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Mathematics, Interdisciplinary Applications
Cheng-Yuan Liou, Aleksandr A. Simak, Wei-Chen Cheng
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Mathematics, Interdisciplinary Applications
Cheng-Yuan Liou, Tai-Hei Wu, Chia-Ying Lee
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Mathematical & Computational Biology
Cheng-Yuan Liou, Shen-Han Tseng, Wei-Chen Cheng, Huai-Ying Tsai
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
(2013)
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Computer Science, Interdisciplinary Applications
Jyh-Ying Peng, John A. D. Aston, Roger N. Gunn, Cheng-Yuan Liou, John Ashburner
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2008)
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Computer Science, Artificial Intelligence
Wei-Chen Cheng, Jau-Chi Huang, Cheng-Yuan Liou
KNOWLEDGE-BASED SYSTEMS
(2012)
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Computer Science, Artificial Intelligence
Jiann-Ming Wu, Pei-Hsun Hsu, Cheng-Yuan Liou
Article
Computer Science, Artificial Intelligence
Cheng-Yuan Liou, Wei-Chen Cheng, Jiun-Wei Liou, Daw-Ran Liou
Article
Computer Science, Artificial Intelligence
Wei-Chen Cheng, Cheng-Yuan Liou
Proceedings Paper
Computer Science, Artificial Intelligence
Yi-Chun Lin, Chao-I Tuan, Cheng-Yuan Liou
2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE, COGNITIVE ALGORITHMS, MIND, AND BRAIN (CCMB)
(2013)
Proceedings Paper
Computer Science, Artificial Intelligence
Daw-Ran Liou, Chia-Ching Lin, Cheng-Yuan Liou
INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2012), PT II
(2012)
Proceedings Paper
Computer Science, Artificial Intelligence
Chang-Hsian Uang, Jiun-Wei Liou, Cheng-Yuan Liou
INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2012), PT I
(2012)
Proceedings Paper
Computer Science, Artificial Intelligence
Jiun-Wei Liou, Cheng-Yuan Liou
INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2012), PT I
(2012)
Proceedings Paper
Computer Science, Artificial Intelligence
Jiun-Wei Liou, Cheng-Yuan Liou
ADVANCES IN SWARM INTELLIGENCE, ICSI 2012, PT II
(2012)
Proceedings Paper
Computer Science, Artificial Intelligence
Wei-Chen Cheng, Jiun-Wei Liou, Cheng-Yuan Liou
2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2012)
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Computer Science, Artificial Intelligence
Jyh-Ying Peng, John A. D. Aston, Cheng-Yuan Liou
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL
(2011)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
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Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
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Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
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Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
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Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
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Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.