The Cranfield paradigm has served as a foundational approach for developing test collections, with relevance judgments typically conducted by human assessors. However, the emergence of large language models (LLMs) has...
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Video magnification (VM) provides an alternative health monitoring solution by enabling contactless and remote measurement of vital signs such as heart rate (HR). HR is a crucial biomarker for assessing the overall he...
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Technology improvements have changed how crimes are solved, which has led to more collaborative study into how criminals act. "Prophet," an additive model-based method for predicting complicated, nonlinear t...
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This article compares the multilingual texts that are used for bilingual lexicon extraction and plagiarism detection. A collection of related sentences and sentences that are translations of one another, a parallel co...
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Data security and user privacy have become crucial elements in multi-tenant data *** traffic types in the multi-tenant data center in the cloud environment have their characteristics and *** the data center network(DC...
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Data security and user privacy have become crucial elements in multi-tenant data *** traffic types in the multi-tenant data center in the cloud environment have their characteristics and *** the data center network(DCN),short and long flows are sensitive to low latency and high throughput,*** traditional security processing approaches,however,neglect these characteristics and *** paper proposes a fine-grained security enhancement mechanism(SEM)to solve the problem of heterogeneous traffic and reduce the traffic completion time(FCT)of short flows while ensuring the security of multi-tenant traffic ***,for short flows in DCN,the lightweight GIFT encryption method is *** Intra-DCN long flows and Inter-DCN traffic,the asymmetric elliptic curve encryption algorithm(ECC)is *** NS-3 simulation results demonstrate that SEM dramatically reduces the FCT of short flows by 70%compared to several conventional encryption techniques,effectively enhancing the security and anti-attack of traffic transmission between DCNs in cloud computing ***,SEM performs better than other encryption methods under high load and in largescale cloud environments.
Affinity propagation(AP)is a classic clustering *** improve the classical AP algorithms,we propose a clustering algorithm namely,adaptive spectral affinity propagation(AdaSAP).In particular,we discuss why AP is not su...
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Affinity propagation(AP)is a classic clustering *** improve the classical AP algorithms,we propose a clustering algorithm namely,adaptive spectral affinity propagation(AdaSAP).In particular,we discuss why AP is not suitable for non-spherical clusters and present a unifying view of nine famous arbitrary-shaped clustering *** propose a strategy of extending AP in non-spherical clustering by constructing category similarity of *** the monotonicity that the clusters’number increases with the self-similarity in AP,we propose a model selection procedure that can determine the number of clusters *** the parameters introduced by extending AP in non-spherical clustering,we provide a grid-evolving strategy to optimize them *** effectiveness of AdaSAP is evaluated by experiments on both synthetic datasets and real-world clustering *** results validate that the superiority of AdaSAP over benchmark algorithms like the classical AP and spectral clustering algorithms.
Large Language Models (LLMs) have shown great potential in the biomedical domain with the advancement of retrieval-augmented generation (RAG). However, existing retrieval-augmented approaches face challenges in addres...
Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset. Current state-of-the-art approaches are based on supervised learning with a conditioned policy. However, th...
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Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset. Current state-of-the-art approaches are based on supervised learning with a conditioned policy. However, these approaches fall short in real-world applications that involve complex tasks with rich temporal and logical structures. In this paper, we propose temporal logic Specification-conditioned Decision Transformer (SDT), a novel framework that harnesses the expressive power of signal temporal logic (STL) to specify complex temporal rules that an agent should follow and the sequential modeling capability of Decision Transformer (DT). Empirical evaluations on the DSRL benchmarks demonstrate the better capacity of SDT in learning safe and high-reward policies compared with existing approaches. In addition, SDT shows good alignment with respect to different desired degrees of satisfaction of the STL specification that it is conditioned on. Copyright 2024 by the author(s)
The integration of sensor devices into an IoT network is experiencing significant growth. Along with the rise of several application demands, it is necessary to continuously develop new designs to accommodate these ch...
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Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. Howev...
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