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arXiv 2025年

作者： Ding, Bonan Xie, Jin Nie, Jing Cao, Jiale School of Big Data and Software Engineering Chongqing University Chongqing China School of Microelectronics and Communication Engineering Chongqing University Chongqing China School of Electrical and Information Engineering Tianjin University Tianjin China Shanghai Artificial Intelligence Laboratory Shanghai China

Multimodal 3D object detection based on deep neural networks has indeed made significant progress. However, it still faces challenges due to the misalignment of scale and spatial information between features extracted from 2D images and those derived from 3D point clouds. Existing methods usually aggregate multimodal features at a single stage. However, leveraging multi-stage cross-modal features is crucial for detecting objects of various scales. Therefore, these methods often struggle to integrate features across different scales and modalities effectively, thereby restricting the accuracy of detection. Additionally, the time-consuming Query-Key-Value-based (QKV-based) cross-attention operations often utilized in existing methods aid in reasoning the location and existence of objects by capturing non-local contexts. However, this approach tends to increase computational complexity. To address these challenges, we present SSLFusion, a novel Scale & Space Aligned Latent Fusion Model, consisting of a scale-aligned fusion strategy (SAF), a 3D-to-2D space alignment module (SAM), and a latent cross-modal fusion module (LFM). SAF mitigates scale misalignment between modalities by aggregating features from both images and point clouds across multiple levels. SAM is designed to reduce the inter-modal gap between features from images and point clouds by incorporating 3D coordinate information into 2D image features. Additionally, LFM captures cross-modal non-local contexts in the latent space without utilizing the QKV-based attention operations, thus mitigating computational complexity. Experiments on the KITTI and DENSE datasets demonstrate that our SSLFusion outperforms state-of-the-art methods. Our approach obtains an absolute gain of 2.15% in 3D AP, compared with the state-of-art method GraphAlign on the moderate level of the KITTI test set. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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Multi-view clustering guided by unconstrained non-negative matrix factorization

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Knowledge-Based Systems 2023年 266卷

作者： Deng, Ping Li, Tianrui Wang, Dexian Wang, Hongjun Peng, Hong Horng, Shi-Jinn School of Computer and Software Engineering Xihua University Chengdu610039 China 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 Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Chengdu611756 China Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known method for handling high-dimensional multi-view data. To satisfy the non-negativity constraint of the matrix, NMFMvC is usually solved using the Karush–Kuhn–Tucker (KKT) conditions. However, this optimization method is poorly scalable. To this end, we propose an unconstrained non-negative matrix factorization multi-view clustering (uNMFMvC) model. First, the objective function was constructed by decoupling the elements of the matrix and combining the elements with a non-linear mapping function in a non-negative value domain. The objective function was then optimized using the stochastic gradient descent (SGD) algorithm. Subsequently, three uNMFMvC methods were constructed based on different mapping functions and detailed reasoning was provided. Finally, experiments were conducted on eight public datasets and compared with cutting-edge multi-view clustering methods. The experimental results demonstrate that the proposed model has significant advantages. © 2023 Elsevier B.V.

关键词： Non-negative matrix factorization

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A Survey of Stance Detection on Social Media: New Directions and Perspectives

arXiv

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arXiv 2024年

作者： Zhang, Bowen Dai, Genan Niu, Fuqiang Yin, Nan Fan, Xiaomao Wang, Senzhang Cao, Xiaochun Huang, Hu College of Big Data and Internet Shenzhen Technology University Shenzhen518000 China College of Computer Science and Software Shenzhen University Shenzhen518000 China Hong Kong University of Science and Technology 999077 Hong Kong School of Computer Science and Engineering Central South University Changsha410083 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China Peking University Shenzhen Graduate School Shenzhen518000 China

In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these opinions offers valuable insights for decision-making in various sectors, including marketing and politics. As a result, stance detection has emerged as a crucial subfield within affective computing, enabling the automatic detection of user stances in social media conversations and providing a nuanced understanding of public sentiment on complex issues. Recent years have seen a surge of research interest in developing effective stance detection methods, with contributions from multiple communities, including natural language processing, web science, and social computing. This paper provides a comprehensive survey of stance detection techniques on social media, covering task definitions, datasets, approaches, and future works. We review traditional stance detection models, as well as state-of-the-art methods based on large language models, and discuss their strengths and limitations. Our survey highlights the importance of stance detection in understanding public opinion and sentiment, and identifies gaps in current research. We conclude by outlining potential future directions for stance detection on social media, including the need for more robust and generalizable models, and the importance of addressing emerging challenges such as multi-modal stance detection and stance detection in low-resource languages. Copyright © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

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Boosting segmentation accuracy of the deep learning models based on the synthetic data generation 4

Boosting segmentation accuracy of the deep learning models b...

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4th International Workshop on Photogrammetric and Computer Vision Techniques for Video Surveillance, Biometrics and Biomedicine, PSBB 2021

作者： Danilov, V.V. Gerget, O.M. Kolpashchikov, D.Yu. Laptev, N.V. Manakov, R.A. Hérnandez-Gómez, L.A. Alvarez, F. Ledesma-Carbayo, M.J. Research Laboratory for Processing and Analysis of Big Data Tomsk Polytechnic University Tomsk Russia Department of Software Engineering Research Laboratory"Gamma Technologies" Almaty Kazakhstan Department of Signals Systems and Radiocommunications Technical University of Madrid Madrid Spain Group of Application of Visual Telecommunications Technical University of Madrid Madrid Spain Biomedical Image Technologies Group Technical University of Madrid Madrid Spain

In the era of data-driven machine learning algorithms, data represents a new oil. The application of machine learning algorithms shows they need large heterogeneous datasets that crucially are correctly labeled. However, data collection and its labeling are time-consuming and labor-intensive processes. A particular task we solve using machine learning is related to the segmentation of medical devices in echocardiographic images during minimally invasive surgery. However, the lack of data motivated us to develop an algorithm generating synthetic samples based on real datasets. The concept of this algorithm is to place a medical device (catheter) in an empty cavity of an anatomical structure, for example, in a heart chamber, and then transform it. To create random transformations of the catheter, the algorithm uses a coordinate system that uniquely identifies each point regardless of the bend and the shape of the object. It is proposed to take a cylindrical coordinate system as a basis, modifying it by replacing the Z-axis with a spline along which the h-coordinate is measured. Having used the proposed algorithm, we generated new images with the catheter inserted into different heart cavities while varying its location and shape. Afterward, we compared the results of deep neural networks trained on the datasets comprised of real and synthetic data. The network trained on both real and synthetic datasets performed more accurate segmentation than the model trained only on real data. For instance, modified U-net trained on combined datasets performed segmentation with the Dice similarity coefficient of 92.6±2.2%, while the same model trained only on real samples achieved the level of 86.5±3.6%. Using a synthetic dataset allowed decreasing the accuracy spread and improving the generalization of the model. It is worth noting that the proposed algorithm allows reducing subjectivity, minimizing the labeling routine, increasing the number of samples, and improving the heterog

关键词： Echocardiography

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BCPPAS : Blockchain-Based Cross-Domain Identity Authentication Scheme for IoT with Privacy Protection

BCPPAS : Blockchain-Based Cross-Domain Identity Authenticati...

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International Conference on Computer Supported Cooperative Work in Design

作者： Qianqian Li Yubing Han Qi Liu Guijuan Wang Hao Yu Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

In Internet of Things(IoT) systems, ensuring the secure exchange of information between devices from different domains is crucial. Current cross-domain authentication schemes based on a single blockchain struggle to meet the confidentiality requirements for data and information exchange in large-scale IoT systems. This paper proposes a blockchain-based identity authentication scheme (BCPPAS) for IoT, featuring dual-chain collaboration, and designs a novel certificateless aggregate signature algorithm to address complex certificate management and key escrow issues. The edge server is capable of aggregating different signatures to achieve batch authentication, markedly improving authentication efficiency and reducing computational and storage overhead. Also, BCPPAS is designed to avoid costly bilinear pairing operations, providing less computational overhead for IoT devices with limited resources. To protect the privacy of IoT devices, BCPPAS uses the pseudonym instead of the real identity. Finally, efficiency of BCPPAS are demonstrated through theoretical analysis and experiments.

关键词： Privacy Federated learning Aggregates Authentication Blockchains Computational efficiency Internet of Things Servers Protection Information exchange

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A new approach for approximately mining frequent itemsets 21

A new approach for approximately mining frequent itemsets

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21st International Conference on data Analytics and Management in data Intensive Domains, DAMDID/RCDL 2019

作者： Valiullin, Timur Huang, Joshua Zhexue Yin, Jianfei Wu, Dingming Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Mining frequent itemsets in transaction databases is an important task in many applications. This task becomes challenging when dealing with a very large transaction database because traditional algorithms are not scalable due to the memory limit. In this paper, we propose a new approach for approximately mining of frequent itemsets in a transaction database. First, we partition the set of transactions in the database into disjoint subsets and make the distribution of frequent itemsets in each subset similar to that of the entire database. Then, we randomly select a set of subsets and independently mine the frequent itemsets in each of them. After that, each frequent itemset discovered from these subsets is voted and the one appearing in the majority subsets is determined as a frequent itemset, called a popular frequent itemset. All popular frequent itemsets are compared with the frequent itemsets discovered directly from the entire database using the same frequency threshold. The recalls and precisions of the frequent itemsets from selected subsets are analyzed against the entire database. The experiment results demonstrate that the use of less than 10 percent of the transaction data in the database can achieve more than 87 percent accuracy. The new approach is very suitable for parallel implementation for large transaction database mining. Copyright © 2019 for this paper by its authors.

关键词： database systems

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PScalpel: A Machine Learning-based Guider for Protein Phase-Separating Behaviour Alteration 39

PScalpel: A Machine Learning-based Guider for Protein Phase-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Jia Zhu, Liyan Wang, Zhe Zhang, Chenqiu Wu, Yaoxing Cui, Jun Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China MOE Key Laboratory of Gene Function and Regulation Guangdong Province Key Laboratory of Pharmaceutical Functional Genes State Key Laboratory of Biocontrol School of Life Sciences Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Pharmaceutical Functional Genes First Affiliated Hospital of Sun Yat-sen University School of Life Sciences Sun Yat-sen University Guangzhou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)157735897X

Missense mutations could affect the Liquid-Liquid Phase Separation (LLPS) propensity of proteins and lead to aberrant phase-separating behaviours, which are recently found to be associated with many diseases including Alzheimer’s and cancer. However, the regulatory role of mutations in LLPS remains unclear due to challenges in accurately characterizing the LLPS ability of mutants, including the high similarity in features, lack of labeled data, and vast amounts of data involved. To bridge this gap and facilitate the discovery of therapeutic strategies, we propose the first machine learning-based guider for protein phase-separating behaviour alteration, PScalpel. PScalpel leverages both structural information and an auxiliary tasks-based graph contrastive learning framework to distinguish the mutants’ LLPS ability, and incorporates a genetic algorithms-based recommendation method to identify mutants with desired LLPS properties. Comprehensive computational and biological experiments validate the effectiveness of PScalpel as a versatile tool for guiding alterations in protein phase separation behavior. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Genetic algorithms

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A novel method for driving path planning with spark

TechRxiv

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TechRxiv 2021年

作者： Li, Leixiao Lin, Hao Wan, Jianxiong Wang, Yongsheng Gao, Jing College of Data Science and Application Inner Mongolia University of Technology China School of Computer Science and Engineering Tianjin University of Technology China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service China College of Computer and Information Engineering Inner Mongolia Agricultural University China Inner Mongolia Autonomous Region Key Laboratory of big data research and application for agriculture and animal husbandry China

Efficient and accurate driving path planning can help drivers drive. To solve the problem of low efficiency of traditional heuristic algorithms such as PSO and GA in solving driving path planning, we introduce Excellence Coefficient into heuristic algorithms and make a parallel design based on Spark, which called EC-SPPSOGA. Excellence Coefficient can increase the probability of good edges being left, simultaneously, preserves the possibility of longer side being selected. The parallel design is based on time-consuming analysis of heuristic algorithms. We validate the performance of EC-SPPSOGA based on the data in TSPLIB. It is verified that the EC-SPPSOGA can improve efficiency of driving path planning and has good scalability. © 2021, CC BY.

关键词： Heuristic algorithms

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Action-Manipulation Attack and Defense to X-Armed Bandits

Action-Manipulation Attack and Defense to X-Armed Bandits

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

作者： Zhi Luo Youqi Li Lixing Chen Zichuan Xu Pan Zhou School of Cyber Science and Engineering Huazhong University of Science and Technology School of Computer Science School of Cyberspace Science and Technology Beijing Institute of Technology Institute of Cyber Science and Technology Shanghai Jiao Tong University School of Software Dalian University of Technology Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology

As a continuous variant of Multi-armed bandits (MAB), $\mathcal{X}$-armed bandits have enriched many applications of online machine learning like personalized recommendation system. However, the attack and defense to the $\mathcal{X}$-armed bandits remain largely unexplored, though the MAB has proved to be vulnerable. In this paper, we aim to bridge this gap and investigate the robustness analysis for the $\mathcal{X}$-armed bandits. Specifically, we consider action-manipulation attack, which is practical but harder than the existing reward-manipulation attack. We propose an attack algorithm based on a lower bound tree (LBT), which can continuously hijack the learner’s action by perturbing $\mathcal{X}$-armed bandits’ high confidence tree (HCT) construction. As a result, the nodes including the arm targeted by the attacker is selected frequently with a sublinear attack cost. To defend against the LBT attack, we propose a robust version of the HCT algorithm, called RoHCT. We theoretically analyze that the regret of RoHCT is related to the upper bound of the total cost Q and still sublinear to total number of rounds T. We carry out experiments to evaluate the effectiveness of LBT and RoHCT.

关键词： Threat modeling Bridges Privacy Costs Upper bound Machine learning big data

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MetaGCD: Learning to Continually Learn in Generalized Category Discovery

arXiv

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arXiv 2023年

作者： Wu, Yanan Chi, Zhixiang Wang, Yang Feng, Songhe Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering University of Toronto TorontoM5G1V7 Canada Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 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. Our code is available at https://***/ynanwu/MetaGCD. © 2023, CC0.

关键词： Learning systems

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