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arXiv 2025年

作者： Tian, Zhibo Quan, Ruijie Ma, Fan Zhan, Kun Yang, Yi School of Information Science and Engineering Lanzhou University China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University China

Reconstructing perceived images from human brain activity forms a crucial link between human and machine learning through Brain-Computer Interfaces. Early methods primarily focused on training separate models for each individual to account for individual variability in brain activity, overlooking valuable cross-subject commonalities. Recent advancements have explored multisubject methods, but these approaches face significant challenges, particularly in data privacy and effectively managing individual variability. To overcome these challenges, we introduce BRAINGUARD, a privacy-preserving collaborative training framework designed to enhance image reconstruction from multisubject fMRI data while safeguarding individual privacy. BRAINGUARD employs a collaborative global-local architecture where individual models are trained on each subject’s local data and operate in conjunction with a shared global model that captures and leverages cross-subject patterns. This architecture eliminates the need to aggregate fMRI data across subjects, thereby ensuring privacy preservation. To tackle the complexity of fMRI data, BRAINGUARD integrates a hybrid synchronization strategy, enabling individual models to dynamically incorporate parameters from the global model. By establishing a secure and collaborative training environment, BRAINGUARD not only protects sensitive brain data but also improves the image reconstructions accuracy. Extensive experiments demonstrate that BRAINGUARD sets a new benchmark in both high-level and low-level metrics, advancing the state-of-the-art in brain decoding through its innovative design. © 2025, CC BY.

关键词： Differential privacy

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GoMatching: a simple baseline for video text spotting via long and short term matching 24

GoMatching: a simple baseline for video text spotting via lo...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haibin He Maoyuan Ye Jing Zhang Juhua Liu Bo Du Dacheng Tao School of Computer Science National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science at Nanyang Technological University

ISBN: (纸本)9798331314385

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets.

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Deep Learning and Quantum-Enabled Cloud Platform Approach on Optimized Crop Health Predictions 5th

Deep Learning and Quantum-Enabled Cloud Platform Approach on...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Spiking Transformer with Experts Mixture 38

Spiking Transformer with Experts Mixture

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Zhaokun Lu, Yijie Jia, Yanhao Che, Kaiwei Niu, Jun Huang, Liwei Shi, Xinyu Zhu, Yuesheng Li, Guoqi Yu, Zhaofei Yuan, Li School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China Peng Cheng Laboratory China College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science Peking University China Institute for Artificial Intelligence Peking University China Institute of Automation Chinese Academy of Sciences China Deep NeuroCognition Lab I2R and CFAR Agency for Science Technology and Research Singapore

Spiking Neural Networks (SNNs) provide a sparse spike-driven mechanism which is believed to be critical for energy-efficient deep learning. Mixture-of-Experts (MoE), on the other side, aligns with the brain mechanism of distributed and sparse processing, resulting in an efficient way of enhancing model capacity and conditional computation. In this work, we consider how to incorporate SNNs' spike-driven and MoE's conditional computation into a unified framework. However, MoE uses softmax to get the dense conditional weights for each expert and TopK to hard-sparsify the network, which does not fit the properties of SNNs. To address this issue, we reformulate MoE in SNNs and introduce the Spiking Experts Mixture Mechanism (SEMM) from the perspective of sparse spiking activation. Both the experts and the router output spiking sequences, and their element-wise operation makes SEMM computation spike-driven and dynamic sparse-conditional. By developing SEMM into Spiking Transformer, the Experts Mixture Spiking Attention (EMSA) and the Experts Mixture Spiking Perceptron (EMSP) are proposed, which performs routing allocation for head-wise and channel-wise spiking experts, respectively. Experiments show that SEMM realizes sparse conditional computation and obtains a stable improvement on neuromorphic and static datasets with approximate computational overhead based on the Spiking Transformer baselines. © 2024 Neural information processing systems foundation. All rights reserved.

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A subjective questionnaire system based on privacy-friendly incentive protocols 2

A subjective questionnaire system based on privacy-friendly ...

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2nd International Symposium on Computer Applications and Information Systems, ISCAIS 2023

作者： Liu, Xin Sun, Shiwen School of Information Engineering Shandong Youth University of Political Science Shandong Jinan250103 China New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong Shandong Jinan250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Shandong Jinan250103 China

ISBN: (纸本)9781510666665

Questionnaire survey systems are a kind of computer-aided survey tool, which are helpful for the questionnaire publisher to collect the opinions and appeals of the respondents. Nevertheless, it is difficult for the questionnaire publisher to solve tricky questions such as respondents’ inactivity in answering questions, especially in the subjective questionnaire. The paper extends the privacy-friendly incentive protocols of Camenisch et al. based on the technique of signatures of knowledge and the Au-Susilo-Mu signature and put forward a subjective questionnaire system oriented to professional course teaching. The new system controls the range of participants by specific attributes. It also allows participants to remain anonymous at all phases, so they can truly feedback on their subjective evaluations. Moreover, when students are not within the scope of the survey, they are encouraged to help to review the collected subjective questionnaire answers. Furthermore, the new system introduces a token mechanism to stimulate students’ participation. Security analysis shows that the new system satisfies correctness, balance, anonymity, and unlinkability. © 2023 SPIE.

关键词： Network security

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Event Argument Extraction with Two-Dimensional Enhanced Fusion Mechanism

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Computer engineering and Applications 2025年第10期61卷 111-119页

作者： Wang, Luxiang Chen, Yanping Huang, Hui Huang, Ruizhang Qin, Yongbin Engineering Research Center of Ministry of Education for Text Computing and Cognitive Intelligence School of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

Addressing the lack of interaction between trigger and arguments, and the lack of interaction within channels in existing studies of event argument extraction, this paper proposes a two-dimensional enhanced fusion mechanism for event argument extraction (W2-ARG) that incorporates a 2D enhanced fusion method. It inserts identifiers on both sides of a trigger word in a sentence, which encodes information about the event type. In the learning process, it is effective to enhance the interaction between trigger and argument, and encode the trigger separately to highlight its information in the sentence. The event argument extraction is implemented as a two-dimensional representation of label prediction. It has the advantage to capture semantic interactions of words at different distances through dilated convolution. Subsequently, a channel attention module is applied to enhance the interactions within the channel to strengthen the information transfer within the channel. Finally, the Laplace operator is utilized to highlight the positional features of event arguments in the semantic space to improve the performance. The proposed model is experimented on the ACE05-EN and ERE-EN datasets. Experimental results show clear performance improvement of the proposed model compared with other event argument extraction methods. © 2025 Journal of Computer engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

关键词： BERT channel attention event argument extraction Laplace operator sentence planarization representation

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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Vankadari Mohith Kalpana Raja I. R. Oviya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven USA

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing.

关键词： Glaucoma Deep learning Diabetic retinopathy Accuracy Computational modeling Neural networks Medical treatment

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Structured multi-track accompaniment arrangement via style prior modelling 24

Structured multi-track accompaniment arrangement via style p...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jingwei Zhao Gus Xia Ziyu Wang Ye Wang Institute of Data Science NUS and Integrative Sciences and Engineering Programme NUS Graduate School Machine Learning Department MBZUAI and Computer Science Department NYU Shanghai Computer Science Department NYU Shanghai and Machine Learning Department MBZUAI School of Computing NUS and Institute of Data Science NUS and Integrative Sciences and Engineering Programme NUS Graduate School

ISBN: (纸本)9798331314385

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency. In this paper, we introduce a novel system that leverages prior modelling over disentangled style factors to address these challenges. Our method presents a two-stage process: initially, a piano arrangement is derived from the lead sheet by retrieving piano texture styles; subsequently, a multi-track orchestration is generated by infusing orchestral function styles into the piano arrangement. Our key design is the use of vector quantization and a unique multi-stream Transformer to model the long-term flow of the orchestration style, which enables flexible, controllable, and structured music generation. Experiments show that by factorizing the arrangement task into interpretable sub-stages, our approach enhances generative capacity while improving efficiency. Additionally, our system supports a variety of music genres and provides style control at different composition hierarchies. We further show that our system achieves superior coherence, structure, and overall arrangement quality compared to existing baselines.

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RFC:a feature selection algorithm for software defect prediction

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Journal of Systems engineering and Electronics 2021年第2期32卷 389-398页

作者： XU Xiaolong CHEN Wen WANG Xinheng Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Big Data Research at Yancheng Nanjing University of Posts and TelecommunicationsYancheng 224000China School of Computing and Engineering University of West LondonLondon W55RFUK

Software defect prediction(SDP)is used to perform the statistical analysis of historical defect data to find out the distribution rule of historical defects,so as to effectively predict defects in the new ***,there are redundant and irrelevant features in the software defect datasets affecting the performance of defect *** order to identify and remove the redundant and irrelevant features in software defect datasets,we propose ReliefF-based clustering(RFC),a clusterbased feature selection ***,the correlation between features is calculated based on the symmetric *** to the correlation degree,RFC partitions features into k clusters based on the k-medoids algorithm,and finally selects the representative features from each cluster to form the final feature *** the experiments,we compare the proposed RFC with classical feature selection algorithms on nine National Aeronautics and Space Administration(NASA)software defect prediction datasets in terms of area under curve(AUC)and *** experimental results show that RFC can effectively improve the performance of SDP.

关键词： software defect prediction(SDP) feature selection cluster

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Software-Defined, Fast and Strongly-Consistent data Replication for RDMA-Based PM datastores

Software-Defined, Fast and Strongly-Consistent Data Replicat...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Haodi Lu Haikun Liu Chencheng Ye Xiaofei Liao Fubing Mao Yu Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology China

Modern storage systems typically replicate data on multiple servers to provide high reliability and availability. However, most commercially-deployed datastores often fail to offer low latency, high throughput, and strong consistency at the same time. This paper presents Whale, a Remote Direct Memory Access (RDMA) based primary-backup replication system for in-memory datastores. Whale achieves both low latency and strong consistency by decoupling metadata multicasting from data replication for all backup nodes, and using an optimistic commitment mechanism to respond to client write requests earlier. Whale achieves high throughput by propagating writes from the primary node to backup nodes asynchronously via RDMA-optimized chain replication. To further reduce the cost of data replication, we design a log-structured datastore to fully exploit the advantages of one-sided RDMA and Persistent Memory (PM). We implement Whale on a cluster equipped with PM and InfiniBand RDMA networks. Experimental results show that Whale achieves much higher throughput and lower latency than state-of-the-art replication protocols.

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