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arXiv 2021年

作者： Soemers, Dennis J.N.J. Mella, Vegard Piette, Éric Stephenson, Matthew Browne, Cameron Teytaud, Olivier Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Facebook AI Research Paris France

In this paper, we use fully convolutional architectures in AlphaZero-like self-play training setups to facilitate transfer between variants of board games as well as distinct games. We explore how to transfer trained parameters of these architectures based on shared semantics of channels in the state and action representations of the Ludii general game system. We use Ludii’s large library of games and game variants for extensive transfer learning evaluations, in zero-shot transfer experiments as well as experiments with additional fine-tuning time. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Fast One-Pass Approximation for Matrix-Based Rényi's Entropy

SSRN

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SSRN 2024年

作者： Gong, Tieliang Dong, Yuxin Gao, Zeyu Chen, Hong Zhang, Weizhan Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi’an710049 China Department of Oncology University of Cambridge CambridgeCB2 1TN United Kingdom Department of Mathematics and Statistics Huazhong Agriculture University Hubei Wuhan430070 China

This paper is concerned with improving the computational efficiency of the matrix-based Rényi's α-order entropy, which enables directly being accessed from the given data without knowing the probability density function of the underlying data. However, leveraging either trace estimation or the eigenvalue-based methods will cost O(n3) in time complexity, where n is the number of observed samples. The intensive computational demands impede its utility in modern large-scale machine learning tasks. Recently, the Hutch++ based entropy approximation was developed, by converting the original entropy estimation problem into stochastic trace estimation. It reduces the time complexity to O(n2s) (s © 2024, The Authors. All rights reserved.

关键词： Distillation

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NSPG-Miner: Mining Repetitive Negative Sequential Patterns

arXiv

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arXiv 2025年

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300401 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Hebei Key Laboratory of Big Data Computing 300401 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this paper explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner, but also verify that NSPG-Miner can discover more valuable information than the state-ofthe-art algorithms. Algorithms and datasets can be downloaded from https://***/wuc567/PatternMining/tree/master/NSPG-Miner. Copyright © 2025, The Authors. All rights reserved.

关键词： Miners

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Digital Imaging Analyzing and Reproducing Color, Texture, and Material Appearance

Digital Imaging Analyzing and Reproducing Color, Texture, an...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sujin Jose Arul Sajja Suneel Manish Gupta K. Varada Rajkumar Monica Garg Ali Abduhussien Hameed Department of Automobile Engineering Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Digital photography has made great strides in showing colour and texture along with specific material details. Standard approaches usually do not attain realistic representations. The Multifaceted Realism Synthesis presented in this document resolves these problems and outshines classic strategies in accuracy and realism. The MRS framework incorporates three core algorithms: With this approach we can obtain more consistent and accurate colour reproduction using the Colour Harmonisation Algorithm. The Physically Informed Material Rendering and Typical Texture Synthesis (ATS) generate more natural textures that adapt to local details rather than using median filtering or morphological methods. The MRS approach adjusts materials appearance texture and colour to deliver superior realism adaptability and precision beating current methods. This new approach can change digital photography across different areas such as entertainment and healthcare. The technique of MRS has made a substantial improvement in the quest for hyper-real visuals in digital photography.

关键词： Measurement Visualization Accuracy Image color analysis MIMICs Transforms Medical services Rendering (computer graphics) Digital photography Standards

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Contrastive Coding for Active Learning under Class Distribution Mismatch

Contrastive Coding for Active Learning under Class Distribut...

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International Conference on Computer Vision (ICCV)

作者： Pan Du Suyun Zhao Hui Chen Shuwen Chai Hong Chen Cuiping Li Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China Beijing China Renmin University of China Beijing China

ISBN: (纸本)9781665428132

Active learning (AL) is successful based on the assumption that labeled and unlabeled data are obtained from the same class distribution. However, its performance deteriorates under class distribution mismatch, wherein the un-labeled data contain many samples out of the class distribution of labeled data. To effectively handle the problems under class distribution mismatch, we propose a contrastive coding based AL framework named CCAL. Unlike the existing AL methods that focus on selecting the most informative samples for annotating, CCAL extracts both semantic and distinctive features by contrastive learning and combines them in a query strategy to choose the most informative un-labeled samples with matched categories. Theoretically, we prove that the AL error of CCAL has a tight upper bound. Experimentally, we evaluate its performance on CIFAR10, CIFAR100, and an artificial cross-dataset that consists of five datasets; consequently, CCAL achieves state-of-the-art performance by a large margin with remarkably lower annotation cost. To the best of our knowledge, CCAL is the first work related to AL for class distribution mismatch.

关键词： Computer vision Costs Upper bound Annotations Semantics Text categorization Computer architecture

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Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

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SN Computer Science 2023年第4期4卷 363页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

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Assessing Extraneous and Intrinsic Cognitive Load in Space Teleoperated Tasks: A Physiological Metrics Study with Cognitive Load Theory

SSRN

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SSRN 2024年

作者： Yan, Zhao Wang, Yahui Chen, Shanguang Yang, Xiaonan Hua, Yaoguang Fang, Haonan Wang, Chaoran School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing314019 China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China Astronaut Center of China National Key Laboratory of Human Factors Engineering China

This study aimed to explore the relationship between physiological performance indicators and both intrinsic and extrinsic cognitive loads during a space robot grasping task, which involves teleoperation requiring high levels of spatial and motor coordination, attention, and decision-making. The hypothesis posited that operational mode would influence extrinsic cognitive load, while task difficulty would impact intrinsic cognitive load. Additionally, a correlation was expected between physiological performance indicators and cognitive load. To test this, a 2x2 factorial experiment was conducted with independent variables being task difficulty (high vs. low) and operational mode (joystick vs. keys), and dependent variables including cognitive load, performance, and learning outcomes. Subjective scales were used to assess intrinsic and extrinsic cognitive load, and physiological data were collected to examine their correlation with cognitive load. The results indicate that operational mode and task difficulty significantly affect cognitive load in space exploration contexts. Specifically, the 'Joystick' mode was associated with higher extrinsic cognitive load, likely due to the challenges it presents in precise control during complex tasks. Performance metrics showed that attention allocation varied by operational mode, with the 'Joystick' mode requiring greater focus on the side view and end-effector, in line with cognitive load theory. Physiological metrics, particularly pupil diameter and EEG data, were crucial in understanding these effects;a decrease in pupil diameter with increased task difficulty reflected heightened intrinsic cognitive load. EEG data further revealed significant variations in brain activity across frontal, prefrontal, and occipital regions, with distinct changes in alpha, theta, and beta frequency bands, highlighting the substantial impact of cognitive load on neural processes. These findings underscore the importance of integrating cognitive

关键词： Space research

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Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation

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ACM Transactions on Recommender Systems 2024年第4期2卷 1-27页

作者： Zhiyong Cheng Jianhua Dong Fan Liu Lei Zhu Xun Yang Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shandong Artificial Intelligence Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computing National University of Singapore Singapore Singapore School of Electronic and Information Engineering Tongji University Shanghai China School of Information Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China and Hefei Comprehensive National Science Center Hefei China

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation of benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations.

关键词： Multi-Behavior Recommendation disentangled representation learning graph convolutional network multi-behavior recommendation multi-task learning

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DualConvNet: Enhancing CNN Inference Efficiency Through Compressed Convolutions and Reparameterization

DualConvNet: Enhancing CNN Inference Efficiency Through Comp...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Haipeng Du Muyan Jiao Jiageng Zhang Xin Lv Jie Zhang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China Network Information Center Xi’an Jiaotong University Xi’an China School of Computer Science and Engineering Xi’an University of Technology Xi’an China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Convolutional Neural Networks (CNNs) have significantly advanced computer vision tasks, but their increasing complexity poses challenges for efficient inference, particularly on resource-constrained devices. We present DualConvNet, a novel CNN architecture that enhances inference efficiency through two key innovations: compressed convolutions and reparameterization. Compressed convolutions reduce computational complexity by selectively processing input channel subsets during both training and inference. For inference, we introduce a reparameterization technique that merges the multi-branch structure into a single, efficient operation, significantly improving speed. Experiments on CIFAR-10, CIFAR-100, and ImageNet-1k demonstrate DualConvNet’s effectiveness, consistently outperforming state-of-the-art models in both accuracy and inference speed. On the COCO dataset, DualConvNet shows competitive accuracy in object detection and instance segmentation tasks while substantially reducing GPU latency. Ablation studies validate the impact of our dual-strategy approach, revealing significant improvements in both accuracy and computational efficiency compared to alternative designs. These results demonstrate DualConvNet’s effectiveness in improving inference efficiency while maintaining high accuracy across various tasks and datasets, making it particularly suitable for real-time applications in resource-constrained scenarios.

关键词： Training Technological innovation Accuracy Image coding Computational modeling Computer architecture Transformers Computational efficiency Convolutional neural networks Computational complexity

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What matters for ad-hoc video search? A large-scale evaluation on TRECVID

arXiv

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arXiv 2021年

作者： Chen, Aozhu Hu, Fan Wang, Zihan Zhou, Fangming Li, Xirong AIMC Lab. School of Information Renmin University of China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China

For quantifying progress in Ad-hoc Video Search (AVS), the annual TRECVID AVS task is an important international evaluation. Solutions submitted by the task participants vary in terms of their choices of cross-modal matching models, visual features and training data. As such, what one may conclude from the evaluation is at a high level that is insufficient to reveal the influence of the individual components. In order to bridge the gap between the current solution-level comparison and the desired component-wise comparison, we propose in this paper a large-scale and systematic evaluation on TRECVID. By selected combinations of state-of-the-art matching models, visual features and (pre-)training data, we construct a set of 25 different solutions and evaluate them on the TRECVID AVS tasks 2016–2020. The presented evaluation helps answer the key question of what matters for AVS. The resultant observations and learned lessons are also instructive for developing novel AVS solutions. Copyright © 2021, The Authors. All rights reserved.

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