Human emotions are the mind's responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep ...
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Most remote systems require user authentication to access ***-based passwords are still widely used as a standard method of user *** conventional text-based passwords are rather hard to remember,users often write thei...
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Most remote systems require user authentication to access ***-based passwords are still widely used as a standard method of user *** conventional text-based passwords are rather hard to remember,users often write their passwords down in order to compromise *** of the most complex challenges users may face is posting sensitive data on external data centers that are accessible to others and do not be controlled directly by *** user authentication methods have recently been proposed to verify the user ***,the fundamental limitation of a graphi-cal password is that it must have a colorful and rich image to provide an adequate password space to maintain security,and when the user clicks and inputs a pass-word between two possible grids,the fault tolerance is adjusted to avoid this *** paper proposes an enhanced graphical authentication scheme,which comprises benefits over both recognition and recall-based graphical techniques besides image *** combination of graphical authentication and steganography technologies reduces the amount of sensitive data shared between users and service providers and improves the security of user *** evaluate the effectiveness of the proposed scheme,peak signal-to-noise ratio and mean squared error parameters have been used.
Multi-hop reasoning for incomplete Knowledge Graphs(KGs)demonstrates excellent interpretability with decent *** Learning(RL)based approaches formulate multi-hop reasoning as a typical sequential decision *** intractab...
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Multi-hop reasoning for incomplete Knowledge Graphs(KGs)demonstrates excellent interpretability with decent *** Learning(RL)based approaches formulate multi-hop reasoning as a typical sequential decision *** intractable shortcoming of multi-hop reasoning with RL is that sparse reward signals make performance *** mainstream methods apply heuristic reward functions to counter this ***,the inaccurate rewards caused by heuristic functions guide the agent to improper inference paths and unrelated object *** this end,we propose a novel adaptive Inverse Reinforcement Learning(IRL)framework for multi-hop reasoning,called AInvR.(1)To counter the missing and spurious paths,we replace the heuristic rule rewards with an adaptive rule reward learning mechanism based on agent’s inference trajectories;(2)to alleviate the impact of over-rewarded object entities misled by inaccurate reward shaping and rules,we propose an adaptive negative hit reward learning mechanism based on agent’s sampling strategy;(3)to further explore diverse paths and mitigate the influence of missing facts,we design a reward dropout mechanism to randomly mask and perturb reward parameters for the reward learning *** results on several benchmark knowledge graphs demonstrate that our method is more effective than existing multi-hop approaches.
Objective: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to...
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Chronic Obstructive Pulmonary Disease (COPD) is a predominant global health concern, ranking third in mortality rates, yet frequently remains undiagnosed until its advanced stages. Given its prevalence, the need for i...
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For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but faul...
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For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network *** saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor *** of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to *** increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor *** Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster *** data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the ***,the MCH overhead *** the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.
Artificial intelligence traffic controllers are being designed with the primary goal of enabling them to adapt to the most recent sensor data in order to perform ongoing optimizations on the signal timing plan for int...
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Development in Quantum computing paves the path to Quantum key distribution (QKD) by using the principles of quantum physics. QKD enables two remote parties to produce and share secure keys while removing all computin...
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Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait an...
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While reinforcement learning has shown promising abilities to solve continuous control tasks from visual inputs, it remains a challenge to learn robust representations from high-dimensional observations and generalize...
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