With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreove...
With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.
Multi-Modal Entity Alignment (MMEA) is a pivotal task in Multi-Modal Knowledge Graphs (MMKGs), seeking to identify identical entities by leveraging associated modal attributes. However, real-world MMKGs confront the c...
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Lung cancer is one of the main causes of death globally and adds to the burden of disease and mortality. Early detection of lung cancer may reduce the chance of developing lung cancer. Artificial intelligence (AI) in ...
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Sailing is not a new concept. The use of artificial intelligence (AI) within the field of sailing however is a relatively new concept. AI is being applied to a variety of different aspects within the field of sailing ...
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Vision is one of the important pathways for human perception of external information, with over 80% of perception being acquired through vision. How to enable computers to possess efficient and flexible visual systems...
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This paper explores the implementation of a distributed model of the Self-Organizing Map (SOM) and its subsequent validation through the implementation of a proof-of-concept prototype using the Typed-Akka tool kit. Th...
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Purpose Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assis...
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Purpose Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling. Design/methodology/approach In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data. Findings The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling. Originality/value The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.
Vanilla image completion approaches exhibit sensitivity to large missing regions, attributed to the limited availability of reference information for plausible generation. To mitigate this, existing methods incorporat...
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Benefitting from the discovery of wireless power transfer (WPT) technology, the wireless rechargeable sensor network (WRSN) has become a promising way for lifetime extension for wireless sensor networks. In practical ...
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Benefitting from the discovery of wireless power transfer (WPT) technology, the wireless rechargeable sensor network (WRSN) has become a promising way for lifetime extension for wireless sensor networks. In practical WRSN scenarios, obstacles can be found almost everywhere. Most state-of-the-art researches believe that obstacles will always degrade signal strength, and omit the influence of obstacles for simplifying the computation process. However, overlooking the positive impacts of obstacles on signal propagation is inconsistent with the intrinsic features of electromagnetic waves. To address this issue, in this paper, we explore the wireless signal propagation process and provide a theoretical charging model to enhance the charging efficiency by leveraging obstacles. Through utilizing the concept of the Fresnel Zone model, we re-formalize the wireless charging model and discretize the charging area and charging time to determine the best charging locations as well as charging duration. We model the charging Efficiency Maximization with Obstacles (EMO) problem as a submodular function maximization problem and propose a cost-efficient algorithm to solve it. Finally, test-bed experiments and extensive simulations are both conducted to verify that our schemes outperform baseline algorithms by $33.46\%$33.46% on average in charging efficiency improvement.
With the rapid development of smart health care, accurate heart rate variability (HRV) estimation for the early detection of diseases has become a hot research topic. Advanced work uses the wireless signal to estimate...
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With the rapid development of smart health care, accurate heart rate variability (HRV) estimation for the early detection of diseases has become a hot research topic. Advanced work uses the wireless signal to estimate the heartbeat in a contact-free way, which usually cannot separate multiple users or work in a dynamic environment. In this article, we propose a lightweight heartbeat-sensing method based on RFID tag pairs, which focuses on HRV extraction in a more general sensing scenario. Based on the tag-pair design, we build a novel heartbeat and respiration model to describe the signal relationship between the two tags from the time and space domains. Based on the model, we propose a Calibrated Temporal-Spatial IQ-Shaping-based signal cancellation algorithm to cancel the respiration and extract the heartbeat. To remove the interference in dynamic measurement, we build an IQ-based signal model via a Principal Component Analysis-based interference estimation. To reduce the statistical error in HRV extraction, we further design a neural network to predict the HRV index. We have implemented a system prototype in a real environment with COTS RFID devices. Extensive experiments show that our system can achieve a median RMSSD error of 7.51 ms, which satisfies the medical demand in HRV measurement.
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