Recommendation has been widely used in business scenarios to provide users with personalized and accurate item lists by efficiently analyzing complex user-item ***,existing recommendation methods have significant shor...
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Recommendation has been widely used in business scenarios to provide users with personalized and accurate item lists by efficiently analyzing complex user-item ***,existing recommendation methods have significant shortcomings in capturing the dynamic preference changes of users and discovering their true potential *** address these problems,a novel framework named Intent-Aware Graph-Level Embedding Learning(IaGEL)is proposed for *** this framework,the potential user interest is explored by capturing the co-occurrence of items in different periods,and then user interest is further improved based on an adaptive aggregation algorithm,forming generic intents and specific *** addition,for better representing the intents,graph-level embedding learning is designed based on the mutual information comparison among positive intents and negative ***,an intent-based recommendation strategy is designed to further mine the dynamic changes in user *** on three public and industrial datasets demonstrate the effectiveness of the proposed IaGEL in the task of recommendation.
Hardware Trojans (HTs) present significant security threats to integrated circuits. Detecting and locating HTs is crucial for mitigating these threats. Thus, this paper proposes a method called HTs-GCN, which utilizes...
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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.
Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in *** general graphs,edge closeness centrality indicates the importance of edges by the shorte...
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Edge closeness and betweenness centralities are widely used path-based metrics for characterizing the importance of edges in *** general graphs,edge closeness centrality indicates the importance of edges by the shortest distances from the edge to all the other *** betweenness centrality ranks which edges are significant based on the fraction of all-pairs shortest paths that pass through the ***,extensive research efforts go into centrality computation over general graphs that omit time ***,numerous real-world networks are modeled as temporal graphs,where the nodes are related to each other at different time *** temporal property is important and should not be neglected because it guides the flow of information in the *** state of affairs motivates the paper’s study of edge centrality computation methods on temporal *** introduce the concepts of the label,and label dominance relation,and then propose multi-thread parallel labeling-based methods on OpenMP to efficiently compute edge closeness and betweenness centralities *** types of optimal temporal *** edge closeness centrality computation,a time segmentation strategy and two observations are presented to aggregate some related temporal edges for uniform *** edge betweenness centrality computation,to improve efficiency,temporal edge dependency formulas,a labeling-based forward-backward scanning strategy,and a compression-based optimization method are further proposed to iteratively accumulate centrality *** experiments using 13 real temporal graphs are conducted to provide detailed insights into the efficiency and effectiveness of the proposed *** with state-ofthe-art methods,labeling-based methods are capable of up to two orders of magnitude speedup.
Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a t...
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Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a typical virtual reality game that entails multi-user *** a user approaches and interacts with a target user in the VE,the user is expected to approach and interact with the target user in the corresponding PE as *** methods of multi-user RDW mainly focus on obstacle avoidance,which does not account for the relative positional relationship between the users in both VE and *** To enhance the user experience and facilitate potential interaction,this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking(DA-RDW)in a shared PE where the target user and other users are *** algorithm adopts improved artificial potential fields,where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic *** the best alignment,this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal ***,this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent *** balance obstacle avoidance and alignment,this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force *** The efficacy of the proposed method was evaluated using a series of simulations and live-user *** experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.
Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework f...
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Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.
In the era of large-scale pretrained models, artificial neural networks(ANNs) have excelled in natural language understanding(NLU) tasks. However, their success often necessitates substantial computational resourc...
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In the era of large-scale pretrained models, artificial neural networks(ANNs) have excelled in natural language understanding(NLU) tasks. However, their success often necessitates substantial computational resources and energy consumption. To address this, we explore the potential of spiking neural networks(SNNs) in NLU——a promising avenue with demonstrated advantages, including reduced power consumption and improved efficiency due to their event-driven characteristics. We propose the SpikingMiniLM,a novel spiking Transformer model tailored for natural language understanding. We first introduce a multi-step encoding method to convert text embeddings into spike trains. Subsequently, we redesign the attention mechanism and residual connections to make our model operate on the pure spike-based paradigm without any normalization technique. To facilitate stable and fast convergence, we propose a general parameter initialization method grounded in the stable firing rate principle. Furthermore, we apply an ANN-to-SNN knowledge distillation to overcome the challenges of pretraining SNNs. Our approach achieves a macro-average score of 75.5 on the dev sets of the GLUE benchmark, retaining 98% of the performance exhibited by the teacher model MiniLMv2. Our smaller model also achieves similar performance to BERTMINIwith fewer parameters and much lower energy consumption, underscoring its competitiveness and resource efficiency in NLU tasks.
Travel time estimation(TTE)is a fundamental task to build intelligent transportation ***,most existing TTE solutions design models upon simple homogeneous graphs and ignore the heterogeneity of traffic networks,where,...
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Travel time estimation(TTE)is a fundamental task to build intelligent transportation ***,most existing TTE solutions design models upon simple homogeneous graphs and ignore the heterogeneity of traffic networks,where,e.g.,main roads typically contribute differently from side *** terms of spatial dimension,few studies consider the dynamic spatial correlations across road segments,e.g.,the traffic speed/volume on road segment A may correlate with the traffic speed/volume on road segment B,where A and B could be adjacent or non-adjacent,and such correlations may vary across *** terms of temporal dimension,even fewer studies consider the dynamic temporal dependences,where,e.g.,the historical states of road A may directly correlate with the recent state of A,and may also indirectly correlate with the recent state of road *** track all aforementioned issues of existing TTE approaches,we provide HDTTE,a solution that employs heterogeneous and dynamic spatio-temporal predictive ***,we first design a general multi-relational graph constructor that extracts hidden heterogeneous information of road segments,where we model road segments as nodes and model correlations as edges in the multi-relational ***,we propose a dynamic graph attention convolution module that aggregates dynamic spatial dependence of neighbor roads to focal *** also present a novel correlation-augmented temporal convolution module to capture the influence of states at past time steps on current traffic ***,in view of the periodic dependence of traffic,we develop a multi-scale adaptive fusion layer to enable HDTTE to exploit periodic patterns from recent,daily,and weekly traffic *** experimental study using real-life highway and urban datasets demonstrates the validity of the approach and its advantage over others.
The widespread adoption of wearable devices has led to a surge in the development of multi-device wearable human activity recognition (WHAR) systems. Nevertheless, the performance of traditional supervised learning-ba...
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Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy ...
Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy *** learning(FL) enhances RS's privacy by enabling model training on decentralized data [2]. Although integrating KG and FL can address both data sparsity and privacy issues in RSs [3], several challenges persist. CH1,Each client's local model relies on a consistent global model from the server, limiting personalized deployment to endusers.
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