This paper presents a fast Affine Motion Estimation (AME) of Versatile Video Coding (VVC) Standard, based on Machine Learning and using Random Forest (RF) classification method. This encoding approach develops an RF m...
This paper presents a fast Affine Motion Estimation (AME) of Versatile Video Coding (VVC) Standard, based on Machine Learning and using Random Forest (RF) classification method. This encoding approach develops an RF model for each block size. The models were trained with information extracted during the VVC encoding process of the current, parent, and neighboring Coding Units (CU). Each model is applied to predict whether the Affine Motion Estimation (AME) will be skipped or not for that CU size. The proposed solution achieves a reduction of 20% on average in AME encoding time, with an insignificant impact of 0.07% on BD-BR.
Trusting others and reciprocating the received trust with trustworthy actions are fundaments of economic and social interactions. The trust game (TG) is widely used for studying trust and trustworthiness and entails a...
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Score-based generative models can effectively learn the distribution of data by estimating the gradient of the distribution. Due to the multi-step denoising characteristic, researchers have recently considered combini...
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Copy-move forgeries often exploit homogeneous regions in images with large-scale attacks to either highlight or conceal target objects. These manipulations are simple to execute but challenging to notice. Forgery dete...
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ISBN:
(数字)9798350350067
ISBN:
(纸本)9798350350074
Copy-move forgeries often exploit homogeneous regions in images with large-scale attacks to either highlight or conceal target objects. These manipulations are simple to execute but challenging to notice. Forgery detection techniques like Copy-Move Forgery Detection (CMFD) cannot detect these forged documents because they are unable to identify a sufficient number of effective keypoints in homogeneous areas, leading to inaccurate and inefficient results. SURF stands for Speeded-Up Robust Features and is used in this paper along with A-KAZE and Scale-Invariant Feature Transforms. According to our experiment, A-KAZE offers superior detection performance under diverse attacks, especially when it comes to large-scale attacks targeting homogeneous regions. A-KAZE is found to be more accurate than SIFT, SURF, and A-KAZE when applied to the NB-CASIA dataset, achieving detection accuracies of $\mathbf{8 9. 2 \%}, \mathbf{9 3. 9 \%}$ and $\mathbf{9 8. 9 8 \%}$.)
We present progress towards realizing electronic-photonic quantum systems on-chip;particularly, entangled photon-pair sources, placing them in the context of previous work, and outlining our vision for mass-producible...
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Feature engineering (FE) consists of generating new, better features to improve Machine Learning models. Very often, FE is performed in a series of trial-and-error steps conducted manually by data scientists. Moreover...
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ISBN:
(纸本)9781728190495
Feature engineering (FE) consists of generating new, better features to improve Machine Learning models. Very often, FE is performed in a series of trial-and-error steps conducted manually by data scientists. Moreover, FE requires data-specific and domain knowledge, both rarely easy to acquire. To alleviate these problems, we propose the Self-Organizing Automatic Feature engineering (SOAFE), a novel approach for Automatic Feature engineering (AFE). Different from the majority of the existing AFEs, SOAFE employs an unsupervised technique (Self-Organizing Maps) to identify patterns in the data, and apply a form of cooperative training, inspired by Generative Adversarial Networks, to improve the feature construction. Our results on several datasets show that SOAFE can improve classification models when compared with existing AFE approaches.
Exploring large datasets in search for valuable insights requires time and sufficient technical knowledge. In order to alleviate this task, we propose and implemented a prototype of a data exploration tool. It is base...
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ISBN:
(纸本)9781728188652
Exploring large datasets in search for valuable insights requires time and sufficient technical knowledge. In order to alleviate this task, we propose and implemented a prototype of a data exploration tool. It is based on Self-Organizing Maps (SOM) and helps non-technical users with limited technical expertise and time. Our proposed approach employs SOM as a clustering mechanism to group and recommend exploratory data views to the user. This recommendation process can also be personalized to meet user’s intention in an interactive manner. Experimental results show that the reported prototype is effective in recommending valuable views, hence, being of aid in data exploration tasks.
Feature engineering (FE) consists of generating new, better features to improve the results obtained by Machine Learning models. Very often, FE is performed in a series of trial-and-error steps conducted manually by d...
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ISBN:
(纸本)9781728188652
Feature engineering (FE) consists of generating new, better features to improve the results obtained by Machine Learning models. Very often, FE is performed in a series of trial-and-error steps conducted manually by data scientists. Moreover, FE requires data-specific and domain knowledge, both rarely easy to acquire. To alleviate these problems, we propose an automatic FE approach based on Self-Organizing Maps (SOM) in which new features are generated via pattern recognition. The use of the SOM algorithm in variable generation tasks can identify data elements that help Machine Learning models to obtain better results and points out to a broad direction for future researches.
This work presents experimental electrical characteristics and circuit prediction at cryogenic temperatures (down to 10 K) for three different kinds of germanium (Ge)-based FETs with advanced Fin/GAA structures. Among...
A future networking design called "software-defined networking"combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regula...
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