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9th IEEE Smart World Congress, SWC 2023

作者： Yao, Jingwang Lv, Hui Xiao, Yijun Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China

ISBN: (纸本)9798350319804

Biochemical process control is often achieved through feedback control strategies for theoretical analysis and optimization of biochemical reaction processes. Here, the design improves the robustness of the biochemical process by taking advantage of the Smith prediction structure and internal mode control within the chemical reaction network theory framework to intervene against the time delays and perturbations present in the biochemical process. First, an IMC representation of Smith prediction is constructed using the presence of parameter rectification of the control method, and then an IMC-MSP control scheme is proposed. A first-order time-lag controlled object is established in the construction of the IMC-MSP control scheme. Besides, CRNs and kinetic representations are established based on the control structure to facilitate the evaluation of the control system performance. In this paper, CRNs representations of parabolic functions as signal inputs are defined for the first time and optimized for stepping signals to facilitate the design and study of biomolecular circuits. Notably, for the CRNs-based IMC-MSP control scheme, on the one hand, numerical simulation experiments are designed to verify the effectiveness of the whole scheme from multiple perspectives of time delays, external perturbations, and filter tuning parameters;on the other hand, using DNA strand displacement is reflected to verify the feasibility of the whole scheme. © 2023 IEEE.

关键词： Chemical reaction networks (CRNs) DNA strand displacement (DSD) Internal model control (IMC control) Smith predictor

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Multimodal Knowledge Graph-Guided Cross-Modal Graph Network for Image-Text Retrieval

Multimodal Knowledge Graph-Guided Cross-Modal Graph Network ...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Juncheng Zheng Meiyu Liang Yang Yu Junping Du Zhe Xue Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

Image-text retrieval is a fundamental cross-modal task, which dedicates to align the representation space between image modality and text modality. Existing cross-interactive image-text retrieval methods generate image and sentence em-beddings independently, introduce interaction-based networks for cross-modal reasoning, and then retrieve them using matching metrics. However, existing approaches do not consider fully utilizing semantic relationships among multimodal knowledge to enhance cross-modal fine-grained implicit semantic reasoning capabilities. In this paper, we propose Multimodal Knowledge Graph-guided Cross-modal Graph Network (MKCGN) that exploits multimodal knowledge graphs to explore cross-modal relationships and enhance global representations. In MKCGN, images generate semantic and spatial graphs, which are used to represent visual graphs, and sentences generate textual graphs based on word semantic relations. The visual and textual graphs are used to implement inter-modal reasoning respectively. Then we obtain interest embeddings of image regions and text words based on entity embed dings in Multimodal Knowledge Graph (MKG), which approximates and aligns the representation space of regions and words to a certain extent, thus obtaining effective inter-modal interactions and learning fine-grained cross-modal communication through graph node contrast loss for inter-modal semantic reasoning. Finally, we mine the implicit semantics and potential relationships of images and texts through the MKG as a means of enhancing the global representations and use cross-modal contrast loss to narrow the space of coarse-grained cross-modal representations. Experiments on the MS-COCO and Flickr30K benchmark datasets show that our proposed MKCGN outperforms state-of-the-art image-text retrieval methods.

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A topic detection method based on KM-LSH Fusion algorithm and improved BTM model

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Soft Computing 2024年第19期28卷 11421-11438页

作者： Liu, Wenjun Guo, Huan Gan, Jiaxin Wang, Hai Wang, Hailan Zhang, Chao Peng, Qingcheng Sun, Yuyan Yu, Bao Hou, Mengshu Li, Bo Li, Xiaolei School of Computer and Software Engineering XiHua University Chengdu610039 China Chengdu611731 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China School of Big Data and Artificial Intelligence Chengdu Technological University Chengdu611730 China Sichuan Provincial Engineering Research Center of Hydroelectric Energy Power Equipment Technology Chengdu610039 China

Topic detection is an information processing technology designed to help people deal with the growing problem of data information on the Internet. In the research literature, topic detection methods are used for topic classification through word embedding, supervised-based and unsupervised-based approaches. However, most methods for topic detection only address the problem of clustering and do not focus on the problem of topic detection accuracy reduction due to the cohesiveness of topics. Also, the sequence of biterm during topic detection can cause substantial deviations in the detected topic content. To solve the above problems, this paper proposes a topic detection method based on KM-LSH fusion algorithm and improved BTM model. KM-LSH fusion algorithm is a fusion algorithm that combines K-means clustering and LSH refinement clustering. The proposed method can solve the problem of cohesiveness of topic detection, and the improved BTM model can solve the influence of the sequence of biterm on topic detection. First, the text vector is constructed by processing the collected set of microblog texts using text preprocessing methods. Secondly, the KM-LSH fusion algorithm is used to calculate text similarity and perform topic clustering and refinement. Finally, the improved BTM model is used to model the texts, which is combined with the word position and the improved TF-IDF weight calculation algorithm to adjust the microblogging texts in clustering. The experiment results indicate that the proposed KM-LSH-IBTM method improves the evaluation indexes compared with the other three topic detection methods. In conclusion, the proposed KM-LSH-IBTM method promotes the processing capability of topic detection in terms of cohesiveness and the sequence of biterm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： K-means clustering

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Improving Cross-Domain Named Entity Recognition from the Perspective of Representation 28th

Improving Cross-Domain Named Entity Recognition from the P...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Xu, Jingyun Cai, Yi School of Software Engineering South China University of Technology Guangzhou510650 China The Key Laboratory of Big Data and Intelligent Robots South China University of Technology and the Pazhou Lab Guangzhou510335 China

ISBN: (纸本)9783031306778

Recently, cross-domain named entity recognition (cross-domain NER), which can reduce the high data annotation costs faced by fully-supervised methods, has drawn attention. Most competitive approaches mainly rely on pre-trained language models like BERT to represent words. As such, the original chaotic representations may bring challenges (e.g., entity span detection errors and entity type misclassification) for them. Motivated by this, this proposal proposes to improve cross-domain NER by refining the original representations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Contrastive Learning Cross-domain Named Entity Recognition Representation

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Unsupervised Multimodal Graph Contrastive Semantic Anchor Space Dynamic Knowledge Distillation Network for Cross-Media Hash Retrieval

Unsupervised Multimodal Graph Contrastive Semantic Anchor Sp...

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International Conference on Data engineering

作者： Yang Yu Meiyu Liang Mengran Yin Kangkang Lu Junping Du Zhe Xue Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Cross-media hash retrieval are efficient and effective techniques for retrieval on multi-media database. The success of the Multimodal Large Models (MLM) provides a valuable direction to enhance the accuracy of multimodal hash retrieval, which achieves decent retrieval accuracy with finetuning the pretrained multimodal large models, but their massive model parameters significantly reduce retrieval efficiency. Knowledge Distillation (KD) methods enable small models to learn from the knowledge of larger models, achieving a reduction in model parameter count while ensuring a certain level of accuracy. However, current KD methods face challenges when applied in the multimodal domain, as it requires preserving the multimodal semantic information while minimizing accuracy degradation. To address these challenges, we propose a novel unsupervised multimodal graph contrastive semantic anchor space dynamic knowledge distillation network for cross-media hash retrieval (GASKN). Firstly, to obtain a multimodal semantic anchor space, we construct a large multimodal fusion teacher model using the BEiT-3 model as the backbone. This teacher model is capable of encoding data from different modalities, such as images and text, using the same multimodal encoder to acquire multimodal hash codes that contain rich information from both modalities simultaneously. Secondly, to ensure efficient retrieval capabilities for the student model, we utilize the ALBERT text encoding model and the BiFormer image encoding model as the compact student model's backbones. This allows us to build a lightweight student model with only a twentieth of the parameter count of the teacher model. We propose a dynamic knowledge distillation technique to transfer the multimodal semantic anchor space knowledge embedded in the multimodal large teacher model to the lightweight student model as much as possible. Thirdly, to further distill the structural knowledge of the semantic anchor space from the teacher model to

关键词： Knowledge engineering Degradation Accuracy Image coding Databases Heuristic algorithms Semantics

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An Automatic Question Classification Model Based on Solving Programs for Online Judge Platforms 9

An Automatic Question Classification Model Based on Solving ...

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9th International Conference on Advanced Cloud and Big Data, CBD 2021

作者： Li, Qi Qi, Peng Min, JiangSong Sun, Yan Beijing University of Posts and Telecomm. Beijing Key Laboratory of Intelligent Telecomm Software and Multimedia Beijing100876 China National Engineering Research Center Supporting Software for Enterprise Internet Services Beijing100876 China

ISBN: (纸本)9781665407458

Nowadays, online judge platforms are popular among programming learners. However, the questions of online judge platforms are usually arranged randomly and without regard to their knowledge relations, which makes it inefficient for users to learn to program. To solve this issue, we develop an automatic question classification model based on the knowledge points of question-solving programs. First, the question-solving programs are processed to abstract syntax trees. Based on the abstract syntax trees, three different sampling schemes are designed to extract token sequences while preserving semantic and structural information of programs. Then, the token sequences are input into Word2Vec for learning program representations. After that, the TextCNN is adopted to generate significant program features from program representations. Finally, the program features are merged and input to a linear classifier for classifying predictions. Experimental results show that our model can effectively preserve the semantic and structural features of programs, and our model performs better than other competing methods in the question classifying task. © 2022 IEEE.

关键词： Semantics

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Comparing Puncture-Detection Approaches for Manual Needle Insertions Through the Parietal Pleura

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年第2期7卷 455-468页

作者： L'orsa, Rachael Zareinia, Kourosh Sutherland, Garnette R. Westwick, David Kuchenbecker, Katherine J. University of Calgary Schulich School of Engineering Department of Electrical and Software Engineering Project neuroArm's Surgical Performance Laboratory CalgaryABT2N 1N4 Canada Max Planck Institute for Intelligent Systems Haptic Intelligence Department Stuttgart70569 Germany Toronto Metropolitan University Department of Mechanical Industrial and Mechatronics Engineering TorontoONM5B 2K3 Canada University of Calgary Project neuroArm Cumming School of Medicine CalgaryABT2N 1N4 Canada University of Calgary Schulich School of Engineering Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada

Tube thoracostomy (chest tube insertion) is a surgical procedure that treats pneumothorax, a potentially life-threatening condition where air accumulates between the chest wall and the lungs. The literature reports high complication rates for this procedure, including accidental fatality due to poor manual depth control during tool insertion. We hypothesize that an instrumented needle-holder could help operators recognize pleural puncture and improve depth control, and we present a puncture-detection experiment that contributes toward this goal. An operator manually inserted a bevel-tip needle into ex vivo porcine ribs and through the parietal pleura via a sensorized percutaneous device that records position, force, and videos. We use this rich dataset of 63 insertions to thoroughly test four previously published data-driven puncture-detection (DDPD) algorithms against two new real-time algorithms: a custom recursive digital filter with coefficients optimized for our application, and a difference equation that compares standard deviations between adjacent sliding windows. Our algorithms achieve a precision (true positives over total identified punctures) of 23% and 22%, respectively, while the precision of existing DDPD algorithms ranges from 0% to 21%. Despite these performance improvements, our results show the limitations of DDPD algorithms and motivate new methods for detecting pleural membrane punctures in thoracostomy. © 2018 IEEE.

关键词： Needles

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Updating Large Language Models' Memories with Time Constraints

Updating Large Language Models' Memories with Time Constrain...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wu, Xin Bu, Yuqi Cai, Yi Wang, Tao School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Biostatistics and Health Informatics King's College London United Kingdom

ISBN: (纸本)9798891761681

By incorporating the latest external knowledge, large language models (LLMs) can modify their internal memory. However, in practical applications, LLMs may encounter outdated information, necessitating the filtering of such data and updating of knowledge beyond internal memory. This paper explores whether LLMs can selectively update their memories based on the time constraints between internal memory and external knowledge. We evaluate existing LLMs using three types of data that exhibit different time constraints. Our experimental results reveal the challenges most LLMs face with time-constrained knowledge and highlight the differences in how various LLMs handle such information. Additionally, to address the difficulties LLMs encounter in understanding time constraints, we propose a two-stage decoupling framework that separates the identification and computation of time constraint into a symbolic system. Experimental results demonstrate that the proposed framework yields an improvement of over 60% in ChatGPT's performance, and achieves a 12-24% enhancement in state-of-the-art LLM GPT-4. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Designing Uncorrelated Address Constrain for DNA Storage by DMVO Algorithm

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022年第2期19卷 866-877页

作者： Cao, Ben Ii, Xue Zhang, Xiaokang Wang, Bin Zhang, Qiang Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Ministry of Education School of Software Engineering Dalian University Dalian116622 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China

At present, huge amounts of data are being produced every second, a situation that will gradually overwhelm current storage technology. DNA is a storage medium that features high storage density and long-term stability and is now considered to be a feasible storage solution. Errors are easily made during the sequencing and synthesis of DNA, however. In order to reduce the error rate, novel uncorrelated address constrain are reported, and a Damping Multi-Verse Optimizer (DMVO)algorithm is proposed to construct a set of DNA coding, which is used as the non-payload. The DMVO algorithm exchanges objects through black/white holes in order to achieve a stable state and adds damping factors as disturbances. Compared with previous work, the coding set obtained by the DMVO algorithm is larger in size and of higher quality. The results of this study reveal that the size of the DNA storage coding set obtained by the DMVO algorithm increased by 4-16 percent, and the variance of the melting temperature decreased by 3-18 percent. © 2004-2012 IEEE.

关键词： Damping

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