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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yali Zhao Rong Yan College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

Document-level biomedical relation extraction refers to extract relationship facts from unstructured biomedical literature. Due to the fact that many relationship facts span multiple sentences and involve complex interactions between entities, it requires models with strong logical reasoning capabilities. However, previous methods have had limitations in terms of logical reasoning, such as lack logical reasoning and the entities merely based on a single granularity. Although other efforts are based on multiple granularity, including mention pairs and entity pairs, the inference mechanism is weak. In this paper, we propose a module to enhance the model’s logical reasoning capabilities from four kinds of granularity (mention, entity, mention pairs, and entity pairs). Specifically, we encode the entities and mentions separately using BiGRU to capture contextual features. Simultaneously, we employ a U-Net network to model entity pairs and mention pairs, and enhance inter-sentence reasoning abilities. Additionally, to support the model’s reasoning capabilities, we introduce topic nodes into the traditional heterogeneous graph to fully extract document information. Experimental results on CDR and GDA datasets in the biomedical field demonstrate the outstanding performance of the proposed model.

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Specific Emitter Identification based on CNN and Transformer

Specific Emitter Identification based on CNN and Transformer

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Science and technology Innovation (IAECST), International Academic Exchange Conference on

作者： Bin Wang Ning Gao Feng Wang The 38th Research Institute of China Electronics Technology Group Corporation Key Laboratory of Aperture Array and Space Application Key Laboratory of Intelligent Information Processing Hefei Anhui China School of Computer and Information Hefei University of Technology Hefei China

Specific emitter identification(SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. In this paper, we present a novel radiation source identification method named CNNFormer based on a hybrid architecture of Convolutional Neural Network and Transformer, harnessing the strengths of both architectures to enhance feature extraction and sequence modeling. We demonstrate the effectiveness of our approach on simulation datasets, achieving state-of-the-art accuracy in emitter identification. We showcase outstanding performance of our method on simulation datasets, achieving 98.29% three-class identification accuracy the emitter dataset with three devices and 97.60% ten-class identification accuracy on an RF fingerprint dataset with ten XSPR transmitters. Furthermore, our model allows for insight into the key features contributing to identification. This work contributes to enhancing the security and reliability of wireless communication systems.

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Firzen: Firing Strict Cold-Start Items with Frozen Heterogeneous and Homogeneous Graphs for Recommendation

Firzen: Firing Strict Cold-Start Items with Frozen Heterogen...

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International Conference on Data Engineering

作者： Hulingxiao He Xiangteng He Yuxin Peng Zifei Shan Xin Su Wangxuan Institute of Computer Technology & National Key Laboratory for Multimedia Information Processing Peking University Beijing China WeiXin Group Tencent Shanghai China WeiXin Group Tencent Shenzhen China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Recommendation models utilizing unique identities (IDs) to represent distinct users and items have dominated the recommender systems literature for over a decade. Since multi-modal content of items (e.g., texts and images) and knowledge graphs (KGs) may reflect the interaction-related users' preferences and items' characteristics, they have been utilized as useful side information to further improve the recommendation quality. However, the success of such methods often limits to either warm-start or strict cold-start item recommendation in which some items neither appear in the training data nor have any interactions in the test stage: (1) Some fail to learn the embedding of a strict cold-start item since side information is only utilized to enhance the warm-start ID representations; (2) The others deteriorate the performance of warm-start recommendation since unrelated multi-modal content or entities in KGs may blur the final representations. In this paper, we propose a unified framework incorporating multi-modal content of items and KGs to effectively solve both strict cold-start and warm-start recommendation termed Firzen, which extracts the user-item collaborative information over frozen heterogeneous graph (collaborative knowledge graph), and exploits the item-item semantic structures and user-user behavioral association over frozen homogeneous graphs (item-item relation graph and user-user co-occurrence graph). Furthermore, we build four unified strict cold-start evaluation benchmarks based on publicly available Amazon datasets and a real-world industrial dataset from Weixin Channels via rearranging the interaction data and constructing KGs. Extensive empirical results demonstrate that our model yields significant improvements for strict cold-start recommendation and outperforms or matches the state-of-the-art performance in the warm-start scenario. The code is available at https://***/PKU-ICST-MIPL/Firzen_ICDE2024.

关键词： Codes Firing Semantics Collaboration Training data Knowledge graphs Benchmark testing

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Scheduling Jobs with Release and Delivery Times Subject to Nested Eligibility Constraints

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Journal of the Operations Research Society of China 2021年第1期9卷 63-77页

作者： Yu-Zhong Zhang Shu-Guang Li Institute of Operations Research Qufu Normal UniversityRizhao 276826ShandongChina Key Laboratory of Intelligent Information Processing in Universities of Shandong(Shandong Institute of Business and Technology) Yantai 264005ShandongChina College of Computer Science and Technology Shandong Institute of Business and TechnologyYantai 264005ShandongChina

The problem of scheduling jobs with release and delivery time subject to machine eligibility constraints is *** eligible sets of the jobs are nested,and pre-emptions are not *** goal is to minimize the maximum delivery completion time,i.e.,the time by which all jobs are *** the special case of equal release time,a 2-approximation algorithm is presented whose running time depends linearly on the number of *** the general case of unequal release time,a polynomial time approximation scheme is derived.

关键词： Scheduling Nested eligibilityconstraints Release time Deliverytime Polynomial timeapproximationscheme

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MUSICAL TEMPO ESTIMATION USING A MULTI-SCALE NETWORK 22

MUSICAL TEMPO ESTIMATION USING A MULTI-SCALE NETWORK

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22nd International Conference on Music information Retrieval, ISMIR 2021

作者： Sun, Xiaoheng He, Qiqi Gao, Yongwei Li, Wei School of Computer Science and Technology Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9781732729902

Recently, some single-step systems without onset detection have shown their effectiveness in automatic musical tempo estimation. Following the success of these systems, in this paper we propose a Multi-scale Grouped Attention Network to further explore the potential of such methods. A multi-scale structure is introduced as the overall network architecture where information from different scales is aggregated to strengthen contextual feature learning. Furthermore, we propose a Grouped Attention Module as the key component of the network. The proposed module separates the input feature into several groups along the frequency axis, which makes it capable of capturing long-range dependencies from different frequency positions on the spectrogram. In comparison experiments, the results on public datasets show that the proposed model outperforms existing state-of-the-art methods on Accuracy1. © 2021 Proceedings of the 22nd International Conference on Music information Retrieval, ISMIR 2021. All Rights Reserved.

关键词： Network architecture

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Firzen: Firing Strict Cold-Start Items with Frozen Heterogeneous and Homogeneous Graphs for Recommendation

arXiv

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arXiv 2024年

作者： He, Hulingxiao He, Xiangteng Peng, Yuxin Shan, Zifei Su, Xin Wangxuan Institute of Computer Technology National Key Laboratory for Multimedia Information Processing Peking University Beijing China WeiXin Group Tencent Shanghai China WeiXin Group Tencent Shenzhen China

Recommendation models utilizing unique identities (IDs) to represent distinct users and items have dominated the recommender systems literature for over a decade. Since multimodal content of items (e.g., texts and images) and knowledge graphs (KGs) may reflect the interaction-related users’ preferences and items’ characteristics, they have been utilized as useful side information to further improve the recommendation quality. However, the success of such methods often limits to either warm-start or strict cold-start item recommendation in which some items neither appear in the training data nor have any interactions in the test stage: (1) Some fail to learn the embedding of a strict cold-start item since side information is only utilized to enhance the warm-start ID representations;(2) The others deteriorate the performance of warm-start recommendation since unrelated multi-modal content or entities in KGs may blur the final representations. In this paper, we propose a unified framework incorporating multi-modal content of items and KGs to effectively solve both strict cold-start and warm-start recommendation termed Firzen, which extracts the user-item collaborative information over frozen heterogeneous graph (collaborative knowledge graph), and exploits the item-item semantic structures and user-user behavioral association over frozen homogeneous graphs (item-item relation graph and user-user co-occurrence graph). Furthermore, we build four unified strict cold-start evaluation benchmarks based on publicly available Amazon datasets and a real-world industrial dataset from Weixin Channels via rearranging the interaction data and constructing KGs. Extensive empirical results demonstrate that our model yields significant improvements for strict cold-start recommendation and outperforms or matches the state-of-the-art performance in the warm-start scenario. The code is available at https://***/PKU-ICST-MIPL/Firzen_ICDE2024. Copyright © 2024, The Authors. All righ

关键词： Knowledge graph

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End-to-End Tibetan Speech Recognition Study Based on Byte Pair Coding 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Cai, Yuqing Wang, Chao Duojie, Renzeng Zhu, Yulei Zhang, Jin Tashi, Nyima Tibet University School of Information Science and Technology Lhasa850000 China State Key Laboratory of Tibetan Intelligent Information Processing and Application Lhasa850000 China Qinghai Normal University College of Computer Xining810016 China

ISBN: (纸本)9780000000002

Aiming at the problems of inconsistent modeling units and unsatisfactory recognition results in Tibetan end-to-end speech recognition research, this paper proposes a BPE-Conformer-CTC/Attention end-to-end Tibetan speech recognition method. Firstly, the method adopts the byte-pair encoding algorithm for speech modeling, which solves the problem of inconsistent modeling units in Tibetan speech recognition by repeatedly merging the most frequent character pairs, segmenting the text into manageable and meaningful units, and balancing the granularity of modeling units. Secondly, the Con-former encoder is utilized to effectively fuse the global and local dependencies of audio sequences, thus enhancing the model's representational capability. Finally, the alignment and decoding process is accelerated by a joint CTC/Attention decoding strategy, which in turn improves the accuracy and efficiency of the recognition results. Experimental results on the open-source datasets XBMU-AMD031 and TIBMD@MUC show that the proposed BPE-Conformer-CTC/Attention model achieves a word error rate of 9.0% and 4.6%, respectively, and compared to the baseline Transformer-CTC/Attention model, the word error rate is reduced by 14.2% and 30.3%, respectively. This proposed approach provides an effective solution for the Tibetan end-to-end speech recognition task. © 2024 China National Conference on Computational Linguistics.

关键词： Decoding

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Sign Language Translation using Frame and Event Stream: Benchmark Dataset and Algorithms

arXiv

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arXiv 2025年

作者： Wang, Xiao Li, Yuehang Wang, Fuling Jiang, Bo Wang, Yaowei Tian, Yonghong Tang, Jin Luo, Bin School of Computer Science and Technology Anhui University Hefei China Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China National Key Laboratory for Multimedia Information Processing Peking University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China

Accurate sign language understanding serves as a crucial communication channel for individuals with disabilities. Current sign language translation algorithms predominantly rely on RGB frames, which may be limited by fixed frame rates, variable lighting conditions, and motion blur caused by rapid hand movements. Inspired by the recent successful application of event cameras in other fields, we propose to leverage event streams to assist RGB cameras in capturing gesture data, addressing the various challenges mentioned above. Specifically, we first collect a large-scale RGB-Event sign language translation dataset using the DVS346 camera, termed VECSL, which contains 15,676 RGB-Event samples, 15,191 glosses, and covers 2,568 Chinese characters. These samples were gathered across a diverse range of indoor and outdoor environments, capturing multiple viewing angles, varying light intensities, and different camera motions. Due to the absence of benchmark algorithms for comparison in this new task, we retrained and evaluated multiple state-of-the-art SLT algorithms, and believe that this benchmark can effectively support subsequent related research. Additionally, we propose a novel RGB-Event sign language translation framework (i.e., M2-SLT) that incorporates fine-grained micro-sign and coarse-grained macro-sign retrieval, achieving state-of-the-art results on the proposed dataset. Both the source code and dataset will be released on https://***/Event-AHU/OpenESL. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Formulation of Optimization Problems with Radiation Field Phases as Design Variables for Pattern Synthesis of Linear Antenna Arrays

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China Communications 2022年第12期19卷 86-100页

作者： Fei Zhao Jun He Sanyou Zeng Changhe Li Qinghui Xu Zhigao Zeng School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China Science and Technology on Blind Signal Processing Laboratory Southwest Electronics and Telecommunication Technology Research InstituteChengdu 610041China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China College of Computer Hunan University of TechnologyZhuzhou CityHunan Province412007China

Pattern synthesise of antenna arrays is usually complicated optimization problems,while evolutionary algorithms(EAs)are promising in solving these *** paper does not propose a new EA,but does construct a new form of optimization *** new optimization formulation has two differences from the common *** is the objective function is the field error between the desired and the designed,not the usual amplitude error between the desired and the *** difference is beneficial to decrease complexity in some *** second difference is that the design variables are changed as phases of desired radiation field within shaped-region,instead of excitation *** difference leads to the reduction of the number of design variables.A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied,and the effectiveness and advantages of the proposed new optimization problems are *** results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.

关键词： pattern synthesis evolutionary algorithms linear antenna arrays

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Lightweight Transformer Network and Self-supervised Task for Kinship Verification 8

Lightweight Transformer Network and Self-supervised Task for...

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Li, Yunwei Li, Danyang Dong, Lingyun Chen, Xiaopan School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results. © 2022 IEEE.

关键词： Image recognition

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