As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention...
详细信息
As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.
The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of ***,the mass disease that needs attention in this context is *** deep learning has significantly advanced the analys...
详细信息
The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of ***,the mass disease that needs attention in this context is *** deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty ***,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the *** prepares convolutional neural network(CNN)and BCNN *** proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN *** study then trains them on a dataset of cataract images filtered from the ocular disease fundus images *** deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye *** compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.
Pesticides are the most common method used to eliminate pests, including aphids. Pesticides are the most common method used to eliminate pests, including aphids. Nonetheless, numerous farmers incorporate ladybugs into...
详细信息
Pesticides are the most common method used to eliminate pests, including aphids. Pesticides are the most common method used to eliminate pests, including aphids. Nonetheless, numerous farmers incorporate ladybugs into their pest management strategies as they serve as natural predators of aphids. By integrating these methods, farmers aim to achieve optimal outcomes in mitigating the detrimental effects of aphids on the agricultural sector. In this paper, the dynamics of interactions between aphids and ladybugs, including the impact of pesticides on aphid mortality, are represented using a system of nonlinear differential equations. This study treats the parameter representing aphid mortality caused by pesticides as a fuzzy number to account for variations in resistance levels. Additionally, the model incorporates four parameters that depict the interaction between aphids and ladybugs beyond considering the effect of pesticides. The parameters include the proportion of aphids consumed by ladybugs, the proportion of aphids capable of evading ladybugs, and the growth rates of both aphids and ladybugs. The triangular form is chosen to depict the fuzzy membership function because it reflects the resistance of aphids when pesticides are applied excessively. The dynamic model, incorporating a fuzzy parameter, is transformed into discrete-time models using the Non-Standard Finite Difference (NSFD) method for simulation purposes. The simulation outcomes align with the analysis findings, indicating a potential equilibrium between the populations of aphids and ladybugs. Various examinations on the impact of fuzzy pesticide parameters on the growth of aphids and ladybugs are provided. The findings demonstrate that pesticide application can substantially decrease the aphid population and can be tailored based on the interplay between aphids and ladybugs. Moreover, pesticide usage can be diminished with heightened ladybug growth and predation rates, thereby minimizing the occurren
Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image *** science combined with artificial intelligence is advancing to automate...
详细信息
Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image *** science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous *** to this is the development of robust algorithms for image classification and detection,crucial in designing sophisticated systems for diagnosis and *** study makes a small contribution to endoscopic image *** proposed approach involves multiple operations,including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and ***,feature optimization utilizes the binary dragonfly algorithm(BDA),with the fusion of the obtained feature *** fused feature set is input into the ensemble subspace k nearest neighbors(ESKNN)*** Kvasir-V2 benchmark dataset,and the COMSATS University Islamabad(CUI)Wah private dataset,featuring three classes of endoscopic stomach images were *** assessments considered various feature selection techniques,including genetic algorithm(GA),particle swarm optimization(PSO),salp swarm algorithm(SSA),sine cosine algorithm(SCA),and grey wolf optimizer(GWO).The proposed model excels,achieving an overall classification accuracy of 98.25% on the Kvasir-V2 benchmark and 99.90% on the CUI Wah private *** approach holds promise for developing an automated computer-aided system for classifying GI tract syndromes through endoscopy images.
The streaming model has been a popular model in big data computation. Streaming kernelization algorithms can be regarded as data compression processes on streaming data. In this study, we give a general method for dev...
The streaming model has been a popular model in big data computation. Streaming kernelization algorithms can be regarded as data compression processes on streaming data. In this study, we give a general method for developing computational lower bounds for streaming kernelization algorithms that is applicable to a large class of computational problems. As an example, we use the method to prove computational lower bounds for the well-known problem d-Hi TTin GSET.
Vision-based grasp pattern recognition1)identifies the specific hand pose（fingers and palm configuration） for the object to be grasped based on visual information of household objects, which benefits tasks such as u...
详细信息
Vision-based grasp pattern recognition1)identifies the specific hand pose（fingers and palm configuration） for the object to be grasped based on visual information of household objects, which benefits tasks such as upper limb prosthesis control and gesture-based human-robot interaction. Various methods, including convolutional neural networks（CNN）,have been proposed for grasp pattern recognition, leading to advancements in fields such as robot imitation, humanrobot interaction, prosthetic design, and robot control.
The earthquake early warning(EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is ...
详细信息
The earthquake early warning(EEW) system provides advance notice of potentially damaging ground shaking. In EEW, early estimation of magnitude is crucial for timely rescue operations. A set of thirty-four features is extracted using the primary wave earthquake precursor signal and site-specific *** Japan's earthquake magnitude dataset, there is a chance of a high imbalance concerning the earthquakes above strong impact. This imbalance causes a high prediction error while training advanced machine learning or deep learning models. In this work, Conditional Tabular Generative Adversarial Networks(CTGAN), a deep machine learning tool, is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information. The result obtained using actual and mixed(synthetic and actual) datasets will be used for training the stacked ensemble magnitude prediction model, MagPred, designed specifically for this study. There are 13295, 3989, and1710 records designated for training, testing, and validation. The mean absolute error of the test dataset for single station magnitude detection using early three, four, and five seconds of P wave are 0.41, 0.40,and 0.38 MJMA. The study demonstrates that the Generative Adversarial Networks(GANs) can provide a good result for single-station magnitude prediction. The study can be effective where less seismic data is available. The study shows that the machine learning method yields better magnitude detection results compared with the several regression models. The multi-station magnitude prediction study has been conducted on prominent Osaka, Off Fukushima, and Kumamoto earthquakes. Furthermore, to validate the performance of the model, an inter-region study has been performed on the earthquakes of the India or Nepal region. The study demonstrates that GANs can discover effective magnitude estimation compared with non-GAN-based methods. This has a high potential for wid
Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’daily ***,due to feedback delays or high costs,existing methods make large-scale,fine-grained waterlog...
详细信息
Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’daily ***,due to feedback delays or high costs,existing methods make large-scale,fine-grained waterlogging monitoring impossible.A common method is to forecast the city’s global waterlogging status using its partial waterlogging *** method has two challenges:first,existing predictive algorithms are either driven by knowledge or data alone;and second,the partial waterlogging data is not collected selectively,resulting in poor *** overcome the aforementioned challenges,this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus *** framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and *** predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural *** combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming *** selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget *** experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost,high accuracy,wide coverage,and fine granularity.
In the rapidly evolving field of education, a semantic search engine is essential to efficiently retrieve knowledge experts’ data. Universities and colleges continuously generate a vast amount of educational and rese...
详细信息
In 1994, Jonathan Grudin wrote his famous paper Eight Challenges for Groupware Developers;The question is whether these challenges still persist, or have we moved on here 30 years later? We revisit the challenges empi...
详细信息
暂无评论