Guiding enterprises towards green technology innovation (GTI) is a pivotal strategy for mitigating pollution and energy wastage, thereby facilitating the transition towards a green and low-carbon economy. In order to ...
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This paper addresses the problem of predicting consumer behavior in live webcast banding and explores the application of machine learning algorithms in this field and their effectiveness. The study mainly focuses on t...
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In the field of computational intelligence and data analytics, the detection of fake food reviews has emerged as a pressing challenge, exacerbated by the widespread use of social media. These fraudulent reviews, parti...
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In SP ARQL federated query system, cardinality estimation is one of the most critical steps that affect query performance. In recent years, a large number of federated query cardinality estimation methods have been pr...
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Computer vision(CV)algorithms have been extensively used for a myriad of applications *** the multimedia data are generally well-formatted and regular,it is beneficial to leverage the massive parallel processing power...
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Computer vision(CV)algorithms have been extensively used for a myriad of applications *** the multimedia data are generally well-formatted and regular,it is beneficial to leverage the massive parallel processing power of the underlying platform to improve the performances of CV *** Instruction Multiple data(SIMD)instructions,capable of conducting the same operation on multiple data items in a single instruction,are extensively employed to improve the efficiency of CV *** this paper,we evaluate the power and effectiveness of RISC-V vector extension(RV-V)on typical CV algorithms,such as Gray Scale,Mean Filter,and Edge *** our examinations,we show that compared with the baseline OpenCV implementation using scalar instructions,the equivalent implementations using the RV-V(version 0.8)can reduce the instruction count of the same CV algorithm up to 24x,when processing the same input ***,the actual performances improvement measured by the cycle counts is highly related with the specific implementation of the underlying RV-V *** our evaluation,by using the vector co-processor(with eight execution lanes)of Xuantie C906,vector-version CV algorithms averagely exhibit up to 2.98x performances speedups compared with their scalar counterparts.
Unlike their success in solving complex and difficult problems, there is a lack of proper mathematical analysis and discussion of metaheuristic algorithms. Even though some researches are done on these aspects, the ga...
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The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the...
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The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.
In recent years, China has introduced numerous policies aimed at fostering the growth of the traditional Chinese medicine (TCM) industry. Concurrently, the enrichment of TCM healthcare resources has s purred an increa...
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ISBN:
(数字)9798350363609
ISBN:
(纸本)9798350363616
In recent years, China has introduced numerous policies aimed at fostering the growth of the traditional Chinese medicine (TCM) industry. Concurrently, the enrichment of TCM healthcare resources has s purred an increase in demand for TCM services. To address the needs of TCM practitioners and to offer the public more high-quality, precise, and intelligent TCM services, we have designed a comprehensive WeChat applet for TCM services that incorporates text recognition technology. This applet leverages text recognition to analyze the ingredients listed in prescriptions and employs a cold extraction method to prepare Chinese patent medicines, thereby reducing the cost of TCM treatments for consumers. Developed using the WeChat developer tool, the applet boasts a user-friendly interface and a suite of integrated functionalities, presenting a valuable service model for the TCM sector.
Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems(SRS).However,the current deep model structures are limited in their ability to learn high-...
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Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems(SRS).However,the current deep model structures are limited in their ability to learn high-quality embeddings with insufficient ***,highly skewed long-tail distribution is very common in recommender ***,in this paper,we focus on enhancing the representation of tail items to improve sequential recommendation *** empirical studies on benchmarks,we surprisingly observe that both the ranking performance and training procedure are greatly hindered by the poorly optimized tail item *** address this issue,we propose a sequential recommendation framework named TailRec that enables contextual information of tail item well-leveraged and greatly improves its corresponding *** the characteristics of the sequential recommendation task,the surrounding interaction records of each tail item are regarded as contextual information without leveraging any additional side *** approach allows for the mining of contextual information from cross-sequence behaviors to boost the performance of sequential *** a light contextual filtering component is plug-and-play for a series of SRS *** verify the effectiveness of the proposed TailRec,we conduct extensive experiments over several popular benchmark *** experimental results demonstrate that TailRec can greatly improve the recommendation results and speed up the training *** codes of our methods have been available.
By recognizing students' facial expressions in a classroom, it is possible to determine in real-time whether students are confused, dissatisfied or happy. Understanding students' emotional states helps teacher...
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