The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartp...
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ISBN:
(数字)9798331542573
ISBN:
(纸本)9798331542580
The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector.
Multi-view stereo (MVS) reconstruction is essential for creating 3D models. The approach involves applying epipolar rectification followed by dense matching for disparity estimation. However, existing approaches face ...
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We study the problem of (ε, δ)-differentially private learning of linear predictors with convex losses. We provide results for two subclasses of loss functions. The first case is when the loss is smooth and non-nega...
ISBN:
(纸本)9781713871088
We study the problem of (ε, δ)-differentially private learning of linear predictors with convex losses. We provide results for two subclasses of loss functions. The first case is when the loss is smooth and non-negative but not necessarily Lipschitz (such as the squared loss). For this case, we establish an upper bound on the excess population risk of $\tilde{O}\left(\frac{\Vert w^*\Vert}{\sqrt{n}} + \min\left\{\frac{\Vert w^* \Vert^2}{(n\epsilon)^{2/3}},\frac{\sqrt{d}\Vert w^*\Vert^2}{n\epsilon}\right\}\right)$, where n is the number of samples, d is the dimension of the problem, and w* is the minimizer of the population risk. Apart from the dependence on ‖w*‖, our bound is essentially tight in all parameters. In particular, we show a lower bound of $\tilde{\Omega}\left(\frac{1}{\sqrt{n}} + {\min\left\{\frac{\Vert w^*\Vert^{4/3}}{(n\epsilon)^{2/3}}, \frac{\sqrt{d}\Vert w^*\Vert}{n\epsilon}\right\}}\right)$. We also revisit the previously studied case of Lipschitz losses [SSTT20]. For this case, we close the gap in the existing work and show that the optimal rate is (up to log factors) $\Theta\left(\frac{\Vert w^*\Vert}{\sqrt{n}} + \min\left\{\frac{\Vert w^*\Vert}{\sqrt{n\epsilon}},\frac{\sqrt{\text{rank}}\Vert w^*\Vert}{n\epsilon}\right\}\right)$, where rank is the rank of the design matrix. This improves over existing work in the high privacy regime. Finally, our algorithms involve a private model selection approach that we develop to enable attaining the stated rates without a-priori knowledge of ‖w*‖.
Structure from motion using uncalibrated multi-camera systems is a challenging task. This paper proposes a bundle adjustment solution that implements a baseline constraint respecting that these cameras are static to e...
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Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. T...
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ISBN:
(数字)9798331501488
ISBN:
(纸本)9798331501495
Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. These issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. The data is gathered from a dataset on EV charging. The data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). The proposed MSNN-AO is utilized within the MATLAB platform. The proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. The MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. This highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. The MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.
Semantic segmentation is a way of photo knowhow that entails labeling the objects in a picture on the pixel degree and assigning every pixel within the photograph a category label. This method is utilized in PC imagin...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Semantic segmentation is a way of photo knowhow that entails labeling the objects in a picture on the pixel degree and assigning every pixel within the photograph a category label. This method is utilized in PC imaginative and prescient programs to construct a representation of a scene from digital photographs. It may be used to correctly phase discrete items consisting of vehicles, pedestrians, and timber, as well as more summary standards, which include shadows and reflections. On the way to achieve success, semantic segmentation algorithms require a large amount of designated labeling and accurate pixel-degree statistical analysis. Latest advances in deep mastering strategies have allowed for the improvement of more sophisticated algorithms that could achieve incredible effects on hard-picture segmentation datasets. Those strategies have progressed the accuracy and efficiency of the photograph know-how algorithms, allowing them to carry out duties that include scene understanding, item identification, scene type, and pixel-stage segmentation.
Multidataset independent subspace analysis (MISA) unifies multiple linear blind source separation methods to analyze joint and unique information across multiple datasets. MISA can jointly analyze large multimodal neu...
Multidataset independent subspace analysis (MISA) unifies multiple linear blind source separation methods to analyze joint and unique information across multiple datasets. MISA can jointly analyze large multimodal neuroimaging datasets to advance our understanding of the brain from multiple perspectives. However, a systematic evaluation of the trade-offs between problem scale and sample size is still absent in the literature. Aiming to support flexibility and replicability of deep latent variable modeling, and equip practitioners with crucial tools and usage guidelines, we developed a MISA PyTorch module incorporating the linked multi-network architecture and loss function of the original MISA MATLAB. We then investigated critical performance trade-offs between latent space and sample sizes in independent vector analysis (IVA) problems. Both platforms were highly similar in hundreds of simulation settings, demonstrating successful replication of the original framework and flexibility to evaluate multiple configurations. We observed that a larger sample size, fewer datasets and fewer sources can lead to better IVA model performance. We then performed an IVA experiment on a large multimodal neuroimaging dataset and observed high cross-modal correlation linkage among the identified sources in both platforms, supporting MISA’s effectiveness for replicable multimodal linkage detection.
Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology o...
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ISBN:
(数字)9798331518097
ISBN:
(纸本)9798331518103
Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer.
From a macro viewpoint, it is essential to comprehend the similarities across human illnesses in order to identify underlying mechanisms at the microscopic level. Previous studies have shown that employing machine lea...
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ISBN:
(数字)9798350360165
ISBN:
(纸本)9798350360172
From a macro viewpoint, it is essential to comprehend the similarities across human illnesses in order to identify underlying mechanisms at the microscopic level. Previous studies have shown that employing machine learning methods or merging many data sources can improve performance in sickness similarity measurement. However, there is currently a gap in the development of a useful framework for information extraction and integration from a range of biological data using deep learning models. We introduce CoGO, a Contrastive learning system that predicts commonalities in illnesses based on gene networks and ontology structure. Gene ontology (GO) and gene interaction network domain information is integrated into CoGO using graph deep learning models. Graph deep learning models are first used to encode gene and GO word features from independent graph structure data. Gene and GO characteristics are then mapped via a nonlinear projection to a shared embedding space. Next, by using cross-view contrastive loss to increase the agreement of related gene-GO links, meaningful gene representations are generated. Finally, to calculate disease similarity, CoGO computes the cosine similarity of sickness representation vectors derived from related gene embeddings.
Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool t...
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Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a variety of options. In this manuscript, we developed hybrid associations models ( $\mathop {\mathtt {HAM}}\limits$ ) to generate sequential recommendations using three factors: 1) users’ long-term preferences, 2) sequential, high-order and low-order association patterns in the users’ most recent purchases/ratings, and 3) synergies among those items. $\mathop {\mathtt {HAM}}\limits$ uses simplistic pooling to represent a set of items in the associations, and element-wise product to represent item synergies of arbitrary orders. We compared $\mathop {\mathtt {HAM}}\limits$ models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that $\mathop {\mathtt {HAM}}\limits$ models significantly outperform the state of the art in all the experimental settings. with an improvement as much as 46.6 percent. In addition, our run-time performance comparison in testing demonstrates that $\mathop {\mathtt {HAM}}\limits$ models are much more efficient than the state-of-the-art methods. and are able to achieve significant speedup as much as 139.7 folds.
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