Iris biometrics allow contactless authentication, which makes it widely deployed human recognition mechanisms since the couple of years. Susceptibility of iris identification systems remains a challenging task due to ...
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Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing huma...
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Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.
With the rapid proliferation of Internet ofThings(IoT)devices,ensuring their communication security has become increasingly *** and smart contract technologies,with their decentralized nature,provide strong security g...
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With the rapid proliferation of Internet ofThings(IoT)devices,ensuring their communication security has become increasingly *** and smart contract technologies,with their decentralized nature,provide strong security guarantees for ***,at the same time,smart contracts themselves face numerous security challenges,among which reentrancy vulnerabilities are particularly *** detection tools for reentrancy vulnerabilities often suffer from high false positive and false negative rates due to their reliance on identifying patterns related to specific transfer *** address these limitations,this paper proposes a novel detection method that combines pattern matching with deep ***,we carefully identify and define three common patterns of reentrancy vulnerabilities in smart ***,we extract key vulnerability features based on these ***,we employ a Graph Attention Neural Network to extract graph embedding features from the contract graph,capturing the complex relationships between different components of the ***,we use an attention mechanism to fuse these two sets of feature information,enhancing the weights of effective information and suppressing irrelevant information,thereby significantly improving the accuracy and robustness of vulnerability *** results demonstrate that our proposed method outperforms existing state-ofthe-art techniques,achieving a 3.88%improvement in accuracy compared to the latest vulnerability detection model AME(Attentive Multi-Encoder Network).This indicates that our method effectively reduces false positives and false negatives,significantly enhancing the security and reliability of smart contracts in the evolving IoT ecosystem.
Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud *** a reasonable resource allocation solution is the key to adequately utilize th...
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Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud *** a reasonable resource allocation solution is the key to adequately utilize the hybrid ***,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other *** on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion ***,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model *** algorithm uses opposition-based learning to generate initial populations for faster ***,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search *** comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.
Generative artificial intelligence(GAI) has recently achieved significant success, enabling anyone to create texts, images, videos and even computer codes while providing insights that might not be possible with tradi...
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Generative artificial intelligence(GAI) has recently achieved significant success, enabling anyone to create texts, images, videos and even computer codes while providing insights that might not be possible with traditional tools. To stimulate future research, this work provides a brief summary of the ongoing and historical developments in GAI over the past 70 years. The achievements are grouped into four categories:(i) rule-based generative systems that follow specialized rules and instructions,(ii) model-based generative algorithms that produce new content based on statistical or graphical models,(iii) deep generative methodologies that utilize deep neural networks to learn how to generate new content from data and(iv)foundation models that are trained on extensive datasets and capable of performing a variety of generative tasks. This paper also reviews successful generative applications and identifies open challenges posed by remaining issues. In addition, this paper describes potential research directions aimed at better utilizing,understanding and harnessing GAI technologies.
Extracting useful details from images is essential for the Internet of Things ***,in real life,various external environments,such as badweather conditions,will cause the occlusion of key target information and image d...
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Extracting useful details from images is essential for the Internet of Things ***,in real life,various external environments,such as badweather conditions,will cause the occlusion of key target information and image distortion,resulting in difficulties and obstacles to the extraction of key information,affecting the judgment of the real situation in the process of the Internet of Things,and causing system decision-making errors and *** this paper,we mainly solve the problem of rain on the image occlusion,remove the rain grain in the image,and get a clear image without ***,the single image deraining algorithm is studied,and a dual-branch network structure based on the attention module and convolutional neural network(CNN)module is proposed to accomplish the task of rain *** order to complete the rain removal of a single image with high quality,we apply the spatial attention module,channel attention module and CNN module to the network structure,and build the network using the coder-decoder *** the experiment,with the structural similarity(SSIM)and the peak signal-to-noise ratio(PSNR)as evaluation indexes,the training and testing results on the rain removal dataset show that the proposed structure has a good effect on the single image deraining task.
Deepfake technology uses generative adversarial networks to produce highly authentic counterfeit images and videos, which raises a significant global security concern by disseminating fake information through online p...
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In the long-term repeated cycling process of lithium-ion batteries（LIBs）, the seamless transmission of lithium ions through the separator is crucial for the normal operation of the batteries. However, the irregular ...
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In the long-term repeated cycling process of lithium-ion batteries（LIBs）, the seamless transmission of lithium ions through the separator is crucial for the normal operation of the batteries. However, the irregular porous structure of the commonly used polyethylene（PE） separator leads to the accumulation of chaotic lithium ions and the formation of lithium dendrites, which pose serious safety risks. To enhance the safety performance of LIBs, we propose a novel composite separator design that incorporates ultrafine Al2O3particles and a multifunctional gel polymer binder, which are mixed and coated onto PE membranes. This composite separator improves the wettability and lithium-ion transference number, resulting in impressive cycling lifespan and high average Coulombic efficiencies for large-scale prismatic LiFePO4//graphite batteries. These batteries exhibit approximately 80 % capacity retention over 1900 cycles with average Coulombic efficiencies of 99.95 %. Furthermore,even after 1000 cycles, LIBs fabricated with the composite separator pass the rigorous nail penetration test. These enhancements in safety performance offer promising prospects for achieving dendrite-free LIBs during long-term cycling processes.
In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge ...
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In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic *** have extensive studies on malicious traffic detection with single challenge,but the detection of complex traffic has not been widely *** adaptive random forests(QARF) is proposed to detect traffic streams with concept drift,imbalance and lack of labeled *** is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling *** achieves querying a small number of instances from unlabeled traffic streams to obtain effective *** conduct experiments using the NSL-KDD dataset to evaluate the performance of *** is compared with other state-of-the-art *** experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD *** performs better than other state-of-the-art methods in comparisons.
Service Composition and Optimization Selection (SCOS) is crucial in Cloud Manufacturing (CMfg), but the uncertainties in service states and working environments pose challenges for existing QoS-based methods. Recently...
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