390 research outputs found
Herding Effect based Attention for Personalized Time-Sync Video Recommendation
Time-sync comment (TSC) is a new form of user-interaction review associated
with real-time video contents, which contains a user's preferences for videos
and therefore well suited as the data source for video recommendations.
However, existing review-based recommendation methods ignore the
context-dependent (generated by user-interaction), real-time, and
time-sensitive properties of TSC data. To bridge the above gaps, in this paper,
we use video images and users' TSCs to design an Image-Text Fusion model with a
novel Herding Effect Attention mechanism (called ITF-HEA), which can predict
users' favorite videos with model-based collaborative filtering. Specifically,
in the HEA mechanism, we weight the context information based on the semantic
similarities and time intervals between each TSC and its context, thereby
considering influences of the herding effect in the model. Experiments show
that ITF-HEA is on average 3.78\% higher than the state-of-the-art method upon
F1-score in baselines.Comment: ACCEPTED for ORAL presentation at IEEE ICME 201
A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding
Multi-Intent Spoken Language Understanding (SLU), a novel and more complex
scenario of SLU, is attracting increasing attention. Unlike traditional SLU,
each intent in this scenario has its specific scope. Semantic information
outside the scope even hinders the prediction, which tremendously increases the
difficulty of intent detection. More seriously, guiding slot filling with these
inaccurate intent labels suffers error propagation problems, resulting in
unsatisfied overall performance. To solve these challenges, in this paper, we
propose a novel Scope-Sensitive Result Attention Network (SSRAN) based on
Transformer, which contains a Scope Recognizer (SR) and a Result Attention
Network (RAN). Scope Recognizer assignments scope information to each token,
reducing the distraction of out-of-scope tokens. Result Attention Network
effectively utilizes the bidirectional interaction between results of slot
filling and intent detection, mitigating the error propagation problem.
Experiments on two public datasets indicate that our model significantly
improves SLU performance (5.4\% and 2.1\% on Overall accuracy) over the
state-of-the-art baseline
Worst-case delay control in multigroup overlay networks
This paper proposes a novel and simple adaptive control algorithm for the effective delay control and resource utilization of end host multicast (EMcast) when the traffic load becomes heavy in a multigroup network with real-time flows constrained by (sigma, rho) regulators. The control algorithm is implemented at the overlay networks and provides more regulations through a novel (sigma, rho, lambda) regulator at each group end host who suffers from heavy input traffic. To our knowledge, it is the first work to incorporate traffic regulators into the end host multicast to control heavy traffic output. Our further contributions include a theoretical analysis and a set of results. We prove the existence and calculate the value of the rate threshold rho* such that for a given set of K groups, when the average rate of traffic entering the group end hosts rho macr > rho*, the ratio of the worst-case multicast delay bound of the proposed (sigma, rho, lambda) regulator over the traditional (sigma, rho) regulator is O(1/Kn) for any integer n. We also prove the efficiency of the novel algorithm and regulator in decreasing worst-case delays by conducting computer simulations
Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach
In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload
computationally-intensive tasks to edge nodes, where they are executed within
containers, reducing the reliance on centralized cloud infrastructure. Frequent
upgrades are essential to maintain the efficient and secure operation of edge
clusters. However, traditional cloud cluster upgrade strategies are ill-suited
for edge clusters due to their geographically distributed nature and resource
limitations. Therefore, it is crucial to properly schedule containers and
upgrade edge clusters to minimize the impact on running tasks. In this paper,
we propose a low-latency container scheduling algorithm for edge cluster
upgrades. Specifically: 1) We formulate the online container scheduling problem
for edge cluster upgrade to minimize the total task latency. 2) We propose a
policy gradient-based reinforcement learning algorithm to address this problem,
considering the unique characteristics of MEC. 3) Experimental results
demonstrate that our algorithm reduces total task latency by approximately 27\%
compared to baseline algorithms
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