To the best of our assessment, this is a pioneering forensic approach specializing in the detection of Photoshop inpainting. The PS-Net's architecture is formulated to address difficulties with the inpainted images that are both delicate and professional in nature. aromatic amino acid biosynthesis It is composed of two subordinate networks, namely the primary network (P-Net) and the secondary network (S-Net). The P-Net's objective is to extract the frequency cues of subtle inpainting artifacts using a convolutional network, subsequently pinpointing the manipulated area. By boosting the weight of frequently co-occurring features and introducing features the P-Net misses, the S-Net somewhat safeguards the model against compression and noise attacks. The localization accuracy of PS-Net is improved by the incorporation of dense connections, Ghost modules, and channel attention blocks (C-A blocks). Experimental results showcase PS-Net's ability to accurately discern fabricated regions in elaborately inpainted pictures, outperforming several state-of-the-art alternatives. The proposed PS-Net possesses a high degree of resilience against post-processing operations typically used in Photoshop.
This article proposes a novel scheme for model predictive control (RLMPC) of discrete-time systems, employing reinforcement learning techniques. Through policy iteration (PI), model predictive control (MPC) and reinforcement learning (RL) are integrated, with MPC generating the policy and RL performing the evaluation. The outcome of the value function calculation becomes the terminal cost within MPC, thus optimizing the derived policy. This action grants an advantage by eliminating the need for the terminal cost, the auxiliary controller, and the terminal constraint within the offline design paradigm commonly used in traditional Model Predictive Control (MPC). Furthermore, the RLMPC algorithm, as presented in this paper, offers a more adaptable prediction horizon, owing to the removal of the terminal constraint, potentially reducing computational demands significantly. RLMPC's convergence, feasibility, and stability properties are subjected to a rigorous analytical assessment. The simulation data indicates that RLMPC yields comparable performance to conventional MPC for linear systems, while outperforming it for nonlinear ones.
Deep neural networks (DNNs) are affected by adversarial examples, and adversarial attack models, specifically DeepFool, are experiencing a surge in performance, outpacing the effectiveness of defenses against adversarial examples. This article introduces a new adversarial example detector that significantly outperforms the existing state-of-the-art detectors, specifically in identifying the most current adversarial attacks on image datasets. The proposed method for identifying adversarial examples leverages sentiment analysis, specifically analyzing the progressively influencing effects of adversarial perturbations on a deep neural network's hidden layer feature maps. For the purpose of transforming hidden-layer feature maps into word vectors and assembling sentences for sentiment analysis, a modular embedding layer with a minimum of learnable parameters is designed. The new detector, through extensive experimentation, demonstrably outperforms existing state-of-the-art detection algorithms in identifying the recent attacks on ResNet and Inception neural networks on the benchmark datasets of CIFAR-10, CIFAR-100, and SVHN. A Tesla K80 GPU facilitates the detector's identification of adversarial examples, produced by cutting-edge attack models, in less than 46 milliseconds, despite boasting only about 2 million parameters.
With the continuous progress of educational informatization, more and more contemporary technologies are finding their way into teaching. Massive and multi-dimensional data, a consequence of these technologies, benefits educational research but also leads to a tremendous expansion in the amount of information absorbed by teachers and students. Employing text summarization techniques to distill the core information from class records, concise class minutes can be generated, thereby significantly enhancing the efficiency of both teachers and students in accessing pertinent details. A new model, HVCMM, for the automatic generation of class minutes utilizing a hybrid view, is proposed in this article. To prevent memory overload during calculations following input, the HVCMM model utilizes a multi-layered encoding technique for the voluminous text found within input class records. By integrating coreference resolution and role vectors, the HVCMM model aims to alleviate the confusion that a large number of participants in a class can introduce regarding referential logic. To uncover the structural information contained within a sentence's topic and section, machine learning algorithms are used. Experiments using the Chinese class minutes (CCM) and augmented multiparty interaction (AMI) datasets revealed that the HVCMM model consistently achieved higher ROUGE scores than competing baseline models. By employing the HVCMM model, teachers can refine their post-instructional reflection and improve their overall teaching standards. To further their understanding of the lessons, students can use the automatically generated class minutes from the model, which detail the key content.
The examination, diagnosis, and prognosis of respiratory illnesses rely on precise airway segmentation, yet its manual delineation proves to be overly demanding and inefficient. To streamline the often-lengthy and potentially biased manual procedure of airway extraction from computed tomography (CT) images, researchers have developed automated methods. Nonetheless, the comparatively small bronchi and terminal bronchioles significantly obstruct the capacity of machine learning models for automatic segmentation tasks. The variance in voxel values, combined with the substantial data imbalance within airway branches, renders the computational module vulnerable to discontinuous and false-negative predictions, especially in cohorts with varying lung diseases. The attention mechanism's capacity to segment complex structures is noteworthy, alongside fuzzy logic's efficacy in lessening the uncertainty in feature representations. selleck products In conclusion, integrating deep attention networks with fuzzy theory, particularly through the implementation of the fuzzy attention layer, provides a more sophisticated solution for improved generalization and robustness. A novel fuzzy attention neural network (FANN) integrated with a sophisticated loss function forms the core of an efficient airway segmentation method presented in this article, prioritizing spatial continuity. Voxels in the feature map and a learned Gaussian membership function are used to define the deep fuzzy set. Departing from existing attention mechanisms, the introduced channel-specific fuzzy attention effectively addresses the challenge of diverse features in separate channels. Medical implications In addition, a new evaluation metric is presented for assessing the connectedness and the wholeness of airway structures. The training of the proposed method on normal lung disease, and its subsequent evaluation on datasets encompassing lung cancer, COVID-19, and pulmonary fibrosis, affirmed its efficiency, generalization, and robustness.
Deep learning-based interactive image segmentation methods have effectively minimized user input requirements, with click interactions being the sole engagement needed. However, the process of adjusting the segmentation still requires too many clicks for satisfactory results. The present article delves into strategies for achieving accurate segmentation of target users, minimizing the burden on the user experience. We advocate for a one-click interactive segmentation technique in this research, enabling the achievement of the objective mentioned above. Addressing this complex interactive segmentation problem, we introduce a top-down framework, dissecting the initial task into a one-click-based preliminary localization stage and a subsequent fine segmentation process. First, a two-stage interactive object localization network is crafted with the objective of completely encapsulating the target object using object integrity (OI) as a supervisory mechanism. Overlapping objects are also addressed through the use of click centrality (CC). This broad localization approach diminishes the search space and enhances the sharpness of the click target at an elevated level of detail. A multilayer segmentation network, guided by a layer-by-layer approach, is subsequently designed to accurately perceive the target with a very limited amount of prior information. The diffusion module is further designed for the purpose of augmenting the exchange of information across layers. The model's design permits a smooth transition to multi-object segmentation tasks. Our method's one-click operation yields superior results compared to the best-in-class methods on several benchmark datasets.
Within the complex neural network of the brain, regions and genes work cooperatively to effectively store and transmit information. The collaboration network of brain regions and genes is formalized as the brain-region gene community network (BG-CN), and we introduce a new deep learning method, the community graph convolutional network (Com-GCN), to examine information exchange within and between the communities. The potential for diagnosing and identifying the root causes of Alzheimer's disease (AD) exists in these results. To model the exchange of information within and between BG-CN communities, an affinity aggregation model is presented. Our second step is to create the Com-GCN architecture, which integrates both inter-community and intra-community convolutions, using the affinity aggregation methodology. Rigorous experimental validation on the ADNI dataset demonstrates that Com-GCN's design closely mirrors physiological mechanisms, enhancing interpretability and classification accuracy. Furthermore, the Com-GCN model can identify the location of lesions in the brain and pinpoint the genes associated with the disease, which could prove beneficial for precision medicine and drug development in Alzheimer's disease, and provide a significant reference point for other neurological conditions.