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3rd Conference on Language, Data and Knowledge, LDK 2021

作者： Armaselu, Florentina Apostol, Elena-Simona Khan, Anas Fahad Liebeskind, Chaya McGillivray, Barbara Truică, Ciprian-Octavian Oleškevičiene, Giedre Valūnaite University of Luxembourg Luxembourg Department of Computer Science and Engineering Faculty of Automatic Control and Computer University Politehnica of Bucharest Romania Institute for Computational Linguistics «A. Zampolli» National Research Council of Italy Pisa Italy Department of Computer Science Jerusalem College of Technology Israel Theoretical and Applied Linguistics Faculty of Modern and Medieval Languages and Linguistics University of Cambridge United Kingdom The Alan Turing Institute London United Kingdom Department of Computer Science and Engineering Faculty of Automatic Control and ComputerUniversity Politehnica of Bucharest Romania Institute of Humanities Mykolas Romeris University Vilnius Lithuania

ISBN: (纸本)9783959771993

The paper proposes an interdisciplinary approach including methods from disciplines such as history of concepts, linguistics, natural language processing (NLP) and Semantic Web, to create a comparative framework for detecting semantic change in multilingual historical corpora and generating diachronic ontologies as linguistic linked open data (LLOD). Initiated as a use case (UC4.2.1) within the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, the study will explore emerging trends in knowledge extraction, analysis and representation from linguistic data science, and apply the devised methodology to datasets in the humanities to trace the evolution of concepts from the domain of socio-cultural transformation. The paper will describe the main elements of the methodological framework and preliminary planning of the intended workflow. © Florentina Armaselu, Elena-Simona Apostol, Anas Fahad Khan, Chaya Liebeskind, Barbara McGillivray, Ciprian-Octavian Truică, and Giedre Valūnaite Oleškevičien e;licensed under Creative Commons License CC-BY 4.0

关键词： Linguistics

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Clustering Strategy for Megaconstellation with Synergistic Energy and Size Considerations

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhang, Chengxi Yin, Tianle Wang, Wenshuo Wu, Jin Wang, Jihe Qi, Yuhua He, Wei Jiangnan University Key Laboratory of Advanced Control for Light Industry Processes Ministry of Education Wuxi214122 China Beijing Institute of Technology Mechanical Engineering China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong SAR Hong Kong University of Science and Technology Beijing School of Intelligent Science and Technology Beijing China School of Aeronautics and Astronautics Shenzhen518107 China Sun Yat-sen University No. 135 Xingang West Road Guangzhou China

This paper proposes an energy- and size-aware clustering (ESC) strategy for megaconstellation systems. The methodology constructs a hierarchical architecture with designated cluster heads and member satellites through role differentiation. Cluster heads serve as relay nodes, reducing the communication load on ground infrastructure by interacting with ground stations and other entities. This hierarchical approach enables efficient constellation control through interactions primarily with cluster heads. The ESC strategy begins with a cluster head election process, incorporating orphan handling management to select cluster heads, ensuring full coverage of the entire constellation. Then, a two-stage multi-objective optimization framework is used to deal with division of members to clusters and intra-cluster multi-hop connectivity configurations. This dual-phase approach optimizes energy consumption while maintaining balanced cluster sizes. Simulations on the OneWeb system validate the ESC strategy as a viable solution in that it achieves 55.9% energy consumption reduction compared to traditional discrete gradient optimization methods while maintaining more balanced cluster sizes. © 2025 IEEE.

关键词： Multiobjective optimization

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Trends and Challenges of Sustainable Agriculture in the Iraqi Environment

Trends and Challenges of Sustainable Agriculture in the Iraq...

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2022 IEEE International Conference on Blockchain, Smart Healthcare and Emerging Technologies, SmartBlock4Health 2022

作者： Al-Asadi, Hanan Hussein Goga, Maria Ali, Haider Abdullah Popa, Ramona Cristina Oludele, Awodele University POLITEHNICA of Bucharest Faculty of Automatic Control and Computers Bucharest Romania University of Technology-Iraq Baghdad Iraq Technical University of Civil Engineering of Bucharest UTCB Department for Training of Teaching Personnel Bucharest Romania Madenat Alelem University College Baghdad Iraq Babcock University Computer Science Department Ogun State Nigeria

ISBN: (纸本)9781665460149

The use of Agriculture Intelligent Systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternative energy sources like solar, thermal, and photovoltaic systems for microclimate control. Greenhouses are considered a successful solution with developments and achievements in the technology of this field. However, problems like high costs, difficulties in installation and maintenance, or the need for some procedures should be considered. Because Iraq is close to the equator (3669 km), it receives sufficient solar power. It also has large areas available for the building of solar greenhouse systems. The farmers need an affordable and functional smart greenhouse to enable and encourage them to use the environment efficiently. We intend to develop low-cost, accurate, and easy-to-use greenhouse systems controlled by artificial intelligence algorithms. Farmers will be able to control the greenhouse by using simple commands. Machine learning algorithms provide real-time and effective analysis that could support farmers in decision-making and provide relevant data for researchers and policymakers. AISs algorithms can lead to a sustainable and productive agriculture system. The system can be used in Iraq with sufficient government support and subsidy. © 2022 IEEE.

关键词： Intelligent systems

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Energy allocation for activity recognition in wearable devices with kinetic energy harvesting

Energy allocation for activity recognition in wearable devic...

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作者： Xiao, Ling Meng, Yu Tian, Xiaobing Luo, Haibo College of Computer Science and Electronic Engineering Hunan University Changsha China College of Computer Science Guangdong University of Science And Technology Dongguan China Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China College of Computer and Control Engineering Minjiang University Fujian China

Harvesting kinetic energy from body movement is regarded as a promising rechargeable energy source for wearable devices with low-power. Energy allocation is essential for motion-based rechargeable devices since the great variability of energy gained from movement. Based on the realistic characteristics of an ultra-low-power wearable devices and our measurement observations, we propose the optimization framework allocating energy to maximize the average accuracy of human activity recognition and provide an offline and online algorithm, respectively. We evaluate the proposed energy allocation approach with real-world human activity and kinetic energy harvesting datasets. Experimental results validate that our proposed energy allocation approach can maximize the energy allocation utility and improve energy efficiency of wearable devices. © 2021 John Wiley & Sons, Ltd.

关键词： Kinetic energy

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Skin-like cryogel electronics from suppressed-freezing tuned polymer amorphization

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Nature Communications 2023年第1期14卷 1-10页

作者： Zhang, Xiansheng Yan, Hongwei Xu, Chongzhi Dong, Xia Wang, Yu Fu, Aiping Li, Hao Lee, Jin Yong Zhang, Sheng Ni, Jiahua Gao, Min Wang, Jing Yu, Jinpeng Ge, Shuzhi Sam Jin, Ming Liang Wang, Lili Xia, Yanzhi State Key Laboratory of Bio-Fibers and Eco-Textiles College of Textiles and Clothing Qingdao University Qingdao 266071 China Institute for Future Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Qingdao 266071 China College of Materials Science and Engineering Qingdao University Qingdao 266071 China CAS Key Laboratory of Engineering Plastics Institute of Chemistry Chinese Academy of Sciences Beijing 100190 China Department of Chemistry SungKyunKwan University Suwon 16419 South Korea State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou 310027 China College of Biological Science and Medical Engineering Donghua University Shanghai 201620 China Institute of Environmental Engineering ETH Zürich Zürich 8093 Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore

The sole situation of semi-crystalline structure induced single performance remarkably limits the green cryogels in the application of soft devices due to uncontrolled freezing field. Here, a facile strategy for achieving multifunctionality of cryogels is proposed using total amorphization of polymer. Through precisely lowering the freezing point of precursor solutions with an antifreezing salt, the suppressed growth of ice is achieved, creating an unusually weak and homogenous aggregation of polymer chains upon freezing, thereby realizing the tunable amorphization of polymer and the coexistence of free and hydrogen bonding hydroxyl groups. Such multi-scale microstructures trigger the integrated properties of tissue-like ultrasoftness (Young’s modulus <10 kPa) yet stretchability, high transparency (~92%), self-adhesion, and instantaneous self-healing (<0.3 s) for cryogels, along with superior ionic-conductivity, antifreezing (−58 °C) and water-retention abilities, pushing the development of skin-like cryogel electronics. These concepts open an attractive branch for cryogels that adopt regulated crystallization behavior for on-demand functionalities. © 2023, Springer Nature Limited.

关键词： Gels and hydrogels Polymers

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Bias-Eliminated Semantic Refinement for Any-Shot Learning

arXiv

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arXiv 2022年

作者： Feng, Liangjun Zhao, Chunhui Li, Xi The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China

When training samples are scarce, the semantic embedding technique, i.e., describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects. However, semantic descriptions are usually obtained in an external paradigm, such as manual annotation, resulting in weak consistency between descriptions and visual features. In this paper, we refine the coarse-grained semantic description for any-shot learning tasks, i.e., zero-shot learning (ZSL), generalized zero-shot learning (GZSL), and few-shot learning (FSL). A new model, namely, the semantic refinement Wasserstein generative adversarial network (SRWGAN) model, is designed with the proposed multihead representation and hierarchical alignment techniques. Unlike conventional methods, semantic refinement is performed with the aim of identifying a bias-eliminated condition for disjoint-class feature generation and is applicable in both inductive and transductive settings. We extensively evaluate model performance on six benchmark datasets and observe state-of-the-art results for any-shot learning;e.g., we obtain 70.2% harmonic accuracy for the Caltech UCSD Birds (CUB) dataset and 82.2% harmonic accuracy for the Oxford Flowers (FLO) dataset in the standard GZSL setting. Various visualizations are also provided to show the bias-eliminated generation of SRWGAN. Our code is available. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Saliency-Aware Regularized Graph Neural Network

arXiv

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arXiv 2024年

作者： Pei, Wenjie Xu, WeiNa Wu, Zongze Li, Weichao Wang, Jinfan Lu, Guangming Wang, Xiangrong Department of Computer Science Harbin Institute of Technology at Shenzhen Shenzhen518172 China Institute of Future Networks Southern University of Science and Technology Shenzhen518055 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518066 China

The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the representation for the entire graph by aggregating node features. Such methods have two potential limitations: 1) the global node saliency w.r.t. graph classification is not explicitly modeled, which is crucial since different nodes may have different semantic relevance to graph classification;2) the graph representation directly aggregated from node features may have limited effectiveness to reflect graph-level information. In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone. We first estimate the global node saliency by measuring the semantic similarity between the compact graph representation and node features. Then the learned saliency distribution is leveraged to regularize the neighborhood aggregation of the backbone, which facilitates the message passing of features for salient nodes and suppresses the less relevant nodes. Thus, our model can learn more effective graph representation. We demonstrate the merits of SAR-GNN by extensive experiments on seven datasets across various types of graph data. Code will be released. © 2024, CC BY.

关键词： Graph neural networks

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Humic Acid Mediated Self-Signal Molecularly Imprinted Electrochemical Sensor for Cortisol Detection

SSRN

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SSRN 2025年

作者： Ma, Jiao Fang, Rong Li, Xiaoting Wang, Meiling Sun, Huijun Guo, Kunpeng Key Laboratory of Interface Science and Engineering in Advanced Materials Ministry of Education Taiyuan University of Technology Shanxi Taiyuan030024 China North Automatic Control Technology Institute Shanxi Taiyuan030006 China College of Materials Science and Engineering Taiyuan University of Technology Taiyuan030024 China Institute of Scientific and Technical Information of Shanxi NO.129 South Section of Binhe West Rd Wanbailin Dist Shanxi Taiyuan030024 China

When detecting cortisol through molecularly imprinted electrochemical sensors, it is essential to add additional redox probes to the detection solution. However, this method complicates the detection process, impedes real-time monitoring, and moreover it may cause serious interference during body fluid analysis. To address the issue, this study innovatively proposed a self-signal sensor based on humic acid (HA)-mediated redox nanoprobe. The amphiphilic HA, featuring a polycyclic aromatic core and numerous hydrophilic multifunctional groups, can effectively coat multi-walled carbon nanotubes through π-π interactions and meanwhile exhibited an exceptionally high adsorption capacity towards various types of redox molecules, particularly cationic redox species. Thus, a conductive redox nanoprobe could be obtained in a facile way with a strong response signal and acted as a bottom layer to prepare an electrochemical sensor in combination with cortisol-imprinted polydopamine as an upper layer. Finally, a self-signal sensor for cortisol was successfully fabricated with a detection limit of 1.45 × 10-9 M, as well as a wide detection range from 1 nM to 1 mM and moreover a good recovery in artificial human sweat. In all, we believe the HA-mediated strategy proposed in this study could inspire more innovation in the field of self-signal sensing. © 2025, The Authors. All rights reserved.

关键词： Multiwalled carbon nanotubes (MWCN)

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Multi-step Ahead Time Series Forecasting Based on the Improved Process Neural Networks 9th

Multi-step Ahead Time Series Forecasting Based on the Improv...

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9th International Conference on computer engineering and Networks, CENet2019

作者： Shao, Haijian Hu, Chunlong Deng, Xing Jiang, Dengbiao School of Computer Science and Engineering Jiangsu University of Science and Technology JiangsuZhenjiang China Key Laboratory of Measurement and Control for CSE School of Automation Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9789811537523

This paper proposes a multi-step ahead time series forecasting based on the improved process neural network. The intelligent algorithm particle swarm optimization (PSO) is used to overcome the potential disadvantages of the neural network, such as slow convergence speed and derivative local minima. Firstly, the theoretical analysis of the PSO is given to optimize the multilayer perceptron (MLP) neural network architecture. Secondly, the theoretical analysis and processing flow about the MLP architecture optimization is given. Thirdly, the performance criteria are applied to verify the performance of the proposed approach. Finally, the experimental evaluation based on a typically chaotic time series with rich spectrum information is utilized to demonstrate that the proposed approach has comparative results and superior on forecasting accuracy comparing to the traditional methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Time series analysis

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