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17th International Conference on intelligent Robotics and Applications, ICIRA 2024

作者： Zuo, Guoyu Wang, Zihao Luo, Yongkang Yu, Shuangyue Zhao, Min School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819607761

As robotic technologies advance, robotic arm manipulation tasks in complex environments become increasingly important. This paper presents a new collaborative pushing and grasping strategy to address two major challenges robotic arms encounter in complex environments: invisibility and tight enclosure of target objects. We propose a generator-evaluator network architecture based on multiple action primitives, incorporating hierarchical reinforcement learning and Double DQN techniques to optimize the operational efficiency of the robotic arm. By introducing a new action primitive to prioritize pushing operations around the topmost objects in space, our approach effectively reveals and locates obscured target objects. Additionally, utilizing the Spatial Correlation Test (SCT), our method explores all possible collision-free operational positions in the action space, selecting the optimal position from among them. Experimental results demonstrate that our approach achieves high efficiency and success rates in scenarios with randomly distributed objects, objects tightly enclosed by others, and invisible objects. This strategy enhances the speed and accuracy of tasks and improves the robot’s adaptability to complex environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Video Frame Interpolation Based on Lightweight Convolutional Unit and Three-scale Encoder 23

Video Frame Interpolation Based on Lightweight Convolutional...

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15th International Conference on Digital Image Processing, ICDIP 2023

作者： Liu, Weijing Yang, Xiaohui Feng, Zhiquan Xu, Tao Guo, Qingbei School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9798400708237

Video frame interpolation (VFI) achieves temporal super-resolution by synthesizing intermediate frame between two original adjacent frames. This paper proposes a lightweight VFI network based on lightweight convolutional unit and three-scale encoder. We first introduce a three-scale encoding-decoding structure with two-level attention cascades to represent the multi-scale motion information. Then, recurrent convolutional layer (RCL) and residual operation are adopted to design the recurrent residual convolutional unit (RRCU) to refine the three-scale structure. Finally, we propose a lightweight unit by combining the depth separable convolution and RRCU, and introduce the local lightweight idea to reduce the model parameters. Experimental results show that the proposed method achieves a better performance against the state-of-the-art methods with fewer parameters. © 2023 ACM.

关键词： Convolution

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A Bald Eagle Search Optimization Based Weighted Rank Aggregation Method for Microarray data Classification 23

A Bald Eagle Search Optimization Based Weighted Rank Aggrega...

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15th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2023

作者： Li, Wei Shi, Tengfei Wang, Linjie Xie, Weidong Key Laboratory of Intelligent Computing in Medical Image MIIC Northeastern University Ministry of Education China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798400700385

The rapid development of microarray technology has generated a large amount of microarray data, and the classification of these data is meaningful for cancer diagnosis, treatment and prognosis. The classification of high-dimensional microarray data with small samples is a challenging problem, which usually requires feature selection methods to reduce the data dimensionality first. However, different feature selection methods usually generate different feature lists for the same data. Researchers need to choose among many feature selection methods, which reduces the research efficiency. Therefore, rank aggregation method is used to generate a optimal list by aggregating all ordered feature lists generated by different feature selection methods. It can combine the advantages of multiple feature selection methods and does not prefer a particular method, so it is more robust to outliers, noises and errors. In this paper, we propose a weighted rank aggregation method based on the Bald Eagle Search optimization. A positional weight is designed to emphasize the importance of the top features in the list, so that the distance between lists can be measured more accurately. In addition, we improve the Bald Eagle Search algorithm for optimizing rank aggregation method to obtain a optimal ordered list. The experimental results on six public microarray datasets indicate that the features selected by our method can significantly improve the classification performance. © 2023 ACM.

关键词： Feature Selection

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Enhancing Drug-Drug Interaction Prediction by Knowledge Graph Embedding 23

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Liu, Chao Wang, Xizhao Liu, Qiang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518000 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug). © 2024 IEEE.

关键词： Knowledge graph

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Efficient Parameter Learning of Bayesian Network with Latent Variables from High-Dimensional data 5

Efficient Parameter Learning of Bayesian Network with Latent...

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5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Wu, Xinran Chen, Xiang Yue, Kun Yunnan University School of Information Science and Engineering Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9798350332674

Bayesian network with latent variables (BNLV) plays an important role in the representation of dependence relations and inference of uncertain knowledge with unobserved variables. The variables with large cardinalities in high-dimensional data make it challenging to efficiently learn the large-scaled probability parameters as the conditional probability distributions (CPDs) of BNLV. In this paper, we first propose the multinomial parameter network to parameterize the CPDs w.r.t. latent variables. Then, we extend the M-step of the classic EM algorithm and give the efficient algorithm for parameter learning of BNLV. Experimental results show that our proposed method outperforms some state-of-the-art competitors. © 2023 IEEE.

关键词： Bayesian networks

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Graph Spatial-Spectral Deep Subspace Clustering for Hyperspectral Image Classification 2nd

Graph Spatial-Spectral Deep Subspace Clustering for Hyperspe...

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2nd International Conference on Image, Vision and intelligent Systems, ICIVIS 2022

作者： Han, Tianhao Dong, Jiwen Niu, Sijie Yu, Wenyue Gao, Xizhan School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789819909223

Deep subspace clustering (DSC) has become a research hotspot and achieved considerable success in unsupervised hyperspectral image (HSI) classification domain. However, previous researches seldom consider global spatial-spectral information, which leads to an unsatisfactory performance when processing HSI with background (can be seen as a type of noise). In this work, we propose a Graph Spatial-Spectral Deep Subspace Clustering (GS2DSC) method which can make full use of the spatial-spectral information of HSI data. Specifically, a graph attention auto-encoder is utilized to extract the graph-structed features of bands and the spectral information. Then, we employ a fusion strategy to fuse the band-level global spatial-spectral features and the patch-level local spatial-spectral features into joint features to enhance the spatial-spectral information. Our proposed method alleviates the bad influence of the background information and produce an informative affinity matrix. The experimental results on two classic HSI datasets show that the overall accuracy of our approach is improved by 5.31% and 3.05% respectively compared with other DSC-based methods, which demonstrates that the structure can enhance the spatial-spectral information and perform better classification results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification 23

Domain Adaptation Random Vector Functional Link for White Bl...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wang, Qin Wang, Xi-Zhao Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells. © 2024 IEEE.

关键词： Deep learning

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Optimizing Empathetic Response by Generating and Integrating Emotion Feedback and Topic Discussion 28th

Optimizing Empathetic Response by Generating and Integrati...

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

作者： Li, Jing Han, Donghong Feng, Shi Zhang, Yifei School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China

ISBN: (纸本)9783031306747

Expressing empathy is a trait in human daily conversation, in which people are willing to give responses containing appropriate emotions and topics on the basis of understanding the interlocutor’s situation. However, empathetic dialogue models trained by data-driven training method tend to generate general responses, which are usually monotonous and difficult to infuse emotions and topics concurrently. To solve this issue, we propose a novel model that generates two sub-responses, namely, emotion feedback and topic discussion, then integrates them to optimize empathetic responses. Specifically, in the sub-response generation stage, we introduce emotion lexicon and commonsense knowledge to make sub-responses focus on emotional words and topic-related words respectively, which drives the sub-responses to be contextually related from different perspectives. Afterward, we utilize cross attention to integrate the global information to optimize the final response. Our model is trained on the pre-trained language model BART. Experimental results show that our method can generate responses involving emotion and topic well, and compared with existing methods, empathy and relevance are improved. Our code is available at https://***/outsider-lj/edsgi_bart. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital storage

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Indoor Multi-Anchor Collaborative Localization for Unmanned Systems

Indoor Multi-Anchor Collaborative Localization for Unmanned ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Tai, Beijia Wei, Yangjie Zhang, Guangyu He, Yuqing Cui, Siyao Ministry of Education College of Computer Science and Engineering Northeastern University Key Laboratory of Intelligent Computing in Medical Image Wenhua Street 3 Shenyang110819 China Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781665481090

Accurate tracking of unmanned devices is essen-tial for higher-level decision-making in collaborative confrontation scenarios. However, the robustness of current positioning methods based on Ultra-Wideband (UWB) is limit, and the positioning error is unstable. This paper presents a collabo-rative localization algorithm specifically designed for indoor unmanned systems, which leverages UWB technology combined with Least Squares (LS) and Extended Kalman Filter (EKF) models to achieve anchor calibration, as well as precise and stable positioning of unmanned devices. First, the positions of anchors is estimated using fixed-point ranging information;Second, the positions of anchors are simultaneously corrected while locating the tags on unmanned devices. The experimental results show that our algorithm achieves positioning accuracy within 10 cm in the three-dimensional space, even with a ranging error of 5 cm. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Generalized Goldwasser and Micali’s Type Cryptosystem

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Journal of Computer science & Technology 2022年第2期37卷 459-467页

作者： Ying Guo Zhen-Fu Cao Xiao-Lei Dong Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China Institute of Intelligent Science and Technology Tongji UniversityShanghai 200092China

In 1982,Goldwasser and Micali proposed the first probabilistic public key cryptosystem with indistinguishability under chosen plaintext attack security based on the quadratic residuosity *** expansion of Goldwasser's scheme is quite large,thereby the scheme is inefficient.A lot of schemes have been proposed to reduce the ciphertext *** schemes use the same encryption algorithm as Goldwasser's scheme with different parameters and keys,which we call them Goldwasser and Micali's type(GM-type)***-type schemes can be divided into two categories according to different parameters and decryption *** this paper,we propose the first generalized GM-type scheme combining these two *** GM-type schemes are special cases of our generalized GM-type *** ciphertext expansion of our scheme is smaller than that of any other GM-type schemes.

关键词： Goldwasser and Micali's type(GM-type)scheme k-th power residuosity discrete logarithm problem

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