameters is designed to translate the hidden layer's feature maps into word vectors, enabling sentence preparation for sentiment analysis. Rigorous experiments indicate that the novel detector consistently outperforms state-of-the-art detection algorithms in detecting the most recent attacks against ResNet and Inception networks on the CIFAR-10, CIFAR-100, and SVHN image datasets. In less than 46 milliseconds, the detector, powered by a Tesla K80 GPU and possessing about 2 million parameters, accurately identifies adversarial examples produced by the latest attack models.
The ongoing advancement of educational information technology sees a growing integration of cutting-edge technologies into teaching practices. While these technologies furnish a wealth of information for research and education, the quantity of data teachers and students are exposed to is expanding at an alarming rate. Through the application of text summarization technology, the core substance of class record text can be condensed into concise class minutes, leading to a considerable increase in the efficiency of teachers and students in accessing this information. This article focuses on the automatic generation of hybrid-view class minutes, employing the model HVCMM. The HVCMM model, encountering potential memory overflow issues with long input class record texts, opts for a multi-layered encoding strategy, preempting such issues after the single-level encoder process. Coreference resolution, coupled with role vector integration, is utilized by the HVCMM model to mitigate the confusion potentially induced by a large number of participants in a class regarding referential logic. Machine learning algorithms are instrumental in extracting structural information from the topic and section of a sentence. By testing the HVCMM model with the Chinese class minutes (CCM) and augmented multiparty interaction (AMI) dataset, we discovered its marked advantage over other baseline models, which is quantitatively verified using the ROUGE metric. Teachers can effectively enhance the quality of their post-class reflection processes, thanks to the assistance of the HVCMM model, thereby improving their teaching standards. Students can improve their understanding of the material by using the model-generated class minutes to review the essential information.
For the assessment, diagnosis, and prognosis of lung diseases, airway segmentation is indispensable, while its manual delineation process is disproportionately taxing. Researchers have proposed novel automated methods for airway extraction from computed tomography (CT) images, thereby improving upon the lengthy and potentially subjective manual segmentation processes. Nevertheless, the minute divisions of the respiratory tract, such as bronchi and terminal bronchioles, present considerable obstacles to accurate automated segmentation by machine learning algorithms. The diversity of voxel values and the substantial data disparity in airway branching results in a computational module that is vulnerable to discontinuous and false-negative predictions, particularly within cohorts with varying lung conditions. Fuzzy logic diminishes the uncertainty in feature representations, whereas the attention mechanism demonstrates its ability to segment complex structures. Biofouling layer Ultimately, the combination of deep attention networks and fuzzy theory, facilitated by the fuzzy attention layer, leads to a more effective solution for better generalization and robustness. This article proposes a novel approach to airway segmentation, leveraging a fuzzy attention neural network (FANN) and a comprehensive loss function to improve spatial continuity in the segmentation. Voxels in the feature map and a learned Gaussian membership function are used to define the deep fuzzy set. Diverging from existing attention mechanisms, this proposed channel-specific fuzzy attention method specifically addresses the issue of heterogeneous features manifesting in various channels. this website Along these lines, a new evaluation metric is put forth to measure both the connectedness and the comprehensiveness of the airway structures. By training on normal lung disease and evaluating on lung cancer, COVID-19, and pulmonary fibrosis datasets, the proposed method's efficiency, generalization, and robustness were empirically verified.
Through the implementation of deep learning, interactive image segmentation has substantially reduced the user's interaction burden, with just simple clicks required. Still, a large number of clicks are required to accurately and consistently correct the segmentation for satisfactory results. This article analyzes methods to generate accurate segmentations of users of interest, while reducing the demands placed on user inputs. This work introduces a one-click interactive segmentation approach to achieve the aforementioned objective. For this especially intricate interactive segmentation problem, we've developed a top-down framework, which involves initial coarse localization via a one-click approach, followed by a more precise segmentation. To begin with, an interactive object localization network, operating in two stages, is developed. It seeks to completely surround the target of interest, leveraging object integrity (OI) supervision. To mitigate the problem of overlapping objects, click centrality (CC) is also applied. This broad localization approach diminishes the search space and enhances the sharpness of the click target at an elevated level of detail. A principled segmentation network, comprised of progressive layers, is then developed to precisely perceive the target with minimal prior knowledge. The diffusion module's contribution to the network architecture is in optimizing the exchange of data across layers. Beyond this, the proposed model's capabilities readily extend to the segmentation of multiple objects. With a single interaction, our methodology achieves the current best performance on various benchmark tests.
The intricate collaboration of brain regions and genes, within the complex neural network framework, is crucial for effective storage and transmission of information. We model the correlations in collaboration as a brain region-gene community network (BG-CN), and introduce a new deep learning approach, the community graph convolutional neural network (Com-GCN), to investigate the transmission of information between and within these communities. The utilization of these results facilitates the diagnosis and extraction of causal factors contributing to Alzheimer's disease (AD). A BG-CN affinity aggregation model is formulated to illustrate how information spreads both within and across communities. The second stage of our design involves constructing the Com-GCN architecture with inter-community and intra-community convolutions, underpinned by the affinity aggregation model. Utilizing the ADNI dataset for experimental validation, the Com-GCN design exhibits a superior match to physiological mechanisms, leading to increased interpretability and improved classification capabilities. Furthermore, the Com-GCN approach allows for the identification of affected brain regions and the genes contributing to disease, thus potentially supporting precision medicine and drug development efforts in AD, and serving as a valuable reference for other neurological disorders.