Entity and relation extraction is a critical task in information *** approaches have emphasized obtaining improved span ***,existing work suffers from two major ***,there is an overabundance of low-quality candidate s...
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Entity and relation extraction is a critical task in information *** approaches have emphasized obtaining improved span ***,existing work suffers from two major ***,there is an overabundance of low-quality candidate spans,which hinders the effective extraction of information from high-quality candidate ***,the information encoded by existing marker strategies is often too simple to fully capture the nuances of the span,resulting in the loss of potentially valuable *** address these issues,we propose an enhancing entity and relation extraction with high-quality spans and enhanced marker(HSEM)strategies,it assigns adaptive weights to different spans in order to make the model more focused on high quality ***,the HSEM model enriches marker representation to incorporate more span information and enhance entity ***,we design a span scoring framework that assesses span quality based on the fusion of internal information and focuses the model on training high-quality samples to improve *** results on six benchmark datasets demonstrate that our model achieves state-of-the-art results after discriminating span quality.
Image captioning is an interdisciplinary research hotspot at the intersection of computer vision and natural language processing, representing a multimodal task that integrates core technologies from both fields. This...
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In recent years, deep learning has significantly advanced skin lesion segmentation. However, annotating medical image data is specialized and costly, while obtaining unlabeled medical data is easier. To address this c...
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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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With the development of artificial intelligence, deep learning has been increasingly used to achieve automatic detection of geographic information, replacing manual interpretation and improving efficiency. However, re...
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With the rapid development of mobile communication technology and intelligent applications,the quantity of mobile devices and data traffic in networks have been growing exponentially,which poses a great burden to netw...
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With the rapid development of mobile communication technology and intelligent applications,the quantity of mobile devices and data traffic in networks have been growing exponentially,which poses a great burden to networks and brings huge challenge to servicing user *** caching,which utilizes the storage and computation resources of the edge to bring resources closer to end users,is a promising way to relieve network burden and enhance user *** this paper,we aim to survey the edge caching techniques from a comprehensive and systematic *** first present an overview of edge caching,summarizing the three key issues regarding edge caching,i.e.,where,what,and how to cache,and then introducing several significant caching *** then carry out a detailed and in-depth elaboration on these three issues,which correspond to caching locations,caching objects,and caching strategies,*** particular,we innovate on the issue“what to cache”,interpreting it as the classification of the“caching objects”,which can be further classified into content cache,data cache,and service ***,we discuss several open issues and challenges of edge caching to inspire future investigations in this research area.
Detecting dangerous driving behavior is a critical research area focused on identifying and preventing actions that could lead to traffic accidents, such as smoking, drinking, yawning, and drowsiness, through technica...
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Low-frequency structural vibrations caused by poor rigidity are one of the main obstacles limiting the machining efficiency of robotic *** vibration suppression strategies primarily focus on passive vibration absorpti...
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Low-frequency structural vibrations caused by poor rigidity are one of the main obstacles limiting the machining efficiency of robotic *** vibration suppression strategies primarily focus on passive vibration absorption at the robotic end and feedback control at the joint *** these strategies have a certain vibration suppression effect,the limitations of robotic flexibility and the extremely limited applicable speed range remain to be *** this study,a Magnetorheological Joint Damper(MRJD)is *** joint-mounted feature ensures machining flexibility of the robot,and the millisecond response time of the Magnetorheological Fluid(MRF)ensures a large effective spindle speed *** importantly,the evolution law of the damping performance of MRJD was revealed based on a low-frequency chatter mechanism,which guarantees the application of MRJD in robotic milling *** analyze the influence of the robotic joint angle on the suppression effect of the MRJD,the joint braking coefficient and end braking coefficient were *** coordinate plots were used to visualize the joint range with the optimal vibration suppression ***,a combination of different postures and cutting parameters was used to verify the vibration suppression effect and feasibility of the joint angle *** experimental results show that the MRJD,which directly improves the joint vibration resistance,can effectively suppress the low-frequency vibration of robotic milling under a variety of cutting conditions.
Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplor...
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Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.
As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy conce...
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As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
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