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IEEE International Conference on data Mining (ICDM)

作者： Yangcheng Gao Zhao Zhang Richang Hong Haijun Zhang Jicong Fan Shuicheng Yan School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Department of Computer Science Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Singapore

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance.

关键词： Degradation Training Quantization (signal) Semantics Neural networks Memory management data models

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Multivariate multi-horizon time-series forecasting for real-time patient monitoring based on cascaded fine tuning of attention-based models

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Computers in Biology and Medicine 2025年 194卷 110406页

作者： Saleh, Hager El-Sappagh, Shaker McCann, Michael Alsamhi, Saeed Hamood Breslin, John G. Insight Research Ireland Centre for Data Analytics School of Engineering University of Galway University Road GalwayH91 TK33 Ireland Atlantic Technological University Letterkenny Donegal Ireland Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 Egypt Department of Computing Atlantic Technological University Letterkenny Donegal Ireland Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Department of Electronics Engineering IBB University Ibb Ibb Yemen

The real-time forecasting of critical physiological indicators in intensive care units (ICUs) is essential for early intervention and clinical decision support. This study introduces a novel framework, StreamHealth Multi-Horizon AI, which has been designed to perform multivariate, multi-horizon time-series forecasting for vital signs, specifically for a person's blood oxygen saturation level (SpO2) and respiratory rate (RR). The framework leverages advanced attention-based models, with a particular emphasis on the Temporal Fusion Transformer (TFT) and Temporal Convolutional Network (TCN), and we benchmark its performance against classical deep learning architectures, including LSTM, GRU, Bi-LSTM, Bi-GRU, CNN, and Sequence-to-Sequence (Seq2Seq) models with and without attention mechanisms. Both univariate and multivariate forecasting tasks are explored across multiple prediction horizons (i.e., 7, 15 and 25 min), using physiological time-series data from the MIMIC-III database. The proposed system incorporates a cascaded fine-tuning strategy, wherein the TFT model is sequentially fine-tuned on individual patients’ data, significantly enhancing the model's generalizability to unseen patient profiles. Empirical results demonstrate that the TFT model consistently outperforms baseline models across all forecasting settings, achieving lower RMSE and MAE values, and exhibiting superior capacity for capturing long-sequence dependencies and temporal feature dynamics. To validate its applicability in real-time clinical environments, the framework integrates a simulated streaming infrastructure using Apache Kafka and Apache Flink, enabling continuous data ingestion, forecasting, and visualization of vital signs. This end-to-end deployment underscores the system's potential for ICU monitoring, allowing clinicians to anticipate patient deterioration proactively. In summary, we introduce a comprehensive framework that uniquely integrates TFT with cascaded fine-tuning for multivar

关键词： Electrotherapeutics

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

arXiv

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arXiv 2024年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing314001 China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs) to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent (BCD) algorithm to iteratively optimize the antenna position vector (APV), array rotation vector (ARV), receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Open-domain Dialogue Generation Grounded with Dynamic Multi-form Knowledge Fusion

arXiv

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arXiv 2022年

作者： Xu, Feifei Zhou, Shanlin Wang, Xinpeng Ma, Yunpu Zhang, Wenkai Li, Zhisong School of Computer Science and Technology Shanghai University of Electric Power 201306 China Database Systems and Data Mining University of Munich Munich80538 Germany School of Electronics and Information Engineering Tongji University 201804 China Data Intelligence Center Alibaba Local Life Service Co. Ltd 200062 China

Open-domain multi-turn conversations normally face the challenges of how to enrich and expand the content of the conversation. Recently, many approaches based on external knowledge are proposed to generate rich semantic and information conversation. Two types of knowledge have been studied for knowledge-aware open-domain dialogue generation: structured triples from knowledge graphs and unstructured texts from documents. To take both advantages of abundant unstructured latent knowledge in the documents and the information expansion capabilities of the structured knowledge graph, this paper presents a new dialogue generation model, Dynamic Multi-form Knowledge Fusion based Open-domain Chatt-ing Machine (DMKCM). In particular, DMKCM applies an indexed text (a virtual Knowledge Base) to locate relevant documents as 1st hop and then expands the content of the dialogue and its 1st hop using a commonsense knowledge graph to get apposite triples as 2nd hop. To merge these two forms of knowledge into the dialogue effectively, we design a dynamic virtual knowledge selector and a controller that help to enrich and expand knowledge space. Moreover, DMKCM adopts a novel dynamic knowledge memory module that effectively uses historical reasoning knowledge to generate better responses. Experimental results indicate the effectiveness of our method in terms of dialogue coherence and informativeness. Copyright © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

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Multiagent Q-Learning for Multicrew Dynamic Scheduling and Routing in Road Network Restoration

Multiagent Q-Learning for Multicrew Dynamic Scheduling and R...

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Chinese Control and Decision Conference, CCDC

作者： Yufeng Shen Lianqi Duan Qianshun Zhu Zhaopin Su Guofu Zhang School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 School of Computer Science and Information Engineering Hefei University of Technology Hefei Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) Hefei Anhui Province Key Laboratory of Industry Safety and Emergency Technology (Hefei University of Technology) Heifei

ISBN: (纸本)9781665478977

Road network restoration is an important issue in the post-disaster disposal and rescue, especially when extraordinarily serious natural disasters (e.g., floods and earthquakes) occur. Central to this endeavour is the problem of determining how to reasonably schedule and route the repair crew to quickly restore the damaged road network and establish reliable supply lines from supply nodes to demand nodes. However, most existing work focuses on the activities of the single repair crew in the static road network, which is unable to adapt to the dynamic changes of the road network caused by secondary disasters, such as rock slides and debris flows. Consequently, this work is concentrated on multicrew dynamic scheduling and routing in road network restoration. Specifically, a model of multicrew dynamic scheduling and routing is first presented. Next, the Markov decision process is adopted to construct a decision model for repair crews, based on which a multiagent Q-learning algorithm is developed to find the best scheduling and routing strategy. Finally, experimental results demonstrate that the proposed method can make repair crews adjust their scheduling and routing strategies according to the dynamic changes of the damaged road network and provides a useful attempt to restore the damaged road network in complex, dynamic emergency scenarios of post-disaster.

关键词： Schedules Q-learning Roads Heuristic algorithms Maintenance engineering Markov processes Dynamic scheduling

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Ontology-based data interestingness: A state-of-the-art review

Natural Language Processing Journal

引用

Natural Language Processing Journal 2023年 4卷

作者： Abhilash C.B. Kavi Mahesh Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad India Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad India

In recent years, there has been significant growth in the use of ontology-based methods to enhance data interestingness. These methods play a crucial role in knowledge management by providing explicit, shareable, and reusable knowledge descriptions. However, limited research has been conducted to explore the impact and practical benefits of employing ontologies to increase data interestingness. A comprehensive analysis of the current state-of-the-art approaches in ontology-based data interestingness is essential to identify future advancements and directions in this field. The main objective of this study is to present a clear roadmap showcasing how ontologies contribute to data interestingness and to evaluate their effectiveness in the existing literature. The findings of this research offer an integrated and comprehensive understanding of the following aspects: (1) interestingness metrics in data mining, (2) association rule mining to identify interesting patterns, (3) ontology-based methods to improve data interestingness, and (4) techniques that use ontologies to derive interestingness in data. The paper concludes with a summary of ontology-based methods for data interestingness and outlines potential avenues for future research.

关键词： Ontology data interestingness Knowledge management Semantics Association rule mining data mining Interestingness metrics Ontology-based methods

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Revocable and certificateless public auditing for cloud storage

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science China(information sciences) 2020年第10期63卷 282-284页

作者： Yinghui ZHANG Tiantian ZHANG Shengmin XU Guowen XU Dong ZHENG School of Cyberspace Security Xi'an University of Posts and Telecommunications Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology National Engineering Laboratory for Wireless Security Xi'an University of Posts and Telecommunications Secure Mobile Centre School of Information SystemsSingapore Management University School of Computer Science and Engineering University of Electronic Science and Technology of China

Dear editor,Plenty of computing and storage resources in the cloud are provided for users with restricted computing and storage resources,which has attracted the attention of many researchers[1,2].A generic blockchain-based cloud data auditing scheme[3] is proposed,which is compatible with any blockchains including the bitcoin *** the data integrity checking scheme,certificate-

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Gaze-music Media Transformation by Similarity of Impression Words

Gaze-music Media Transformation by Similarity of Impression ...

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2020 International Electronics Symposium, IES 2020

作者： Iskandar, Abdullah Nakanishi, Takafumi Basuki, Achmad Okada, Ryotaro Kitagawa, Takashi Electronics Engineering Polytechnic Institute of Surabaya Department of Creative Multimedia Technology Surabaya Indonesia Musashino University Department of Data Science Tokyo Japan Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781728195308

In this paper, we present gaze-music transformation realization method by realizing gaze emotion detection system and interconnecting the detection system and an automatic music media creation system. This method automatically creates a music media contents by a detected emotion from human gaze. When it is possible to realize a system that acquires human gaze data from a camera device, detects emotions from the data, and creates media content in response to those emotions, it will contribute to creative activities corresponding to human emotion. Our method connects a gaze emotion detection system and an automatic music creation system by the similarity of words that represent emotions and impressions. It is possible to compare emotions by comparing the emotions expressed by each different vocabulary using the similarity of words. © 2020 IEEE.

关键词： Emotion Recognition

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Highly Sensitive Acetic Anhydride Sensor Based on Cd-Doped Biphase In2o3 Microspheres

SSRN

引用

SSRN 2023年

作者： Meng, Fanli Zhang, Hua Guo, Yinghao Zhang, Haoting College of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

New drugs emerge in an endless stream, the global drug supply is increasing, and the number of drug users is steadily rising, so the detection of acetic anhydride-a raw material for drug preparation is of great significance for controlling the spread of drugs. The crystal phase of semiconductor metal oxides has an important impact on oxygen vacancy, microscopic morphology, specific surface area, and high-energy surface exposure, which in turn affects the catalytic and gas-sensing effects of materials. Ions doped can sometimes achieve the purpose of changing the crystal phase. In this paper, Cd-doped biphase (cubic phase and hexagonal phase) In2O3 nanospheres sensitive to acetic anhydride were synthesized via one easy hydrothermal process. The fabricated sensor has high sensitivity (372) to 100 ppm acetic anhydride in the dry environment of 220 °C, excellent selectivity and good stability. X-ray diffraction (XRD), energy dispersive spectroscopy (EDS) analysis, and scanning electron microscopy (SEM) were used to observe the crystal phase, elemental information and nanoscale appearance of the prepared compounds, and the gas-sensitive mechanism was briefly analyzed according to the characterization information. The enhancing acetic anhydride sensing properties are owing to the change of internal electronic channels and the crystal structure of In2O3 material caused by Cd-doped. © 2023, The Authors. All rights reserved.

关键词： Scanning electron microscopy

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Hollow Urchin Co-Fe2o3 With Outstanding Selectivity and Fast Responding For Ppb Level Nh3 Sensing Via Lewis Acid-Base Effect

SSRN

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SSRN 2023年

作者： Liu, Yang Ji, Hanyang Yuan, Zhenyu Zhu, Hongmin Kong, Lu Gao, Hongliang Meng, Fanli College of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang110819 China

Layered double hydroxides (LDH) are a class of layered compounds, which are popular among researchers because of the large specific surface area and excellent catalytic properties. In this work, Co-Fe2O3 2 was prepared with urchin Co-Fe LDH as a precursor. The morphology of Co-Fe2O3 2 was not changed, and burrs were evenly distributed on the surface with a diameter of about 3-5 nm. Co-Fe2O3 2 showed outstanding gas sensitivity to NH3, with favourable repeatability, stability and selectivity, as well as an ultra-low application detection limit of 10 ppb (response was 5%). The response of the sensor to 10 ppm NH3 at 275 °C was 298% with a fast response/recovery rate (7.2/5.4 s). BET revealed that the specific surface area of Co-Fe2O3 2 was 114.5 m2/g, which could provide more sites for gas adsorption and reaction. The results of XPS, Raman, and FTIR spectras indicated that some Co2+ replaced the position of Fe3+ to generate abundant oxygen vacancy, which could enhance the activity of the sensitive material. The small size effect and the formation of p-n junction increased the adsorption capacity of the sensitive material. Moreover, the synergistic effect of Lewis acid-base and catalytic of Co3O4 greatly improved the selectivity of the sensor to NH3. The growth process and principle of Co-Fe LDH were discussed and analyzed in detail, which provided a new idea for the controlled synthesis of LDH. Co-Fe2O3 2 is a promising gas-sensitive material for the rapid detection of ammonia. © 2023, The Authors. All rights reserved.

关键词： Oxygen vacancies

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