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18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021

作者： Li, Zhibin Li, Shuai Luo, Xin University of Chinese Academy of Sciences School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing School Chongqing China Swansea University Department of Electronics and Electrical Engineering Swansea United Kingdom University of Chinese Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing School Chongqing China

ISBN: (纸本)9781665440486

Robot arms have been widely used in industry. The absolute positioning error of robots without calibration can reach several millimeters, which cannot meet the application requirements of accurate operation. Therefore, it is almost a mandatory procedure for industrial robots to take on-site calibration before being used. Generally, most researchers on robot calibration have mechanical and instrumentation background as the collection of calibration data is tedious and it is usually difficult to access to industrial robots for researchers in other fields. This research explores the calibration problem from a machine learning perspective and provides the first open-access dataset called "RobotCali"in this area so that machine learning scientists can step into this field and verify their algorithms on this problem. In the meanwhile, a new calibration method based on the Levenberg-Marquardt (LM) algorithm and extended Kalman filter (EKF) algorithm is proposed, which can significantly improve the absolute positioning accuracy of the robot after calibration. Firstly, the error model of robot is established, and kinematic parameters are initially identified by LM algorithm. Then the EKF algorithm is used to further calibrate these parameters, which has been verified the effectiveness of the proposed method by experimental results. Lastly, the future research work is discussed. © 2021 IEEE.

关键词： Calibration

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Z16b,a natural compound from Ganoderma cochlear is a novel RyR2 stabilizer preventing catecholaminergic polymorphic ventricular tachycardia

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中国药理学报（英文版） 2022年第9期43卷 2340-2350页

作者： Jiang-fan Wan Gang Wang Fu-ying Qin Dan-ling Huang Yan Wang Ai-ling Su Hai-ping Zhang Yang Liu Shao-yin Zeng Chao-liang Wei Yong-xian Cheng Jie Liu Guangdong Key Laboratory of Genome Stability and Human Disease Prevention Department of PathophysiologySchool of MedicineShenzhen UniversityShenzhen 518000China College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Genome Stability and Human Disease Prevention Department of PathophysiologySchool of MedicineShenzhen UniversityShenzhen 518000China School of Pharmaceutical Sciences Shenzhen University Health Science CenterShenzhen 518000China Center for Translation Medicine Research and Development Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China Guangdong Cardiovascular Institute Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510000China Guangdong Provincial key laboratory of South China Structure Heart Disease Department of Pediatric CardiologyGuangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510000China

Catecholaminergic polymorphic ventricular tachycardia(CPVT)is an inherited,lethal ventricular arrhythmia triggered by *** in genes that encode cardiac ryanodine receptor(RyR2)and proteins that regulate RyR2 activity cause enhanced diastolic Ca2+release(leak)through the RyR2 channels,resulting in *** therapies for CPVT are *** found that Z16b,a meroterpenoid isolated from Ganoderma cochlear,inhibited Ca2+spark frequency(CaSF)in R2474S/+cardiomyocytes in a dose-dependent manner,with an IC50 of 3.2 μM.Z16b also dose-dependently suppressed abnormal post-pacing Ca2+release *** injection(i.p.)of epinephrine and caffeine stimulated sustained ventricular tachycardia in all R2474S/+mice,while pretreatment with Z16b(0.5 mg/kg,i.p.)prevented ventricular arrhythmia in 9 of 10 mice,and Z16b administration immediately after the onset of VT abolished sVT in 9 of 12 *** translational significance,Z16b significantly inhibited CaSF and abnormal Ca2+release events in human CPVT ***,Z16b interacts with RyR2,enhancing the"zipping"state of the N-terminal and central domains of RyR2.A molecular docking simulation and point mutation and pulldown assays identified Z16b forms hydrogen bonds with Arg626,His1670,and Gln2126 in RyR2 as a triangle shape that anchors the NTD and CD interaction and thus stabilizes RyR2 in a tight"zipping"*** findings support that Z16b is a novel RyR2 stabilizer that can prevent *** may also serve as a lead compound with a new scaffold for the design of safer and more efficient drugs for treating CPVT.

关键词： arrhythmia catecholaminergic polymorphic ventricular tachycardia natural product ryanodine receptor induced pluripotent stem cell-derived cardiomyocytes

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LungX-Net: Lung Cancer Diagnosis from CT and Histopathological Images via Attention Based Multi-Level Feature Fusion Network

LungX-Net: Lung Cancer Diagnosis from CT and Histopathologic...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Sohaib Asif Vicky Yang Wang Dong Xu Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence Taizhou China Center of Intelligent Diagnosis and Therapy (Taizhou) Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Taizhou China Wenling Institute of Big Data and Artificial Intelligence in Medicine Taizhou China Department of Diagnostic Ultrasound Imaging & Interventional Therapy Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Hangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Lung cancer ranks among the top causes of cancer mortality worldwide. However, early detection can significantly boost survival rates, potentially increasing them by as much as 70%. While deep learning algorithms have shown promise in detecting lung cancer from CT scans, current methods are often inefficient, and single-model approaches struggle with complex medical images. Leveraging diverse model features can enhance accuracy and simplify detection. This paper introduces LungX-Net, an innovative and cost-effective deep learning model aimed at improving lung cancer diagnostic accuracy using CT and histopathological images. The proposed model unifies two networks into a unified, trainable feature extraction architecture, employing model compression to preserve essential core blocks. This approach enhances the model’s ability to propagate strong and robust features while significantly reducing computational resource requirements. The model also integrates a Convolutional Block Attention Module (CBAM) to enhance feature extraction by emphasizing the most important regions, which further improves overall accuracy. The integration of these techniques optimizes diagnostic performance while strategically minimizing complexity, making LungX-Net a powerful tool for efficient and precise lung cancer diagnosis. The proposed LungX-Net was evaluated on both CT and histopathological datasets, achieving a recognition accuracy of 99.09% on the CT dataset and 99.30% on the histopathological dataset. LungX-Net demonstrated superior performance compared to previous approaches, achieving greater accuracy with fewer parameters and faster testing times, highlighting its efficiency and cost-effectiveness.

关键词： Deep learning Accuracy Computed tomography Computational modeling Lungs Lung cancer Mortality Feature extraction Medical diagnostic imaging Testing

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Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-graph Embedding for Next-POI Recommendation

Mandari: Multi-Modal Temporal Knowledge Graph-aware Sub-grap...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoqian Liu Xiuyun Li Yuan Cao Fan Zhang Xiongnan Jin Jinpeng Chen School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China The Technology Innovation Center of Cultural Tourism Big Data of Hebei Province Chengde China Hebei Normal University for Nationalities Chengde China Knowledge Discovery and Data Mining Research Center Zhejiang Lab Hangzhou China

Next-POI recommendation aims to explore from user check-in sequence to predict the next possible location to be visited. Existing methods are often difficult to model the implicit association of multi-modal data with user choices. Moreover, traditional methods struggle to fully explore the variation of user preferences at variable time intervals. To tackle these limitations, we propose a Multi-Modal Temporal Knowledge Graph-aware Sub-graph Embedding approach (Mandari). We first construct a novel Multi-Modal Temporal Knowledge Graph. Based on the proposed knowledge graph, we integrate multi-modal information and leverage the graph attention network to calculate sub-graph prediction probability. Next, we implement a temporal knowledge mining method to model the segmentation and periodicity of user check-in and obtain temporal prediction probability. Finally, we fuse temporal prediction probability with the previous sub-graph prediction probability to obtain the final result. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art methods.

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An Explainable and Lightweight Deep Learning Model with Attention Mechanism for Efficient Lung Disease Detection

An Explainable and Lightweight Deep Learning Model with Atte...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qian Tingting Sohaib Asif Yuke Lin Jincao Yao Enyu Wang Vicky Yang Wang Dong Xu Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence Taizhou China Center of Intelligent Diagnosis and Therapy (Taizhou) Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Taizhou China Wenling Institute of Big Data and Artificial Intelligence in Medicine Taizhou China Department of Diagnostic Ultrasound Imaging & Interventional Therapy Zhejiang Cancer Hospital Chinese Academy of Sciences China Zhejiang Chinese Medical University Hangzhou China Department of Radiology Imaging Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Taizhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

COVID-19 has rapidly spread across the world as an extremely contagious disease. Therefore, early detection of the virus is essential to effectively control its transmission and prevent further spread. It is widely reported in the literature that convolutional neural networks (CNNs) are commonly utilized for COVID-19 detection. However, the majority of these models require substantial computational resources and possess a large number of parameters, rendering them less feasible for deployment on real-time devices. This study proposes a solution to address COVID-19 detection challenges by introducing LWIA-Net, a rapid and highly efficient lightweight CNN architecture. The LWIA-Net is equipped with channel attention squeeze and excitation (SE) blocks, as well as a naïve inception module, to enhance the network’s learning ability, reduce computational complexity, and maintain a high level of detection performance. By incorporating the naïve inception module, the LWIA-Net enables multi-level feature extraction, while the inclusion of the SE block helps focuses on informative channels. This integration contributes to the production of high-quality image features and effectively reduces redundancy. We performed thorough experiments using both our locally developed dataset and a publicly available dataset. The statistical analysis demonstrate that LWIA-Net achieves superior performance compared to pre-trained CNN architectures, despite its extremely lightweight architecture which demands low computational cost, memory space, and consists of only 1.11 million parameters. We conducted a comprehensive ablation study to gain a deeper understanding of the significance of each component and to verify their individual contributions to the LWIA-Net. Additionally, we utilized Grad-CAM analysis to confirm that the proposed model effectively identifies and emphasizes the most critical regions of interest. Experimental results have shown that LWIA-Net achieves remarkable accuracy rate

关键词： COVID-19 Statistical analysis Computational modeling Pulmonary diseases Redundancy Computer architecture Production Rendering (computer graphics) Real-time systems Convolutional neural networks

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A Hybrid Prediction Method Based on MODWT-EMD for Time Series in Wireless Sensor Networks

A Hybrid Prediction Method Based on MODWT-EMD for Time Serie...

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Zhongmin Wang Jingwen Jia Cong Gao Yanping Chen Hong Xia Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an University of Posts and Telecommunications Xi’an China School of Computer Science & Technology Xi’an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (纸本)9781665405546

Time series is the main form of sensor data. Time series prediction is conducive to reduce the energy consumption of sensor nodes and increase the service life. As time cost and computation cost of the existing hybrid prediction methods for time series are not good, this paper proposes a hybrid prediction method based on MODWT-EMD for time series. Firstly, the original time series is decomposed into multiple frequency components by Maximal Overlapping Discrete Wavelet Transform (MODWT). Secondly, the multiple frequency components are decomposed successively by Empirical Mode Decomposition (EMD) to obtain IMF components and residuals. IMFs and residuals contain the feature information of time series. Then, the importance of the extracted feature information is scored and sorted by random forest. We select feature information with a higher score. Finally, the selected feature information is input into Bi-GRU for training. We predicted the original time series by the trained Bi-GRU and obtained the prediction results. The experimental results show that the method proposed in this paper can predict the time series effectively.

关键词： Training Wireless communication Wireless sensor networks Time-frequency analysis Costs Time series analysis Predictive models

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An intelligent Resource Scheduling Method with Edge Channel Deployment for BPM

An Intelligent Resource Scheduling Method with Edge Channel ...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Bowen Liu Wanchun Dou Xiaokang Zhou Xuyun Zhang Lianyong Qi Fei Dai Chaochao Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Data Science Shiga University Hikone Japan Department of Computing Macquarie University Sydney Australia College of Computer and Software China University of Petroleum (East China) Qingdao China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China College of Computer Science and Technology Zhejiang University Hangzhou China

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users’ status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method.

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A Stepwise Path Selection Scheme Based on Multiple QoS Parameters Evaluation in SDN 17th

A Stepwise Path Selection Scheme Based on Multiple QoS Param...

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17th EAI International Conference on Collaborative computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Liu, Lin Zhou, Jian-Tao Xing, Hai-Feng Guo, Xiao-Yong College of Computer Science Ecological Big Data Engineering Research Center of the Ministry of Education Cloud Computing and Service Software Engineering Laboratory of Inner Mongolia Autonomous Region National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Social Computing and Data Processing Key Laboratory of Inner Mongolia Autonomous Region Big Data Analysis Technology Engineering Research Center of Inner Mongolia Autonomous Region Inner Mongolia University Inner Mongolia Hohhot China College of Computer Science and Technology Inner Mongolia Normal University Inner Mongolia Hohhot China College of Computer Information and Management Inner Mongolia University of Finance and Economics Inner Mongolia Hohhot China

ISBN: (纸本)9783030926342

Nowadays, the best-effort service can not guarantee the quality of service (QoS) for all kinds of services. QoS routing is an important method to guarantee QoS requirements. It involves path selection for flows based on the current network status and the performance criteria of the service requirements. However, it is difficult for proposed solutions to obtain all the available paths owing to not fully considering all the QoS parameters of paths. In this paper, we propose SWQoS, a novel, universal, and stepwise QoS guarantee scheme based on multiple QoS parameter evaluation for selecting the available paths including preferred paths, satisfied paths and reluctant paths in SDN. The experiments show that SWQoS can select all the available paths that meet the performance criteria of the service requirements and have better QoS parameter performance compared with other path selection methods. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Quality of service

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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

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IEEE Transactions on Affective computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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Stand-in Backdoor: A Stealthy and Powerful Backdoor Attack

Stand-in Backdoor: A Stealthy and Powerful Backdoor Attack

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Shuang Li Hongwei Li Hanxiao Chen School of Computer Science and Engineering University of Electronic Science and Technology of China China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9781728181059

Lack of transparency in deep learning models makes them vulnerable to backdoor attack, which can cause severe security consequences. For a backdoored model, the specific inputs can trigger misclassification rules while it performs normal behaviors on clean data. Existing backdoor attacks usually generate poisoned data by adding an obvious trigger to the original data and mislabeling them, which suffers from poor invisibility and hence can be easily detected. In this paper, we propose Stand-in Backdoor, a more stealthy and powerful backdoor attack, which can completely hide the trigger while maintaining correct labels of poisoned data. Specifically, we design a novel optimization strategy to transform triggers into imperceptible perturbation in the feature space. Furthermore, utilizing the transferability of feature perturbation, we fine-tune the victim model with well-constructed poisoned data that are correctly labeled. Extensive experiments conducted on various image classification tasks demonstrate that our attack outperforms the state-of-the-art work in terms of backdoor stealth and attack performance, without sacrificing the model's utility.

关键词： Perturbation methods Transforms Resists Manuals Inspection data models Security

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