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2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022

作者： Arora, Ashok Sharma, Aniket Singh, Pramod Kumar Abv - Indian Institute of Information Technology Computational Intelligence and Data Mining Research Lab Madhya Pradesh Gwalior India

ISBN: (纸本)9781665477192

User-generated content (UGC) has garnered much attention as it allows users to consume and produce content at a fast rate. Although such flexibility encourages contribution and knowledge-sharing, it also raises a question about the quality of the content. In this work, we present an automated method to estimate the quality of the collaboratively created content on Web2.0 platforms using the example of wikiHow. We define quality as a collection of independent feature groups, each focusing on a distinct quality aspect. We analyze these quality indicators' contribution to estimating the content's quality. © 2022 IEEE.

关键词： Automation

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Advancing Semantic Edge Detection through Cross-Modal Knowledge Learning 24

Advancing Semantic Edge Detection through Cross-Modal Knowle...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Deng, Ruoxi Yu, Bin Lu, Jinxuan Zhou, Caixia Chen, Zhao-Min Hu, Jie Wenzhou Institute of Data Research School of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China School of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400706868

Semantic edge detection (SED) is pivotal for the precise demarcation of object boundaries, yet it faces ongoing challenges due to the prevalence of low-quality labels in current methods. In this paper, we present a novel solution to bolster SED through the encoding of both language and image data. Distinct from antecedent language-driven techniques, which predominantly utilize static elements such as dataset labels, our method taps into the dynamic language content that details the objects in each image and their interrelations. By encoding this varied input, we generate integrated features that utilize semantic insights to refine the high-level image features and the ultimate mask representations. This advancement improves the quality of these features and elevates SED performance. Experimental evaluation on benchmark datasets, including SBD and Cityscape, showcases the efficacy of our method, achieving leading ODS F-scores of 79.0 and 76.0, respectively. Our approach signifies a notable advancement in SED technology by seamlessly integrating multimodal textual information, embracing both static and dynamic aspects. © 2024 ACM.

关键词： Convolutional neural networks

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An Effective Implementation of Detection and Retrieval Property of Episodic Memory 6

An Effective Implementation of Detection and Retrieval Prope...

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6th International Conference on Advances in Artificial intelligence, ICAAI 2022

作者： Sharma, Aniket Singh, Pramod Kumar Prakash, Jay Computational Intelligence and Data Mining Research Laboratory ABV-Indian Institute of Information Technology and Management Gwalior India Department of Computer Science and Engineering National Institute of Technology Calicut India

ISBN: (纸本)9781450396943

A deep understanding of the brain can lead to significant breakthroughs in Artificial intelligence. Many researchers concentrate their efforts on simulating the human mind to comprehend its complexities better. With the intention of better understanding the episodic memory aspect of the human mind, we propose a deep learning model to implement the detection and retrieval properties of human episodic memory, a part of long-term memory. A model based on LSTM and CNN is proposed, which follows the architectural methodology of Rosenblatt's experiential memory model. A comparison of detection efficiency and accuracy and the proposed model's retrieval property with a recently suggested method demonstrate its effectiveness and superiority. © 2022 Association for Computing Machinery.

关键词： Long short-term memory

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Novel camera self-calibration method with clustering prior and nonlinear optimization from an image sequence

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Multimedia Tools and Applications 2024年第42期83卷 90553-90572页

作者： Jiang, Xiaohui Zhu, Haijiang An, Ning Xie, Binjian Wei, Hao Tang, Fulin Wu, Yihong College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China State Key Laboratory of Multimodal Artificial Intelligence Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Intelligent Coal Mining and Strata Control Beijing100013 China Research Institute of Mine Big Data China Coal Research Institute Beijing100013 China

In many 3D reconstruction, Simultaneous Localization and Mapping (SLAM), AR, and VR systems, the camera focal length constantly changes slightly due to loose hardware, causing their performances to deteriorate. To address this problem, we propose a novel camera self-calibration method that incorporates clustering prior and nonlinear optimization. By leveraging the Kruppa equation, we roughly estimate multiple analytical solutions for the camera focal length from an image sequence. Then, robust clustering based on the Gaussian mixture model is introduced to obtain an initial camera focal length value. Finally, a nonlinear cost function is proposed to refine the initial camera focal length value. Extensive experimental results on simulation and real data demonstrate that the proposed method outperforms the state-of-the-art methods, exhibiting good robustness and high accuracy. Moreover, the proposed method can be used in real-time videos for SLAM systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cost functions

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TabLog: Test-Time Adaptation for Tabular data Using Logic Rules 41

TabLog: Test-Time Adaptation for Tabular Data Using Logic Ru...

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41st International Conference on Machine Learning, ICML 2024

作者： Ren, Weijieying Li, Xiaoting Chen, Huiyuan Rakesh, Vineeth Wang, Zhuoyi Das, Mahashweta Honavar, Vasant Artificial Intelligence Research Laboratory Center for Artificial Intelligence Foundations and Scientific Applications Institute for Computational and Data Sciences College of Information Sciences and Technology The Pennsylvania State University PA United States Visa Research CA United States College of Information Sciences and Technology The Pennsylvania State University PA United States

We consider the problem of test-time adaptation of predictive models trained on tabular data. Effective solution of this problem requires adaptation of predictive models trained on the source domain to a target domain, using only unlabeled target domain data, without access to source domain data. Existing test-time adaptation methods for tabular data have difficulty coping with the heterogeneous features and their complex dependencies inherent in tabular data. To overcome these limitations, we consider test-time adaptation in the setting wherein the logical structure of the rules is assumed to remain invariant despite distribution shift between source and target domains whereas the numerical parameters associated with the rules and the weights assigned to them can vary to accommodate distribution shift. TabLog discretizes numerical features, models dependencies between heterogeneous features, introduces a novel contrastive loss for coping with distribution shift, and presents an end-to-end framework for efficient training and test-time adaptation by taking advantage of a logical neural network representation of a rule ensemble. We present results of experiments using several benchmark data sets that demonstrate TabLog is competitive with or improves upon the state-of-the-art methods for test-time adaptation of predictive models trained on tabular data. Our code is available at https://***/WeijieyingRen/TabLog. Copyright 2024 by the author(s)

关键词： data assimilation

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A genetic engineering algorithm for the generalized quadratic assignment problem

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Neural Computing and Applications 2025年 1-27页

作者： Sohrabi, Majid Fathollahi-Fard, Amir M. Gromov, Vasilii A. Dulebenets, Maxim A. School of Data Analysis and Artificial Intelligence National Research University Higher School of Economics 11 Pokrovsky Boulevard Moscow109028 Russia Laboratory for Models and Methods of Computational Pragmatics National Research University Higher School of Economics Moscow Russia Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Taipa China College of Engineering 2035 E Paul Dirac Dr. Sliger Building Suite 275 TallahasseeFL32310 United States

The generalized quadratic assignment problem (GQAP) poses a significant challenge in optimization, known for its NP-hard complexity and wide-ranging applications in supply chain and manufacturing contexts. While various metaheuristic algorithms have addressed the GQAP, this paper introduces a novel modification of the genetic algorithm (GA), namely the genetic engineering algorithm (GEA). A traditional GA initializes the population of solutions randomly and refines these solutions iteratively through crossover and mutation operators. However, its reliance on randomness often leads to premature convergence, susceptibility to local optima, and suboptimal solutions at termination of the algorithmic run. To address these drawbacks, our research presents a modified GA framework inspired by genetic engineering principles. This framework introduces novel search mechanisms mimicking gene mining processes, targeting population enhancement through insertion, purification, and genetic material exchange from elite solutions. The GEA adds three search scenarios to the traditional GA framework to exhibit desired characteristics from chromosomes, enhancing solution quality and convergence speed. This GEA demonstrates a superior performance across various search scenarios against state-of-the-art algorithms and exact solutions for the benchmark instances of the GQAP. Additionally, this study conducts comprehensive sensitivity analyses on the search scenarios of the GEA. By employing gene mining strategies, the proposed GEA offers promising solutions for effectively addressing the NP-hard optimization challenges of the GQAP on large-scale datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Sensitivity analysis

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Key Clues Guided Video Character Social Relationship Recognition Enhanced by LLM

Key Clues Guided Video Character Social Relationship Recogni...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Wenlong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Video Character Social Relationship Recognition (VCSRR) requires a comprehensive consideration about spatio-temporal and multi-modal clues in videos. Most existing methods mainly focus on integrating multi-modal clues and modeling interactions among characters. However, they fail to discover key clues in the complex video data or fully understand the clues related to social relationships. In this article, we propose a novel Large Language Model Enhanced Key Clues Selection (LEKCS) framework to address the aforementioned issues. The core of LE-KCS is to mine multi-scale key clues from the perspectives of time, space and multi-modality, then transfer the knowledge about social relationships of the Large Language Model to VCSRR for understanding the selected clues. We evaluated LEKCS on the MovieGraphs dataset and the experimental results indicate that our proposed LE-KCS achieves state-of-the-art performance. © 2025 IEEE.

关键词： large language model Social relationship recognition video understanding

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An Effective Implementation of Detection and Retrieval Property of Episodic Memory 22

An Effective Implementation of Detection and Retrieval Prope...

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Proceedings of the 6th International Conference on Advances in Artificial intelligence

作者： Aniket Sharma Pramod Kumar Singh Jay Prakash Computational Intelligence and Data Mining Research Laboratory ABV-Indian Institute of Information Technology and Management India Department of Computer Science and Engineering National Institute of Technology India

ISBN: (纸本)9781450396943

A deep understanding of the brain can lead to significant breakthroughs in Artificial intelligence. Many researchers concentrate their efforts on simulating the human mind to comprehend its complexities better. With the intention of better understanding the episodic memory aspect of the human mind, we propose a deep learning model to implement the detection and retrieval properties of human episodic memory, a part of long-term memory. A model based on LSTM and CNN is proposed, which follows the architectural methodology of Rosenblatt’s experiential memory model. A comparison of detection efficiency and accuracy and the proposed model’s retrieval property with a recently suggested method demonstrate its effectiveness and superiority.

关键词： Long Short-Term Memory Rosenblatt’s Experiential Memory Model Episodic Memory Recurrent Convolutional Networks

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On Learning Frequency-Instance Correlations by Model-Agnostic Training for Synthetic Speech Detection 16

On Learning Frequency-Instance Correlations by Model-Agnosti...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Wang, Zining Gao, Lijian Zhang, Jialin Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Zhenjiang China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

The goal of Synthetic Speech Detection (SSD) is to detect spoofing speech synthesized by text-to-speech and voice conversion. Most existing SSD methods focus only on mining frequency-wise dependency by customizing frequency-aggregation modules in SSD models. However, the instance-wise dependency is usually under-explored, which is critical for identifying the synthetic speech from a global view. In this paper, we propose a novel model-agnostic training strategy for SSD that exploits both local (frequency-wise) and global (instance-wise) contexts, which do not rely on a customized architecture and can be flexibly integrated into previous SSD models. Specifically, we propose an inter-frequency correlation module to capture the local context by reconstructing the masked frequency information from the unmasked frequency context. Meanwhile, an inter-instance correlation module is performed to explore the global context among different instances by promoting intra-class compactness and inter-class dispersion in the latent space. These two complementary modules operate from distinct contextual perspectives, leading to improvements in SSD performance. Extensive experiments show that our method significantly improves the performance of two state-of-the-art models on the 2019 dataset and 2021 dataset of ASVspoof. © 2024 Z. Wang, L. Gao, J. Zhang & Q. Mao.

关键词： Speech recognition

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TabLog: test-time adaptation for tabular data using logic rules 24

TabLog: test-time adaptation for tabular data using logic ru...

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Proceedings of the 41st International Conference on Machine Learning

作者： Weijieying Ren Xiaoting Li Huiyuan Chen Vineeth Rakesh Zhuoyi Wang Mahashweta Das Vasant Honavar Artificial Intelligence Research Laboratory Center for Artificial Intelligence Foundations and Scientific Applications Institute for Computational and Data Sciences College of Information Sciences and Technology The Pennsylvania State University PA Visa Research CA Artificial Intelligence Research Laboratory Center for Artificial Intelligence Foundations and Scientific Applications Institute for Computational and Data Sciences College of Information Sciences and Technology and College of Information Sciences and Technology The Pennsylvania State University PA

We consider the problem of test-time adaptation of predictive models trained on tabular data. Effective solution of this problem requires adaptation of predictive models trained on the source domain to a target domain, using only unlabeled target domain data, without access to source domain data. Existing test-time adaptation methods for tabular data have difficulty coping with the heterogeneous features and their complex dependencies inherent in tabular data. To overcome these limitations, we consider test-time adaptation in the setting wherein the logical structure of the rules is assumed to remain invariant despite distribution shift between source and target domains whereas the numerical parameters associated with the rules and the weights assigned to them can vary to accommodate distribution shift. TabLog discretizes numerical features, models dependencies between heterogeneous features, introduces a novel contrastive loss for coping with distribution shift, and presents an end-to-end framework for efficient training and test-time adaptation by taking advantage of a logical neural network representation of a rule ensemble. We present results of experiments using several benchmark data sets that demonstrate TabLog is competitive with or improves upon the state-of-the-art methods for testtime adaptation of predictive models trained on tabular data. Our code is available at https://***/WeijieyingRen/TabLog.

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