This paper explores fractional-order derivatives to model the apparent arterial compliance dynamics in human vascular aging subjects. The proposed model employs fractional-order capacitor (FOC) elements that combine c...
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Information overload is a major problem for many people who use the internet. This is because there is so much information available that it can be hard to know where to start. Filtering tools, like the Recommender Sy...
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ISBN:
(数字)9798350364729
ISBN:
(纸本)9798350364736
Information overload is a major problem for many people who use the internet. This is because there is so much information available that it can be hard to know where to start. Filtering tools, like the Recommender System, are needed to solve this problem. These tools give users access to relevant information and let them customise their search results based on their liking. The Collaborative Filtering Recommendation System finds the group of active users who are your closest neighbours. This study shows many ways to use the supervised machine learning algorithm (KNN) method for the Movie recommendation System with different similarity measures, such as the cosine metric, Squared Error Similarity, Linear Correlation Coefficient, and Baseline Predictor Correlation. For the movie recommender system, these many changes to the KNN algorithm were tested on real data from the given dataset and compared based on accuracy measures such as the number of matching pairs and different types of error like mean absolute error, squared mean error, and squared root mean error. By making changes to the web browser, this recommendation system processing tool could be used as a substitute in the real world.
The Covid-19 pandemic is influenced by many environmental, health, and socioeconomic aspects such as air pollution, comorbidity, occupation, etc. To better manage future pandemics, decision-makers need comprehensive d...
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Reconstructing the 2D or 3D shape of an object from several 2D drawings is a crucial problem in computer-aided design (CAD). Despite the advancement of deep neural networks, automatic isometric image generation from t...
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ISBN:
(数字)9798331515942
ISBN:
(纸本)9798331515959
Reconstructing the 2D or 3D shape of an object from several 2D drawings is a crucial problem in computer-aided design (CAD). Despite the advancement of deep neural networks, automatic isometric image generation from three orthographic views line drawings using deep learning remains unresolved. Existing image-to-image translation techniques often generate images from just one input image. In this paper, we propose a novel method for the above task using a GAN-based model, namely IsoGAN. This method takes three images of object’s front, side, and top view as input, then analyzes the spatial and geometrical relations between each view and finally generates the corresponding isometric view image of the object. Extensive experiments on SPARE3D dataset show promising results of IsoGAN on isometric view generation task, demonstrating the effectiveness of the proposed IsoGAN.
Recently, large Vision Language (VL) models, i.e., CLIP, have demonstrated impressive capabilities in training solely on internet-scale image-language pairs. Moreover, almost all VL models have tackled indoor objects ...
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ISBN:
(数字)9798350365474
ISBN:
(纸本)9798350365481
Recently, large Vision Language (VL) models, i.e., CLIP, have demonstrated impressive capabilities in training solely on internet-scale image-language pairs. Moreover, almost all VL models have tackled indoor objects under controlled illumination and camera views. However, outdoor 3D environments are time-varying uncontrolled scenes under natural phenomena. Therefore, captions from such unseen scenes and objects are hard to obtain in a state-of-the-art (SOTA) one-shot algorithm, resulting in insufficient captions. This paper proposes PV-Cap (Physics-based Vocabulary for Caption) for enhancing 3D scene understanding through enriched captions. Since many tasks in understanding 3D dynamic scenes are hard to deal with, PVCap aims to disentangle such complexities through multiple grouped Deep Learning and Vision Language models step-wisely. Proposed i-VQA (iterative VQA) and 3D-CPP (3D Contrastive Physical-Scale Pretraining) extended from SOTA 2D-CLIP also contribute to generating physical and 3D-based captions. Using many images with 3D dynamic events, i.e., road scenes with traffic flow and accidents, experiments have demonstrated the usability and effectiveness of proposed PV-Cap over SOTA models in terms of segmentation and captions.
Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners. Wide adoption of FL faces the fundamental challenges of data heterogeneity and th...
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ISBN:
(数字)9798350390155
ISBN:
(纸本)9798350390162
Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners. Wide adoption of FL faces the fundamental challenges of data heterogeneity and the large scale of data owners involved. In this paper, we investigate the prospect of Foundation Model (e.g., transformers)-based FL for achieving generalization and personalization in this setting. Different from existing research efforts which mostly focus on studying Transformer-based FL on small scales, we conduct extensive comparative experiments involving FL with Transformers, ResNet, and personalized ResNet-based FL approaches under various large-scale scenarios. These experiments consider varying numbers of data owners to demonstrate Transformers’ advantages over deep neural networks in large-scale heterogeneous FL tasks. In addition, we analyze the superior performance of Transformers by comparing the Centered Kernel Alignment (CKA) representation similarity across different layers and FL models to gain insight into the reasons behind their promising capabilities.
Dental caries is a distinct disease strategy caused by the metabolic action of dental biofilm, which continually demineralizes lacquer and dentine. Its commonly known as tooth rot, is one of the most prevalent and lon...
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Advancements in next-generation sequencer(NGS)platforms have improved NGS sequence data production and reduced the cost involved,which has resulted in the production of a large amount of genome *** downstream analysis...
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Advancements in next-generation sequencer(NGS)platforms have improved NGS sequence data production and reduced the cost involved,which has resulted in the production of a large amount of genome *** downstream analysis of multiple associated sequences has become a bottleneck for the growing genomic data due to storage and space utilization issues in the domain of *** traditional string-matching algorithms are efficient for small sized data sequences and cannot process large amounts of data for downstream *** study proposes a novel bit-parallelism algorithm called BitmapAligner to overcome the issues faced due to a large number of sequences and to improve the speed and quality of multiple sequence alignment(MSA).The input files(sequences)tested over BitmapAligner can be easily managed and organized using the Hadoop distributed file *** proposed aligner converts the test file(the whole genome sequence)into binaries of an equal length of the sequence,line by line,before the sequence alignment *** Hadoop distributed file system splits the larger files into blocks,based on a defined block size,which is 128 MB by *** can accurately process the sequence alignment using the bitmask approach on large-scale sequences after sorting the *** experimental results indicate that BitmapAligner operates in real time,with a large number of ***,BitmapAligner achieves the exact start and end positions of the pattern sequence to test the MSA application in the whole genome query *** MSA’s accuracy is verified by the bitmask indexing property of the bit-parallelism extended shifts(BXS)*** dynamic and exact approach of the BXS algorithm is implemented through the MapReduce function of Apache ***,the traditional seeds-and-extend approach faces the risk of errors while identifying the pattern sequences’***,the proposed model resolves the largescale data challen
Open Government data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency...
Open Government data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all; thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format.
Visual grounding aims to ground an image region through natural language, which heavily relies on cross-modal alignment. Most existing methods transfer visual/linguistic knowledge separately by fully fine-tuning uni-m...
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