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arXiv 2023年

作者： Xing, Zhibo Zhang, Zijian Liu, Jiamou Zhang, Ziang Li, Meng Zhu, Liehuang Russello, Giovanni The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China The School of Computer Science The University of Auckland Auckland New Zealand Southeast Institute of Information Technology Beijing Institute of Technology Fujian China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China

With the rapid advancement of artificial intelligence technology, the usage of machine learning models is gradually becoming part of our daily lives. High-quality models rely not only on efficient optimization algorithms but also on the training and learning processes built upon vast amounts of data and computational power. However, in practice, due to various challenges such as limited computational resources and data privacy concerns, users in need of models often cannot train machine learning models locally. This has led them to explore alternative approaches such as outsourced learning and federated learning. While these methods address the feasibility of model training effectively, they introduce concerns about the trustworthiness of the training process since computations are not performed locally. Similarly, there are trustworthiness issues associated with outsourced model inference. These two problems can be summarized as the trustworthiness problem of model computations: How can one verify that the results computed by other participants are derived according to the specified algorithm, model, and input data? To address this challenge, verifiable machine learning (VML) has emerged. This paper presents a comprehensive survey of zero-knowledge proof-based verifiable machine learning (ZKP-VML) technology. We first analyze the potential verifiability issues that may exist in different machine learning scenarios. Subsequently, we provide a formal definition of ZKP-VML. We then conduct a detailed analysis and classification of existing works based on their technical approaches. Finally, we discuss the key challenges and future directions in the field of ZKP-based VML. © 2023, CC BY.

关键词： Machine learning

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Graph-Based Terminal Ranking for Sparsification of Interference Coordination Parameters in Ultra-Dense Networks 10th

Graph-Based Terminal Ranking for Sparsification of Interfere...

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10th EAI International Conference on Mobile Networks and Management, MONAMI 2020

作者： Gou, Junxiang Wang, Lusheng Lin, Hai Peng, Min Ministry of Education School of Computer Science and Information Engineering Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230601 China Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan University Wuhan430072 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230601 China

ISBN: (纸本)9783030640019

In the future mobile communication system, inter-cell interference becomes a serious problem due to the intensive deployment of cells and terminals. Traditional interference coordination schemes take long time for optimization in ultra-dense networks. Meanwhile, due to the increase of factors affecting communication and in order to better meet the communication needs of each terminal, an interference coordination scheme needs to fully consider multiple characteristic parameters of the terminal, which will further increase the scheme’s computational time. Therefore, we should compress all the data through sparsification of parameters before optimization. There are many terminal parameters, and the essence of sparsification of parameters is to rank terminals. In this paper, a graph-based terminal ranking scheme is designed. First, each terminal can be represented by its multiple parameters. Then, all terminals are used as the vertexes in the graph to form a complete weighted graph, and edge weights represent the degree of dissimilarity between terminals. A ranking of terminals is obtained by finding a minimum Hamiltonian path in the graph. Finally, the ranking of all parameter sequences is obtained according to terminals ranking, which makes the sparsity of all parameter sequences better. Simulation results show that the proposed scheme can accomplish sparsification of parameter sequences effectively, especially when the number of sequences increases. In addition, compared with the optimal coordination of traditional scheme, this scheme improves the fairness of the system while ensuring high system capacity, and dramatically reduces the computational time of interference coordination. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2020.

关键词： Approximation algorithms

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MetaGCD: Learning to Continually Learn in Generalized Category Discovery

MetaGCD: Learning to Continually Learn in Generalized Catego...

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International Conference on Computer Vision (ICCV)

作者： Yanan Wu Zhixiang Chi Yang Wang Songhe Feng Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method.

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Rethinking CLIP-based Video Learners in Cross-Domain Open-Vocabulary Action Recognition

arXiv

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arXiv 2024年

作者： Lin, Kun-Yu Ding, Henghui Zhou, Jiaming Tang, Yu-Ming Peng, Yi-Xing Zhao, Zhilin Loy, Chen Change Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China S-Lab Nanyang Technological University Singapore Institute of Big Data Fudan University China AI Thrust Hong Kong University of Science and Technology Guangzhou China Data Science Lab School of Computing & DataX Research Centre Macquarie University Australia

Building upon the impressive success of CLIP (Contrastive Language-Image Pretraining), recent pioneer works have proposed to adapt the powerful CLIP to video data, leading to efficient and effective video learners for open-vocabulary action recognition. Inspired by that humans perform actions in diverse environments, our work delves into an intriguing question: Can CLIP-based video learners effectively generalize to video domains they have not encountered during training? To answer this, we establish a CROSS-domain Open-Vocabulary Action recognition benchmark named XOV-Action, and conduct a comprehensive evaluation of five state-of-the-art CLIP-based video learners under various types of domain gaps. The evaluation demonstrates that previous methods exhibit limited action recognition performance in unseen video domains, revealing potential challenges of the cross-domain open-vocabulary action recognition task. In this paper, we focus on one critical challenge of the task, namely scene bias, and accordingly contribute a novel scene-aware video-text alignment method. Our key idea is to distinguish video representations apart from scene-encoded text representations, aiming to learn scene-agnostic video representations for recognizing actions across domains. Extensive experiments demonstrate the effectiveness of our method. The benchmark and code will be available at https://***/KunyuLin/XOV-Action/. Copyright © 2024, The Authors. All rights reserved.

关键词： Video recording

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Coupling Photothermal and Piezoelectric Effect in Bi4Ti3O12 for Enhanced Photodegradation of Tetracycline Hydrochloride

SSRN

引用

SSRN 2023年

作者： Huang, Kai Tian, Junlong Xu, Wenjie Zheng, Shuaizhi Key Laboratory of Low Dimensional Materials and Application Technology Ministry of Education School of Materials Science and Engineering Xiangtan University Xiangtan411105 China Hunan Key Laboratory of Micro-Nano Energy Materials and Devices Laboratory for Quantum Engineering and Micro-Nano Energy Technology School of Physics and Optoelectronic Xiangtan University Hunan Xiangtan411105 China Department of Electronic Science College of Big Data and Information Engineering Guizhou University Guiyang550025 China

Semiconductor photocatalytic technology is an effective way to remove antibiotic pollution. However, the recombination of photogenerated carriers limits the efficiency of photocatalytic degradation of antibiotic. In this work, a Bi4Ti3O12 (BTO) micro-hollow sphere (HS-BTO) was successfully self-assembled by BTO nano-sheet. In 7 min, the photodegradation efficiency of TC solution with an initial concentration of 10 mg/L is reached as high as 90 % at 2.6 °C, and the reaction rate constant (k) is up to 0.1775 min-1. With the increase of temperature up to 50 °C, 90 % of TC solution is photodegraded only in 1 min. This work reveals that heating can effectively reduce the charge transfer resistance, accelerate the charge transfer, thus promoting the charge separation and improving the photocatalytic performance. In addition, through ultrasound vibration, HS-BTO generates polarization charges and built-in polarization fields accelerating the photogenerated charge carriers separation and greatly suppressed the recombination rate of e-/h+ pairs, which achieved piezoelectric catalytic degradation of TC. Consequently, a developed photocatalytic degradation of tetracycline hydrochloride (TC) via coupling photothermal and piezoelectric effect is achieved. Thus, this work proposes an innovative approach to coupling thermal and mechanical energy to advance photocatalysis. © 2023, The Authors. All rights reserved.

关键词： Bismuth compounds

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Disentangling clusters from non-Euclidean data via graph frequency reorganization

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Information Sciences 2024年 662卷

作者： Geng, Yangli-ao Chi, Chong-Yung Sun, Wenju Zhang, Jing Li, Qingyong Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Power Automation Department China Electric Power Research Institute Beijing100192 China

In light of the growing need for non-Euclidean data analysis, graphs have been recognized as an effective tool for characterizing the distribution and correlation of such data, thus inspiring many graph-based developments for various applications such as clustering, of non-Euclidean data. However, under unsupervised scenarios, the construction of graphs from unlabeled data often involves numerous noisy links, consequently leading to serious performance degradation in concerned applications. To resolve this issue, we propose a novel method, referred to as Graph Frequency Reorganization (GFR), to enhance the discriminability of potential clusters and the associated graph quality. GFR shows capability far beyond the suboptimality in unsupervised graph construction. Furthermore, a fast version of GFR is proposed to reduce its computation overhead for large-scale datasets. Consequently, the obtained unsupervised clustering results can be significantly upgraded using the GFR data (i.e., the data after the GFR processing). To evaluate the effectiveness of the GFR, some experimental results on ten real-world datasets are provided to demonstrate that the overall clustering performance of a simple k-means using the GFR data is superior to several state-of-the-art graph-based clustering methods1. © 2024 Elsevier Inc.

关键词： K-means clustering

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Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting

arXiv

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arXiv 2024年

作者： Xiong, Qi Tang, Kai Ma, Minbo Zhang, Ji Xu, Jie Li, Tianrui School of Computing and Artificial Intelligence Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education National Engineering Laboratory of Integrated Transportation Big Data Application Technology Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China School of Computing The University of Leeds LeedsLS2 9JT United Kingdom

Long-term time series forecasting (LTSF) is a critical task across diverse domains. Despite significant advancements in LTSF research, we identify a performance bottleneck in existing LTSF methods caused by the inadequate modeling of Temporal Dependencies within the Target (TDT). To address this issue, we propose a novel and generic temporal modeling framework, Temporal Dependency Alignment (TDAlign), that equips existing LTSF methods with TDT learning capabilities. TDAlign introduces two key innovations: 1) a loss function that aligns the change values between adjacent time steps in the predictions with those in the target, ensuring consistency with variation patterns, and 2) an adaptive loss balancing strategy that seamlessly integrates the new loss function with existing LTSF methods without introducing additional learnable parameters. As a plug-and-play framework, TDAlign enhances existing methods with minimal computational overhead, featuring only linear time complexity and constant space complexity relative to the prediction length. Extensive experiments on six strong LTSF baselines across seven real-world datasets demonstrate the effectiveness and flexibility of TDAlign. On average, TDAlign reduces baseline prediction errors by 1.47% to 9.19% and change value errors by 4.57% to 15.78%, highlighting its substantial performance improvements. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series analysis

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Cascaded Aggregation Convolution Network for Saliency Detection Of Stored-Grain Pests

SSRN

引用

SSRN 2023年

作者： Yu, Junwei Zhai, Fupin Liu, Nan Key Laboratory of Grain Information Processing and Control Ministry of Education Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China College of Information Scinence and Engineering Henan University of Technology Zhengzhou450001 China PLA SSF Information Engineering University Zhengzhou450001 China

The behaviors exhibited by stored grain pests, including feeding, respiration, excretion, and reproduction, engender multifarious threats to grain storage. Consequently, it is imperative to diligently undertake the task of detecting and effectively controlling these pests. At present, Great progress has made in both traditional methods and deep learning techniques for salient object detection. It is still a challenge, especially for small objects and non-salient images, biological vision. This paper presents a grain pests detection method based on cascaded strous convolution . We collet(collect) a novel dataset GrainPest, which comprises 500 images covering various salient pests in grains. We develop a simple and effective framework named cascaded aggregation convolution network(CACNet). CACNet outperforms various state-of-the-art object detection baselines on all datasets tested, making it a robust, general framework that can help facilitate future research in COD. Finally, we conduct a large-scale COD study, evaluating 13 cutting-edge models, providing some interesting findings, and showing several potential applications. Our research offers the community an opportunity to explore more in this new field. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Cross-View Fuzziness and Intra-view Uncertainty-Based Weight Reconstruction for Multi-view Feature Selection 11th

Cross-View Fuzziness and Intra-view Uncertainty-Based Weigh...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Xiang, Biao Chen, Hongmei Mi, Yong Sang, Binbin Horng, Shi-Jinn Li, Tianrui Institute of Artificial Intelligence School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China College of Computer and Information Science Chongqing Normal University Chongqing401331 China Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan

ISBN: (纸本)9783031927430

Weight assignment is a universal and indispensable process in multi-view feature selection. However, most existing methods overlook the fuzziness and uncertainty implied in multi-view data. This may result in improper weight assignment, degrading the quality of selected features. A cross-view fuzziness and intra-view uncertainty-based weight reconstruction method (CFIUWR) is proposed for multi-view feature selection. With the fuzzy rough set theory, fuzzy approximation operators recognize cross-view fuzziness and intra-view uncertainty, which are evaluated by classification-related measurement. Cross-view fuzziness and intra-view uncertainty are incorporated to develop a reconstruction mechanism for view and feature weights. An optimization algorithm is derived, guaranteeing rapid convergence. Comparisons with state-of-the-art methods on several real-world datasets demonstrate the superiority of the CFIUWR. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Rough set theory

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Web Log Analysis in Genealogy System

Web Log Analysis in Genealogy System

引用

IEEE International Conference on big knowledge (ICBK)

作者： Xiaojian Liu Yi Zhu Shengwei Ji Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

A large amount of log data is generated every moment in many real-world applications. System logs, mobile application logs, web logs etc., all of which contain rich information and can be analyzed for valuable information. At the same time, genealogy attracts more and more attention in recent decades, especially for Chinese. People utilize the genealogy system to seek roots, find source of family, build a family tree and so on. Due to the complexity of genealogy data and specific features of genealogy system, how to extract and analyze the valued genealogy information effectively is a huge challenge. Motivated by this, we propose a novel analysis method using web logs in genealogy system for drawing user profile and providing personalized recommendations. More specifically, firstly, all web logs are extracted from genealogy system based on the enhanced logs which record the behaviors and data of user. Secondly, the extracted logs are formatted for analysis, and we analyze the logs to extract various user-related attributes for drawing user profile. Finally, personalized recommendations like interested people and family tree are provided for each user in system. The high availability and effectiveness of our proposed method is verified on genealogy system-Huapu system.

关键词： data mining databases data visualization Tools big data Feature extraction knowledge engineering

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