In today’s digital landscape, the pervasive use of digital images across diverse domains has led to growing concerns regarding their authenticity and reliability. The potential for malicious manipulation of these ima...
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The diversified development of the service ecosystem,particularly the rapid growth of services like cloud and edge computing,has propelled the flourishing expansion of the service trading ***,in the absence of appropr...
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The diversified development of the service ecosystem,particularly the rapid growth of services like cloud and edge computing,has propelled the flourishing expansion of the service trading ***,in the absence of appropriate pricing guidance,service providers often devise pricing strategies solely based on their own interests,potentially hindering the maximization of overall market *** challenge is even more severe in edge computing scenarios,as different edge service providers are dispersed across various regions and influenced by multiple factors,making it challenging to establish a unified pricing *** paper introduces a multi-participant stochastic game model to formalize the pricing problem of multiple edge ***,an incentive mechanism based on Pareto improvement is proposed to drive the game towards Pareto optimal direction,achieving optimal ***,an enhanced PSO algorithm was proposed by adaptively optimizing inertia factor across three *** optimization significantly improved the efficiency of solving the game model and analyzed equilibrium states under various evolutionary *** results demonstrate that the proposed pricing incentive mechanism promotes more effective and rational pricing allocations,while also demonstrating the effectiveness of our algorithm in resolving game problems.
Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent *** research studies have achieved splendid results with the help of ma...
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Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent *** research studies have achieved splendid results with the help of machine learning models from different applications such as healthcare services,sign language translation,security,context awareness,and the internet of ***,most of these adopted studies have some shortcomings in the machine learning algorithms as they rely on recurrence and convolutions and,thus,precluding smooth sequential ***,in this paper,we propose a deep-learning approach based solely on attention,i.e.,the sole Self-Attention Mechanism model(Sole-SAM),for activity and motion recognition using Wi-Fi *** Sole-SAM was deployed to learn the features representing different activities and motions from the raw CSI *** were carried out to evaluate the performance of the proposed Sole-SAM *** experimental results indicated that our proposed system took significantly less time to train than models that rely on recurrence and convolutions like Long Short-Term Memory(LSTM)and Recurrent Neural Network(RNN).Sole-SAM archived a 0.94%accuracy level,which is 0.04%better than RNN and 0.02%better than LSTM.
With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of *** distribu...
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With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of *** distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent *** address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing ***,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource *** novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT *** experiments demonstrate that optimizing the execution efficiency of components can significantly improve system *** instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%***,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%***,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance *** study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.
Polymer-derived SiOC materials are widely regarded as a new generation of anodes owing to their high specific capacity,low discharge platform,tunable chemical/structural composition,and good structural ***,tailoring t...
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Polymer-derived SiOC materials are widely regarded as a new generation of anodes owing to their high specific capacity,low discharge platform,tunable chemical/structural composition,and good structural ***,tailoring the structure of SiOC to improve its electrochemical performance while simultaneously achieving elemental doping remains a ***,the lithium storage mechanism and the structural evolution process of SiOC are still not fully understood due to its complex *** this study,a hollow porous SiOCN(Hp-SiOCN)featuring abundant oxygen defects is successfully prepared,achieving both the creation of a hollow porous structure and nitrogen element doping in one step,finally enhancing the structural stability and improving the lithium storage kinetics of *** addition,the formation of a fully reversible structural unit,SiO3C─N,through the chemical interaction between N and Si/C,showcases a strong lithium adsorption *** advantage of these combined benefits,the as-prepared Hp-SiOCN electrode delivers a reversible specific capacity of 412 mAh g^(−1)(93%capacity retention)after 500 cycles at 1.0 A g^(−1) and exhibited only 4%electrode *** work offers valuable mechanistic insights into the synergistic optimization of elemental doping and structural design in SiOC,paving the way for advanced developments in battery technology.
We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the c...
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We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.
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.
Biosignal representation learning (BRL) plays a crucial role in emotion recognition for game users (ERGU). Unsupervised BRL has garnered attention considering the difficulty in obtaining ground truth emotion labels fr...
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Biosignal representation learning (BRL) plays a crucial role in emotion recognition for game users (ERGU). Unsupervised BRL has garnered attention considering the difficulty in obtaining ground truth emotion labels from game users. However, unsupervised BRL in ERGU faces challenges, including overfitting caused by limited data and performance degradation due to unbalanced sample distributions. Faced with the above challenges, we propose a novel method of biosignal contrastive representation learning (BCRL) for ERGU, which not only serves as a unified representation learning approach applicable to various modalities of biosignals but also derives generalized biosignals representations suitable for different downstream tasks. Specifically, we solve the overfitting by introducing perturbations at the embedding layer based on the projected gradient descent (PGD) adversarial attacks and develop the sample balancing strategy (SBS) to mitigate the negative impact of the unbalanced sample on the performance. Further, we have conducted comprehensive validation experiments on the public dataset, yielding the following key observations: 1) BCRL outperforms all other methods, achieving average accuracies of 76.67%, 71.83%, and 63.58% in 1D-2C Valence, 1D-2C Arousal and 2D-4C Valence/Arousal, respectively;2) The ablation study shows that both the PGD module (+7.58% in accuracy on average) and the SBS module (+14.60% in accuracy on average) have a positive effect on the performance of different classifications;3) BCRL model exhibits the certain generalization ability across the different games, subjects and classifiers. IEEE
Due to the small size of the annotated corpora and the sparsity of the event trigger words, the event coreference resolver cannot capture enough event semantics, especially the trigger semantics, to identify coreferen...
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Due to the small size of the annotated corpora and the sparsity of the event trigger words, the event coreference resolver cannot capture enough event semantics, especially the trigger semantics, to identify coreferential event mentions. To address the above issues, this paper proposes a trigger semantics augmentation mechanism to boost event coreference resolution. First, this mechanism performs a trigger-oriented masking strategy to pre-train a BERT (Bidirectional Encoder Representations from Transformers)-based encoder (Trigger-BERT), which is fine-tuned on a large-scale unlabeled dataset Gigaword. Second, it combines the event semantic relations from the Trigger-BERT encoder with the event interactions from the soft-attention mechanism to resolve event coreference. Experimental results on both the KBP2016 and KBP2017 datasets show that our proposed model outperforms several state-of-the-art baselines.
As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empi...
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As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical *** in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock *** study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend *** study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random *** the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as ***,the parameter combination of the model is optimized through random parameter *** experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine *** with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.
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