Federated learning is a novel machine learning paradigm in which the model is trained on local data by distributed clients. Most of the current research on federated learning assumes that clients are unconditionally p...
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ISBN:
(数字)9798350349184
ISBN:
(纸本)9798350349191
Federated learning is a novel machine learning paradigm in which the model is trained on local data by distributed clients. Most of the current research on federated learning assumes that clients are unconditionally providing data and training models, and little consideration has been given to how to incentivize clients with high-quality data to participate in the model training task. Therefore, this paper proposes a blockchain-based federated learning incentive mechanism combining data quality verification and reverse auction. Firstly, by verifying the quality of client data, the client with high-quality data that meets the task requirements is selected, and then the client sends its bid for the task to the reverse auction smart contract. Secondly, some clients with the best performance in different training phases are selected to participate in the task training using smart contracts, and a certain number of clients are selected to form a committee among the unselected participants. The committee members are responsible for validating the local model parameters of the clients while receiving validation rewards. Finally, we conduct simulation experiments on two datasets separately, and the experimental results demonstrate the effectiveness of our proposal.
While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that ...
The significance of the real estate search engine in the economy necessitates the development of a reliable room image luxury level annotation method that addresses current limitations, including the inability to asse...
The significance of the real estate search engine in the economy necessitates the development of a reliable room image luxury level annotation method that addresses current limitations, including the inability to assess room quality, underutilization of deep network capacities, and the need for more annotated house images. This paper proposes a novel real estate image annotation model, leveraging the diffusion model and contrastive language-image pre-training (CLIP) network, through a multi-stage algorithm. First, the diffusion network is employed as a data augmentation technique to generate additional real estate images for network training. Then, a CLIP model is utilized to categorize images into the kitchen, bathroom, dining room, living room, and foyer. Finally, five CLIP models assess the condition of each room, categorizing it as contemporary and standard. Experimental results on a newly collected real estate image dataset demonstrate that the proposed approach surpasses existing house image classification algorithms.
Transactional stream processing engines (TSPEs) have gained increasing attention due to their capability of processing real-time stream applications with transactional semantics. However, TSPEs remain susceptible to s...
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ISBN:
(数字)9798350317152
ISBN:
(纸本)9798350317169
Transactional stream processing engines (TSPEs) have gained increasing attention due to their capability of processing real-time stream applications with transactional semantics. However, TSPEs remain susceptible to system failures and power outages. Existing TSPEs mainly focus on performance improvement, but still face a significant challenge to guarantee fault tolerance while offering high-performance services. We revisit commonly-used fault tolerance approaches in stream processing and database systems, and find that these approaches do not work well on TSPEs due to complex data dependencies. In this paper, we propose a novel TSPE called MorphStreamR to achieve fast failure recovery while guaranteeing low performance overhead at runtime. The key idea of MorphStreamR is to record intermediate results of resolved dependencies at runtime, and thus eliminate data dependencies to improve task parallelism during failure recovery. MorphStreamR further mitigates the runtime overhead by selectively tracking data dependencies and incorporating workload-aware log commitment. Experimental results show that MorphStreamR can significantly reduce the recovery time by up to 3.1 x while experiencing much less performance slowdown at runtime, compared with other applicable fault tolerance approaches.
The long-range wide area network (LoRaWAN) is a standard for the Internet of Things (IoT) because it has low cost, long range, not energy-intensive, and capable of supporting massive end devices (EDs). The adaptive da...
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Synapses are the special connection structure between brain neurons, and they are the core element of brain plasticity and information transmission. In order to better understand the process of brain information trans...
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Semantic reasoning techniques based on knowledge graphs have been widely studied since they were proposed. Previous studies are mostly based on closed-world assumptions, which cannot reason about unknown facts. To thi...
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In India manually evaluating court records was time-consuming and prone to errors, leading to delays in justice administration. Legal Sarathi, an automated event extraction tool was proposed to address this issue, it ...
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ISBN:
(数字)9798350388602
ISBN:
(纸本)9798350388619
In India manually evaluating court records was time-consuming and prone to errors, leading to delays in justice administration. Legal Sarathi, an automated event extraction tool was proposed to address this issue, it utilized Large Language Models coupled with other cutting-edge Natural Language Processing (NLP) techniques. Legal Sarathi analyzes unstructured legal case documents to extract time frames, significant participants and major events to improve speed and accuracy. It featured state-of-the-art Natural Language Processing (NLP) capabilities for prompt and precise case analysis whose aims included speeding up the analysis of court proceedings and real-time information retrieval, while simultaneously providing critical data for informed decision-making. The tools deliverables comprised an automated event extraction system, a highly preprocessed data corpus, a sophisticated NLP model, a user-friendly interface and thorough validation results. Ultimately, this established an improved and equitable legal system in India to ensure that all parties received quick and fair access to the courts and fostered public trust.
Medical image analysis algorithms have been quite popular for automating the segmentation of the liver and liver tumours in recent years. A system like this would also lessen radiologists’ workload and subjectiv...
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This study underscores the potential of variational representation as a tool for uncertainty estimation in the energy market, which would help Independent System Operators (ISO) in risk assessment and demand response....
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ISBN:
(数字)9798350395075
ISBN:
(纸本)9798350395082
This study underscores the potential of variational representation as a tool for uncertainty estimation in the energy market, which would help Independent System Operators (ISO) in risk assessment and demand response. Forecasts of future supply curves are also very valuable to energy suppliers seeking to optimize bidding strategies in an oligopolistic power market characterized by information privacy and volatility of individual bidding behaviors and variability in renewable energy generation. However, traditional deterministic modelling methods may not adequately capture the inherent uncertainty and volatility of the electricity market. Our variational encoding technique produces a resultant embedding space that is stochastic and exhibits substantial interpretability, which we exploit to demonstrate the model's transparency. By using the learned posterior probability distribution of the embedded aggregated supply curves (ASCs) to determine the statistical dispersion, we identify and study periods of high uncertainty in the Singapore electricity market between December 2021 and March 2023. We seek to provide insights into the origins of these uncertainties, showing that the method can capture changes in the market useful for risk modelling in decision-making.
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